Coupled Thorens and Soil Conservation Service Models for Soil Erosion Assessment in a Loess Plateau Watershed, China
Coupled Thorens and Soil Conservation Service Models for Soil Erosion Assessment in a Loess Plateau Watershed, China
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DOI：
10.3390/rs15030803
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Changjia Li;Tong Lu;Shuai Wang;Jiren Xu
中科院分区：
文献类型：
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作者：
Changjia Li;Tong Lu;Shuai Wang;Jiren Xu
黄土高原土壤侵蚀的评价是一个迫切需要解决的问题，但目前的土壤侵蚀模型和长期的土壤侵蚀观测资料还很有限。在这项研究中，我们耦合Thorens和土壤保持服务（SCS）模型来评估在黄土高原小里河流域在20世纪80年代和2010年代的径流和泥沙。结果表明，所提出的模型框架在模拟径流和泥沙的空间分布方面具有令人满意的性能。月径流量的Nash-Sutcliffe效率（NSE）、百分比偏差（PBIAS）和均方根误差测量标准偏差比（RSR）分别为0.93%、4.42%和0.27;月产沙量分别为0.31%、62.31%和0.82。SCS和Thorens模型较好地反映了土地利用变化对产流产沙的影响。建议的建模框架分布结构简单，需要相对较少的数据，可以从公共数据集获得，并可用于预测径流和泥沙产量在其他类似的ungagged或监测不力的流域。这项工作对其他干旱和半干旱地区的径流和侵蚀评估具有重要意义，可以在缺乏实地测量的情况下获得大面积的径流和侵蚀率。
Assessing soil erosion in China’s severely eroded Loess Plateau is urgently needed but is usually limited by suitable erosion models and long-term field measurements. In this study, we coupled the Thorens and Soil Conservation Service (SCS) models to evaluate runoff and sediment yield during the 1980s and 2010s in the Xiaolihe watershed on the Loess Plateau. Results showed the proposed model framework had a satisfactory performance in modelling spatially distributed runoff and sediment yield. The Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS) and the root mean square error-measured standard deviation ratio (RSR) were 0.93, 4.42% and 0.27 for monthly runoff; and 0.31, 62.31% and 0.82 for monthly sediment yield. The effects of land use changes on runoff and sediment yield were well captured by the SCS and Thorens models. The proposed modelling framework is distributed with a simple structure, requires relatively little data that can be obtained from public datasets, and can be used to predict runoff and sediment yield in other similar ungagged or poorly monitored watersheds. This work has important implications for runoff and erosion assessment in other arid and semi-arid regions, to derive runoff and erosion rates across large areas with scarce field measurements.