Thousands of stream miles in the southern Piedmont region are impaired because of high levels of suspended sediment. It is unclear if the source is upland erosion from agricultural sources or bank erosion of historic sediment deposited in the flood plains between 1830 and 1930 when cotton farming was extensive. The objective of this study was to determine the source of high stream suspended sediment concentrations in a typical southern Piedmont watershed using sediment fingerprinting techniques. Twenty-one potential tracers were tested for their ability to discriminate between sources, conservative behavior, and lack of redundancy. Tracer concentrations were determined in potential sediment sources (forests, pastures, row crop fields, stream banks, and unpaved roads and construction sites), and suspended sediment samples collected from the stream and analyzed using mixing models. Results indicated that 137Cs and 15N were the best tracers to discriminate potential sediment sources in this watershed. The delta15N values showed distinct signatures in all the potential suspended sediment sources, and delta15N was a unique tracer to differentiate stream bank soil from upland subsurface soils, such as soil from construction sites, unpaved roads, ditches, and field gullies. Mixing models showed that about 60% of the stream suspended sediment originated from eroding stream banks, 23 to 30% from upland subsoil sources (e.g., construction sites and unpaved roads), and about 10 to 15% from pastures. The results may be applicable to other watersheds in the Piedmont depending on the extent of urbanization occurring in these watersheds. Better understanding of the sources of fine sediment has practical implications on the type of sediment control measures to be adopted. Investment of resources in improving water quality should consider the factors causing stream bank erosion and erosion from unpaved roads and construction sites to water quality impairment.

Information on the nature and relative contribution of different watershed sediment sources is recognized as a key requirement in the design and implementation of targeted management strategies for sediment control. A direct method of assessing sediment sources in a watershed that has attracted attention in recent years is sediment fingerprinting. The aim of this article is to describe the development of sediment fingerprinting as a research tool and to consider how the method might be transformed from a research tool to a management tool within a regulatory framework, with special reference to the United States total maximum daily load (TMDL) program. When compared with the current source assessment tools in developing sediment TMDLs, sediment fingerprinting offers considerable improvement as a tool for quantifying sources of sediment in terms of source type (e.g., channel vs. hillslope) as well as spatial location (subwatershed). While developing a conceptual framework for sediment TMDLs, we recognize sediment fingerprinting along with sediment budgeting and modeling as valuable tools in the TMDL process for developing justifiable sediment TMDLs. The discussions presented in this article may be considered as a first step toward streamlining the sediment fingerprinting approach for its wider application in a regulatory framework.

Abstract:Snow is an important component of the water resources of New York State and the watersheds and reservoirs of New York City (NYC) water supply. In many of the NYC water supply watersheds the hydrologic regimes of high-elevation headwaters are linked to streamflow and channel processes in low-elevation stream reaches that serve as inputs to water supply reservoirs. To better simulate this linkage there is a need to understand spatial variations in snowpack and snowmelt. Snowmelt hydrology is an important component of the Soil and Water Assessment Tool (SWAT) model in watersheds where spring runoff is strongly affected by melting snow. This study compares model simulated snowpack and snowmelt at different elevation bands with snow survey data available for the Cannonsville reservoir watershed. Simulations examine the effects of parameterising the SWAT snowmelt sub-model using 1, 3, and 5 elevation bands by comparison with measured snow and streamflow. Comparison between measured and simulated snowpack produced correlation coefficients ranging from 0Ð35 to 0Ð85. Simulations of both daily and seasonal streamflow, improved when using 3 elevation bands with r 2 of 0Ð73 and E NS of 0Ð72. Streamflow simulations showed slightly lower model performance when basin elevation was assumed to be equal to snow survey site elevation, due to the snow survey sites being somewhat biased toward lower elevations. The effect of climate change was also evaluated and showed that under higher air temperatures in future climate change scenarios, SWAT indicated more precipitation falling as rain, increased and earlier snowmelt, and a reduced snowpack leading to a change in the pattern of streamflow, particularly during winter and early spring.

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