2016
DOI: 10.1002/hyp.10804
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Could operational hydrological models be made compatible with satellite soil moisture observations?

Abstract: Soil moisture is a significant state variable in flood forecasting. Nowadays more and more satellite soil moisture products are available, yet their usage in the operational hydrology is still limited. This is because the soil moisture state variables in most operational hydrological models (mostly conceptual models) are over‐simplified—resulting in poor compatibility with the satellite soil moisture observations. A case study is provided to discuss this in more detail, with the adoption of the XAJ model and t… Show more

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Cited by 26 publications
(19 citation statements)
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“…Both in situ and satellite data requires a model tailored to their use. It would mean including a shallow soil layer close to the surface and a distributed spatial discretization for fully exploiting the potential of satellite soil moisture observations [112,120]. On the other hand, remote sensing scientists should improve the characterization of the errors associated to the soil moisture products, increase their spatial and temporal resolution, their temporal coverage and temporal consistency.…”
Section: Runoff Modellingmentioning
confidence: 99%
“…Both in situ and satellite data requires a model tailored to their use. It would mean including a shallow soil layer close to the surface and a distributed spatial discretization for fully exploiting the potential of satellite soil moisture observations [112,120]. On the other hand, remote sensing scientists should improve the characterization of the errors associated to the soil moisture products, increase their spatial and temporal resolution, their temporal coverage and temporal consistency.…”
Section: Runoff Modellingmentioning
confidence: 99%
“…However, a review of these approaches reveals a wide disparity in conclusions regarding the value of soil moisture assimilation for forecasting streamflow [Crow and Ryu, 2009;Massari et al, 2015b;Lievens et al, 2015]. This lack of consistency arises, at least in part, from significant sensitivity to the structure and calibration of the particular hydrologic model applied in the assimilation system [Chen et al, 2011;Zhuo and Han, 2016;Massari et al, 2015a]. Therefore, evaluation results are nonrobust in that they are affected by the accuracy of the assumed parametric relationship connecting precipitation, runoff, and soil moisture imbedded within these models.…”
Section: Introductionmentioning
confidence: 99%
“…The SA methods can, therefore, enhance our control on spatiotemporal model behavior [5]. There are local (LSA) and global sensitivity analysis (GSA) methods that evaluate distinct and joint effects between different model parameters, respectively [6][7][8].…”
Section: Introductionmentioning
confidence: 99%