In Situ, Modeled, and Earth Observation Monitoring of Surface Water Availability in West African Rangelands

Kimberly Slinski^1,2*Gabriel Senay³Adoum Alkhalil⁴Shraddhanand Shukla⁴Amy Mcnally²James Rowland³Erwann Fillol⁵Soni Yatheendradas^1,2Chris Funk⁴Andrew Hoell⁶Michael Jasinski⁷

• ¹University of Maryland, College Park, College Park, United States
• ²Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, Maryland, United States
• ³Earth Resources Observation and Science Center, United States Geological Survey (USGS), Sioux Falls, South Dakota, United States
• ⁴Climate Hazards Center, Department of Geography, College of Letters & Science, University of California, Santa Barbara, Santa Barbara, California, United States
• ⁵Action Against Hunger West and Central Africa Regional Office, Dakar, Senegal
• ⁶NOAA Physical Sciences Laboratory, Boulder, Colorado, United States
• ⁷Hydrological Sciences Laboratory, Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, United States

Rangeland ponds are vital to the livelihoods of pastoral and agropastoral communities in Africa, providing an important source of water for livestock. However, sparse instrumentation across much of Africa makes it extremely challenging to monitor surface water availability in these areas. Model estimates of surface water, for example as used by the Famine Early Warning Systems Network (FEWS NET) Water Point Viewer, are one of the few operational tools available to monitor surface water stress across pastoral areas of the Sahel and East Africa. Water availability data from these models are difficult to validate. New methods using satellite data to classify surface water provide an opportunity to assess the performance of these tools. This study compares water availability estimates derived from Landsat and Sentinel 1 satellite imagery to in situ observations and model simulations of water availability in 22 ephemeral ponds located in the Ferlo region of Senegal. The Active-Passive Water Classification (APWC) algorithm detected surface water at each location. Over 2022 and 2023, water was detected in pond locations annually at a frequency of 68.2% for all ponds and at frequency of 43.8% in ponds with a surface area less than 10,000 square meters (m 2 ). The APWC results outperform global and continental surface water datasets in the Ferlo region. Seasonal water availability was captured in twelve ponds over the 2022 and 2023 seasons. The twelve locations can function as sentinel ponds to monitor local water availability. Study results demonstrate the viability of satellite methods to assess water availability in the region as well as the challenges to using satellite-based methods to estimate water availability in small ponds.