Black, Kevin B.

Cole, Steven J.

Mattingley, Paul S.

Moore, Robert J.

Wells, Steven C.

NERC Open Research Archive

Geophysical Research AbstractsVol. 17, EGU2015-6028, 2015EGU General Assembly 2015© Author(s) 2015. CC Attribution 3.0 License.Hydrological assessment of radar-raingauge merging techniques using theGrid-to-Grid modelPaul S. Mattingley, Robert J. Moore, Steven J. Cole, Steven C. Wells, and Kevin B. BlackCentre for Ecology & Hydrology, Wallingford, United Kingdom (paumat@ceh.ac.uk)High quality space-time grids of rainfall are required as input to achieve accurate predictions of river flow foroperational flood forecasting. In order to produce the best possible rainfall inputs it is common practice to mergeobservational data from raingauge and radar networks to obtain improved gridded rainfall estimates.The relative performance of merging methods is commonly assessed at radar pixels coincident with raingaugelocations employing evaluation statistics such as the cross-validated Root Mean Square Error (RMSE). Such as-sessments indicate the extent to which the merged rainfall estimate can reproduce the raingauge value omitted ateach cross-validation step and given time but not whether this leads to improved hydrological model performance.Here, results of a UK case study will be presented that demonstrate the differences in hydrological performancebetween the selected merging methods. A spatially distributed hydrological model, called Grid-to-Grid or G2G, isused to simulate river flows across several major flood events over the 6 years 2007 to 2012.G2G is applied operationally across Britain by the Flood Forecasting Centre and Scottish Flood Forecasting Ser-vice. The current real time set-up employs 1km gridded rainfalls at a 15 minute interval obtained using a staticmultiquadric merging approach where any incidental parameters are calibrated off-line. Here, the current mergingmethod is compared with adaptive multiquadric merging and variants of Kriging.Attention is focussed on whether a reduction in rainfall estimation RMSE necessarily leads to improvement inmodelled river flow, when judged in terms of flow R2 Efficiency, magnitude and timing of peak flows, and overallflow bias. Discussion of computational time and algorithm complexity is included to guide recommendations forimprovements to the current operational use of rainfall merging methods.

Hydrological assessment of radar-raingauge merging techniques using the Grid-to-Grid model

http://nora.nerc.ac.uk/511331/1/N511331AB.pdf

Hydrological assessment of radar-raingauge merging techniques using the Grid-to-Grid model

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