Water resources are important to both natural ecosystems and human societies. Surface water is the most readily accessible water resource and provides an array of ecosystem services. Water stress, the ratio of water demand to supply, is a global concern as water resources are stressed by changes in climate, land cover, and population size. Understanding current and projected spatial and temporal factors of surface water dynamics is key to better managing our water resources and limiting the effects of water stress. However, few studies estimating changes in surface water account for climate and human drivers synergistically. Therefore, we compared three sets of statistical models using climate only, anthropogenic only, and the combination of climate and anthropogenic explanatory variables to assess the influence of each set of drivers on estimating surface water. We then used the most accurate model, the combination of climate and anthropogenic drivers (-0.17% average watershed mean percent error), with climate and land use projection data to project surface water areas under different climate and land use scenarios. For climate drivers, we used precipitation and temperature data from ensembles of the Inter-Comparison of Coupled Models-Phase 5 (CMIP5) Global Climate Models under three Representative Concentration Pathways (RCPs)–RCP4.5, RCP6.0, and RCP8.5. For anthropogenic drivers, we used three land use/land cover change projections from the U.S. Geological Survey’s FOR-SCE model corresponding to Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) that have RCP counterparts. Our models suggest an uneven distribution of projected change in surface water area, where watersheds with more natural land cover will experience less change (positive or negative) and watersheds with less natural land cover will experience more change. We also expect to find that, under the business-as-usual scenario, watersheds with greater urbanization will see a reduction in surface water area by 2100. These results highlight our ability to mitigate water stress with land use management and also emphasize the need to account for both climate and anthropogenic drivers when estimating and predicting surface water area.

Surface water is the most readily accessible water resource and provides an array of ecosystem services, but is stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000-2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the Southeastern U.S. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean -0.17%) and the highest explained variance (R2 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land use policies can be highly effective tools in controlling surface water variations in the Southeastern U.S.