A WATER QUALITY MONITORING NETWORK ... - Springer Link

Environmental Monitoring and Assessment (2006) 122: 319–334 DOI: 10.1007/s10661-006-0358-4

c Springer 2006

A WATER QUALITY MONITORING NETWORK DESIGN METHODOLOGY FOR THE SELECTION OF CRITICAL SAMPLING POINTS: PART II R. O. STROBL1,∗ , P. D. ROBILLARD2 , R. L. DAY3 , R. D. SHANNON4 and A. J. McDONNELL5 1

Water Resources Department, Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands; 2 World Water Watch, Cambridge, Massachusetts, U.S.A.; 3 Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, Pennsylvania, U.S.A.; 4 Department of Agricultural and Biological Engineering, The Pennsylvania State University, University Park, Pennsylvania, U.S.A.; 5 Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania, U.S.A. (∗ author for correspondence, e-mail: [email protected])

(Received 2 July 2004; accepted 10 January 2005)

Abstract. In order to resolve the spatial component of the design of a water quality monitoring network, a methodology has been developed to identify the critical sampling locations within a watershed. This methodology, called Critical Sampling Points (CSP), focuses on the contaminant total phosphorus (TP), and is applicable to small, predominantly agricultural-forested watersheds. The CSP methodology was translated into a model, called Water Quality Monitoring Station Analysis (WQMSA). It incorporates a geographic information system (GIS) for spatial analysis and data manipulation purposes, a hydrologic/water quality simulation model for estimating TP loads, and an artificial intelligence technology for improved input data representation. The model input data include a number of hydrologic, topographic, soils, vegetative, and land use factors. The model also includes an economic and logistics component. The validity of the CSP methodology was tested on a small experimental Pennsylvanian watershed, for which TP data from a number of single storm events were available for various sampling points within the watershed. A comparison of the ratios of observed to predicted TP loads between sampling points revealed that the model’s results were promising. Keywords: critical source areas, design methodology, monitoring network, sampling points, water quality

1. Introduction The principal instrument to temporally and spatially administer water bodies is the installation of a water quality monitoring network. In light of the on-going degradation of water bodies world-wide, a well-designed monitoring network becomes a very important prerequisite in identifying water quality problems and establishing baseline values and long-term trends. Additionally, a better appreciation of the process dynamics of a watershed can be obtained via the gained information from a monitoring network. Ideally, the design of a well-configured water quality monitoring network should adhere to a universally adaptable and standardized design methodology, which not only incorporates scientifically based procedures, but also

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allows for flexibility in the design to accommodate for periodic modifications in the design. However, past approaches to the design of monitoring networks have been for the most part on an arbitrary basis without a structured or logical design methodology. Inadequately designed water quality monitoring networks not only can lead to erroneous and inaccurate appraisals, but also to very expensive monitoring data with little management or planning value. An adequately designed monitoring network, on the other hand, cannot only provide insight into the existing water quality conditions, but also foresight into the possible effects of any future changes occurring within the watershed. Also, a well-designed monitoring network is a prerequisite to well spent man-power and financial resources. When designing a water quality monitoring network, especially with respect to deciding upon the optimal placement of sampling locations, the generation and transport mechanisms of the contaminant need to be taken into account. The principal factors influencing contaminant transport in a watershed are topography, vegetation, soils, and climate (Barnes, 1997). From a hydrologic perspective, the spatial variability of these influencing factors will induce distinct runoff generation mechanisms. In many rural watersheds, for example, soil erosion and nutrient losses affect the water quality and often generate long-term problems, such as accelerated eutrophication (Honisch et al., 2002). Ideally, the identified sampling sites correspond to the most critical stream reaches with respect to the given pollutant. In order to accurately identify the critical stream locations, an inventory of the watershed-related data needs to be undertaken. More specifically, if the watershed is subdivided into individual land parcels, then each of these will have a certain pollution potential associated with it, depending principally on its topographic, land use, vegetation, and soil attributes (Vieux and Farajalla, 1994). Furthermore, since these attributes vary extensively across the watershed, the exact spatial location of each land unit plays a major role in defining its pollution potential. Consequently, critical land elements that are located spatially far away from the stream, may not pose as much of a pollution threat as a land parcel of equal critical magnitude that is situated adjacent to the water course. It is important to note that the transport of a pollutant from any given watershed parcel is a function of the surface and subsurface flow paths toward the stream. In addition, if the flow paths of land parcels with high pollution potential are intersected by vegetation or land uses that are known to retain pollutants, their potential threat is further reduced. Therefore, it becomes quite clear that the spatial dependency and topographic position as well as the pollution potential of each land parcel have great importance in the decision-making process of designating sensible candidate sampling points along a stream.

