@article{awasthi_chalise_wang_erkyihun_asefa_sankarasubramanian_2024, title={Hydroclimatic scenario generation using two-stage stochastic simulation framework}, volume={190}, ISSN={["1872-9657"]}, url={https://doi.org/10.1016/j.advwatres.2024.104739}, DOI={10.1016/j.advwatres.2024.104739}, abstractNote={Climate change poses significant challenges for decision-making processes across a range of sectors. From the water resources planning and management perspective, the interest is often in evaluating the performance of a water supply system in a future state considering the potential changes in rainfall and streamflow characteristics. With observed climate change signals, scenario-based projections of rainfall and streamflow simulations are crucial for evaluating the potential impacts of climate change on water resource systems. Given the complexity of the existing approaches, their applications for generating scenario-based projections of streamflow and rainfall are limited. We developed a non-parametric bootstrapping approach, NPScnGen, for future scenario generation of any hydroclimatic variable. The developed approach is flexible and can be used with any physical hydrological or data-driven stochastic model that provides simulations of hydroclimatic variables of interest for the historical climate condition. In NPScnGen, samples of any set of time-series characteristics, such as mean and standard deviation, are generated from a multivariate Gaussian process for the considered scenario, and then bootstrapping is performed to select the closest sample from the historical simulation of that hydroclimatic variable. We have also proposed a modified wavelet-based model, Wavelet-HMM, and used that model to synthetically generate historical climate time-series as a baseline. We present the application of the developed framework consisting of historical climate simulation and future climate projection approaches on rainfall and streamflow datasets for the Tampa Bay region in Florida. Plain Language Summary: Water resources managers require a wide range of hypothetical but potential changes in hydroclimatic variables such as streamflow and rainfall to evaluate the sustenance of water supply systems in future. Existing scenario generation approaches are limited by either the complexity of statistical models or dependency on climate models which have their own limitations. In such a scenario, the developed non-parametric scenario generation framework in this study, NPScnGen, can be very useful. The developed framework can be applied with any sophisticated time-series generation model that can generate synthetic hydroclimatic traces for baseline climate condition, and it is also flexible in generating a wide range of potential climate change scenarios. We show the application of the framework on both streamflow and rainfall datasets.}, journal={ADVANCES IN WATER RESOURCES}, author={Awasthi, Chandramauli and Chalise, Dol Raj and Wang, Hui and Erkyihun, Solomon Tassew and Asefa, Tirusew and Sankarasubramanian, A.}, year={2024}, month={Aug} }
 @article{fang_johnson_yeghiazarian_sankarasubramanian_2024, title={Improved National-Scale Above-Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio-Temporal Hierarchical Model}, volume={60}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2023WR034557}, DOI={10.1029/2023WR034557}, abstractNote={AbstractFloods cause hundreds of fatalities and billions of dollars of economic loss each year in the United States. To mitigate these damages, accurate flood prediction is needed for issuing early warnings to the public. This situation is exacerbated in larger model domains flood prediction, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio‐temporal hierarchical model (STHM) using above‐normal flow estimation with a 10‐day window of modeled National Water Model (NWM) streamflow and a variety of catchment characteristics as input. The STHM is calibrated (1993–2008) and validated (2009–2018) in controlled, natural, and coastal basins over three broad groups, and shows significant improvement for the first two basin types. A seasonal analysis shows the most influential predictors beyond NWM streamflow reanalysis are the previous 3‐day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving above‐normal streamflow in ungauged basins, 20‐fold cross‐validation is performed by leaving 5% of sites. Results show that the STHM increases predictive skill in over 50% of sites' by 0.1 Nash‐Sutcliffe efficiency (NSE) and improves over 65% of sites' streamflow prediction to an NSE > 0.67, which demonstrates that the STHM is one of the first of its kind and could be employed for flood prediction in both gauged and ungauged basins.}, number={1}, journal={WATER RESOURCES RESEARCH}, author={Fang, Shiqi and Johnson, J. Michael and Yeghiazarian, Lilit and Sankarasubramanian, A.}, year={2024}, month={Jan} }
 @article{das bhowmik_budamala_sankarasubramanian_2024, title={Influence of long-term observed trends on the performance of seasonal hydroclimate forecasts}, volume={188}, ISSN={["1872-9657"]}, url={https://doi.org/10.1016/j.advwatres.2024.104707}, DOI={10.1016/j.advwatres.2024.104707}, abstractNote={Skillful forecasts of hydroclimate variables are essential for operational water management, agricultural planning, and food supply. Several studies have attempted to improve the skill of raw forecasts either by post-processing or by incorporating sea surface conditions into raw forecasts. However, to the best of our knowledge, limited to no study has investigated temporal trend, which is present in observed records but is absent from retrospective forecasts (also known as, hindcasts). The current study understands that a temporal trend can be yielded in raw meteorological forecasts by i) updating surface boundary forcings and ii) applying statistical models for either post-processing meteorological forecasts or issuing streamflow forecasting using weather forecasts as predictors. To analytically derive the relationship between temporal trend and forecast performance, this study applies three statistical approaches for post-processing season-ahead hindcasts of the Indian monsoon obtained from three general circulation models (GCM). The findings show that raw hindcasts of the Indian monsoons typically ignore the temporal trend present in the observed records. Furthermore, analytical derivations confirm that the absence of a trend in GCM hindcasts significantly influences post-processing performance. Moreover, a semi-parametric approach could not overcome the limitations of a parametric linear model in yielding a temporal trend in the hindcasts. Potential reasons for the absence of a trend in the hindcast is also discussed.}, journal={ADVANCES IN WATER RESOURCES}, author={Das Bhowmik, Rajarshi and Budamala, Venkatesh and Sankarasubramanian, A.}, year={2024}, month={Jun} }
 @article{levey_arumugam_2024, title={Is Reservoir Storage Effectively Utilized in Southeast? A Regional Assessment to Reduce Drought Risk considering Potential Storage and Flood Scenarios}, url={https://doi.org/10.22541/essoar.172286680.05710842/v1}, DOI={10.22541/essoar.172286680.05710842/v1}, author={Levey, Jessica Rose and Arumugam, Sankar}, year={2024}, month={Aug} }
 @article{fang_johnson_sankarasubramanian_2024, title={Leveraging Synthetic Aperture Radar (SAR) to improve above-normal flow prediction in ungauged basins}, url={https://doi.org/10.22541/essoar.171320277.73530520/v1}, DOI={10.22541/essoar.171320277.73530520/v1}, abstractNote={Effective flood prediction significantly enhances risk management and response strategies, yet remains challenging, particularly in ungauged basins.This study investigates the capacity for integrating streamflow derived from Synthetic Aperture Radar (SAR) and U.S. National Water Model (NWM) output to provide enhanced predictions of above-normal flow (ANF).Leveraging the Global Flood Detection System (GFDS) and Principal Component Regression (PCR) of SAR data, we apply the Spatial-temporal Hierarchical model (STHM) for ANF prediction replacing antecedent streamflow with SAR-derived flow.Our evaluation shows promising results, with STHM-SAR significantly improving prediction accuracy of NWM, especially coastal regions where approximately 60% of sites demonstrated enhanced performance compared to previous efforts.Spatial and temporal validations underscore the model's robustness, with SAR data contributing to explained variance by 24% on average.This approach not only streamlines post-processing modeling but also uniquely combines existing data, showcasing its potential to improve hydrological modeling, particularly in regions with limited measurements. Hosted fileRS_main.docx available at https://authorea.com/users/577650/articles/737256-leveragingsynthetic-aperture-radar}, author={Fang, Shiqi and Johnson, J. Michael and Sankarasubramanian, Arumugam}, year={2024}, month={Apr} }
 @article{awasthi_archfield_reich_sankarasubramanian_2023, title={Beyond Simple Trend Tests: Detecting Significant Changes in Design-Flood Quantiles}, volume={50}, ISSN={["1944-8007"]}, url={https://doi.org/10.1029/2023GL103438}, DOI={10.1029/2023GL103438}, abstractNote={AbstractChanges in annual maximum flood (AMF), which are usually detected using simple trend tests (e.g., Mann‐Kendall test (MKT)), are expected to change design‐flood estimates. We propose an alternate framework to detect significant changes in design‐flood between two periods and evaluate it for synthetically generated AMF from the Log‐Pearson Type‐3 (LP3) distribution due to changes in moments associated with flood distribution. Synthetic experiments show MKT does not consider changes in all three moments of the LP3 distribution and incorrectly detects changes in design‐flood. We applied the framework on 31 river basins spread across the United States. Statistically significant changes in design‐flood quantiles were observed even without a significant trend in AMF and basins with statistically significant trend did not necessarily exhibit statistically significant changes in design‐flood. We recommend application of the framework for evaluating changes in design‐flood estimates considering changes in all the moments as opposed to simple trend tests.}, number={13}, journal={GEOPHYSICAL RESEARCH LETTERS}, author={Awasthi, C. and Archfield, S. A. and Reich, B. J. and Sankarasubramanian, A.}, year={2023}, month={Jul} }
 @article{johnson_fang_sankarasubramanian_rad_cunha_jennings_clarke_mazrooei_yeghiazarian_2023, title={Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection}, volume={128}, ISSN={["2169-8996"]}, url={https://doi.org/10.1029/2023JD038534}, DOI={10.1029/2023JD038534}, abstractNote={AbstractWith an increasing number of continental‐scale hydrologic models, the ability to evaluate performance is key to understanding uncertainty and making improvements to the model(s). We hypothesize that any model, running a single set of physics, cannot be “properly” calibrated for the range of hydroclimatic diversity as seen in the contenintal United States. Here, we evaluate the NOAA National Water Model (NWM) version 2.0 historical streamflow record in over 4,200 natural and controlled basins using the Nash‐Sutcliffe Efficiency metric decomposed into relative performance, and conditional, and unconditional bias. Each of these is evaluated in the contexts of meteorologic, landscape, and anthropogenic characteristics to better understand where the model does poorly, what potentially causes the poor performance, and what similarities systemically poor performing areas share. The primary objective is to pinpoint traits in places with good/bad performance and low/high bias. NWM relative performance is higher when there is high precipitation, snow coverage (depth and fraction), and barren area. Low relative skill is associated with high potential evapotranspiration, aridity, moisture‐and‐energy phase correlation, and forest, shrubland, grassland, and imperviousness area. We see less bias in locations with high precipitation, moisture‐and‐energy phase correlation, barren, and grassland areas and more bias in areas with high aridity, snow coverage/fraction, and urbanization. The insights gained can help identify key hydrological factors underpinning NWM predictive skill; enforce the need for regionalized parameterization and modeling; and help inform heterogenous modeling systems, like the NOAA Next Generation Water Resource Modeling Framework, to enhance ongoing development and evaluation.}, number={24}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, author={Johnson, J. Michael and Fang, Shiqi and Sankarasubramanian, Arumugam and Rad, Arash Modaresi and Cunha, Luciana Kindl and Jennings, Keith S. and Clarke, Keith C. and Mazrooei, Amir and Yeghiazarian, Lilit}, year={2023}, month={Dec} }
 @article{johnson_fang_sankarasubramanian_rad_cunha_clarke_mazrooei_yeghiazarian_2023, title={Comprehensive analysis of the NOAA National Water Model: A call for heterogeneous formulations and diagnostic model selection}, url={https://doi.org/10.22541/essoar.167415214.45806648/v1}, DOI={10.22541/essoar.167415214.45806648/v1}, abstractNote={With an increasing number of continental-scale hydrologic models, the
ability to evaluate performance is key to understanding uncertainty in
prediction and making improvements to the model(s). In 2016, the NOAA
National Water Model (NWM) was put into operations to improve the
spatial and temporal resolution of hydrologic prediction in the U.S.
Here, we evaluate the NWM 2.0 historical streamflow record in natural
and controlled basins using the Nash Sutcliffe Efficiency metric
decomposed into relative error, conditional, and unconditional bias.
Each of these is evaluated in the contexts of categorized meteorologic,
landscape, and anthropogenic characteristics to assess model performance
and diagnose error types. Broadly speaking greater rainfall and snow
coverage leads to improved performance while larger potential
evapotranspiration (PET), aridity, and phase correlation reduce
performance. More rainfall and phase correlation reduce overall bias,
while increasing PET, aridity, snow coverage/fraction increase model
bias. With respect to landscape traits, more barren and agricultural
land yeild improved performance while more forest, shrubland, grassland
and imperviousness tend to decrease performance. Lastly, more barren and
herbaceous land tend to decrease bias, while greater imperviousness,
urban, forest, and shrubland cover increase bias. The insights gained
can help identify key hydrological factors in NWM predictions; enforce
the need for regionalized physics and modeling; and help develop hybid
post-processing methods to improve prediction. Finally, we demonstrate
how the NOAA Next Generation Water Resource Modeling Framework can help
reduce the structural bias through the application of heterogenous model
processes and highlight opportunities for ongoing development and
evaluation.}, author={Johnson, J. Michael and Fang, Shiqi and Sankarasubramanian, Arumugam and Rad, Arash Modaresi and Cunha, Luciana Kindl and Clarke, Keith C and Mazrooei, Amirhossein and Yeghiazarian, Lilit}, year={2023}, month={Jan} }
 @article{karimi_miller_sankarasubramanian_obenour_2023, title={Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model}, volume={59}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2022WR033088}, DOI={10.1029/2022WR033088}, abstractNote={AbstractNutrient pollution is a widespread environmental problem that degrades water quality worldwide. Addressing this issue calls for characterizing nutrient sources and retention rates, especially in seasons when water quality problems are most severe. Hybrid (statistical‐mechanistic) watershed models have been used to quantify nutrient loading from various source categories. However, these models are generally developed for long‐term average conditions, limiting their ability to assess temporal drivers of nutrient loading. They also have not been calibrated for season‐specific estimates of loading and retention rates. To address these issues, we developed a hybrid watershed model that incorporates interannual variability in land use and precipitation as temporal drivers of phosphorus loading and transport. We calibrate the hybrid watershed model within a Bayesian hierarchical framework on both an annual and summer basis over a multi‐decadal period (1982–2017). For our study area in the North Carolina Piedmont region (USA), we find that urban lands developed before 1980 are the largest contributor of phosphorus (per unit area), especially under dry conditions. Seasonally, summer phosphorus export rates are generally found to be lower than corresponding annual rates (kg/ha/mo), while in‐stream retention is found to be elevated in summer. In addition, we find that precipitation has a substantially larger influence on phosphorus export from agricultural lands than other source types, especially in summer, and that antecedent (May) precipitation significantly influences summer phosphorus export. Overall, our approach provides a data‐driven and probabilistic line of evidence to support watershed phosphorus management across different sources and seasons.}, number={1}, journal={WATER RESOURCES RESEARCH}, author={Karimi, K. and Miller, J. W. and Sankarasubramanian, A. and Obenour, D. R.}, year={2023}, month={Jan} }
 @article{ford_sankarasubramanian_2023, title={Generalizing Reservoir Operations Using a Piecewise Classification and Regression Approach}, volume={59}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2023WR034890}, DOI={10.1029/2023WR034890}, abstractNote={AbstractInflow anomalies at varying temporal scales, seasonally varying storage mandates, and multipurpose allocation requirements contribute to reservoir operational decisions. The difficulty of capturing these constraints across many basins in a generalized framework has limited the accuracy of streamflow estimates in land‐surface models for locations downstream of reservoirs. We develop a Piecewise Linear Regression Tree to learn generalized daily operating policies from 76 reservoirs from four major basins across the coterminous US. Reservoir characteristics, such as residence time and maximum storage, and daily state variables, such as storage and inflow, are used to group similar observations across all reservoirs. Linear regression equations are then fit between daily state variables and release for each group. We recommend two models—Model 1 (M1) that performs the best when simulating untrained records but is complex and Model 2 (M2) that is nearly as performant as M1 but more parsimonious. The simulated release median root mean squared error is 49.7% (53.2%) of mean daily release with a median Nash‐Sutcliffe efficiency of 0.62 (0.52) for M1 (M2). Long‐term residence time is shown to be useful in grouping similar operating reservoirs. Release from low residence time reservoirs can be mostly described using inflow‐based variables. Operations at higher residence time reservoirs are more related to previous release variables or storage variables, depending on the current inflow. The ability of the models presented to capture operational dynamics of many types of reservoirs indicates their potential to be used for untrained and limited data reservoirs.}, number={9}, journal={WATER RESOURCES RESEARCH}, author={Ford, Lucas and Sankarasubramanian, A.}, year={2023}, month={Sep} }
 @article{ford_sankarasubramanian_2023, title={Generalizing Reservoir Operations using a Piecewise Classification and Regression Approach}, url={https://doi.org/10.22541/essoar.167979630.09661764/v1}, DOI={10.22541/essoar.167979630.09661764/v1}, abstractNote={Inflow anomalies at varying temporal scales, seasonally varying storage
mandates, and multi-purpose allocation requirements contribute to
reservoir operational decisions. The difficulty of capturing these
constraints across many basins in a generalized framework has limited
the accuracy of streamflow estimates in Land Surface Models for
locations downstream of reservoirs. We develop a Piece Wise Linear
Regression Tree to learn generalized daily operating policies from 76
reservoirs from four major basins across the coterminous US. Reservoir
characteristics, such as residence time and maximum storage, and daily
state variables, such as storage and inflow, are used to group similar
observations across all reservoirs. Linear regression equations are then
fit between daily state variables and release for each group. We
recommend two models – Model 1 (M1) that performs the best when
simulating untrained records but is complex, and Model 2 (M2) that is
nearly as performant as M1 but more parsimonious. The simulated release
median root mean squared error is 49.7% (53.2%) of mean daily release
with a median Nash-Sutcliffe Efficiency of 0.62 (0.52) for M1 (M2).
Long-term residence time is shown to be useful in grouping similar
operating reservoirs. Release from low residence time reservoirs can be
mostly described using inflow-based variables. Operations at higher
residence time reservoirs are more related to previous release variables
or storage variables, depending on the current inflow. The ability of
the models presented to capture operational dynamics of many types of
reservoirs indicates their potential to be used for untrained and
limited data reservoirs.}, author={Ford, Lucas and Sankarasubramanian, Arumugam}, year={2023}, month={Mar} }
 @article{fang_johnson_yeghiazarian_sankarasubramanian_2023, title={Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model}, url={https://doi.org/10.22541/essoar.167590827.70275868/v1}, DOI={10.22541/essoar.167590827.70275868/v1}, abstractNote={Floods cause hundreds of fatalities and billions of dollars of economic
loss each year in the United States. To mitigate these damages, accurate
flood prediction is needed for issuing early warnings to the public.
This situation is exacerbated in larger model domains for high flows,
particularly in ungauged basins. To improve flood prediction for both
gauged and ungauged basins, we propose a spatio-temporal hierarchical
model (STHM) to improve high flow estimation using a 10-day window of
modeled National Water Model (NWM) streamflow and a variety of catchment
characteristics as input. The STHM is calibrated (1993-2008) and
validated (2009-2018) in controlled, natural, and coastal basins over
three broad groups, and shows significant improvement for the first two
basin types. A seasonal analysis shows the most influential predictors
are the previous 3-day average streamflow and the aridity index for
controlled and natural basins, respectively. To evaluate the STHM in
improving streamflow in ungauged basins, 20-fold cross-validation is
performed by leaving 5% of sites. Results show that the STHM increases
predictive skill in over 50% of sites by 0.1 Nash-Sutcliffe efficiency
(NSE) and improves over 65% of sites’ streamflow prediction to an
NSE>0.67, which demonstrates that the STHM is one of the
first of its kind and could be employed for flood prediction in both
gauged and ungauged basins.}, author={Fang, Shiqi and Johnson, J. Michael and Yeghiazarian, Lilit and Sankarasubramanian, Arumugam}, year={2023}, month={Feb} }
 @article{han_sankarasubramanian_wang_wan_yao_2023, title={One-Parameter Analytical Derivation in Modified Budyko Framework for Unsteady-State Streamflow Elasticity in Humid Catchments}, volume={59}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2023WR034725}, DOI={10.1029/2023WR034725}, abstractNote={AbstractThe changes in climate and catchment properties have altered the hydrological processes significantly at different spatiotemporal scales around the world. In particular for finer time scales, changes in water storage, which has been commonly neglected for long‐term temporal scales, may play an important role on hydrological processes. Nevertheless, few studies addressed this question in modifying the Budyko framework, with most of them remaining a steady‐state assumption on catchment characteristics. Here, we derive new analytical formulas of unsteady‐state streamflow elasticity in a modified Budyko framework, incorporating both storage change and one specific parameter for catchment properties. We study 78 humid catchments in the USA with simulation data of daily hydrological processes from a probability‐distribution‐based hydrological model (the modified HyMOD). As indicated by results, the annual storage change ratio is linearly correlated with the annual aridity index, and this relationship can be used to estimate elasticity coefficients with our formulas. The estimated elasticity coefficients perform well in simulating the annual streamflow with the power‐law model. For different catchments, variability of the unsteady‐state elasticity is higher than that of the steady‐state elasticity. Unsteady‐state streamflow coefficients show significant linear correlation with catchment properties, such as the average slope, average elevation, and catchment area. This study provides a new analytical approach to investigate the interannual stability of catchments with varying climate and catchment properties.}, number={9}, journal={WATER RESOURCES RESEARCH}, author={Han, Peng-Fei and Sankarasubramanian, Arumugam and Wang, Xu-Sheng and Wan, Li and Yao, Lili}, year={2023}, month={Sep} }
 @article{awasthi_vogel_sankarasubramanian_2023, title={Regionalization of Climate Elasticity Preserves Dooge's Complementary Relationship}, url={https://doi.org/10.22541/essoar.169947261.16344787/v1}, DOI={10.22541/essoar.169947261.16344787/v1}, abstractNote={Climate elasticity of streamflow represents a nondimensional measure of
the sensitivity of streamflow to climatic factors. Estimation of such
elasticities from observational records has become an important
alternative to scenario-based methods of evaluating streamflow
sensitivity to climate. Nearly all previous elasticity studies have used
a definition of elasticity known as arc elasticity, which measures
changes in streamflow about mean values of streamflow and climate. Using
observational records in western U.S., our findings reveal that
elasticity definitions based on power law models lead to both regional
and basin specific estimates of elasticity which are physically more
realistic than estimates based on arc elasticity. Evaluating the ability
of arc and power law elasticity estimators in reproducing Dooge’s
complementary relationship (DCR) between potential evapotranspiration
and precipitation elasticities reveal that power law elasticities
estimated from at-site, panel and hierarchical statistical models
reproduce DCR, whereas corresponding estimators based on arc elasticity
cannot reproduce DCR. Importantly, our regional elasticity formulations
using either panel and/or hierarchical formulations led to estimates of
both regional and basin specific estimates of elasticities, enabling and
contrasting streamflow sensitivity to climate across both basins and
regions.}, author={Awasthi, Chandramauli and Vogel, Richard M. and Sankarasubramanian, Arumugam}, year={2023}, month={Nov} }
 @article{levey_sankarasubramanian_2023, title={Spatial and Temporal Variation of Subseasonal-to-Seasonal (S2S) Precipitation Reforecast Skill Across CONUS}, url={https://doi.org/10.22541/essoar.169461965.53386198/v1}, DOI={10.22541/essoar.169461965.53386198/v1}, abstractNote={Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S)
time scale, are essential for informed and proactive water resources
management. Although S2S precipitation forecasts have been evaluated, no
systematic decomposition of the skill, Nash-Sutcliffe Efficiency (NSE)
coefficient, has been analyzed towards understanding the forecast
accuracy. We decompose the NSE of S2S precipitation forecast into its
three components – correlation, conditional bias, and unconditional
bias – by four seasons, three lead times (1–12-day, 1-22 day, and 1-32
day), and three models (ECMWF, CFS, NCEP) over the Conterminous United
States (CONUS). Application of dry mask is critical as the NSE and
correlation are lower across all seasons after masking areas with low
precipitation values. Further, a west-to-east gradient in S2S forecast
skill exists and forecast skill was better during the winter months and
for areas closer to the coast. Overall, ECMWF’s model performance was
stronger than both ECCC and NCEP CFS’s performance, mainly for the
forecasts issued during fall and winter months. However, ECCC and NCEP
CFS performed better for the forecast issued during the spring months,
and also performed better in in-land areas. Post-processing using simple
Model Output Statistics could reduce both unconditional and conditional
bias to zero, thereby offering better skill for regimes with high
correlation. Our decomposition results also show efforts should focus on
improving model parametrization and initialization schemes for climate
regimes with low correlation values.}, author={Levey, Jessica Rose and Sankarasubramanian, Arumugam}, year={2023}, month={Sep} }
 @article{chalise_sankarasubramanian_olden_ruhi_2023, title={Spectral Signatures of Flow Regime Alteration by Dams Across the United States}, volume={11}, ISSN={["2328-4277"]}, url={https://doi.org/10.1029/2022EF003078}, DOI={10.1029/2022EF003078}, abstractNote={AbstractRiver scientists strive to understand how streamflow regimes vary across space and time because it is fundamental to predicting the impacts of climate change and human activities on riverine ecosystems. Here we tested whether flow periodicity differs between rivers that are regulated or unregulated by large dams, and whether dominant periodicities change over time in response to dam regulation. These questions were addressed by calculating wavelet power at different timescales, ranging from 6 hr to 10 years, across 175 pairs of dam‐regulated and unregulated USGS gages with long‐term discharge data, spanning the conterminous United States. We then focused on eight focal reservoirs with high‐quality and high‐frequency data to examine the spectral signatures of dam‐induced flow alteration and their time‐varying nature. We found that regulation by dams induces changes not only in flow magnitude and variability, but also in the dominant periodicities of a river's flow regime. Our analysis also revealed that dams generally alter multi‐annual and annual periodicity to a higher extent than seasonal or daily periodicity. Based on the focal reservoirs, we illustrate that alteration of flow periodicity is time varying, with dam operations (e.g., daily peaking vs. baseload operation), changes in dam capacity, and environmental policies shifting the relative importance of periodicities over time. Our analysis demonstrates the pervasiveness of human signatures now characterizing the U.S. rivers' flow regimes, and may inform the restoration of environmental periodicity downstream of reservoirs via controlled flow releases—a critical need in light of new damming and dam retrofitting for hydropower globally.}, number={2}, journal={EARTHS FUTURE}, author={Chalise, Dol Raj and Sankarasubramanian, A. and Olden, Julian D. D. and Ruhi, Albert}, year={2023}, month={Feb} }
 @article{kumar_zhu_sankarasubramanian_2023, title={Understanding the Food-Energy-Water Nexus in Mixed Irrigation Regimes Using a Regional Hydroeconomic Optimization Modeling Framework}, volume={59}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2022WR033691}, DOI={10.1029/2022WR033691}, abstractNote={AbstractUnderstanding the nexus between food, energy, and water systems (FEW) is critical for basins with intensive agricultural water use as they face significant challenges under changing climate and regional development. We investigate the food, energy, and water nexus through a regional hydroeconomic optimization (RHEO) modeling framework. The crop production in RHEO is estimated through a hierarchical regression model developed using a biophysical model, AquaCropOS, forced with daily climatic inputs. Incorporating the hierarchical model within the RHEO also reduces the computation time by enabling parallel programming within the AquaCropOS and facilitates mixed irrigation—rainfed, fully irrigated and deficit irrigation—strategies. To demonstrate the RHEO framework, we considered a groundwater‐dominated basin, South Flint River Basin, Georgia, for developing mixed irrigation strategies over 31 years. Our analyses show that optimal deficit irrigation is economically better than full irrigation, which increases the groundwater pumping cost. Thus, considering deficit irrigation in a groundwater‐dominated basin reduces the water, carbon, and energy footprints, thereby reducing FEW vulnerability. The RHEO also could be employed for analyzing FEW nexus under potential climate change and future regional development scenarios.}, number={6}, journal={WATER RESOURCES RESEARCH}, author={Kumar, Hemant and Zhu, Tingju and Sankarasubramanian, A.}, year={2023}, month={Jun} }
 @article{ford_queiroz_decarolis_sankarasubramanian_2022, title={Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation}, volume={8}, ISSN={["2352-4847"]}, url={https://doi.org/10.1016/j.egyr.2022.06.017}, DOI={10.1016/j.egyr.2022.06.017}, abstractNote={Climate variability accounts for distinct seasonal differences in electricity demand and streamflow potential, which power systems rely on to assess available hydropower and to cool thermal power plants. Understanding the interactions between reservoir and power networks under varying climate conditions requires an integrated analysis of both systems. In this study, we develop Co-Optimization of Reservoir and Electricity Generation Systems (COREGS), a generalized, open-source, modeling framework that optimizes both systems with respect to reducing power generation costs using a multireservoir model (GRAPS) and an electricity system model (TEMOA). Three optimization schemes of varying degrees of model integration are applied to Tennessee Valley Authority's reservoir and electricity systems for the summer and winters from 2003 to 2015. We find that co-optimization of the systems results in more efficient water allocation decisions than separate optimization. Co-optimization solutions reduce reservoir spill and allocate water for hydropower only when and where it is beneficial to the power system as compared to stand-alone water system optimization. As the penetration of solar and wind power continues to increase, power systems will be more reliant on flexible reliable generating services such as reservoir systems and co-optimization of both systems will become more essential for efficient seasonal planning and operation.}, journal={ENERGY REPORTS}, publisher={Elsevier BV}, author={Ford, Lucas and Queiroz, Anderson and DeCarolis, Joseph and Sankarasubramanian, A.}, year={2022}, month={Nov}, pages={8061–8078} }
