Content area
Abstract
ABSTRACT
Owing to the increased frequency of short‐duration extreme rainfall events caused by climate change, peak flood flows are expected to increase substantially in small and medium‐sized rivers (SMRs) with a short time of concentration for a catchment (Tc). Accurate flood forecasts and corresponding evacuation are effective in reducing the number of casualties caused by flash floods in SMRs. Currently, flood forecasting using observed rainfall in SMRs has a short lead time, which often delays the issuance of evacuation orders by local governments. Moreover, the large number of SMRs necessitates a system that can be widely used by local governments for disaster response tasks, such as issuing evacuation orders. Therefore, we developed a system that can accurately predict when river water levels will reach the Flood Risk Level (FRL). This forecasting approach uses the rainfall–runoff–inundation (RRI) model and the H–Q equation. The parameters in the RRI model were optimized using the Shuffled Complex Evolution algorithm developed at the University of Arizona (SCE‐UA) to reduce the required time and effort. The system uses real‐time water level observation data to sequentially modify the basin state quantities in the RRI model using the particle filter method to improve the water level forecast accuracy. The system was implemented in 200 rivers in Japan with diverse rainfall and geological characteristics and was tested during the flood season. Accuracy verification was conducted when the forecasted water levels were operated within a range of ± 50 cm. The results showed that 75% of the flood events could be forecasted more than 2 h before reaching the FRLs. Furthermore, 89% of the flood events could be predicted with a lead time (LT; time that water levels reach the FRL—time of first forecast) of 2 h or more or a lead time equal to the Tc or more. These findings show that this system has the potential to enhance and strengthen flood warning and evacuation systems.
Details
Flash floods;
Flood forecasting;
Rainfall-runoff modeling;
Rainfall-climatic change relationships;
Topography;
Evacuation systems;
Rivers;
Water levels;
Flood risk;
Moisture content;
Climate change;
Rainfall-runoff relationships;
Peak floods;
Accuracy;
Local government;
Evacuation;
Flood predictions;
Casualties;
Forecasting;
Forecast accuracy;
Floods;
Disaster management;
Flash flooding;
Hydrology;
Environmental risk;
Flooding;
Flood warnings;
Evolutionary algorithms;
Risk levels;
River water;
Concentration time;
Lead time;
Precipitation;
Runoff;
Flood flow;
Storm damage;
Rain;
Rainfall;
Watersheds
; Kakinuma, Daiki 2 ; Nakamura, Yosuke 3 ; Numata, Shingo 3 ; Mochizuki, Takafumi 4 ; Kubota, Keijiro 2 ; Yasukawa, Masaki 5 ; Nemoto, Toshihiro 5 ; Koike, Toshio 6 1 Foundation of River & Basin Integrated Communications, Tokyo, Japan, The University of Tokyo, Tokyo, Japan
2 International Centre for Water Hazard and Risk Management Under the Auspices of UNESCO, Public Works Research Institute, Tsukuba, Japan
3 Mitsui Consultants Co. Ltd., Tokyo, Japan
4 Ministry of Land, Infrastructure, Transport and Tourism, Tokyo, Japan
5 Global Environment Data Commons, The University of Tokyo, Tokyo, Japan
6 The University of Tokyo, Tokyo, Japan, International Centre for Water Hazard and Risk Management Under the Auspices of UNESCO, Public Works Research Institute, Tsukuba, Japan