Assessing the Impacts of Multi-spatial Scales of Annual Landscape Dynamics on Nitrate-Nitrogen Fluxes in the Soil Profile in Southeastern Catchments

The study aims for a multi-spatial impact assessment of different landscapes on nitrate-nitrogen fluxes (NNF) in the soil profile for the Southeastern United States. The landscapes include waterbody, urbanization, forests, agriculture, and barren land. The spatial scales considered include catchment units, geologic influence units, and hydrologic influence units. We implemented Soil and Water Assessment Tool (SWAT) and Soil and Water Assessment Tool Calibration Uncertainty Program (SWAT-CUP) models in the soil profile of 0-1000 mm in three catchments of the Southeastern United States, namely Fish River (Alabama), Tampa Bay (Florida), and Winyah Bay (South Carolina). The statistical modeling techniques of ANOVA, Pearson r correlation, and minimum-maximum standardization were employed to evaluate multiscale correlations of NNF to different landscapes. The study estimated the annual nitrate loads contributed by surface runoff (68%), lateral flow (24%), and percolation (8%) averaged between 2000 and 2014 and pointed to rising NNF estimates at all the spatial units with annual landscape dynamics. The study results indicated that the correlations of NNF to forests and urbanization were significantly high at geologic influence units (r = 0.58-0.63, p < 0.001) and hydrologic influence units (r = 0.44-0.56, p < 0.001), and less pronounced at catchment units (r = 0.34-0.39, p < 0.01). On the other hand, the water bodies showed statistically significant associations with NNF at the catchment scale (r = 0.46, p < 0.001), while geological influence units (r = 0.26, p < 0.01), and hydrologic influence units (r = 0.28, p < 0.01) showed less significant associations. However, there exists uncertainty in spatial predictions for barren land (r = 0.11, 0 < p < 0.1). These uncertainties and differences in associations to NNF at various spatial scales are primarily due to the statistical bias, namely the modifiable areal unit problem. The study results will provide useful information for effective monitoring and management of nutrient pollution and water quality modeling in catchments of the Southeastern United States and comparable watersheds in other geographical regions.