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- aggregation classification "A1".
- aggregation creator B218502.
- aggregation creator B218503.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2012".
- aggregation format "application/pdf".
- aggregation hasFormat 2024286.bibtex.
- aggregation hasFormat 2024286.csv.
- aggregation hasFormat 2024286.dc.
- aggregation hasFormat 2024286.didl.
- aggregation hasFormat 2024286.doc.
- aggregation hasFormat 2024286.json.
- aggregation hasFormat 2024286.mets.
- aggregation hasFormat 2024286.mods.
- aggregation hasFormat 2024286.rdf.
- aggregation hasFormat 2024286.ris.
- aggregation hasFormat 2024286.txt.
- aggregation hasFormat 2024286.xls.
- aggregation hasFormat 2024286.yaml.
- aggregation isPartOf urn:issn:1027-5606.
- aggregation language "eng".
- aggregation rights "I have retained and own the full copyright for this publication".
- aggregation subject "Earth and Environmental Sciences".
- aggregation title "The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter".
- aggregation abstract "The Sequential Importance Sampling with Resampling (SISR) particle filter and the SISR with parameter resampling particle filter (SISR-PR) are evaluated for their performance in soil moisture assimilation and the consequent effect on baseflow generation. With respect to the resulting soil moisture time series, both filters perform appropriately. However, the SISR filter has a negative effect on the baseflow due to inconsistency between the parameter values and the states after the assimilation. In order to overcome this inconsistency, parameter resampling is applied along with the SISR filter, to obtain consistent parameter values with the analyzed soil moisture state. Extreme parameter replication, which could lead to a particle collapse, is avoided by the perturbation of the parameters with white noise. Both the modeled soil moisture and baseflow are improved if the complementary parameter resampling is applied. The SISR filter with parameter resampling offers an efficient way to deal with biased observations. The robustness of the methodology is evaluated for 3 model parameter sets and 3 assimilation frequencies. Overall, the results in this paper indicate that the particle filter is a promising tool for hydrologic modeling purposes, but that an additional parameter resampling may be necessary to consistently update all state variables and fluxes within the model.".
- aggregation authorList BK482151.
- aggregation endPage "390".
- aggregation issue "2".
- aggregation startPage "375".
- aggregation volume "16".
- aggregation aggregates 2024747.
- aggregation isDescribedBy 2024286.
- aggregation similarTo hess-16-375-2012.
- aggregation similarTo LU-2024286.