[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2168":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":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":46},2168,"From soil loss to sediment delivery: GeoAI-enhanced RUSLE modeling of erosion and sediment dynamics in a hyper-arid watershed","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffeart.2026.1893163","In the delineated watershed containing Wadi Samnan near Az Zulfi, Saudi Arabia, soil erosion is a local management concern because sparse vegetation, erodible sandy surface materials, escarpment-influenced terrain, and episodic rainfall events can concentrate runoff and sediment movement along wadi channels and drainage corridors. These conditions make it difficult to identify erosion-prone zones using field observations alone, particularly in data-scarce hyper-arid environments. This study presents a GeoAI-enhanced RUSLE-based framework to model water-induced soil loss and sediment delivery dynamics in the study watershed. The Revised Universal Soil Loss Equation (RUSLE) was integrated with geospatial datasets, remote sensing products, digital elevation model-derived terrain attributes, and machine-learning-based spatial analysis to estimate the spatial distribution of water-induced erosion risk. Rainfall erosivity, soil erodibility, topographic influence, land-cover conditions, and conservation-practice factors were derived and mapped within a GIS environment. In addition, the Sediment Delivery Ratio (SDR) was incorporated to evaluate relative sediment-delivery potential and improve the interpretation of how estimated hillslope soil loss may be transferred into sediment yield within the drainage system. The results show that RUSLE-derived potential soil-loss estimates ranged from 0 to 319.5 t ha -1 yr -1 , with the slight erosion class covering 43.2% of the watershed and modeled severe-erosion hotspots covering 10.9%. High-risk areas were mainly concentrated along steep slopes, drainage corridors, sparsely vegetated surfaces, and erodible sandy soils. SDR results indicated low overall sediment connectivity, with approximately 85%–99% of eroded material likely retained locally within the watershed, depending on the spatially varying SDR values. The Random Forest internal consistency analysis explained 81.73% of the variance in RUSLE-derived soil-loss estimates, indicating that the input factors captured much of the modeled spatial variability; however, this result should not be interpreted as independent field validation. Overall, the integration of RUSLE, SDR, remote sensing, GIS, and GeoAI provides a useful framework for identifying erosion-prone zones, distinguishing soil-loss potential from sediment-delivery potential, prioritizing conservation interventions, and supporting sustainable watershed management in hyper-arid environments.","在沙特阿拉伯祖尔菲附近萨姆南干谷所在的划定流域内，土壤侵蚀是当地管理关注的问题，因为稀疏植被、易侵蚀的砂质表层物质、受崖坡影响的地形以及偶发性降雨事件会将径流和泥沙运动集中沿干谷河道和排水廊道输送。这些条件使得仅凭野外观测难以识别侵蚀易发区，尤其是在数据稀缺的极端干旱环境中。本研究提出了一种GeoAI增强的RUSLE框架，用于模拟研究流域内水力驱动的土壤流失和泥沙输移动态。修正通用土壤流失方程（RUSLE）与地理空间数据集、遥感产品、数字高程模型衍生的地形属性以及基于机器学习的空间分析相结合，以估算水力侵蚀风险的空间分布。降雨侵蚀力、土壤可蚀性、地形影响、土地覆盖条件和保护措施因子在GIS环境中被导出并制图。此外，引入泥沙输移比（SDR）以评估相对泥沙输移潜力，并改进对坡面土壤流失估算值如何转化为排水系统内产沙量的解释。结果表明，RUSLE衍生的潜在土壤流失估算值范围为0至319.5 t ha⁻¹ yr⁻¹，其中轻度侵蚀等级覆盖流域面积的43.2%，模拟的严重侵蚀热点区域覆盖10.9%。高风险区主要集中在陡坡、排水廊道、植被稀疏地表和易侵蚀砂质土壤沿线。SDR结果表明整体泥沙连通性较低，约85%–99%的侵蚀物质可能就地滞留于流域内，具体取决于空间变化的SDR值。随机森林内部一致性分析解释了RUSLE衍生土壤流失估算值中81.73%的方差，表明输入因子捕捉了大部分模拟的空间变异性；然而，该结果不应被解释为独立的野外验证。总体而言，RUSLE、SDR、遥感、GIS和GeoAI的整合为识别侵蚀易发区、区分土壤流失潜力与泥沙输移潜力、优先安排保护干预措施以及支持极端干旱环境中的可持续流域管理提供了一个有用的框架。",null,"Frontiers in Earth Science","2026-09-09T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,17,13,1,"GeoAI融合RUSLE与SDR模型量化超干旱流域侵蚀与泥沙输移，方法新颖、数据扎实，但属区域案例研究，对国内三农实践的直接价值有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","遥感","水土保持","干旱区农业","流域治理",0,"10.3389\u002Ffeart.2026.1893163",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":38,"card":39,"direction":43,"ingested_from":45},"W7212020416",[36],{"name":37,"orcid":9},"Ali R. Alruzuq","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fearth-science\u002Farticles\u002F10.3389\u002Ffeart.2026.1893163\u002Fpdf",{"tldr":40,"method":41,"finding":42,"direction":43,"opportunity":44},"用GeoAI增强RUSLE与SDR模型，评估沙特超干旱流域土壤侵蚀与泥沙输移空间格局。","RUSLE结合遥感、DEM、GIS与随机森林空间分析，并引入泥沙输移比SDR。","侵蚀量0–319.5 t\u002Fha\u002Fyr，严重区占10.9%，约85%–99%侵蚀物就地滞留。","农业遥感与作物表型","可将GeoAI-RUSLE-SDR框架迁移至数据稀缺区，结合实地监测验证并耦合泥沙连通性指数。","openalex","2026-09-11T23:30:31.280278Z"]