Abstract. The Thailand floods in 2011 caused unprecedented economic damage in the Chao Phraya River basin. To diagnose the flood hazard characteristics, this study analyses the hydrologic sensitivity of flood runoff and inundation to rainfall. The motivation is to address why the seemingly insignificant monsoon rainfall, or 1.2 times more rainfall than for past large floods, including the ones in 1995 and 2006, resulted in such devastating flooding. To quantify the hydrologic sensitivity, this study simulated longterm rainfall-runoff and inundation for the entire river basin (160 000 km 2 ). The simulation suggested that the flood inundation volume was 1.6 times more in 2011 than for the past flood events. Furthermore, the elasticity index suggested that a 1 % increase in rainfall causes a 2.3 % increase in runoff and a 4.2 % increase in flood inundation. This study highlights the importance of sensitivity quantification for a better understanding of flood hazard characteristics; the presented basin-wide rainfall-runoff-inundation simulation was an effective approach to analyse the sensitivity of flood runoff and inundation at the river basin scale.

A devastating flood disaster occurred in Thailand in 2011. In case of such large‐scale flooding, it is important to predict the dynamics of inundation on a near real‐time basis for safe evacuation. To predict widespread inundation, where both rainfall‐runoff from surrounding mountains and rainfall on floodplains contribute to the event, this paper applied a rainfall‐runoff‐inundation (RRI) model to the entire Chao Phraya River basin (160 000 km2). Near real‐time simulation was conducted for emergency responses with globally available dataset including satellite‐based topography (HydroSHEDS derived from SRTM) and rainfall (TRMM 3B42RT) during the disaster. Post‐flood simulation was also carried out with more local information. The RRI model was found capable of representing the peak inundation extent with an acceptable accuracy and correctly predicting a 1‐month lasting inundation in the lower part of the basin. On the other hand, the prediction overestimated the river discharge by 40% and the inundation water level by 2 m mainly due to the neglect of the evapotranspiration effect. The post‐flood simulation improved its accuracy by up to 10% for river discharges and 1 m for peak inundation water levels, but it did not lead to better agreement of flood extents with those based on the remote sensing. Further study is recommended to improve accuracy for modelling of spatial extent of flooding. Furthermore, sensitivity analysis with different input suggested what information should be prioritised for emergency response‐type flood simulations.

Lagged ensemble forecasting of rainfall and rainfall-runoff-inundation (RRI) forecasting were applied to the devastating flood in the Kabul River basin, the first strike of the 2010 Pakistan flood. The forecasts were performed using the Global Forecast System of the National Centers for Environmental Prediction (NCEP-GFS) and were provided four times daily. Dynamical downscaling was also applied to the forecasts by the Weather Research and Forecasting Model (WRF), a regional model. The forecasts of the rainfall and inundation area were verified by comparing rain gauge-corrected Global Satellite Mapping of Precipitation (GSMaP) data and the observed indicator of an inundation map based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The GFS predicted a sign of heavy rainfall in northern Pakistan 4 days ahead of the onset. However, most of the forecasts predicted it in wrong places, and only those performed after the rainfall onset predicted it in the accurate location. Downscaling corrected the locations of the misplaced GFS forecasts and also underestimated or overestimated rainfall amount derived from GFS. Finally, downscaled forecasts predicted a reliable amount of rainfall in the Kabul River basin 1 day ahead of the rainfall onset and predicted a high probability of heavy rainfall 3 days ahead. Lagged ensemble forecasts of discharge and inundation distribution based on GFS rainfall predicted the probability of the actual discharge and inundation distribution, but in low reliability. The reliability substantially improved when downscaled rainfall was used. The reliability of the flood alert system combining NCEP-GFS, dynamical downscaling by WRF, and the RRI model was at an acceptable level in this study.