- Email: [email protected]
A weight-of-evidence approach to integrate suspended sediment source information
Journal of Environmental Management 128 (2013) 182e191
Contents lists available at SciVerse ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
A weight-of-evidence approach to integrate suspended sediment source information B. Fu a, *, L.T.H. Newham a, J.B. Field a, O. Vigiak b a
Fenner School of Environment and Society, The Australian National University, Canberra, ACT 0200, Australia Department of Primary Industries, Future Farming Systems Research Division, Rutherglen Centre, RMB 1145 Chiltern Valley Road, Rutherglen, VIC 3685, Australia b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 October 2011 Received in revised form 15 April 2013 Accepted 8 May 2013 Available online
Sediment monitoring, tracing and modelling are widely used to identify suspended sediment sources. Although each method has inherent limitations and uncertainties, their integration provides opportunities to form collective knowledge and encourages robust management strategies. This paper presents a Weight-of-Evidence approach to integrate multiple Lines-of-Evidence for identifying suspended sediment sources. Three sources of evidence were used: i) stream flow and suspended sediment monitoring at river gauges; ii) geochemical sediment tracing at river junctions; and iii) catchment-scale suspended sediment modelling of hillslope, gully, streambank and unsealed road erosion. We applied this approach on two data-poor catchments in Australia. Some reaches were consistently identified as major sources of sediment from all Lines-of-Evidence. However, inconsistencies between the types of evidence in other areas highlighted the high uncertainty in identifying suspended sediment sources in these areas and the need for further investigation. The integration framework maximised the use of scarce information, enabled explicit consideration of uncertainties for catchment management and identified where future monitoring and research should be targeted. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Sediment sources Weight-of-Evidence Integration Sediment modelling Sediment tracing
1. Introduction Water quality is important for the health of rivers and estuarine ecosystems. Water quality decline resulting from excessive suspended sediment loads and sediment-derived contaminants is a world-wide problem and has attracted much scientific and public interest (Collins and Owens, 2006; McCulloch et al., 2003). Identifying sources of suspended sediment enables catchment managers to prioritise remediation efforts. However, this task can be challenging at catchment scales due to the complex processes of sediment mobilisation, transport and deposition. Methods for identifying sediment sources can be broadly classified into three types: monitoring (Jansson, 2002), tracing (Nagle et al., 2007; Walling, 2005), and modelling (Fu et al., 2010; Merritt et al., 2003). Each method has its own data requirements, is applied under different assumptions and constraints, and has inherent limitations and uncertainties. Integrating information
* Corresponding author. Tel.: þ61 2 6125 0850; fax: þ61 2 6125 8395. E-mail address: [email protected] (B. Fu). 0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.05.005
gathered with different methods, however, provides opportunities to form collective knowledge and reduce the impact of each method’s limitations on final evaluations (Malhotra et al., 1999). From a management perspective, integration encourages the development of transparent and robust strategies whilst recognising method limits and uncertainties, and helps reduce the risk of management actions taken based on a single information type, especially when that information is derived from limited data. Several studies have integrated different information to investigate suspended sediment sources. Some use heterogeneous information derived from a single method. For example, many sediment models adopt a modular structure using submodels to identify sediment sources from different erosion and landuse types (Flanagan and Nearing, 1995; Kinsey-Henderson et al., 2005; Prosser et al., 2001). Information integration has also been achieved by combining multiple methods, such as monitoring and tracing (Croke et al., 1999; Murray et al., 1993; Walling et al., 2001), tracing and modelling (Newham et al., 2001), and monitoring, tracing and modelling (Hateley et al., 2007; Rustomji et al., 2008). Although information integration is not a new concept in identifying suspended sediment sources, the integration is usually qualitative. No
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
clear guidance is available for selecting appropriate suspended sediment source methods and for systematic synthesis of available information. An approach that could provide a framework for integrating information to identify suspended sediment sources is the Weight-ofEvidence approach which has been reviewed by Weed (2005). This approach has been used in environmental assessments and involves the combining of multiple Lines-of-Evidence to assess an environmental system (Burton et al., 2002). For example, a typical sediment quality assessment often involves three major Lines-of-Evidence: sediment chemistry, laboratory toxicity, and biota community alteration information (Chapman, 1996). By combining the information from multiple Lines-of-Evidence, more confident conclusions on sediment quality and ecosystem impairment can be achieved. Linesof-Evidence can be combined qualitatively, or quantitatively by attributing weights to the Lines-of-Evidence which reflect the importance of and/or confidence in the information provided (Suter and Cormier, 2011). The Weight-of-Evidence approach may require subjective decisions or qualitative information (e.g. expert opinion) (Chapman et al., 2002). The main advantage of the Weight-of-Evidence approach is that it provides a systematic framework for utilising all available information and identifying data gaps, which is especially useful in data-poor situations. This paper presents a Weight-of-Evidence approach for identifying suspended sediment sources. In the following section we summarise three types of information integration that have been used in suspended sediment sourcing studies. Next, we propose a framework for integrating multiple Lines-of-Evidence using the Weight-of-Evidence approach for suspended sediment sourcing. This integration framework is then applied at two data-poor catchments in Australia. Finally, we discuss the advantages and limitations of the integration framework and make recommendations for improvements. 2. Types of information integration Three types of information integration can be summarised from the literature: consolidation, confirmation and verification (Fig. 1). Consolidation unites heterogeneous information. Different Lines-of-Evidence provide additional information forming collective knowledge that is more complete than a single Line-ofEvidence. The assumption is that each Line-of-Evidence provides accurate (termed as “true”) information. An example is given by Walling et al. (2001), who integrated monitoring and tracing methods to identify suspended sediment sources for a small agricultural catchment in Zambia. River monitoring was used to establish sediment flux and yield data at the catchment outlet; 137 Cs was used to measure the rates of soil erosion, deposition and floodplain accumulation; and multiple tracers were used to identify the proportional contributions of suspended sediment sources. A detailed suspended sediment budget of soil erosion, transport and deposition within the catchment was obtained by uniting these three types of information (Walling et al., 2001). Confirmation intersects heterogeneous information. Different Lines-of-Evidence reach the same conclusion and thus increase the overall certainty in the study outcomes. Confirmation is common when different Lines-of-Evidence give the same type of results. In this type of integration, the uncertainties are acknowledged in every Line-of-Evidence, and none of them alone is considered sufficient to reach a sound conclusion. For example, Newham et al. (2001) compared the results of Sediment River Network Model (SedNet) with sediment tracing in the upper Murrumbidgee catchment, Australia. In finding general agreement between the two Lines-of-Evidence, the authors argued that management recommendations could be formulated with greater confidence.
Consolidation
Sediment sources
183
Sediment sources
Integrated Information
Information Information A B
Confirmation
Sediment sources
Sediment sources
Integrated Information
Information Information B A
Verification
Sediment sources
Sediment sources
Information Information A B
Integrated information
Fig. 1. Diagrams showing the conceptual frameworks for three types of information integration: consolidation, confirmation and verification. In consolidation, additional information on sediment sources is added. In confirmation, the sediment sources are confirmed by two independent information (A and B, darker colour indicates higher confidence). In verification, the third configuration of Information B is supported by independent Information A.
Verification tests heterogeneous information. In this type of integration, the result from at least one Line-of-Evidence is assumed “true” and is used as a reference to test or calibrate other Lines-of-Evidence. For example, Rustomji et al. (2008) used stream monitoring and sediment tracing to test different model configurations. In their study, information from stream monitoring and sediment tracing was considered “true” and the best model configuration to match this information was recommended and used for prediction (Rustomji et al., 2008). The three types of integration can be used in conjunction. For example, Murray et al. (1993) investigated suspended sediment sources in the Murrumbidgee River of Australia using information from monitoring (aerial photographs, suspended sediment and discharge data, turbidity records) and tracing (137Cs and excess 210 Pb). Event-based suspended sediment and historical turbidity data (from separate sources) both suggested the upper catchment as the dominant sediment source during flood events (confirmation), while sediment tracing suggested top soil as the major source. Event-based monitoring and sediment tracing provided evidence that during flood events suspended sediment mainly came from top soil in the upper catchment (consolidation).
3. An integration framework for identifying suspended sediment sources The Weight-of-Evidence approach is adapted here to develop a suitable framework to integrate the information on suspended sediment sources from multiple Lines-of-Evidence (Fig. 2). The framework involves the study design, obtaining Lines-of-Evidence and information integration. As there are limitations and uncertainties within each Line-of-Evidence, information consolidation is not used; rather, the Weight-of-Evidence approach is analysed using the confirmation type of integration.
184
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
Define study purpose
Identify prior knowledge and resource availability
Study design
C ¼
Define study strategy
Obtaining LOEs
standardised to account for differences in their outputs. Then, weights are assigned to individual Lines-of-Evidence based on their reliability and importance to the research question. Finally, the Linesof-Evidence are combined with consideration of their weights: n X i¼1
LOE 1
LOE 2
LOE 3
(e.g. monitoring)
(e.g. tracing)
(e.g. modelling)
E i Wi =
n X
! Wi
(1)
i¼1
Here C denotes the Weight-of-Evidence results on sediment contribution; Ei is the sediment contribution of the source according to Line-of-Evidence i; Wi is the weight assigned to Line-ofEvidence i; and n is the number of Lines-of-Evidence. 4. Application of the integration framework to case studies
Information integration
Confirmation (Weight-of-Evidence approach)
Weight-of-Evidence results
Fig. 2. A framework for integrating multiple Lines-of-Evidence (LOE) to identify sediment sources.
3.1. The study design The study design involves establishing a strategy based on the purpose of the study and available resources. In shaping the study design the following questions require consideration: Is information on the relative contribution of suspended sediment sources or the volume of suspended sediment yields required? Is information on the spatial distribution of suspended sediment sources required? Is information on the temporal variability of the suspended sediment sources required? What is the spatial extent of the study? The content and scale of the study are defined in this step, and the Lines-of-Evidence are identified. A carefully designed strategy is crucial to the effective use of Lines-of-Evidence in achieving the study objective. This requires attention to several aspects of the design, such as: identifying timing and locations of suspended sediment sampling for monitoring and tracing; selection of suitable tracers; and selection of models at an appropriate scale and commensurate to data availability. 3.2. Obtaining Lines-of-Evidence The second step involves identifying the characteristics of each Line-of-Evidence and Lines-of-Evidence implementation. The characteristics of Line-of-Evidence include assumptions, scale and accuracy of measurements, data interpolation, and method limitations. For example, spatial and temporal dimensions of Line-ofEvidence can be defined based on a “scale triplet” of spacing (i.e. the distance or time between samples), extent (i.e. the overall coverage of the data in space or time), and support (i.e. the averaging volume or area or time of the samples) (Blöschl and Grayson, 2001). The combination of these characteristics forms the basis to assess the reliability of the Lines-of-Evidence and their relative importance to the research question. 3.3. Information integration The Lines-of-Evidence are integrated based on the Weight-ofEvidence approach. First, the information from Lines-of-Evidence is
4.1. The case study catchments The Moruya-Deua (1500 km2) and Tuross (2100 km2) River catchments are located on the southeast coast of Australia (Fig. 3). The climate is warm temperate with coastal influences. The annual average rainfall ranges from approximately 700e1100 mm increasing from west to east. Much of the catchments consist of early Ordovician metasedimentary rocks, covering about 45% in each catchment. The rest of the catchments contain sedimentary rocks of younger ages and plutonic rocks. Basalt is limited in both catchments, with a scattered and disparate distribution pattern in coastal regions. Areas of steep terrain remain forested. Over 70% of the forests are dominated by native Eucalyptus and Mimosacae forests. Most of the flat regions have been developed into urban townships or agricultural villages for grazing and cropping. There are approximately 1000 km of unsealed roads in the Moruya-Deua catchment and 2000 km in the Tuross River catchment. Water quality in the catchments is a concern for local communities and industries (Beeton et al., 2006; Drewry et al., 2009). The catchments provide drinking water supplies for local towns and villages. Maintaining good water quality is also vital for the local industries such as dairying and oyster farming, and the estuaries and marine ecosystems. However, in both catchments little is known about sediment sources at the catchment scale, and cost-effective actions on erosion control are difficult to identify and justify. 4.2. The study design 4.2.1. Aim The aim of the case studies was to inform catchment managers on priority sites for erosion control and ongoing monitoring. Thus, this study focused on identifying the spatial distribution and relative contribution of suspended sediment sources. 4.2.2. Prior knowledge and data availability The study area is relatively data-poor. Basic landscape data including 25 m-resolution Digital Elevation Model and geological mapping were available, but very little data were available for identifying suspended sediment sources. Historical aerial photographs were available at scales of 1:23,270 to 1:81,700 although these proved too coarse for estimating gully head cut retreat, gully wall erosion, and stream migration rates in the region. Likewise, resolution of available remote sensing data was too coarse spatially and/or temporally to identify sediment sources in the study catchments (Vrieling, 2006). Previous water quality studies in the Moruya-Deua and Tuross River catchments focused on the estuary conditions (Haines and Cavanagh, 2002). An exception was the basin-scale sediment budget study as part of the Australian National Land and Water Resources Audit, which reported over 70% of sediment to come
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
185
Fig. 3. Location of the study catchments, sampled junctions (J1eJ5) for sediment tracing and the gauges, and a schematic diagram showing sampling sites at each junction.
