Sediment Source Fingerprinting: Transforming From a Research Tool ...

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION Vol. 48, No. 6

AMERICAN WATER RESOURCES ASSOCIATION

December 2012

SEDIMENT SOURCE FINGERPRINTING: TRANSFORMING FROM A RESEARCH TOOL TO A MANAGEMENT TOOL1

Rajith Mukundan, Desmond E. Walling, Allen C. Gellis, Michael C. Slattery, and David E. Radcliffe2

ABSTRACT: 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. (KEY TERMS: sediment TMDLs: watersheds: sediment sources: water-quality standards.) Mukundan, Rajith, Desmond E. Walling, Allen C. Gellis, Michael C. Slattery, and David E. Radcliffe, 2012. Sediment Source Fingerprinting: Transforming from a Research Tool to a Management Tool. Journal of the American Water Resources Association (JAWRA) 48(6): 1241-1257. DOI: 10.1111 ⁄ j.1752-1688.2012.00685.x

INTRODUCTION

Fluvial sediment can contribute substantially to habitat degradation, reduction in reservoir-storage capacity, and impairment of water quality (Walling, 2009; Larsen et al., 2010; Kemp et al., 2011). As a result, sediment is typically listed as one of the top three pollutants in the United States (U.S.) (USEPA, 2009). In North America, the physical, chemical, and

biological damage attributable to fluvial sediment has been estimated to range from $20 to $50 billion annually (Pimentel et al., 1995; Osterkamp, 2004). Soil erosion resulting from poor land management is a threat to sustainable agricultural production and global food security (Montgomery, 2007) and the sediment mobilized by erosion can have wide-ranging offsite impacts as it is transported from a field to the stream network and through the river system. Although estimates of sediment yield based on

1 Paper No. JAWRA-11-0115-P of the Journal of the American Water Resources Association (JAWRA). Received September 26, 2011; accepted July 10, 2012. ª 2012 American Water Resources Association. Discussions are open until six months from print publication. 2 Respectively, Research Associate, Institute for Sustainable Cities, City University of New York, 695 Park Avenue, New York, New York 10065; Emeritus Professor, Geography, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, United Kingdom; Research Hydrologist-Geomorphologist, United States Geological Survey, Water Science Center, Baltimore, Maryland 21228; Professor, School of Geology, Energy, and the Environment and the Institute for Environmental Studies, Texas Christian University, Fort Worth, Texas 76129; and Professor, Crop and Soil Sciences Department, University of Georgia, Athens, Georgia 30602 (E-Mail ⁄ Mukundan: [email protected]).

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MUKUNDAN, WALLING, GELLIS, SLATTERY, stream monitoring are available for many rivers in the U.S. and elsewhere, little information is available on the primary source of the sediment in many river basins. The importance of reliable information on the nature and relative contribution of different watershed sediment sources is increasingly recognized (Collins and Walling, 2004). Such information is a key requirement for establishing a watershed sediment budget, developing meaningful watershedscale models of suspended sediment yield, and, more importantly, in the design and implementation of targeted management strategies for sediment control. If sediment fluxes from a watershed are to be reduced, management strategies must target the primary sediment sources within that watershed. The sediment yield at the outlet of a watershed reflects a complex suite of geomorphic processes, including hillslope erosion, sediment delivery to stream channels, sediment storage on colluvial slopes and floodplains, stream channel erosion, and the magnitude of storage or conveyance losses within the system (Figure 1). Quantification of sediment sources is further compounded by spatial and temporal variability in the interaction between the factors that govern sediment yield. This spatial and temporal variation can be related to natural factors, such as climate variability and change, or anthropogenic factors, such as land use. Indirect methods of sediment source assessment, based on documenting the intensity of different erosion processes and thus an assess-

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ment of the likely importance of the related sources, include field observations, use of aerial photography, and use of erosion plots for surface erosion monitoring and erosion pins for streambank erosion monitoring. Collins and Walling (2004) and Walling (2005) discuss in detail the many problems associated with these traditional indirect methods of assessing sediment sources in a watershed, and point to the need for alternative approaches. A direct method of assessing sediment sources in a watershed that has been developed in recent years is sediment source fingerprinting. The technique is based on two assumptions (Walling et al., 1999): 1. The various potential sediment sources within a watershed can be discriminated by a number of different diagnostic physical and chemical properties of the source materials. 2. Comparing the properties of suspended sediment with those of potential source materials permits the relative importance of each individual source to be assessed. Although the sediment source fingerprinting technique has been successfully used worldwide as a research tool, with the exception of Gellis and Walling (2011), who proposed that investigations into sediment sources should be the first step in any program to reduce sediment, there are very few reported applications of this technique as a watershed sediment management tool within a regulatory framework. The aim of this article is to consider how sediment fingerprinting might be transformed from a research tool to a management tool. The specific objectives of this study are to: 1. Provide a brief overview of the sediment source fingerprinting approach, its development, and its current status; 2. Develop a conceptual framework for applying sediment source fingerprinting in a regulatory framework, with special reference to the U.S. total maximum daily load (TMDL) program; and 3. Discuss the potential challenges and possible solutions while incorporating the approach in regional TMDL plans.

