Improving operating policies of large-scale surface-groundwater systems through stochastic programming

Abstract

[EN] The management of large-scale water resource systems with surface and groundwater resources requires considering stream-aquifer interactions. Optimization models applied of large-scale systems have either employed deterministic optimization (with perfect foreknowledge of future inflows, which hinders their applicability to real-life operations) or stochastic programming (in which stream-aquifer interaction is often neglected due to the computational burden associated with these methods). In this paper, stream-aquifer interaction is integrated in a stochastic programming framework by combining the Stochastic Dual Dynamic Programming (SDDP) optimization algorithm with the Embedded Multireservoir Model (EMM). The resulting extension of the SDDP algorithm, named Combined Surface-Groundwater SDDP (CSG-SDDP), is able to properly represent the stream-aquifer interaction within stochastic optimization models of large-scale surface-groundwater resources systems. The algorithm is applied to build a hydroeconomic model for the Jucar River Basin (Spain), in which stream-aquifer interactions are essential to the characterization of water resources in the system. Besides the uncertainties regarding the economic characterization of the demand functions, the results show that the economic efficiency of the operating policies under the current system can be improved by better management of groundwater and surface resources