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  • Author or Editor: J. M. Schuurmans x
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J. M. Schuurmans and M. F. P. Bierkens

Abstract

This study mimics an online forecast system to provide nine day-ahead forecasts of regional soil moisture. It uses modified ensemble rainfall forecasts from the numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF), which is provided by the Royal Netherlands Meteorological Office (KNMI). Both the individual ensembles as well as the mean of the ensembles are used as input for a hydrological model of a 70-km2 study area during March–November 2006. The outcomes are compared to the model run with high-resolution rainfall fields (based on 14 rain gauges within the study area and meteorological radar) as input. It is shown that the total spatial mean rainfall is forecasted very well for all lead times. The measured rainfall (spatial mean) shows a distribution with peaks at 0–1 and >10 mm day−1. These peaks are underestimated by the ensemble mean of the forecasts and this underestimation increases with lead time. This is not the case when ensemble members are used. Besides, the modeled uncertainty in rainfall by ECMWF underestimates the true uncertainty for all lead times and the number of rainfall events (thresholds 0.1, 0.5, and 1.0 mm) is overestimated. Absolute temporal mean bias values in root zone storage—that is, soil moisture—larger than 1 mm start to show for lead times longer than 3 days. The lower and upper bounds of bias for a lead time of 9 days are approximately −4 and 7 mm, respectively (negative values mean the forecasted soil moisture is underestimated). The bias in root zone storage shows a spatial pattern that represents the spatial pattern of total rainfall: areas with less rainfall than spatial average show a negative bias and vice versa. Local differences within this spatial pattern are due to land use and soil type. The results suggest that ensemble forecasts of soil moisture using ensemble rainfall forecasts from ECMWF are of practical use for water management, even at regional scales.

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J. M. Schuurmans, M. F. P. Bierkens, E. J. Pebesma, and R. Uijlenhoet

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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