 @article{ruhi_hwang_devineni_mukhopadhyay_kumar_comte_worland_sankarasubramanian_2022, title={How Does Flow Alteration Propagate Across a Large, Highly Regulated Basin? Dam Attributes, Network Context, and Implications for Biodiversity}, url={https://doi.org/10.1029/2021EF002490}, DOI={10.1029/2021EF002490}, abstractNote={AbstractLarge dams are a leading cause of river ecosystem degradation. Although dams have cumulative effects as water flows downstream in a river network, most flow alteration research has focused on local impacts of single dams. Here we examined the highly regulated Colorado River Basin (CRB) to understand how flow alteration propagates in river networks, as influenced by the location and characteristics of dams as well as the structure of the river network—including the presence of tributaries. We used a spatial Markov network model informed by 117 upstream‐downstream pairs of monthly flow series (2003–2017) to estimate flow alteration from 84 intermediate‐to‐large dams representing >83% of the total storage in the CRB. Using Least Absolute Shrinkage and Selection Operator regression, we then investigated how flow alteration was influenced by local dam properties (e.g., purpose, storage capacity) and network‐level attributes (e.g., position, upstream cumulative storage). Flow alteration was highly variable across the network, but tended to accumulate downstream and remained high in the main stem. Dam impacts were explained by network‐level attributes (63%) more than by local dam properties (37%), underscoring the need to consider network context when assessing dam impacts. High‐impact dams were often located in sub‐watersheds with high levels of native fish biodiversity, fish imperilment, or species requiring seasonal flows that are no longer present. These three biodiversity dimensions, as well as the amount of dam‐free downstream habitat, indicate potential to restore river ecosystems via controlled flow releases. Our methods are transferrable and could guide screening for dam reoperation in other highly regulated basins.}, journal={Earth's Future}, author={Ruhi, Albert and Hwang, Jeongwoo and Devineni, Naresh and Mukhopadhyay, Sudarshana and Kumar, Hemant and Comte, Lise and Worland, Scott and Sankarasubramanian, A.}, year={2022}, month={Jun} }
 @article{johnson_narock_singh-mohudpur_fils_clarke_saksena_shepherd_arumugam_yeghiazarian_2022, title={Knowledge graphs to support real-time flood impact evaluation}, volume={43}, ISSN={["2371-9621"]}, DOI={10.1002/aaai.12035}, abstractNote={AbstractA digital map of the built environment is useful for a range of economic, emergency response, and urban planning exercises such as helping find places in app driven interfaces, helping emergency managers know what locations might be impacted by a flood or fire, and helping city planners proactively identify vulnerabilities and plan for how a city is growing. Since its inception in 2004, OpenStreetMap (OSM) sets the benchmark for open geospatial data and has become a key player in the public, research, and corporate realms. Following the foundations laid by OSM, several open geospatial products describing the built environment have blossomed including the Microsoft USA building footprint layer and the OpenAddress project. Each of these products use different data collection methods ranging from public contributions to artificial intelligence, and if taken together, could provide a comprehensive description of the built environment. Yet, these projects are still siloed, and their variety makes integration and interoperability a major challenge. Here, we document an approach for merging data from these three major open building datasets and outline a workflow that is scalable to the continental United States (CONUS). We show how the results can be structured as a knowledge graph over which machine learning models are built. These models can help propagate and complete unknown quantities that can then be leveraged in disaster management.}, number={1}, journal={AI MAGAZINE}, author={Johnson, J. Michael and Narock, Tom and Singh-Mohudpur, Justin and Fils, Doug and Clarke, Keith C. and Saksena, Siddharth and Shepherd, Adam and Arumugam, Sankar and Yeghiazarian, Lilit}, year={2022}, month={Mar}, pages={40–45} }
 @article{awasthi_archfield_ryberg_kiang_sankarasubramanian_2022, title={Projecting Flood Frequency Curves Under Near-Term Climate Change}, volume={58}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2021WR031246}, DOI={10.1029/2021WR031246}, abstractNote={AbstractFlood‐frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate‐informed methodology for estimating flood‐frequency curves under non‐stationary future climate conditions. The methodology develops an asynchronous, semiparametric local‐likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log‐Pearson Type‐3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR‐MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR‐MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood‐frequency quantiles resulting from the ASLLR‐MM and GCM members compare well with the flood‐frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land‐surface conditions and a multi‐level model structure that includes other basins in a region could further improve the model performance in arid basins.}, number={8}, journal={WATER RESOURCES RESEARCH}, author={Awasthi, C. and Archfield, S. A. and Ryberg, K. R. and Kiang, J. E. and Sankarasubramanian, A.}, year={2022}, month={Aug} }
 @article{miller_karimi_sankarasubramanian_obenour_2021, title={Assessing inter-annual variability in nitrogen sourcing and retention
through hybrid Bayesian watershed modeling}, volume={2}, url={http://dx.doi.org/10.5194/hess-2021-52}, DOI={10.5194/hess-2021-52}, abstractNote={Abstract. Excessive nutrient loading is a major cause of water quality problems worldwide, including in North Carolina (NC), where reservoirs and coastal systems are often subject to excessive algae and hypoxia. Efficient nutrient management requires that loading sources are accurately quantified. However, loading rates from various urban and rural non-point sources remain highly uncertain especially with respect to climatological variation. Furthermore, statistical calibration of loading models does not always yield plausible results, given the noisiness and paucity of observational data common to many locations. To address these issues, we leverage data for two large NC Piedmont basins collected over three decades (1982–2017) using a mechanistically parsimonious watershed loading and transport model calibrated within a Bayesian hierarchical framework. We explore temporal drivers of loading by incorporating annual changes in precipitation, land use, livestock, and point sources within the model formulation. Also, different representations of urban development are compared based on how they constrain model uncertainties. Results show that urban lands built before 1980 are the largest source of nutrients, exporting over twice as much nitrogen per hectare than agricultural and post-1980 urban lands. In addition, pre-1980 urban lands are the most hydrologically constant source of nutrients, while agricultural lands show the most variation among high and low flow years. Finally, undeveloped lands export an order of magnitude (~ 7–13x) less nitrogen than built environments.
                        }, journal={Hydrology and Earth System Sciences}, publisher={Copernicus GmbH}, author={Miller, Jonathan W. and Karimi, Kimia and Sankarasubramanian, Arumugam and Obenour, Daniel R.}, year={2021}, month={Feb} }
 @article{yao_sankarasubramanian_wang_2021, title={Climatic and Landscape Controls on Long-term Baseflow}, volume={57}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2020WR029284}, DOI={10.1029/2020WR029284}, abstractNote={AbstractFor evaluating the climatic and landscape controls on long‐term baseflow, baseflow index (BFI, defined as the ratio of baseflow to streamflow) and baseflow coefficient (BFC, defined as the ratio of baseflow to precipitation) are formulated as functions of climate aridity index, storage capacity index (defined as the ratio of average soil water storage capacity to precipitation), and a shape parameter for the spatial variability of storage capacity. The derivation is based on the two‐stage partitioning framework and a cumulative distribution function for storage capacity. Storage capacity has a larger impact on BFI than on BFC. When storage capacity index is smaller than 1, BFI is less sensitive to storage capacity index in arid regions compared to that in humid regions; whereas, when storage capacity index is larger than 1, BFI is less sensitive to storage capacity index in humid regions. The impact of storage capacity index on BFC is only significant in humid regions. The shape parameter plays an important role on fast flow generation at the first‐stage partitioning in humid regions and baseflow generation at the second‐stage partitioning in arid regions. The derived formulae were applied to more than 400 catchments where storage capacity index was found to follow a logarithmic function with climate aridity index. The role of climate forcings at finer timescales on baseflow were quantified, indicating that seasonality in climate forcings has a significant control especially on BFI.}, number={6}, journal={Water Resources Research}, publisher={Water Resources Research}, author={Yao, L. and Sankarasubramanian, A. and Wang, D.}, year={2021}, month={Jun}, pages={e2020WR029284} }
 @article{chalise_sankarasubramanian_ruhi_2021, title={Dams and Climate Interact to Alter River Flow Regimes Across the United States}, volume={9}, ISSN={["2328-4277"]}, url={https://doi.org/10.1029/2020EF001816}, DOI={10.1029/2020EF001816}, abstractNote={AbstractStoring and managing river flows through reservoirs could dampen or increase climate‐induced fluctuations in streamflow, but interactions between the effects of dams and climate are poorly understood. Here, we examined how dam properties control different facets of flow alteration across the coterminous United States (CONUS), and compared alteration trends between dam‐affected and reference stream gages. We quantified departures from the natural flow regime using 730 stations with long‐term daily discharge data. Dam size and purpose explained high variation in flow alteration, and alteration was particularly severe in water‐stressed regions. Importantly, regulation of river flows by dams often dampened climate‐driven alteration (48% of the flow metrics), particularly in watersheds with positive flow trends; while worsening climatic impacts in other cases (44%), or even having dual effects (8%). Our results show that dam and climate impacts on streamflow need to be assessed jointly, and based on a diverse range of flow regime facets (e.g., event magnitude and duration, frequency, and timing) instead of mean annual flows only. By pairing the USGS streamflow records available from upstream and downstream of 209 dams across the CONUS, we advance the notion that dams amplify flow alteration, but also ameliorate some flow alteration metrics. Understanding such potential and limitations is important in light of climate non‐stationarity and a new wave of damming in developing economies, and will be key to further advancing environmental flow science into the future.}, number={4}, journal={Earth’s Future}, publisher={American Geophysical Union (AGU)}, author={Chalise, D. and Sankarasubramanian, A. and Ruhi, A.}, year={2021}, month={Apr}, pages={e2020EF001816} }
 @article{kumar_hwang_devineni_sankarasubramanian_2021, title={Dynamic Flow Alteration Index for Complex River Networks With Cascading Reservoir Systems}, volume={58}, ISSN={0043-1397 1944-7973}, url={http://dx.doi.org/10.1029/2021WR030491}, DOI={10.1029/2021WR030491}, abstractNote={AbstractLarge dams degrade the river’s health by heavily regulating the natural flows. Despite a long history of research on flow regulation due to dams, most studies focused only on the impact of a single dam and ignored the combined impact of flow regulation on a river network. We propose a new Dynamic Flow Alteration Index (DFAI) to quantify the local and cumulative degree of regulation by comparing the observed controlled flows with the naturalized flows based on a moving time horizon for the highly regulated Colorado River Basin. The proposed DFAI matches closely to dam’s localized regulation for headwater gages and starts to diverge as we move downstream due to increase in cumulative impact of the dams. DFAI considers the impact of dam operations on flow characteristics such as shifting of peak flow occurrence and dampening of peak flows. DFAI estimates the degree of regulation to be small for upstream dams and finds the maximum network regulation to be 2.52 years at Glen Canyon reservoir. DFAI also successfully captures the reduction in cumulative regulation when dam operations (e.g., Hoover Dam) bring the altered flow in synchronization with natural regime due to downstream flow requirements. The impact of San Juan River Basin Recovery Implementation Program is also captured by DFAI as the reduction in network regulation drops by 1.5 years for Navajo Dam. Our findings using DFAI suggest the need to develop naturalized flows for major river basins to quantify the flow alteration under continually changing climate and human influences.}, number={1}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Kumar, Hemant and Hwang, Jeongwoo and Devineni, Naresh and Sankarasubramanian, A.}, year={2021}, month={Dec} }
 @article{mukhopadhyay_sankarasubramanian_de queiroz_2021, title={Performance Comparison of Equivalent Reservoir and Multireservoir Models in Forecasting Hydropower Potential for Linking Water and Power Systems}, volume={147}, ISSN={0733-9496 1943-5452}, url={http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001343}, DOI={10.1061/(asce)wr.1943-5452.0001343}, abstractNote={To link water and power systems on a regional scale, equivalent reservoir models—an aggregated representation of a multireservoir system—are commonly used because conventional river-basin scale optimization models become computationally expensive with increasing dimensionality. Although equivalent reservoir models are widely applied in power system operation, analyses comparing the performance of equivalent reservoir models with multireservoir cascade models are limited. To this end, this study systematically compares two equivalent reservoir models, an aggregated water balance and an energy balance representation, with a multireservoir cascade representation for a system of three reservoirs in series in Savannah, South Carolina, in terms of the total end-of-period release, hydropower and storage based on simulation, simulation optimization, and analytically over a 30-year period. Findings from the pilot basin are generalized by altering the storage-to-demand ratio (SDR) to understand the effect of different system characteristics on the equivalent reservoir representation under observed and predicted inflows of different skills. Equivalent reservoir models perform similarly to the cascade model for systems with large SDRs, but for systems with smaller SDRs, equivalent reservoir models perform poorly because spill and other losses from individual reservoirs cannot be effectively represented in the aggregated approach.}, number={4}, journal={Journal of Water Resources Planning and Management}, publisher={American Society of Civil Engineers (ASCE)}, author={Mukhopadhyay, Sudarshana and Sankarasubramanian, A. and de Queiroz, Anderson Rodrigo}, year={2021}, month={Apr}, pages={04021005} }
 @article{esraghi_queiroz_sankarasubramanian_decarolis_2021, title={Quantification of climate-induced interannual variability in residential U.S. electricity demand}, volume={236}, ISSN={["1873-6785"]}, url={http://dx.doi.org/10.1016/j.energy.2021.121273}, DOI={10.1016/j.energy.2021.121273}, abstractNote={We assess the sensitivity of residential electricity demand in 48 U S. states to seasonal climate variations and structural changes pertaining to state-level household electricity demand. The main objective is to quantify the effects of seasonal climate variability on residential electricity demand variability during the winter and summer seasons. We use state-level monthly demographic, energy, and climate data from 2005 to 2017 in a linear regression model and find that interannual climate variability explains a significant share of seasonal household electricity demand variation: in 42 states, more than 70% and 50% of demand variability in summer and winter, respectively, is driven by climate. Our work suggests the need for new datasets to quantify unexplained variance in the winter and summer electricity demand. Findings from this study are critical to developing seasonal electricity demand forecasts, which can aid power system operation and management, particularly in a future with greater electrification of end-use demands.}, journal={Energy}, publisher={Elsevier BV}, author={Esraghi, H. and Queiroz, Ade and Sankarasubramanian, A. and DeCarolis, J.}, year={2021}, month={Dec}, pages={121273} }
 @article{hwang_kumar_ruhi_sankarasubramanian_devineni_2021, title={Quantifying Dam-Induced Fluctuations in Streamflow Frequencies Across the Colorado River Basin}, volume={57}, ISSN={0043-1397 1944-7973}, url={http://dx.doi.org/10.1029/2021WR029753}, DOI={10.1029/2021WR029753}, abstractNote={AbstractPeriodic fluctuations in natural streamflow are a major driver of river ecosystem dynamics and water resource management. However, most U.S. rivers are impacted both by long‐term hydroclimatic trends and dams that alter flow variability. Despite these impacts, it remains largely unexplored how dams affect the dominant frequencies of natural streamflow over a highly regulated river network. We investigated the entire Colorado River Basin (CRB) to understand how the annual (10–14 months) and multi‐annual (24–60 months) frequencies in natural flow regimes have been progressively altered by dams. Given the significant alteration over the CRB, we captured changes in streamflow frequencies between naturalized and observed monthly flows via wavelet analysis. Based on the similarity of changes in streamflow frequencies (annual and multi‐annual) over the last 30 years, sections of the riverine network were classified into four groups. The annual frequency was relatively well preserved downstream of Hoover Dam, while showing a systematic trend of alteration downstream of Glen Canyon Dam until Hoover Dam. Meanwhile, the multi‐annual frequency component was highly altered for the entire Lower Colorado main stem (i.e., downstream of Glen Canyon). We also identified dams with significant impacts on streamflow frequency by comparing wavelet coherence estimates. This study advances the notion that dams fundamentally alter river flow regimes across multiple frequencies and with varying amplitudes over time and space, with alteration propagating – or being dampened – by both hydroclimatic fluctuations and water resource management.}, number={10}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Hwang, J. and Kumar, H. and Ruhi, A. and Sankarasubramanian, A. and Devineni, N.}, year={2021}, month={Oct}, pages={e2021WR029753} }
 @article{cawthorne_rodrigo de queiroz_eshraghi_sankarasubramanian_decarolis_2021, title={The Role of Temperature Variability on Seasonal Electricity Demand in the Southern US}, volume={3}, ISSN={["2624-9634"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85123099133&partnerID=MN8TOARS}, DOI={10.3389/frsc.2021.644789}, abstractNote={The reliable and affordable supply of energy through interconnected systems represent a critical infrastructure challenge. Seasonal and interannual variability in climate variables—primarily precipitation and temperature—can increase the vulnerability of such systems during climate extremes. The objective of this study is to understand and quantify the role of temperature variability on electricity consumption over representative areas of the Southern United States. We consider two states, Tennessee and Texas, which represent different climate regimes and have limited electricity trade with adjacent regions. Results from regression tests indicate that regional population growth explains most of the variability in electricity demand at decadal time scales, whereas temperature explains 44–67% of the electricity demand variability at seasonal time scales. Seasonal temperature forecasts from general circulation models are also used to develop season-ahead power demand forecasts. Results suggest that the use of climate forecasts can potentially help to project future residential electricity demand at the monthly time scale.Capsule Summary: Seasonal temperature forecasts from GCMs can potentially help in predicting season-ahead residential power demand forecasts for states in the Southern US.}, journal={Frontiers in Sustainable Cities}, author={Cawthorne, D. and Rodrigo de Queiroz, A. and Eshraghi, H. and Sankarasubramanian, A. and DeCarolis, J.F.}, year={2021}, month={Jun} }
 @article{chalise_aiyyer_sankarasubramanian_2021, title={Tropical cyclones contribution to seasonal precipitation and streamflow using station-based data in Southeastern and Southcentral United States}, volume={48}, ISSN={["1944-8007"]}, url={https://doi.org/10.1029/2021GL094738}, DOI={10.1029/2021GL094738}, abstractNote={AbstractStudies have quantified the contribution of tropical cyclones (TCs) toward seasonal precipitation, but limited analysis is available on TC contribution toward seasonal streamflow over the southeastern and southcentral (SESC) United States (U.S.). Using an extensive network of hydroclimatic data that consists of 233 TC tracks and daily precipitation and streamflow, we estimate TC contribution toward precipitation and streamflow during the hurricane season over the SESC U.S. We found that TCs account for 12% of seasonal streamflow and 6% of seasonal precipitation over the region. Florida, North Carolina, and Louisiana have the highest fractional occurrence of TC‐generated annual maximum precipitation (∼20%–32%) and streamflow (∼15%–27%). We also found the fractional occurrence of TCs associated with peak‐over threshold precipitation (streamflow) events ranges from 5% to 8% in coastal regions (10%–20% over FL and 5%–10% over coastal NC). Increased antecedent moisture results in increased TCs contribution to streamflow leading to different land‐surface responses for similar hurricane events.}, number={15}, journal={Geophysical Research Letters}, publisher={American Geophysical Union (AGU)}, author={Chalise, D. and Aiyyer, A. and Sankarasubramanian, A.}, year={2021}, month={Aug}, pages={e2021GL094738} }
 @article{mazrooei_reitz_wang_sankarasubramanian_2021, title={Urbanization Impacts on Evapotranspiration Across Various Spatio-Temporal Scales}, volume={9}, ISSN={["2328-4277"]}, DOI={10.1029/2021EF002045}, abstractNote={AbstractUrbanization has been shown to locally increase the nighttime temperatures creating urban heat islands, which partly arise due to evapotranspiration (ET) reduction. It is unclear how the direction and magnitude of the change in local ET due to urbanization varies globally across different climatic regimes. This knowledge gap is critical, both for the key role of ET in the energy and water balance accounting for the majority of local precipitation, and for reducing the urban heat island effect. We explore and assess the impacts of urbanization on monthly and mean annual ET across a range of landscapes from local to global spatial scales. Remotely sensed land cover and ET available at 1 km resolution are used to quantify the differences in ET between urban and surrounding non‐urban areas across the globe. The observed patterns show that the statistically significant difference between urban and non‐urban ET can be estimated to first order as a function of local hydroclimate, with arid regions seeing increased ET, and humid regions showing decreased ET. Cities under cold climates also evaporate more than their non‐urban surroundings during the winter, as the urban micro‐climate has increased energy availability resulting from human activities. Increased ET in arid cities arises from municipal water withdrawals and increased irrigation during drought conditions. These results can help inform planners to improve the integration of environmental conditions into the design and management of urban landscapes.}, number={8}, journal={Earth’s Future}, author={Mazrooei, A. and Reitz, M. and Wang, D. and Sankarasubramanian, A.}, year={2021}, month={Aug}, pages={e2021EF002045} }
 @inproceedings{chalise_arumugam_mahinthakumar_ranjithan_eaton_vidal_2020, title={A framework for ecological flow allocation in multiple reservoir operation}, author={Chalise, D. and Arumugam, S. and Mahinthakumar, G. and Ranjithan, R.S. and Eaton, Mi and Vidal, A.Ruhi}, year={2020}, month={Dec} }
 @article{das bhowmik_sankarasubramanian_2020, title={A performance‐based multi‐model combination approach to reduce uncertainty in seasonal temperature change projections}, volume={41}, ISSN={0899-8418 1097-0088}, url={http://dx.doi.org/10.1002/joc.6870}, DOI={10.1002/joc.6870}, abstractNote={AbstractFuture changes in climate variable exhibit prominent impact on flood magnitudes, crop yields, and freshwater withdrawal. Researchers typically ignore the large degree of uncertainty translated from climate projections to the estimated climate change magnitudes while applying pre‐processing approaches on climate change projections. General Circulation Models (GCM) exhibit substantial uncertainty in projecting future changes in the seasonal temperature, which is evaluated by estimating the shift in either the mean or variance. Bias between the observed changes (1950–1999) and GCM simulated changes vary across models, climate regions, seasons, and under emission scenarios. The simplest approach to reduce model structural uncertainty, equal weighting of GCMs, does not consider superiority of one or multiple GCMs compared to the rest. The current study adopts a performance‐based model combination approach that has shown efficiency in streamflow and weather forecasting, and GCM precipitation simulation. The optimal model combination approach has been modified to combine multi‐model climate change information, while yielding the spatial correlation in climate change information within a geographic region. The optimal model combination approach, along with a simple bias‐correction, is applied on 10 GCMs over nine climate regions across the coterminous United States (CONUS). We found that the optimal combination exhibits lower RMSE values as compared to the equal combination. Correlations between the model combined projections under optimal combination and the observed changes are strong and positive (>0.5). Future (2000–49) model combined projections exhibit an increase in the mean seasonal temperature by 2°C for winter and by 1°C for summer over almost all climate regions.}, number={S1}, journal={International Journal of Climatology}, publisher={Wiley}, author={Das Bhowmik, Rajarshi and Sankarasubramanian, Arumugam}, year={2020}, month={Oct}, pages={E2615–E2632} }
 @inproceedings{lall_arumugam_cioffi_devineni_doss-gollin_kwon_rajagopalan_2020, title={America's Water: Multiscale Forecasting and Innovation in Infrastructure Design & Management Instruments is critical for Climate Adaptation}, author={Lall, U. and Arumugam, S. and Cioffi, F. and Devineni, N. and Doss-Gollin, J. and Kwon, H. and Rajagopalan, B.}, year={2020}, month={Dec} }
 @inproceedings{ford_queiroz_decarolis_arumugam_2020, title={COREGS: An optimization framework to analyze water and power systems together under a changing climate}, author={Ford, L.C. and Queiroz, A. and DeCarolis, J. and Arumugam, S.}, year={2020}, month={Dec} }
 @article{basu_bhowmik_sankarasubramanian_2020, title={Changing Seasonality of Annual Maximum Floods over the Conterminous US}, url={https://doi.org/10.1002/essoar.10504706.1}, DOI={10.1002/essoar.10504706.1}, abstractNote={Understanding the flood generating mechanisms that influence flood seasonality in a region provides information on setting up relevant contingency measures. While former studies had estimated flood...}, journal={Earth and Space Science Open Archive ESSOAr;}, publisher={Water Resources Research}, author={Basu, B. and Bhowmik, R. D. and Sankarasubramanian, A.}, year={2020} }
 @misc{arumugam_2020, title={Climate and Water Management: Opportunities and Challenges” by the Government of India (Invited talk)}, author={Arumugam, S.}, year={2020}, month={Oct} }
 @article{mukhopadhyay_sankarasubramanian_awasthi_2020, title={Developing the hydrological dependency structure between streamgage and reservoir networks}, volume={7}, ISSN={2052-4463}, url={http://dx.doi.org/10.1038/s41597-020-00660-6}, DOI={10.1038/s41597-020-00660-6}, abstractNote={AbstractReliable operation of physical infrastructures such as reservoirs, dikes, nuclear power plants positioned along a river network depends on monitoring riverine conditions and infrastructure interdependency with the river network, especially during hydrologic extremes. Developing this cascading interdependency between the riverine conditions and infrastructures for a large watershed is challenging, as conventional tools (e.g., watershed delineation) do not provide the relative topographic information on infrastructures along the river network. Here, we present a generic geo-processing tool that systematically combines three geospatial layers: topographic information from the National Hydrographic Dataset (NHDPlusV2), streamgages from the USGS National Water Information System, and reservoirs from the National Inventory of Dams, to develop the interdependency between reservoirs and streamgages along the river network for upper and lower Colorado River Basin (CRB) resulting in River and Infrastructure Connectivity Network (RICON) that shows the said interdependency as a concise edge list for the CRB. Another contribution of this study is an algorithm for developing the cascading interdependency between infrastructure and riverine networks to support their management and operation.}, number={1}, journal={Scientific Data}, publisher={Springer Science and Business Media LLC}, author={Mukhopadhyay, Sudarshana and Sankarasubramanian, A. and Awasthi, Chandramauli}, year={2020}, month={Oct}, pages={319} }
 @article{xuan_ford_mahinthakumar_de souza filho_lall_sankarasubramanian_2020, title={GRAPS: Generalized Multi-Reservoir Analyses using probabilistic streamflow forecasts}, volume={133}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2020.104802}, DOI={10.1016/j.envsoft.2020.104802}, abstractNote={A multi-reservoir simulation-optimization model GRAPS, Generalized Multi-Reservoir Analyses using Probabilistic Streamflow Forecasts, is developed in which reservoirs and users across the basin are represented using a node-link representation. Unlike existing reservoir modeling software, GRAPS can handle probabilistic streamflow forecasts represented as ensembles for performing multi-reservoir prognostic water allocation and evaluate the reliability of forecast-based allocation with observed streamflow. GRAPS is applied to four linked reservoirs in the Jaguaribe Metropolitan Hydro-System (JMH) in Ceará, North East Brazil. Results from the historical simulation and the zero-inflow policy over the JMH system demonstrate the model's capability to support monthly water allocation and reproduce the observed monthly releases and storages. Additional analyses using streamflow forecast ensembles illustrate GRAP's abilities in developing storage-reliability curves under inflow-forecast uncertainty. Our analyses show that GRAPS is versatile and can be applied for 1) short-term operating policy studies, 2) long-term basin-wide planning evaluations, and 3) climate-information based application studies.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Xuan, Yi and Ford, Lucas and Mahinthakumar, Kumar and De Souza Filho, Assis and Lall, Upmanu and Sankarasubramanian, A.}, year={2020}, month={Nov}, pages={104802} }
 @article{sankarasubramanian_wang_archfield_reitz_vogel_mazrooei_mukhopadhyay_2020, title={HESS Opinions: Beyond the long-term water balance:  evolving Budyko's supply–demand framework for the  Anthropocene towards a global synthesis of land-surface  fluxes under natural and human-altered watersheds}, volume={24}, ISSN={1607-7938}, url={http://dx.doi.org/10.5194/hess-24-1975-2020}, DOI={10.5194/hess-24-1975-2020}, abstractNote={Abstract. Global hydroclimatic conditions have been substantially altered over the past century by anthropogenic influences that arise from the warming global climate and from local/regional anthropogenic disturbances. Traditionally, studies have used coupling of multiple models to understand how land-surface water fluxes vary due to changes in global climatic patterns and local land-use changes. We argue that because the basis of the Budyko framework relies on the supply and demand concept, the framework could be effectively adapted and extended to quantify the role of drivers – both changing climate and local human disturbances – in altering the land-surface response across the globe. We review the Budyko framework, along with these potential extensions, with the intent of furthering the applicability of the framework to emerging hydrologic questions. Challenges in extending the Budyko framework over various spatio-temporal scales and the use of global datasets to evaluate the water balance at these various scales are also discussed.}, number={4}, journal={Hydrology and Earth System Sciences}, publisher={Copernicus GmbH}, author={Sankarasubramanian, A. and Wang, Dingbao and Archfield, Stacey and Reitz, Meredith and Vogel, Richard M. and Mazrooei, Amirhossein and Mukhopadhyay, Sudarshana}, year={2020}, month={Apr}, pages={1975–1984} }
 @article{das bhowmik_seo_das_sankarasubramanian_2020, title={Synthesis of Irrigation Water Use in the United States: Spatiotemporal Patterns}, volume={146}, ISSN={["1943-5452"]}, DOI={10.1061/(ASCE)WR.1943-5452.0001249}, abstractNote={AbstractThe role of large-scale drivers—climate, population, and adaption of efficient irrigation practices—in controlling irrigation water use efficiency has rarely been addressed. The primary obj...}, number={7}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, author={Das Bhowmik, Rajarshi and Seo, Seung Beom and Das, Priyam and Sankarasubramanian, A.}, year={2020}, month={Jul} }
 @article{mazrooei_sankarasubramanian_lakshmi_2020, title={Technical Note: Evaluation of the skill in monthly-to-seasonal  soil moisture forecasting based on SMAP satellite  observations over the southeastern US}, volume={24}, ISSN={1607-7938}, url={http://dx.doi.org/10.5194/hess-24-1073-2020}, DOI={10.5194/hess-24-1073-2020}, abstractNote={Abstract. Providing accurate soil moisture (SM) conditions is a critical step in model initialization in weather forecasting, agricultural planning, and water resources management. This study develops monthly-to-seasonal (M2S) top layer SM forecasts by forcing 1- to 3-month-ahead precipitation forecasts with Noah3.2 Land Surface Model. The SM forecasts are developed over the southeastern US (SEUS), and the SM forecasting skill is evaluated in comparison with the remotely sensed SM observations collected by the Soil Moisture Active Passive (SMAP) satellite. Our results indicate potential in developing real-time SM forecasts. The retrospective 18-month (April 2015–September 2016) comparison between SM forecasts and the SMAP observations shows statistically significant correlations of 0.62, 0.57, and 0.58 over 1-, 2-, and 3-month lead times respectively.
