[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-OpenET":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"OpenET","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":13,"published_at":18,"category":19,"cover_url":16,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":30,"tags":32,"primary_topic":36,"secondary_topics":42,"classification":43,"search_phrases":88,"slug":91,"view_count":28,"doi":92,"paper":93,"created_at":117},1313,"Agricultural water balance and variability in Utah irrigation canal companies with OpenET products and spatial data","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00271-026-01172-8","Abstract Agricultural water optimization is critical for improving water security for the arid Western USA and other water-scarce regions. Optimization includes assessment of agricultural water diversions within conveyance systems managed by irrigation canal companies (ICC), yet few water balance approaches exist at this level. Satellite data provides insight into historical agricultural water use and the potential for optimization. In this study, historical remote sensing data from OpenET and gridMET were coupled with Geographic Information Systems data (fields\u002Fcanals distribution and agricultural diversion records) to implement a water balance model for three ICCs in Northern Utah. The monthly water balance model determined the change of water depletion at the root zone (∆Dr) with precipitation, irrigation and capillary rise from groundwater (CR) as inflows; and actual crop evapotranspiration (ET c act ), runoff and deep percolation (DP) as outflows. The water balance residual (WBR) was calculated as the unresolved portion of the water balance and reflect runoff, DP and other unquantified hydrologic exchanges (CR was considered only where supported). The spatial distribution of ∆Dr was captured by a Random Forest model using local observed data (ET c act and soil water content). Across the study area, WBRs varied spatially and temporally (2020–2023) from 0.01 to 2.7 cubic hectometers (12 − 2,210 acre-feet) per month, with the highest values occurring during peak irrigation season (July-August). Seasonally (Apr-Oct), WBR represented 5–29% of the sum of all water balance components across the studied ICCs and should be interpreted as an illustrative screening range. Overall, this study demonstrates the feasibility of approximating an ICC-scale water balance under data-limited conditions, and highlights the need for improved monitoring, data collection, and research.","农业用水优化对于改善美国西部干旱地区及其他水资源短缺地区的水安全至关重要。优化工作包括对灌溉渠公司（ICC）管理的输水系统内农业引水进行评估，然而在此层面上可用的水量平衡方法寥寥无几。卫星数据为历史农业用水情况及优化潜力提供了洞察。本研究将来自OpenET和gridMET的历史遥感数据与地理信息系统数据（田地\u002F渠道分布及农业引水记录）相结合，为犹他州北部的三个灌溉渠公司实施了水量平衡模型。月水量平衡模型确定了根区耗水变化量（∆Dr），其中降水、灌溉和地下水毛细上升（CR）为流入项；实际作物蒸散发（ET c act ）、径流和深层渗漏（DP）为流出项。水量平衡残差（WBR）被计算为水量平衡中未解决的部分，反映了径流、深层渗漏及其他未量化的水文交换（仅在数据支持的情况下考虑毛细上升）。∆Dr的空间分布通过使用当地观测数据（实际作物蒸散发和土壤含水量）的随机森林模型进行捕捉。在整个研究区域内，水量平衡残差在空间和时间上（2020—2023年）变化范围为每月0.01至2.7立方百米（12至2,210英亩-英尺），最高值出现在灌溉高峰期（7月至8月）。在季节尺度上（4月至10月），水量平衡残差占所研究灌溉渠公司所有水量平衡分量总和的5%至29%，应将其视为一个说明性的筛选范围。总体而言，本研究证明了在数据有限条件下近似估算灌溉渠公司尺度水量平衡的可行性，并强调了改进监测、数据收集和研究的必要性。",null,"Irrigation Science","2026-08-31T00:00:00Z","论文",10,false,67,{"impact":24,"substance":25,"depth":26,"authority":27,"freshness":28,"relevant":7,"comment":29},15,20,18,12,2,"研究利用OpenET和空间数据构建灌溉公司尺度水量平衡模型，方法新颖，数据详实，对干旱区农业水资源优化有参考价值。",[31],{"name":17,"url":13},[33,34,35,4],"农业遥感","水资源管理","灌溉优化",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":44,"locked":21,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":50},[49,58,66,74,81],{"code":37,"score":26,"supported":50,"title":51,"abstract":52,"tags":57},true,[38],[53,54,55,38,56],"灌溉","水资源","蒸散发","evapotranspiration",[34],{"code":59,"score":60,"supported":50,"title":61,"abstract":62,"tags":65},"I3",7,[],[63,64],"遥感","remote sensing",[33],{"code":67,"score":68,"supported":50,"title":69,"abstract":70,"tags":73},"H2",6,[],[71,72],"土壤","soil",[],{"code":75,"score":68,"supported":50,"title":76,"abstract":77,"tags":80},"I4",[],[78,79],"地理信息","geographic information",[],{"code":82,"score":83,"supported":50,"title":84,"abstract":85,"tags":87},"H7",3,[],[86],"干旱",[],[89,90],"水资源管理 农业遥感 灌溉优化 OpenET","水资源管理 农业遥感","水资源管理农业遥感灌溉优化OpenET-1313","10.1007\u002Fs00271-026-01172-8",{"doi":92,"openalex_id":94,"authors":95,"venue":17,"cited_by_count":108,"oa_url":109,"card":110,"direction":114,"ingested_from":116},"W7204819722",[96,99,102,105],{"name":97,"orcid":98},"Katherine Osorio-Diaz","https:\u002F\u002Forcid.org\u002F0000-0001-5301-6389",{"name":100,"orcid":101},"Alfonso F. Torres‐Rua","https:\u002F\u002Forcid.org\u002F0000-0002-2238-9550",{"name":103,"orcid":104},"Matt Yost","https:\u002F\u002Forcid.org\u002F0000-0001-5012-8481",{"name":106,"orcid":107},"Lawrence E. Hipps","https:\u002F\u002Forcid.org\u002F0000-0002-7658-8571",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs00271-026-01172-8.pdf",{"tldr":111,"method":112,"finding":113,"direction":114,"opportunity":115},"利用OpenET和空间数据构建犹他州灌溉公司水均衡模型，评估农业用水优化潜力。","耦合OpenET、gridMET遥感数据与GIS数据，建立月水均衡模型，并用随机","水均衡残差时空变化大，峰值在灌溉旺季，占组分5-29%，表明数据有限下可近似估算。","农业遥感与作物表型","可延伸研究灌溉公司尺度水均衡的不确定性量化，结合更高分辨率遥感与实地监测，提升模型精度。","openalex","2026-09-01T23:30:20.143754Z"]