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Appropriate temporal resolution of precipitation data for discharge modelling in pre-alpine catchments
Univ Zurich, Dept Geog, Zurich, Switzerland.;Warsaw Univ Life Sci SGGW, Dept Hydraul Engn, Warsaw, Poland..ORCID iD: 0000-0002-5273-1038
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. Univ Zurich, Dept Geog, Zurich, Switzerland.ORCID iD: 0000-0002-6314-2124
2018 (English)In: Hydrological Sciences Journal, ISSN 0262-6667, E-ISSN 2150-3435, Vol. 63, no 1, p. 1-16Article in journal (Refereed) Published
Abstract [en]

Precipitation time series with high temporal resolution are desired for hydrological modelling and flood studies. Yet the choice of an appropriate resolution is not straightforward because the use of too high a temporal resolution increases the data requirements, computational costs and, presumably, associated uncertainty, while performance improvement may be indiscernible. In this study, the effect of averaging hourly precipitation on model performance and associated uncertainty is investigated using two data sources: station network precipitation (SNP) and radar-based precipitation (RBP). From these datasets, time series of different temporal resolutions were generated, and runoff was simulated for 13 pre-alpine catchments with a bucket-type model. Our results revealed that different temporal resolutions were required for an acceptable model performance depending on the catchment size and data source. These were 1-12h for small (16-59km(2)), 3-21h for medium (60-200km(2)), and 24h for large (200-939km(2)) catchments.

Place, publisher, year, edition, pages
TAYLOR & FRANCIS LTD , 2018. Vol. 63, no 1, p. 1-16
Keywords [en]
radar-based precipitation, station network precipitation, averaging length, uncertainty, Bayesian methods, bucket-type model
National Category
Oceanography, Hydrology and Water Resources
Identifiers
URN: urn:nbn:se:uu:diva-342455DOI: 10.1080/02626667.2017.1410279ISI: 000422685200001OAI: oai:DiVA.org:uu-342455DiVA, id: diva2:1185540
Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-02-26Bibliographically approved

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Seibert, Jan

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