Model training accuracy.

Under the increasing pressure of global climate change, water conservation (WC) in semi-arid regions is experiencing unprecedented levels of stress. WC involves complex, nonlinear interactions among ecosystem components like vegetation, soil structure, and topography, complicating research. This study introduces a novel approach combining InVEST modeling, spatiotemporal transfer of Water Conservation Reserves (WCR), and deep learning to uncover regional WC patterns and driving mechanisms. The InVEST model evaluates Xiong’an New Area’s WC characteristics from 2000 to 2020, showing a 74% average increase in WC depth with an inverted “V” spatial distribution. Spatiotemporal analysis identifies temporal changes, spatial patterns of WCR and land use, and key protection areas, revealing that the WCR in Xiong’an New Area primarily shifts from the lowest WCR areas to lower WCR areas. The potential enhancement areas of WCR are concentrated in the northern region. Deep learning quantifies data complexity, highlighting critical factors like land use, precipitation, and drought influencing WC. This detailed approach enables the development of personalized WC zones and strategies, offering new insights into managing complex spatial and temporal WC data.

在全球气候变化压力日益加剧的背景下，半干旱地区的水源涵养（Water Conservation, WC）正面临前所未有的压力。水源涵养涉及植被、土壤结构、地形等生态系统组分间复杂的非线性相互作用，这为相关研究带来了极大挑战。本研究提出一种结合InVEST模型、水源涵养储备（Water Conservation Reserves, WCR）时空转移分析与深度学习的全新研究方法，以揭示区域水源涵养格局及其驱动机制。通过InVEST模型对2000至2020年雄安新区的水源涵养特征进行评估，结果显示该区域水源涵养深度平均增幅达74%，且空间分布呈倒“V”型特征。时空分析明确了水源涵养储备的时间变化规律、土地利用空间格局与重点保护区域，研究发现雄安新区的水源涵养储备主要从极低等级储备区域向低等级储备区域转移。水源涵养储备的潜在提升区域集中于北部区域。深度学习方法量化了数据集的复杂度，识别出土地利用、降水与干旱等影响水源涵养的关键因子。这套精细化研究路径可支撑差异化水源涵养分区与管理策略的制定，为复杂时空尺度下的水源涵养数据管理提供了全新研究视角。