Exploring Drivers of Regional Water-Quality Change Using Differential Spatially Referenced Regression-A Pilot Study in the Chesapeake Bay Watershed

摘要

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.Plain Language Summary Large watersheds are a mosaic of differing land types, climate, and human influences, which store and export pollutants over time scales ranging from days to centuries. Scientists' understanding of these watersheds' response to changing conditions has historically been based on complex models with many built-in assumptions. This work describes a simpler alternative, designed to link a regional pattern of observed changes in pollutant export to maps of changes in suspected causes, while keeping "rules" to a minimum. We modeled changes in the export of nitrogen, a nutrient whose excess is responsible for decline in Chesapeake Bay fisheries, from 43 bay tributaries between 1990 and 2010. Our model explained 80% of the observed changes, linking overall reductions to changes in inputs from point, agricultural, atmospheric, and urban sources, coupled with increases in temperature and precipitation. Delayed export of nitrogen input before 1990 had a smaller influence, indicating that the watershed's "memory" of degradation that occurred long ago, although evident, does not overshadow actions taken to improve regional water quality at the 20-year time scale. By providing hard evidence of the effects of past changes, this approach can better inform today's management choices and help set realistic expectations for their outcomes.