Accurate estimates of soil moisture as initial conditions to hydrological models are expected to ... more Accurate estimates of soil moisture as initial conditions to hydrological models are expected to greatly increase the accuracy of flood and drought predictions. As in-situ soil moisture observations are scarce, satellite-based estimates are a suitable alternative. The validation of remotely sensed soil moisture products is generally hampered by the difference in spatial support of in-situ observations and satellite footprints. Unsaturated zone modeling may serve as a valuable validation tool because it could bridge the gap of different spatial supports. A stochastic, distributed unsaturated zone model (SWAP) was used in which the spatial support was matched to these of the satellite soil moisture retrievals. A comparison between point observations and the SWAP model was performed to enhance understanding of the model and to assure that the SWAP model could be used with confidence for other locations in Spain. A timeseries analysis was performed to compare surface soil moisture from the SWAP model to surface soil moisture retrievals from three different microwave sensors, including AMSR-E, SMOS and ASCAT. Results suggest that temporal dynamics are best captured by AMSR-E and ASCAT resulting in an averaged correlation coefficient of 0.68 and 0.71, respectively. SMOS shows the capability of capturing the long-term trends, however on short timescales the soil moisture signal was not captured as well as by the other sensors, resulting in an averaged correlation coefficient of 0.42. Root mean square errors for the three sensors were found to be very similar (± 0.05 m 3 m −3 ). The satellite uncertainty is spatially correlated and distinct spatial patterns are found over Spain.
This study investigates the added value of operational radar with respect to rain gauges in obtai... more 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.
Accurate estimates of soil moisture as initial conditions to hydrological models are expected to ... more Accurate estimates of soil moisture as initial conditions to hydrological models are expected to greatly increase the accuracy of flood and drought predictions. As in-situ soil moisture observations are scarce, satellite-based estimates are a suitable alternative. The validation of remotely sensed soil moisture products is generally hampered by the difference in spatial support of in-situ observations and satellite footprints. Unsaturated zone modeling may serve as a valuable validation tool because it could bridge the gap of different spatial supports. A stochastic, distributed unsaturated zone model (SWAP) was used in which the spatial support was matched to these of the satellite soil moisture retrievals. A comparison between point observations and the SWAP model was performed to enhance understanding of the model and to assure that the SWAP model could be used with confidence for other locations in Spain. A timeseries analysis was performed to compare surface soil moisture from the SWAP model to surface soil moisture retrievals from three different microwave sensors, including AMSR-E, SMOS and ASCAT. Results suggest that temporal dynamics are best captured by AMSR-E and ASCAT resulting in an averaged correlation coefficient of 0.68 and 0.71, respectively. SMOS shows the capability of capturing the long-term trends, however on short timescales the soil moisture signal was not captured as well as by the other sensors, resulting in an averaged correlation coefficient of 0.42. Root mean square errors for the three sensors were found to be very similar (± 0.05 m 3 m −3 ). The satellite uncertainty is spatially correlated and distinct spatial patterns are found over Spain.
This study investigates the added value of operational radar with respect to rain gauges in obtai... more 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.
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Papers by M. Bierkens