G auge 217002 (D eua at W am ban)
20,000
G auge 218008 (Tuross at Eurobodalla)
16,000
4.2.3. Study strategy Three Lines-of-Evidence were selected for this study: stream monitoring at river gauges, sediment tracing at river junctions, and
Sediment (mg/L)
24,000
sediment concentration data was collected in 2005 covering three events at gauge 217002 (Moruya-Deua River catchment) and gauge 218008 (Tuross River catchment) (Fig. 3) (Drewry et al., 2005, 2009).
12,000 8,000
150 100
4,000 0 1 /J u n
1 /J u l
1 /Aug
1 /Sep 1 /O c t
G auge 217002
200
Sediment (mg/L)
M ean discharge (M L/day)
from gully erosion (Marston et al., 2001). However, the model used for the audit was not verified by local data. Our field reconnaissance suggested that gully density was low in the Tuross River catchment, whereas significant streambank erosion was observed in the lower Tuross River. Thus further investigation of sediment sources was warranted. Monitoring of suspended sediment concentration in the catchments was spatially and temporally sparse. Event-based suspended
40 0
0 10000 20000 30000 15 min discharge (ML) G auge 218008
80 60 40 20 0
0
5000 10000 15 min discharge (ML)
1 /Nov 1 /D e c
Fig. 4. Examples of suspended sediment concentrations and discharge ratio graphs, showing daily mean discharge and the ratio graphs for the event in November 2005.
186
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
catchment-scale sediment modelling. These Lines-of-Evidence were chosen based on data availability at the time of the study. 4.3. Obtaining Lines-of-Evidence 4.3.1. Line-of-Evidence 1: sediment monitoring Event-based suspended sediment concentration and discharge data were obtained from the studies of Drewry et al. (2005, 2009). We used relationships between the suspended sediment concentrations (SSC) and discharge (Q) to identify the likely locations and types of major suspended sediment sources (Lefrançois et al., 2007; Williams, 1989). The SSCeQ ratio graphs (or sediment hysteresis loops), exhibited clockwise loops, most with a depletion of suspended sediment occurring on the rising stage of the hydrograph (Fig. 4). After the first depletion, suspended sediment concentrations rose again with the increase of discharge until suspended sediment was exhausted, and the concentrations decreased again before the peak of discharge. The behaviour of “double depletion” in the SSCeQ ratio graphs may indicate primary (instant) and secondary sources of suspended sediment during most events for both catchments. The instant source of suspended sediment is directly available during the early phase of the event, and could be deposited suspended sediment in the river or loose surface material from streambanks near the gauges. The instant source exhausts quickly after the rise of discharge, at which time the suspended sediment from the secondary source has not yet arrived. The secondary sources of suspended sediment may be derived from the middle reaches of the catchments, such as near-stream hillslope and road erosion. The critical information from analysis of the SSC-Q ratio graphs is that i) the suspended sediment in the catchments is supply-limited; ii) the major suspended sediment sources are likely to be deposited sediment in the rivers and/or streambank erosion as instant sources; and iii) hillslope and/or road erosion near the stream channels in the lower catchments may be secondary sources. The above qualitative information was translated into the index of a site delivering suspended sediment to gauges 217002 and 218008. This index was expressed spatially using upstream distance from the gauges e the shorter the distance, i.e. the closer the location to the stream and the gauge, the higher the potential of suspended sediment delivered to the stream and the higher the index value. The method is similar to the index-based approach for predicting risk of nutrient loss (Heathwaite et al., 2000; Sharpley et al., 2003). 4.3.2. Line-of-Evidence 2: sediment tracing Geochemical and mineralogical tracers were used to investigate the origins of sediments. These tracers were used because: i) geochemical and mineralogical differences have been identified for sediment in eastern Australia (Bhatia and Crook, 1986; White and Chappell, 1977); and ii) the concentrations of suspended sediment were too low to have sufficient samples for radioisotope analysis. Sediments were collected from the top 0.5 cm of deposits in active stream channels and floodplains at five stream junctions (J1eJ5 in Fig. 3). At each junction three samples were collected, one in the upstream main reach (A), one in the upstream tributary reach (B) and one in the downstream reach (C) (Fig. 3). Sites A and B were located approximately 1 km upstream of Site C. It was assumed that sediment samples from Sites A and B at each junction represented the sediment eroded and transported from the upstream subcatchments (denoted Subcatchments A and B respectively). Four to six replicate samples were collected at each site (with an extent of about 0.01 km2). Wet sieving was used to extract the fraction of the sediment less than 63 mm. The samples were then treated with 30% (w/w)
hydrogen peroxide to remove organic matter. The X-ray diffraction technique was used to analyse the bulk (<63 mm) and clay (<2 mm) mineralogy. An Inductively Coupled Plasma e Atomic Emission Spectrometer was used to quantify the concentrations of 17 elements (Al, Ba, Ca, Ce, Cu, Fe, Ga, Gd, K, Mg, Mn, Na, Nd, Pr, Ti, V, Zn). For each junction, tracers suitable for suspended sediment source discrimination were selected based on two criteria: i) the median of the tracer concentration from the mixture C is between those from Subcatchments A and B; and ii) the tracer concentrations from Subcatchments A and B were significantly different with 95% confidence. Mineralogical tracers were found to be of limited use because no significant differences were found to differentiate suspended sediment sources. In contrast, geochemical tracers could be successfully used for discriminating relative suspended sediment contributions from sampled subcatchments using a multivariate mixing model modified from Collins et al. (1998):
Minimize
n X
ððCi Ei Þ=Ci Þ2
(2)
i¼1
where Ei is the estimated concentration of tracer i in the mixture sample, and Ci is the median concentrations of tracer i in the mixture C samples. Given that Ei ¼ Aix þ Biy, constrained by: x þ y ¼ 1, and 0 x 1, where Ai and Bi are the median concentrations of tracer i in Subcatchment A and Subcatchment B, respectively; the model can be solved to get x and y, i.e. the proportional contributions of suspended sediment from Subcatchments A and B, respectively, at each junction. The results suggest that in the Moruya-Deua River catchment, the Upper Moruya-Deua River, Burra Creek and Donalds Creek subcatchments contribute 45%, 17% and 38% of the suspended sediment, respectively. The Donalds Creek subcatchment (with a higher density of unsealed roads) contributes a significantly higher proportion of suspended sediment per km2 than the other two subcatchments. In the Tuross River catchment, the Upper Tuross River subcatchment was estimated to contribute the most suspended sediment (%), followed by the Wadbilliga River. Belimbla Creek subcatchment has the largest undisturbed forest area, and was found to contribute the least amount of suspended sediment (16%). Detailed descriptions of geochemical sediment tracing for the study catchments are described in Fu et al. (2006). 4.3.3. Line-of-Evidence 3: sediment modelling A hybrid conceptual-empirical and spatially-distributed model, the Catchment Scale Management of Distributed Sources Model (CatchMODS) (Newham et al., 2008), was used to estimate sediment yields from hillslope, gully and streambank erosion in the catchments. Because unsealed roads were also considered a potentially important source of sediment in the region, we modified the Washington Road Surface Erosion Model (WARSEM) (Dubé et al., 2004) and integrated it with CatchMODS to account for sediment from unsealed roads (Fu et al., 2009). Sediment routing from each subcatchment was estimated as below:
Xout; i ¼ Xout; j þ Hi þ Gi þ Si þ Ri Fi
(3)
where Xout,i is the sediment routing from subcatchment i; Xout,j is the sediment routing from the upper subcatchment j; Hi, Gi, Si and Ri are the simulated suspended sediment from hillslope, gully, streambank and road erosion, respectively, from subcatchment i; and Fi is the floodplain deposition in subcatchment i. The model results suggest that major suspended sediment sources included the subcatchments along the middle to lower
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
187
Table 1 Characteristics of the three Lines-of-Evidence (monitoring, tracing and modelling) used in the two case studies to identify suspended sediment sources. Monitoring
Tracing
Modelling
Spatial resolution
25-m DEM grid cell
Small to medium subcatchments (1e85 km2)
Spatial extent
Upstream of 217002 and 218008 sites (km2) Three events One year (2005) 1.2e63 mm Index of suspended sediment contribution
Medium to large subcatchment upstream sampled junctions (50e900 km2) Upstream of junctions J1-J5 (1200 and 1580 km2) February 2006 A few decades <63 mm Proportional contribution of suspended sediment
Temporal resolution Temporal extent Particle size Output
Moruya-Deua River, the Araluen Creek, and the middle to lower Tuross River (Fig. 5). In the Moruya-Deua River catchment, hillslope, gully, streambank and road erosion contributed 32%, 33%, 26% and 9% of suspended sediment, respectively. In contrast, streambank erosion in the Tuross River catchment contributed nearly half of the suspended sediment. Gully erosion was less significant in the Tuross River catchment, and provided only 14% of the suspended sediment. The estimated total suspended sediment yields were compared with the reported sediment yields for the Southern Tablelands region in Australia (Wasson, 1994) and the estimated suspended sediment yields from event-based monitoring in the study
Whole catchments (less small estuary areas due to unavailable data) (1420 and 1960 km2) Mean annual average 1983e2008 <63 mm Annual average suspended sediment yields
catchments (Drewry et al., 2005, 2009). Our model outputs showed general agreement with the results compiled by Wasson (1994), but were higher than the sediment yields estimated from eventbased monitoring. The latter was expected because the monitoring data was obtained in a very dry year which had only 46e60% of annual average flow. 4.4. Information integration 4.4.1. Standardising the Lines-of-Evidence Information from the three Lines-of-Evidence described previously was provided at different scales and resolutions. Therefore,
Fig. 5. Estimated suspended sediment yields from the sediment model, showing (a) annual total suspended sediment yield and annual yields from (b) hillslope; (c) gully; (d) streambank; and (e) unsealed road.
