A high-resolution global flood hazard model

Abstract

Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and economies have grown across the developing world, so too has demand from governments, businesses, and NGOs for modeled flood hazard data in these data-scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally and present a framework methodology that leverages recent cross-disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ∼90 m resolution for the whole terrestrial land surface between 56°S and 60°N, and results are validated against high-resolution government flood hazard data sets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ∼1 km, mean absolute error in flooded fraction falls to ∼5%. The full complexity global model contains an automatically parameterized subgrid channel network, and comparison to both a simplified 2-D only variant and an independently developed pan-European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. While careful processing of existing global terrain data sets enables reasonable model performance in urban areas, adoption of forthcoming next-generation global terrain data sets will offer the best prospect for a step-change improvement in model performance.

Keywords: flooding; global; hydraulic; large‐scale modeling.

Figure 1

Conceptual flowchart of global flood hazard model framework. The colors associate areas of the flowchart to the six key challenges presented in this paper: global terrain data (green); extreme flow generation (blue); global river network and geometry (yellow); flood defenses (purple); computational hydraulic engine (orange); and automation framework (red).

Figure 2

Comparison of (top left) raw SRTM DEM to LiDAR and (top middle) corrected SRTM to LiDAR for Western Belize. Cross sections (a, b, and c) transect the Belize River valley and compare the LiDAR elevation profile (black) to the uncorrected SRTM profile (green) and corrected SRTM profile (purple).

Figure 3

Example output from global flood model, showing 1 in 100 year maximum flood depth for (a) all of Africa, (b) Inland Niger Delta, and (c) Zambezi River floodplain.

Figure 4

Map showing fit between global model and Canadian state benchmark data for the Bow River (and its main tributaries) in Calgary, Alberta. Green shading represents matching flooded area in both benchmark data and the global model; blue shading represents flooded area unique to the global model; and red shading represents flooded area unique to the benchmark data.

Figure 5

Map showing fit between global model and Canadian state benchmark data for the North Saskatchewan River (and its main tributaries) in Edmonton, Alberta. Green shading represents matching flooded area in both benchmark data and the global model; blue shading represents flooded area unique to the global model; and red shading represents flooded area unique to the benchmark data.

Figure 6

Map showing fit between global model and Canadian state benchmark data for the Red Deer River in Red Deer, Alberta. Green shading represents matching flooded area in both benchmark data and the global model; blue shading represents flooded area unique to the global model; and red shading represents flooded area unique to the benchmark data.

Figure 7

Map showing fit between global model and UK Environment Agency benchmark data for the Severn catchment (∼11,000 km²). Green shading represents matching flooded area in both benchmark data and the global model; blue shading represents flooded area unique to the global model; and red shading represents flooded area unique to the benchmark data.

Figure 8

Map showing fit between global model and UK Environment Agency benchmark data for the Thames catchment (∼16,000 km²). Green shading represents matching flooded area in both benchmark data and the global model; blue shading represents flooded area unique to the global model; and red shading represents flooded area unique to the benchmark data. The present omission of tidal flooding within the global model is visible in the underprediction of flood hazard relative to the benchmark data in the lower reach of the Thames.