A first collective validation of global fluvial flood models for major floods in Nigeria and Mozambique

Abstract

Global flood models (GFMs) are becoming increasingly important for disaster risk management internationally. However, these models have had little validation against observed flood events, making it difficult to compare model performance. In this paper, we introduce the first collective validation of multiple GFMs against the same events and we analyse how different model structures influence performance. We identify three hydraulically diverse regions in Africa with recent large scale flood events: Lokoja, Nigeria; Idah, Nigeria; and Chemba, Mozambique. We then evaluate the flood extent output provided by six GFMs against satellite observations of historical flood extents in these regions. The critical success index of individual models across the three regions ranges from 0.45 to 0.7 and the percentage of flood captured ranges from 52% to 97%. Site specific conditions influence performance as the models score better in the confined floodplain of Lokoja but score poorly in Idah's flat extensive floodplain. 2D hydrodynamic models are shown to perform favourably. The models forced by gauged flow data show a greater level of return period accuracy compared to those forced by climate reanalysis data. Using the results of our analysis, we create and validate a three-model ensemble to investigate the usefulness of ensemble modelling in a flood hazard context. We find the ensemble model performs similarly to the best individual and aggregated models. In the three study regions, we found no correlation between performance and the spatial resolution of the models. The best individual models show an acceptable level of performance for these large rivers.

Flood Inundation Mapping of the Sparsely Gauged Large-Scale Brahmaputra Basin Using Remote Sensing Products

“…The accuracy of the simulated flood maps was evaluated using the Critical Success Index (CSI), which is frequently used in estimating the accuracy of flood forecasts [58][59][60][61][62]. In particular the following indices were derived: Probability of Detection (POD, shows what flood fraction of the observed events was correctly simulated), False Alarm Ratio (FAR, shows what dry fraction of the flood event was incorrectly simulated as flooded) and Critical Success Index (CSI, is an indication of the goodness of fit between the simulated and observed flood).…”

Global flood hazard map and exposed GDP comparison: a China case study

“…For example, Ward et al (2015) discuss the quality of elevation data, accuracy of boundary conditions used to force inundation models, and the knowledge of river morphology, among other things. Bernhofen et al (2018) also discuss the importance of forcing boundary conditions, especially input flow, as well as the influence of morphological features, such as floodplain size and the steepness of the terrain. Another major 55 challenge for GFMs is to account for the impact that structural flood defenses have on flood hazard, especially in regions with high protection standards.…”

Advancing global flood hazard simulations by improving comparability, benchmarking, and integration of global flood models

“…Yet, they all inherently have, depending on their governing processes and structure, distinct properties, strengths, and weaknesses. Since validation data, period, and location are usually not consistent between GFM description studies, model differences do not directly become visible while in fact they can result in locally remarkable deviations when compared with each other (Trigg et al 2016, Bernhofen et al 2018.…”

“…Such model intercomparison projects are a great way to narrow the above-mentioned knowledge gap, let alone the stimulus for intensified scientific collaboration and exchange. To our knowledge, there is no such detailed comparison yet for GFMs besides first benchmarking efforts focussing on overall model only (Trigg et al 2016, Bernhofen et al 2018. Consequently, it is fair to say that GFMs are behind in terms of collaborative testing as they lack of more consistent and regular comparison, hampering a better understanding of the discrepancies in model outputs.…”