Improved Drought Management of Falls Lake Reservoir: Role of Multimodel Streamflow Forecasts in Setting up Restrictions

Abstract

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.