Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets
Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets
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DOI：
10.3390/rs15092417
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发表时间：
2023-05
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影响因子：
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通讯作者：
Sijal Dangol;Xuesong Zhang;Xin-Zhong Liang;Martha C. Anderson;W. Crow;Sangchul Lee;G. Moglen;G. McCarty
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作者：
Sijal Dangol;Xuesong Zhang;Xin-Zhong Liang;Martha C. Anderson;W. Crow;Sangchul Lee;G. Moglen;G. McCarty
遥感水文变量与径流数据一起越来越多地被用于对水文模型参数进行多变量校准。在这里，我们使用径流和遥感水文变量的不同组合来校准土壤和水评估工具(SWAT)模型，包括大气-土地交换逆(ALESI)蒸散(ET)、中分辨率成像光谱辐射计(MODIS)ET和土壤合并(SMERGE)土壤湿度。结果表明，在传统的径流模型校正方法中加入遥感ET值和土壤湿度，可以影响对水文模型敏感的参数的个数和数值，但不一定能改善模型的性能。然而，与只使用径流相比，单独使用遥感ET或土壤湿度数据会导致模型性能下降。此外，我们观察到Alexi或MODIS ET数据之间的巨大差异，在这两个数据集之间进行模型校准的选择可能会对SWAT模型的性能产生重大影响。径流、ET和土壤水分数据的不同组合的使用也导致了模拟的水文过程的显著差异，如径流、渗透和地下水排放。最后，我们比较了SWAT和SWAT-C(SWAT-C)模型在不同的多变量校准设置下的性能，这两个模型在验证期内的性能表现出了显著的差异。基于这些结果，我们建议(1)在选择各种遥感数据作为传统使用的径流数据的补充之前，评估用于模型校准的各种遥感数据(当有多种选择时)；以及(2)在模型校准过程中考虑不同的模型结构，以支持稳健的水文模拟。
Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.