Author(s): Md Safat Sikder; Faisal Hossain

Linked Author(s):

Keywords: Monsoon; Flooding forecasting; Quantitative precipitation forecast; Numerical weather prediction; Ganges; Brahmaputra and Mekong

Abstract: For flood-prone countries subject to large-scale and seasonal flooding, precipitation forecasting is the single most important factor for improving the skill of flood forecasting for such large river basins dominated by the monsoon. Several flood forecasting agencies in South and Southeast Asia, where monsoon floods dominate (e.g. Bangladesh, Pakistan, India, Thailand and Vietnam), are currently using quantitative precipitation forecast (QPF) from numerical weather prediction (NWP) models. Although there are numerous studies reported in the literature to evaluate QPF precipitation performance, there appears to be lack of studies about the impact on the flood forecasting skill. In this study, we demonstrate tangible improvements in flood forecasting based on NWP precipitation forecast using an approach that is operationally feasible in resource-limited settings of many flood agencies. Our improvement is based on a bias correction methodology for enhancing the skill of QPF using observed and QPF climatology. The proposed approach can be applied to any type of QPF dataset such as those dynamically downscaled from regional NWP. We demonstrate clear and consistent improvement in the enhancement of flood forecasting skill at longer lead times of up to 7 days in three river basins of Ganges, Brahmaputra and Mekong by about 50% (reduction in RMSE) or 25% improvement in correlation when compared to the forecasts obtained from uncorrected QPF. Furthermore, our proposed bias correction methodology yields significantly higher skill improvement in flood forecast for global (non-downscaled) QPF than those dynamically downscaled QPFs for the macroscale hydrologic model used for forecasting stream flows. The simplicity of the QPF bias correction methodology along with the numerical efficiency can be of tremendous appeal to operational flood forecasting agencies of the developing world faced with large-scale monsoonal flooding and limited computational resources and time for disaster response.

DOI: https://doi.org/10.1080/15715124.2018.1476368

Year: 2019