2. Critical Sampling Points Methodology A design methodology for identifying the critical sampling points in small agricultural-forested watersheds with respect to TP loads has been developed and

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described by Strobl et al. (2006). It was called the Critical Sampling Points (CSP) methodology and assists the watershed manager to achieve the spatial dimension portion of water quality monitoring network design. To be of practical use, it has been translated into a GIS-, fuzzy logic-, and simulation model-based model, called the Water Quality Monitoring Station Analysis (WQMSA) model. 3. WQMSA Model 3.1. G ENERAL

MODEL DESCRIPTION

The WQMSA model embodies the developed CSP methodology. A computed overall potential stream pollution index (PSPI) is used to ultimately rank each stream reach with respect to other stream reaches in the watershed according to its potential TP load. To arrive at a PSPI for each stream cell, the WQMSA model is fundamentally subdivided into two principal modules: potential surface pollution and potential subsurface pollution analysis components. The potential surface pollution analysis component evaluates the impact of several input variables, including land use, permeability, slope, plan curvature, profile curvature, potential solar radiation, buffering potential, flow path length, topographic wetness index, stream power index, and sediment transport index. The user of the model is asked to weight the importance of each input variable to each other via an interactive slider menu (Figure 1). All surface input variables, except for the inherently non-continuous land use variable, are used in a fuzzy logic operation to more realistically represent and evaluate the potential of surface loading. A detailed description of the employed

Figure 1. Weight assignment menu for fuzzy and non-fuzzy input variables.

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fuzzy logic operation can be found in Strobl et al. (2005). The fuzzy logic output and the land use variable are linked to ultimately compute a normalized index indicating the potential surface pollution for every cell in the watershed, excluding the stream cells. The highest index value of the computed potential surface pollution is normalized to a value of 1.00. In a further analysis step, a normalized potential surface pollution index (PSurfPI) is provided for each stream cell, based upon the surface contributing area (SCA) for a given stream cell and the previously calculated normalized index denoting the potential surface load of every cell in the watershed. This computed PSurfPI can hence be used to distribute the overall TP load from all source areas, as estimated by the GWLF model, along the stream cells to estimate the TP loads that can potentially be expected along the entire stream. This provides further quantitative insight into which stream reaches of the watershed are critical. For clarification, it should be noted that the term “SCA” refers to the exclusive surface drainage area of a stream cell excluding the surface drainage area of all other stream cells further upstream. In an optional step, the user may also distribute the overall TP load from all source areas across the watershed via the normalized index indicating the potential surface loading for every cell in the watershed. This provides further quantitative insight into which stream reaches of the watershed are critical from a surface runoff perspective. On the other hand, the subsurface loading potential is estimated by exploiting the TP loads estimated by the GWLF model for groundwater, septic systems, point sources, and stream bank erosion. Once again, however, in a simpler and more straightforward manner, a normalized potential subsurface pollution index as well as the subsurface TP load for each stream cell is computed. In a final step, the computed surface and subsurface loads for each stream cell are aggregated to receive a total stream load. Subsequently, the PSPI is calculated from the total stream load. In order to eliminate logistically impossible sampling sites along the stream from the analysis, the user is further asked to identify these reaches. The user then has the opportunity to specify the number of economically possible sampling points for a given year. A separate interface is available in the case that the user should require assistance in resolving how many sampling points will be economically possible for that given year. According to the number of economically possible sampling points, the most critical sampling locations in the watershed are identified and the sampling locations are highlighted. The WQMSA model has been designed to account for the unavailability of data in many countries and watershed applications. In other words, in order to assure that this methodology is as practical and thus universal as possible, the WQMSA model relies solely upon three data layers as input, namely layers of topography, land use, and permeability. Further watershed characteristics detail is, however, desirable for a successful execution of the GWLF model. Nonetheless, GWLF includes a wide consortium of default values which make the model suitable for use in remote regions where detailed data about watershed characteristics may be scarce.


