A review of gauge-radar merging methods for quantitative precipitation estimation in hydrology

In the case of a significant precipitation event, hydrological models play a key role in flood mitigation. To develop a hydrological model that produces results with a high degree of confidence, it is imperative that the model be provided with accurate quantitative precipitation estimates as input. For flood forecasting purposes, quantitative precipitation estimates at high spatial and temporal resolutions are preferable. Rain gauges and weather radar are the most widely used instruments for near real-time collection of precipitation estimates. While rain gauges and radar demonstrate certain strengths, both instruments suffer from a wide variety of well-known errors which inhibit their ability to provide optimal precipitation estimates for hydrological models. Considering this, several methods have been developed to merge the estimates of these two instruments in order to minimize their individual weaknesses and take advantage of their respective strengths. The goal of this paper is to provide a comprehensive review of gauge-radar merging methods and assess the opportunity for near real-time application of gauge-radar merging methods in hydrology. Methods presented include: mean field bias correction, Brandes spatial adjustment, local bias correction with ordinary kriging, range-dependent bias correction, Bayesian data combination, conditional merging, kriging with external drift and statistical objective analysis. While comparison of gauge-radar merging methods is difficult, several factors, including gauge network design, storm type and the temporal resolution of adjustment, have demonstrated a large effect on the overall accuracy of a particular merging method. The majority of research carried out on near real-time application of gauge-radar merging methods has been conducted outside of Canada. Further research is recommended to assess the capability of using gauge-radar merging schemes with Canadian radar products for precipitation estimation in hydrological applications, including operational hydrological modelling for flood forecasting.