                    }, number={3}, journal={Hydrology and Earth System Sciences}, publisher={Copernicus GmbH}, author={Mazrooei, Amirhossein and Sankarasubramanian, Arumugam and Lakshmi, Venkat}, year={2020}, month={Mar}, pages={1073–1079} }
 @article{yao_libera_kheimi_sankarasubramanian_wang_2020, title={The Roles of Climate Forcing and Its Variability on Streamflow at Daily, Monthly, Annual, and Long‐Term Scales}, volume={56}, ISSN={0043-1397 1944-7973}, url={http://dx.doi.org/10.1029/2020WR027111}, DOI={10.1029/2020WR027111}, abstractNote={AbstractThe temporal variability of precipitation and potential evapotranspiration affects streamflow from daily to long‐term scales, but the relative roles of different climate variabilities on streamflow at daily, monthly, annual, and mean annual scales have not been systematically investigated in the literature. This paper developed a new daily water balance model, which provides a unified framework for water balance across timescales. The daily water balance model is driven by four climate forcing scenarios (observed daily climate and observed daily climate with its intra‐monthly, intra‐annual, and inter‐annual variability removed) and applied to 78 catchments. Daily streamflow from the water balance model is aggregated to coarser timescales. The relative roles of intra‐monthly, intra‐annual, and inter‐annual climate variability are evaluated by comparing the modeled streamflow forced with the climate forcings at two consecutive timescales. It is found that daily, monthly, and annual streamflow is primarily controlled by the climate variability at the same timescale. Intra‐monthly climate variability plays a small role in monthly and annual streamflow variability. Intra‐annual climate variability has significant effects on streamflow at all the timescales, and the relative roles of inter‐annual climate variability are also significant to the monthly and mean annual streamflow, which is often disregarded. The quantitative evaluation of the roles of climate variability reveals how climate controls streamflow across timescales.}, number={7}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Yao, Lili and Libera, Dominic A. and Kheimi, Marwan and Sankarasubramanian, A. and Wang, Dingbao}, year={2020}, month={Jul} }
 @article{yao_libera_kheimi_arumugam_wang_2020, title={The Roles of Climate Variability on Runoff at Daily, Monthly, Annual, and Long-term Scales}, volume={1}, url={https://doi.org/10.1002/essoar.10501784.1}, DOI={10.1002/essoar.10501784.1}, abstractNote={Climate variability, in terms of the climatic fluctuations in precipitation and potential evapotranspiration, impacts the variability of runoff at different timescales. This paper developed a new d...}, publisher={Wiley}, author={Yao, Lili and Libera, Dominic and Kheimi, Marwan Mustafa A. and Arumugam, Sankarasubramanian and Wang, Dingbao}, year={2020}, month={Jan} }
 @inproceedings{merwade_yeghiazarian_arumugam_cai_shepherd_johnson_riasi_2020, title={The Urban Flooding Open Knowledge Network: Delivering Flood Information to AnyOne, AnyTime AnyWhere}, author={Merwade, V. and Yeghiazarian, L. and Arumugam, S. and Cai, X. and Shepherd, A. and Johnson, M. and Riasi, M.S.}, year={2020}, month={Dec} }
 @inproceedings{kumar_arumugam_zhu_2020, title={Understanding the food-energy-water nexus through hydroeconomic modeling under near-term climate change and regional development portfolios}, author={Kumar, H. and Arumugam, S. and Zhu, T.}, year={2020}, month={Dec} }
 @misc{arumugam_2020, place={Boulder}, title={Water Security under changing climate: Stressors, Opportunities and Challenges}, author={Arumugam, S.}, year={2020}, month={Oct} }
 @inproceedings{mukhopadhyay_mazrooei_arumugam_2019, title={A Geo-processing Tool for co-locating the dependency of critical infrastructure with hydrologic information network}, author={Mukhopadhyay, S. and Mazrooei, A. and Arumugam, S.}, year={2019} }
 @misc{arumugam_2019, title={Challenges in Understanding Continental Scale Groundwater Variability, Change and Appropriation}, author={Arumugam, S.}, year={2019} }
 @inproceedings{ford_queiroz_decarolis_arumugam_2019, title={Climate-Water-Energy Nexus: An Integrated Modeling Framework to Analyze Water and Power Systems Under a Changing Climate}, author={Ford, L. and Queiroz, A. and DeCarolis, J. and Arumugam, S.}, year={2019} }
 @article{huang_reich_fuentes_sankarasubramanian_2019, title={Complete spatial model calibration}, volume={13}, ISSN={1932-6157}, url={http://dx.doi.org/10.1214/18-aoas1219}, DOI={10.1214/18-aoas1219}, abstractNote={Computer simulation models are central to environmental science. These mathematical models are used to understand complex weather and climate patterns and to predict the climate's response to different forcings. Climate models are of course not perfect reflections of reality, and so comparison with observed data is needed to quantify and to correct for biases and other deficiencies. We propose a new method to calibrate model output using observed data. Our approach not only matches the marginal distributions of the model output and gridded observed data, but it simultaneously postprocesses the model output to have the same spatial correlation as the observed data. This comprehensive calibration method permits realistic spatial simulations for regional impact studies. We apply the proposed method to global climate model output in North America and show that it successfully calibrates the model output for temperature and precipitation.}, number={2}, journal={The Annals of Applied Statistics}, publisher={Institute of Mathematical Statistics}, author={Huang, Yen-Ning and Reich, Brian J. and Fuentes, Montserrat and Sankarasubramanian, A.}, year={2019}, month={Jun}, pages={746–766} }
 @inproceedings{mazrooei_arumugam_wang_2019, title={How Regional Evapotranspiration Fluxes are altered due to Urbanization?}, author={Mazrooei, A. and Arumugam, S. and Wang, D.}, year={2019} }
 @inproceedings{ruhi_hwang_devineni_mukhopadhyay_kumar_comte_worland_arumugam_2019, title={How does flow alteration propagate across a large, highly-regulated basin? Dam attributes, network context, and implications for biodiversity}, author={Ruhi, A. and Hwang, J. and Devineni, N. and Mukhopadhyay, S. and Kumar, H. and Comte, L. and Worland, S. and Arumugam, S.}, year={2019} }
 @inproceedings{chalise_arumugam_ruhi_2019, title={Human and climate variables explain spatio-temporal patterns of streamflow variation across the United States}, author={Chalise, D. and Arumugam, S. and Ruhi, A.}, year={2019} }
 @article{mazrooei_sankarasubramanian_2019, title={Improving monthly streamflow forecasts through assimilation of observed streamflow for rainfall-dominated basins across the CONUS}, volume={575}, ISSN={0022-1694}, url={http://dx.doi.org/10.1016/j.jhydrol.2019.05.071}, DOI={10.1016/j.jhydrol.2019.05.071}, abstractNote={Among different sources of uncertainty in hydrologic modeling (i.e., model structure, parameter estimation, input data, etc.), consecutive error reduction of model initial conditions can prevent a model from drifting away from reality and consequently improving model estimates. Most approaches that evaluated the correction of initial conditions through data assimilation (DA) have focused on improving hydrologic model simulations (i.e., under observed forcings) rather than evaluating the model performance in a forecasting context. This paper investigates the utility of Ensemble Kalman Filter (EnKF) data assimilation in which available observed streamflow is exploited to update state variables of a conceptual water balance model for forecasting monthly streamflow over 340 rainfall-dominated river basins across the contiguous United States (CONUS). Our results demonstrate that after EnKF application, streamflow simulation skill improves in terms of both Relative Root Mean Square Error (R-RMSE) and correlation coefficient (CC) for almost 90% of the selected river basins. Evaluating the model performance under different flow conditions shows that EnKF has stronger positive effect on monthly low flow predictions comparing to monthly high flows particularly during the summer season. The utility of EnKF is also assessed in the context of 1-month ahead streamflow forecasting. Due to the updated model initial conditions, streamflow forecasts are improved throughout the year even though the skill in hydrologic forecasts is predominantly dependent on the accuracy of precipitation forecasts.}, journal={Journal of Hydrology}, publisher={Elsevier BV}, author={Mazrooei, Amirhossein and Sankarasubramanian, A.}, year={2019}, month={Aug}, pages={704–715} }
 @article{bhowmik_sankarasubramanian_2019, title={Limitations of univariate linear bias correction in yielding cross‐correlation between monthly precipitation and temperature}, volume={39}, ISSN={0899-8418 1097-0088}, url={http://dx.doi.org/10.1002/joc.6086}, DOI={10.1002/joc.6086}, abstractNote={AbstractStatistical bias correction techniques are commonly used in climate model projections to reduce systematic biases. Among the several bias correction techniques, univariate linear bias correction (e.g., quantile mapping) is the most popular, given its simplicity. Univariate linear bias correction can accurately reproduce the observed mean of a given climate variable. However, when performed separately on multiple variables, it does not yield the observed multivariate cross‐correlation structure. In the current study, we consider the intrinsic properties of two candidate univariate linear bias‐correction approaches (simple linear regression and asynchronous regression) in estimating the observed cross‐correlation between precipitation and temperature. Two linear regression models are applied separately on both the observed and the projected variables. The analytical solution suggests that two candidate approaches simply reproduce the cross‐correlation from the general circulation models (GCMs) in the bias‐corrected data set because of their linearity. Our study adopts two frameworks, based on the Fisher z‐transformation and bootstrapping, to provide 95% lower and upper confidence limits (referred as the permissible bound) for the GCM cross‐correlation. Beyond the permissible bound, raw/bias‐corrected GCM cross‐correlation significantly differs from those observed. Two frameworks are applied on three GCMs from the CMIP5 multimodel ensemble over the coterminous United States. We found that (a) the univariate linear techniques fail to reproduce the observed cross‐correlation in the bias‐corrected data set over 90% (30–50%) of the grid points where the multivariate skewness coefficient values are substantial (small) and statistically significant (statistically insignificant) from zero; (b) the performance of the univariate linear techniques under bootstrapping (Fisher z‐transformation) remains uniform (non‐uniform) across climate regions, months, and GCMs; (c) grid points, where the observed cross‐correlation is statistically significant, witness a failure fraction of around 0.2 (0.8) under the Fisher z‐transformation (bootstrapping). The importance of reproducing cross‐correlations is also discussed along with an enquiry into the multivariate approaches that can potentially address the bias in yielding cross‐correlations.}, number={11}, journal={International Journal of Climatology}, publisher={Wiley}, author={Bhowmik, R. Das and Sankarasubramanian, A.}, year={2019}, month={Apr}, pages={4479–4496} }
 @article{de queiroz_mulcahy_sankarasubramanian_deane_mahinthakumar_lu_decarolis_2019, title={Repurposing an energy system optimization model for seasonal power generation planning}, volume={181}, ISSN={0360-5442}, url={http://dx.doi.org/10.1016/j.energy.2019.05.126}, DOI={10.1016/j.energy.2019.05.126}, abstractNote={Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate electricity demand and utilizing energy models to estimate monthly electricity generation and associated operational costs. The objective of this paper is to develop and test a computationally efficient model that can support seasonal planning while preserving key aspects of system operation over hourly and daily timeframes. To do so, an energy system optimization model is repurposed for seasonal planning using features drawn from a unit commitment model. Different scenarios utilizing a well-known test system are used to evaluate the errors associated with both the repurposed energy system model and an imperfect load forecast. The results show that the energy system optimization model using an imperfect load forecast produces differences in monthly cost and generation levels that are less than 2% compared with a unit commitment model using a perfect load forecast. The enhanced energy system optimization model can be solved approximately 100 times faster than the unit commitment model, making it a suitable tool for future work aimed at evaluating seasonal electricity generation and demand under uncertainty.}, journal={Energy}, publisher={Elsevier BV}, author={de Queiroz, A.R. and Mulcahy, D. and Sankarasubramanian, A. and Deane, J.P. and Mahinthakumar, G. and Lu, N. and DeCarolis, J.F.}, year={2019}, month={Aug}, pages={1321–1330} }
 @inproceedings{awasthi_archfield_kiang_ryberg_arumugam_2019, title={Revising Flood-Frequency Curves under Climate Change in the United States}, author={Awasthi, C. and Archfield, S.A. and Kiang, J.E. and Ryberg, K.R. and Arumugam, S.}, year={2019} }
 @inproceedings{yeghiazarian_merwede_golden_arumugam_welty_bales_cai_2019, title={Sustainable Urban Systems: Managing the Urban Multiplex and its Hydrologic Challenges}, author={Yeghiazarian, L. and Merwede, V. and Golden, H.E. and Arumugam, S. and Welty, C. and Bales, J. and Cai, X.}, year={2019} }
 @inproceedings{tu_arumugam_dong_dyckman_grantham_olson_ruddell_ulibarri_ruhi_2019, title={Sustainable Water Management in the Lower Colorado Basin: Influence of Climate and Regulatory Context on the Ability to Meet Human and Environmental Water Needs}, author={Tu, T. and Arumugam, S. and Dong, X. and Dyckman, C. and Grantham, T. and Olson, L.J. and Ruddell, B. L. and Ulibarri, N. and Ruhi, A.}, year={2019} }
 @inproceedings{yao_libera_kheimi_arumugam_wang_2019, title={The Roles of Climate Variability on Runoff at Daily, Monthly, Inter-annual, and Mean Annual Scales}, author={Yao, L. and Libera, D. and Kheimi, M. and Arumugam, S. and Wang, D.}, year={2019} }
 @inproceedings{mazrooei_arumugam_wood_2019, title={The Utility of Gauge-measured Streamflow Records in Improving Month-ahead Streamflow Forecasts through Variational Data Assimilation}, author={Mazrooei, A. and Arumugam, S. and Wood, A.}, year={2019} }
 @inproceedings{kumar_arumugam_2019, title={The role of climate on crop yield per unit area across the contiguous United States}, author={Kumar, H. and Arumugam, S.}, year={2019} }
 @article{seo_das bhowmik_sankarasubramanian_mahinthakumar_kumar_2019, title={The role of cross-correlation between precipitation and temperature in basin-scale simulations of hydrologic variables}, volume={570}, ISSN={0022-1694}, url={http://dx.doi.org/10.1016/J.JHYDROL.2018.12.076}, DOI={10.1016/J.JHYDROL.2018.12.076}, abstractNote={Uncertainty in climate forcings causes significant uncertainty in estimating streamflow and other land-surface fluxes in hydrologic model simulations. Earlier studies primarily analyzed the importance of reproducing cross-correlation between precipitation and temperature (P-T cross-correlation) using various downscaling and weather generator schemes, leaving out how such biased estimates of P-T cross-correlation impact streamflow simulation and other hydrologic variables. The current study investigates the impacts of biased P-T cross-correlation on hydrologic variables using a fully coupled hydrologic model (Penn-state Integrated Hydrologic Model, PIHM). For this purpose, a synthetic weather generator was developed to generate multiple realizations of daily climate forcings for a specified P-T cross-correlation. Then, we analyzed how reproducing/neglecting P-T cross-correlation in climate forcings affect the accuracy of a hydrologic simulation. A total of 50 synthetic data sets of daily climate forcings with different P-T cross-correlation were forced into to estimate streamflow, soil moisture, and groundwater level under humid (Haw River basin in NC, USA) and arid (Lower Verde River basin in AZ, USA) hydroclimate settings. Results show that climate forcings reproducing the P-T cross-correlation yield lesser root mean square errors in simulated hydrologic variables (primarily on the sub-surface variables) as compared to climate forcings that neglect the P-T cross-correlation. Impacts of P-T cross-correlation on hydrologic simulations were remarkable to low flow and sub-surface variables whereas less significant to flow variables that exhibit higher variability. We found that hydrologic variables with lower internal variability (for example: groundwater and soil-moisture depth) are susceptible to the bias in P-T cross-correlation. These findings have potential implications in using univariate linear downscaling techniques to bias-correct GCM forcings, since univariate linear bias-correction techniques reproduce the GCM estimated P-T cross-correlation without correcting the bias in P-T cross-correlation.}, journal={Journal of Hydrology}, publisher={Elsevier BV}, author={Seo, S.B. and Das Bhowmik, R. and Sankarasubramanian, A. and Mahinthakumar, G. and Kumar, M.}, year={2019}, month={Mar}, pages={304–314} }
 @inproceedings{mukhopadhyay_wood_arumugam_rajagopalan_2019, title={Understanding Drivers of Subseasonal to Seasonal Streamflow Variability over Contiguous United States}, author={Mukhopadhyay, S. and Wood, A. and Arumugam, S. and Rajagopalan, B.}, year={2019} }
 @inproceedings{libera_wang_arumugam_2019, title={Using an Integrated Groundwater and Surface Water Model for Understanding the Effects of Climate Change Scenarios on the Food-Energy-Water Nexus}, author={Libera, D. and Wang, D. and Arumugam, S.}, year={2019} }
 @article{mazrooei_sankarasubramanian_wood_2019, title={Variational Assimilation of Streamflow Observations in Improving Monthly Streamflow Forecasting}, DOI={10.5194/hess-2019-288}, abstractNote={Abstract. Uncertainties associated with the initial conditions (e.g. soil moisture content) of a hydrologic model have been recognized as one of the main sources of errors in hydrologic predictions, specifically over a rainfall-runoff regime. Apart from the recent advances in Data Assimilation (DA) for improving hydrologic predictions, this study explores variational assimilation (VAR) of gauge-measured daily streamflow data for updating initial state of soil moisture content of Variable Infiltration Capacity (VIC) Land Surface Model (LSM) in order to improve streamflow simulations as well as monthly streamflow forecasting. The study is conducted for the Tar River basin in North Carolina over 20-year period (1991–2010). The role of two critical parameters of VAR DA – the frequency of DA application and the length of assimilation window – in determining the skill of DA-improved streamflow predictions is also assessed. We found that correcting VIC model's initial conditions using a 7-day assimilation window results in the highest improvement in the skill of streamflow predictions quantified by Kling-Gupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE) metrics. In addition, the potential gain from VAR DA framework is quantified and compared under two 1-month ahead streamflow forecasting schemes: 1) deterministic forecasts developed by using ECHAM4.5 GCM 1-month ahead precipitation forecasts and 2) Probabilistic forecasts from Ensemble Streamflow Prediction (ESP) approach. This study also examines the persistence of the DA impact in the monthly predictions by quantifying the enhanced accuracy in daily flows over extending forecast lead time blocks. Analyses show that the the corrected initial state conditions continually enhance the 7–8 days ahead predictions, but after that the errors in forcings dominate the DA effects. Still, the overall impact of VAR DA in monthly streamflow forecasting is positive.
