 |
| Titel |
Towards observation-based gridded runoff estimates for Europe |
| VerfasserIn |
L. Gudmundsson, S. I. Seneviratne |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 6 ; Nr. 19, no. 6 (2015-06-22), S.2859-2879 |
| Datensatznummer |
250120747
|
| Publikation (Nr.) |
 copernicus.org/hess-19-2859-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| Terrestrial water variables are the key to understanding ecosystem
processes, feed back on weather and climate, and are a prerequisite
for human activities. To provide context for local investigations and to
better understand phenomena that only emerge at large spatial scales,
reliable information on continental-scale freshwater dynamics
is necessary. To date streamflow is among the best-observed variables of
terrestrial water systems. However, observation networks have a
limited station density and often incomplete temporal coverage,
limiting investigations to locations and times with observations.
This paper presents a methodology to estimate continental-scale
runoff on a 0.5° spatial grid with monthly resolution. The
methodology is based on statistical
upscaling of observed streamflow from small catchments in Europe and
exploits readily available gridded atmospheric forcing data combined
with the capability of machine learning techniques.
The resulting runoff estimates are validated against (1) runoff from
small catchments that were not used for model training, (2) river
discharge from nine continental-scale river basins and (3) independent
estimates of long-term mean evapotranspiration at the pan-European
scale. In addition it is shown that the produced gridded runoff
compares on average better to observations than a multi-model
ensemble of comprehensive land surface models (LSMs), making it an
ideal candidate for model evaluation and model development.
In particular, the presented machine learning approach may help
determining which factors are most relevant for an efficient modelling
of runoff at regional scales. Finally, the resulting data product is used to derive a
comprehensive runoff climatology for Europe and its potential for
drought monitoring is illustrated. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|