188
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
the information had to be standardised prior to integration. The standardisation process potentially downgrades the scale and richness of the available information to the characteristic of the coarsest Lines-of-Evidence. The advantage, however, is like-to-like comparison and effective integration. The characteristics of individual Lines-of-Evidence are listed in Table 1. Monitoring gave the index of a site delivering suspended sediment to the location of gauges 217002 and 218008. Tracing provided the relative proportional contributions of suspended sediment at five stream junctions. Modelling provided suspended sediment yields from various erosion processes and subcatchments. Temporally, monitoring results were limited to events in a dry year, whereas both tracing and modelling reflect suspended sediment source contributions in the medium to long term (>10 years). However, the sediment model was simulated under a dry climate scenario, and the samples for tracing were likely to be deposited in the last decade which has been relatively dry. Therefore, despite differences in temporal resolution and extent (Table 1), all three Lines-of-Evidence were representative of low rainfall conditions, therefore could be integrated. There are also slight variations in particle size fractions represented in the three Lines-of-Evidence. The particle size fraction used in monitoring is 1.2e63 mm, whereas tracing and modelling use sediment less than 63 mm. We assumed that these differences in particle size between the Lines-of-Evidence can be neglected. Lines-of-Evidence were standardised spatially, using the subcatchments defined by tracing as the common basis. Results from monitoring and modelling were converted to proportional contributions of suspended sediment for the subcatchments defined in sediment tracing (at the selected junctions). The suspended sediment indices obtained from monitoring were averaged for the tracing subcatchments and rescaled so that each pair of subcatchments at a junction sum to 100%. Similarly, modelled suspended sediment yields were summed and scaled to 100% for each tracing subcatchment. 4.4.2. Weighting the Lines-of-Evidence Weights were given to the Lines-of-Evidence based on our judgement on the reliability of each Line-of-Evidence. Three criteria were used for assigning the weights: i) relevance of the outputs with the research objective; ii) quality of the input data; and iii) method suitability in relation to study objective. A score between 1 and 5 was assigned to each Line-of-Evidence for each criterion (Table 2). The final weight for each Line-of-Evidence was the sum of the scores from all criteria. The rationales for the score given to each Line-of-Evidence are detailed below. 4.4.2.1. Relevance of outputs to study objective. The focus of this research was the relative contribution of suspended sediment sources and their spatial distribution. Hence the Lines-of-Evidence were scored by how well their results reflect suspended sediment generation and transport. Scores of 5, 4 and 3 are given to monitoring, tracing and modelling, respectively. A lower score is given to modelling because we believed that sediment deposition was under-represented by the current model. 4.4.2.2. Precision and accuracy of the data used for each Line-ofEvidence. This reflects consideration of the representativeness of Table 2 Weights assigned to the Lines-of-Evidence (monitoring, tracing and modelling). Scoring criteria
Monitoring
Tracing
Modelling
Relevance to study objective Data precision and accuracy Method suitability Total
5 4 1 10
4 4 4 12
3 3 2 8
samples, the quality of laboratory analysis, and the accuracy of the model inputs. Scores of 4, 4 and 3 were given to monitoring, tracing and modelling, respectively. The model inputs were judged less reliable due to the resolution of the data and lack of testing. For instance, the Digital Elevation Model (DEM) used was at 25 m resolution, which is a coarse resolution for estimating the slope of road segments. 4.4.2.3. Suitability of the method relative to study objective. The major consideration of this criterion involved the suitability of the assumptions and algorithms. Scores of 1, 4 and 2 were given to monitoring, tracing and modelling, respectively. For the stream monitoring, the spatial distribution of the suspended sediment sources was based on the qualitative interpretation of the relationship between suspended sediment concentration and river discharge. Relatively high uncertainty was introduced when converting this qualitative information into categorised quantitative data (index). In contrast, the procedures for geochemical sediment tracing are generally well acknowledged (Walling, 2005) and were supported in this research by further analysis of the influence of particle size on tracing results (Fu et al., 2008). Some potential sources of uncertainties associated with the design of sediment tracing included the selection of tracers and the design of laboratory analysis, especially the treatment of organic matter. As a result, a score of 4 was given to tracing. Integrated sediment modelling relied on a large number of assumptions about the empirical relationships between suspended sediment yields and the biophysical characteristics of the catchments, many of which were not able to be thoroughly tested for the study catchments. Consequently, a value of 2 was given to modelling. 4.4.3. Combining the Lines-of-Evidence The Weight-of-Evidence results were calculated from Equation (1), and compared with the results of monitoring, tracing and modelling taken individually (Fig. 6). The combined proportional contributions of suspended sediment from the upstream subcatchments for J1 to J5 were 45%, 51%, 73%, 57% and 70%, respectively. The Weight-of-Evidence results slightly hinged towards the results of tracing and monitoring as larger weights were assigned to these two Lines-of-Evidence. The percentage contributions of the subcatchments were standardised by their drained subcatchment areas. The results show that in the Moruya-Deua River catchment, the Donalds Creek subcatchment at J1 contributed four times more sediment per unit area than Burra Creek subcatchment. At J2, the combined Donalds Creek and Burra Creek subcatchments contributed three times more sediment per unit area than the upper Moruya-Deua River subcatchment. In the Tuross River catchment, the three subcatchments in the upper reach of the Tuross River (i.e. Belimbla Creek, Wadbilliga River and Upper Tuross River subcatchments) had similar proportional contributions per unit area. At J3, the Reedy Creek subcatchment contributed five times more sediment per unit area than the mid and upper Tuross River subcatchment. These results highlight the hotspot subcatchments (Donalds Creek and Reedy Creek) in terms of sediment control management, where investment in sediment control could be prioritised. Outcomes for J5 were generally consistent among the three Lines-of-Evidence (ranging from 60% to 84%). However, outcomes for J2 varied significantly among the three Lines-of-Evidence, with sediment contribution from Subcatchment A (i.e. upper MoruyaDeua River subcatchment) ranging from 21% estimated by monitoring to 98% by modelling. From a management perspective, the uncertainty for J2 is too high to make sensible decisions in management priority and more data would be required to better identify sediment sources in this particular area.
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
189
J1 0
20
40
60
80
100 %
0
20
40
60
80
100 %
0
20
40
60
80
100 %
0
20
40
60
80
100 %
J2
J3
Monitoring
J4
Tracing Modelling WOE results
J5 0
20
40
60
80
100 %
Fig. 6. The proportional contributions of suspended sediment from the upstream subcatchments of J1 to J5, derived from monitoring, tracing, modelling and the integration framework.
4.4.4. Sensitivity analysis A sensitivity analysis was undertaken to investigate how sensitive the Weight-of-Evidence outputs were to the weights assigned to each Line-of-Evidence. This was done by increasing and decreasing the score of each Line-of-Evidence (one at a time) by 3 points, which is 20% of the maximum score a Line-of-Evidence can be assigned (i.e. 15). In most cases the Weight-of-Evidence outputs were insensitive to the scores assigned to Lines-of-Evidence (Fig. 7). On average, Weight-of-Evidence outputs changed by 2% for J1 and J3-J5 as a result of increasing or decreasing any Line-of-Evidence score by 3 points. The exception was at junction J2 where the Weight-of-Evidence output increased by 6.5% when monitoring result was decreased by 3 points (i.e. from 10 to 7), and decreased by 5.4% when monitoring result was increased by 3 points (i.e. from 10 to 13). Conversely, a 3-point increase in modelling score (from 8 to 11) led to a 8.4% increase in Weight-of-Evidence output; while a 3-point decrease in modelling score (from 8 to 5) led to a 10.2% decrease in Weight-of-Evidence output. A change in Line-ofEvidence 2 (tracing) instead was relatively insensitive to the Weight-of-Evidence outputs. This outcome at J2 reflected the greater differences among the monitoring and modelling results for this junction (Fig. 7).
Fig. 7. Sensitivity of the Weight-of-Evidence outputs to the weights assigned to each Line-of-Evidence (i.e. monitoring, tracing and modelling). The bars are the Weight-ofEvidence outputs for each junction under the assigned weights. The error bars indicate the Weight-of-Evidence outputs with changes (3 points) in scores for each Line-ofEvidence.
5. Discussion 5.1. Advantages and limitations Integrating heterogeneous information can increase confidence in a study outcome sensibly. This is particularly the case when verification of individual Lines-of-Evidence is unavailable. In the case study applications, uncertainties and limits of each Line-ofEvidence were identified, albeit subjectively, during the Weightof-Evidence weighting processes, and demonstrated that no Lineof-Evidence alone could provide a conclusive answer on the sources of suspended sediment. The Weight-of-Evidence approach did not remove uncertainties in each Line-of-Evidence, rather, it enabled their explicit consideration, improving the overall confidence in the identification of suspended sediment sources to meet the needs of catchment managers. The most significant limitation of the Weight-of-Evidence approach was that the outcomes of the integration were limited by the lowest resolutions among the Lines-of-Evidence. In the case study applications, the spatial resolution of the information on suspended sediment sources was reduced to the scale of the medium to large subcatchments defined for sediment tracing, and thus significant information from other Lines-of-Evidence was lost in the Weight-of-Evidence integrated results. Information loss can be substantial when the tracing subcatchments are large and contain several critical sources of suspended sediment that are of management interest. For example, the upper Moruya-Deua River subcatchment (upstream J2) covers two-thirds of the Moruya-Deua River catchment. Although sediment modelling identified specific subcatchments to contribute high sediment loads (such as the Araluen Creek subcatchment), this information was lost during integration and the significance of the Araluen Creek in contributing suspended sediment to the river systems could not be confirmed. The information was not only lost spatially, but also quantitatively. For example, average annual suspended sediment yields estimated by the sediment model could not be confirmed with other Lines-of-Evidence. Losing information on the magnitude of suspended sediment yields was the cost of increasing confidence in the relative suspended sediment contributions. However, given that the priority of the study was to identify relative suspended sediment sources, this loss of information is acceptable.
190
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
The Weight-of-Evidence approach depends on expert judgement in the weighting assignment, which can be largely subjective. Although clear criteria were set to minimise subjectivities in weighting, it is likely that different scores could be given by different experts, thus altering the outcomes. Subjectivity is not necessarily negative, and techniques exist to elicit expert knowledge or to formulate weights that reflect the opinion of a large group of experts (Cooke, 1994). Even with such limitations, weighting towards well-defined criteria is a more transparent approach than simply averaging Lines-of-Evidence without consideration of their relative reliability. 5.2. Recommendations to improve the integrated approach Confidence in Weight-of-Evidence outcomes depends on the confidence in individual Lines-of-Evidence. This implies that overall confidence can be increased by reducing the uncertainty in individual Lines-of-Evidence. The integration framework outcomes can be improved by increasing the resolution of the coarsest Lineof-Evidence, in this case geochemical sediment tracing (i.e. medium to large subcatchments). Increasing the number of junctions sampled for sediment tracing, in particular at junctions that other Lines-of-Evidence indicated as potentially critical suspended sediment sources (e.g. Araluen Creek), would improve the spatial identification of the suspended sediment sources. Another means to improve Weight-of-Evidence outcomes is by increasing the number of independent Lines-of-Evidence. In the case studies, additional Lines-of-Evidence may include spatially distributed suspended sediment monitoring and sediment tracing on targeted erosion areas. For example, the Donalds Creek subcatchment in the Moruya-Deua River catchment and the Reedy Creek subcatchment in the Tuross River catchment were both estimated to contribute large proportions of suspended sediment relative to their catchment size. Stream monitoring at the outlets of these subcatchments could provide valuable information on suspended sediment loads from these areas. Field observations suggest that these subcatchments contain a relatively high density of forest roads and unstable streambanks. Sediment tracing aiming at discriminating suspended sediment contributions from unsealed roads and streambanks in these subcatchments could help to clarify further suspended sediment sources. This new Line-of-Evidence could be then integrated in the Weight-ofEvidence approach to confirm suspended sediment source outcomes. Improvements in assigning weights to the Lines-of-Evidence could be achieved using a Bayesian framework by specifying prior probabilities for the suspended sediment source estimates and estimating observation-error variances from the numerical results of individual Lines-of-Evidence. Bayes’ theorem could be used to assess the influence of each Line-of-Evidence, and weights could be then set as the Bayesian posterior probabilities. This would not exclude subjectiveness entirely, but would put the weighting exercise into a systematic framework. An example of using Bayes’ theorem to weight multiple Lines-of-Evidence has been provided by Neuhäuser and Terhorst (2007) for a landslide susceptibility assessment. Alternatively, a panel of experts judging Lines-of-Evidence reliability could be established to review and refine the weights we selected for this study. The use of formal elicitation techniques could improve confidence in the weights substantially (McGraw and Seale, 1988). In the case studies, the sensitivity analysis of the weights to the integration framework outputs suggested that a more complex weight assignment is not warranted for J1 and J3-J5. However, the integration outputs are relatively sensitive to the scores given to sediment monitoring and modelling at J2, and thus improved weighting processes may be required.