THE SEDIMENT SOURCE FINGERPRINTING APPROACH AND ITS CURRENT STATUS

FIGURE 1. Components of Watershed Sediment Generation and Export (modified from USEPA, 1999).

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The use of the fingerprinting approach to provide information on the source of the suspended sediment 1242

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SEDIMENT SOURCE FINGERPRINTING: TRANSFORMING transported by a stream or river can be traced back to the 1970s and the work of researchers such as Klages and Hsieh (1975), Wall and Wilding (1976), and Walling et al. (1979). In these three studies, mineralogy, mineralogy plus geochemical properties, and mineral magnetic properties, respectively, were used as the fingerprints to discriminate potential sources and to establish the likely source of the suspended sediment output from the study watersheds. In the first study, source was defined in terms of spatial location and more particularly the tributary catchments that represented the most important sediment sources. In contrast, in the latter two studies, source was defined in terms of source type and a distinction was made between surficial and subsurface or channel bank sources. The sources were only broadly defined and the assessment of the importance or relative contribution of the potential source was essentially qualitative, pointing to the source that was most likely to be the dominant source or to changes in the relative importance of a particular source during storm events. Since these early sediment source fingerprinting studies, most work has focused on discriminating source types, rather than spatial sources. Information on the latter can be obtained by measuring and comparing the sediment loads of individual tributaries, but information on source type is difficult to obtain using other approaches. As the potential shown by these early studies has been further explored and developed, a number of important methodological advances have occurred, which have greatly extended the utility and potential of the sediment fingerprinting approach. In reviewing these advances, seven key areas can be identified. These relate to: 1. The use of multiple properties or composite fingerprints, involving a wide range of different physical and chemical measurements, to improve the discrimination between different sources and to permit a greater number of potential sources to be identified; 2. The incorporation of statistical tests to confirm the ability of particular fingerprint properties to discriminate between potential sediment sources and to assist in the selection of the ‘‘best’’ composite fingerprint; 3. The use of numerical mixing (or unmixing) models to provide quantitative assessments of the relative contribution of different potential sources; 4. Testing for conservative behavior and incorporation of grain size and organic matter enrichment ⁄ depletion effects into the mixing models or related procedures used for source apportionment; JOURNAL

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5. Extension of the approach to consider an increased range of surrogates for watersheddelivered sediment ‘‘targets,’’ in addition to discrete samples of suspended sediment; 6. Extension of the approach to incorporate a temporal dimension and to consider changes in sediment source through time; and 7. Taking account of the uncertainty associated with source apportionment procedures. Further details of these developments are provided below.

Composite Fingerprints for Discriminating Multiple Sediment Sources Although some early studies made use of a single sediment property as a fingerprint, subsequent work has emphasized that use of a composite fingerprint involving a combination of several properties will provide better discrimination between several potential sources and therefore provide more reliable estimates of the relative contribution of the potential sources. A wide range of soil and sediment properties have since been used to fingerprint potential sources, and in addition to mineralogy, geochemistry and mineral magnetic measurements (Walden et al.,1997; Slattery et al., 2000), color (Grimshaw and Lewin, 1980; Martı´nez-Carreras et al., 2010a), isotopic signatures (Douglas et al., 1995, 2003; Fox and Papanicolaou, 2007; Gellis et al., 2009), fallout radionuclides (Walling and Woodward, 1992; Olley et al., 1993; He and Owens, 1995; Wallbrink et al., 1998, 1999), plant pollen (Brown, 1985), and properties of the organic fraction (Papanicolaou et al., 2003) have been used. Fallout radionuclides have proved particularly useful in providing discrimination between surface and subsurface sources and more particularly between topsoil from cultivated and noncultivated soils (Walling and Woodward, 1992; Wallbrink et al., 1998). It has frequently been suggested that discrimination between a range of sources can be improved if a composite fingerprint includes properties drawn from different property subsets that respond to different environmental controls. Recent work in testing and introducing novel fingerprint properties has proved particularly important in providing increased scope for source discrimination and in reducing analytical demands. In the first context, the use of compoundspecific stable isotopes (e.g., Gibbs, 2008) has provided a very valuable basis for discriminating sediment mobilized from areas supporting different vegetation types or crops. In the second context, the use of diffuse spectral reflectance measurements have been shown by Poulenard et al. (2009) and 1243

