Reliable freshwater access drives wildlife movement and distribution across the globe. However, rising temperatures and destabilizing rainfall regimes threaten the persistence of small, rain-fed seasonal water sources. In southern Africa, expected to warm rapidly by 2100, drying surface water may cause a breakdown in seasonal migrations, restricting the habitat of endangered, water-reliant wildlife like the African savanna elephant (Loxodonta africana). This drying may concentrate elephant around remaining surface water, increasing resource competition and human-elephant conflict. An accurate understanding of the dynamics and drivers of seasonal surface water will therefore be critical to wildlife and human health as climate change intensifies.
Here, we present a flexible and reproducible framework for fine-scale mapping of surface water extent in southern Africa’s 520,000 km2 Kavango Zambezi Transfrontier Conservation Area (KAZA). We implemented Otsu’s method on an Automated Water Extraction Index in Google Earth Engine to threshold bi- and tri-monthly median Sentinel-2 MSI imagery from 2019-2025, creating >35 binary rasters of ephemeral water and three seasonal recurrence rasters. We leveraged the high vegetation-water contrast in the wet season as a positive mask on dryland surface water in the dry season. Mapping validation in eight topographically and hydrologically distinct subregions of KAZA showed a water classification accuracy of 88-100% in the wet season, a 41% improvement over existing water products. We then compared our Ephemeral Surface Water (ESW) maps to existing 30m Landsat products in a case study of 43 GPS-collared L. africana in Namibia and Botswana. Models relying on the 30m water recurrence product did not reproduce known biological rhythms, estimating that only 42% of elephants visited water within the 48 hour time frame reported in the literature. Using our ESW product 99% of elephant GPS data conformed to biological reality, supporting the use of ESW in animal movement models.
As aridification threatens to diminish essential surface water resources, it is imperative to model the drivers of wildlife movements at the scale of wildlife needs. With our ESW, we address the dearth of fine scale accessible surface water extent data and provide a straightforward coding architecture for applications beyond KAZA.