TY - CHAP
T1 - Automatic prediction of high-resolution daily rainfall fields for multiple extents
T2 - The potential of operational radar
AU - Schuurmans, J. M.
AU - Bierkens, M. F.P.
AU - Pebesma, E. J.
AU - Uijlenhoet, R.
PY - 2008
Y1 - 2008
N2 - 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 modelling. To this end we combine data from the Netherlands operational national rain gauge network (330 gauges nation wide) with our own 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 km 2), 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 colocated cokriging (OCCK), 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). Focus here is on automatic prediction. For 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.
AB - 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 modelling. To this end we combine data from the Netherlands operational national rain gauge network (330 gauges nation wide) with our own 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 km 2), 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 colocated cokriging (OCCK), 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). Focus here is on automatic prediction. For 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.
UR - http://www.scopus.com/inward/record.url?scp=56249149276&partnerID=8YFLogxK
M3 - Chapter
AN - SCOPUS:56249149276
SN - 9789068094220
T3 - Nederlandse Geografische Studies
SP - 17
EP - 44
BT - Hydrological now-and forecasting - Integration of operationally available remotely sensed and forecasted hydrometeorological variables into distributed hydrological models
ER -