Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

Abstract

Abstract. Both M s 8.0 Wenchuan earthquake on 12 May 2008 and M s 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity-duration (I -D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall-reflectivity (R − Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I -D threshold derived by the frequentist method is I = 10.1D −0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I -D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I -D thresholds and likewise underestimate I -D thresholds due to undershooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the strong small-scale rainfall process in the study area.

Rainfall intensity in geomorphology: Challenges and opportunities

“…Their conclusions suggest that rain gauges severely underestimate the duration-intensity thresholds. Shi et al (2018) further explored this effect and highlighted the importance of correct recording of the intensity in the core of the limited areas covered by convective rainfall events. Nikolopoulos et al (2014, 2015) have also explored the limitations of conventional rain gauge-based approaches for deriving debris flow occurrence thresholds, and additional studies are presented by Destro et al (2017) and Rossi et al (2017).…”

Citizen rain gauges improve hourly radar rainfall bias correction using a two-step Kalman filter

“…Mean field bias (MFB) adjustment is a common technique for bias correction in radar rainfall relative to ground stations. It can be computed as the ratio of the mean hourly radar rainfall estimate to the rain gauge measurement (Anagnostou and Krajewski, 1999;Yoo and Yoon, 2010;Hanchoowong et al, 2013;Shi et al, 2018). However, direct application of the MFB does not account for uncertainty in the bias associated with each radar-gauge measurement.…”

“…However, direct application of the MFB does not account for uncertainty in the bias associated with each radar-gauge measurement. Alternatively, a KF has previously been used to estimate the spatially uniform MFB in real time in several studies, including Anhert et al (1986), Smith and Krajewski (1991), Anagnostou et al (1998), Seo et al (1999), Chumchean et al (2006), Kim and Yoo (2014), and Shi et al (2018). The KF has the benefit of accounting for uncertainties in the observations by weighting the contribution of measurements by their respective error variances (Kalman, 1960).…”

Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars

“…The composite reflectivity factor (CR) and vertically integrated liquid water content (VIL) are chosen to segregate the convective and stratiform precipitation. Convective precipitation is identified based on that CR > 50 dBZ or VIL > 6.5 kgm −2 [23,35]. Otherwise, the precipitation is classified as being stratiform.…”