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 km 2 ). Based on 74 selected rainfall events (March-October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km 2 ), medium (10 000 km 2 ), and large (82 875 km 2 ). 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.
Abstract. We investigate the effect of spatial variability of daily rainfall on soil moisture, groundwater level and discharge using a physically-based, fully-distributed hydrological model. We focus on the effect of rainfall spatial variability on day-to-day variability of the interior catchment response, as well as on its effect on the general hydrological behavior of the catchment. The study is performed in a flat rural catchment (135 km2) in The Netherlands, where climate is semi-humid (average precipitation 800 mm/year, evapotranspiration 550 mm/year) and rainfall is predominantly stratiform. Both range-corrected radar data (resolution 2.5×2.5 km2) as well as data from a dense network of 30 raingauges are used, observed for the period March–October 2004. Eight different rainfall scenarios, either spatially distributed or spatially uniform, are used as input for the hydrological model. The main conclusions from this study are: (i) using a single raingauge as rainfall input carries a great risk for the prediction of discharge, groundwater level and soil moisture, especially if the raingauge is situated outside the catchment; (ii) taking into account the spatial variability of rainfall instead of using areal average rainfall as input for the model is needed to get insight into the day-to-day spatial variability of discharge, groundwater level and soil moisture content; (iii) to get insight into the general behavior of the hydrological system it is sufficient to use correct predictions of areal average rainfall over the catchment.
Abstract.We investigate the effect of spatial variability of daily rainfall on soil moisture, groundwater level and discharge using a physically-based, fully-distributed hydrological model. This model is currently in use with the district water board and is considered to represent reality. We focus on the effect of rainfall spatial variability on day-to-day variability of the interior catchment response, as well as on its effect on the general hydrological behaviour of the catchment. The study is performed in a flat rural catchment (135 km 2 ) in the Netherlands, where the climate is semi-humid (average precipitation 800 mm/year, evapotranspiration 550 mm/year) and rainfall is predominantly stratiform (i.e. large scale). Both range-corrected radar data (resolution 2.5×2.5 km 2 ) as well as data from a dense network of 30 raingauges are used, observed for the period March-October 2004. Eight different rainfall scenarios, either spatially distributed or spatially uniform, are used as input for the hydrological model. The main conclusions from this study are: (i) using a single raingauge as rainfall input carries a great risk for the prediction of discharge, groundwater level and soil moisture, especially if the raingauge is situated outside the catchment; (ii) taking into account the spatial variability of rainfall instead of using areal average rainfall as input for the model is needed to get insight into the day-to-day spatial variability of discharge, groundwater level and soil moisture content; (iii) to get insight into the general behaviour of the hydrological system it is sufficient to use correct predictions of areal average rainfall over the catchment.
Abstract. This study shows that remotely sensed ET act is useful in hydrological modelling for the procedure of model calibration and shows it potential to update soil moisture predictions. Comparison of modeled and remotely sensed ET act together with the outcomes of our data assimilation procedure points out potential model errors, both conceptual and flux-related. Assimilation of remotely sensed ET act results in a realistic spatial adjustment of soil moisture, except for the area where the model suffers from conceptual errors (forest with deep groundwater levels). By using operational (i.e. available for community in practice) data and models we aim to show the potential and limitations of using remotely sensed ET act in the practice of hydrological modelling. We use satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ET act . The model, used by the local water board, is a physically based distributed hydrological model of a small catchment (70 km 2 ) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Model outcomes of ET act show values that are at least 20% lower than those estimated by SEBAL, which is due to the fact that different evapotranspiration methods are used. The spatial pattern of ET act from the hydrological model resembles the soil map, whereas the ET act from SEBAL resembles the land use map. As both ASTER and MODIS images were available for the same days, this study provides an opportunity to compare the worth of these two satellite sources. It is shown that,
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