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Abstract Ephemeral ponds (EPs) are seasonally flooded isolated wetlands that provide a variety of hydroecological benefits, including the provision of breeding habitat for several amphibian and invertebrate species. However, the lack of their explicit representation in hydrological models limits a comprehensive understanding of their interaction with surrounding landscapes and their vulnerability in the context of human interventions and climate change. The purpose of this research was to improve the isolated wetland module of the Soil Water Assessment Tool (SWAT) to better represent EP hydrology. The changes include (1) representation of groundwater and hypodermic flow as the only inflows from the pond drainage surface, due to the intermittent and negligible presence of inflow from surface runoff in forested ponds, (2) revision of how evapotranspiration within EPs is represented and (3) implementation of distinct volume‐area‐depth relationships for ponds based on their geometrical shape. The accuracy of these improvements was assessed against that of a previous isolated wetland formulation in replicating water depth observations of 10 EPs of a portion of the Kenauk forest (68 km 2 ) in the Canadian Shield of the Outaouais region (Québec, Canada). The comparison results show that the revised SWAT model presented here significantly improves the distinct filling and drying water cycle of EPs (average root mean square error of 0.1 m of the revised model vs. 0.23 m for the original model). Besides, the new module allowed to identify that hypodermic flow, evapotranspiration and seepage to the underlying soil are the main EP source and sinks. The new module also allowed to explicitly quantify the differences in filling/drying pattern of the EPs of the Kenauk forest and unlike the original model structure, the new module was able to closely replicate the interannual variation of spring and annual hydroperiod duration.
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Abstract High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying ‘hotspots’ characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.