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Interpolation of Daily Rain Gauge Data for Hydrological Modeling in Data Sparse Regions Using Pattern Information from Satellite Data

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dc.creator Stisen, Simon
dc.creator Tumbo, Madaka
dc.date 2016-05-26T08:56:38Z
dc.date 2016-05-26T08:56:38Z
dc.date 2015
dc.date.accessioned 2018-04-18T11:18:00Z
dc.date.available 2018-04-18T11:18:00Z
dc.identifier Stisen, S. and Tumbo, M., 2015. Interpolation of daily raingauge data for hydrological modelling in data sparse regions using pattern information from satellite data. Hydrological Sciences Journal, 60(11), pp.1911-1926.
dc.identifier http://hdl.handle.net/20.500.11810/2256
dc.identifier 10.1080/02626667.2014.992789
dc.identifier.uri http://hdl.handle.net/20.500.11810/2256
dc.description In order to cope with a severe reduction of the rain gauge network in the Great Ruaha River Basin over the past 30 years, an interpolation scheme using spatial patterns from satellite images as covariate has been evaluated. The regression based interpolation attempts to combine the advantages of accurate rainfall amounts from rain gauge records with the unique spatial pattern information obtained from satellite based rainfall estimates. A spatial pattern analysis reveals that the simple interpolation of the sparse current rain gauge network compares very poorly to the pattern originating from the much denser historic network. In contrast, the rainfall data sets that include patterns from satellite data show good correlation with the historic pattern. The evaluation based on hydrological modeling, showed similar and good performance for all rainfall products including rain gauge records, whereas the purely satellite based product performed poorly.
dc.language en
dc.subject Daily raingauge data
dc.subject Satellite rainfall estimates
dc.subject Regression-based interpolation
dc.subject Hydrological modelling
dc.subject Tanzania
dc.title Interpolation of Daily Rain Gauge Data for Hydrological Modeling in Data Sparse Regions Using Pattern Information from Satellite Data
dc.type Journal Article, Peer Reviewed


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