Cherry May R. Mateo

Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models

A catastrophic flood event which caused massive economic losses occurred in Thailand, in 2011. Several studies have already been conducted to analyze the Thai floods, but none of them have assessed the impacts of reservoir operation on flood inundation. This study addresses this gap by combining physically based hydrological models to explicitly simulate the impacts of reservoir operation on flooding in the Chao Phraya River Basin, Thailand. H08, an integrated water resources model with a reservoir operation module, was combined with CaMa-Flood, a river routing model with representation of flood dynamics. The combined H08-CaMa model was applied to simulate and assess the historical and alternative reservoir operation rules in the two largest reservoirs in the basin. The combined H08-CaMa model effectively simulated the 2011 flood: regulated flows at a major gauging station have high daily NSE-coefficient of 92% as compared with observed discharge; spatiotemporal extent of simulated flood inundation match well with those of satellite observations. Simulation results show that through the operation of reservoirs in 2011, flood volume was reduced by 8.6 billion m3 and both depth and area of flooding were reduced by 40% on the average. Nonetheless, simple modifications in reservoir operation proved to further reduce the flood volume by 2.4 million m3 and the depth and area of flooding by 20% on the average. By modeling reservoir operation with a hydrodynamic model, a more realistic simulation of the 2011 Thai flood was made possible, and the potential of reducing flood inundation through improved reservoir management was quantified.

Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

Global-scale river models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC) is assumed, simulation results deteriorate with finer spatial resolution; Nash–Sutcliffe efficiency coefficients decreased by more than 50 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC) is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions at finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings should be universal and may have significant impacts on largeto globalscale simulations, especially in regions where mega deltas exist.These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.