Abstract
Demand for radar Quantitative Precipitation Estimates (QPEs) as precipitation forcing to hydrological models in operational flood forecasting has increased in the recent past. It is practically impossible to get error-free QPEs due to the intrinsic limitations of weather radar as a precipitation measurement tool. Adjusting radar QPEs with gauge observations by combining their advantages while minimizing their weaknesses increases the accuracy and reliability of radar QPEs. This study deploys several techniques to merge two dual-polarized King City radar (WKR) C-band and two KBUF Next-Generation Radar (NEXRAD) S-band operational radar QPEs with rain gauge data for the Humber River (semi-urban) and Don River (urban) watersheds in Ontario, Canada. The relative performances are assessed against an independent gauge network by comparing hourly rainfall events. The Cumulative Distribution Function Matching (CDFM) method performed best, followed by Kriging with Radar-based Error correction (KRE). Although both WKR and NEXRAD radar QPEs improved significantly, NEXRAD Level III Digital Precipitation Array (DPA) provided the best results. All methods performed better for low- to medium-intensity precipitation but deteriorated with the increasing rainfall intensities. All methods outperformed radar only QPEs for all events, but the agreement is best in the summer.
Highlights
Flooding has been identified as the world’s deadliest natural disaster after earthquakes and tsunamis due to the associated damage to life, property, economy, and infrastructure [1,2]
The agreement between gauge observations and radar Quantitative Precipitation Estimates (QPEs) has been improved after applying radar-gauge merging; the degree of improvement varies for each method as well as for each radar QPE (Figure 3)
The existing error after applying Cumulative Distribution Function Matching (CDFM) may be caused by the random component of the radar QPE errors that are not removed by the CDFM method
Summary
Flooding has been identified as the world’s deadliest natural disaster after earthquakes and tsunamis due to the associated damage to life, property, economy, and infrastructure [1,2]. A well-developed flood forecasting system that can deliver accurate and reliable forecasts with proper lead time is a vital part of nonstructural flood management. Flood forecasting and warning utilizing hydrological models to forecast river flow have a widespread application in disaster management [3,4]. The frequency and magnitude of flooding are mostly influenced by precipitation [5,6]. The accuracy and reliability of hydrological model predictions depend heavily on the accuracy of the forcing data, especially the Quantitative Precipitation Estimates (QPEs) [7,8]. Accurate QPEs produce model parameter sets that represent watershed characteristics after hydrological
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