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Assimilation of backscatter observations into a hydrological model: a case study in Belgium using ASCAT data

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Baguis, P., Carrassi, A. orcid id iconORCID: https://orcid.org/0000-0003-0722-5600, Roulin, E., Vannitsem, S., Modanesi, S., Lievens, H., Bechtold, M. orcid id iconORCID: https://orcid.org/0000-0002-8042-9792 and De Lannoy, G. orcid id iconORCID: https://orcid.org/0000-0002-6743-7122 (2022) Assimilation of backscatter observations into a hydrological model: a case study in Belgium using ASCAT data. Remote Sensing, 14 (22). 5740. ISSN 2072-4292 doi: 10.3390/rs14225740

Abstract/Summary

We investigated the possibilities of improving hydrological simulations by assimilating radar backscatter observations from the advanced scatterometer (ASCAT) in the hydrological model SCHEME using a calibrated water cloud model (WCM) as an observation operator. The WCM simulates backscatter based on soil moisture and vegetation data and can therefore be used to generate observation predictions for data assimilation. The study was conducted over two Belgian catchments with different hydrological regimes: the Demer and the Ourthe catchment. The main differences between the two catchments can be summarized in precipitation and streamflow levels, which are higher in the Ourthe. The data assimilation method adopted here was the ensemble Kalman filter (EnKF), whereby the uncertainty of the state estimate was described via the ensemble statistics. The focus was on the optimization of the EnKF, and possible solutions to address biases introduced by ensemble perturbations were investigated. The latter issue contributes to the fact that backscatter data assimilation only marginally improves the overall scores of the discharge simulations over the deterministic reference run, and only for the Ourthe catchment. These performances, however, considerably depend on the period considered within the 5 years of analysis. Future lines of research on bias correction, the data assimilation of soil moisture and backscatter data are also outlined.

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Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/109246
Item Type Article
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Uncontrolled Keywords Article, hydrological model, water cloud model, data assimilation, backscatter, leaf area index, soil moisture, bias correction
Publisher MDPI
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