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dc.contributor.author
Meier, Philipp
dc.contributor.author
Froemelt, Andreas
dc.contributor.author
Kinzelbach, Wolfgang
dc.date.accessioned
2019-06-25T10:52:04Z
dc.date.available
2017-06-09T10:00:03Z
dc.date.available
2019-06-25T10:52:04Z
dc.date.issued
2011
dc.identifier.issn
1027-5606
dc.identifier.issn
1607-7938
dc.identifier.other
10.5194/hess-15-999-2011
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/31812
dc.identifier.doi
10.3929/ethz-b-000031812
dc.description.abstract
Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Copernicus
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Hydrological real-time modelling in the Zambezi river basin using satellite-based soil moisture and rainfall data
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
dc.date.published
2011-03-23
ethz.journal.title
Hydrology and Earth System Sciences
ethz.journal.volume
15
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
Hydrol. Earth Syst. Sci.
ethz.pages.start
999
en_US
ethz.pages.end
1008
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
001881462
ethz.publication.place
Göttingen
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
03432 - Kinzelbach, Wolfgang
en_US
ethz.leitzahl.certified
03432 - Kinzelbach, Wolfgang
ethz.date.deposited
2017-06-09T10:00:23Z
ethz.source
ECIT
ethz.identifier.importid
imp59364dc793a4241318
ethz.ecitpid
pub:52236
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-15T08:47:15Z
ethz.rosetta.lastUpdated
2022-03-28T23:09:05Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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