Improving SWAT Model Calibration Using Soil MERGE (SMERGE)
Improving SWAT Model Calibration Using Soil MERGE (SMERGE)
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DOI：
10.3390/w12072039
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发表时间：
2020-07
期刊：
影响因子：
3.4
通讯作者：
K. Tobin;M. Bennett
中科院分区：
文献类型：
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作者：
K. Tobin;M. Bennett
这项研究调查了8个中等大小(832至4892平方公里)的大平原流域。土壤和水资源评估工具(SWAT)自动校准程序SUFI-2从1995年至2015年在每个流域使用23个模型参数执行，以确定高度敏感的参数(HSP)。该模型随后按年运行，为每一年(1995年至2015年)生成最佳参数值。HSP与年降水量(独立坡度参数-高程回归模型-PRISM)和根区土壤水分(土壤Merge-SMERGE 2.0)异常数据相关。具有稳健相关性(r>；0.5)的HSP被用于按年度(2016-2018)校准模型。将结果与基线模拟进行了比较，在基线模拟中，通过在整个期间(1992至2015)运行该模型获得了最佳参数。这种方法提高了2016至2018年生成的年度模拟的性能。与PRISM产品相比，SMERGE 2.0产生了更可靠的结果。这种方法的主要优点是它限制了参数空间，最大限度地减少了等定性，并促进了基于更真实的参数值的建模。
This study examined eight Great Plains moderate-sized (832 to 4892 km2) watersheds. The Soil and Water Assessment Tool (SWAT) autocalibration routine SUFI-2 was executed using twenty-three model parameters, from 1995 to 2015 in each basin, to identify highly sensitive parameters (HSP). The model was then run on a year-by-year basis, generating optimal parameter values for each year (1995 to 2015). HSP were correlated against annual precipitation (Parameter-elevation Regressions on Independent Slopes Model—PRISM) and root zone soil moisture (Soil MERGE—SMERGE 2.0) anomaly data. HSP with robust correlation (r > 0.5) were used to calibrate the model on an annual basis (2016 to 2018). Results were compared against a baseline simulation, in which optimal parameters were obtained by running the model for the entire period (1992 to 2015). This approach improved performance for annual simulations generated from 2016 to 2018. SMERGE 2.0 produced more robust results compared with the PRISM product. The main virtue of this approach is that it constrains parameter space, minimizesing equifinality and promotesing modeling based on more physically realistic parameter values.