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MUKUNDAN, WALLING, GELLIS, SLATTERY, Martı´nez-Carreras et al. (2010a) to offer a rapid means of characterizing and fingerprinting potential sources, both directly, by means of color coefficients and spectra parameters, and indirectly, by using the reflectance spectra to generate estimates of specific geochemical properties that are subsequently used as fingerprint properties. Spectral reflectance measurements can be undertaken on the small amounts of sediment contained on a filter paper and can thus avoid the need to collect large samples. More generally, both near-infrared and mid-infrared spectroscopy techniques would appear to offer considerable potential for rapidly characterizing source materials and inferring geochemical properties. As the range of different fingerprint properties that have been successfully used for source discrimination and apportionment has increased, so too has the range of potential sources that can be considered. Whereas, in many early studies, only a simple distinction between surface and subsurface or channel sources was made, more recent studies have incorporated as many as five potential sources, including, for example, stream channels, topsoil from cultivated and pasture areas (Walling et al., 2008), unmetalled roads and tracks (Gruszowski et al., 2003; Motha et al., 2004; Collins et al., 2010b), construction sites (Gellis et al., 2009; Mukundan et al., 2010), and damaged road verges (Collins et al., 2010c). The vast range of fingerprint properties that has been used by individual studies and the essentially site-specific nature of most studies could be seen as a problem in terms of developing standardized procedures. In many studies, a large number of properties are initially analyzed, with their selection often being determined by the available analytical equipment. The best properties for source discrimination at that location are then identified empirically using statistical procedures to test for discrimination and select the final composite fingerprint. Few attempts have yet been made to develop general guidance for selecting appropriate fingerprint properties.

Statistical Tests of Ability to Discriminate Among Sources As indicated above, the set of sediment properties to be included in a composite fingerprint is frequently selected empirically by testing the discrimination of the potential sources afforded by a wide range of properties, identifying those that perform well, and selecting a subset of these that together provide a high level of discrimination. The application of statistical techniques, including the use of the Mann-Whitney U-test (Carter et al., 2003; Porto et al., 2005), the Kruskal-Wallis H-test (Collins et al., 1998, 2001; JAWRA

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Walling et al., 1999), the Wilcoxon rank-sum test (Juracek and Ziegler, 2009), and the Tukey test (Motha et al., 2003) to identify properties providing good discrimination is often followed by the use of discriminant function analysis and other classification techniques to select optimum combinations of those properties (Walling and Woodward, 1995; Collins et al., 1998). These statistical tests have greatly increased the rigor of property selection by confirming the ability of the properties selected to discriminate the potential sources. Further work is nevertheless arguably required to explore further the potential for using nonparametric statistics, where the requirements of parametric statistical methods are not met.

Mixing Models for Relative Sediment Source Apportionment The use of numerical mixing (or unmixing) models and related techniques to provide quantitative estimates of the relative magnitude of the contributions of the potential sources to a sediment sample represented a key advance in sediment source fingerprinting. In most cases, use of a substantial number of fingerprint properties, in combination with several potential sources, means that the model is overparameterized and it is necessary to use an optimization procedure to derive the estimates of the source contributions (e.g., Yu and Oldfield, 1989; Collins et al., 1997; Krause et al., 2003). These procedures are commonly based on minimization of the difference between the recorded property values associated with the target sediment sample and those predicted by the mixing model using a given set of relative source contributions. In this situation, it is important that the goodness of fit of the mixing model should be objectively tested to ensure that the result obtained is meaningful, rather than simply providing a mechanistic forced fit that is characterized by poor agreement between observed and predicted property values. The mixing models have been modified by some workers to include the use of weightings for the individual properties included in the model, based, for example, on their measurement precision, their variability for a given source or their discriminatory power, and the inclusion of prior information to restrict the potential range of the optimized source contributions (Collins et al., 1998, 2010a). Collins et al. (2010c) have also recently demonstrated that the use of alternative optimization procedures, including local and global genetic algorithm (GA) routines, may produce different results. Bayesian approaches have also been applied to provide mixing model solutions (Fox and Papanicolaou, 2008b; 1244

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SEDIMENT SOURCE FINGERPRINTING: TRANSFORMING Palmer and Douglas, 2008) and, as indicated below, these also possess advantages in terms of providing a basis for incorporating uncertainty into the results. Alternatives to the numerical mixing model include the approach described by Poulenard et al. (2009) where artificial mixtures representing a range of different source contributions were produced and these were used to establish partial least squares regression models, which were used to generate relationships between spectral reflectance parameters and the relative contribution of the different potential sources, which could then be used to derive an estimate of the source contribution proportions, directly from the spectral reflectance spectrum.

Conservative Behavior of Tracers The use of sediment source fingerprinting techniques necessarily assumes that the tracer properties used behave conservatively within the fluvial system and also that the properties of source material and sediment samples can be directly compared. It is often difficult to confirm the conservative behavior of tracers directly, but, in many studies, a simple range test, based on confirming that the property values used to characterize the target sediment fall within the range of the property values associated with the potential sources, has been introduced to identify fingerprint properties that are not conservative. Enrichment and depletion effects associated with the grainsize composition and organic matter content caused by selective mobilization and transport can be viewed as a form of nonconservative behavior. This effect can introduce important problems when comparing sediment and source material properties. Sediment mobilized from a given source, which is enriched in fines or organic material due to selective erosion or selective deposition during transport, is likely to have properties that differ from those of its source. In this situation, the fundamental assumption that the properties of sediment can be compared directly with those of potential source material, in order to determine its source, will clearly be violated. A number of different approaches have been employed to deal with this potential problem. These include, first, processing both source material and target samples to focus on the same grain-size fraction, and, secondly, use of correction factors to take account of contrasts in organic matter content and grain-size composition. In the first case, a range of size fractions including the