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Figure 2. Overview of the WQMSA model.

The WQMSA model consists of five principal phases, with a total of 30 substeps, as depicted in Figure 2. It is an automated process requiring minimal user interaction. 3.2. SOFTWARE

USED

The majority of the WQMSA model was programmed using the AVENUETM lanR guage of the GIS program ArcView 3.2. In order to allow spatial modeling and analysis of cell-based raster data, the application requires the extension Spatial R AnalystTM . Since certain data manipulation cannot be carried out by ArcView R 3.2, these operations were consequently programmed in VisualBasic within the R Microsoft EXCEL 2000 software. In addition, the hydrologic simulation model GWLF 2.0 was code-structured from its original version in QuickBasic 4.5 to FORTRAN 90 and modified to output daily values. Furthermore, a stream bank erosion algorithm, as suggested by Evans (2002), was incorporated into the original GWLF model. On a PC platform, it is recommended to use at least a Pentium and 64 MB RAM. 3.3. M ODEL

ASSUMPTIONS

The WQMSA model has been developed for application to small, mainly agriculturally-dominated/forested watersheds with respect to the contaminant TP.

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Successful execution of the WQMSA model depends greatly upon the detail and accuracy of the input data. Since the WQMSA model utilizes the output from the GWLF model, the truthfulness of the developed methodology is inherently dependent on the accuracy and validity of applying the GWLF model to the watershed under study. The GWLF model uses universally accepted surface runoff and erosion algorithms, such as the Soil Conservation Service Curve Number (SCS-CN) method and the Universal Soil Loss Equation (USLE). Furthermore, the GWLF model has been used in various geographically diverse watersheds, including watersheds in the United States and Mexico (Haith et al., 1992; Lee et al., 2000; Izurieta et al., 2001). In terms of the assessment of the loading potentially coming from the land surface, it is assumed that the 11 input variables are sufficiently representative to provide an accurate account thereof (Strobl et al., 2005). Although such a weighting scheme might be rather subjective, it does allow the user to control and decide which input variables are more important for a given watershed under study, and thus permits the CSP methodology to be adapted to the local conditions of any watershed. The user of the WQMSA model may base the weighting process on specific watershed data, generalized watershed data, or intuitive experience. A further assumption used in the WQMSA model is that the model is designed to predict the logistically and economically possible sampling points for a given year. Hence, it is rather important that the input data, especially the land use data, accurately reflect the current watershed characteristics with reference to the year in question. Finally, the user should realize that the critical stream sampling point locations derived by the WQMSA model cannot be endorsed to correspond to the absolute truth, but rather to represent a likely TP load allocation in the watershed. The model has been developed to embody processes and variables that are widely accepted to be important in terms of phosphorus movement to a stream. The results of the model will inherently be representative of a reasonable estimation of which stream sections in a watershed will have the greatest TP loads.

3.4. C OMPILATION

AND DEVELOPMENT OF DATASETS

The WQMSA model relies solely on three primary GIS data layers as input for deriving the input layers needed for the analysis of the most critical sampling points in the watershed. The grid size must be determined beforehand in accordance with the resolution of the available data. Three GIS layers for the watershed are required, namely a gridded land use layer, categorized into six preset classes, a gridded soil permeability layer, and a topography layer that provides the geographic coordinates and corresponding elevations throughout the watershed is necessary.