                        }, journal={Hydrology and Earth System Sciences}, author={Mazrooei, A. and Sankarasubramanian, A. and Wood, A.W.}, year={2019} }
 @inproceedings{archfield_ryberg_blum_barth_awasthi_li_abeshu_arumugam_2019, title={What defines a flood? Building shared understanding across differing attributes and definitions of flooding}, author={Archfield, S.A. and Ryberg, K.R. and Blum, A.G. and Barth, N.A. and Awasthi, C. and Li, H. and Abeshu, G.W. and Arumugam, S.}, year={2019} }
 @inproceedings{arumugam_wang_devineni_2018, title={A Conceptual Approach using the Limits Concept for Extending Budyko's Framework for Natural Watersheds and Human-Altered Landscapes}, author={Arumugam, S. and Wang, D. and Devineni, N.}, year={2018} }
 @article{libera_sankarasubramanian_sharma_reich_2018, title={A non-parametric bootstrapping framework embedded in a toolkit for assessing water quality model performance}, volume={107}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2018.05.013}, DOI={10.1016/j.envsoft.2018.05.013}, abstractNote={Assessing the ability to predict nutrient concentration in streams is important for determining compliance with the Numeric Nutrient Water Quality Criteria for Nitrogen in the U.S.A. Evaluation of the USGS's Load Estimator (LOADEST) and the Weighted Regression on Time, Discharge, and Season (WRTDS) models in predicting total nitrogen loads over 18 stations from the Water Quality Network show good performance (Nash-Sutcliffe Efficiency (NSE) > 0.8) in capturing the observed variability even for stations with limited data. However, both models captured only 40% of observed variance in total nitrogen (TN) concentration (NSE < 0.4). Thus, the same dataset performed differently in predicting two attributes – TN load and concentration – questioning the predictive skill of the models. This study proposes a non-parametric re-sampling approach for assessing the performance of water quality models particularly in predicting TN concentration. Null distributions for three common performance metrics belonging to populations of metrics with no skill in capturing the observed variability are constructed through a bootstrap resampling technique. Sample metrics from the LOADEST and WRTDS model in predicting TN concentration are used to calculate p-values for determining if the sample metrics belongs to the null distributions. .}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Libera, Dominic A. and Sankarasubramanian, A. and Sharma, Ashish and Reich, Brian J.}, year={2018}, month={Sep}, pages={25–33} }
 @inproceedings{mukhopadhyay_arumugam_2018, title={Application of Sub-seasonal to Seasonal (S2S) precipitation forecast in multipurpose multi-reservoir system for water and energy management}, author={Mukhopadhyay, S. and Arumugam, S.}, year={2018} }
 @article{seo_mahinthakumar_sankarasubramanian_kumar_2018, title={Assessing the restoration time of surface water and groundwater systems under groundwater pumping}, volume={32}, ISSN={1436-3240 1436-3259}, url={http://dx.doi.org/10.1007/s00477-018-1570-9}, DOI={10.1007/s00477-018-1570-9}, number={9}, journal={Stochastic Environmental Research and Risk Assessment}, publisher={Springer Science and Business Media LLC}, author={Seo, S. B. and Mahinthakumar, G. and Sankarasubramanian, A. and Kumar, M.}, year={2018}, month={Jun}, pages={2741–2759} }
 @inproceedings{arumugam_2018, place={Atlanta}, title={Climate-Water-Energy Nexus: Opportunities and Challenge}, author={Arumugam, S.}, year={2018}, month={Oct} }
 @inproceedings{arumugam_2018, title={Climate-Water-Energy Nexus: Uncertainty Reduction in Climate Forecasts Using Multimodel Combination and their Relevance to Water and Energy Management}, author={Arumugam, S.}, year={2018} }
 @inproceedings{climate-water-energy nexus: uncertainty reduction in climate forecasts using multimodel combination and their relevance to water and energy management_2018, year={2018}, month={Jun} }
 @article{seo_mahinthakumar_sankarasubramanian_kumar_2018, title={Conjunctive Management of Surface Water and Groundwater Resources under Drought Conditions Using a Fully Coupled Hydrological Model}, volume={144}, ISSN={["1943-5452"]}, DOI={10.1061/(asce)wr.1943-5452.0000978}, abstractNote={AbstractA conjunctive management model has been developed to obtain optimal allocation of surface water and groundwater under different constraints during a drought. Two simulation models—a fully d...}, number={9}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, author={Seo, S. B. and Mahinthakumar, G. and Sankarasubramanian, A. and Kumar, M.}, year={2018}, month={Sep} }
 @inproceedings{chalise_arumugam_ruhi_2018, title={Effects of dam purpose and degree of regulation on flow regime alteration over the continental United States}, author={Chalise, D.R. and Arumugam, S. and Ruhi, A.}, year={2018} }
 @inproceedings{yao_wang_libera_arumugam_2018, title={Evaluating the Controls of the Long-Term Water Balance through a Conceptual Hydrological Model Representing Saturation Excess and Infiltration Excess Runoff Generations}, author={Yao, L. and Wang, D. and Libera, D. and Arumugam, S.}, year={2018} }
 @inproceedings{mazrooei_reitz_arumugam_2018, title={Global assessment of evapotranspiration impacted by anthropogenic land cover changes}, author={Mazrooei, A. and Reitz, M. and Arumugam, S.}, year={2018} }
 @article{das_patskoski_sankarasubramanian_2018, title={Modeling the Irrigation Withdrawals Over the Coterminous US Using a Hierarchical Modeling Approach}, volume={54}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/2017wr021723}, DOI={10.1029/2017wr021723}, abstractNote={AbstractStudies focusing on national/global water scarcity require water availability and water use to quantify the imbalance. In this regard, annual irrigation withdrawal data reported by the USGS every 5 years provide a valuable information on the water use patterns over the United States. This study develops an empirical model to estimate annual irrigation water withdrawal using irrigated area, climate information, and population as predictors. Given the hierarchy in the data sources, we propose a predictive linear hierarchical regression model to develop annual irrigation water withdrawal models using varying intercepts (VI) and varying intercepts and slopes (VIS) approaches. Estimates from hierarchical models are compared with pooled and unpooled classical regression models. Overall, both hierarchical models outperform the classical models with the adjusted R2 between USGS‐reported and modeled withdrawal estimates being above 0.6 in most states using county and climate division level data. However, due to the spatial difference between the supply (rural areas) and demand (urban areas) for agriculture products, climate division level estimates exhibit a higher adjusted R2 than county level estimates. The VIS model is able to capture local effects better, particularly for states whose irrigation withdrawal patterns significantly differ from the national pattern. The performance of the models is also validated by leaving out the entire nation's water‐use data out (i.e., leave‐one‐out cross‐validation) to ensure the reported skill is not due to overfitting. Split‐sample validation in predicting 2010 irrigation withdrawal also shows the potential of the developed hierarchical model in estimating the annual irrigation withdrawals for the years with no data within the once in 5 year USGS database.}, number={6}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Das, Priyam and Patskoski, Jason and Sankarasubramanian, A.}, year={2018}, month={Jun}, pages={3769–3787} }
 @article{petersen_devineni_sankarasubramanian_2018, title={Monthly hydroclimatology of the continental United States}, volume={114}, ISSN={["1872-9657"]}, DOI={10.1016/j.advwatres.2018.02.010}, abstractNote={Physical/semi-empirical models that do not require any calibration are of paramount need for estimating hydrological fluxes for ungauged sites. We develop semi-empirical models for estimating the mean and variance of the monthly streamflow based on Taylor Series approximation of a lumped physically based water balance model. The proposed models require mean and variance of monthly precipitation and potential evapotranspiration, co-variability of precipitation and potential evapotranspiration and regionally calibrated catchment retention sensitivity, atmospheric moisture uptake sensitivity, groundwater-partitioning factor, and the maximum soil moisture holding capacity parameters. Estimates of mean and variance of monthly streamflow using the semi-empirical equations are compared with the observed estimates for 1373 catchments in the continental United States. Analyses show that the proposed models explain the spatial variability in monthly moments for basins in lower elevations. A regionalization of parameters for each water resources region show good agreement between observed moments and model estimated moments during January, February, March and April for mean and all months except May and June for variance. Thus, the proposed relationships could be employed for understanding and estimating the monthly hydroclimatology of ungauged basins using regional parameters.}, journal={ADVANCES IN WATER RESOURCES}, author={Petersen, Thomas and Devineni, Naresh and Sankarasubramanian, A.}, year={2018}, month={Apr}, pages={180–195} }
 @article{libera_sankarasubramanian_2018, title={Multivariate bias corrections of mechanistic water quality model predictions}, volume={564}, ISSN={0022-1694}, url={http://dx.doi.org/10.1016/j.jhydrol.2018.07.043}, DOI={10.1016/j.jhydrol.2018.07.043}, abstractNote={Water quality networks usually do not include observations on a continuous timescale over a long period. Statistical models that use streamflow and mechanistic models that use meteorological information and land-use are commonly employed to develop continuous streamflow and nutrient records. Given the availability of long meteorological records, mechanistic models have the potential to develop continuous water quality records, but such predictions suffer from systematic biases on both streamflow and water quality constituents. This study proposes a multivariate bias correction technique based on canonical correlation analysis (CCA) – a dimension reduction technique based on multivariate multiple regression – that reduces the bias in both streamflow and loadings simultaneously by preserving the cross-correlation. We compare the performance of CCA with linear regression (LR) in removing the systematic bias from the SWAT model forced with precipitation and temperature for three selected watersheds from the Southeastern US. First, we compare the performance of CCA with LR in removing the bias in SWAT model outputs in predicting the observed streamflow and total nitrogen (TN) loadings from the Water Quality Network (WQN) dataset. We also evaluate the potential of CCA in removing the bias in SWAT model predictions at daily and monthly time scales by considering the LOADEST model predicted loadings as the predictand for CCA and LR. Evaluation of CCA with the observed dataset and at daily and streamflow time scales shows that the proposed multivariate technique not only reduces the bias in the cross-correlation between streamflow and loadings, but also improves the joint probability of estimating observed streamflow and loadings. Potential implications of the proposed bias-correction technique, CCA, in water quality forecasting and management are also discussed.}, journal={Journal of Hydrology}, publisher={Elsevier BV}, author={Libera, Dominic A. and Sankarasubramanian, A.}, year={2018}, month={Sep}, pages={529–541} }
 @article{mukhopadhyay_patskoski_sankarasubramanian_2018, title={Role of Pacific SSTs in improving reconstructed streamflow over the coterminous US}, volume={8}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/s41598-018-23294-6}, DOI={10.1038/s41598-018-23294-6}, abstractNote={AbstractReconstructed annual streamflows using tree-ring chronologies provide useful information on moisture availability during the pre-historic period, but they have limitations in estimating high flows due to the upper bound on soil water holding capacity and trees’ metabolic growth limits. We propose a hybrid approach that uses tree-ring chronologies and climatic indices for improving high flows in 301 basins whose annual streamflows are modulated by ENSO and/or PDO. The hybrid decomposition approach relies on separating the moisture supply into the basin as outside-the-region moisture and within-the-region moisture with the former being estimated by SST indices and the latter being estimated by tree-ring chronologies. Analyses over the 301 stations from coterminous US show that the proposed approach improves the high flows and improves the overall error in the reconstructed streamflows. Potential utility of the improved reconstructed annual streamflows with improved high flows is also discussed.}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Mukhopadhyay, Sudarshana and Patskoski, Jason M. and Sankarasubramanian, A.}, year={2018}, month={Mar} }
 @inbook{wood_sankarasubramanian_mendoza_2018, title={Seasonal Ensemble Forecast Post-processing}, ISBN={9783642404573 9783642404573}, url={http://dx.doi.org/10.1007/978-3-642-40457-3_37-1}, DOI={10.1007/978-3-642-40457-3_37-1}, booktitle={Handbook of Hydrometeorological Ensemble Forecasting}, publisher={Springer Berlin Heidelberg}, author={Wood, Andrew W. and Sankarasubramanian, A. and Mendoza, Pablo}, year={2018}, pages={1–27} }
 @misc{synthesis of public water supply use in the united states and china: spatio-temporal patterns and socio-economic controls_2018, year={2018}, month={May} }
 @misc{arumugam_2018, title={Synthesis on National Water Use: Spatial Patterns and Socio-economic Controls, State of America’s Water: Present and Future}, author={Arumugam, S.}, year={2018}, month={Apr} }
 @misc{arumugam_2018, title={Synthesis on National Water Use: Spatial Patterns and Socio-economic Controls, State of America’s Water: Present and Future}, author={Arumugam, S.}, year={2018}, month={Jan} }
 @inproceedings{libera_wang_yao_arumugam_2018, title={The Role of Spatial Variability of Climate Variables on the Long-Term Water Balance Using a Semi-distributed (SWAT) Mechanistic Model}, author={Libera, D. and Wang, D. and Yao, l. and Arumugam, S.}, year={2018} }
 @inproceedings{ruhi_worland_mukhopadhyay_devineni_chalise_salinas_mazrooei_abeshu_arumugam_2018, title={Understanding the cumulative effects of dams on regional streamflow dynamics}, author={Ruhi, A. and Worland, S. and Mukhopadhyay, S. and Devineni, N. and Chalise, D. and Salinas, J.L. and Mazrooei, A. and Abeshu, G. and Arumugam, S.}, year={2018} }
 @book{sankarasubramanian_mahinthakumar_berglund_2018, title={WSC - Category 3: Collaborative Research: Water Sustainability under Near-term Climate Change: A Cross-Regional Analysis Incorporating Socio-Ecological Feedbacks and Adaptations}, author={Sankarasubramanian, A. and Mahinthakumar, G. and Berglund, E.}, year={2018} }
 @book{lall_johnson_colohan_aghakouchak_arumugam_brown_mccabe_pulwarty_2018, place={Washington, D.C.}, title={Water}, url={http://dx.doi.org/10.7930/NCA4.2018.CH3}, DOI={10.7930/NCA4.2018.CH3}, abstractNote={This report is an authoritative assessment of the science of climate change, with a focus on the United States. It represents the second of two volumes of the Fourth National Climate Assessment, mandated by the Global Change Research Act of 1990.}, journal={Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II}, institution={U.S. Global Change Research Program}, author={Lall, Upmanu and Johnson, Thomas and Colohan, Peter and Aghakouchak, Amir and Arumugam, Sankar and Brown, Casey and Mccabe, Gregory J. and Pulwarty, Roger S.}, editor={Reidmiller, D.R. and Avery, C.W. and Easterling, D.R. and Kunkel, K.E. and Lewis, K.L.M and Maycock, T.K. and Stewart, B.C.Editors}, year={2018} }
 @inproceedings{johnson_lall_aghakouchak_arumugam_brown_mccabe_pulwarty_colohan_lewis_lustig_2018, title={Water: Fourth U.S. National Climate Assessment (NCA4) Volume 2, Chapter 3}, author={Johnson, T. and Lall, U. and AghaKouchak, A. and Arumugam, S. and Brown, C. and McCabe, G.J., Jr. and Pulwarty, R.S. and Colohan, P. and Lewis, K. and Lustig, A.}, year={2018} }
 @inproceedings{patskoski_mukhopadhyaya_arumugam_2017, place={EWRI, Sacramento}, title={A Hybrid Approach Towards Streamflow Reconstruction Using Tree-ring Chronologies and Ssts over the United States}, author={Patskoski, J.S. and Mukhopadhyaya, S. and Arumugam, S.}, year={2017}, month={May} }
 @article{jayaprakasan_uma_sankarasubramanian_2018, title={Characterizing and Predicting Yelp Users' Behavior}, volume={27}, ISBN={["978-3-319-60254-7"]}, ISSN={["2197-6511"]}, DOI={10.1007/978-3-319-60255-4_2}, abstractNote={A business’ revenue is significantly dependent on Yelp user ratings (Luca, Reviews, reputation, and revenue: the case of Yelp.com. Harvard Business School Working Paper, No. 12-016, 2016; Anderson and Magruder J Econ J. 122(563):957–989, 2012). Knowing the characteristics of Yelp users will influence their business practices that would eventually help improve their Yelp ratings and consequently their revenue. We categorize Yelp users based on the average number of stars given by each user for their reviews. We determine the common characteristics and differences of users between these user groups; and determine whether these characteristics change by business category. We conclude that users whose average rating falls between 3.7 and 4.0 are the most influential and socially connected and that the type of business does not affect the characteristics of the users. Additionally, we design a two-stage predictive model to predict the average star rating of users given their features or attributes and compare its performance to standard models such as random forest and generalized additive model.}, journal={HIGHLIGHTING THE IMPORTANCE OF BIG DATA MANAGEMENT AND ANALYSIS FOR VARIOUS APPLICATIONS}, author={Jayaprakasan, Parvathy and Uma, R. N. and Sankarasubramanian, A.}, year={2018}, pages={17–35} }
 @book{sankarasubramanian_2017, title={Climate Informed Uncertainty Analyzes for Integrated Water Resources Sustainability}, institution={National Science Foundation}, author={Sankarasubramanian, A.}, year={2017} }
 @inproceedings{mazrooei_arumugam_lakshmi_wood_2017, title={Data Assimilation using observed streamflow and remotely-sensed soil moisture for improving sub-seasonal-to-seasonal forecasting}, author={Mazrooei, A. and Arumugam, S. and Lakshmi, V. and Wood, A.}, year={2017}, month={Dec} }
 @misc{arumugam_2017, title={Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the US Sunbelt}, author={Arumugam, S.}, year={2017}, month={Jul} }
 @inproceedings{mukhopadhyaya_quieroz_arumugam_2017, title={Equivalent Reservoir Modeling for Multipurpose and Multi-reservoir Systems over the Southern United States}, author={Mukhopadhyaya, S. and Quieroz, A. and Arumugam, S.}, year={2017}, month={May} }
 @inproceedings{seo_bhowmik_mahinthakumar_arumugam_2017, title={Impact of Correlation Between Precipitation and Temperature on Hydrologic Simulations}, author={Seo, S.B. and Bhowmik, R. Das and Mahinthakumar, G. and Arumugam, S.}, year={2017}, month={May} }
 @inproceedings{arumugam_mazrooei_cumbie-ward_2017, place={New Orleans}, title={Integrated Drought Monitoring and Forecasts for Decision Making in Water and Agricultural Sectors over the Southeastern US under Changing Climate}, author={Arumugam, S. and Mazrooei, A. and Cumbie-Ward, R.}, year={2017}, month={Dec} }
 @inproceedings{libera_arumugam_2017, title={Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model}, author={Libera, D. and Arumugam, S.}, year={2017}, month={Dec} }
 @article{bhowmik_sankarasubramanian_sinha_patskoski_mahinthakumar_kunkel_2017, title={Multivariate Downscaling Approach Preserving Cross Correlations across Climate Variables for Projecting Hydrologic Fluxes}, volume={18}, ISSN={1525-755X 1525-7541}, url={http://dx.doi.org/10.1175/JHM-D-16-0160.1}, DOI={10.1175/JHM-D-16-0160.1}, abstractNote={Abstract
               Most of the currently employed procedures for bias correction and statistical downscaling primarily consider a univariate approach by developing a statistical relationship between large-scale precipitation/temperature with the local-scale precipitation/temperature, ignoring the interdependency between the two variables. In this study, a multivariate approach, asynchronous canonical correlation analysis (ACCA), is proposed and applied to global climate model (GCM) historic simulations and hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to downscale monthly precipitation and temperature over the conterminous United States. ACCA is first applied to the CNRM-CM5 GCM historical simulations for the period 1950–99 and compared with the bias-corrected dataset based on quantile mapping from the Bureau of Reclamation. ACCA is also applied to CNRM-CM5 hindcasts and compared with univariate asynchronous regression (ASR), which applies regular regression to sorted GCM and observed variables. ACCA performs better than ASR and quantile mapping in preserving the cross correlation at grid points where the observed cross correlations are significant while reducing fractional biases in mean and standard deviation. Results also show that preservation of cross correlation increases the bias in standard deviation slightly, but estimates observed precipitation and temperature with increased likelihood, particularly for months exhibiting significant cross correlation. ACCA also better estimates the joint likelihood of observed precipitation and temperature under hindcasts since hindcasts estimate the observed variability in precipitation better. Implications of preserving cross correlations across climate variables for projecting runoff and other land surface fluxes are also discussed.}, number={8}, journal={Journal of Hydrometeorology}, publisher={American Meteorological Society}, author={Bhowmik, Rajarshi Das and Sankarasubramanian, A. and Sinha, Tushar and Patskoski, Jason and Mahinthakumar, G. and Kunkel, Kenneth E.}, year={2017}, month={Aug}, pages={2187–2205} }
 @article{kominoski_ruhí_hagler_petersen_sabo_sinha_sankarasubramanian_olden_2017, title={Patterns and drivers of fish extirpations in rivers of the American Southwest and Southeast}, volume={24}, ISSN={1354-1013 1365-2486}, url={http://dx.doi.org/10.1111/gcb.13940}, DOI={10.1111/gcb.13940}, abstractNote={AbstractEffective conservation of freshwater biodiversity requires spatially explicit investigations of how dams and hydroclimatic alterations among climate regions may interact to drive species to extinction. We investigated how dams and hydroclimatic alterations interact with species ecological and life history traits to influence past extirpation probabilities of native freshwater fishes in the Upper and Lower Colorado River (CR), Alabama‐Coosa‐Tallapoosa (ACT), and Apalachicola‐Chattahoochee‐Flint (ACF) basins. Using long‐term discharge data for continuously gaged streams and rivers, we quantified streamflow anomalies (i.e., departure “expected” streamflow) at the sub‐basin scale over the past half‐century. Next, we related extirpation probabilities of native fishes in both regions to streamflow anomalies, river basin characteristics, species traits, and non‐native species richness using binomial logistic regression. Sub‐basin extirpations in the Southwest (n = 95 Upper CR, n = 130 Lower CR) were highest in lowland mainstem rivers impacted by large dams and in desert springs. Dampened flow seasonality, increased longevity (i.e., delayed reproduction), and decreased fish egg sizes (i.e., lower parental care) were related to elevated fish extirpation probability in the Southwest. Sub‐basin extirpations in the Southeast (ACT n = 46, ACF n = 22) were most prevalent in upland rivers, with flow dependency, greater age and length at maturity, isolation by dams, and greater distance upstream. Our results confirm that dams are an overriding driver of native fish species losses, irrespective of basin‐wide differences in native or non‐native species richness. Dams and hydrologic alterations interact with species traits to influence community disassembly, and very high extirpation risks in the Southeast are due to interactions between high dam density and species restricted ranges. Given global surges in dam building and retrofitting, increased extirpation risks should be expected unless management strategies that balance flow regulation with ecological outcomes are widely implemented.}, number={3}, journal={Global Change Biology}, publisher={Wiley}, author={Kominoski, John S. and Ruhí, Albert and Hagler, Megan M. and Petersen, Kelly and Sabo, John L. and Sinha, Tushar and Sankarasubramanian, Arumugam and Olden, Julian D.}, year={2017}, month={Nov}, pages={1175–1185} }
 @article{bhowmik_sharma_sankarasubramanian_2017, title={Reducing Model Structural Uncertainty in Climate Model Projections—A Rank-Based Model Combination Approach}, volume={30}, ISSN={0894-8755 1520-0442}, url={http://dx.doi.org/10.1175/JCLI-D-17-0225.1}, DOI={10.1175/JCLI-D-17-0225.1}, abstractNote={ Future changes in monthly precipitation are typically evaluated by estimating the shift in the long-term mean/variability or based on the change in the marginal distribution. General circulation model (GCM) precipitation projections deviate across various models and emission scenarios and hence provide no consensus on the expected future change. The current study proposes a rank/percentile-based multimodel combination approach to account for the fact that alternate model projections do not share a common time indexing. The approach is evaluated using 10 GCM historical runs for the current period and is validated by comparing with two approaches: equal weighting and a non-percentile-based optimal weighting. The percentile-based optimal combination exhibits lower values of RMSE in estimating precipitation terciles. Future (2000–49) multimodel projections show that January and July precipitation exhibit an increase in simulated monthly extremes (25th and 75th percentiles) over many climate regions of the conterminous United States. }, number={24}, journal={Journal of Climate}, publisher={American Meteorological Society}, author={Bhowmik, R. Das and Sharma, A. and Sankarasubramanian, A.}, year={2017}, month={Dec}, pages={10139–10154} }
 @article{patskoski_sankarasubramanian_2017, title={Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed, reconstructed and projected streamflow}, volume={32}, ISSN={1436-3240 1436-3259}, url={http://dx.doi.org/10.1007/s00477-017-1456-2}, DOI={10.1007/s00477-017-1456-2}, number={4}, journal={Stochastic Environmental Research and Risk Assessment}, publisher={Springer Science and Business Media LLC}, author={Patskoski, Jason and Sankarasubramanian, A.}, year={2017}, month={Sep}, pages={1065–1083} }
 @inproceedings{mukhopadhyaya_arumugam_2017, title={Sub-seasonal-to-seasonal Reservoir Inflow Forecast using Bayesian Hierarchical Hidden Markov Model}, author={Mukhopadhyaya, S. and Arumugam, S.}, year={2017}, month={Dec} }
 @article{sankarasubramanian_sabo_larson_seo_sinha_bhowmik_vidal_kunkel_mahinthakumar_berglund_et al._2017, title={Synthesis of public water supply use in the United States: Spatio‐temporal patterns and socio‐economic controls}, volume={5}, ISSN={2328-4277 2328-4277}, url={http://dx.doi.org/10.1002/2016EF000511}, DOI={10.1002/2016ef000511}, abstractNote={AbstractRecent U.S. Geological Survey water‐use report suggests that increasing water‐use efficiency could mitigate the supply‐and‐demand imbalance arising from changing climate and growing population. However, this rich data have neither analyzed to understand the underlying patterns, nor have been investigated to identify the factors contributing to this increased efficiency. A national‐scale synthesis of public supply withdrawals (“withdrawals”) reveals a strong North–south gradient in public supply water use with the increasing population in the South contributing to increased withdrawal. Contrastingly, a reverse South–north gradient exists in per capita withdrawals (“efficiency”), with northern states consistently improving the efficiency, while the southern states' efficiency declined. Our analyses of spatial patterns of per capita withdrawals further demonstrate that urban counties exhibit improved efficiency over rural counties. Improved efficiency is also demonstrated over high‐income and well‐educated counties. Given the potential implications of the findings in developing long‐term water conservation measures (i.e., increasing block rates), we argue the need for frequent updates, perhaps monthly to annual, of water‐use data for identifying effective strategies that control the water‐use efficiency in various geographic settings under a changing climate.}, number={7}, journal={Earth's Future}, publisher={American Geophysical Union (AGU)}, author={Sankarasubramanian, A. and Sabo, J. L. and Larson, K. L. and Seo, S. B. and Sinha, T. and Bhowmik, R. and Vidal, A. Ruhi and Kunkel, K. and Mahinthakumar, G. and Berglund, E. Z. and et al.}, year={2017}, month={Jul}, pages={771–788} }
 @misc{arumugam_2017, title={Uncertainty Reduction in Climate Forecasts using Multimodel Combination and their Relevance to Water and Energy Management}, author={Arumugam, S.}, year={2017}, month={Mar} }
 @misc{arumugam_2017, title={Utilizing Climate Information for Improving Water, Energy, & Ecological Management}, author={Arumugam, S.}, year={2017}, month={Apr} }
 @inproceedings{mazrooei_arumugan_2017, title={Utilizing Enkf Data Assimilation in Improving ‘abcd’ Lumped Water Balance Model Performance}, author={Mazrooei, A. and Arumugan, S.}, year={2017}, month={May} }
 @article{mazrooei_sankarasubramanian_2017, title={Utilizing Probabilistic Downscaling Methods to Develop Streamflow Forecasts from Climate Forecasts}, volume={18}, ISSN={1525-755X 1525-7541}, url={http://dx.doi.org/10.1175/JHM-D-17-0021.1}, DOI={10.1175/JHM-D-17-0021.1}, abstractNote={Abstract
               Statistical information from ensembles of climate forecasts can be utilized in improving the streamflow predictions by using different downscaling methods. This study investigates the use of multinomial logistic regression (MLR) in downscaling large-scale ensemble climate forecasts into basin-scale probabilistic streamflow forecasts of categorical events over major river basins across the U.S. Sun Belt. The performance of MLR is then compared with the categorical forecasts estimated from the traditional approach, principal component regression (PCR). Results from both cross validation and split sampling reveal that in general, the probabilistic categorical forecasts from the MLR model have more accuracy and exhibit higher rank probability skill score (RPSS) compared to the PCR probabilistic forecasts. MLR forecasts are also more skillful than PCR forecasts during the winter season as well as for basins that exhibit high interannual variability in streamflows. The role of ensemble size of precipitation forecasts in developing MLR-based streamflow forecasts was also investigated. Because of its simplicity, MLR offers an alternate, reliable approach to developing categorical streamflow forecasts.}, number={11}, journal={Journal of Hydrometeorology}, publisher={American Meteorological Society}, author={Mazrooei, Amirhossein and Sankarasubramanian, A.}, year={2017}, month={Nov}, pages={2959–2972} }
 @misc{arumugam_2016, title={Climate Variability and its Impacts over the Southeast}, author={Arumugam, S.}, year={2016}, month={Feb} }
 @inproceedings{mazrooei_arumugam_2016, title={Developing Categorical Streamflow Forecasts from Climate Forecasts Using Probabilistic Downscaling Methods}, author={Mazrooei, A. and Arumugam, S.}, year={2016}, month={May} }
 @article{vogel_arumugam_2016, title={Flood forecasting a global perspective foreword}, DOI={10.1016/b978-0-12-801884-2.10000-3}, journal={Flood Forecasting: A Global Perspective}, author={Vogel, R. M. and Arumugam, Sankarasubramanian}, year={2016}, pages={XVII-} }
 @article{seo_sinha_mahinthakumar_sankarasubramanian_kumar_2016, title={Identification of dominant source of errors in developing streamflow and groundwater projections under near-term climate change}, volume={121}, ISSN={["2169-8996"]}, DOI={10.1002/2016jd025138}, abstractNote={AbstractUncertainties in projecting the changes in hydroclimatic variables (i.e., temperature and precipitation) under climate change partly arises from the inability of global circulation models (GCMs) in explaining the observed changes in hydrologic variables. Apart from the unexplained changes by GCMs, the process of customizing GCM projections to watershed scale through a model chain—spatial downscaling, temporal disaggregation, and hydrologic model—also introduces errors, thereby limiting the ability to explain the observed changes in hydrologic variability. Toward this, we first propose metrics for quantifying the errors arising from different steps in the model chain in explaining the observed changes in hydrologic variables (streamflow and groundwater). The proposed metrics are then evaluated using a detailed retrospective analyses in projecting the changes in streamflow and groundwater attributes in four target basins that span across a diverse hydroclimatic regimes over the U.S. Sunbelt. Our analyses focused on quantifying the dominant sources of errors in projecting the changes in eight hydrologic variables—mean and variability of seasonal streamflow, mean and variability of 3 day peak seasonal streamflow, mean and variability of 7 day low seasonal streamflow, and mean and standard deviation of groundwater depth—over four target basins using an Penn state Integrated Hydrologic Model (PIHM) between the period 1956–1980 and 1981–2005. Retrospective analyses show that small/humid (large/arid) basins show increased (reduced) uncertainty in projecting the changes in hydrologic attributes. Further, changes in error due to GCMs primarily account for the unexplained changes in mean and variability of seasonal streamflow. On the other hand, the changes in error due to temporal disaggregation and hydrologic model account for the inability to explain the observed changes in mean and variability of seasonal extremes. Thus, the proposed metrics provide insights on how the error in explaining the observed changes being propagated through the model under different hydroclimatic regimes.}, number={13}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, author={Seo, S. B. and Sinha, T. and Mahinthakumar, G. and Sankarasubramanian, A. and Kumar, M.}, year={2016}, month={Jul}, pages={7652–7672} }
