Seasonal hydroclimatology of the continental United States: Forecasting and its relevance to water management

Recent research in seasonal climate prediction has focused on combining multiple atmospheric General Circulation Models (GCMs) to develop multimodel ensembles. A new approach to combine multiple GCMs is proposed by analyzing the skill of candidate models contingent on the relevant predictor(s) state. To demonstrate this approach, we combine historical simulations of winter (December-February, DJF) precipitation and temperature from seven GCMs by evaluating their skill -- represented by Mean Square Error (MSE) -- over similar predictor (DJF Nino3.4) conditions. The MSE estimates are converted into weights for each GCM for developing multimodel tercile probabilities. A total of six multimodel schemes are considered that includes combinations based on pooling of ensembles as well as based on the long-term skill of the models. To ensure the improved skill exhibited by the multimodel scheme is statistically significant, we perform rigorous hypothesis tests comparing the skill of multimodels with individual models' skill. The multimodel combination contingent on Nino3.4 show improved skill particularly for regions whose winter precipitation and temperature exhibit significant correlation with Nino3.4.;Analyses of weights also show that the proposed multimodel combination methodology assigns higher weights for GCMs and lesser weights for climatology during El Nino and La Nina conditions. On the other hand, due to the limited skill of GCMs during neutral conditions over the tropical Pacific, the methodology assigns higher weights for climatology resulting in improved skill from the multimodel combinations. The proposed methodology is also evaluated within a forecasting context by combining real-time precipitation forecasts from five different coupled GCMs contingent on the forecasted Nino3.4. Thus, analyzing GCMs' skill contingent on the relevant predictor state provide an alternate approach for multimodel combination such that years with limited skill could be replaced with climatology.;The utility of the proposed multimodel combination methodology in the context of short-term (monthly to seasonal) water management is investigated by utilizing 3-month ahead probabilistic multimodel streamflow forecasts developed using climate 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, NC. 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 supply releases and the end of the season target storage. 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. This research also presents a systematic analysis for understanding the seasonal hydroclimatology of the continental United States. The relationship of seasonality in precipitation and temperature to mean monthly runoff are analyzed for 1373 watersheds across the U.S. using a physical model with no calibration.