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4. Example Application In order to test the validity of the CSP methodology as embodied by the WQMSA model, a small experimental watershed was selected that not only had water quality measurements at its outlet, but also at several points along the stream within the watershed. Such a configuration allows for a relative comparison of how well the developed methodology performs by comparing the computed PSPI to the observed loads at those points within the watershed. 4.1. WATERSHED

LOCATION AND DESCRIPTION

The FD-36 watershed is an upland rural watershed of 39.7 ha, located in eastern Pennsylvania (Figure 3). It is a subwatershed of east Mahantango Creek, which represents a tributary of the Susquehanna River that eventually flows into the Chesapeake Bay. USDA-ARS land use data from 1996 were reorganized according to the categories of the WQMSA model, and indicate that 43, 29, 15, 12, and 1% of the land use are row crops, forest, non-row crops, grassland, and urban, respectively. Cropland is the most dominant land use in the watershed, except directly adjacent to the stream channel. Major crops are wheat (Triticum aestivum), corn (Zea mays), and soybeans (Glycine max) (McDowell et al., 2001). There are three principal forested areas, located in the southern parts of the watershed. The land

Figure 3. FD-36 watershed location and land use distribution.

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surface elevations in the watershed range from approximately 240 to 295 m above mean sea level. The climate of the watershed can be described as humid and temperate (McDowell et al., 2001). Average annual precipitation is about 1100 mm and well distributed throughout the year (Gburek and Folmar, 1999). Continuous streamflow measurements have been made since May 1996 at the four recording H-flumes within the FD-36 watershed. 4.2. AVAILABLE

DATA AND DATA PRE - PROCESSING

In constructing the necessary GIS theme layers, a square cell grid size of 5 m was chosen, resulting in a total of 15 225 watershed cells and 113 cells defining the stream bed. For the septic system load component, a GIS theme layer representing the two dwellings in the watershed that have septic systems was built from the land use GIS theme. In addition, meteorological data were available from April 1995 to March 2001 from a station just outside the watershed. Daily streamflow data, measured by H-flumes at the four existing sampling locations, were available from April 1997 to March 2001. Also available were data of the event mean concentrations of sediment and TP of composite samples taken during various storm events during the period June 1999 until March 2001 at the four sampling points. For these storm events, the storm sediment and TP loads were calculated with knowledge of the corresponding stormflow hydrographs. Unfortunately, the major storm events generally occurred during the winter months, during which no water quality sampling was undertaken. The GWLF model was calibrated with the available data and yielded the necessary TP load input for the WQSMA model (Table I). 4.3. WQMSA

MODEL RESULTS

There were two major goals of the performed WQMSA model simulations, namely: 1. to investigate how well the WQMSA model performed in light of the actual observed TP data at the four sampling points within the FD-36 watershed, and 2. to hypothetically re-assign the four existing sampling points according to the results of the WQMSA model. TABLE I Four-year (1997–2001) average annual TP loads from the GWLF model for the FD-36 watershed

TP load (kg/year)

Source areas

Stream bank erosion

Groundwater

Point sources

Septic systems

210.3

0.0

7.9

0.0

0.0


4.4. WQMSA

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MODEL PERFORMANCE INVESTIGATION

The actual TP loads were computed for each of the four sampling points from the event mean concentrations and estimated streamflow volumes for 22 single storm events. In order to make a comparison between the simulated and observed TP loads possible, the ratios of the observed loads from the 22 storm events between the sampling points were computed. Equal weighting for all fuzzy and non-fuzzy variables was used in the WQMSA model simulation. In the case of the simulated loads from the WQMSA model, it should be noted that in order to arrive at the estimated ratios, it would have also been possible to accumulate the PSPI values since these are directly proportional to the estimated TP loads. Figure 4 depicts the boxplots of the TP load ratios between the four sampling points along with the respective location of the WQMSA model-estimated TP load ratio (shown with an). It shows that the ratio estimated by the WQMSA model falls within the range of the observed data, in most cases at least falling within the respective interquartile ranges. The relationships between streamflow volume and these ratios as well as peak streamflow rate and these ratios were also examined. For all ratios, similar results were obtained. For this reason, only two representative scatterplots of these relationships are provided (Figure 5). A logarithmic trend line was added to these scatterplots to indicate the general tendency. The estimated WQMSA TP ratio is given in the upper right-hand corner of each graph in Figure 5.

