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| Titel |
Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose |
| VerfasserIn |
D. L. Shrestha, D. E. Robertson, Q. J. Wang, T. C. Pagano, H. A. P. Hapuarachchi |
| 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. 5 ; Nr. 17, no. 5 (2013-05-21), S.1913-1931 |
| Datensatznummer |
250018879
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| Publikation (Nr.) |
 copernicus.org/hess-17-1913-2013.pdf |
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| Zusammenfassung |
The quality of precipitation forecasts from four Numerical Weather
Prediction (NWP) models is evaluated over the Ovens catchment in Southeast
Australia. Precipitation forecasts are compared with observed precipitation
at point and catchment scales and at different temporal resolutions. The
four models evaluated are the Australian Community Climate Earth-System
Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km.
The skill of the NWP precipitation forecasts varies considerably between
rain gauging stations. In general, high spatial resolution (ACCESS-A and
ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in
dry, low elevation areas and underestimate in wet, high elevation areas. The
global model (ACCESS-G) consistently underestimates the precipitation at all
stations and the bias increases with station elevation. The skill varies
with forecast lead time and, in general, it decreases with the increasing
lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km,
hourly), the precipitation forecasts appear to have very little skill.
There is moderate skill at short lead times when the forecasts are averaged
up to daily and/or catchment scale. The precipitation forecasts fail to
produce a diurnal cycle shown in observed precipitation. Significant
sampling uncertainty in the skill scores suggests that more data are
required to get a reliable evaluation of the forecasts. The non-smooth decay
of skill with forecast lead time can be attributed to diurnal cycle in the
observation and sampling uncertainty.
Future work is planned to assess the benefits of using the NWP rainfall
forecasts for short-term streamflow forecasting. Our findings here suggest
that it is necessary to remove the systematic biases in rainfall forecasts,
particularly those from low resolution models, before the rainfall forecasts
can be used for streamflow forecasting. |
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