6. Conclusion An integration framework was described here for the identification of suspended sediment sources using a Weight-of-Evidence approach that incorporated multiple Lines-of-Evidence. The framework was driven by the study purpose, prior knowledge, and data availability. Its practical application to two case study catchments allowed for the integration of three Lines-of-Evidence (stream monitoring, geochemical sediment tracing, and sediment modelling). As a result, the contribution of suspended sediment at river junctions was more tightly identified, and more confident statements with regard to sources of sediment could be formulated. The major limitation of the integration framework was that the quality and quantity of the information were downgraded to match the Line-of-Evidence with the lowest resolution. For example, in this study information on suspended sediment yields was lost and detailed identification of some critical suspended sediment sources could not be identified. This limitation is inherent to the approach and can only be improved by increasing the resolution of the coarsest Line-of-Evidence (e.g. sediment tracing in this study), or by obtaining additional Lines-of-Evidence. In this sense, the Weight-of-Evidence approach can help identify where and which type of additional data is required, and therefore help planning data collection campaigns. A secondary limit of the approach was that it involved some subjectivity in assigning weights to the Lines-of-Evidence. This is not a negative aspect per se, as subjective choices were transparent, being explicitly formulated in the framework. However, more elaborate weighting processes (using for example a Bayesian framework or enlarging the panel of experts) may be pursued if sensitivity of the Weight-of-Evidence outcomes warrants it. The integration framework builds a coherent structure to interpret multiple Lines-of-Evidence, and can be widely applied to suspended sediment source studies. Such a rapid and cost-effective approach is a practical tool for catchment managers in integrating and communicating information effectively, and developing focused monitoring programmes and management strategies to meet the pressing demands for effective water quality management, particularly in the absence of long term spatially-distributed suspended sediment load data. Acknowledgements This study was undertaken with funding from the Australian National University, Cooperative Research Centre for Landscape Environments and Mineral Exploration and as part of Australian Research Council Linkage project LP0560439 with funding from Eurobodalla Shire Council. We wish to thank Prof Tony Jakeman, Prof John Norton and Dr John Drewry for their valuable comments on the draft of this paper. Dr Barry Croke also provided great support in the development of modelling research. The DEM data was provided by the New South Wales Department of Environment and Climate Change. References Beeton, R.J.S., Buckley, K.I., Jones, G.J., Morgan, D., Reichelt, R.E., Trewin, D., 2006. Australia State of the Environment 2006, Independent Report to the Australian Government Minister for the Environment and Heritage. Department of the Environment and Heritage, Canberra. Bhatia, M.R., Crook, K.A.W., 1986. Trace element characteristics of graywackes and tectonic setting discrimination of sedimentary basins. Contrib. Mineral Petr. 92, 181e193. Blöschl, G., Grayson, R., 2001. Spatial observations and interpolation. In: Grayson, R., Blöschl, G. (Eds.), Spatial Patterns in Catchment Hydrology; Observations and Modelling. Cambridge University Press, Cambridge, pp. 17e50. Burton, G.A., Chapman, P.M., Smith, E.P., 2002. Weight-of-Evidence approaches for assessing ecosystem impairment. Hum. Ecol. Risk Assess. 8, 1657e1673. Chapman, P.M., 1996. Presentation and interpretation of sediment quality triad data. Ecotoxicology 5, 327e339.
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191 Chapman, P.M., McDonald, B.G., Lawrence, G.S., 2002. Weight-of-Evidence issues and frameworks for sediment quality (and other) assessments. Hum. Ecol. Risk Assess. 8, 1489e1515. Collins, A.L., Owens, P.N., 2006. Introduction to soil erosion and sediment redistribution in river catchments: measurement, modelling and management in the 21st century. In: Owens, P.N., Collins, A.L. (Eds.), Soil Erosion and Sediment Redistribution in River Catchments. CAB International, Cambridge, pp. 3e9. Collins, A.L., Walling, D.E., Leeks, G.J.L., 1998. Use of composite fingerprints to determine the provenance of the contemporary suspended sediment load transported by rivers. Earth Surf. Proc. Land. 23, 31e52. Cooke, N.J., 1994. Varieties of knowledge elicitation techniques. Int. J. Hum-Comput St. 41, 801e849. Croke, J., Wallbrink, P., Fogarty, P., Hairsine, P., Mockler, S., McCormack, B., Brophy, J., 1999. Managing Sediment Sources, and Movement in Forests: The Forest Industry and Water Quality. Industry report 99/11. Cooperative Research Centre for Catchment Hydrology. Drewry, J.J., Greene, R.S.B., Newham, L.T., Croke, B.F.W., 2005. The role of soil regolith properties, river suspended sediment and nutrient concentrations during storm events in the Moruya and Tuross catchments, NSW. In: Roach, I.C. (Ed.), Regolith 2005 e Ten Years of CRC LEME. Cooperative Research Centre for Landscape Environments and Mineral Exploration, Adelaide and Canberra, pp. 78e82. Drewry, J.J., Newham, L.T.H., Croke, B.F.W., 2009. Suspended sediment, nitrogen and phosphorus concentrations and exports during storm-events to the Tuross estuary, Australia. J. Environ. Manage. 90, 879e887. Dubé, K., Megahan, W., McCalmon, M., 2004. Washington Road Surface Erosion Model (WARSEM) Manual. Department of Natural Resources, State of Washington. Flanagan, D.C., Nearing, M.A., 1995. Usda e Water Erosion Prediction Project: Hillslope Profile and Watershed Model Documentation. National Soil Erosion Research Laboratory Report No.10. US Department of Agriculture e Agricultural Research Service, West Lafayette. Fu, B., Field, J.B., Newham, L.T., 2006. Tracing the source of sediment in Australian coastal catchments. In: Fitzpatrick, R.W., Shand, P. (Eds.), Regolith 2006Consolidation and Dispersion of Ideas. CRC LEME, Perth, Western Australian, pp. 100e104. Fu, B., Newham, L.T.H., Field, J.B., 2008. Influence of particle size on geochemical suspended sediment tracing in Australia. In: Schmidt, J. (Ed.), Sediment Dynamics in Changing Environments (Proceedings of a Symposium Held in Christchurch, New Zealand, December 2008). IAHS Press, Wallingford, pp. 23e30. Fu, B., Newham, L.T.H., Field, J.B., 2009. Modelling erosion and sediment delivery from unsealed roads in southeast Australia. Math. Comput. Simul. 79, 2679e2688. Fu, B., Newham, L.T.H., Ramos-Scharrón, C.E., 2010. A review of surface erosion and sediment delivery models for unsealed roads. Environ. Model. Softw. 25, 1e14. Haines, P., Cavanagh, D., 2002. Tuross Estuary and Coila Lake Estuary Processes Study: Addendum to Final Report. WBM Oceanics Australia, Broadmeadow. Hateley, L.R., Armour, J.D., Brodie, J., Faithful, J., Pitt, G.L., Nelson, P.N., 2007. Modelling, monitoring and sediment tracing in the Tully River Catchment, north Queensland: a comparison of techniques. In: Oxley, L., Kulasiri, D. (Eds.), MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Christchurch, pp. 867e873. Heathwaite, L., Sharpley, A., Gburek, W., 2000. A conceptual approach for integrating phosphorus and nitrogen management at watershed scales. J. Environ. Qual. 29, 158e166. Jansson, M.B., 2002. Determining sediment source areas in a tropical river basin, Costa Rica. CATENA 47, 63e84. Kinsey-Henderson, A.E., Post, D.A., Prosser, I.P., 2005. Modelling sources of sediment at sub-catchment scale: an example from the Burdekin catchment, North Queensland, Australia. Math. Comput. Simul. 69, 90e102. Lefrançois, J., Grimaldi, C., Gascuel-Odoux, C., Gilliet, N., 2007. Suspended sediment and discharge relationships to identify bank degradation as a main sediment source on small agricultural catchments. Hydrol Process. 21, 2923e2933. Malhotra, M.K., Sharma, S., Nair, S.S., 1999. Decision making using multiple models. Eur. J. Oper. Res. 114, 1e14.
191
Marston, F., Prosser, I.P., Hughes, A., Lu, H., Stevenson, J., 2001. Waterborne Erosion e an Australian Story. Technical Report 17/01. CSIRO Land and Water, Canberra. McCulloch, M., Fallon, S., Wyndham, T., Hendy, E., Lough, J., Barnes, D., 2003. Coral record of increased sediment flux to the inner Great Barrier Reef since European settlement. Nature 421, 727e730. McGraw, K.L., Seale, M.R., 1988. Knowledge elicitation with multiple experts: considerations and techniques. Artif. Intell. Rev. 2, 31e44. Merritt, W.S., Letcher, R.A., Jakeman, A.J., 2003. A review of erosion and sediment transport models. Environ. Model. Softw. 18, 761e799. Murray, A.S., Olive, L.J., Olley, J.M., Caitcheon, G.C., Wasson, R.J., Wallbrink, P.J., 1993. Tracing the source of suspended sediment in the Murrumbidgee River, Australia. IAHS Publication No. 215. In: Peters, N.E., Hoehn, E., Leibundgut, C., Tase, N., Walling, D.E. (Eds.), Tracer in Hydrology. IAHS Press, Wallingford, pp. 293e302. Nagle, G.N., Fahey, T.J., Ritchie, J.C., Woodbury, P.B., 2007. Variations in sediment sources and yields in the Finger Lakes and Catskills regions of New York. Hydrol Process. 21, 828e838. Neuhäuser, B., Terhorst, B., 2007. Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SWGermany). Geomorphology 86, 12e24. Newham, L.T.H., Drewry, J.J., Fu, B., 2008. Catchment-scale water quality modelling and integration of collateral information. In: Sànchez-Marrè, M., Béjar, J., Comas, J., Rizzoli, A.E., Guariso, G. (Eds.), Integrating Sciences and Information Technology for Environmental Assessment and Decision Making. Proceedings of the 4th Biennial Meeting of the International Environmental Modelling and Software Society, vol. 1. International Environmental Modelling and Software Society, Barcelona, pp. 584e591. Newham, L.T.H., Prosser, J.P., Norton, J.P., Croke, B.F.W., Jakeman, A.J., 2001. Techniques for assessing the performance of a landscape-based sediment source and transport model: sensitivity trials and physical methods. In: Ghassemi, F., Post, D., Sivapalan, M., Vertessy, R. (Eds.), MODSIM 2001 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Canberra, pp. 149e154. Prosser, I.P., Rustomji, P., Young, B., Moran, C., Hughes, A., 2001. Constructing River Basin Sediment Budgets for the National Land and Water Resources Audit. Technical Report 15/01. CSIRO Land and Water, Canberra. Rustomji, P., Caitcheon, G., Hairsine, P., 2008. Combining a spatial model with geochemical tracers and river station data to construct a catchment sediment budget. Water Resour. Res. 44, W01422. DOI:01410.01029/02007WR006112. Sharpley, A.N., Weld, J.L., Beegle, D.B., Kleinman, P.J.A., Gburek, W.J., Moore, P.A., Mullins, G., 2003. Development of phosphorus indices for nutrient management planning strategies in the United States. J. Soil Water Conserv. 58, 137e152. Suter, G.W., Cormier, S.M., 2011. Why and how to combine evidence in environmental assessments: weighing evidence and building cases. Sci. Total Environ. 409, 1406e1417. Vrieling, A., 2006. Satellite remote sensing for water erosion assessment: a review. CATENA 65, 2e18. Walling, D.E., 2005. Tracing suspended sediment sources in catchments and river systems. Sci. Total Environ. 344, 159e184. Walling, D.E., Collins, A.L., Sichingabula, H.M., Leeks, G.J.L., 2001. Integrated assessment of catchment suspended sediment budgets: a Zambian example. Land Degrad. Dev. 12, 387e415. Wasson, R.J., 1994. Annual and decadal variation of sediment yield in Australia, and some global comparisons. International Association of Hydrological Sciences (IAHS) Publication No. 224. In: Olive, L.J., Loughran, R.J., Kesby, J.A. (Eds.), Variability in Stream Erosion and Sediment Transport. IAHS Press, Wallingford, pp. 269e279. Weed, D.L., 2005. Weight of evidence: a review of concept and methods. Risk Anal. 25, 1545e1557. White, A.J.R., Chappell, B.W., 1977. Ultrametamorphism and granitoid genesis. Tectonophysics 43, 7e22. Williams, G.P., 1989. Sediment concentration versus water discharge during single hydrologic events in rivers. J. Hydrol. 111, 89e106.
