Evaluating the use of bias-corrected radar rainfall data in three flood events in Samsun, Turkey

Abstract

In applied hydrology, estimating the peak flood discharge in ungauged or poorly gauged river sections is vital for urbanized areas. Spatially distributed rainfall data such as weather radar data may be a good choice to represent the driving force in hydrologic models for ungauged regions. However, it is important to examine the accuracy of this product, especially over mountainous regions. The bias between radar rainfall and rain gauge rainfall can be progressively removed by using information provided by rain gauges. The Kalman Filter algorithm is applied for the mean field bias correction of radar rainfall data using past estimates and observations. Regarding the bias-correction methods, two filtering approaches are developed from 8 events observed at 13 rain gauge stations, and the bias-corrected radar (BCR) rainfall data are used to compare simulated and observed hydrographs for the three flood events that caused severe consequences in Samsun–Terme. It is found out that in frontal type rainfall, BCR rainfall estimates improve the Nash–Sutcliffe efficiency from 0.56 to 0.80 in runoff simulation of the event occurred on 22 November 2014; however, simulations of the event occurred on 2 August 2015 and 28 May 2016 have poorer statistical results probably owing to the effect of convective type rainfall and snow melting, respectively.