Staff Publications

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 402651
Title Climatology of daily rainfall semi-variance in The Netherlands
Author(s) Beek, C.Z. van de; Leijnse, H.; Torfs, P.J.J.F.; Uijlenhoet, R.
Source Hydrology and Earth System Sciences 15 (2011)1. - ISSN 1027-5606 - p. 171 - 183.
Department(s) Hydrology and Quantitative Water Management
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) spatial variability - daily precipitation - gauge measurements - extreme rainfall - united-states - radar - calibration - resolution - hydrology - sensitivity
Abstract Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted.
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