@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} }
 @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_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}, 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} }
 @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} }