)1 Department of Civil and Environmental Engineering, Duke University, Duke University, NC 27708-0287, USA; 1 [EMAIL_ADDRESS] (T.M.W. )1 Department of Civil and Environmental Engineering, Duke University, NC 27708-0287, USA; 1 [EMAIL_ADDRESS] (T.M.W. )1 Department of Civil and Environmental Engineering, Duke University, NC 27708-0287, USA; 1 [EMAIL_ADDRESS] (T.M.W. )1 Department of Civil and Environmental Engineering, Duke University, NC 27708-0287, USA; 1 [EMAIL_ADDRESS] (T.M.W. )1 Department of Civil and Environmental Engineering, Duke University, NC 27708-0287, USA; 1 el7@duke. activities, and many existing wetlands are degraded from drainage, agriculture, and dam construction, among other uses [1; 8]. Therefore, it is increasingly important to accurately map wetlands dynamically, particularly in order to target wetland restoration and conservation efforts. The Upper Zambezi River Basin (UZRB) in Sub-Saharan Africa is an area of complex topography and hydrometeorology where interactions between the Angola High Plateau (AHP), the Inter-tropical Convergence Zone (ITCZ), and the Congo Air Boundary Zone (CABZ) determine the spatial and temporal distribution of water resources, and inter-annual climate variability likely maps to the variability observed in vegetation and wetland density [9]. However, long-term records and detailed spatial observations of surface water, groundwater, and boundary layer water, carbon and energy fluxes are scarce for this region. In order to accurately describe spatiotemporal changes in ecosystem water availability, surface-groundwater interactions must be considered at the floodplain scale. Characterization of surface-groundwater interactions in the UZRB requires dynamic identification of ephemeral wetland areas that expand to store water during the wet season, and recede with aquifer discharge in the dry season. Methods for determining the spatial and temporal variation of wetlands using remote sensing observations include: delineating wetland extent with active [10; 11; 12; 13; 14; 15; 16; 17] and passive microwave sensors [18; 19]; classifying wetland areas with optical imagers including Landsat ETM+, SPOT, ASTER, and MODIS [20; 21; 22; 23; 24; 25; 26; 27]; and inferring wetland area from indices derived from infrared satellite bands sensitive to water, for example, the Normalized Difference Water Index (NDWI) [28; 29]. The spectral wavebands of microwave sensors and optical imagers tend to miss smaller scale features when used to delineate wetlands and have difficulty distinguishing narrow wetlands, canopy density, and the spectral signatures of other land cover classes [24; 30; 31]. Indices constructed from visible and infrared satellite reflectance bands highlight specific surface properties like greenness and water content [28; 29; 31]. While these indices are unable to distinguish effectively between different wetland types or vegetation species, they map the physical surface characteristics that are indicative of wetlands. We propose a combined approach where four different indices constructed from the visible and infrared surface reflectances are treated as predictors for wetlands within a logistic regression model that allows for dynamic wetland mapping. Understanding the role of the wetland vegetation in the water budget of the UZRB is crucial for evaluating the impact of changes in wetland extent on land-atmosphere interactions. It is of particular importance to evaluate how seasonal precipitation impacts the persistence of wetlands through the dry season and, in turn, how persistent wetlands support environments favorable to rainfall processes. These areas serve as an additional moisture source for vegetation undergoing photosynthesis and evapotranspiration. Thus, inter-annual and inter-seasonal variability in wetland extent have implications for both the carbon and water cycles. This manuscript aims to show how changes in wetland extent relates to local precipitation patterns and how ephemeral wetland persistence impacts the coupled water-carbon cycles.