EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Water Management and Transfers Optimization with Probabilistic Seasonal Forecasts

Tingju Zhu1, Guilherme Marques2, Josué Medellin-Azuara3, and Jay Lund4
Tingju Zhu et al.
  • 1ZJU-UIUC Institute, International Campus, Zhejiang University, Haining, China (
  • 2Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
  • 3School of Engineering, University of California, Merced, U.S.A.
  • 4Department of Civil and Environmental Engineering, University of California, Davis, U.S.A.

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 develop 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 onfarm 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, for managing complex regional water systems, including agricultural and urban water uses, water transfers, and conjunctive use of surface water and groundwater.

How to cite: Zhu, T., Marques, G., Medellin-Azuara, J., and Lund, J.: Water Management and Transfers Optimization with Probabilistic Seasonal Forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20981,, 2020