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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. [ABSTRACT FROM AUTHOR]

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a Bayesian hierarchical model (BHM) based on the generalized extreme value (GEV) and the generalized Pareto distribution for regional flood frequency analysis at high resolution across a large part of North America. Our model leverages annual maximum flow data from ≈20,000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include peaks over threshold flow data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improvable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield a satisfying agreement when compared with the available design peak discharge from various government sources. The assessment of the covariates' contributions to the model is also informative with regard to the most relevant underlying factors influencing flood-inducing peak flows. According to the developed aggregate importance score, the key covariates in our model are temperature-related bioindicators, the catchment drainage area and the geographical location. [ABSTRACT FROM AUTHOR]

Despite the high historical losses attributed to flood events, Canadian flood mitigation efforts have been hindered by a dearth of current, accessible flood extent/risk models and maps. Such resources often entail large datasets and high computational requirements. This study presents a novel, computationally efficient flood inundation modelling framework ("InundatEd") using the height above nearest drainage-based solution for Manning's equation, implemented in a big-data discrete global grid systems-based architecture with a web-GIS platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to known flood extents. The framework is divided into multiple swappable modules including: GIS pre- processing; regional regression; inundation model; and web-GIS visualization. Extent testing and processing speed results indicate the value of a DGGS-based architecture alongside a simple conceptual inundation model and a dynamic user interface. [ABSTRACT FROM AUTHOR]