Contents lists available at SciVerse ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
A weight-of-evidence approach to integrate suspended sediment source information B. Fu a, *, L.T.H. Newham a, J.B. Field a, O. Vigiak b a
Fenner School of Environment and Society, The Australian National University, Canberra, ACT 0200, Australia Department of Primary Industries, Future Farming Systems Research Division, Rutherglen Centre, RMB 1145 Chiltern Valley Road, Rutherglen, VIC 3685, Australia b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 October 2011 Received in revised form 15 April 2013 Accepted 8 May 2013 Available online
Sediment monitoring, tracing and modelling are widely used to identify suspended sediment sources. Although each method has inherent limitations and uncertainties, their integration provides opportunities to form collective knowledge and encourages robust management strategies. This paper presents a Weight-of-Evidence approach to integrate multiple Lines-of-Evidence for identifying suspended sediment sources. Three sources of evidence were used: i) stream flow and suspended sediment monitoring at river gauges; ii) geochemical sediment tracing at river junctions; and iii) catchment-scale suspended sediment modelling of hillslope, gully, streambank and unsealed road erosion. We applied this approach on two data-poor catchments in Australia. Some reaches were consistently identified as major sources of sediment from all Lines-of-Evidence. However, inconsistencies between the types of evidence in other areas highlighted the high uncertainty in identifying suspended sediment sources in these areas and the need for further investigation. The integration framework maximised the use of scarce information, enabled explicit consideration of uncertainties for catchment management and identified where future monitoring and research should be targeted. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Sediment sources Weight-of-Evidence Integration Sediment modelling Sediment tracing
1. Introduction Water quality is important for the health of rivers and estuarine ecosystems. Water quality decline resulting from excessive suspended sediment loads and sediment-derived contaminants is a world-wide problem and has attracted much scientific and public interest (Collins and Owens, 2006; McCulloch et al., 2003). Identifying sources of suspended sediment enables catchment managers to prioritise remediation efforts. However, this task can be challenging at catchment scales due to the complex processes of sediment mobilisation, transport and deposition. Methods for identifying sediment sources can be broadly classified into three types: monitoring (Jansson, 2002), tracing (Nagle et al., 2007; Walling, 2005), and modelling (Fu et al., 2010; Merritt et al., 2003). Each method has its own data requirements, is applied under different assumptions and constraints, and has inherent limitations and uncertainties. Integrating information
* Corresponding author. Tel.: þ61 2 6125 0850; fax: þ61 2 6125 8395. E-mail address: [email protected] (B. Fu). 0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.05.005
gathered with different methods, however, provides opportunities to form collective knowledge and reduce the impact of each method’s limitations on final evaluations (Malhotra et al., 1999). From a management perspective, integration encourages the development of transparent and robust strategies whilst recognising method limits and uncertainties, and helps reduce the risk of management actions taken based on a single information type, especially when that information is derived from limited data. Several studies have integrated different information to investigate suspended sediment sources. Some use heterogeneous information derived from a single method. For example, many sediment models adopt a modular structure using submodels to identify sediment sources from different erosion and landuse types (Flanagan and Nearing, 1995; Kinsey-Henderson et al., 2005; Prosser et al., 2001). Information integration has also been achieved by combining multiple methods, such as monitoring and tracing (Croke et al., 1999; Murray et al., 1993; Walling et al., 2001), tracing and modelling (Newham et al., 2001), and monitoring, tracing and modelling (Hateley et al., 2007; Rustomji et al., 2008). Although information integration is not a new concept in identifying suspended sediment sources, the integration is usually qualitative. No
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
clear guidance is available for selecting appropriate suspended sediment source methods and for systematic synthesis of available information. An approach that could provide a framework for integrating information to identify suspended sediment sources is the Weight-ofEvidence approach which has been reviewed by Weed (2005). This approach has been used in environmental assessments and involves the combining of multiple Lines-of-Evidence to assess an environmental system (Burton et al., 2002). For example, a typical sediment quality assessment often involves three major Lines-of-Evidence: sediment chemistry, laboratory toxicity, and biota community alteration information (Chapman, 1996). By combining the information from multiple Lines-of-Evidence, more confident conclusions on sediment quality and ecosystem impairment can be achieved. Linesof-Evidence can be combined qualitatively, or quantitatively by attributing weights to the Lines-of-Evidence which reflect the importance of and/or confidence in the information provided (Suter and Cormier, 2011). The Weight-of-Evidence approach may require subjective decisions or qualitative information (e.g. expert opinion) (Chapman et al., 2002). The main advantage of the Weight-of-Evidence approach is that it provides a systematic framework for utilising all available information and identifying data gaps, which is especially useful in data-poor situations. This paper presents a Weight-of-Evidence approach for identifying suspended sediment sources. In the following section we summarise three types of information integration that have been used in suspended sediment sourcing studies. Next, we propose a framework for integrating multiple Lines-of-Evidence using the Weight-of-Evidence approach for suspended sediment sourcing. This integration framework is then applied at two data-poor catchments in Australia. Finally, we discuss the advantages and limitations of the integration framework and make recommendations for improvements. 2. Types of information integration Three types of information integration can be summarised from the literature: consolidation, confirmation and verification (Fig. 1). Consolidation unites heterogeneous information. Different Lines-of-Evidence provide additional information forming collective knowledge that is more complete than a single Line-ofEvidence. The assumption is that each Line-of-Evidence provides accurate (termed as “true”) information. An example is given by Walling et al. (2001), who integrated monitoring and tracing methods to identify suspended sediment sources for a small agricultural catchment in Zambia. River monitoring was used to establish sediment flux and yield data at the catchment outlet; 137 Cs was used to measure the rates of soil erosion, deposition and floodplain accumulation; and multiple tracers were used to identify the proportional contributions of suspended sediment sources. A detailed suspended sediment budget of soil erosion, transport and deposition within the catchment was obtained by uniting these three types of information (Walling et al., 2001). Confirmation intersects heterogeneous information. Different Lines-of-Evidence reach the same conclusion and thus increase the overall certainty in the study outcomes. Confirmation is common when different Lines-of-Evidence give the same type of results. In this type of integration, the uncertainties are acknowledged in every Line-of-Evidence, and none of them alone is considered sufficient to reach a sound conclusion. For example, Newham et al. (2001) compared the results of Sediment River Network Model (SedNet) with sediment tracing in the upper Murrumbidgee catchment, Australia. In finding general agreement between the two Lines-of-Evidence, the authors argued that management recommendations could be formulated with greater confidence.
Consolidation
Sediment sources
183
Sediment sources
Integrated Information
Information Information A B
Confirmation
Sediment sources
Sediment sources
Integrated Information
Information Information B A
Verification
Sediment sources
Sediment sources
Information Information A B
Integrated information
Fig. 1. Diagrams showing the conceptual frameworks for three types of information integration: consolidation, confirmation and verification. In consolidation, additional information on sediment sources is added. In confirmation, the sediment sources are confirmed by two independent information (A and B, darker colour indicates higher confidence). In verification, the third configuration of Information B is supported by independent Information A.
Verification tests heterogeneous information. In this type of integration, the result from at least one Line-of-Evidence is assumed “true” and is used as a reference to test or calibrate other Lines-of-Evidence. For example, Rustomji et al. (2008) used stream monitoring and sediment tracing to test different model configurations. In their study, information from stream monitoring and sediment tracing was considered “true” and the best model configuration to match this information was recommended and used for prediction (Rustomji et al., 2008). The three types of integration can be used in conjunction. For example, Murray et al. (1993) investigated suspended sediment sources in the Murrumbidgee River of Australia using information from monitoring (aerial photographs, suspended sediment and discharge data, turbidity records) and tracing (137Cs and excess 210 Pb). Event-based suspended sediment and historical turbidity data (from separate sources) both suggested the upper catchment as the dominant sediment source during flood events (confirmation), while sediment tracing suggested top soil as the major source. Event-based monitoring and sediment tracing provided evidence that during flood events suspended sediment mainly came from top soil in the upper catchment (consolidation).
3. An integration framework for identifying suspended sediment sources The Weight-of-Evidence approach is adapted here to develop a suitable framework to integrate the information on suspended sediment sources from multiple Lines-of-Evidence (Fig. 2). The framework involves the study design, obtaining Lines-of-Evidence and information integration. As there are limitations and uncertainties within each Line-of-Evidence, information consolidation is not used; rather, the Weight-of-Evidence approach is analysed using the confirmation type of integration.
184
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
Define study purpose
Identify prior knowledge and resource availability
Study design
C ¼
Define study strategy
Obtaining LOEs
standardised to account for differences in their outputs. Then, weights are assigned to individual Lines-of-Evidence based on their reliability and importance to the research question. Finally, the Linesof-Evidence are combined with consideration of their weights: n X i¼1
LOE 1
LOE 2
LOE 3
(e.g. monitoring)
(e.g. tracing)
(e.g. modelling)
E i Wi =
n X
! Wi
(1)
i¼1
Here C denotes the Weight-of-Evidence results on sediment contribution; Ei is the sediment contribution of the source according to Line-of-Evidence i; Wi is the weight assigned to Line-ofEvidence i; and n is the number of Lines-of-Evidence. 4. Application of the integration framework to case studies
Information integration
Confirmation (Weight-of-Evidence approach)
Weight-of-Evidence results
Fig. 2. A framework for integrating multiple Lines-of-Evidence (LOE) to identify sediment sources.
3.1. The study design The study design involves establishing a strategy based on the purpose of the study and available resources. In shaping the study design the following questions require consideration: Is information on the relative contribution of suspended sediment sources or the volume of suspended sediment yields required? Is information on the spatial distribution of suspended sediment sources required? Is information on the temporal variability of the suspended sediment sources required? What is the spatial extent of the study? The content and scale of the study are defined in this step, and the Lines-of-Evidence are identified. A carefully designed strategy is crucial to the effective use of Lines-of-Evidence in achieving the study objective. This requires attention to several aspects of the design, such as: identifying timing and locations of suspended sediment sampling for monitoring and tracing; selection of suitable tracers; and selection of models at an appropriate scale and commensurate to data availability. 3.2. Obtaining Lines-of-Evidence The second step involves identifying the characteristics of each Line-of-Evidence and Lines-of-Evidence implementation. The characteristics of Line-of-Evidence include assumptions, scale and accuracy of measurements, data interpolation, and method limitations. For example, spatial and temporal dimensions of Line-ofEvidence can be defined based on a “scale triplet” of spacing (i.e. the distance or time between samples), extent (i.e. the overall coverage of the data in space or time), and support (i.e. the averaging volume or area or time of the samples) (Blöschl and Grayson, 2001). The combination of these characteristics forms the basis to assess the reliability of the Lines-of-Evidence and their relative importance to the research question. 3.3. Information integration The Lines-of-Evidence are integrated based on the Weight-ofEvidence approach. First, the information from Lines-of-Evidence is
4.1. The case study catchments The Moruya-Deua (1500 km2) and Tuross (2100 km2) River catchments are located on the southeast coast of Australia (Fig. 3). The climate is warm temperate with coastal influences. The annual average rainfall ranges from approximately 700e1100 mm increasing from west to east. Much of the catchments consist of early Ordovician metasedimentary rocks, covering about 45% in each catchment. The rest of the catchments contain sedimentary rocks of younger ages and plutonic rocks. Basalt is limited in both catchments, with a scattered and disparate distribution pattern in coastal regions. Areas of steep terrain remain forested. Over 70% of the forests are dominated by native Eucalyptus and Mimosacae forests. Most of the flat regions have been developed into urban townships or agricultural villages for grazing and cropping. There are approximately 1000 km of unsealed roads in the Moruya-Deua catchment and 2000 km in the Tuross River catchment. Water quality in the catchments is a concern for local communities and industries (Beeton et al., 2006; Drewry et al., 2009). The catchments provide drinking water supplies for local towns and villages. Maintaining good water quality is also vital for the local industries such as dairying and oyster farming, and the estuaries and marine ecosystems. However, in both catchments little is known about sediment sources at the catchment scale, and cost-effective actions on erosion control are difficult to identify and justify. 4.2. The study design 4.2.1. Aim The aim of the case studies was to inform catchment managers on priority sites for erosion control and ongoing monitoring. Thus, this study focused on identifying the spatial distribution and relative contribution of suspended sediment sources. 4.2.2. Prior knowledge and data availability The study area is relatively data-poor. Basic landscape data including 25 m-resolution Digital Elevation Model and geological mapping were available, but very little data were available for identifying suspended sediment sources. Historical aerial photographs were available at scales of 1:23,270 to 1:81,700 although these proved too coarse for estimating gully head cut retreat, gully wall erosion, and stream migration rates in the region. Likewise, resolution of available remote sensing data was too coarse spatially and/or temporally to identify sediment sources in the study catchments (Vrieling, 2006). Previous water quality studies in the Moruya-Deua and Tuross River catchments focused on the estuary conditions (Haines and Cavanagh, 2002). An exception was the basin-scale sediment budget study as part of the Australian National Land and Water Resources Audit, which reported over 70% of sediment to come
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
185
Fig. 3. Location of the study catchments, sampled junctions (J1eJ5) for sediment tracing and the gauges, and a schematic diagram showing sampling sites at each junction.
