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| Titel |
Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills |
| VerfasserIn |
M. Arias-Hidalgo, B. Bhattacharya, A. E. Mynett, A. Griensven |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 7 ; Nr. 17, no. 7 (2013-07-24), S.2905-2915 |
| Datensatznummer |
250018944
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| Publikation (Nr.) |
 copernicus.org/hess-17-2905-2013.pdf |
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| Zusammenfassung |
| At present, new technologies are becoming available to extend the coverage of
conventional meteorological datasets. An example is the TMPA-3B42R dataset
(research – v6). The usefulness of this satellite rainfall product has been
investigated in the hydrological modeling of the Vinces River catchment
(Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some
features of the precipitation spatial pattern (e.g., decreasing southwards
and westwards). It showed a remarkable bias compared to the ground-based
rainfall values. Several time scales (annual, seasonal, monthly, etc.) were
considered for bias correction. High correlations between the TMPA-3B42R and
the rain gauge data were still found for the monthly resolution, and
accordingly a bias correction at that level was performed. Bias correction
factors were calculated, and, adopting a simple procedure, they were
spatially distributed to enhance the satellite data. By means of rain gauge
hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated
to daily resolution. These synthetic time series were inserted in a
hydrological model to complement the available rain gauge data to assess the
model performance. The results were quite comparable with those using only
the rain gauge data. Although the model outcomes did not improve remarkably,
the contribution of this experimental methodology was that, despite a high
bias, the satellite rainfall data could still be corrected for use in
rainfall-runoff modeling at catchment and daily level. In absence of rain
gauge data, the approach may have the potential to provide useful data at
scales larger than the present modeling resolution (e.g., monthly/basin). |
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