Figure 4. Computed WQMSA model TP load ratios with respect to box plots of observed TP load ratios between sampling points (SP1, SP2, SP3, and SP4).

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Figure 5. Scatter plots of TP load ratios versus stormflow volume and peak storm flow rate.

In all cases, the scatterplots indicate that most of the TP load observations are solely based upon low flow volumes and low peak flow rates. For each of these ratios, the logarithmic trend line reveals that at higher flow volumes and peak flow rates, the estimated WQMSA TP ratio is approached. This is reasonable since the CSP methodology represents the average annual expected TP load, of which up to 90% is generally determined by a few large storm events (Pionke et al., 2000). Unfortunately, as stated earlier, the larger storm events are normally not sampled in the FD-36 watershed. 4.5. WQMSA

MODEL SAMPLING STATION ASSIGNMENT

In order to re-evaluate the locations of the current four sampling stations in the FD-36 watershed with respect to the CSP methodology and indicate where these four sampling points hypothetically should be located in terms of better assessing and ultimately controlling the TP pollution potential in the watershed, the WQMSA model was run with all surface input variables equally weighted. A field visit to the site indicated that all stream reaches were logistically accessible. Figure 6 shows the distribution of the SCAs as well as the computed potential surface pollution index ≥0.80 and 0.90. The CSP methodology has identified the near stream areas to be particularly potentially TP load-prone. Figure 7 illustrates the computed PSPI for the stream, where larger circles indicate higher PSPI values. The watershed outlet was automatically considered to be a sampling point of interest. Thus, it was pertinent only to seek the three highest ranking stream reaches. The three next highest ranking stream reaches were also included in the analysis (Table II). Table II indicates that the stream reach denominated as 1 has a much


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Figure 6. Potential surface pollution indices ≥0.80 and 0.90 for all weights = 1.00.

Figure 7. Computed PSPI for the stream and locations of the four existing sampling points SP1, SP2, SP3, and SP4.

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TABLE II PSPI and ranking of the critical and existing sampling locations X-coordinate

Y-coordinate

PSPI

Rank

Comments (relative to Figure 8)

364275.2561 364435.2561 364350.2561 364675.2561 364305.2561 364465.2561 364830.2561 364755.2561 364565.2561 364460.2561

4507931.3415 4508001.3415 4507981.3415 4508041.3415 4507941.3415 4508011.3415 4508126.3415 4508086.3415 4508026.3415 4508011.3415

1.0000 0.7062 0.3256 0.3130 0.2532 0.2266 0.0025 0.0332 0.0007 0.0003

1 2 3 4 5 6 73 31 87 96

Denominated as 1 Denominated as 2 Denominated as 3 Denominated as 4 Denominated as 5 Denominated as 6 Watershed outlet (=SP1) SP2 SP3 SP4

higher PSPI value than stream reach 2 and especially than stream reach 3. Stream reach 1 is the furthest upstream point of the stream and is drained by the largest SCA of the watershed. It should be noted that in the FD-36 watershed case, the surface component of the WQMSA model is solely responsible for the distinction of potential polluting watershed areas. This can be explained by the fact that the subsurface component of the WQMSA model does not contribute to the differentiation of watershed cells that may add to the potential of stream pollution due to TP loads because, according to the results of the GWLF model simulation, the stream bank erosion, septic systems, and point sources are considered to be negligible in the watershed. The only subsurface component of the WQMSA model contributing to the total TP load in the watershed is the groundwater component which, according to the WQMSA model, distributes its TP load uniformly to all watershed cells and, therefore, does not assist in distinguishing watershed areas having higher pollution potential. The computed PSPI value and the overall ranking for the watershed outlet are relatively low. In terms of selecting the third sampling point, the very similar PSPIs reported in Table II suggest that the watershed manager would have a choice between the stream reaches 3 and 4, and possibly even could reasonably select stream reach 5. The geographical locations of the WQMSA model-suggested as well as the currently existing sampling points are depicted in Figure 8. The calculated PSPIs for the presently existing sampling stations of the FD-36 watershed are relatively low in terms of identifying the critical stream reaches that contribute the greatest TP loads. However, Figure 8 discloses that sampling station 4 (SP4) is located within 5 m to stream reach 6, which ranks sixth in stream pollution potential. It should be noted that the geographic locations of the WQMSA model are not topographically absolute since the results are highly dependent on the resolution and accuracy of the DEM.