 @inproceedings{bhowmik_arumugam_2016, title={Importance of Preserving Cross-correlation in developing Statistically Downscaled Climate Forcings}, publisher={EWRI, West Palm Beach}, author={Bhowmik, R.D. and Arumugam, S.}, year={2016}, month={May} }
 @inproceedings{libera_arumugam_2016, title={Multivariate Bias Correction Procedures for Improving Water Quality Predictions using Mechanistic Models}, author={Libera, D. and Arumugam, S.}, year={2016}, month={May} }
 @inproceedings{das_arumugam_2016, title={Predictive Classical and Hierarchical Bayesian Modeling for Yearly Irrigation Water Withdrawal over the Coterminous USA}, author={Das, P. and Arumugam, S.}, year={2016}, month={May} }
 @inproceedings{arumugam_ruhi_sabo_sinha_seo_bhowmik_2016, title={Synthesis of US Public Water Supply: Spatio-temporal Patterns and Socio-Economic Controls}, author={Arumugam, S. and Ruhi, A. and Sabo, J. and Sinha, T. and Seo, S.B. and Bhowmik, R.D.}, year={2016}, month={Dec} }
 @inproceedings{seo_mahinthakumar_arumugam_kumar_2016, title={Understanding the Effects of Groundwater Pumping on Streamflow: Human-Feedback Analysis on Downstream Impacts and Relevance to Reservoir Management}, publisher={EWRI, West Palm Beach}, author={Seo, S.B. and Mahinthakumar, G. and Arumugam, S. and Kumar, M.}, year={2016}, month={May} }
 @inproceedings{arumugam_quieroz_patskoski_mahinthakumar_decarolis_2016, title={Utilizing Climate Forecasts for Improving Water and Power Systems Coordination}, author={Arumugam, S. and Quieroz, A. and Patskoski, J. and Mahinthakumar, G. and DeCarolis, J.}, year={2016}, month={Dec} }
 @misc{arumugam_2016, title={Water and ecological sustainability under near-term climate change}, author={Arumugam, S.}, year={2016}, month={Mar} }
 @article{wang_brill_ranjithan_sankarasubramanian_2015, title={A framework for incorporating ecological releases in single reservoir operation}, volume={78}, ISSN={0309-1708}, url={http://dx.doi.org/10.1016/j.advwatres.2015.01.006}, DOI={10.1016/j.advwatres.2015.01.006}, abstractNote={Most reservoir operation practices consider downstream environmental flow as a constraint to meet a minimum release. The resulting flow regime may not necessarily provide downstream aquatic conditions to support healthy ecosystems. These effects can be quantified in terms of changes in values of parameters that represent the flow regimes. Numerous studies have focused on determining the ecological response to hydrological alteration caused by reservoir operation. To mitigate hydrological alteration and restore the natural flow regime as much as possible, a reservoir operation framework is proposed to explicitly incorporate ecological flow requirements. A general optimization-based decision model is presented to consider simultaneously the multiple anthropogenic uses of the reservoir and desirable ecological releases represented by parameters that capture the flow regime. Multiple uses of the reservoir, including water supply, hydropower generation, etc., are modeled as a mixed integer programming problem. Hydropower generation, which is represented by a nonlinear function that usually depends on head and water flow, is linearized using a two-dimensional function. Investigations using a reservoir in Virginia, located in the southeastern United States, demonstrate that compared to standard releases based on current operation practice, releases simulated using this framework perform better in mimicking pre-development flows. The tradeoff between anthropogenic use and ecological releases is investigated. The framework is first demonstrated for instances with perfect stream flow information. To examine the flexibility of this framework in reservoir release management, monthly flow forecasts and disaggregated daily flow conditions are incorporated. Retrospective monthly flow forecasts are obtained through regression models that use gridded precipitation forecasts and gridded soil moisture estimates as predictors. A nonparametric method is chosen to disaggregate monthly flow forecasts to daily flow conditions. Compared with daily flow climatology, forecasted monthly and daily flow better preserves flow variability and result in lower changes of flow parameters under the proposed framework.}, journal={Advances in Water Resources}, publisher={Elsevier BV}, author={Wang, Hui and Brill, Earl D. and Ranjithan, Ranji S. and Sankarasubramanian, A.}, year={2015}, month={Apr}, pages={9–21} }
 @inproceedings{arumugam_2015, title={Climate-Water-Energy Nexus: Opportunities and Challenges}, author={Arumugam, S.}, year={2015}, month={Mar} }
 @article{mazrooei_sinha_sankarasubramanian_kumar_peters‐lidard_2015, title={Decomposition of sources of errors in seasonal streamflow forecasting over the U.S. Sunbelt}, volume={120}, ISSN={2169-897X 2169-8996}, url={http://dx.doi.org/10.1002/2015jd023687}, DOI={10.1002/2015jd023687}, abstractNote={AbstractSeasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real‐time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8° to 1/8° spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel‐nearest neighbor (K‐NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS‐2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS‐2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991–2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.}, number={23}, journal={Journal of Geophysical Research: Atmospheres}, publisher={American Geophysical Union (AGU)}, author={Mazrooei, Amirhossein and Sinha, Tushar and Sankarasubramanian, A. and Kumar, Sujay and Peters‐Lidard, Christa D.}, year={2015}, month={Dec} }
 @book{sankarasubramanian_boyles_mazoorei_singh_2015, place={Raleigh, NC}, title={Experimental Reservoir Storage Forecasts Utilizing Climate-Information Based Streamflow Forecasts}, institution={NC Water Resources Research Institute}, author={Sankarasubramanian, A. and Boyles, R. and Mazoorei, A. and Singh, H.}, year={2015}, month={Mar} }
 @article{singh_sinha_sankarasubramanian_2015, title={Impacts of Near-Term Climate Change and Population Growth on Within-Year Reservoir Systems}, volume={141}, ISSN={0733-9496 1943-5452}, url={http://dx.doi.org/10.1061/(ASCE)WR.1943-5452.0000474}, DOI={10.1061/(ASCE)WR.1943-5452.0000474}, abstractNote={AbstractClimate change and increased urban demand can significantly stress water supply systems, emphasizing the importance of reallocating reservoir storage for the designed uses. Most studies on climate change assessment have analyzed arid region reservoirs due to high interannual variability in streamflows. This study focuses on a within-year reservoir system, Lake Jordan in North Carolina, from a temperate region that has been experiencing rapid growth since the 1990s. Given the interest in utilizing climate change projections for planning purposes, the current operational policies are evaluated, and revised rules for operating the within-year system over 30 year period (2012–2041) are suggested. Downscaled general circulation model (GCM) projections are used to implement the soil and water assessment tool (SWAT) model for the Upper Cape Fear River basin to estimate changes in mean monthly streamflows during 2012–2041 at Lake Jordan. Projected monthly streamflows from four GCMs indicate wet winter con...}, number={6}, journal={Journal of Water Resources Planning and Management}, publisher={American Society of Civil Engineers (ASCE)}, author={Singh, Harminder and Sinha, Tushar and Sankarasubramanian, A.}, year={2015}, month={Jun}, pages={04014078} }
 @article{patskoski_sankarasubramanian_2015, title={Improved reservoir sizing utilizing observed and reconstructed streamflows within a Bayesian combination framework}, volume={51}, ISSN={0043-1397 1944-7973}, url={http://dx.doi.org/10.1002/2014WR016189}, DOI={10.1002/2014wr016189}, abstractNote={AbstractReservoir sizing is a critical task as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, sequent peak algorithm (SQP) is used with observed streamflow to obtain reservoir storage estimates. To overcome the limited sample length of observed streamflow, synthetic streamflow traces estimated from observed streamflow characteristics are provided with SQP to estimate the distribution of storage. However, the parameters in the stochastic streamflow generation model are derived from the observed record and are still unrepresentative of the long‐term drought records. Paleo‐streamflow time series, usually reconstructed using tree‐ring chronologies, span for a longer period than the observed streamflow and provide additional insight into the preinstrumental drought record. This study investigates the capability of reconstructed streamflow records in reducing the uncertainty in reservoir storage estimation. For this purpose, we propose a Bayesian framework that combines observed and reconstructed streamflow for estimating the parameters of the stochastic streamflow generation model. By utilizing reconstructed streamflow records from two potential stations over the Southeastern U.S., the distribution of storage estimated using the combined streamflows is compared with the distribution of storage estimated using observed streamflow alone based on split‐sample validation. Results show that combining observed and reconstructed streamflow yield stochastic streamflow generation parameters more representative of the longer streamflow record resulting in improved reservoir storage estimates. We also generalize the findings through a synthetic experiment by generating reconstructed streamflow records of different sample length and skill. The analysis shows that uncertainty in storage estimates reduces by incorporating reconstruction records with higher skill and longer sample lengths. Potential applications of the proposed methodology are also discussed.}, number={7}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Patskoski, Jason and Sankarasubramanian, A.}, year={2015}, month={Jul}, pages={5677–5697} }
 @misc{arumugam_2015, title={Leonardo da Vinci: Contribution of the renaissance artist towards water management}, author={Arumugam, S.}, year={2015}, month={Oct} }
 @article{patskoski_sankarasubramanian_wang_2015, title={Reconstructed streamflow using SST and tree-ring chronologies over the southeastern United States}, volume={527}, ISSN={["1879-2707"]}, DOI={10.1016/j.jhydrol.2015.05.041}, abstractNote={A hybrid approach that considers both tree-ring chronologies and sea surface temperature (SST) data for reconstructing annual streamflow is proposed. The most common approach to reconstruct annual streamflow is to develop statistical regression relationships between principal components of tree rings and observed annual flow values and then extend the relationship to estimate annual streamflow values over the period for which tree-ring chronology is available. The primary limitation of this approach is in estimating high flow values since tree-ring growth reaches its potential limit during wet years. The proposed hybrid approach overcomes this limitation by using SST conditions from the tropical Pacific and tree-ring chronologies from the watershed for reconstructing annual streamflows. For this purpose, we considered eight virgin watersheds having long tree-ring chronologies over the southeastern United States. Given the role of El Nino Southern Oscillation (ENSO) in influencing the hydroclimatology of the southeastern United States, we estimated the periodic component of streamflow using Nino3.4 – an index representing ENSO – and the non-periodic component of streamflow using the non-periodic component of tree rings that represent interannual variability of moisture supply within the region. We employed Singular Spectrum Analysis (SSA) for extracting periodic and non-periodic components from tree-ring chronologies, Nino3.4 and streamflow data. The proposed tree ring and SST hybrid approach was compared with the traditional principal component regression (PCR) approach based on cross-validation. Results show that inclusion of SST provided better reconstructed flow values during high flow years but also resulted in overestimation of flow during low flow years. Combination of annual streamflow estimates from the two models – PCR and the hybrid approach – resulted in improved estimates of reconstructed annual streamflow for the selected eight watersheds. Potential applications for such improved reconstructed streamflow estimates is also discussed.}, journal={JOURNAL OF HYDROLOGY}, author={Patskoski, Jason and Sankarasubramanian, A. and Wang, Hui}, year={2015}, month={Aug}, pages={761–775} }
 @article{li_sankarasubramanian_ranjithan_sinha_2016, title={Role of multimodel combination and data assimilation in improving streamflow prediction over multiple time scales}, volume={30}, ISSN={1436-3240 1436-3259}, url={http://dx.doi.org/10.1007/s00477-015-1158-6}, DOI={10.1007/s00477-015-1158-6}, number={8}, journal={Stochastic Environmental Research and Risk Assessment}, publisher={Springer Science and Business Media LLC}, author={Li, Weihua and Sankarasubramanian, A. and Ranjithan, R. S. and Sinha, Tushar}, year={2016}, month={Sep}, pages={2255–2269} }
 @misc{arumugam_2015, title={Synthesis on National Water Use: Spatial Patterns and Controls}, author={Arumugam, S.}, year={2015}, month={Oct} }
 @misc{arumugam_2015, title={Synthesis on National Water Use: Spatial Patterns and Socio-economic Controls, State of America’s Water: Present and Future}, author={Arumugam, S.}, year={2015}, month={May} }
 @inproceedings{arumugam_ruhi_sabo_sinha_seo_bhowmik_2015, title={The role of hydroclimate and water use on freshwater sustainability over the Coterminous US}, author={Arumugam, S. and Ruhi, A. and Sabo, J. and Sinha, T. and Seo, S.B. and Bhowmik, R.D.}, year={2015}, month={Dec} }
 @article{wang_sankarasubramanian_ranjithan_2015, title={Understanding the low-frequency variability in hydroclimatic attributes over the southeastern US}, volume={521}, ISSN={["1879-2707"]}, DOI={10.1016/j.jhydrol.2014.09.081}, abstractNote={Most studies on evaluating the potential in developing seasonal to interannual hydroclimatic forecasts have focused on associating low-frequency climatic conditions with basin-level precipitation/streamflow. The motivation of this study is to provide an understanding on how land surface characteristics modulate the low-frequency (interannual to decadal) variability in precipitation to develop low-frequency signal in streamflow. For this purpose, we consider basins with minimum anthropogenic impacts over southeastern United States and apply Singular Spectrum Analysis (SSA), a data-driven spectrum analysis tool, on annual precipitation and streamflow time series for detecting the dominant frequencies and for estimating the associated variability with them. Hypothesis test against an AR(1) process is carried out via Monte Carlo SSA for detecting significant (at 90% confidence level) low-frequency oscillations. Thus, the study investigates how the observed low-frequency oscillations in precipitation/streamflow vary over the southeastern United States and also their associations with climatic conditions. For most study basins, precipitation exhibits higher low-frequency oscillations than that of streamflow primarily due to reduction in variability by basin storage. Investigating this further, we found that the percentage variance accounted by low-frequency oscillations in streamflow being higher for larger basins which primarily indicates the increased role of climate and basin storage. To develop a fundamental understanding on how basin storage controls the low-frequency oscillations in streamflow, a simple annual hydrological model is employed to explore how the given low-frequency signal in precipitation being modified under different baseflow index conditions and groundwater residence time. Implications of these analyses relating to streamflow predictions and model calibration are also discussed.}, journal={JOURNAL OF HYDROLOGY}, author={Wang, Hui and Sankarasubramanian, A. and Ranjithan, R. S.}, year={2015}, month={Feb}, pages={170–181} }
 @inproceedings{ali_shafiee_berglund_arumugam_2014, title={An Agent-Based Modeling Approach to Simulate the Dynamics of Water Supply and Water Demand}, ISBN={9780784413548}, url={http://dx.doi.org/10.1061/9780784413548.179}, DOI={10.1061/9780784413548.179}, abstractNote={Water resources management requires an insightful balance between water demand and water supply. US water supply is at risk of shortage due to population growth, land use changes, climate change, and water use behaviors of customers. Long-term water supply planning is conventionally based on projections of population growth and demands; however, the sustainability of water resources depends on the dynamic interactions among the environmental, technological, and social characteristics of the water system and local population. This research develops a sociotechnical model to simulate the interactions among the social and engineering systems. An agent-based model (ABM) is used to simulate households and water-use behaviors and is coupled with a set of technical models, including climate change projections, a hydrological watershed model, and a water reservoir model. The ABM framework simulates population growth as an increase in the number of household agents, which affects the water supply and demand balance through increasing demands. Household agents increase irrigation demands due to climate change and decrease indoor demands as they adopt low-flow appliances. Agents also respond to drought restrictions by limiting their use of water for outdoor application. The effects of these actions on the reservoir storage are simulated using engineering models and data describing the climatological and hydrological conditions of the watershed. The ABM framework is developed and demonstrated for the Raleigh, NC, water supply system, which withdraws water from the Falls Lake Reservoir. The model is tested against historic data (1983-2013) and is used to explore the effectiveness of policies for the period 2013-2033. Conservation programs and drought restrictions are simulated and to evaluate the need to develop new water source in the future. The ABM framework facilitates simulations that generate new insight about the dynamics involved in the sustainability of water supply and demands. 1806 World Environmental and Water Resources Congress 2014: Water without Borders © ASCE 2014}, booktitle={World Environmental and Water Resources Congress 2014}, publisher={American Society of Civil Engineers}, author={Ali, Alireza Mashhadi and Shafiee, M. Ehsan and Berglund, Emily Zechman and Arumugam, Sankarasubramanian}, year={2014}, month={May} }
 @article{robertson_baethgen_block_lall_sankarasubramanian_de assis de souza filho_j verbist_2014, title={Climate risk management for water in semi–arid regions}, volume={1}, ISSN={2194-6434}, url={http://dx.doi.org/10.1186/2194-6434-1-12}, DOI={10.1186/2194-6434-1-12}, abstractNote={New sources of hydroclimate information based on forecast models and observational data have the potential to greatly improve the management of water resources in semi-arid regions prone to drought. Better management of climate-related risks and opportunities requires both new methods to develop forecasts of drought indicators and river flow, as well as better strategies to incorporate these forecasts into drought, river or reservoir management systems. In each case the existing institutional and policy context is key, making a collaborative approach involving stakeholders essential. This paper describes work done at the IRI over the past decade to develop statistical hydrologic forecast and water allocation models for the semi arid regions of NE Brazil (the "Nordeste") and central northern Chile based on seasonal climate forecasts. In both locations, downscaled precipitation forecasts based on lagged SST predictors or GCM precipitation forecasts exhibit quite high skill. Spring-summer melt flow in Chile is shown to be highly predictable based on estimates of previous winter precipitation, and moderately predictable up to 6 months in advance using climate forecasts. Retrospective streamflow forecasts here are quite effective in predicting reductions in water rights during dry years. For the multi-use Oros reservoir in NE Brazil, streamflow forecasts have the most potential to optimize water allocations during multi-year low-flow periods, while the potential is higher for smaller reservoirs, relative to demand. This work demonstrates the potential value of seasonal climate forecasting as an integral part of drought early warning and for water allocation decision support systems in semi-arid regions. As human demands for water rise over time this potential is certain to rise in the future.}, number={1}, journal={Earth Perspectives}, publisher={Springer Science and Business Media LLC}, author={Robertson, Andrew W and Baethgen, Walter and Block, Paul and Lall, Upmanu and Sankarasubramanian, Arumugam and de Assis de Souza Filho, Francisco and J Verbist, Koen M}, year={2014}, pages={12} }
 @article{sinha_sankarasubramanian_mazrooei_2014, title={Decomposition of Sources of Errors in Monthly to Seasonal Streamflow Forecasts in a Rainfall-Runoff Regime}, volume={15}, ISSN={["1525-7541"]}, DOI={10.1175/jhm-d-13-0155.1}, abstractNote={Abstract
               Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.}, number={6}, journal={JOURNAL OF HYDROMETEOROLOGY}, author={Sinha, Tushar and Sankarasubramanian, A. and Mazrooei, Amirhossein}, year={2014}, month={Dec}, pages={2470–2483} }
 @article{zaved_khan_mehrotra_sharma_sankarasubramanian_2014, title={Global Sea Surface Temperature Forecasts Using an Improved Multimodel Approach}, volume={27}, ISSN={["1520-0442"]}, DOI={10.1175/jcli-d-13-00486.1}, abstractNote={Abstract
               With the availability of hindcasts or real-time forecasts from a number of coupled climate models, multimodel ensemble forecasting systems have gained popularity in recent years. However, many models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Assigning equal weights to each model in space and time may result in a biased forecast with narrower confidence limits than is appropriate. Although methods for combining forecasts that take into consideration differences in model accuracy over space and time exist, they suffer from a lack of consideration of the intermodel dependence that may exist. This study proposes an approach that considers the dependence among models while combining multimodel ensemble forecast. The approach is evaluated by combining sea surface temperature (SST) forecasts from five climate models for the period 1960–2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, is predicted three months in advance using the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for all the seasons over the majority of grid points compared to the case in which the dependence among the models is ignored. Consequently, the proposed approach of combining multiple models, taking into account the interdependence that exists, provides an attractive strategy to develop improved SST forecasts.}, number={10}, journal={JOURNAL OF CLIMATE}, author={Zaved, Mohammad and Khan, Kaiser and Mehrotra, Rajeshwar and Sharma, Ashish and Sankarasubramanian, A.}, year={2014}, month={May}, pages={3505–3515} }
 @inproceedings{arumugam_2014, title={Implications of Water Use and Hydroclimatic Anomalies on the Freshwater Sustainability across the US Sunbelt}, author={Arumugam, S.}, year={2014}, month={Dec} }
 @inproceedings{arumugam_lall_2014, title={Improved Water and Energy Management Utilizing Seasonal to Interannual Hydroclimatic Forecasts}, author={Arumugam, S. and Lall, U.}, year={2014}, month={Dec} }
 @article{li_sankarasubramanian_ranjithan_brill_2014, title={Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework}, volume={50}, ISSN={0043-1397}, url={http://dx.doi.org/10.1002/2013WR015248}, DOI={10.1002/2013wr015248}, abstractNote={AbstractRegional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate‐information‐based inflow forecast for minimizing the deviations from the end‐of‐season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no‐transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season‐ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system.}, number={8}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Li, Weihua and Sankarasubramanian, A. and Ranjithan, R. S. and Brill, E. D.}, year={2014}, month={Aug}, pages={6810–6827} }
 @article{arumugam_wood_rajagopalan_schaake_2014, title={Improving Forecasts for Water Management}, volume={95}, ISSN={0096-3941}, url={http://dx.doi.org/10.1002/2014EO010004}, DOI={10.1002/2014EO010004}, abstractNote={Recent advances in seasonal to interannual hydroclimate predictions provide an opportunity for developing a proactive approach toward water management. This motivated a recent AGU Chapman Conference (see program details at http://chapman.agu.org/watermanagement/). Approximately 85 participants from the United States, Oceania, Asia, Europe, and South America presented and discussed the current state of successes, challenges, and opportunities in seasonal to interannual hydroclimate forecasts and water management, and a number of key messages emerged.}, number={1}, journal={Eos, Transactions American Geophysical Union}, publisher={American Geophysical Union (AGU)}, author={Arumugam, Sankar and Wood, Andy and Rajagopalan, Balaji and Schaake, John}, year={2014}, month={Jan}, pages={3–3} }
 @article{almanaseer_sankarasubramanian_bales_2014, title={Improving Groundwater Predictions Utilizing Seasonal Precipitation Forecasts from General Circulation Models Forced with Sea Surface Temperature Forecasts}, volume={19}, ISSN={["1943-5584"]}, DOI={10.1061/(asce)he.1943-5584.0000776}, abstractNote={Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly ground- water levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models (principle component regression (PCR) and canonical correlation analysis (CCA)) for predicting groundwater and streamflow at both seasonal and monthly time- scales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using precipitation forecasts in climate models improves the ability to predict the interannual variability of winter and spring streamflow and groundwater levels over the basin. However, significant conditional bias exists in all the three modeling schemes, which indicates the need to consider improved modeling schemes as well as the availability of longer time-series of observed hydroclimatic information over the basin. DOI: 10.1061/(ASCE)HE .1943-5584.0000776. © 2014 American Society of Civil Engineers.}, number={1}, journal={JOURNAL OF HYDROLOGIC ENGINEERING}, author={Almanaseer, Naser and Sankarasubramanian, A. and Bales, Jerad}, year={2014}, month={Jan}, pages={87–98} }
 @misc{patskoski_arumugam_2014, title={Reducing the uncertainty in projecting future streamflow using paleo and instrumental records along with near-term climate change projections}, author={Patskoski, J. and Arumugam, S.}, year={2014}, month={Jun} }
 @article{singh_sankarasubramanian_2014, title={Systematic uncertainty reduction strategies for developing streamflow forecasts utilizing multiple climate models and hydrologic models}, volume={50}, ISSN={["1944-7973"]}, DOI={10.1002/2013wr013855}, abstractNote={Recent studies show that multimodel combinations improve hydroclimatic predictions by reducing model uncertainty. Given that climate forecasts are available from multiple climate models, which could be ingested with multiple watershed models, what is the best strategy to reduce the uncertainty in streamflow forecasts? To address this question, we consider three possible strategies: (1) reduce the input uncertainty first by combining climate models and then use the multimodel climate forecasts with multiple watershed models (MM‐P), (2) ingest the individual climate forecasts (without multimodel combination) with various watershed models and then combine the streamflow predictions that arise from all possible combinations of climate and watershed models (MM‐Q), (3) combine the streamflow forecasts obtained from multiple watershed models based on strategy (1) to develop a single streamflow prediction that reduces uncertainty in both climate forecasts and watershed models (MM‐PQ). For this purpose, we consider synthetic schemes that generate streamflow and climate forecasts, for comparing the performance of three strategies with the true streamflow generated by a given hydrologic model. Results from the synthetic study show that reducing input uncertainty first (MM‐P) by combining climate forecasts results in reduced error in predicting the true streamflow compared to the error of multimodel streamflow forecasts obtained by combining streamflow forecasts from all‐possible combination of individual climate model with various hydrologic models (MM‐Q). Since the true hydrologic model structure is unknown, it is desirable to consider MM‐PQ as an alternate choice that reduces both input uncertainty and hydrologic model uncertainty. Application on two watersheds in NC also indicates that reducing the input uncertainty first is critical before reducing the hydrologic model uncertainty.}, number={2}, journal={WATER RESOURCES RESEARCH}, author={Singh, Harminder and Sankarasubramanian, A.}, year={2014}, month={Feb}, pages={1288–1307} }
 @inproceedings{sinha_arumugam_2014, title={The Utility of CMIP5 Climate Change Projections in Estimating Hydrologic Impacts in the Conterminous US}, author={Sinha, T. and Arumugam, S.}, year={2014}, month={Jun} }
 @article{oh_sinha_sankarasubramanian_2014, title={The role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the southeast US}, volume={18}, ISSN={1607-7938}, url={http://dx.doi.org/10.5194/hess-18-2885-2014}, DOI={10.5194/hess-18-2885-2014}, abstractNote={Abstract. It is well known in the hydrometeorology literature that developing real-time daily streamflow forecasts in a given season significantly depends on the skill of daily precipitation forecasts over the watershed. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings so that daily nutrient loadings and the associated concentration could be predicted using daily precipitation forecasts and previously observed streamflow as surrogates of antecedent land surface conditions. By selecting 18 relatively undeveloped basins in the southeast US (SEUS), we evaluate the skill in predicting observed total nitrogen (TN) loadings in the Water Quality Network (WQN) by first developing the daily streamflow forecasts using the retrospective weather forecasts based on K-nearest neighbor (K-NN) resampling approach and then forcing the forecasted streamflow with a nutrient load estimation (LOADEST) model to obtain daily TN forecasts. Skill in developing forecasts of streamflow, TN loadings and the associated concentration were computed using rank correlation and RMSE (root mean square error), by comparing the respective forecast values with the WQN observations for the selected 18 Hydro-Climatic Data Network (HCDN) stations. The forecasted daily streamflow and TN loadings and their concentration have statistically significant skill in predicting the respective daily observations in the WQN database at all 18 stations over the SEUS. Only two stations showed statistically insignificant relationships in predicting the observed nitrogen concentration. We also found that the skill in predicting the observed TN loadings increases with the increase in drainage area, which indicates that the large-scale precipitation reforecasts correlate better with precipitation and streamflow over large watersheds. To overcome the limited samplings of TN in the WQN data, we extended the analyses by developing retrospective daily streamflow forecasts over the period 1979–2012 using reforecasts based on the K-NN resampling approach. Based on the coefficient of determination (R2Q-daily) of the daily streamflow forecasts, we computed the potential skill (R2TN-daily) in developing daily nutrient forecasts based on the R2 of the LOADEST model for each station. The analyses showed that the forecasting skills of TN loadings are relatively better in the winter and spring months, while skills are inferior during summer months. Despite these limitations, there is potential in utilizing the daily streamflow forecasts derived from real-time weather forecasts for developing daily nutrient forecasts, which could be employed for various adaptive nutrient management strategies for ensuring better water quality.
