[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2329":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":58},2329,"Spatio-temporal characteristics and driving factor identification of precipitation use efficiency: Based on machine learning and SHAP analysis","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110763","Precipitation use efficiency (PUE) is a key indicator of the \"carbon-water\" coupling in ecosystems; research on its spatiotemporal distribution and the identification of its driving factors is of great significance for regional ecohydrological processes and resource management. This study focuses on the cold region of Northeast China, an important agricultural and natural ecological zone. Based on multi-source remote sensing data from 2001 to 2024, it employs the Theil-Sen trend estimation and Mann–Kendall significance tests to identify trends in PUE, and combined the Hurst index and coefficient of variation to analyze its future evolution. Furthermore, by integrating the SHAP explainable machine learning model framework, this study quantifies the multiscale driving contributions of climate, vegetation, and topographic factors to PUE and identifies the ecological thresholds of key factors. The results show that: (1) The spatial distribution of PUE exhibits a gradient pattern characterized by higher values in the northwest and lower values in the southeast. Interannual variations follow a three-phase pattern of “gradual increase, significant decline, and fluctuation at high levels,” with values ranging from 0.67 to 0.96 gC·m⁻²·mm⁻¹ . (2) Spatially, PUE is not only regulated by multiple individual factors, but the interactions among these factors further reinforce its patterns; temporally, PUE is jointly regulated by dynamic meteorological and vegetation factors, and key influencing factors exhibit significant nonlinear threshold responses. (3) The dominant driving mechanisms in regions with different PUE values exhibit significant heterogeneity: regions with low PUE values are primarily constrained by high evapotranspiration and weak water-holding capacity, whereas regions with high PUE values rely on efficient water conversion and utilization strategies under low-precipitation conditions. This study reveals the multiscale factors driving PUE in the cold regions of Northeast China and identifies key regulatory thresholds, providing quantitative evidence for regional “carbon-water” integrated management, precision-based vegetation restoration, and climate change adaptation strategies.","降水利用效率（PUE）是生态系统“碳水”耦合的关键指标，研究其时空分布及驱动因素识别对区域生态水文过程与资源管理具有重要意义。本研究以我国东北寒区这一重要农业与自然生态区为对象，基于2001—2024年多源遥感数据，采用Theil-Sen趋势估计与Mann-Kendall显著性检验识别PUE变化趋势，并结合Hurst指数与变异系数分析其未来演变；进一步集成SHAP可解释机器学习模型框架，量化气候、植被与地形因素对PUE的多尺度驱动贡献，并识别关键因子的生态阈值。结果表明：（1）PUE空间分布呈西北高、东南低的梯度格局，年际变化呈“缓慢上升—显著下降—高位波动”的三阶段特征，数值范围为0.67~0.96 gC·m⁻²·mm⁻¹。（2）空间上，PUE不仅受多个单因子调控，因子间交互作用进一步强化其格局；时间上，PUE受气象与植被动态因子共同调控，关键影响因子表现出显著的非线性阈值响应。（3）不同PUE值区域的主导驱动机制存在显著异质性：低PUE值区域主要受高蒸散与弱持水能力制约，而高PUE值区域则依赖低降水条件下高效的水分转化利用策略。本研究揭示了东北寒区PUE的多尺度驱动因素并识别了关键调控阈值，为区域“碳水”一体化管理、精准植被恢复及气候变化适应策略提供了定量依据。",null,"Agricultural Water Management","2026-09-10T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,8,1,"基于2001—2024年多源遥感与SHAP可解释机器学习量化东北寒区降水利用效率驱动机制与生态阈值，方法新颖、数据规模大，对区域水土资源精准管理有参考价值，但属细分领域研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","农业水资源","遥感","气候变化适应","东北黑土区",0,"10.1016\u002Fj.agwat.2026.110763",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":51,"direction":55,"ingested_from":57},"W7212162650",[37,39,42,45,47,49],{"name":38,"orcid":9},"Shuai Zou",{"name":40,"orcid":41},"Fanxiang Meng","https:\u002F\u002Forcid.org\u002F0009-0000-5015-1120",{"name":43,"orcid":44},"Ennan Zheng","https:\u002F\u002Forcid.org\u002F0000-0002-8186-8738",{"name":46,"orcid":9},"Tianxiao Li",{"name":48,"orcid":9},"Gang Li",{"name":50,"orcid":9},"Mo Li",{"tldr":52,"method":53,"finding":54,"direction":55,"opportunity":56},"基于多源遥感与可解释机器学习，揭示东北寒区降水利用效率时空特征及驱动因子。","2001-2024多源遥感数据，Theil-Sen、Mann-Kendall、H","PUE呈西北高东南低格局，驱动因子具非线性阈值与区域异质性。","农业遥感与作物表型","可结合碳通量数据，探究PUE与碳汇耦合机制及阈值调控的生态管理策略。","openalex","2026-09-13T23:30:25.912065Z"]