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Figure 8. WQMSA model suggested sampling locations (1, 2, 3, 4, 5, and 6) and existing sampling points (SP1, SP2, SP3, and SP4).

To investigate the importance of the size of the SCAs in the ranking of the critical stream reaches, each watershed cell was given a potential surface pollution index value of 1.0. Table III presents this analysis. The results show that only with minor alterations in individual rankings, the results of the WQMSA model are greatly influenced by the size of the SCAs. A unit area PSPI was computed for each of the sampling points in Table III. The calculations show that the highest PSPI ranked stream reach (sampling point 1) has a lower unit area PSPI than the lower PSPI ranked stream reaches. This observation highlights the point that a larger sized SCA can rank high although on average the source areas within the TABLE III PSPI and ranking for SCAs only Sampling point

WQMSA rank

WQMSA PSPI

WQMSA unit area PSPI (105 m−2 )

Rank relative to SCA

PSPI according to SCA

Watershed area (%) represented by SCA

1 2 3 4 5 6 SP1 SP2 SP3 SP4

1 2 3 4 5 6 73 31 87 96

1.0000 0.7062 0.3256 0.3130 0.2532 0.2266 0.0025 0.0332 0.0007 0.0003

1.126 1.300 1.316 1.211 1.307 1.349 1.429 1.443 0.933 0.600

1 2 4 3 5 6 74 30 85 89

1.0000 0.6119 0.2788 0.2912 0.2182 0.1892 0.0020 0.0259 0.0008 0.0006

23.32 14.27 6.79 6.50 5.09 4.41 0.05 0.60 0.02 0.01

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SCA are not the potentially most critical areas of the watershed. On the other hand, the outlet of the FD-36 watershed (SP1) has a higher unit area PSPI than all of the top six PSPI ranked stream cells. However, the SCA for the outlet is extremely small (0.01% of the watershed area) and hence load control or reduction measures in the outlet’s SCA will ultimately not have a great impact on the watershed’s total loading. Nevertheless, for watersheds without great differences in SCA sizes, the surface component input factors of the WQMSA model will have a much greater impact on the final results. Moreover, if there are areas within a watershed that have a very high TP load pollution potential, this will be reflected in the final WQMSA model’s results, despite differences in SCA sizes. A brief investigation was undertaken to inspect how changing a single input variable would affect the WQMSA model’s results. Weights are assigned to each of the 11 input variables to highlight the importance of each input variable. Even though such a weighting scheme might appear to be rather subjective, it permits the watershed manager to dictate and decide which input variables are more crucial for a given watershed under study. Such a scheme allows the local conditions of the watershed to be adapted into the methodology. For example, for watersheds exhibiting just a single land use, the land use input variable will not have an impact on the relative loading of the watershed cells. Therefore, in this case, including the land use variable into the analysis would not shed any light on the critical source areas. Several runs were performed and had shown that modifications to a single input variable may improve the pollution situation to some extent; however, that usually a number of important hydrologic, topographic, vegetative, etc. factors should be targeted to achieve more observable results. This also highlights the fact that in any watershed management plan, a number of factors should be targeted in order to achieve visible results in pollution control measures.

