Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model
Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model
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DOI：
10.5194/hess-22-2575-2018
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发表时间：
2018-04
影响因子：
6.3
通讯作者：
E. Pinnington;T. Quaife;E. Black
中科院分区：
文献类型：
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作者：
E. Pinnington;T. Quaife;E. Black
抽象的。我们表明，利用数据同化技术，卫星对浅层土壤湿度的估计可用于校准加纳区域尺度的地表模型。修改后的校准显着改进了土壤湿度的模型估计。具体来说，我们发现在吸收一年的土壤湿度观测值来更新模型参数后，5 年后的加纳北部的无偏均方根差异减少了 18%，加纳南部的无偏均方根差异减少了 21%。使用改进的遥感降雨数据集使加纳北部的偏差减少了 6%，加纳南部的偏差减少了 10%。改进的降雨数据对土壤季节性润湿期间的模型估计影响最大，而遥感土壤湿度的同化在干燥期间影响最大。在加纳北部，我们能够在数据同化后恢复对土壤质地的改进估计。然而，我们无法为南方做到这一点。在吸收一年的观测结果后，我们发现无偏均方根差值显着减小，这对于改进撒哈拉以南非洲地表模型土壤湿度估计值来说是个好兆头。
Abstract. We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find an 18 % reduction in unbiased root-mean-squared differences in the north of Ghana and a 21 % reduction in the south of Ghana for a 5-year hindcast after assimilating a single year of soil moisture observations to update model parameters. The use of an improved remotely sensed rainfall dataset contributes to 6 % of this reduction in deviation for northern Ghana and 10 % for southern Ghana. Improved rainfall data have the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying-down. In the north of Ghana we are able to recover improved estimates of soil texture after data assimilation. However, we are unable to do so for the south. The significant reduction in unbiased root-mean-squared difference we find after assimilating a single year of observations bodes well for the production of improved land surface model soil moisture estimates over sub-Saharan Africa.