                    }, number={8}, journal={Hydrology and Earth System Sciences}, publisher={Copernicus GmbH}, author={Oh, J. and Sinha, T. and Sankarasubramanian, A.}, year={2014}, month={Aug}, pages={2885–2898} }
 @inproceedings{arumugam_2014, title={Water and ecological sustainability under near-term climate change}, author={Arumugam, S.}, year={2014}, month={Jan} }
 @inproceedings{lord_zechman_arumugam_2013, title={A Complex Adaptive System Approach Assessing the Dynamics of Population Growth, Land Use, and Climate Change for Urban Water Resources Management}, ISBN={9780784412947}, url={http://dx.doi.org/10.1061/9780784412947.281}, DOI={10.1061/9780784412947.281}, abstractNote={Urban water resources management requires careful planning to balance water supply and demand. Under increasing population growth and land use change through urbanization, water shortages may become increasingly frequent, and climate change can alter the availability and timing of water from expected levels. While long-term water supply planning is conventionally based on projections of population growth, demands, and system capacity under a stationary climate, the sustainability of water resources depends on the dynamic interactions among the environmental, technological, and social characteristics of the water system and local population. The response of consumers to water use regulations will affect future water availability, and to address the challenges of water resources management and provide insight to system dynamics a new modeling approach is needed that goes beyond simple assumptions about water availability, population growth, and demand increases, to explicitly incorporate the feedbacks among these systems and their impacts on water availability. A dynamic modeling approach is developed to provide insight about the supply-demand dynamics and feedbacks arising from urban growth dynamics, consumer behaviors, and potential changes in climate and land use. This research couples engineering and hydro-climatology models with complex adaptive system modeling techniques to assess the influence of social dynamics on water resources availability. Land use change is simulated using cellular automata modeling. Consumer adaptations of water demands and policy decisions about water restrictions are simulated using agent-based modeling. Watershed and reservoir simulation are implemented using the Soil Water Assessment Tool (SWAT) and integrated within a complex adaptive system simulation framework. This framework is developed for the Falls Lake Reservoir near Raleigh, North Carolina, to simulate the performance of alternative water shortage response plan and supply-side management scenarios under increased population and climate change scenarios.}, booktitle={World Environmental and Water Resources Congress 2013}, publisher={American Society of Civil Engineers}, author={Lord, Benjamin and Zechman, Emily and Arumugam, Sankar}, year={2013}, month={May} }
 @inproceedings{ranjithan_touma_brill_sankarasubramanian_2013, title={A Quantitative Framework for Assessing the Effects of Climate and Land-use Change on Streamflow}, author={Ranjithan, R.S. and Touma, D. and Brill, E.D. and Sankarasubramanian, A.}, year={2013}, month={May} }
 @misc{arumugam_2013, title={Climate, Water and Energy Management in the Greater Horn of Africa}, author={Arumugam, S.}, year={2013}, month={Mar} }
 @misc{arumugam_2013, title={Climate-informed Uncertainty Analyses for Water and Energy Management}, author={Arumugam, S.}, year={2013}, month={Feb} }
 @inproceedings{mazoorei_sinha_arumugam_2013, place={Portland}, title={Decomposition of Sources of Errors in developing Seasonal Streamflow Forecasts over the Sunbelt}, author={Mazoorei, A. and Sinha, T. and Arumugam, S.}, year={2013}, month={Jul} }
 @inproceedings{sinha_mazoorei_arumugam_boyles_2013, place={Portland}, title={Experimental Inflow and Storage Forecasts for the State of NC}, author={Sinha, T. and Mazoorei, A. and Arumugam, S. and Boyles, R.}, year={2013}, month={Jul} }
 @inbook{sun_caldwell_georgakakos_cruise_mcnider_terando_conrads_feldt_misra_romolo_et al._2013, place={Washington, DC}, series={NCA Regional Input Reports}, title={Impacts of Climate Change and Variability on Water Resources in the Southeastern USA}, booktitle={Climate of the southeast United States : variability, change, impacts, and vulnerability}, publisher={Island Press}, author={Sun, G. and Caldwell, P.V. and Georgakakos, Aris P. and Cruise, James and McNider, Richard T. and Terando, Adam and Conrads, Paul A. and Feldt, John and Misra, Vasu and Romolo, Luigi and et al.}, editor={Ingram, Keith and Dow, Kristin and Carter, Lynne and Anderson, Julie and Sommer, Eleanor K.Editors}, year={2013}, collection={NCA Regional Input Reports} }
 @book{sankarasubramanian_ranjithan_2013, title={Improved Water Resources Sustainability utilizing Multi Time-scale Streamflow Forecasts}, institution={National Science Foundation}, author={Sankarasubramanian, A. and Ranjithan, R.S.}, year={2013}, month={May} }
 @article{wang_sankarasubramanian_ranjithan_2013, title={Integration of Climate and Weather Information for Improving 15-Day-Ahead Accumulated Precipitation Forecasts}, volume={14}, ISSN={["1525-7541"]}, DOI={10.1175/jhm-d-11-0128.1}, abstractNote={Abstract
               Skillful medium-range weather forecasts are critical for water resources planning and management. This study aims to improve 15-day-ahead accumulated precipitation forecasts by combining biweekly weather and disaggregated climate forecasts. A combination scheme is developed to combine reforecasts from a numerical weather model and disaggregated climate forecasts from ECHAM4.5 for developing 15-day-ahead precipitation forecasts. Evaluation of the skill of the weather–climate information (WCI)-based biweekly forecasts under leave-five-out cross validation shows that WCI-based forecasts perform better than reforecasts in many grid points over the continental United States. Correlation between rank probability skill score (RPSS) and disaggregated ECHAM4.5 forecast errors reveals that the lower the error in the disaggregated forecasts, the better the performance of WCI forecasts. Weights analysis from the combination scheme also shows that the biweekly WCI forecasts perform better by assigning higher weights to the better-performing candidate forecasts (reforecasts or disaggregated ECHAM4.5 forecasts). Particularly, WCI forecasts perform better during the summer months during which reforecasts have limited skill. Even though the disaggregated climate forecasts do not perform well over many grid points, the primary reason WCI-based forecasts perform better than the reforecasts is due to the reduction in the overconfidence of the reforecasts. Since the disaggregated climate forecasts are better dispersed than the reforecasts, combining them with reforecasts results in reduced uncertainty in predicting the 15-day-ahead accumulated precipitation.}, number={1}, journal={JOURNAL OF HYDROMETEOROLOGY}, author={Wang, Hui and Sankarasubramanian, A. and Ranjithan, Ranji S.}, year={2013}, month={Feb}, pages={186–202} }
 @inproceedings{bhowmik_arumugam_patskoski_2013, title={Multivariate Downscaling of Decadal Climate Change Projections over the Sunbelt}, author={Bhowmik, R. and Arumugam, S. and Patskoski, J.}, year={2013}, month={Jul} }
 @inproceedings{seo_sinha_mahinthakumar_arumugam_2013, title={Near-term Climate Change Impacts on Surface water and groundwater interactions over the Sunbelt}, author={Seo, S.B and Sinha, T. and Mahinthakumar, G. and Arumugam, S.}, year={2013}, month={Jul} }
 @inproceedings{arumugam_2013, title={Near-term Climate Change and Water Management over the Sunbelt}, author={Arumugam, S.}, year={2013}, month={Mar} }
 @article{sinha_arumugam_2013, title={Role of climate forecasts and initial conditions in developing streamflow and soil moisture forecasts in a rainfall-runoff regime}, volume={17}, journal={Hydrology and Earth System Sciences}, author={Sinha, T. and Arumugam, S.}, year={2013}, pages={721–733} }
 @article{oludhe_sankarasubramanian_sinha_devineni_lall_2013, title={The Role of Multimodel Climate Forecasts in Improving Water and Energy Management over the Tana River Basin, Kenya}, volume={52}, ISSN={["1558-8432"]}, DOI={10.1175/jamc-d-12-0300.1}, abstractNote={AbstractThe Masinga Reservoir located in the upper Tana River basin, Kenya, is extremely important in supplying the country's hydropower and protecting downstream ecology. The dam serves as the primary storage reservoir, controlling streamflow through a series of downstream hydroelectric reservoirs. The Masinga dam's operation is crucial in meeting power demands and thus contributing significantly to the country's economy. La Niña–related prolonged droughts of 1999–2001 resulted in severe power shortages in Kenya. Therefore, seasonal streamflow forecasts contingent on climate information are essential to estimate preseason water allocation. Here, the authors utilize reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with constructed analog SSTs and multimodel precipitation forecasts developed from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project to improve water allocation during the April–June and October–December seasons for the Masinga Reservoir. Three-month-ahead inflow forecasts developed from ECHAM4.5, multiple GCMs, and climatological ensembles are used in a reservoir model to allocate water for power generation by ensuring climatological probability of meeting the end-of-season target storage required to meet seasonal water demands. Retrospective reservoir analysis shows that inflow forecasts developed from single GCM and multiple GCMs perform better than use of climatological values by reducing the spill and increasing the allocation for hydropower during above-normal inflow years. Similarly, during below-normal inflow years, both of these forecasts could be effectively utilized to meet the end-of-season target storage by restricting releases for power generation. The multimodel forecasts preserve the end-of-season target storage better than the single-model inflow forecasts by reducing uncertainty and the overconfidence of individual model forecasts.}, number={11}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, author={Oludhe, C. and Sankarasubramanian, A. and Sinha, Tushar and Devineni, Naresh and Lall, Upmanu}, year={2013}, month={Nov}, pages={2460–2475} }
 @article{segura_lazzati_sankarasubramanian_2013, title={The use of broken power-laws to describe the distributions of daily flow above the mean annual flow across the conterminous U.S.}, volume={505}, ISSN={0022-1694}, url={http://dx.doi.org/10.1016/j.jhydrol.2013.09.016}, DOI={10.1016/j.jhydrol.2013.09.016}, abstractNote={A recent study employed a broken power-law (BPL) distribution for understanding the scaling frequency of bankfull discharge in snowmelt-dominated basins. This study, grounded from those findings, investigated the ability of a BPL function to describe the distribution of daily flows above the mean annual flow in 1217 sites across the conterminous U.S. (CONUS). The hydrologic regime in all the sites is unregulated and spans a wide range in drainage areas (2–120,000 km2) and elevation (0–3000 m). Available daily flow records in all sites varied between 15 and 108 years. Comparing the performance of BPL distribution and the traditionally used lognormal distribution, we found that BPL provides stronger fit in ∼80% of the sites. Thus the BPL function provides a suitable tool to model daily flows in most areas of the CONUS. The potential for developing a model for predicting the frequency distribution of daily flows in ungauged sites was analyzed. We found that such model is possible using drainage area, mean basin elevation, and mean annual precipitation as predicting variables for any site located above 600 m across the CONUS. We also found strong continental-wide correlations between 3 of the 4 parameters that describe the BPL and basin characteristics. Our results indicate that the BPL function provides a robust alternative to traditional functions such as the lognormal to model the statistical variation of daily flows above the mean annual in most basins of the CONUS.}, journal={Journal of Hydrology}, publisher={Elsevier BV}, author={Segura, Catalina and Lazzati, Davide and Sankarasubramanian, Arumugam}, year={2013}, month={Nov}, pages={35–46} }
 @book{sankarasubramanian_sinha_2013, title={Uncertainty in Surface Water Availability over North Carolina under climate and land use changes}, institution={NC Water Resources Research Institute}, author={Sankarasubramanian, A. and Sinha, T.}, year={2013}, month={Mar} }
 @article{sankarasubramanian_lall_2012, title={Climate Forecasts and Water Management}, journal={AGU Hydrology Section Newsletter}, author={Sankarasubramanian, A. and Lall, U.}, year={2012}, month={Jul} }
 @book{sankarasubramanian_semazzi_hatch_2012, title={Climate Information based Urban Water Supply and Energy Management in the Greater Horn of Africa}, institution={National Oceanic and Atmospheric Administration}, author={Sankarasubramanian, A. and Semazzi, F. and Hatch, U.}, year={2012}, month={Oct} }
 @inproceedings{sinha_arumugam_2012, title={Decomposition of Sources of Errors in Seasonal Streamflow Forecasts in a Rainfall-Runoff Dominated Basin}, author={Sinha, T. and Arumugam, S.}, year={2012}, month={Dec} }
 @inproceedings{patskoski_arumugam_2012, title={Estimating Required Reservoir Storage from Synthetically Generated Streamflow through a Hierarchical Bayesian Framework Combining Observed and Paleo Streamflow}, author={Patskoski, J. and Arumugam, S.}, year={2012}, month={Dec} }
 @inbook{sankarasubramanian_lall_rajagopalan_2012, place={Chichester}, edition={2nd}, title={Floods and Changing Climate: Seasonal Forecasts and Reconstruction}, ISBN={0471899976 9780471899976 0470057335 9780470057339}, url={http://dx.doi.org/10.1002/9780470057339.vnn045}, DOI={10.1002/9780470057339.vnn045}, abstractNote={Abstract
          It is widely acknowledged that both climate and land use changes modify flood frequency, thereby challenging the traditional assumption that the underlying stochastic process is stationary in time, and that the annual maximum flood corresponds to an independent identically distributed (iid) process. In this article, we employ a semiparametric approach to estimate flood quantiles conditional on selected “climate indices” that carry the signal of structured low‐frequency climate variation, and influence the atmospheric mechanisms that enhance or retard local precipitation and flood potential. The semiparametric approach that maximizes the local likelihood of the observed annual maximum peak in the climatic predictor state space is applied to estimate conditional flood quantiles for the Blacksmith Fork River near Hyrum (BFH), Utah. The estimated conditional flood quantiles correlate well with the observed annual maximum peaks, thus offering prospects for reconstructing past flood series as well as for short‐term forecasting.}, booktitle={Encyclopedia of Environmetrics}, publisher={John Wiley & Sons, Ltd}, author={Sankarasubramanian, A. and Lall, Upmanu and Rajagopalan, Balaji}, editor={El-Shaarawl, A.H. and Piegorsch, Walter W.Editors}, year={2012}, month={Jan} }
 @inbook{sun_caldwell_georgakakos_sankarasubramanian_cruise_mcnider_terando_conrads_feldt_misra_et al._2012, title={Impacts of Climate Change and Variability on Water Resources in the Southeastern US}, booktitle={Southeastern Regional Technical Report to the National Climate Change Assessment}, publisher={Island Press}, author={Sun, G. and Caldwell, P.V. and Georgakakos, Aris P. and Sankarasubramanian, A. and Cruise, James and McNider, Richard T. and Terando, Adam and Conrads, Paul A. and Feldt, John and Misra, Vasu and et al.}, year={2012} }
 @article{oh_sankarasubramanian_2012, title={Interannual hydroclimatic variability and its influence on winter nutrient loadings over the Southeast United States}, volume={16}, ISSN={1607-7938}, url={http://dx.doi.org/10.5194/hess-16-2285-2012}, DOI={10.5194/hess-16-2285-2012}, abstractNote={Abstract. It is well established in the hydroclimatic literature that the interannual variability in seasonal streamflow could be partially explained using climatic precursors such as tropical sea surface temperature (SST) conditions. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings so that nutrient loadings could be predicted using season-ahead climate forecasts forced with forecasted SSTs. By selecting 18 relatively undeveloped basins in the Southeast US (SEUS), we relate winter (January-February-March, JFM) precipitation forecasts that influence the JFM streamflow over the basin to develop winter forecasts of nutrient loadings. For this purpose, we consider two different types of low-dimensional statistical models to predict 3-month ahead nutrient loadings based on retrospective climate forecasts. Split sample validation of the predictive models shows that 18–45% of interannual variability in observed winter nutrient loadings could be predicted even before the beginning of the season for at least 8 stations. Stations that have very high coefficient of determination (> 0.8) in predicting the observed water quality network (WQN) loadings during JFM exhibit significant skill in predicting seasonal total nitrogen (TN) loadings using climate forecasts. Incorporating antecedent flow conditions (December flow) as an additional predictor did not increase the explained variance in these stations, but substantially reduced the root-mean-square error (RMSE) in the predicted loadings. Relating the dominant mode of winter nutrient loadings over 18 stations clearly illustrates the association with El Niño Southern Oscillation (ENSO) conditions. Potential utility of these season-ahead nutrient predictions in developing proactive and adaptive nutrient management strategies is also discussed.
