Novel extensions to the Fisher copula to model flood spatial dependence over North America

Taking into account the spatial dependence of floods is essential for an accurate assessment of fluvial flood risk. We propose novel extensions to the Fisher copula to statistically model the spatial structure of observed historical flood record data across North America. These include the machine-learning-based XGBoost model, exploiting the information contained in 130 catchment-specific covariates to predict discharge Kendall's τ coefficients between pairs of gauged–ungauged catchments. A novel conditional simulation strategy is utilized to simulate coherent flooding at all catchments efficiently. After subdividing North America into 14 hydrological regions and 1.8 million catchments, applying our methodology allows us to obtain synthetic flood event sets with spatial dependence, magnitudes, and frequency resembling those of the historical events. The different components of the model are validated using several measures of dependence and extremal dependence to compare the observed and simulated events. The obtained event set is further analyzed and supports the conclusions from a reference paper in flood spatial modeling. We find a nontrivial relationship between the spatial extent of a flood event and its peak magnitude.