Investigating agricultural water sustainability in arid regions with Bayesian network and water footprint theories

作者：Lingyun Zhang, Yang Yu, Zengkun Guo, Xiaoyun Ding, Jing Zhang, Ruide Yu · 发表于：The Science of The Total Environment · 年份：2024 · DOI：10.1016/j.scitotenv.2024.175544 · 被引用次数：25 · 研究领域：Water resources management and optimization、Hydrology and Watershed Management Studies、Water-Energy-Food Nexus Studies

Water scarcity is a significant constraint in agricultural ecosystems of arid regions, necessitating sustainable development of agricultural water resources. This study innovatively combines Bayesian theory and Water Footprint (WF) to construct a Bayesian Network (BN). Water quantity and quality data were evaluated comprehensively by WF in agricultural production. This evaluation integrates WF and local water resources to establish a sustainability assessment framework. Selected nodes are incorporated into a BN and continuously updated through structural and parameter learning, resulting in a robust model. Results reveal a nearly threefold increase of WF in the arid regions of Northwest China from 1989 to 2019, averaging 189.95 × 10 8 m 3 annually. The region's agricultural scale is expanding, and economic development is rapid, but the unsustainability of agricultural water use is increasing. Blue WF predominates in this region, with cotton having the highest WF among crops. The BN indicates a 70.1 % probability of unsustainable water use. Sensitivity analysis identifies anthropogenic factors as primary drivers influencing water resource sustainability. Scenario analysis underscores the need to reduce WF production and increase agricultural water supply for sustainable development in arid regions. Proposed strategies include improving irrigation methods, implementing integrated water-fertilizer management, and selecting drought-resistant, economically viable crops to optimize...