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

Abstract

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.

Changing suspended sediment in United States rivers and streams: linking sediment trends to changes in land use/cover, hydrology and climate

“…See the Supplement for additional explanation of trend methods. The efficacy of using WRTDS for estimating trends in sediment concentration and flux has been explored and discussed in Moyer et al (2012), Chanat et al (2016), and Lee et al (2016).…”

“…Using static land use conditions (either current or long-term average conditions) and recent water-quality conditions provides information about spatial variability but does not explicitly explore how temporal changes in land use/cover or other human activities affect water quality. Other studies have begun to explore the effect of temporal changes on water quality by explicitly considering land use/cover, land management and hydrologic changes over time using empirical approaches such as structural equation models (Ryberg, 2017;Ryberg et al, 2018), hybrid deterministic-empirical approaches (Chanat and Yang, 2018) or focusing on a couple of specific potential causes in a limited geographic area (Schottler et al, 2014;Panthi et al, 2017). Historically, fieldbased assessments in specific areas have been successful at identifying and supporting a causal understanding of changes in river sediment (e.g., Wolman and Schick, 1967;Trimble and Lund, 1982;Gellis et al, 1991).…”

Factors driving nutrient trends in streams of the Chesapeake Bay watershed

“…This advancement, F I G U R E 3 Estimated flow-normalized total and source specific annual fluxes of nitrogen and phosphorus to Chesapeake Bay for selected years from different models (Ator et al, 2019; among others, has contributed to a series of papers that cover a broad range of topics ranging from local-scale responses to management practices to regional interpretations of water-quality changes (Fanelli, Blomquist, & Hirsch, 2019;Oelsner & Stets, 2019;Zhang, Brady, & Ball, 2013;Zhang, Brady, Boynton, & Ball, 2015). Perhaps most important to basin-wide understanding of nutrient trends in streams, however, are the results from broad regional statistical analyses in which SPAtially Referenced Regression On Watershed attributes (SPARROW) modeling (Schwarz, Hoos, Alexander, & Smith, 2006;Smith, Schwarz, & Alexander, 1997) was extended to include a temporal component, thus providing regionally consistent insights into the spatiotemporal drivers of trends in the bay watershed (Ator, García, Schwarz, Blomquist, & Sekellick, 2019;Chanat & Yang, 2018). Although both of these models were calibrated to flow-normalized fluxes, they differ in conceptualization.…”

Depth of Solute Generation Is a Dominant Control on Concentration‐Discharge Relations

“…While annual estimates of fertilizer input in a catchment or a region is sometimes available, the spatially explicit and time‐varying description of solute sources is typically insufficient for modeling applications (Costa et al, 2017; Ilampooranan et al, 2019). Consequently, recent studies investigated the spatiotemporal variabilities of solute inputs and their impact on surface water quality (Böhlke et al, 2002; Chanat & Yang, 2018; Garnier et al, 2018; Kennedy et al, 2018; McCrackin et al, 2017; McInerney et al, 2018; Zimmer et al, 2019). In some cases, inverse modeling approaches for solute input estimation are suggested (Goyette et al, 2019; Luscz et al, 2017; Miller et al, 2017).…”