Exploring drivers of regional water-quality change using differential spatially referenced regression – A pilot study in the Chesapeake Bay watershed

An understanding of riverine water-quality dynamics in regional mixed-land use watersheds is the foundation for advances in landscape biogeochemistry and informed land management. A differential implementation of the statistical/process-based model SPAtially Referenced Regressions on Watershed attributes (SPARROW; Smith et al., https://doi.org/10.1029/97wr02171) is proposed to empirically relate a regional pattern of changes in flow-normalized constituent flux, over a multiyear period, to contemporaneous changes in spatially referenced explanatory variables. In a pilot application, the differential model, called Spatiotemporal Watershed Accumulation of Net effects (SWAN), is used to explore factors influencing changes in flow-normalized flux of total nitrogen over the period 1990–2010 at 43 sites in the nontidal Chesapeake Bay watershed. A seven-parameter model explains 80% of the transformed variability in independently estimated flux changes, indicating that storage effects having characteristic time scales greater than 20 years had a small influence, relative to changes in inputs, on regional water-quality response. Results suggest that 1990–2010 changes in total-nitrogen flux are largely the outcome of increased nonpoint-source pollution associated with urban and suburban development, modulated to the point of negation by terrestrial losses stemming from widespread increases in air temperature and precipitation. The loss mechanism is qualitatively consistent with denitrification; however, increases in aboveground biomass, agricultural nitrogen exports, or hydrologic flushing are also plausible contributors. Although qualified by a small sample size and constraints on explanatory data availability, the pilot suggests that SWAN is a promising approach for broadening scientific understanding of factors driving regional water-quality change and for supporting evidence-based land-management decisions.