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Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 361637
Title Automatic prediction of high-resolution daily rainfall fields for multiple extents: the potential of operational radar
Author(s) Schuurmans, J.M.; Bierkens, M.F.P.; Pebesma, E.J.; Uijlenhoet, R.
Source Journal of Hydrometeorology 8 (2007)6. - ISSN 1525-755X - p. 1204 - 1224.
Department(s) Hydrology and Quantitative Water Management
Publication type Refereed Article in a scientific journal
Publication year 2007
Keyword(s) radar - regen - hydrologie - modelleren - voorspelling - hydrologische gegevens - meettechnieken - meteorologische waarnemingen - rain - hydrology - modeling - prediction - hydrological data - measurement techniques - meteorological observations - gauge data - stochastic interpolation - geostatistics - variability - design - scales - model
Categories Meteorology (General) / Hydrology
Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March¿October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites¿a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.
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