Multi-Objective Optimization of Water Resource Allocation for Groundwater Recharge and Surface Runoff Management in Watershed Systems

2025-02-21

AI Summary

The desiccation of Lake Urmia has triggered severe salinization, dust storms, and ecosystem degradation. Method: This study develops a multi-objective water resources optimization model integrating surface runoff allocation and groundwater recharge to dynamically sustain the lake’s minimum ecological water level. It innovatively couples Sobol’–Jansen and Morris global sensitivity analysis methods to uncover strong nonlinear interactions between runoff and groundwater in controlling lake level, and proposes a seasonally differentiated multiplicative runoff regulation paradigm. Optimization employs genetic algorithms, nonlinear programming, and pattern search methods. Contribution/Results: Quantitative results indicate that surface runoff must increase by 8.7× during wet seasons and 33.5× during dry seasons to stabilize lake level. The framework delivers a quantifiable, operationally feasible water management strategy for ecological restoration of arid-zone saline lakes.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Land degradation and air pollution are primarily caused by the salinization of soil and desertification that occurs from the drying of salinity lakes and the release of dust into the atmosphere because of their dried bottom. The complete drying up of a lake has caused a community environmental catastrophe. In this study, we presented an optimization problem to determine the total surface runoff to maintain the level of salinity lake (Urmia Lake). The proposed process has two key stages: identifying the influential factors in determining the lake water level using sensitivity analysis approaches based upon historical data and optimizing the effective variable to stabilize the lake water level under changing design variables. Based upon the Sobol'-Jansen and Morris techniques, the groundwater level and total surface runoff flow are highly effective with nonlinear and interacting impacts of the lake water level. As a result of the sensitivity analysis, we found that it may be possible to effectively manage lake levels by adjusting total surface runoff. We used genetic algorithms, non-linear optimization, and pattern search techniques to solve the optimization problem. Furthermore, the lake level constraint is established based on a pattern as a constant number every month. In order to maintain a consistent pattern of lake levels, it is necessary to increase surface runoff by approximately 8.7 times during filling season. It is necessary to increase this quantity by 33.5 times during the draining season. In the future, the results may serve as a guide for the rehabilitation of the lake.

Problem

• Optimizing water resource allocation
• Managing groundwater recharge
• Stabilizing salinity lake levels

Innovation

• Genetic algorithms optimize water allocation
• Sensitivity analysis identifies key lake factors
• Pattern search maintains consistent lake levels