G auge 217002 (D eua at W am ban)
20,000
G auge 218008 (Tuross at Eurobodalla)
16,000
4.2.3. Study strategy Three Lines-of-Evidence were selected for this study: stream monitoring at river gauges, sediment tracing at river junctions, and
Sediment (mg/L)
24,000
sediment concentration data was collected in 2005 covering three events at gauge 217002 (Moruya-Deua River catchment) and gauge 218008 (Tuross River catchment) (Fig. 3) (Drewry et al., 2005, 2009).
12,000 8,000
150 100
4,000 0 1 /J u n
1 /J u l
1 /Aug
1 /Sep 1 /O c t
G auge 217002
200
Sediment (mg/L)
M ean discharge (M L/day)
from gully erosion (Marston et al., 2001). However, the model used for the audit was not verified by local data. Our field reconnaissance suggested that gully density was low in the Tuross River catchment, whereas significant streambank erosion was observed in the lower Tuross River. Thus further investigation of sediment sources was warranted. Monitoring of suspended sediment concentration in the catchments was spatially and temporally sparse. Event-based suspended
40 0
0 10000 20000 30000 15 min discharge (ML) G auge 218008
80 60 40 20 0
0
5000 10000 15 min discharge (ML)
1 /Nov 1 /D e c
Fig. 4. Examples of suspended sediment concentrations and discharge ratio graphs, showing daily mean discharge and the ratio graphs for the event in November 2005.
186
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
catchment-scale sediment modelling. These Lines-of-Evidence were chosen based on data availability at the time of the study. 4.3. Obtaining Lines-of-Evidence 4.3.1. Line-of-Evidence 1: sediment monitoring Event-based suspended sediment concentration and discharge data were obtained from the studies of Drewry et al. (2005, 2009). We used relationships between the suspended sediment concentrations (SSC) and discharge (Q) to identify the likely locations and types of major suspended sediment sources (Lefrançois et al., 2007; Williams, 1989). The SSCeQ ratio graphs (or sediment hysteresis loops), exhibited clockwise loops, most with a depletion of suspended sediment occurring on the rising stage of the hydrograph (Fig. 4). After the first depletion, suspended sediment concentrations rose again with the increase of discharge until suspended sediment was exhausted, and the concentrations decreased again before the peak of discharge. The behaviour of “double depletion” in the SSCeQ ratio graphs may indicate primary (instant) and secondary sources of suspended sediment during most events for both catchments. The instant source of suspended sediment is directly available during the early phase of the event, and could be deposited suspended sediment in the river or loose surface material from streambanks near the gauges. The instant source exhausts quickly after the rise of discharge, at which time the suspended sediment from the secondary source has not yet arrived. The secondary sources of suspended sediment may be derived from the middle reaches of the catchments, such as near-stream hillslope and road erosion. The critical information from analysis of the SSC-Q ratio graphs is that i) the suspended sediment in the catchments is supply-limited; ii) the major suspended sediment sources are likely to be deposited sediment in the rivers and/or streambank erosion as instant sources; and iii) hillslope and/or road erosion near the stream channels in the lower catchments may be secondary sources. The above qualitative information was translated into the index of a site delivering suspended sediment to gauges 217002 and 218008. This index was expressed spatially using upstream distance from the gauges e the shorter the distance, i.e. the closer the location to the stream and the gauge, the higher the potential of suspended sediment delivered to the stream and the higher the index value. The method is similar to the index-based approach for predicting risk of nutrient loss (Heathwaite et al., 2000; Sharpley et al., 2003). 4.3.2. Line-of-Evidence 2: sediment tracing Geochemical and mineralogical tracers were used to investigate the origins of sediments. These tracers were used because: i) geochemical and mineralogical differences have been identified for sediment in eastern Australia (Bhatia and Crook, 1986; White and Chappell, 1977); and ii) the concentrations of suspended sediment were too low to have sufficient samples for radioisotope analysis. Sediments were collected from the top 0.5 cm of deposits in active stream channels and floodplains at five stream junctions (J1eJ5 in Fig. 3). At each junction three samples were collected, one in the upstream main reach (A), one in the upstream tributary reach (B) and one in the downstream reach (C) (Fig. 3). Sites A and B were located approximately 1 km upstream of Site C. It was assumed that sediment samples from Sites A and B at each junction represented the sediment eroded and transported from the upstream subcatchments (denoted Subcatchments A and B respectively). Four to six replicate samples were collected at each site (with an extent of about 0.01 km2). Wet sieving was used to extract the fraction of the sediment less than 63 mm. The samples were then treated with 30% (w/w)
hydrogen peroxide to remove organic matter. The X-ray diffraction technique was used to analyse the bulk (<63 mm) and clay (<2 mm) mineralogy. An Inductively Coupled Plasma e Atomic Emission Spectrometer was used to quantify the concentrations of 17 elements (Al, Ba, Ca, Ce, Cu, Fe, Ga, Gd, K, Mg, Mn, Na, Nd, Pr, Ti, V, Zn). For each junction, tracers suitable for suspended sediment source discrimination were selected based on two criteria: i) the median of the tracer concentration from the mixture C is between those from Subcatchments A and B; and ii) the tracer concentrations from Subcatchments A and B were significantly different with 95% confidence. Mineralogical tracers were found to be of limited use because no significant differences were found to differentiate suspended sediment sources. In contrast, geochemical tracers could be successfully used for discriminating relative suspended sediment contributions from sampled subcatchments using a multivariate mixing model modified from Collins et al. (1998):
Minimize
n X
ððCi Ei Þ=Ci Þ2
(2)
i¼1
where Ei is the estimated concentration of tracer i in the mixture sample, and Ci is the median concentrations of tracer i in the mixture C samples. Given that Ei ¼ Aix þ Biy, constrained by: x þ y ¼ 1, and 0 x 1, where Ai and Bi are the median concentrations of tracer i in Subcatchment A and Subcatchment B, respectively; the model can be solved to get x and y, i.e. the proportional contributions of suspended sediment from Subcatchments A and B, respectively, at each junction. The results suggest that in the Moruya-Deua River catchment, the Upper Moruya-Deua River, Burra Creek and Donalds Creek subcatchments contribute 45%, 17% and 38% of the suspended sediment, respectively. The Donalds Creek subcatchment (with a higher density of unsealed roads) contributes a significantly higher proportion of suspended sediment per km2 than the other two subcatchments. In the Tuross River catchment, the Upper Tuross River subcatchment was estimated to contribute the most suspended sediment (%), followed by the Wadbilliga River. Belimbla Creek subcatchment has the largest undisturbed forest area, and was found to contribute the least amount of suspended sediment (16%). Detailed descriptions of geochemical sediment tracing for the study catchments are described in Fu et al. (2006). 4.3.3. Line-of-Evidence 3: sediment modelling A hybrid conceptual-empirical and spatially-distributed model, the Catchment Scale Management of Distributed Sources Model (CatchMODS) (Newham et al., 2008), was used to estimate sediment yields from hillslope, gully and streambank erosion in the catchments. Because unsealed roads were also considered a potentially important source of sediment in the region, we modified the Washington Road Surface Erosion Model (WARSEM) (Dubé et al., 2004) and integrated it with CatchMODS to account for sediment from unsealed roads (Fu et al., 2009). Sediment routing from each subcatchment was estimated as below:
Xout; i ¼ Xout; j þ Hi þ Gi þ Si þ Ri Fi
(3)
where Xout,i is the sediment routing from subcatchment i; Xout,j is the sediment routing from the upper subcatchment j; Hi, Gi, Si and Ri are the simulated suspended sediment from hillslope, gully, streambank and road erosion, respectively, from subcatchment i; and Fi is the floodplain deposition in subcatchment i. The model results suggest that major suspended sediment sources included the subcatchments along the middle to lower
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
187
Table 1 Characteristics of the three Lines-of-Evidence (monitoring, tracing and modelling) used in the two case studies to identify suspended sediment sources. Monitoring
Tracing
Modelling
Spatial resolution
25-m DEM grid cell
Small to medium subcatchments (1e85 km2)
Spatial extent
Upstream of 217002 and 218008 sites (km2) Three events One year (2005) 1.2e63 mm Index of suspended sediment contribution
Medium to large subcatchment upstream sampled junctions (50e900 km2) Upstream of junctions J1-J5 (1200 and 1580 km2) February 2006 A few decades <63 mm Proportional contribution of suspended sediment
Temporal resolution Temporal extent Particle size Output
Moruya-Deua River, the Araluen Creek, and the middle to lower Tuross River (Fig. 5). In the Moruya-Deua River catchment, hillslope, gully, streambank and road erosion contributed 32%, 33%, 26% and 9% of suspended sediment, respectively. In contrast, streambank erosion in the Tuross River catchment contributed nearly half of the suspended sediment. Gully erosion was less significant in the Tuross River catchment, and provided only 14% of the suspended sediment. The estimated total suspended sediment yields were compared with the reported sediment yields for the Southern Tablelands region in Australia (Wasson, 1994) and the estimated suspended sediment yields from event-based monitoring in the study
Whole catchments (less small estuary areas due to unavailable data) (1420 and 1960 km2) Mean annual average 1983e2008 <63 mm Annual average suspended sediment yields
catchments (Drewry et al., 2005, 2009). Our model outputs showed general agreement with the results compiled by Wasson (1994), but were higher than the sediment yields estimated from eventbased monitoring. The latter was expected because the monitoring data was obtained in a very dry year which had only 46e60% of annual average flow. 4.4. Information integration 4.4.1. Standardising the Lines-of-Evidence Information from the three Lines-of-Evidence described previously was provided at different scales and resolutions. Therefore,
Fig. 5. Estimated suspended sediment yields from the sediment model, showing (a) annual total suspended sediment yield and annual yields from (b) hillslope; (c) gully; (d) streambank; and (e) unsealed road.
