Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

Abstract

Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting.

Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model

“…As an alternative for capturing a detailed spatial distribution of rainfall, data provided by satellite and meteorological radar has emerged as a solution [9][10][11]. These data sources can effectively be blended with Numerical Weather Predictions (NWP) as in Yoon S.-S. [12] or assimilation methods as in Khaki et al [13]. Moreover, the use of radar rainfall estimates [14] for streamflow forecasting has been extensively documented [15][16][17][18][19] with satisfactory outcomes.…”

Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation

“…This result seems to be based on the fact that the ensemble forecast was made by considering the uncertainty of rainfall propagation, i.e., by considering more feasible cases of rainfall propagation. Also, this limitation, which is the quality of three hour forecast is far lower than that of one hour and two hours can be found in the previous studies in Korea [108][109][110].…”

Inundation Analysis of the Oda River Basin in Japan during the Flood Event of 6–7 July 2018 Utilizing Local and Global Hydrographic Data

“…Recently, remote sensing rainfall data, i.e., mainly satellite-based [16,17,38,40], and weather radar rainfall [10,18,26,32,41] are both quite popular in hydrological modeling, providing a range of options to obtain the most reliable analysis. Estimated rainfall from weather radar observations, however, is characterized by higher spatial resolution for rainfall and can be used for hydrometeorological applications, particularly for specific areas [10,18,26,32,41]. Data for the average basin rainfall in the Oda river basin were obtained and compared using all available rainfall data from the study by P.C.…”

“…Some of them can calculate runoff in each grid of a river basin. These models, however, must be combined with a hydraulic model to analyze flood inundation over a river basin [31,32]. The Rainfall-Runoff-Inundation (RRI) model is a two-dimensional, distributed-parameter, structured grid, hydrological model that has the advantage of simultaneously modeling runoff and flood inundation [14].…”