Optimizing Regional Water Management using Probabilistic Seasonal Forecasts

Abstract:

Advances in probabilistic seasonal flow forecasts sparked renewed interests to improve water management, through explicit incorporation of forecasts and forecast uncertainties into decision-making. Here we developed a three-stage stochastic programming model to optimize integrated agricultural and urban water management decisions by directly considering probabilistic seasonal flow forecasts. The model represents urban water users which make short-term and long-term water conservation choices to maximize supply reliability and minimize conservation costs; it also represents irrigators which optimize land and water allocations to annual and perennial crops to maximize farm revenue, besides water transfers between agricultural and urban uses. Long-term urban conservation measures, areas of perennial crops, and capital investments in on-farm irrigation are considered in the first stage; annual crop areas, which depend on forecasted flows, are considered in the second stage; and reductions of irrigated annual and perennial crop areas due to water scarcity, conjunctive use operations, and water transfers informed by realized hydrologic year types are considered in the third stage. The temporal hierarchy of these decisions intends to approximate actual decision-making process by simultaneously considering long- and short-term decisions, forecasts, and forecasting skills. This paper provides a framework for quantifying the value of probabilistic forecasting information and forecasting skills, as well as for managing complex regional water systems.