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Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An Extensive Experiment in Over 700 Catchments Throughout Europe
Water Resources Research ( IF 5.4 ) Pub Date : 2021-05-05 , DOI: 10.1029/2021wr029643
D. De Santis 1 , D. Biondi 1 , W. T. Crow 2 , S. Camici 3 , S. Modanesi 3 , L. Brocca 3 , C. Massari 3
Affiliation  

In this study, we perform a data assimilation (DA) experiment on a very large number (>700) of small- and medium-scale (150–10,000 km2) European catchments to assess the impact of satellite soil moisture (SM) DA on streamflow simulations for different climatic and hydrologic conditions. In the experiment, Climate Change Initiative SM active, passive and combined products are assimilated over a time period 2003–2016 via an Ensemble Kalman Filter (EnKF). The results show that, on average, the assimilation of the three products provides relatively small improvements as compared to analogous open loop (OL) results (i.e., with an increase on median Kling-Gupta Efficiency equal to 0.0048, 0.0033, and 0.0022 [−] for the active, the passive, and the combined products, respectively). OL performance itself is found to be a significant driver of the assimilation results: greater improvements are observed in catchments with poor OL streamflow predictions and inaccurate precipitation estimates. The remotely sensed product accuracy also emerges as relevant for assimilation efficiency, while factors affecting SM retrievals such as vegetation density, topographic complexity and basin area are found to have only a limited impact on the spatial pattern of performance. Small and detrimental effects of SM assimilation are observed over fully humid catchments and at high latitudes where pre-storm soil moisture has reduced control on runoff generation as well as in basins where the hydrological model contains structural limitations.

中文翻译:

用于河流流量预测的卫星土壤水分产品同化:在整个欧洲 700 多个流域的广泛实验

在本研究中,我们对大量(> 700)中小型(150-10,000 km 2) 欧洲集水区,以评估卫星土壤水分 (SM) DA 对不同气候和水文条件下水流模拟的影响。在实验中,气候变化倡议 SM 主动、被动和组合产品在 2003 年至 2016 年的时间段内通过集合卡尔曼滤波器 (EnKF) 同化。结果表明,平均而言,与类似的开环 (OL) 结果相比,三种产品的同化提供了相对较小的改进(即,中值 Kling-Gupta 效率的增加等于 0.0048、0.0033 和 0.0022 [- ] 分别用于有源、无源和组合产品)。发现 OL 性能本身是同化结果的重要驱动因素:在 OL 流量预测不佳和降水估计不准确的集水区观察到了更大的改进。遥感产品精度也与同化效率相关,而影响 SM 反演的因素,如植被密度、地形复杂性和流域面积,对性能的空间格局只有有限的影响。在完全潮湿的集水区和暴风雨前土壤水分减少对径流生成的控制以及水文模型包含结构限制的流域的高纬度地区,观察到 SM 同化的小而有害的影响。发现地形复杂性和盆地面积对性能的空间格局只有有限的影响。在完全潮湿的集水区和暴风雨前土壤水分减少对径流生成的控制以及水文模型包含结构限制的流域的高纬度地区,观察到 SM 同化的小而有害的影响。发现地形复杂性和盆地面积对性能的空间格局只有有限的影响。在完全潮湿的集水区和暴风雨前土壤水分减少对径流生成的控制以及水文模型包含结构限制的流域的高纬度地区,观察到 SM 同化的小而有害的影响。
更新日期:2021-06-08
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