Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data
| dc.contributor | Roulin, Emmanuel | |
| dc.contributor.author | Baguis, Pierre | |
| dc.contributor.editor | Wagner, Wolfgang | |
| dc.contributor.editor | Notarnicola, Claudia | |
| dc.contributor.editor | Qu, John J. | |
| dc.contributor.editor | Ryu, Dongryeol | |
| dc.contributor.editor | Baghdadi, Nicolas | |
| dc.contributor.editor | Thenkabail, Prasad S. | |
| dc.date | 2017-08-10 | |
| dc.date.accessioned | 2019-05-16T12:28:37Z | |
| dc.date.available | 2019-05-16T12:28:37Z | |
| dc.identifier.uri | https://orfeo.belnet.be/handle/internal/7263 | |
| dc.description | In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar Scatterometer (ASCAT), embarked on the Meteorological Operational satellites (MetOp). The product generated with these measurements has a horizontal resolution of 25 km and represents the upper few centimeters of soil. Our approach is based on the Ensemble Kalman Filter technique (EnKF), where observation and model uncertainties are taken into account, implemented in a conceptual hydrological model. The analysis is carried out in the Demer catchment of the Scheldt River Basin in Belgium, for the period from June 2013–May 2016. In this context, two methodological advances are being proposed. First, the generation of stochastic terms, necessary for the EnKF, of bounded variables like SSM is addressed with the aid of specially-designed probability distributions, so that the bounds are never exceeded. Second, bias due to the assimilation procedure itself is removed using a post-processing technique. Subsequently, the impact of SSM assimilation on the simulated streamflow is estimated using a series of statistical measures based on the ensemble average. The differences from the control simulation are then assessed using a two-dimensional bootstrap sampling on the ensemble generated by the assimilation procedure. Our analysis shows that data assimilation combined with bias correction can improve the streamflow estimations or, at a minimum, produce results statistically indistinguishable from the control run of the hydrological model. | en_US |
| dc.language | eng | en_US |
| dc.publisher | MDPI | en_US |
| dc.title | Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data | en_US |
| dc.type | Article | en_US |
| dc.subject.frascati | Physical sciences | en_US |
| dc.audience | Scientific | en_US |
| dc.subject.free | data assimilation; ensemble Kalman filter; satellite data; remote sensing; soil moisture; hydrological model | en_US |
| dc.source.title | Remote Sensing | en_US |
| dc.source.volume | 9 | en_US |
| dc.source.issue | 8 | en_US |
| dc.relation.project | H-SAF | en_US |
| Orfeo.peerreviewed | Yes | en_US |
| dc.identifier.doi | https://doi.org/10.3390/rs9080820 |
