non-point source water pollution management: improving decision-making information through water...

Journal of Environmental Management (1992) 34, 15-30

Non-point Source Water Pollution Management: Improving Decision-making Information Through Water Quality Monitoring

Lorin E. Reinelt*, Richard R. Hornert and Reinhold Castensson*

*Department of Water and Environmental Studies, Linkrping University, S-581 83 Lh~kb'phzg, Sweden and ~Department of Ovil Enghteering, University of IVashington, Seattle, IVashhtgton 98195, U.S.A.

Received 29 September 1990

The identification, assessment and management of non-point source water pollution problems can be improved through better water quality monitoring program designs. The result is often more useful and reliable information for use by decision-makers. In this study of the Svarth River Basin in south-central Sweden, the results of a modified monitoring program, designed to address non-point source inputs, were compared with the results of the ongoing program. Cost, validity (addressing intended objectives) and reliability (estimated level of uncertainty) were the criteria used to evaluate and compare the design and results obtained from the two programs. The study showed that choices of variables measured, station location and sampling frequency have a direct influence on the type and value of information obtained, especially when considering non-point sources. For a similar cost, the modified program produced more valid and reliable information for decision-making than the ongoing program.

Keywords: water quality monitoring, decision-making information, non-point sources, cost, validity, reliability.

I. Introduction

In the past, good quality water was considered an unlimited resource in Sweden. Widespread visible pollution of lakes and rivers in the mid-1900s, however, led to the establishment of programs to protect water quality and manage valuable water resources (Lrwgren et al., 1989). Early water quality management efforts focused primarily on industrial and municipal point sources, because they were relatively easy to identify and their control was possible with modern treatment technologies. Today, most point source discharges have been reduced and it is increasingly clear that control of non- point, or diffuse, sources is necessary to improve water quality further.

Non-point sources include agricultural and silvicultural operations, urban areas, mining and construction activities and atmospheric deposition. Intensive agricultural

Current address for Lorin Reinelt: Center for Urban Water Resources Management, University of Washington, FX-10 Seattle, Washington 98195, U.S.A.

15

0301-4797/92/010015+ 16 $03.00/0 © 1992 Academic Press Limited

16 Non-point s o u r c e w a t e r pollution

and silvicultural activities and new urban development contribute to increases in land erosion and eutrophication of rivers, lakes and nearshore areas (Siissmann, 1983; Logan, 1987; Sheridan and Hubbard, 1987). Resulting water quality degradation may impact beneficial uses. Total suspended solids (TSS), phosphorus and nitrogen are commonly measured to assess potential eutrophication problems or measure changes (in space or time) resulting from these activities.

The eutrophication of lakes (Forsberg and Ryding, 1985; Henderson-Sellers and Markland, 1987) and nearshore areas (Fleischer et al., 1989) in Sweden continues to be an environmental problem. In recent years, several government decisions were made in Sweden regarding the reduction of nutrient loadings to lakes and nearshore areas, including Lake Ringsjr, Laholm Bay and the Baltic and North Seas. For these four areas, decisions were made to reduce nutrient loadings by as much as 50% (North Sea Conference, 1987; Helsinki Commission, 1988; Fleischer et al., 1989). Once such decisions are made, there is a need for accurate information regarding nutrient loadings to gauge the success of water quality management efforts.

1.1. WATER QUALITY MONITORING AND PROGRAM DESIGN

Water quality monitoring may be carried out for many reasons (e.g. to assess current conditions, detect changes, determine annual pollutant loadings). Although these examples deal with different issues, Reckhow and Chapra (1983) noted that, for most cases, a decision or inference is to be made, and thus information is needed to assist in the decision-making process. If water quality monitoring programs are designed with this purpose in mind, it is likely that the value of data obtained, and ultimately the information available to the decision-maker, will I;.e greater.

A clear definition of objectives is an important first step in the development of a water quality monitoring program (Ward et aL, 1986; Reinelt et al., 1988). Although it is sometimes difficult for decision-makers to define specific, or even general, objectives, it is critical for purposes of monitoring program design. This necessity is because the objectives, or purpose, of the program directly influence the type of design, and thus the data and information eventually available. The available budget for monitoring is also determined at this time. Once the objectives and cost constraints are defined, the design proceeds and results in the choice of: (I) variables under investigation; (2) station locations; and (3) sampling frequency (number of, and intervals-between, occasions).

In 1986, the Swedish Environmental Protection Board (SNV) issued new general recommendations and guidelines for water quality monitoring that included the development of base and special programs (Ryding, 1984; SNV, 1986). The base programs consist of annual investigations at a reduced number of locations, with fewer variables measured on less frequent occasions than in past efforts. The special programs are intermittent and include intensive investigations designed to address specific concerns and problems.

