Soil Moisture Model Calibration and Validation: An ARS Watershed on the South Fork Iowa River
Soil Moisture Model Calibration and Validation: An ARS Watershed on the South Fork Iowa River
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DOI：
10.1175/jhm-d-14-0145.1
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发表时间：
2015-06-01
影响因子：
3.8
通讯作者：
Niemeier, James J.
中科院分区：
文献类型：
--
作者：
Coopersmith, Evan J.;Cosh, Michael H.;Niemeier, James J.
利用原位技术进行土壤水分监测是一项耗时且成本高昂的奋进，因此需要一种方法来提高原位网络空间估计的分辨率。使用一个简单的水文模型，在现场流域网络的估计能力，可以通过使用可用的降水，土壤和地形信息增加超出站点分布。研究地点选在爱荷华州河，其特点是土壤和地形特征均质，减少变量降水。使用北美陆地数据同化系统（NLDAS）2013年10公里降水量估计值，与现场网络协调生成表层土壤水分的高分辨率估计值，该网络作为爱荷华州洪水研究（IFloodS）的一部分部署。一个简单的，桶模型土壤水分在每个原位传感器进行了校准，使用四个降水产品，随后验证在传感器，它被校准和其他近端传感器，后者后的偏差校正步骤。对于在传感器上进行校准的模型和在其他附近传感器上进行验证的模型，分别获得了0.031和0.045 m（3）m（-3）的平均RMSE值。
Soil moisture monitoring with in situ technology is a time-consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the estimation capacity of an in situ watershed network can be increased beyond the station distribution by using available precipitation, soil, and topographic information. A study site was selected on the Iowa River, characterized by homogeneous soil and topographic features, reducing the variables to precipitation only. Using 10-km precipitation estimates from the North American Land Data Assimilation System (NLDAS) for 2013, high-resolution estimates of surface soil moisture were generated in coordination with an in situ network, which was deployed as part of the Iowa Flood Studies (IFloodS). A simple, bucket model for soil moisture at each in situ sensor was calibrated using four precipitation products and subsequently validated at both the sensor for which it was calibrated and other proximal sensors, the latter after a bias correction step. Average RMSE values of 0.031 and 0.045 m(3) m(-3) were obtained for models validated at the sensor for which they were calibrated and at other nearby sensors, respectively.