Most of the world’s population faces freshwater scarcity threats, and reservoirs, built both for ensuring water supply during prolonged droughts and reducing downstream flood risks, are critical infrastructure for water sustainability. Historical inflow data and water demand were used to estimate reservoir storage allocation and operation policies when designing and building reservoirs, 50 to 100 years ago. This study assesses historical reservoir operations in 16 Southeastern reservoirs and evaluates the potential for utilizing existing flood control storage for alternative purposes without increasing downstream flood risk. Using a reservoir simulation model, we evaluate the resulting storage under four initial storage conditions for observed and synthetic seasonal maximum six-day flood pulses. For most reservoirs, we find conservation storage is depleting and did not exceed the flood storage capacity in their historical operation. The simulation model resulted in most of the reservoirs’ storage levels staying within the flood control pool for all scenarios (for observed and synthetic floods). Additional flood risk was lowest for initial storage condition 1 (flood control pool empty) and highest with condition 2 (50% of the flood control pool full). Flood risk increased the most for reservoirs with small ratios of flood control to conservation pool storage. Our study shows the potential for reallocation and utilization of flood control storage to meet the increasing demand. As limited opportunities for new reservoirs exist, utilizing current reservoir storage without introducing additional downstream risk may be an effective management strategy to mitigate flood and drought risk under climate change and population growth.

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.