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

Summary: Improved Drought Management of Falls Lake Reservoir:
Role of Multimodel Streamflow Forecasts in Setting up
Restrictions
Kurt Golembesky1
; A. Sankarasubramanian2
; and Naresh Devineni3
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.

  

Source: Arumugam, Sankar - Department of Civil, Construction, and Environmental Engineering, North Carolina State University

 

Collections: Environmental Sciences and Ecology; Engineering