Concentrated Animal Feeding Operations (CAFOs) apply massive amounts of untreated waste to nearby farmlands, with severe environmental health impacts of swine CAFOs and proximity to disadvantaged communities well documented in some US regions. Most studies documenting the impacts of CAFOs rely almost exclusively on CAFO locations known from incomplete public records. Poultry CAFOs generate dry waste and operate without federal permits; thus, their environmental justice (EJ) impacts are undocumented. North Carolina (NC), a leading poultry producer, has seen a significant increase in poultry CAFOs, particularly since the 1997 swine CAFO moratorium. Using literature-derived heuristics, this study refined the locations of poultry CAFOs derived based on Earth Observation (EO) data and deep learning, reducing the overestimation of poultry CAFO density by 54% after heuristic adjustments. We removed 51.8% of misclassified features in NC and 61.5% across the US, significantly improving dataset accuracy. Spatial analysis, including Moran’s I and Local Indicators of Spatial Association, revealed that poultry CAFOs often cluster in census tracts with high Social Vulnerability Index (SVI) scores, indicating potential EJ issues. Notably, one-third of NC’s census tracts with high poultry CAFO density also have high SVI, primarily in rural eastern regions. Similar patterns were observed in the South and Southeast of the US. However, not all high-density CAFO areas correspond with high SVI, suggesting a complex relationship between CAFO locations and community vulnerabilities. This study highlights the critical need for comprehensive, high-quality data on unpermitted poultry CAFOs to fully understand their impacts on communities and accurately inform EJ evaluations.

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.

Satellite-based as well as reanalysis-based datasets are widely available and useful in detecting spatial and temporal variability of rainfall at a finer resolution. These products have been widely used in weather forecasting, hydrological and climate studies. However, the accuracy of these satellite products varies spatially and across different datasets. In this study, the accuracy of five satellite-based precipitation products with different spatial resolutions i.e., CHIRPS, ERA5, TRMM, GPM, and Terra Clim available on Google Earth Engine (GEE) were compared with India Meteorological Department (IMD) grided data in six climate zones in India. The statistics such as RMSE, R 2, MSE, and PBIAS were computed. It was observed that the performance of each product varies in different climatic zones. The GPM was observed to have high accuracy in arid, semi-arid, and tropical wet zones. TRMM showed a good match in tropical wet & dry, tropical wet, and semi-arid zones. Terra Clim and ERA5 showed high accuracy in humid subtropical and montane regions respectively. It was also observed that CHRIPS was found to be least suitable in all the climate zones across India. The findings from the present studies will serve as a guiding document for the researcher to select appropriate datasets for different applications such as drought monitoring, precipitation anomaly, hydrological models, or other related studies in India.