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| Titel |
Rainfall and temperature estimation for a data sparse region |
| VerfasserIn |
R. L. Wilby, D. Yu |
| 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. 10 ; Nr. 17, no. 10 (2013-10-15), S.3937-3955 |
| Datensatznummer |
250085956
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| Publikation (Nr.) |
 copernicus.org/hess-17-3937-2013.pdf |
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| Zusammenfassung |
| Humanitarian and development agencies face difficult decisions about where
and how to prioritise climate risk reduction measures. These tasks are
especially challenging in regions with few meteorological stations, complex
topography and extreme weather events. In this study, we blend surface
meteorological observations, remotely sensed (TRMM and NDVI) data,
physiographic indices, and regression techniques to produce gridded maps of
annual mean precipitation and temperature, as well as parameters for
site-specific, daily weather generation in Yemen. Maps of annual means were
cross-validated and tested against independent observations. These
replicated known features such as peak rainfall totals in the highlands and
western escarpment, as well as maximum temperatures along the coastal plains
and interior. The weather generator reproduced daily and annual diagnostics
when run with parameters from observed meteorological series for a test site
at Taiz. However, when run with interpolated parameters, the frequency of
wet days, mean wet-day amount, annual totals and variability were
underestimated. Stratification of sites for model calibration improved
representation of the growing season's rainfall totals. Future work should focus
on a wider range of model inputs to better discriminate controls exerted by
different landscape units. |
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