188
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
the information had to be standardised prior to integration. The standardisation process potentially downgrades the scale and richness of the available information to the characteristic of the coarsest Lines-of-Evidence. The advantage, however, is like-to-like comparison and effective integration. The characteristics of individual Lines-of-Evidence are listed in Table 1. Monitoring gave the index of a site delivering suspended sediment to the location of gauges 217002 and 218008. Tracing provided the relative proportional contributions of suspended sediment at five stream junctions. Modelling provided suspended sediment yields from various erosion processes and subcatchments. Temporally, monitoring results were limited to events in a dry year, whereas both tracing and modelling reflect suspended sediment source contributions in the medium to long term (>10 years). However, the sediment model was simulated under a dry climate scenario, and the samples for tracing were likely to be deposited in the last decade which has been relatively dry. Therefore, despite differences in temporal resolution and extent (Table 1), all three Lines-of-Evidence were representative of low rainfall conditions, therefore could be integrated. There are also slight variations in particle size fractions represented in the three Lines-of-Evidence. The particle size fraction used in monitoring is 1.2e63 mm, whereas tracing and modelling use sediment less than 63 mm. We assumed that these differences in particle size between the Lines-of-Evidence can be neglected. Lines-of-Evidence were standardised spatially, using the subcatchments defined by tracing as the common basis. Results from monitoring and modelling were converted to proportional contributions of suspended sediment for the subcatchments defined in sediment tracing (at the selected junctions). The suspended sediment indices obtained from monitoring were averaged for the tracing subcatchments and rescaled so that each pair of subcatchments at a junction sum to 100%. Similarly, modelled suspended sediment yields were summed and scaled to 100% for each tracing subcatchment. 4.4.2. Weighting the Lines-of-Evidence Weights were given to the Lines-of-Evidence based on our judgement on the reliability of each Line-of-Evidence. Three criteria were used for assigning the weights: i) relevance of the outputs with the research objective; ii) quality of the input data; and iii) method suitability in relation to study objective. A score between 1 and 5 was assigned to each Line-of-Evidence for each criterion (Table 2). The final weight for each Line-of-Evidence was the sum of the scores from all criteria. The rationales for the score given to each Line-of-Evidence are detailed below. 4.4.2.1. Relevance of outputs to study objective. The focus of this research was the relative contribution of suspended sediment sources and their spatial distribution. Hence the Lines-of-Evidence were scored by how well their results reflect suspended sediment generation and transport. Scores of 5, 4 and 3 are given to monitoring, tracing and modelling, respectively. A lower score is given to modelling because we believed that sediment deposition was under-represented by the current model. 4.4.2.2. Precision and accuracy of the data used for each Line-ofEvidence. This reflects consideration of the representativeness of Table 2 Weights assigned to the Lines-of-Evidence (monitoring, tracing and modelling). Scoring criteria
Monitoring
Tracing
Modelling
Relevance to study objective Data precision and accuracy Method suitability Total
5 4 1 10
4 4 4 12
3 3 2 8
samples, the quality of laboratory analysis, and the accuracy of the model inputs. Scores of 4, 4 and 3 were given to monitoring, tracing and modelling, respectively. The model inputs were judged less reliable due to the resolution of the data and lack of testing. For instance, the Digital Elevation Model (DEM) used was at 25 m resolution, which is a coarse resolution for estimating the slope of road segments. 4.4.2.3. Suitability of the method relative to study objective. The major consideration of this criterion involved the suitability of the assumptions and algorithms. Scores of 1, 4 and 2 were given to monitoring, tracing and modelling, respectively. For the stream monitoring, the spatial distribution of the suspended sediment sources was based on the qualitative interpretation of the relationship between suspended sediment concentration and river discharge. Relatively high uncertainty was introduced when converting this qualitative information into categorised quantitative data (index). In contrast, the procedures for geochemical sediment tracing are generally well acknowledged (Walling, 2005) and were supported in this research by further analysis of the influence of particle size on tracing results (Fu et al., 2008). Some potential sources of uncertainties associated with the design of sediment tracing included the selection of tracers and the design of laboratory analysis, especially the treatment of organic matter. As a result, a score of 4 was given to tracing. Integrated sediment modelling relied on a large number of assumptions about the empirical relationships between suspended sediment yields and the biophysical characteristics of the catchments, many of which were not able to be thoroughly tested for the study catchments. Consequently, a value of 2 was given to modelling. 4.4.3. Combining the Lines-of-Evidence The Weight-of-Evidence results were calculated from Equation (1), and compared with the results of monitoring, tracing and modelling taken individually (Fig. 6). The combined proportional contributions of suspended sediment from the upstream subcatchments for J1 to J5 were 45%, 51%, 73%, 57% and 70%, respectively. The Weight-of-Evidence results slightly hinged towards the results of tracing and monitoring as larger weights were assigned to these two Lines-of-Evidence. The percentage contributions of the subcatchments were standardised by their drained subcatchment areas. The results show that in the Moruya-Deua River catchment, the Donalds Creek subcatchment at J1 contributed four times more sediment per unit area than Burra Creek subcatchment. At J2, the combined Donalds Creek and Burra Creek subcatchments contributed three times more sediment per unit area than the upper Moruya-Deua River subcatchment. In the Tuross River catchment, the three subcatchments in the upper reach of the Tuross River (i.e. Belimbla Creek, Wadbilliga River and Upper Tuross River subcatchments) had similar proportional contributions per unit area. At J3, the Reedy Creek subcatchment contributed five times more sediment per unit area than the mid and upper Tuross River subcatchment. These results highlight the hotspot subcatchments (Donalds Creek and Reedy Creek) in terms of sediment control management, where investment in sediment control could be prioritised. Outcomes for J5 were generally consistent among the three Lines-of-Evidence (ranging from 60% to 84%). However, outcomes for J2 varied significantly among the three Lines-of-Evidence, with sediment contribution from Subcatchment A (i.e. upper MoruyaDeua River subcatchment) ranging from 21% estimated by monitoring to 98% by modelling. From a management perspective, the uncertainty for J2 is too high to make sensible decisions in management priority and more data would be required to better identify sediment sources in this particular area.
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
189
J1 0
20
40
60
80
100 %
0
20
40
60
80
100 %
0
20
40
60
80
100 %
0
20
40
60
80
100 %
J2
J3
Monitoring
J4
Tracing Modelling WOE results
J5 0
20
40
60
80
100 %
Fig. 6. The proportional contributions of suspended sediment from the upstream subcatchments of J1 to J5, derived from monitoring, tracing, modelling and the integration framework.
4.4.4. Sensitivity analysis A sensitivity analysis was undertaken to investigate how sensitive the Weight-of-Evidence outputs were to the weights assigned to each Line-of-Evidence. This was done by increasing and decreasing the score of each Line-of-Evidence (one at a time) by 3 points, which is 20% of the maximum score a Line-of-Evidence can be assigned (i.e. 15). In most cases the Weight-of-Evidence outputs were insensitive to the scores assigned to Lines-of-Evidence (Fig. 7). On average, Weight-of-Evidence outputs changed by 2% for J1 and J3-J5 as a result of increasing or decreasing any Line-of-Evidence score by 3 points. The exception was at junction J2 where the Weight-of-Evidence output increased by 6.5% when monitoring result was decreased by 3 points (i.e. from 10 to 7), and decreased by 5.4% when monitoring result was increased by 3 points (i.e. from 10 to 13). Conversely, a 3-point increase in modelling score (from 8 to 11) led to a 8.4% increase in Weight-of-Evidence output; while a 3-point decrease in modelling score (from 8 to 5) led to a 10.2% decrease in Weight-of-Evidence output. A change in Line-ofEvidence 2 (tracing) instead was relatively insensitive to the Weight-of-Evidence outputs. This outcome at J2 reflected the greater differences among the monitoring and modelling results for this junction (Fig. 7).
Fig. 7. Sensitivity of the Weight-of-Evidence outputs to the weights assigned to each Line-of-Evidence (i.e. monitoring, tracing and modelling). The bars are the Weight-ofEvidence outputs for each junction under the assigned weights. The error bars indicate the Weight-of-Evidence outputs with changes (3 points) in scores for each Line-ofEvidence.
5. Discussion 5.1. Advantages and limitations Integrating heterogeneous information can increase confidence in a study outcome sensibly. This is particularly the case when verification of individual Lines-of-Evidence is unavailable. In the case study applications, uncertainties and limits of each Line-ofEvidence were identified, albeit subjectively, during the Weightof-Evidence weighting processes, and demonstrated that no Lineof-Evidence alone could provide a conclusive answer on the sources of suspended sediment. The Weight-of-Evidence approach did not remove uncertainties in each Line-of-Evidence, rather, it enabled their explicit consideration, improving the overall confidence in the identification of suspended sediment sources to meet the needs of catchment managers. The most significant limitation of the Weight-of-Evidence approach was that the outcomes of the integration were limited by the lowest resolutions among the Lines-of-Evidence. In the case study applications, the spatial resolution of the information on suspended sediment sources was reduced to the scale of the medium to large subcatchments defined for sediment tracing, and thus significant information from other Lines-of-Evidence was lost in the Weight-of-Evidence integrated results. Information loss can be substantial when the tracing subcatchments are large and contain several critical sources of suspended sediment that are of management interest. For example, the upper Moruya-Deua River subcatchment (upstream J2) covers two-thirds of the Moruya-Deua River catchment. Although sediment modelling identified specific subcatchments to contribute high sediment loads (such as the Araluen Creek subcatchment), this information was lost during integration and the significance of the Araluen Creek in contributing suspended sediment to the river systems could not be confirmed. The information was not only lost spatially, but also quantitatively. For example, average annual suspended sediment yields estimated by the sediment model could not be confirmed with other Lines-of-Evidence. Losing information on the magnitude of suspended sediment yields was the cost of increasing confidence in the relative suspended sediment contributions. However, given that the priority of the study was to identify relative suspended sediment sources, this loss of information is acceptable.
190
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191
The Weight-of-Evidence approach depends on expert judgement in the weighting assignment, which can be largely subjective. Although clear criteria were set to minimise subjectivities in weighting, it is likely that different scores could be given by different experts, thus altering the outcomes. Subjectivity is not necessarily negative, and techniques exist to elicit expert knowledge or to formulate weights that reflect the opinion of a large group of experts (Cooke, 1994). Even with such limitations, weighting towards well-defined criteria is a more transparent approach than simply averaging Lines-of-Evidence without consideration of their relative reliability. 5.2. Recommendations to improve the integrated approach Confidence in Weight-of-Evidence outcomes depends on the confidence in individual Lines-of-Evidence. This implies that overall confidence can be increased by reducing the uncertainty in individual Lines-of-Evidence. The integration framework outcomes can be improved by increasing the resolution of the coarsest Lineof-Evidence, in this case geochemical sediment tracing (i.e. medium to large subcatchments). Increasing the number of junctions sampled for sediment tracing, in particular at junctions that other Lines-of-Evidence indicated as potentially critical suspended sediment sources (e.g. Araluen Creek), would improve the spatial identification of the suspended sediment sources. Another means to improve Weight-of-Evidence outcomes is by increasing the number of independent Lines-of-Evidence. In the case studies, additional Lines-of-Evidence may include spatially distributed suspended sediment monitoring and sediment tracing on targeted erosion areas. For example, the Donalds Creek subcatchment in the Moruya-Deua River catchment and the Reedy Creek subcatchment in the Tuross River catchment were both estimated to contribute large proportions of suspended sediment relative to their catchment size. Stream monitoring at the outlets of these subcatchments could provide valuable information on suspended sediment loads from these areas. Field observations suggest that these subcatchments contain a relatively high density of forest roads and unstable streambanks. Sediment tracing aiming at discriminating suspended sediment contributions from unsealed roads and streambanks in these subcatchments could help to clarify further suspended sediment sources. This new Line-of-Evidence could be then integrated in the Weight-ofEvidence approach to confirm suspended sediment source outcomes. Improvements in assigning weights to the Lines-of-Evidence could be achieved using a Bayesian framework by specifying prior probabilities for the suspended sediment source estimates and estimating observation-error variances from the numerical results of individual Lines-of-Evidence. Bayes’ theorem could be used to assess the influence of each Line-of-Evidence, and weights could be then set as the Bayesian posterior probabilities. This would not exclude subjectiveness entirely, but would put the weighting exercise into a systematic framework. An example of using Bayes’ theorem to weight multiple Lines-of-Evidence has been provided by Neuhäuser and Terhorst (2007) for a landslide susceptibility assessment. Alternatively, a panel of experts judging Lines-of-Evidence reliability could be established to review and refine the weights we selected for this study. The use of formal elicitation techniques could improve confidence in the weights substantially (McGraw and Seale, 1988). In the case studies, the sensitivity analysis of the weights to the integration framework outputs suggested that a more complex weight assignment is not warranted for J1 and J3-J5. However, the integration outputs are relatively sensitive to the scores given to sediment monitoring and modelling at J2, and thus improved weighting processes may be required.