5. Conclusions This paper has described the WQMSA model, which embodies the CSP methodology, as described by Strobl et al. (2005). The model is to be used in the sampling station allocation component of water quality monitoring network design and is intended to achieve improvements in current water quality monitoring network design strategies. It should be noted that it is not intended to provide final results that do not require any further periodic reappraisal and modifications. Instead, the WQMSA model provides a means of periodically reassessing the critical sampling locations identified in previous analysis runs. The proposed methodology emphasizes topographic, hydrologic, transport, vegetative, and soil factors as well as existing land use conditions as indicators of potential for TP load pollution. Surface and subsurface components are included in the model. However, due to the nature of the export and loss of TP, the surface component has been developed in greater detail. A normalized index, called the


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Potential Stream Pollution Index (PSPI), which is based upon all the surface and subsurface factors, is computed to represent an approximation of pollution risk. It provides a means of ranking surface and subsurface potential stream pollution. Watershed managers and planners may use the PSPI to evaluate and prioritize sites to target areas for detailed field investigations. In addition, once the model is used to identify high priority sites, appropriate controls may be identified in contributing areas of highest risk in order to minimize or reduce the total TP load existing in the watershed. To test the applicability and practicality of the CSP methodology, a small experimental watershed in eastern Pennsylvania was chosen. A comparison of the ratios of observed to predicted TP loads at the four sampling locations within the watershed revealed that the WQMSA model’s results approached the high storm flow/high storm volume expected TP load ratios which are what principally determine the expected annual TP loads of a watershed. However, since the largest storm events were not available for the watershed under study, this observation could not be conclusively proven. Nevertheless, the application demonstrated that the WQMSA model has potential in identifying the critical stream reaches. A detailed investigation of the individual input parameters to the WQMSA model in the case of the FD-36 watershed revealed that the size and location of the SCAs strongly influenced the final ranking of the high priority sites with respect to TP load pollution. This finding suggests that any reasonable water quality monitoring network design methodology must necessarily include the spatial aspect of the input parameters. As was suggested by the presented case study, the WQMSA model may not only be utilized in the scientific allocation of sampling stations, but also can potentially be applied in other research areas, such as best management practices assignment or in the determination of which watershed attributes are most critical for a particular watershed.

References Barnes, P. L.: 1997, Row Crop Pollution Control in Northeast Kansas, Kansas State University. Evans, B. M.: 2002, ‘Development of an Automated GIS-Based Modeling Approach to Support Regional Watershed Assessments’, Ph.D. Thesis, The Pennsylvania State University, University Park, PA. Gburek, W. J. and Folmar, G. J.: 1999, ‘Flow and chemical contributions to streamflow in an upland watershed: A baseflow survey’, J. Hydrol. 217, 1–18. Haith, D. A., Mandel, R. and Wu, R. S.: 1992, GWLF – Generalized Watershed Loading Functions, Version 2.0 – User’s Manual, Department of Agricultural Engineering, Cornell University, Ithaca, NY. Honisch, M., Hellmeier, C. and Weiss, K.: 2002, ‘Response of surface and subsurface water quality to land use changes’, Geoderma 105, 277–298.

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Izurieta, J., Gómez, M. A., Evans, B. E. and Mijangos, M. A.: 2001, ‘Evaluación de la contaminación difusa en la cuenca del R´ıo Apatlaco’, in XI Congreso Nacional de Irrigación. Simposio 5, Manejo Integral de Cuencas, September 19–21, Guanajuato, México. Lee, K.-Y., Fisher, T. R., Jordan, T., Correll, D. L. and Weller, D.E.: 2000, ‘Modeling the hydrochemistry of the Choptank River basin using GWLF and GIS’, Biogeochemistry 49, 143–173. McDowell, R. W., Sharpley, A. N., Beegle, D. B. and Weld, J. L.: 2001, ‘Comparing phosphorus management strategies at a watershed scale’, J. Soil Water Conserv. 56, 306–315. Pionke, H. B., Gburek, W. J. and Sharpley, A. N.: 2000, ‘Critical source area controls on water quality in an agricultural watershed located in the Chesapeake Basin’, Ecol. Eng. 14, 325–335. Strobl, R. O., Robillard, P. D., Shannon, R. D., Day, R. L. and McDonnell, A.J.: 2006, ‘A water quality monitoring network design methodology for the selection of critical sampling points. Part I, Environ. Monit. Assess. 112(1–3), 137–158. Vieux, B. E. and Farajalla, N. S.: 1994, ‘Capturing the essential spatial variability in distributed hydrological modelling: Hydraulic roughness’, Hydrol. Process. 8, 221–236.