@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{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{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{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} }
 @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} }