Analysing the Performance of Various Radar-Rain Gauge Merging Methods for Modelling the Hydrologic Response of Upper Thames River Basin, Canada

Abstract

Accurate estimate of precipitation is of paramount importance for assessing the hydrologic response of a river basin. Weather radar data integrated with rain gauge measurements are applied to characterize the spatial feature of the storm event producing precipitation over the basin. Ordinary kriging of rain gauge data, mean field bias, Brandes spatial adjustment, conditional merging (CM), and local bias techniques are applied in this study to evaluate the performance of these radar-rain gauge merging methods for hydrologic modelling of the Upper Thames River basin (UTRb), southwestern Ontario, Canada. Singularity-sensitive Bayesian merging method (SSBM) with a fine spatial resolution was also applied to retain the singularity character of the rainfall event. Rainfall-runoff simulations were carried out for three major storm events recorded in the UTRb using the HEC-HMS 4.0 hydrologic model. River flow analysis was performed for the comparison of results of HEC-RAS 4.1 hydraulic model with the observed rating curve. A novel methodology involving a dual-storage system is proposed to model three sub-basins of UTRb which displayed skewed and spiked observed runoff hydrographs. Using this dual-storage system for the three sub-basins it is found that CM and SSBM merging methods yielded optimal Nash-Sutcliffe efficiency coefficients for the prediction of runoff from these sub-basins.

Evaluation of Radar-Gauge Merging Techniques to Be Used in Operational Flood Forecasting in Urban Watersheds

“…The most recent categorization of the existing radar-gauge merging methods was introduced by Ochoa-Rodriguez et al [54] based on the potential for urban hydrological application; 1. radar bias adjustment methods, 2. rain gauge interpolation methods using spatial radar association as additional information, and 3. radar-rain gauge integration methods. Only two preliminary studies have investigated the three categories of merging methods defined by Ochoa-Rodriguez et al [54,68,71]. Therefore, it is beneficial to consider methods that preserve small-scale features in the merged estimates in fine scale, the limited availability of rain gauges, and computational requirements for operational use when applying radar-gauge merging methods.…”

A Review of Radar‐Rain Gauge Data Merging Methods and Their Potential for Urban Hydrological Applications

“…In relation to noninterpolation methods, KED has been shown to outperform bias adjustment methods and also the CoK integration method (Schuurmans et al, 2007;Velasco-Forero et al, 2009). The only method that has displayed a similar or better performance than KED is the BAY (integration) method (Kumar et al, 2016;Ochoa-Rodriguez, Wang, Bailey, et al, 2015). 6.…”

“…This decomposition method was initially tested in combination with the BAY merging method in an urban context and was shown to effectively improve merging results (Wang, Ochoa-Rodríguez, Onof, & Willems, 2015). Later on, and this time in a larger geographic domain (River Thames catchment, Canada), Kumar et al (2016) compared the performance of the BAY method with singularity decomposition treatment against that of other merging methods (including MFB, KRE and Brandes) and found the singularity-BAY method to outperform others. However, Kumar et al (2016) did not specifically analyze the impact of the singularity treatment on merging performance: they simply applied the singularity-BAY method but did not compare its performance against that of the original BAY method, without singularity treatment.…”

“…Later on, and this time in a larger geographic domain (River Thames catchment, Canada), Kumar et al (2016) compared the performance of the BAY method with singularity decomposition treatment against that of other merging methods (including MFB, KRE and Brandes) and found the singularity-BAY method to outperform others. However, Kumar et al (2016) did not specifically analyze the impact of the singularity treatment on merging performance: they simply applied the singularity-BAY method but did not compare its performance against that of the original BAY method, without singularity treatment. More recently, Cecinati, Wani, and Rico-Ramirez (2017) tested the singularity decomposition approach in combination with KED, with merging performed over a large geographic domain (a 200-km × 200-km area in the North of England) and at hourly temporal resolution.…”

“…While the literature on radar-rain gauge merging is abundant and provides insights into the potential of merging at urban scales, studies to date have not sufficiently investigated important aspects related to radar-rain gauge merging applicability, particularly at the scales of interest. For example, only few studies include an intercomparison of various merging methods, with only two recent preliminary studies providing a comparison of the three categories of merging methods defined in the present paper (Kumar et al, 2016;Ochoa-Rodriguez, Wang, Bailey, et al, 2015). Furthermore, most of the studies focusing on intercomparison of merging methods and on analysis and handling of factors affecting merging performance have focused on large-scale applications, the resolution requirements of which are different from those of urban hydrological applications.…”