                    }, number={7}, journal={Hydrology and Earth System Sciences}, publisher={Copernicus GmbH}, author={Oh, J. and Sankarasubramanian, A.}, year={2012}, month={Jul}, pages={2285–2298} }
 @inproceedings{patskoski_arumugam_2012, title={Low-Dimensional Models of Annual Streamflow Using Tree Ring Data and Nino 3.4 Forecasts}, ISBN={9780784412312}, url={http://dx.doi.org/10.1061/9780784412312.391}, DOI={10.1061/9780784412312.391}, abstractNote={Long time series of streamflow are required for the planning and management of water resources. However, observed streamflow records within the continental United State typically start from 1930. Thus, to compare the observed hydroclimatic extremes with past unobserved events, tree rings are typically used to reconstruct annual streamflow values. The most common approach to reconstruct annual streamflow is to develop a statistical relationship between the principal components of the tree rings and the observed annual flow values and then extend the relationship to reconstruct annual streamflow values for the period for which tree ring data is available. However, this approach has limited skill in reconstructing high flow values since tree ring growth reaches its potential limit during wet years. We propose an alternate approach to overcome this limitation by combining information from Sea Surface Temperature (SST) and from tree ring chronologies. Given the role of El Nino Southern Oscillation (ENSO) in influencing hydroclimatology over the Southeastern US, we predict the periodic component of streamflow using Nino 3.4 - an index representing ENSO - and the non-periodic component of streamflow using the non-periodic variability in tree rings. Given its ability to extract low-dimensional components, we also employ Multi-Channel Singular Spectrum Analysis (MSSA) on tree rings for streamflow reconstruction. These two methods are tested with the traditional approach based on Leave-Five-Percent-Out-Cross-Validation. Results from the study show that MSSA improves the skill of reconstructed streamflows and the hybrid approach (SSTs and tree rings) perform better during high flow years.}, booktitle={World Environmental and Water Resources Congress 2012}, publisher={American Society of Civil Engineers}, author={Patskoski, Jason and Arumugam, Sankarasubramanian}, year={2012}, month={May} }
 @misc{arumugam_2012, title={Probabilistic Water Quality Trading conditional on seasonal nutrient forecasts}, author={Arumugam, S.}, year={2012}, month={Jun} }
 @article{li_sankarasubramanian_2012, title={Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination}, volume={48}, ISSN={["1944-7973"]}, DOI={10.1029/2011wr011380}, abstractNote={Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM‐1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, “abcd” model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM‐1 with optimal model combination scheme (MM‐O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., “abcd” model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM‐1 and MM‐O) consistently performed better than the single model prediction. Overall, MM‐1 performs better than MM‐O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM‐1 assigns weights equally for all the models, whereas MM‐O assigns higher weights for always the best‐performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM‐1 performs better than all single models and optimal model combination scheme, MM‐O, in predicting the monthly flows as well as the flows during wetter months.}, journal={WATER RESOURCES RESEARCH}, author={Li, Weihua and Sankarasubramanian, A.}, year={2012}, month={Dec} }
 @inproceedings{arumugam_2012, title={Role of Climate Variability in Modulating Surface Water and Groundwater Interaction over the Southeastern US}, author={Arumugam, S.}, year={2012}, month={Mar} }
 @article{almanaseer_sankarasubramanian_2012, title={Role of Climate Variability in Modulating the Surface Water and Groundwater Interaction over the Southeast United States}, volume={17}, ISSN={["1084-0699"]}, DOI={10.1061/(asce)he.1943-5584.0000536}, abstractNote={AbstractThis paper presents an investigation of the role of climatic variability on interannual groundwater and streamflow variability in the southeast United States. For this purpose, streamflow and associated groundwater levels are analyzed for 20 basins that are minimally affected by reservoirs and groundwater pumping. Using the spatially averaged monthly precipitation time series obtained from the Precipitation-elevation Regressions on Independent Slopes Model (PRISM), this paper identifies the recharge and discharge periods that influence the groundwater levels during the winter [January, February, March (JFM)] and summer [July, August, September (JAS)] seasons. Recharge-discharge dependency analyses indicate that precipitation during the previous 3 months influences the groundwater level in a given month. Streamflow in any given month depends on the groundwater level during the previous 3 months. Principal component analysis (PCA) on the precipitation, temperature, streamflow, and groundwater data i...}, number={9}, journal={JOURNAL OF HYDROLOGIC ENGINEERING}, author={Almanaseer, Naser and Sankarasubramanian, A.}, year={2012}, month={Sep}, pages={1001–1010} }
 @article{petersen_devineni_sankarasubramanian_2012, title={Seasonality of monthly runoff over the continental United States: Causality and relations to mean annual and mean monthly distributions of moisture and energy}, volume={468}, ISSN={["0022-1694"]}, DOI={10.1016/j.jhydrol.2012.08.028}, abstractNote={Observed monthly climatology of streamflow over the continental United States showed significant differences from the monthly precipitation climatology. The objective of this study is to provide an overview of the seasonality of streamflow over the continental US and also to understand the processes that control the streamflow seasonality. For this purpose, we employ principal component regression on five predictors – both climatic and land-surface characteristics – that explain the spatial variability in the streamflow seasonality. While the distribution of mean monthly precipitation is uniform throughout the year over most of the eastern United States (except peninsular Florida), mean monthly streamflow exhibits pronounced seasonality with peak runoff occurring during the winter (early spring) over the Southeast (Mid-Atlantic and Northeast) regions. The spatial variability in the seasonality index – the ratio of peak mean monthly value to the annual total – of runoff over the eastern US primarily depends on the covariability between monthly moisture and energy cycles. As the coherence between these two cycles change from negative to positive over the eastern US, increased moisture availability during the summer results in decreased seasonality index. In contrast, over the western US, both precipitation and streamflow exhibit strong seasonality with respective monthly peaks occurring in early and late winter months. Given that the moisture and energy cycles over the west exhibit significant negative correlation, limited energy availability during peak months of precipitation results in peak monthly runoff occurring in the same season as that of precipitation. Thus, the spatial variability in runoff seasonality over the western United States is strongly dependent on the basin aridity and the seasonality index in precipitation. For catchments over the Midwest and peninsular Florida, given the significant positive correlations in moisture and energy cycles, mean monthly runoff peaks occur in the spring and early summer season with the magnitude of streamflow seasonality being dependent on the aridity index and soil moisture holding capacity of the basin.}, journal={JOURNAL OF HYDROLOGY}, author={Petersen, Thomas and Devineni, Naresh and Sankarasubramanian, A.}, year={2012}, month={Oct}, pages={139–150} }
 @misc{arumugam_2012, title={Uncertainty Reduction in Climate Forecasts using Multimodel Combination and their Relevance to Water Management}, author={Arumugam, S.}, year={2012}, month={Apr} }
 @misc{arumugam_2012, title={Uncertainty Reduction in Climate Forecasts using Multimodel Combination and their Relevance to Water Management}, author={Arumugam, S.}, year={2012}, month={Aug} }
 @inproceedings{arumugam_2011, title={Climate Forecasts and Water Management: Opportunities and Challenges}, author={Arumugam, S.}, year={2011}, month={Apr} }
 @inproceedings{almanaseer_arumugam_bales_2011, title={Improving Groundwater Predictions using Seasonal Precipitation Forecasts}, author={Almanaseer, N. and Arumugam, S. and Bales, J.}, year={2011}, month={Dec} }
 @inproceedings{wang_arumugam_ranjithan_2011, title={Low-frequency oscillation in annual precipitation and streamflow over Southeastern United States}, author={Wang, H. and Arumugam, S. and Ranjithan, R.S.}, year={2011} }
 @inproceedings{patskoski_arumugam_2011, title={Predicting Streamflow in the Southeastern United States using Climate and Tree Ring Data}, author={Patskoski, J. and Arumugam, S.}, year={2011}, month={Dec} }
 @inproceedings{sinha_arumugam_2011, title={Role of initial conditions and climate variability on seasonal streamflow forecasting in the southeastern US}, author={Sinha, T. and Arumugam, S.}, year={2011} }
 @book{sankarasubramanian_boyles_2011, title={Seasonal Streamflow Forecasts for the Hydrologic Unit Code (HUC-8) Basins in North Carolina utilizing Multimodel Climate Forecasts}, institution={NC-Water Resources Research Institute}, author={Sankarasubramanian, A. and Boyles, R.}, year={2011} }
 @inproceedings{singh_arumugam_2011, title={Systematic uncertainty reduction in streamflow forecasts development: Importance of Input and Hydrologic Model Uncertainty}, author={Singh, H. and Arumugam, S.}, year={2011} }
 @misc{arumugam_2011, title={Uncertainty Reduction in Streamflow Predictions over Multiple Time Scales}, author={Arumugam, S.}, year={2011}, month={Aug} }
 @inproceedings{li_arumugam_ranjithan_2011, title={Utility of Climate Forecasts in promoting optimal inter-basin transfer in the North Carolina Triangle Area}, author={Li, W. and Arumugam, S. and Ranjithan, R.S.}, year={2011} }
 @inproceedings{arumugam_2011, place={Asheville, NC}, title={Utility of Multimodel Climate Forecasts in Improving Reservoir Management}, publisher={National Climatic Data Center}, author={Arumugam, S.}, year={2011}, month={Aug} }
 @book{sankarasubramanian_2011, title={Vulnerability of Coastal Watersheds to Climate Change and Variability}, institution={NC Sea Grant and NC Water Resources Research Institute}, author={Sankarasubramanian, A.}, year={2011} }
 @inproceedings{oh_arumugam_2010, title={Climate, Streamflow, and Nutrients Variability Over the Southeastern United States}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)458}, DOI={10.1061/41114(371)458}, abstractNote={It is well established in hydroclimatic literature that interannual variability in seasonal streamflow could be explained partially using SST conditions. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings such that season-ahead nutrient loadings could be predicted purely based on exogenous climatic conditions such as Sea Surface Temperature (SST). By selecting 40 relatively undeveloped basins in the Southeastern US, we relate the (3-month ahead) SST conditions that influence the seasonal streamflow over the basin with the observed seasonal loadings from the water quality database. For basins whose seasonal nutrient concentrations/loadings modulate according to SST conditions, we develop low-dimensional statistical models that predict 3-month ahead nutrient loadings purely based on principal components of SST grid points. By comparing the estimated loadings using SST conditions with both observed and simulated loadings, we show that on 40–80% of interannual variability in seasonal loadings could be explained contingent on SST conditions even before the beginning of the season. Potential utility of these season-ahead nutrient predictions in developing proactive and adaptive water quality management plans as well as in providing prognostic information to support water quality trading is also discussed.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Oh, Jeseung and Arumugam, Sankarasubramanian}, year={2010}, month={May} }
 @inproceedings{almanaseer_arumugam_2010, title={Climate-Groundwater-Surface Water Interrelationships Over the South Eastern US}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)84}, DOI={10.1061/41114(371)84}, abstractNote={This research focuses in understanding the interaction between climate, groundwater and surface water over the Southeastern US. For this purpose, we consider observed streamflow, precipitation, temperature and groundwater levels from twenty basins from the USGS climate-groundwater network. Seasonality analyses on the four variables indicate both high and low periods of activity for each variable. Singular spectrum analyses on all the four variables indicate that groundwater is the most dominant component in explaining the first component. Relating eigen values with drainage area show that large basins exhibit increased role for groundwater. Analyses on relating precipitation forecasts and sea surface temperature conditions show that there is a potential in predicting groundwater availability purely based on climate information.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Almanaseer, Naser and Arumugam, Sankar}, year={2010}, month={May} }
 @inproceedings{devineni_arumugam_2010, title={Climatology of Monthly Runoff: Causality and Relations to Seasonality in Precipitation and Temperature}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)457}, DOI={10.1061/41114(371)457}, abstractNote={Observed monthly climatology of streamflows over the eastern United States showed significant differences from the month precipitation climatology. While the distribution of monthly precipitation is uniform throughout the year over the eastern United States (except peninsular Florida), the streamflow shows pronounced seasonality with peak flow seasons occurring during the winter over the Southeast and during the spring over the Mid-Atlantic and Northeast regions. A systematic analysis is carried out using simple water balance models that require no calibration for understanding the role of precipitation and temperature seasonality in influencing the streamflow seasonality. Preliminary findings show that for regions where precipitation and temperature are out of phase, streamflow seasonality index — which indicates the high flow season — occurs during the melt season. On the other hand, for regions where monthly distributions of precipitation is uniform, streamflow seasonality index typically coincide with storage seasonality.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Devineni, Naresh and Arumugam, Sankarasubramanian}, year={2010}, month={May} }
 @article{devineni_sankarasubramanian_2010, title={Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs}, volume={37}, ISSN={["0094-8276"]}, DOI={10.1029/2010gl044989}, abstractNote={A new approach to combine precipitation forecasts from multiple models is evaluated by analyzing the skill of the candidate models contingent on the forecasted predictor(s) state. Using five leading coupled GCMs (CGCMs) from the ENSEMBLES project, we develop multimodel precipitation forecasts over the continental United States (U.S) by considering the forecasted Nino3.4 from each CGCM as the conditioning variable. The performance of multimodel forecasts is compared with individual models based on rank probability skill score and reliability diagram. The study clearly shows that multimodel forecasts perform better than individual models and among all multimodels, multimodel combination conditional on Nino3.4 perform better with more grid points having the highest rank probability skill score. The proposed algorithm also depends on the number of years of forecasts available for calibration. The main advantage in using this algorithm for multimodel combination is that it assigns higher weights for climatology and lower weights for CGCM if the skill of a CGCM is poor under ENSO conditions. Thus, combining multiple models based on their skill in predicting under a given predictor state(s) provides an attractive strategy to develop improved climate forecasts.}, journal={GEOPHYSICAL RESEARCH LETTERS}, author={Devineni, Naresh and Sankarasubramanian, A.}, year={2010}, month={Dec} }
 @article{devineni_sankarasubramanian_2010, title={Improving the prediction of winter precipitation and temperature over the continental United States: Role of the ENSO state in developing multimodel combinations}, volume={138}, DOI={10.1175/2009mwr3112a.1}, number={6}, journal={Monthly Weather Review}, author={Devineni, N. and Sankarasubramanian, A.}, year={2010}, pages={2447–2468} }
 @inproceedings{wang_arumugam_ranjithan_2010, title={Interannual to Decadal Variability in Hydroclimatic Data: Analyses and Implications to Water Management}, author={Wang, H. and Arumugam, S. and Ranjithan, R.S.}, year={2010} }
 @article{liu_sankarasubramanian_ranjithan_2011, title={Logistic regression analysis to estimate contaminant sources in water distribution systems}, volume={13}, ISSN={1464-7141 1465-1734}, url={http://dx.doi.org/10.2166/hydro.2010.106}, DOI={10.2166/hydro.2010.106}, abstractNote={Accidental or intentional contamination in a water distribution system (WDS) has recently attracted attention due to the potential hazard to public health and the complexity of the contaminant characteristics. The accurate and rapid characterization of contaminant sources is necessary to successfully mitigate the threat in the event of contamination. The uncertainty surrounding the contaminants, sensor measurements and water consumption underscores the importance of a probabilistic description of possible contaminant sources. This paper proposes a rapid estimation methodology based on logistic regression (LR) analysis to estimate the likelihood of any given node as a potential source of contamination. Not only does this algorithm yield location-specific probability information, but it can also serve as a prescreening step for simulation–optimization methods by reducing the decision space and thus alleviating the computational burden. The applications of this approach to two example water networks show that it can efficiently rule out numerous nodes that do not yield contaminant concentrations to match the observations. This elimination process narrows down the search space of the potential contamination locations. The results also indicate that the proposed method efficiently yields a good estimation even when some noise is incorporated into the measurements and demand values at the consumption nodes.}, number={3}, journal={Journal of Hydroinformatics}, publisher={IWA Publishing}, author={Liu, Li and Sankarasubramanian, A. and Ranjithan, S. Ranji}, year={2011}, month={Jul}, pages={545–557} }
 @inproceedings{oh_arumugam_2010, title={Probabilistic Water quality trading model conditioned on season-ahead nutrient load forecasts}, author={Oh, J. and Arumugam, S.}, year={2010} }
 @inproceedings{almanaseer_arumugam_bales_2010, title={Role of Climate Variability in Modulating Surface Water and Groundwater Interaction over the Southeast United States}, author={Almanaseer, N. and Arumugam, S. and Bales, J.}, year={2010}, month={Dec} }
 @inproceedings{wang_arumugam_ranjithan_2010, title={Seamless Integration of Weather and Climate Information in Developing Operational Streamflow Forecasts}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)470}, DOI={10.1061/41114(371)470}, abstractNote={Various atmospheric and ocean conditions, such as El-Nino Southern Oscillation, affect the monthly and seasonal streamflow potential in a given region. Similarly, at daily to weekly time scales, well known oscillations such as PNA (Pacific-North America) influence the skill of weather forecasts. Thus, to develop bi-weekly streamflow forecasts, we propose an algorithm that combines streamflow forecasts downscaled from medium-range weather information and the disaggregated bi-weekly streamflow forecasts from climate information. For basins, whose monthly and seasonal streamflows are primarily SST driven with limited skill in predicting the bi-weekly weather, such a combination would be expected to yield benefits in developing better weekly streamflow forecasts. To demonstrate this algorithm, we first analyze using a synthetic study that consider streamflow forecasts having different skills at bi-weekly and monthly time scales and combine both of them using the proposed algorithm. Preliminary investigations clearly show that combining medium range weather and climate information could improve the skill in predicting the bi-weekly precipitation/streamflow forecasts for the basin. Findings from the synthetic study is also validated by combining retrospective bi-weekly weather forecasts and the disaggregated bi-weekly forecasts obtained from the retrospective climate forecasts for various regions in the country.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Wang, Hui and Arumugam, Sankarasubramanian and Ranjithan, Ranji S.}, year={2010}, month={May} }
 @inproceedings{li_arumugam_ranjithan_2010, title={The Role of Multimodel Combination and Data assimilation in Improving Streamflow Prediction}, author={Li, W. and Arumugam, S. and Ranjithan, R.S.}, year={2010} }
 @inproceedings{li_arumugam_ranjithan_2010, title={Utility of Climate Forecasts in Promoting Inter-Basin Transfer in the North Carolina Triangle Area}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)270}, DOI={10.1061/41114(371)270}, abstractNote={Droughts experienced by regional water supply systems often result due to reduced streamflow/precipitation potential which could occur due to varying exogenous climatic conditions such as tropical sea surface temperature (SST). Similarly, water supply systems can also experience frequent shortages in supply due to increased water demand resulting from urbanization and population growth in the region. The goal of this study is to identify a sustainable way of managing triangle area's two major reservoir systems while in the meantime improving the water supply reliability for its urban area. In this study, streamflow forecasts downscaled from climate forecasts for the winter season is developed to explore the potential for inter-basin transfer between Falls Lake of the Neuse River basin and Jordan Lake in the Cape Fear River basin. Using the 3-month ahead ensembles of streamflow forecasts, the reservoir simulation model estimates the probability of meeting the end of the season target storage for the two systems. Comparing these two probabilities, various scenarios of inter-basin transfers between the two systems are analyzed in such a way that the water quality releases from both systems are not endangered. Results show that by introducing inter-basin transfer, the reliability of the water supply for the triangle area could be increased, which would help in developing regional drought management strategies.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Li, Weihua and Arumugam, Sankarasubramanian and Ranjithan, Ranji S.}, year={2010}, month={May} }
 @book{sankarasubramanian_devineni_2010, title={Utilizing Three-Month ahead Multimodel Streamflow Forecasts for Improving the Management Of Falls Lake}, institution={NC-Water Resources Research Institute}, author={Sankarasubramanian, A. and Devineni, N.}, year={2010} }
 @inproceedings{arumugam_2009, title={Climate-informed Uncertainty Analyses for Integrated River Basin Management}, author={Arumugam, S.}, year={2009}, month={Jul} }
 @article{vankayala_sankarasubramanian_ranjithan_mahinthakumar_2009, title={Contaminant Source Identification in Water Distribution Networks Under Conditions of Demand Uncertainty}, volume={10}, ISSN={["1527-5930"]}, DOI={10.1080/15275920903140486}, abstractNote={Water distribution systems are susceptible to accidental and intentional chemical or biological contamination that could result in adverse health impact to the consumers. This study focuses on a water distribution forensics problem, contaminant source identification, subject to water demand uncertainty. Due to inherent variability in water consumption levels, demands at consumer nodes remain one of the major sources of uncertainty. In this research, the nodal demands are considered to be stochastic in nature and are varied using Gaussian and Autoregressive models. A hypothetical source identification problem is constructed by simulating observations at the sensor nodes from an arbitrary contaminant source. A simulation-optimization approach is used to solve the source identification problem with EPANET tool as the simulator and Genetic Algorithm (GA) as the optimizer. The goal is to find the source location and concentration by minimizing the difference between the simulated and observed concentrations at the sensor nodes. Two variations of GA, stochastic GA and noisy GA are applied to the same problem for comparison. Results show that noisy GA is more robust and is less computationally expensive than stochastic GA in solving the source identification problem. Moreover, the autoregressive demand uncertainty model better represents the uncertainty in the source identification process than the Gaussian model.}, number={3}, journal={ENVIRONMENTAL FORENSICS}, author={Vankayala, Praveen and Sankarasubramanian, A. and Ranjithan, S. Ranji and Mahinthakumar, G.}, year={2009}, pages={253–263} }
 @article{golembesky_sankarasubramanian_devineni_2009, title={Improved drought management of Falls Lake Reservoir: Role of multimodel streamflow forecasts in setting up restrictions}, volume={135}, DOI={10.1061/(ASCE)0733-9496(2009)135:3(188)}, abstractNote={Droughts, resulting from natural variability in supply and from increased demand due to urbanization, have severe economic implications on local and regional water supply systems. In the context of short-term monthly to seasonal water management, predicting these supply variations well in advance are essential in advocating appropriate conservation measures before the onset of drought. In this study, we utilized 3-month ahead probabilistic multimodel streamflow forecasts developed using climatic information—sea surface temperature conditions in the tropical Pacific, tropical Atlantic, and over the North Carolina coast—to invoke restrictions for Falls Lake Reservoir in the Neuse River Basin, N.C. Multimodel streamflow forecasts developed from two single models, a parametric regression approach and semiparametric resampling approach, are forced with a reservoir management model that takes ensembles to estimate the reliability of meeting the water quality and water supply releases and the end of the season target storage. The analyses show that the entire seasonal releases for water supply and water quality uses could be met purely based on the initial storages 100% reliability of supply, thereby limiting the use of forecasts. The study suggests that, by constraining the end of the season target storage conditions being met with high probability, the climate information based streamflow forecasts could be utilized for invoking restrictions during below- normal inflow years. Further, multimodel forecasts perform better in detecting the below-normal inflow conditions in comparison to single model forecasts by reducing false alarms and missed targets which could improve public confidence in utilizing climate forecasts for developing proactive water management strategies.}, number={3}, journal={Journal of Water Resources Planning and Management}, author={Golembesky, K. and Sankarasubramanian, A. and Devineni, N.}, year={2009}, pages={188–197} }
 @article{sankarasubramanian_lall_souza filho_sharma_2009, title={Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework}, volume={45}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/2009WR007821}, DOI={10.1029/2009wr007821}, abstractNote={Probabilistic, seasonal to interannual streamflow forecasts are becoming increasingly available as the ability to model climate teleconnections is improving. However, water managers and practitioners have been slow to adopt such products, citing concerns with forecast skill. Essentially, a management risk is perceived in “gambling” with operations using a probabilistic forecast, while a system failure upon following existing operating policies is “protected” by the official rules or guidebook. In the presence of a prescribed system of prior allocation of releases under different storage or water availability conditions, the manager has little incentive to change. Innovation in allocation and operation is hence key to improved risk management using such forecasts. A participatory water allocation process that can effectively use probabilistic forecasts as part of an adaptive management strategy is introduced here. Users can express their demand for water through statements that cover the quantity needed at a particular reliability, the temporal distribution of the “allocation,” the associated willingness to pay, and compensation in the event of contract nonperformance. The water manager then assesses feasible allocations using the probabilistic forecast that try to meet these criteria across all users. An iterative process between users and water manager could be used to formalize a set of short‐term contracts that represent the resulting prioritized water allocation strategy over the operating period for which the forecast was issued. These contracts can be used to allocate water each year/season beyond long‐term contracts that may have precedence. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser optimization model that can support such an allocation process is presented. The application of this conceptual model is explored using data for the Jaguaribe Metropolitan Hydro System in Ceara, Brazil. The performance relative to the current allocation process is assessed in the context of whether such a model could support the proposed short‐term contract based participatory process. A synthetic forecasting example is also used to explore the relative roles of forecast skill and reservoir storage in this framework.}, number={11}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Sankarasubramanian, A. and Lall, Upmanu and Souza Filho, Francisco Assis and Sharma, Ashish}, year={2009}, month={Nov} }
 @article{choudhury_sankarasubramanian_2009, title={River Flood Forecasting Using Complementary Muskingum Rating Equations}, volume={14}, ISSN={["1943-5584"]}, DOI={10.1061/(ASCE)HE.1943-5584.0000046}, abstractNote={A model for real-time flood forecasting in river systems with large drainage areas has been developed. Flow variations between upstream and downstream stations are interlinked and are typically governed by reach properties. Unique paired variations establish useful flow correspondence resulting in inflow and outflow forecasting models for a reach. The proposed model can generate forecasts with increased lead time without applying a separate inflow forecasting model and can also provide updated forecasts essential for real-time applications. The model was applied to flood forecasting in Tar River Basin, N.C., covering a drainage area of 13,921 km 2 . The model aggregates multiple upstream flows to provide long range forecasts for two downstream stations in the basin. Applicability of the model in estimating complete upstream and downstream hydrographs was demonstrated using a textbook example. Application results indicate that the new model can provide complete and updatable evolution of hydrographs using the current flow state.}, number={7}, journal={JOURNAL OF HYDROLOGIC ENGINEERING}, author={Choudhury, Parthasarathi and Sankarasubramanian, A.}, year={2009}, month={Jul}, pages={745–751} }
 @misc{arumugam_2009, title={Role of Climate Variability and Change in Improving River Basin Management}, author={Arumugam, S.}, year={2009}, month={Dec} }
 @article{sankarasubramanian_lall_devineni_espinueva_2009, title={The Role of Monthly Updated Climate Forecasts in Improving Intraseasonal Water Allocation}, volume={48}, ISSN={["1558-8432"]}, DOI={10.1175/2009JAMC2122.1}, abstractNote={Abstract
               Seasonal streamflow forecasts contingent on climate information are essential for short-term planning (e.g., water allocation) and for setting up contingency measures during extreme years. However, the water allocated based on the climate forecasts issued at the beginning of the season needs to be revised using the updated climate forecasts throughout the season. In this study, reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with “persisted” SSTs were used to improve both seasonal and intraseasonal water allocation during the October–February season for the Angat reservoir, a multipurpose system, in the Philippines. Monthly updated reservoir inflow forecasts are ingested into a reservoir simulation model to allocate water for multiple uses by ensuring a high probability of meeting the end-of-season target storage that is required to meet the summer (March–May) demand. The forecast-based allocation is combined with the observed inflows during the season to estimate storages, spill, and generated hydropower from the system. The performance of the reservoir is compared under three scenarios: forecasts issued at the beginning of the season, monthly updated forecasts during the season, and use of climatological values. Retrospective reservoir analysis shows that the operation of a reservoir by using monthly updated inflow forecasts reduces the spill considerably by increasing the allocation for hydropower during above-normal-inflow years. During below-normal-inflow years, monthly updated streamflow forecasts could be effectively used for ensuring enough water for the summer season by meeting the end-of-season target storage. These analyses suggest the importance of performing experimental reservoir analyses to understand the potential challenges and opportunities in improving seasonal and intraseasonal water allocation by using real-time climate forecasts.}, number={7}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, author={Sankarasubramanian, A. and Lall, Upmanu and Devineni, Naresh and Espinueva, Susan}, year={2009}, month={Jul}, pages={1464–1482} }
