A spatiotemporal hydro-economic optimization modeling framework for integrating the conjunctive surface water and groundwater management with the crop pattern in an arid endorheic river basin

Abstract

The spatial heterogeneity and temporal dynamics under hydrological and hydrogeological conditions for the large endorheic river basins urgently need to implement spatiotemporal hydro-economic optimization for agricultural and environmental water demand. However, the current optimization lacks a comprehensive modeling framework that fully integrates the crop pattern with conjunctive use of surface water (SW) and groundwater (GW) and the consequent hydrological responses of river–lake-groundwater system. To address this challenge, a novel hydro-economic simulation–optimization framework was developed to alleviate the conflicts between agricultural and environmental sectors. The simulation model integrated a machine learning model namely kernel extreme learning machine with physically-based MODFLOW-NWT to significantly lower the huge computational burden. The optimization model based on the ɛ-MOMA algorithm was used to maximize net agricultural economic profit, GW and lake water storage by optimizing crop pattern, SW/GW irrigation and ecological water diversion. The framework was validated in the Yanqi Basin, an arid endorheic river basin with intensive agricultural expansion. The optimization results demonstrate that the net economic profit conflicts with the GW and lake water storage subject to water scarcity in the arid basin. The representative Pareto schemes including the extreme and compromising solutions are compared with the historical scheme to uncover the management implications. First, the net profit, the lowest lake level can be elevated by up to 14.7 % and 1.60 m, respectively, and the largest GW drawdown can be reduced by up to 3.83 m compared to the pre-optimized scheme. Second, the cash crop acreage dominates net profit and can be increased in the first two management periods to maximize agricultural economic objective. However, the schemes maximizing GW and lake water storage objective need to lower cash crop acreage and reduce irrigation water demand throughout the management period. Then, SW irrigation is largely elevated by collaboratively optimizing the ecological water diversion to significantly reduce GW irrigation in the last two management periods compared to the pre-optimized scheme. Therefore, it is of great importance for achieving the optimal trade-offs between agricultural and environmental water demand to implement conjunctive SW and GW management. Finally, the location-dependent crop pattern and water policy can be optimized to identify spatial adjustment with the preferred objectives in the different irrigation districts. Overall, these results advance our understanding of conjunctive water management and crop pattern regulation in the arid endorheic river basin, and testify the flexibility and usefulness of the hydro-economic optimization framework.