Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan

Li Zhou,Tianqi Ao,Hemakanth Selvarajah,Kuniyoshi Takeuchi,Mohamed Rasmy,Toshio Koike

doi:10.3390/app11031087

Abstract

Flood management is an important topic worldwide. Precipitation is the most crucial factor in reducing flood-related risks and damages. However, its adequate quality and sufficient quantity are not met in many parts of the world. Currently, near real-time satellite precipitation products (NRT SPPs) have great potential to supplement the gauge rainfall. However, NRT SPPs have several biases that require corrections before application. As a result, this study investigated two statistical bias correction methods with different parameters for the NRT SPPs and evaluated the adequacy of its application in the Fuji River basin. We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellitE Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. The results showed that the corrected SPPs by the 10-day ratio based bias correction method are consistent with the gauge data at the watershed scale. Compared with the original SPPs, the corrected SPPs improved the flood discharge simulation considerably. GSMaP-NRT and IMERG-Early have the potential for hourly river-flow simulation on a basin or large scale after bias correction. These findings can provide references for the applications of NRT SPPs in other basins for flood monitoring and early warning applications. It is necessary to investigate the impact of number of ground observation and their distribution patterns on bias correction and hydrological simulation efficiency, which is the future direction of this study.

Highlights

Flooding is an inevitable natural hazard, but it is a valuable water resource
A previous study indicated that 70–80% of the uncertainties of hydrological simulations are due to the uncertainties in precipitation data [3]
Accurate and reliable precipitation data are crucial for hydrological forecasting and water resources management

Summary

Introduction

Flooding is an inevitable natural hazard, but it is a valuable water resource. How to manage and utilize floodwater efficiently has always been a hot and challenging spot in global research. As a non-structural measure, flood forecasting helps to control and take advantage of the excess water effectively. Precipitation data are one of the most important boundary conditions in flood forecasting models [1]. It brings uncertainty to flood forecasting [2], in ungauged or poorly gauged basins. A previous study indicated that 70–80% of the uncertainties of hydrological simulations are due to the uncertainties in precipitation data [3]. Accurate and reliable precipitation data are crucial for hydrological forecasting and water resources management

Methods

Results

Conclusion