Simulation-Optimization Model for Conjunctive Management of Surface Water and Groundwater for Agricultural Use

• Published in: Water (Basel) Vol. 13; no. 23; p. 3444
• Main Authors: Ashu, Agbortoko Bate, Lee, Sang-Il
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 04.12.2021
• Subjects: Agriculture; Algorithms; Aquifers; Creeks & streams; drawdown; Freshwater resources; Groundwater; Irrigation; monsoon season; neural networks; Optimization; Precipitation; rain; resource allocation; Rice; Seasons; Simulation; South Korea; summer; Surface water; water management; Water supply; water table; Watersheds; Iran; South Korea
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w13233444

The conjunctive management of surface water and groundwater resources is essential to sustainably manage water resources. The target study is the Osan watershed, in which approximately 60–70% of rainfall occurs during the summer monsoon in Central South Korea. Surface water resources are overexploited six times as much as groundwater resources in this region, leading to increasing pressure to satisfy the region’s growing agricultural water demand. Therefore, a simulation-optimization (S-O) model at the sub-basin scale is required to optimize water resource allocation in the Osan watershed. An S-O model based on an artificial neural network (ANN) model coupled with Jaya algorithm optimization (JA) was used to determine the yearly conjunctive supply of agricultural water. The objective was to minimize the water deficit in the watershed subject to constraints on the cumulative drawdown in each subarea. The ANN model could predict the behaviour of the groundwater level and facilitate decision making. The S-O model could minimize the water deficit by approximately 80% in response to the gross water demand, thereby proving to be suitable for a conjunctive management model for water resource management and planning.