Reliable freshwater access drives terrestrial wildlife movements and habitat use globally. The small, rain-fed seasonal pools critical for dryland wildlife persistence are vulnerable to rising temperatures and unstable precipitation regimes projected under climate change. In southern Africa, which is expected to warm rapidly by 2100, the drying and disappearance of surface water may cause a breakdown in seasonal migrations of large, area-sensitive, and water-dependent wildlife species. Furthermore, the disappearance of ephemeral water may concentrate wildlife around remaining surface water, increasing resource competition and human-wildlife conflict. An accurate understanding of the dynamics and drivers of seasonal surface water will therefore be crucial to wildlife and human health as climate change intensifies.

Here, we present a framework and empirical analysis of fine-scale surface water mapping in the Kavango Zambezi Transfrontier Conservation Area (KAZA), the world's largest terrestrial conservation area (520,000km2). From 2019-2025, we implemented Otsu thresholding on median Automated Water Extraction Index Sentinel-2 MSI imagery (10m), creating >35 quasi-monthly KAZA-wide Ephemeral Surface Water (ESW) rasters (mean accuracy 87%, compared to 50% for a 30m Global Surface Water (GSW) product). Low wet season (November-May) rainfall drove led to low surface water fill from February-May, especially after multiple drought years (2023-2024), suggesting a critically vulnerable time for water-dependent wildlife.

Finally, we assessed the applicability of our ESW map for wildlife movement analyses. African savanna elephant (Loxodonta africana) are known to visit water approximately every 48 hours; a useful water map should reflect this dependence. We calculated water visitation rates using GPS data from 27 individual elephant and a threshold distance of T = 200m, and found that 99% of GPS tracks maintained ESW visitation rates under 48 hours, compared to only 29% of tracks for GSW visitation. These results were robust when T was varied, and ESW similarly outperformed random controls and several other water products, making ESW an effective proxy for actual water use in animal movement models. As climate change threatens seasonally ephemeral surface water, fine-resolution tools such as ESW will help conservation scientists and managers to understand and predict the drivers of wildlife movements at the scale of wildlife needs.