1.2. USING ~,VATER QUALITY INFORMATION

It is not possible to obtain complete knowledge about the subject of concern through water quality monitoring, but a sampling of the problem usually results in better information. Decision-makers require information that is unbiased, or truly representative of the subject, and contains a certain level of assurance that the information acquired through sampling will lead to a better decision (Reckhow and Chapra, 1983).

Water quality information aids the decision-maker in many areas, including: (1) the

L. E. Reinelt et aL 17

development of water quality management and control strategies; (2) the development of new environmental or water quality policy; (3) integrated land and water use planning (Castensson et al., 1990); and (4) evaluation of previous decisions. As an example for this study, new information might result in policies directed at greater control of non-point sources or a re-affirmation of the status quo. In either case, a better decision can be made if the necessary detail or amount of information is available to address the issues of concern.

1.3. PURPOSE OF THIS STUDY

Changes in pollution sources and accompanying changes in the type of information desired by decision-makers require the development of different water quality monitoring strategies to address the current objectives. The data and information obtained from a water quality monitoring program should be valid and reliable, yet still be cost- effective. For purposes of this study, validity was defined as the ability to address intended objectives and reliability as producing an acceptable level of uncertainty.

In this study a monitoring strategy developed using a non-point source monitoring program design conceptual model (Horner et aL, 1986; Reinelt et al., 1988), was carried out on the Svartfi River Basin in south-central Sweden. The purpose of the study was to evaluate the results of the new monitoring program and compare the information obtained to that of the ongoing monitoring program. The application of the model is explained in detail elsewhere (Reinelt et al., 1987). The new monitoring program was carried out from January to December 1987.

The ongoing monitoring program is carried out by the Motala River Basin Water Protection Alliance (MSV), a voluntary regional organization with an interest in water quality control (Romfis, 1985). Membership in MSV includes local municipalities and industries within the geographic area of interest. Since 1974, MSV has co-ordinated water quality monitoring according to a program authorized by the County Adminis- trative Board of 0sterg6tland. Figure 1 shows the boundaries of the Svartfi and Motala River Basins.

2. Criteria for evaluating monitoring program effectiveness

Cost, validity and reliability were the criteria used to assess the effectivefaess of the monitoring program designs and the results obtained from the two programs. The calculation of monitoring program cost was straightforward, but determining the validity and reliability of the programs was partly subjective and more difficult.

2.1. MONITORING PROGRAM COST

Monitoring program cost may be divided into two components: (I) collection or sampling costs; and (2) analysis or laboratory costs (Mar et al., 1986a). Collection costs are composed of occasion costs (the cost of sending a crew to the field for each sampling occasion) and station costs (the cost of moving to each station and collecting a sample). The analysis costs are based on the number of variables analysed and the cost of each analysis. The number of sampling occasions and stations determine the collection costs, while the type and number of variables analysed determine the analysis costs. Overhead costs also should be included to reflect the total cozt of the monitoring program. In this study, the following equation, modified from Mar et al. (1986b), was used to calculate monitoring program cost.


5 8 o 2 8 '

( ~1 I

I !

/ ( ~j

15°5 f f

!

~ Moto lo Str '6m R ive r B o s i n 0 3 0 k i n • ~ S v o r t 8 R i v e r Bos in I ! I I

Figure 1. Location map of the Svartfi River and Motala River Basins, and the study area.

C = Co+ T x C t + S x Tx (C,+ C a)

where: C=total monitoring program cost; T=number of sampling occasions; S= number of sampling stations; Co= overhead costs; Ct= cost per sampling occasion; C, = cost per sampling station; and Ca = total analysis cost per sample.

2.2. VALIDITY AND RELIABILITY

Validity may be defined as the quality or state of being well-grounded or justifiable. In this study, validity was used to assess the potential effectiveness of the monitoring program design in meeting the program objectives. A "valid" design is one in which the choices of variables, stations and sampling frequency effectively characterize the problem and provide the necessary information to meet the objectives. Determining the validity is somewhat subJective and the authors did not attempt to develop a unit for its measurement.

Reliability is defined as the extent to which an experiment, test or measuring procedure yields the same result on.repeated trials. It also has been defined as the "inverse of uncertainty" (Reckhow and Chapra, 1983). Reliability relates to uncertainty


and precision, all three of which have a statistical basis. In this study, rcliability was measured in terms of the uncertainty associated with annual loading estimates. Generally, reliability increases and uncertainty decreases with cost (Figure 2). It is important to note, however, that two monitoring programs can have different levels of reliability for the same cost.

