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| Titel |
Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling |
| VerfasserIn |
D. Duethmann, J. Zimmer, A. Gafurov, A. Güntner, D. Kriegel, B. Merz, S. Vorogushyn |
| 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 (2013-07-02), S.2415-2434 |
| Datensatznummer |
250018913
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| Publikation (Nr.) |
 copernicus.org/hess-17-2415-2013.pdf |
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| Zusammenfassung |
| In data sparse mountainous regions it is difficult to derive areal
precipitation estimates. In addition, their evaluation by cross validation
can be misleading if the precipitation gauges are not in representative
locations in the catchment. This study aims at the evaluation of
precipitation estimates in data sparse mountainous catchments. In
particular, it is first tested whether monthly precipitation fields from
downscaled reanalysis data can be used for interpolating gauge observations.
Secondly, precipitation estimates from this and other methods are evaluated
by comparing simulated and observed discharge, which has the advantage that
the data are evaluated at the catchment scale. This approach is extended
here in order to differentiate between errors in the overall bias and the
temporal dynamics, and by taking into account different sources of
uncertainties. The study area includes six headwater catchments of the
Karadarya Basin in Central Asia. Generally the precipitation estimate based
on monthly precipitation fields from downscaled reanalysis data showed an
acceptable performance, comparable to another interpolation method using
monthly precipitation fields from multi-linear regression against
topographical variables. Poor performance was observed in only one
catchment, probably due to mountain ridges not resolved in the model
orography of the regional climate model. Using two performance criteria for
the evaluation by hydrological modelling allowed a more informed
differentiation between the precipitation data and showed that the
precipitation data sets mostly differed in their overall bias, while the
performance with respect to the temporal dynamics was similar. Our
precipitation estimates in these catchments are considerably higher than
those from continental- or global-scale gridded data sets. The study
demonstrates large uncertainties in areal precipitation estimates in these
data sparse mountainous catchments. In such regions with only very few
precipitation gauges but high spatial variability of precipitation,
important information for evaluating precipitation estimates may be gained
by hydrological modelling and a comparison to observed discharge. |
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