A spatially referenced regression model (SPARROW) for suspended sediment in streams of the Conterminous U.S.

Abstract

Suspended sediment has long been recognized as an important contaminant affecting water resources. Besides its direct role in determining water clarity, bridge scour and reservoir storage, sediment serves as a vehicle for the transport of many binding contaminants, including nutrients, trace metals, semi-volatile organic compounds, a nd numerous pesticides (U.S. Environmental Protection Agency, 2000a). Recent efforts to addr ess water-quality concerns through the Total Maximum Daily Load (TMDL) process have iden tified sediment as the single most prevalent cause of impairment in the Nation’s streams a nd rivers (U.S. Environmental Protection Agency, 2000b). Moreover, sediment has been identified as a medium for the tran sport and sequestration of organic carbon, playing a potentia lly important role in understa nding sources and sinks in the global carbon budget (Stallard, 1998). A comprehensive understanding of sediment fate a nd transport is considered essential to the design and implementation of effective plans for sediment management (Osterkamp and others, 1998, U.S. General Accounting Office, 1990). An exte nsive literature addr essing the problem of quantifying sediment transport has produced a nu mber of methods for estimating its flux (see Cohn, 1995, and Robertson and Roerish, 1999, for us eful surveys). The accuracy of these methods is compromised by uncertainty in the concentration measurements and by the highly episodic nature of sediment movement, particul arly when the methods are applied to smaller basins. However, for annual or decadal flux es timates, the methods are generally reliable if calibrated with extended periods of data (Robertson and Roerish, 1999). A substantial literature also supports the Universal Soil Loss Equation (U SLE) (Soil Conservation Service, 1983), an engineering method for estimating sheet and rill erosion, although the empirical credentials of the USLE have recently been questioned (Tri mble and Crosson, 2000). Conversely, relatively little direct evidence is available concerning the fate of sediment. The common practice of quantifying sediment fate with a sediment deliv ery ratio, estimated from a simple empirical relation with upstream basin area, does not artic ulate the relative importance of individual storage sites within a basin (Wolman, 1977). Rates of sediment deposition in reservoirs and flood plains can be determined from empirical measurement s , but only a limited number of sites have been monitored, and net rates of deposition or loss from other potential sinks and sources is largely unknown (Stallard, 1998). In particular, little is known about how much sediment loss from fields ultimately makes its way to stream channels, and how much sediment is subsequently stored in or lost from th e streambed (Meade and Parker, 1985, Trimble and Crosson, 2000). This paper reports on recent progress made to a ddress empirically the question of sediment fate and transport on a national scale. The model pres ented here is based on the SPAtially Referenced Regression On Watershed attr ibutes (SPARROW) methodology, fi rst used to estimate the distribution of nutrients in str eams and rivers of the United Stat es, and subsequently shown to describe land and stream processes affecting the delivery of nutrients (Smith and others, 1997, Alexander and others, 2000, Preston and Brakeb ill, 1999). The model makes use of numerous spatial datasets, available at the national level, to explain long-term sediment water-quality conditions in major streams and rivers throughou t the United States. Sediment sources are identified using sediment erosion rates from the National Resources I nventory (NRI) (Natural Resources Conservation Service, 2000) and apportioned over the landscape according to 30- meter resolution land-use information from th e National Land Cover Data set (NLCD) (U.S. Geological Survey, 2000a). More than 76,000 reservoirs from the National Inventory of Dams (NID) (U.S. Army Corps of Engin eers, 1996) are identified as pot ential sediment sinks. Other, non-anthropogenic sources and sinks are identified using soil in formation from the State Soil Survey Geographic (STATSGO) data base (Schwarz and Alexander, 1995) and spatial coverages representing surficial rock t ype and vegetative cover. The SPA RROW model empirically relates these diverse spatial datasets to estimates of long-term, mean annual sediment flux computed from concentration and flow measurements co llected over the period 1985 -95 from more than 400 monitoring stations maintained by the Na tional Stream Quality Accounting Network (Alexander and others, 1998), the National Wa ter Quality Assessment Program, and U.S. Geological Survey District offices (Turcios and Gray, in press). Th e calibrated model is used to estimate sediment flux for over 60,000 stream segments included in the River Reach File 1 (RF1) stream network (Alexander and others, 1999). SPARROW uses statis tical methods to calibrate a simple, structural model of riverine water quality, one that imposes mass ba lance in accounting for changes in contaminant flux. As applied here, the mass-balance approach facilitates the interpretation of model results in terms of physical processes affecting sediment transport, and makes possible the estimation of various rates of sediment generation and loss associated with stream channels and features of the landscape. The statistical approach provides a basi s for assessing the error of these inferred rates and of the error in extrapolated estimates of sediment flux made for streams in the RF1 network. An important implication of the holistic modeling approach adopted in this analysis is that estimates of sediment production and loss ar e based on, and therefore consistent with, measurements of in-stream flux. Other ancillary information, such as direct measurements of long-term sediment storage and release from rese rvoirs (Steffen, 1996), is incorporated into the analysis by specifying additional equations expl aining these ancillary variables. The imposition of cross-equation constraints affords this info rmation a statistically consistent weight in explaining in-stream sediment flux. Thus, the me thodology described here represents a general framework for synthesizing a wide spectrum of available information relevant to the understanding of sediment fate and transport.