@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={Abstract}, 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}, number={6}, journal={JOURNAL OF HYDROMETEOROLOGY}, author={Sinha, Tushar and Sankarasubramanian, A. and Mazrooei, Amirhossein}, year={2014}, month={Dec}, pages={2470–2483} }