Adapting a Global Flood Model for Regional Simulations: the CaMa-Flood Model as Applied to New England Catchments

Monday, 15 December 2014
Danielle S Grogan, University of New Hampshire Main Campus, Durham, NH, United States, Hyungjun KIM, The University of Tokyo, Tokyo, Japan, Dai Yamazaki, JAMSTEC Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan, Richard B Lammers, University of New Hampshire, Durham, NH, United States, Steve E Frolking, Univ New Hampshire, Durham, NH, United States and Taikan Oki, University of Tokyo, Bunkyo-ku, Japan
Flood prediction and mitigation are important aspects of watershed management, especially in New England where a 30-year upward trend in floods has been attributed to a combination of urbanization and climate change. In addition to urban development, New England is currently experiencing an increase in agricultural land development. The land surface component of gridded hydrology models captures how these land use changes alter elements of the hydrologic cycle, leading to changes in river discharge. In this study, we use a gridded hydrology model (WBM) and a physically based river model (CaMa-Flood) to quantify changes in fluvial regime due to past (1980-2010) and projected future (2010-2050) land use change.

The CaMa-Flood model originally has been developed and validated for continental to global scales, accurately modeling discharge and inundation of large rivers. Here, we suggest a methodology incorporating locally available survey data for applying the same model structure to regional studies of past and future flood risk assessments in six New England basins. Locally estimated river geometry parameters using direct measurement results in increased simulation accuracy as validated against USGS in-situ gauged discharge. In sensitivity analysis, we found that accurate calibration of the river geometry parameters is dependent upon the spatial scale of the simulation. In addition to calibrating river geometry parameters, we incorporated direct observational data from field surveys, and compared the resulting flood simulations.