 @inbook{brown_conrad_sankarasubramanian_someshwar_elazegui_2009, place={London}, title={The use of seasonal climate forecasts within a shared reservoir system:  The case of Angat reservoir, the Philippines}, booktitle={Climate Change Adaptation in the Water Sector}, publisher={Earthscan}, author={Brown, C. and Conrad, E. and Sankarasubramanian, A. and Someshwar, S. and Elazegui, Dulze}, editor={Ludwig, F. and Kabat, P. and van Schaik, H. and van der Valk, M.Editors}, year={2009} }
 @inproceedings{devineni_arumugam_2008, title={Improved Drought Management of Falls Lake Reservoir: Role of Multimodel Streamflow Forecasts in Setting up Restrictions}, author={Devineni, N. and Arumugam, S.}, year={2008}, month={Oct} }
 @inproceedings{li_arumugam_2008, title={Improving Hydrological Predictions through better representation of Model Uncertainty}, author={Li, W. and Arumugam, S.}, year={2008}, month={Oct} }
 @article{devineni_sankarasubramanian_ghosh_2008, title={Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations}, volume={44}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/2006WR005855}, DOI={10.1029/2006wr005855}, abstractNote={A new approach for developing multimodel streamflow forecasts is presented. The methodology combines streamflow forecasts from individual models by evaluating their skill, represented by rank probability score (RPS), contingent on the predictor state. Using average RPS estimated over the chosen neighbors in the predictor state space, the methodology assigns higher weights for a model that has better predictability under similar predictor conditions. We assess the performance of the proposed algorithm by developing multimodel streamflow forecasts for Falls Lake Reservoir in the Neuse River Basin, North Carolina (NC), by combining streamflow forecasts developed from two low‐dimensional statistical models that use sea‐surface temperature conditions as underlying predictors. To evaluate the proposed scheme thoroughly, we consider a total of seven multimodels that include existing multimodel combination techniques such as combining based on long‐term predictability of individual models and by simple pooling of ensembles. Detailed nonparametric hypothesis tests comparing the performance of seven multimodels with two individual models show that the reduced RPS from multimodel forecasts developed using the proposed algorithm is statistically significant from the RPSs of individual models and from the RPSs of existing multimodel techniques. The study also shows that adding climatological ensembles improves the multimodel performance resulting in reduced average RPS. Contingency analyses on categorical (tercile) forecasts show that the proposed multimodel combination technique reduces average Brier score and total number of false alarms, resulting in improved reliability of forecasts. However, adding multiple models with climatology also increases the number of missed targets (in comparison to individual models' forecasts) which primarily results from the reduction of increased resolution that is exhibited in the individual models' forecasts under various forecast probabilities.}, number={9}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Devineni, Naresh and Sankarasubramanian, A. and Ghosh, Sujit}, year={2008}, month={Sep} }
 @misc{arumugam_2008, title={Role of Climate Forecasts in Meeting NC Future Water Supply Challenges}, author={Arumugam, S.}, year={2008}, month={Jan} }
 @inproceedings{oh_arumugam_2008, title={Role of Streamflow Seasonality in Influencing Water Quality Variability in the Southeastern US}, author={Oh, J. and Arumugam, S.}, year={2008}, month={Oct} }
 @article{sankarasubramanian_lall_espinueva_2008, title={Role of retrospective forecasts of GCMs forced with persisted SST anomalies in operational streamflow forecasts development}, volume={9}, ISSN={["1525-7541"]}, DOI={10.1175/2007JHM842.1}, abstractNote={Abstract
               Seasonal streamflow forecasts contingent on climate information are essential for water resources planning and management as well as for setting up contingency measures during extreme years. In this study, operational streamflow forecasts are developed for a reservoir system in the Philippines using ECHAM4.5 precipitation forecasts (EPF) obtained using persisted sea surface temperature (SST) scenarios. Diagnostic analyses on SST conditions show that the tropical SSTs influence the streamflow during extreme years, whereas the local SSTs (0°–25°N, 115°–130°E) account for streamflow variability during normal years. Given that the EPF, local, and tropical SST conditions are spatially correlated, principal components regression (PCR) is employed to downscale the GCM-predicted precipitation fields and SST anomalies to monthly streamflow forecasts and to update them every month within the season using the updated EPF and SST conditions. These updated forecasts improve the prediction of monthly streamflows within the season in comparison to the skill of the monthly streamflow forecasts issued at the beginning of the season. It is also shown that the streamflow forecasting model developed using EPF under persisted SST conditions performs well upon employing EPF obtained under predicted SSTs as predictor. This has potential implications in the development of operational streamflow forecasts and statistical downscaling, which requires adequate years of retrospective GCM forecasts for recalibration. Finally, the study also shows that predicting the seasonal streamflow using the monthly precipitation forecasts reproduces the observed seasonal total better than the conventional approach of using seasonal precipitation forecasts to predict the seasonal streamflow.}, number={2}, journal={JOURNAL OF HYDROMETEOROLOGY}, author={Sankarasubramanian, A. and Lall, Upmanu and Espinueva, Susan}, year={2008}, month={Apr}, pages={212–227} }
 @inproceedings{arumugam_li_2008, title={The Role of Multimodel Combinations in improving Streamflow Prediction}, author={Arumugam, S. and Li, W.}, year={2008} }
 @misc{arumugam_2007, title={Climate Informed Water Management}, author={Arumugam, S.}, year={2007}, month={Jul} }
 @article{broad_pfaff_taddei_sankarasubramanian_lall_de assis de souza filho_2007, title={Climate, stream flow prediction and water management in northeast Brazil: societal trends and forecast value}, volume={84}, ISSN={0165-0009 1573-1480}, url={http://dx.doi.org/10.1007/s10584-007-9257-0}, DOI={10.1007/s10584-007-9257-0}, number={2}, journal={Climatic Change}, publisher={Springer Science and Business Media LLC}, author={Broad, Kenneth and Pfaff, Alexander and Taddei, Renzo and Sankarasubramanian, A. and Lall, Upmanu and de Assis de Souza Filho, Franciso}, year={2007}, month={May}, pages={217–239} }
 @inproceedings{golembesky_arumugam_devineni_2007, title={Improved Management of Falls Lake Reservoir during the Summer Season using Climate Information based Monthly Streamflow Forecasts: Role of Restrictions in Water supply and Water quality management}, author={Golembesky, K. and Arumugam, S. and Devineni, N.}, year={2007}, month={Mar} }
 @inproceedings{devineni_arumugam_ghosh_2007, title={Multi-model Ensembling of Probabilistic Streamflow Forecasts: Role of Predictor State Space in Skill Evaluation}, author={Devineni, N. and Arumugam, S. and Ghosh, S.}, year={2007}, month={Mar} }
 @inproceedings{devineni_arumugam_2007, title={Multimodel Ensembles of Streamflow Forecasts: Role ofPredictor State in Developing Optimal Combinations}, author={Devineni, N. and Arumugam, S.}, year={2007}, month={Dec} }
 @inproceedings{arumugam_devineni_2007, title={Predictability of U.S. Winter Precipitation: Role of ENSO state in Developing Multimodel Combinations}, author={Arumugam, S. and Devineni, N.}, year={2007}, month={Dec} }
 @misc{arumugam_2006, title={Climate Forecasts and Reservoir Management – Possibilities and Challenges}, author={Arumugam, S.}, year={2006}, month={Oct} }
 @misc{arumugam_2006, title={Climate Forecasts and Reservoir Management – Possibilities and Challenges}, author={Arumugam, S.}, year={2006}, month={Mar} }
 @inproceedings{brown_lall_arumugam_2006, place={Change, AMS, Atlanta}, title={Climate-informed Decision Tools for the Water and Energy Sector (Invited Presentation)}, author={Brown, A. and Lall, U. and Arumugam, S.}, year={2006} }
 @inproceedings{arumugam_lall_2006, title={Improved Operation of Reservoir Systems – Utility of Seasonal and Monthly Updated Climate Forecasts}, author={Arumugam, S. and Lall, U.}, year={2006} }
 @inproceedings{arumugam_devineni_ghosh_2006, title={Multi-model Ensembling of Probabilistic Streamflow Forecasts: Role of Predictor State Space in Skill Evaluation}, author={Arumugam, S. and Devineni, N. and Ghosh, S.}, year={2006}, month={Dec} }
 @book{sankarasubramanian_devineni_ghosh_2006, place={Raleigh, NC}, title={Multi-model Ensembling of Probabilistic Streamflow Forecasts: Role of Predictor State Space in Skill Evaluation}, number={2595}, institution={North Carolina State University}, author={Sankarasubramanian, A. and Devineni, N. and Ghosh, S.}, year={2006} }
 @misc{arumugam_2005, title={Climate Forecasts and Reservoir Management – Possibilities and Challenges}, author={Arumugam, S.}, year={2005}, month={Nov} }
 @misc{arumugam_lall_2005, title={Climate Forecasts and Reservoir Management – Possibilities and Challenges}, author={Arumugam, S. and Lall, U.}, year={2005}, month={Nov} }
 @misc{arumugam_2005, title={Climate Information based Streamflow Forecasts: Predictor Identification and Model Development,}, author={Arumugam, S.}, year={2005}, month={Nov} }
 @misc{ward_hansen_arumugam_osgood_zubair_brown_mishra_2005, title={Decision Systems Research and Tool Development at the IRI}, author={Ward, N.M. and Hansen, J.W. and Arumugam, S. and Osgood, D. and Zubair, L. and Brown, C. and Mishra, A.}, year={2005}, month={Nov} }
 @inproceedings{sankarasubramanian_lall_2005, series={Water science & technology}, title={Dynamic Water Allocation Framework for Multiple uses: Utility of Climate Forecasts towards Short-term Water Management}, booktitle={Climate change : a challenge or a threat for water management? : selected proceedings of the International Conference "Climate Change: a Challenge or a Threat for Water Management?" held in Amsterdam, the Netherlands, 27-29 September 2004}, author={Sankarasubramanian, A. and Lall, U.}, editor={L.J., BolwidtEditor}, year={2005}, collection={Water science & technology} }
 @misc{arumugam_2005, title={Improved Water Allocation using Climate Information Based Streamflow Forecasts: An Assessment from System Perspective}, author={Arumugam, S.}, year={2005}, month={Jun} }
 @misc{arumugam_2005, title={Improved Water Allocation using Climate Information Based Streamflow Forecasts: An Assessment from System Perspective}, author={Arumugam, S.}, year={2005}, month={Jan} }
 @misc{arumugam_2005, title={Improved Water Allocation using Climate Information Based Streamflow Forecasts: Decision Analyses and Possibilities}, author={Arumugam, S.}, year={2005}, month={Oct} }
 @misc{arumugam_2005, title={Improving Angat Reservoir Operation using Climate Forecasts: Decision Analyses and Possibilities}, author={Arumugam, S.}, year={2005}, month={Aug} }
 @book{vogel_sankarasubramanian_2005, title={Monthly Climate Data for Selected USGS HCDN Sites, 1951-1990, R1}, url={http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=810}, DOI={10.3334/ornldaac/810}, abstractNote={ORNL DAAC: Time series of monthly minimum and maximum temperature, precipitation, and potential evapotranspiration were derived for 1,469 watersheds in the conterminous United States for which stream flow measurements were also available from the national streamflow database, termed the Hydro-Climatic Data Network (HCDN), developed by Slack et al. (1993a,b). Monthly climate estimates were derived for the years 1951-1990. The climate characteristic estimates of temperature and precipitation were estimated using the PRISM (Daly et al. 1994, 1997) climate analysis system as described in Vogel, et al. 1999. Estimates of monthly potential evaporation were obtained using a method introduced by Hargreaves and Samani (1982) which is based on monthly time series of average minimum and maximum temperature data along with extraterrestrial solar radiation. Extraterrestrial solar radiation was estimated for each basin by computing the solar radiation over 0.1 degree grids using the method introduced by Duffie and Beckman (1980) and then summing those estimates for each river basin. This process is described in Sankarasubramanian, et al. (2001). Revision Notes: This data set has been revised to update the number of watersheds included in the data set and to updated the units for the potential evapotranspiration variable. Please see the Data Set Revisions section of this document for detailed information.}, institution={ORNL Distributed Active Archive Center}, author={Vogel, R.M. and Sankarasubramanian, A.}, year={2005} }
 @inproceedings{arumugam_lall_2005, title={Use of Operational Climate Forecasts in Reservoir Management and Operation}, author={Arumugam, S. and Lall, U.}, year={2005}, month={Dec} }
 @misc{arumugam_2004, title={Better Management through Better Decisions – Use of Climate Forecasts}, author={Arumugam, S.}, year={2004}, month={Oct} }
 @misc{arumugam_2004, title={Improved Water Allocation using Climate Information Based Streamflow Forecasts: An Assessment from System Perspective}, author={Arumugam, S.}, year={2004}, month={Apr} }
 @misc{arumugam_2004, title={Improved Water Allocation using Climate Information Based Streamflow Forecasts: An Assessment from System Perspective}, author={Arumugam, S.}, year={2004}, month={Mar} }
 @inproceedings{arumugam_lall_robertson_2004, title={Multimodel Probabilistic Hydroclimatic Ensemble Forecasts}, author={Arumugam, S. and Lall, U. and Robertson, A.W.}, year={2004}, month={May} }
 @inproceedings{arumugam_lall_2004, title={Operational Streamflow Forecasts Development Using GCM Predicted Precipitation Fields}, author={Arumugam, S. and Lall, U.}, year={2004}, month={Dec} }
 @article{ward_sankarasubramanian_hansen_indeje_mutter_2004, title={To What Extent can Climate Information Contribute to Solving Problems}, volume={9}, number={2}, journal={Clivar Exchanges}, author={Ward, M.N. and Sankarasubramanian, A. and Hansen, J. and Indeje, M. and Mutter, C.}, year={2004}, pages={5–8} }
 @misc{arumugam_2004, title={Utility of Climate Information Based Streamflow Forecasts towards Annual Water Allocation in Jaguaribe-Metropolitan Hydro (JMH) System, Ceara, NE Brazil}, author={Arumugam, S.}, year={2004}, month={Mar} }
 @article{matalas_sankarasubramanian_2003, title={Effect of persistence on trend detection via regression}, volume={39}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/2003wr002292}, DOI={10.1029/2003wr002292}, abstractNote={Trends in hydrologic sequences may be assessed in various ways. The coefficient of regression of flow on time may be used, particularly if the sequences are very long. Under the assumption of stationarity the variance of the regression coefficient is expressed as a function of sequence length and the autocorrelation coefficients of relevant order. Thus the variance inflation factor for assessing the statistical significance of estimated regression coefficients may be readily determined for any given stationary process. The variance inflation factor is determined for four stationary processes: independent, Markov, autoregressive‐moving average of order (1, 1), and fractional Gaussian noise. The effectiveness of prewhitening observed sequences with a Markov process is nearly the same whether the first order autocorrelation coefficient is known per se or through estimation.}, number={12}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Matalas, Nicholas C. and Sankarasubramanian, A.}, year={2003}, month={Dec} }
 @inproceedings{sankarasubramanian_lall_2003, title={Flood Quantiles and Changing Climate: Seasonal Forecasts and Reconstruction of Past Flood Records}, ISBN={9780784406854}, url={http://dx.doi.org/10.1061/40685(2003)356}, DOI={10.1061/40685(2003)356}, abstractNote={It is widely acknowledged that both climate and land use changes modify flood frequency, thereby challenging the traditional assumption that the underlying stochastic process is stationary in time, and that the annual maximum flood corresponds to an independent identically distributed (iid) process. In this paper, we employ a semi-parametric approach to estimate flood quantiles conditional on selected climate indices that carry the signal of structured low frequency climate variation, and influence the atmospheric mechanisms that enhance or retard local precipitation and flood potential. The semi-parametric approach that maximizes the local likelihood of the observed annual maximum peak in the climatic predictor state space is applied to estimate conditional flood quantiles for the Blacksmith Fork River near Hyrum (BFH), Utah. The estimated conditional flood quantiles correlate well with the observed annual maximum peaks, thus offering prospects for reconstructing past flood series as well as for short term forecasting.}, booktitle={World Water & Environmental Resources Congress 2003}, publisher={American Society of Civil Engineers}, author={Sankarasubramanian, A. and Lall, Upmanu}, year={2003}, month={Jun} }
 @article{sankarasubramanian_lall_2003, title={Flood quantiles in a changing climate: Seasonal forecasts and causal relations}, volume={39}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/2002wr001593}, DOI={10.1029/2002wr001593}, abstractNote={Recognizing that the frequency distribution of annual maximum floods at a given location may change over time in response to interannual and longer climate fluctuations, we compare two approaches for the estimation of flood quantiles conditional on selected “climate indices” that carry the signal of structured low‐frequency climate variation, and influence the atmospheric mechanisms that modify local precipitation and flood potential. A parametric quantile regression approach and a semiparametric local likelihood approach are compared using synthetic data sets and for data from a streamflow gauging station in the western United States. Their relative utility in different settings for seasonal flood risk forecasting as well as for the assessment of long‐term variation in flood potential is discussed.}, number={5}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Sankarasubramanian, A. and Lall, Upmanu}, year={2003}, month={May} }
 @article{sankarasubramanian_vogel_2003, title={Hydroclimatology of the continental United States}, volume={30}, ISSN={0094-8276}, url={http://dx.doi.org/10.1029/2002gl015937}, DOI={10.1029/2002gl015937}, abstractNote={The overall water balance and the sensitivity of watershed runoff to changes in climate are investigated using national databases of climate and streamflow for 1,337 watersheds in the U.S. We document that 1% changes in precipitation result in 1.5–2.5% changes in watershed runoff, depending upon the degree of buffering by storage processes and other factors. Unlike previous research, our approach to estimating climate sensitivity of streamflow is nonparametric and does not depend on a hydrologic model. The upper bound for precipitation elasticity of streamflow is shown to be the inverse of the runoff ratio. For over a century, investigators [Pike, 1964; Budyko, 1974; Ol'dekop, 1911; and Schreiber, 1904] have suggested that variations in watershed aridity alone are sufficient to predict spatial variations in long‐term watershed runoff. We document that variations in soil moisture holding capacity are just as important as variations in watershed aridity in explaining the mean and variance of annual watershed runoff.}, number={7}, journal={Geophysical Research Letters}, publisher={American Geophysical Union (AGU)}, author={Sankarasubramanian, A. and Vogel, Richard M.}, year={2003}, month={Apr} }
 @misc{arumugam_2003, title={Utility of Climate Forecasts in Improving Reservoir Management}, author={Arumugam, S.}, year={2003}, month={Feb} }
 @misc{sankarasubramanian_lall_sharma_lucas_2003, title={Utility of Climate Information Based Reservoir Inflow Forecasts in Annual Water Allocation: Ceara Case Study}, author={Sankarasubramanian, A. and Lall, U. and Sharma, A. and Lucas, J.}, year={2003}, month={Nov} }
 @article{vogel_sankarasubramanian_2003, title={Validation of a watershed model without calibration}, volume={39}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/2002wr001940}, DOI={10.1029/2002wr001940}, abstractNote={Traditional approaches for the validation of watershed models focus on the “goodness of fit” between model predictions and observations. It is possible for a watershed model to exhibit a “good” fit, yet not accurately represent hydrologic processes; hence “goodness of fit” can be misleading. Instead, we introduce an approach which evaluates the ability of a model to represent the observed covariance structure of the input (climate) and output (streamflow) without ever calibrating the model. An advantage of this approach is that it is not confounded by model error introduced during the calibration process. We illustrate that once a watershed model is calibrated, the unavoidable model error can cloud our ability to validate (or invalidate) the model. We emphasize that model hypothesis testing (validation) should be performed prior to, and independent of, parameter estimation (calibration), contrary to traditional practice in which watershed models are usually validated after calibrating the model. Our approach is tested using two different watershed models at a number of different watersheds in the United States.}, number={10}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Vogel, Richard M. and Sankarasubramanian, A.}, year={2003}, month={Oct} }
 @inproceedings{sankarasubramanian_sharma_lall_2003, title={Water Allocation for Multiple Uses based on Probabilistic Reservoir Inflow Forecasts}, booktitle={Proceedings of the IAHS-IUGG Meeting}, author={Sankarasubramanian, A. and Sharma, A. and Lall, U.}, year={2003}, month={Jun} }
 @article{sankarasubramanian_vogel_2002, title={Annual hydroclimatology of the United States}, volume={38}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/2001wr000619}, DOI={10.1029/2001wr000619}, abstractNote={An overview of the annual hydroclimatology of the United States is provided. Time series of monthly streamflow, temperature, and precipitation are developed for 1337 watersheds in the United States. This unique data set is then used to evaluate several approaches for estimating the long‐term water balance and the interannual variability of streamflow. Traditional relationships which predict either actual evapotranspiration or the interannual variability of streamflow from an aridity index  are shown to perform poorly for basins with low soil moisture storage capacity. A water balance model is used to formulate new relationships for predicting actual evapotranspiration and the interannual variability of streamflow. These relationships depend on both the aridity index  and a new soil moisture storage index. A physically based approach for estimating the soil moisture storage index is introduced which requires monthly time series of precipitation, potential evapotranspiration, and an estimate of maximum soil moisture holding capacity. The net results are improved expressions for the long‐term water balance and the interannual variability of streamflow which do not require either calibration or streamflow data.}, number={6}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Sankarasubramanian, A. and Vogel, Richard M.}, year={2002}, month={Jun} }
 @article{sankarasubramanian_vogel_2002, title={Comment on the paper: “Basin hydrologic response relations to distributed physiographic descriptors and climate” by Karen Plaut Berger, Dara Entekhabi, 2001. Journal of Hydrology 247, 169–182}, volume={263}, ISSN={0022-1694}, url={http://dx.doi.org/10.1016/s0022-1694(02)00061-6}, DOI={10.1016/s0022-1694(02)00061-6}, number={1-4}, journal={Journal of Hydrology}, publisher={Elsevier BV}, author={Sankarasubramanian, A. and Vogel, Richard M.}, year={2002}, month={Jun}, pages={257–261} }
 @inproceedings{lall_sharma_arumugam_filho_2002, title={From Interannual Streamflow Forecasts to New Water Management Strategies for Ceara, North East Brazil}, author={Lall, U. and Sharma, A. and Arumugam, S. and Filho, F. Souza}, year={2002}, month={Dec} }
 @misc{arumugam_2002, title={Hydroclimatology of the United States}, author={Arumugam, S.}, year={2002}, month={May} }
 @article{sankarasubramanian_vogel_limbrunner_2001, title={Climate elasticity of streamflow in the United States}, volume={37}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/2000wr900330}, DOI={10.1029/2000wr900330}, abstractNote={Precipitation elasticity of streamflow, ϵP, provides a measure of the sensitivity of streamflow to changes in rainfall. Watershed model‐based estimates of ϵP are shown to be highly sensitive to model structure and calibration error. A Monte Carlo experiment compares a nonparametric estimator of ϵP with various watershed model‐based approaches. The nonparametric estimator is found to have low bias and is as robust as or more robust than alternate model‐based approaches. The nonparametric estimator is used to construct a map of ϵP for the United States. Comparisons with 10 detailed climate change studies reveal that the contour map of ϵP introduced here provides a validation metric for past and future climate change investigations in the United States. Further investigations reveal that ϵP tends to be low for basins with significant snow accumulation and for basins whose moisture and energy inputs are seasonally in phase with one another. The Budyko hypothesis can only explain variations in ϵP for very humid basins.}, number={6}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Sankarasubramanian, A. and Vogel, Richard M. and Limbrunner, James F.}, year={2001}, month={Jun}, pages={1771–1781} }
 @inproceedings{sankarasubramanian_vogel_2000, title={Annual Hydroclimatology of the United States}, booktitle={Proceedings of the 27th Annual WRPM Conference}, author={Sankarasubramanian, A. and Vogel, R.M.}, year={2000} }
 @article{fernandez_vogel_sankarasubramanian_2000, title={Regional calibration of a watershed model}, volume={45}, ISSN={0262-6667 2150-3435}, url={http://dx.doi.org/10.1080/02626660009492371}, DOI={10.1080/02626660009492371}, abstractNote={Abstract As watershed models become increasingly sophisticated and useful, there is a need to extend their applicability to locations where they cannot be calibrated or validated. A new methodology for the regionalization of a watershed model is introduced and evaluated. The approach involves calibration of a watershed model to many sites in a region, concurrently. Previous research that has sought to relate the parameters of monthly water balance models to physical drainage basin characteristics in a region has met with limited success. Previous studies have taken the two-step approach: (a) estimation of watershed model parameters at each site, followed by (b) attempts to relate model parameters to drainage basin characteristics. Instead of treating these two steps as independent, both steps are implemented concurrently. All watershed models in a region are calibrated simultaneously, with the dual objective of reproducing the behaviour of observed monthly streamflows and, additionally, to obtain good relationships between watershed model parameters and basin characteristics. The approach is evaluated using 33 basins in the southeastern region of the United States by comparing simulations using the regional models for three catchments which were not used to develop the regional regression equations. Although the regional calibration approach led to nearly perfect regional relationships between watershed model parameters and basin characteristics, these “improved” regional relationships did not result in improvements in the ability to model streamflow at ungauged sites. This experiment reveals that improvements in regional relationships between watershed model parameters and basin characteristics will not necessarily lead to improvements in the ability to calibrate a watershed model at an ungauged site.}, number={5}, journal={Hydrological Sciences Journal}, publisher={Informa UK Limited}, author={Fernandez, W. and Vogel, R. M. and Sankarasubramanian, A.}, year={2000}, month={Oct}, pages={689–707} }
 @misc{arumugam_2000, title={Role of Environmental Assessments and Environmental Management Plans in enhancing the Development Effectiveness – A Review on Water and Agriculture Projects in the South Asia Region}, author={Arumugam, S.}, year={2000}, month={Dec} }
 @article{vogel_sankarasubramanian_2000, title={Spatial scaling properties of annual streamflow in the United States}, volume={45}, ISSN={0262-6667 2150-3435}, url={http://dx.doi.org/10.1080/02626660009492342}, DOI={10.1080/02626660009492342}, abstractNote={Abstract The spatial scaling properties of annual average streamflow is examined using records from 1 433 river basins across the continental United States. The log-linear relationship ln(E[Qr i]) = a + br ln(Ai) is representative throughout the United States, where E[Qr i] represents the expectation of the rth moment of annual streamflow at site i, and Ai represents drainage area. The scaling model parameters ar and br follow nearly perfect linear relationships ar = rα and br = rβ throughout the continental United States. We conclude that the probability distribution of annual streamflow follows simple scaling relationships in all regions of the United States. In temperate regions where climate is relatively homogeneous, scale alone describes most of the variability in the moments of annual streamflow. In the more climatically heterogeneous regions, such as in the Upper Colorado and Missouri river basins, scale alone is a poor predictor of the moments of annual flow.}, number={3}, journal={Hydrological Sciences Journal}, publisher={Informa UK Limited}, author={Vogel, Richard M. and Sankarasubramanian, A.}, year={2000}, month={Jun}, pages={465–476} }
 @inproceedings{vogel_sankarasubramanian_limbrunner_wilson_1999, title={Comparisons of Climate Elasticity of Streamflow in the United States}, ISBN={9780784404300}, url={http://dx.doi.org/10.1061/40430(1999)251}, DOI={10.1061/40430(1999)251}, abstractNote={The sensitivity of streamflow to climate is investigated by calcu lating precipitation and potential evapotranspiration elasticity of streamflow for 1,447 watersheds in the United States. A unique dataset of streamflow and climate time-series is constructed which accounts for the complex spatial variations in climate across the U.S. Average annual values of streamflow, precipitation, temperature, and potential evapotransiration are used to estimate climate elasticity of the long-term mean streamflow using a nonparametric (databased) approach, a regional regression approach and a water balance modeling approach. Comparisons are provided with nine previous climate change studies based on monthly water balance models and soil moisture accounting models Our results suggest that streamflow is more sensitive to changes in precipitation than to changes in potential evapotranspiration. Streamflow is particularly sensitive to both precipitation and potential evapotranspiration in the midwestern regions of the U.S. Difficulties with both the concept of elasticity and with its estimation are discussed and ongoing research is summarized. Climate elasticities derived from detailed monthly simulation experiments agree nicely with the simpler annual approaches outlined in this study.}, booktitle={WRPMD'99}, publisher={American Society of Civil Engineers}, author={Vogel, Richard M. and Sankarasubramanian, A. and Limbrunner, James F. and Wilson, Ian}, year={1999}, month={Jun} }
 @article{sankarasubramanian_srinivasan_1999, title={Investigation and comparison of sampling properties of L-moments and conventional moments}, volume={218}, ISSN={0022-1694}, url={http://dx.doi.org/10.1016/s0022-1694(99)00018-9}, DOI={10.1016/s0022-1694(99)00018-9}, abstractNote={The first part of this article deals with fitting of regression equations for the sampling properties, variance of L-standard deviation (l2), and bias and variance of L-skewness (t3), based on Monte-Carlo simulation results, for generalised Normal (Lognormal-3) and Pearson-3 distributions. These fitted equations will be useful in formulating goodness-of-fit test statistics in regional frequency analysis. The second part presents a comparison of the sampling properties between L-moments and conventional product moments for generalised Normal, generalised Extreme Value, generalised Pareto and Pearson-3 distributions, in a relative form. The comparison reveals that the bias in L-skewness is found to be insignificant up to a skewness of about 1.0, even for small samples. In case of higher skewness, for a reasonable sample size of 30, L-skewness is found to be nearly unbiased. However, the conventional skewness is found to be significantly biased, even for a low skewness of 0.5 and a reasonable sample size of 30. The overall performance evaluation in terms of "Relative-RMSE in third moment ratio" reveals that conventional moments are preferable at lower skewness, particularly for smaller samples, while L-moments are preferable at higher skewness, for all sample sizes. This point is illustrated through an application that seeks to obtain an appropriate regional flood frequency distribution for the 98 catchment areas located in the central region of India, spread over six hydrometeorologic subzones.}, number={1-2}, journal={Journal of Hydrology}, publisher={Elsevier BV}, author={Sankarasubramanian, A. and Srinivasan, K.}, year={1999}, month={May}, pages={13–34} }
 @inproceedings{vogel_sankarasubramanian_1999, title={On the Validation of a Watershed Model}, author={Vogel, R.M. and Sankarasubramanian, A.}, year={1999}, month={Aug} }
 @inproceedings{sankarasubramanian_srinivasan_1996, title={Evaluation of Sampling Properties of General Extreme Value (GEV) Distribution-L-Moments Vs Conventional Moments}, booktitle={Proceedings of the 24th Annual WRPM conference}, author={Sankarasubramanian, A. and Srinivasan, K.}, year={1996} }
 @article{srinivasan_sankarasubramanian_1996, title={Flood Frequency Models for Indian Catchments– A Relook}, volume={77}, journal={Journal of the Institution of Engineers}, author={Srinivasan, K. and Sankarasubramanian, A.}, year={1996}, pages={41–46} }
 @inproceedings{arumugam_lall_de souza_brown, place={San Antonio}, title={Climate Forecasts and Reservoir Management – Possibilities and Challenges}, author={Arumugam, S. and Lall, U. and De Souza, A.F. and Brown, C.} }