6. Conclusion An integration framework was described here for the identification of suspended sediment sources using a Weight-of-Evidence approach that incorporated multiple Lines-of-Evidence. The framework was driven by the study purpose, prior knowledge, and data availability. Its practical application to two case study catchments allowed for the integration of three Lines-of-Evidence (stream monitoring, geochemical sediment tracing, and sediment modelling). As a result, the contribution of suspended sediment at river junctions was more tightly identified, and more confident statements with regard to sources of sediment could be formulated. The major limitation of the integration framework was that the quality and quantity of the information were downgraded to match the Line-of-Evidence with the lowest resolution. For example, in this study information on suspended sediment yields was lost and detailed identification of some critical suspended sediment sources could not be identified. This limitation is inherent to the approach and can only be improved by increasing the resolution of the coarsest Line-of-Evidence (e.g. sediment tracing in this study), or by obtaining additional Lines-of-Evidence. In this sense, the Weight-of-Evidence approach can help identify where and which type of additional data is required, and therefore help planning data collection campaigns. A secondary limit of the approach was that it involved some subjectivity in assigning weights to the Lines-of-Evidence. This is not a negative aspect per se, as subjective choices were transparent, being explicitly formulated in the framework. However, more elaborate weighting processes (using for example a Bayesian framework or enlarging the panel of experts) may be pursued if sensitivity of the Weight-of-Evidence outcomes warrants it. The integration framework builds a coherent structure to interpret multiple Lines-of-Evidence, and can be widely applied to suspended sediment source studies. Such a rapid and cost-effective approach is a practical tool for catchment managers in integrating and communicating information effectively, and developing focused monitoring programmes and management strategies to meet the pressing demands for effective water quality management, particularly in the absence of long term spatially-distributed suspended sediment load data. Acknowledgements This study was undertaken with funding from the Australian National University, Cooperative Research Centre for Landscape Environments and Mineral Exploration and as part of Australian Research Council Linkage project LP0560439 with funding from Eurobodalla Shire Council. We wish to thank Prof Tony Jakeman, Prof John Norton and Dr John Drewry for their valuable comments on the draft of this paper. Dr Barry Croke also provided great support in the development of modelling research. The DEM data was provided by the New South Wales Department of Environment and Climate Change. References Beeton, R.J.S., Buckley, K.I., Jones, G.J., Morgan, D., Reichelt, R.E., Trewin, D., 2006. Australia State of the Environment 2006, Independent Report to the Australian Government Minister for the Environment and Heritage. Department of the Environment and Heritage, Canberra. Bhatia, M.R., Crook, K.A.W., 1986. Trace element characteristics of graywackes and tectonic setting discrimination of sedimentary basins. Contrib. Mineral Petr. 92, 181e193. Blöschl, G., Grayson, R., 2001. Spatial observations and interpolation. In: Grayson, R., Blöschl, G. (Eds.), Spatial Patterns in Catchment Hydrology; Observations and Modelling. Cambridge University Press, Cambridge, pp. 17e50. Burton, G.A., Chapman, P.M., Smith, E.P., 2002. Weight-of-Evidence approaches for assessing ecosystem impairment. Hum. Ecol. Risk Assess. 8, 1657e1673. Chapman, P.M., 1996. Presentation and interpretation of sediment quality triad data. Ecotoxicology 5, 327e339.
B. Fu et al. / Journal of Environmental Management 128 (2013) 182e191 Chapman, P.M., McDonald, B.G., Lawrence, G.S., 2002. Weight-of-Evidence issues and frameworks for sediment quality (and other) assessments. Hum. Ecol. Risk Assess. 8, 1489e1515. Collins, A.L., Owens, P.N., 2006. Introduction to soil erosion and sediment redistribution in river catchments: measurement, modelling and management in the 21st century. In: Owens, P.N., Collins, A.L. (Eds.), Soil Erosion and Sediment Redistribution in River Catchments. CAB International, Cambridge, pp. 3e9. Collins, A.L., Walling, D.E., Leeks, G.J.L., 1998. Use of composite fingerprints to determine the provenance of the contemporary suspended sediment load transported by rivers. Earth Surf. Proc. Land. 23, 31e52. Cooke, N.J., 1994. Varieties of knowledge elicitation techniques. Int. J. Hum-Comput St. 41, 801e849. Croke, J., Wallbrink, P., Fogarty, P., Hairsine, P., Mockler, S., McCormack, B., Brophy, J., 1999. Managing Sediment Sources, and Movement in Forests: The Forest Industry and Water Quality. Industry report 99/11. Cooperative Research Centre for Catchment Hydrology. Drewry, J.J., Greene, R.S.B., Newham, L.T., Croke, B.F.W., 2005. The role of soil regolith properties, river suspended sediment and nutrient concentrations during storm events in the Moruya and Tuross catchments, NSW. In: Roach, I.C. (Ed.), Regolith 2005 e Ten Years of CRC LEME. Cooperative Research Centre for Landscape Environments and Mineral Exploration, Adelaide and Canberra, pp. 78e82. Drewry, J.J., Newham, L.T.H., Croke, B.F.W., 2009. Suspended sediment, nitrogen and phosphorus concentrations and exports during storm-events to the Tuross estuary, Australia. J. Environ. Manage. 90, 879e887. Dubé, K., Megahan, W., McCalmon, M., 2004. Washington Road Surface Erosion Model (WARSEM) Manual. Department of Natural Resources, State of Washington. Flanagan, D.C., Nearing, M.A., 1995. Usda e Water Erosion Prediction Project: Hillslope Profile and Watershed Model Documentation. National Soil Erosion Research Laboratory Report No.10. US Department of Agriculture e Agricultural Research Service, West Lafayette. Fu, B., Field, J.B., Newham, L.T., 2006. Tracing the source of sediment in Australian coastal catchments. In: Fitzpatrick, R.W., Shand, P. (Eds.), Regolith 2006Consolidation and Dispersion of Ideas. CRC LEME, Perth, Western Australian, pp. 100e104. Fu, B., Newham, L.T.H., Field, J.B., 2008. Influence of particle size on geochemical suspended sediment tracing in Australia. In: Schmidt, J. (Ed.), Sediment Dynamics in Changing Environments (Proceedings of a Symposium Held in Christchurch, New Zealand, December 2008). IAHS Press, Wallingford, pp. 23e30. Fu, B., Newham, L.T.H., Field, J.B., 2009. Modelling erosion and sediment delivery from unsealed roads in southeast Australia. Math. Comput. Simul. 79, 2679e2688. Fu, B., Newham, L.T.H., Ramos-Scharrón, C.E., 2010. A review of surface erosion and sediment delivery models for unsealed roads. Environ. Model. Softw. 25, 1e14. Haines, P., Cavanagh, D., 2002. Tuross Estuary and Coila Lake Estuary Processes Study: Addendum to Final Report. WBM Oceanics Australia, Broadmeadow. Hateley, L.R., Armour, J.D., Brodie, J., Faithful, J., Pitt, G.L., Nelson, P.N., 2007. Modelling, monitoring and sediment tracing in the Tully River Catchment, north Queensland: a comparison of techniques. In: Oxley, L., Kulasiri, D. (Eds.), MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Christchurch, pp. 867e873. Heathwaite, L., Sharpley, A., Gburek, W., 2000. A conceptual approach for integrating phosphorus and nitrogen management at watershed scales. J. Environ. Qual. 29, 158e166. Jansson, M.B., 2002. Determining sediment source areas in a tropical river basin, Costa Rica. CATENA 47, 63e84. Kinsey-Henderson, A.E., Post, D.A., Prosser, I.P., 2005. Modelling sources of sediment at sub-catchment scale: an example from the Burdekin catchment, North Queensland, Australia. Math. Comput. Simul. 69, 90e102. Lefrançois, J., Grimaldi, C., Gascuel-Odoux, C., Gilliet, N., 2007. Suspended sediment and discharge relationships to identify bank degradation as a main sediment source on small agricultural catchments. Hydrol Process. 21, 2923e2933. Malhotra, M.K., Sharma, S., Nair, S.S., 1999. Decision making using multiple models. Eur. J. Oper. Res. 114, 1e14.
191
Marston, F., Prosser, I.P., Hughes, A., Lu, H., Stevenson, J., 2001. Waterborne Erosion e an Australian Story. Technical Report 17/01. CSIRO Land and Water, Canberra. McCulloch, M., Fallon, S., Wyndham, T., Hendy, E., Lough, J., Barnes, D., 2003. Coral record of increased sediment flux to the inner Great Barrier Reef since European settlement. Nature 421, 727e730. McGraw, K.L., Seale, M.R., 1988. Knowledge elicitation with multiple experts: considerations and techniques. Artif. Intell. Rev. 2, 31e44. Merritt, W.S., Letcher, R.A., Jakeman, A.J., 2003. A review of erosion and sediment transport models. Environ. Model. Softw. 18, 761e799. Murray, A.S., Olive, L.J., Olley, J.M., Caitcheon, G.C., Wasson, R.J., Wallbrink, P.J., 1993. Tracing the source of suspended sediment in the Murrumbidgee River, Australia. IAHS Publication No. 215. In: Peters, N.E., Hoehn, E., Leibundgut, C., Tase, N., Walling, D.E. (Eds.), Tracer in Hydrology. IAHS Press, Wallingford, pp. 293e302. Nagle, G.N., Fahey, T.J., Ritchie, J.C., Woodbury, P.B., 2007. Variations in sediment sources and yields in the Finger Lakes and Catskills regions of New York. Hydrol Process. 21, 828e838. Neuhäuser, B., Terhorst, B., 2007. Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SWGermany). Geomorphology 86, 12e24. Newham, L.T.H., Drewry, J.J., Fu, B., 2008. Catchment-scale water quality modelling and integration of collateral information. In: Sànchez-Marrè, M., Béjar, J., Comas, J., Rizzoli, A.E., Guariso, G. (Eds.), Integrating Sciences and Information Technology for Environmental Assessment and Decision Making. Proceedings of the 4th Biennial Meeting of the International Environmental Modelling and Software Society, vol. 1. International Environmental Modelling and Software Society, Barcelona, pp. 584e591. Newham, L.T.H., Prosser, J.P., Norton, J.P., Croke, B.F.W., Jakeman, A.J., 2001. Techniques for assessing the performance of a landscape-based sediment source and transport model: sensitivity trials and physical methods. In: Ghassemi, F., Post, D., Sivapalan, M., Vertessy, R. (Eds.), MODSIM 2001 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Canberra, pp. 149e154. Prosser, I.P., Rustomji, P., Young, B., Moran, C., Hughes, A., 2001. Constructing River Basin Sediment Budgets for the National Land and Water Resources Audit. Technical Report 15/01. CSIRO Land and Water, Canberra. Rustomji, P., Caitcheon, G., Hairsine, P., 2008. Combining a spatial model with geochemical tracers and river station data to construct a catchment sediment budget. Water Resour. Res. 44, W01422. DOI:01410.01029/02007WR006112. Sharpley, A.N., Weld, J.L., Beegle, D.B., Kleinman, P.J.A., Gburek, W.J., Moore, P.A., Mullins, G., 2003. Development of phosphorus indices for nutrient management planning strategies in the United States. J. Soil Water Conserv. 58, 137e152. Suter, G.W., Cormier, S.M., 2011. Why and how to combine evidence in environmental assessments: weighing evidence and building cases. Sci. Total Environ. 409, 1406e1417. Vrieling, A., 2006. Satellite remote sensing for water erosion assessment: a review. CATENA 65, 2e18. Walling, D.E., 2005. Tracing suspended sediment sources in catchments and river systems. Sci. Total Environ. 344, 159e184. Walling, D.E., Collins, A.L., Sichingabula, H.M., Leeks, G.J.L., 2001. Integrated assessment of catchment suspended sediment budgets: a Zambian example. Land Degrad. Dev. 12, 387e415. Wasson, R.J., 1994. Annual and decadal variation of sediment yield in Australia, and some global comparisons. International Association of Hydrological Sciences (IAHS) Publication No. 224. In: Olive, L.J., Loughran, R.J., Kesby, J.A. (Eds.), Variability in Stream Erosion and Sediment Transport. IAHS Press, Wallingford, pp. 269e279. Weed, D.L., 2005. Weight of evidence: a review of concept and methods. Risk Anal. 25, 1545e1557. White, A.J.R., Chappell, B.W., 1977. Ultrametamorphism and granitoid genesis. Tectonophysics 43, 7e22. Williams, G.P., 1989. Sediment concentration versus water discharge during single hydrologic events in rivers. J. Hydrol. 111, 89e106.