A relatively new statistical method known as "bootstrapping" (Efron, 1982) was used to give a quantitative indication of the reliability of the annual loading estimates. Bootstrapping, a Monte Carlo form of sample re-use, involves random sampling of the water quality data used in the estimation of annual loadings. For this study, bootstrapping was performed on a log load versus Iog flow regression equation that was used to calculate annual loadings (Smith and Stewart, 1977; Reinelt and Grimvall, in press):

log ([(t))= h + b x log (q(t))

where l(t) is the estimated load at time t, h and b are determined by the least squares method, and q(t) is the observed water flow at time t. Repeating the entire random sampling process a large number of times (N= 500 for this ease) yields 500 different regression coefficients. Confidence intervals can then be established for the annual loading estimates. Further discussion of this technique is presented below.

3. Case study of the Svarth River Basin

3. I. HISTORY OF WATER QUALITY MONITORING

Systematic water quality monitoring began in Sweden during the 1950s and in the Motala River Basin in 1955 (Karlsson and L6wgren, 1985). The monitoring program for the Svart~ River Basin, a sub-basin of the Motala River Basin, has gone through many changes during the past three decades. In general, there was an increase in the number of variables under study, station locations and sampling frequencies between 1955 and 1984 (Reinelt et al., 1987). In 1985, there was a decrease in the number of variables and stations, reflecting the new SNV recommendations and guidelines noted above. The frequency of sampling was either increased (4 to 6), decreased (12 to 6) or held constant at 12 times per year, depending on the station.

Ootimum

y.

Cost

Figure 2. Theoretical relationship between reliability and cost for different monitoring program designs (adapted from Reckhow and Chapra, 1983).


3.2. STUDY AREA DESCRIPTION

The Svart~ River Basin is located between Lake V/ittern and the Baltic Sea in south- central Sweden. This study focused on the northern half of the basin, between Lake Sommen and Lake Roxen, an area of 1539 km 2 (Figure 3). The mean annual precipi- tation is 545 ram. Seasonal and short-term flows o f the Svarth River are partly controlled by a dam at the outlet of Lake Sommen and several run-of-river power stations. Land use consists of a mixture of arable land (grains, oil crops, potatoes), pastures, forests and several urban communities. The largest community is Mj61by (population 17 000). The northern half o f the river basin is dominated by agricultural lands and the southern half is primarily forests.

3.3. WATER QUALITY CONCERNS

As noted above, the eutrophication of lakes and nearshore areas continues to be a concern in Sweden. Such concerns also exist in the area of this case study, particularly

Li 13

Li 19

Kopell~]n

Mi~lby U~

%,

BOX -

holm

Bo2

0 5 km I . . . . 1

Figure 3. Svart~ River Basin map showing tributaries, sub-basins and sampling station locations. 0 , MSV and NP (Bo2, M61, M62, Lil3); .k, MSV only (Bo4); II, NP only (LilS, Lil9).


for Lake Roxen and the Baltic Sea. The following is a brief description of the water quality conditions in the area of the Svartfi River Basin.

Lake Sommen, at the southern end of the study area, has high water quality and few impacts from human activities. Its watershed is primarily forested. Beneficial uses of the lake include its water usage downstream (hydropower, irrigation, municipal) and recreation (Goulter and Castensson, 1988). Lake Roxen, on the other hand, has nutrient-rich waters and regular algal blooms (L~insstyrelsen i Osterg6tland, 1988; MSV, 1989). Beneficial uses (e.g. recreation, swimming) have been interrupted by large algal blooms. Potential human health impacts resulting from body contact with certain algae include swimmers' itch, a chronic dermatitus resulting from algal toxins (Henderson- Sellars and Markland, 1987).

Three main rivers flow into Lake Roxen, but the Svartfi River typically contains the highest concentrations and annual loadings of nutrients (MSV, 1989; Karlsson, 1989). All major point sources in the Svartfi. River Basin have tertiary treatment for phosphorus removal, but there has been little change in phosphorus transport (L6wgren and Karlsson, 1987; Karlsson et al., 1988).

3.4. MONITORING PROGRAM OBJECTIVES

With this background information well-documented, the objective of this study was to assess the non-point source sediment and nutrient loadings to Lake Roxen from the Svartfi River Basin. Specific objectives were as follows:

1. Determine the annual sediment and nutrient loadings, their spatial distribution in the watershed and temporal variability during the year.

2. Compare the magnitudes of the annual loadings from the known point sources to those from non-point sources.

3. Compare the ongoing and modified monitoring programs in terms of cost, validity and reliability in meeting objectives 1 and 2.

3.5. DESCRIPTION OF THE TWO MONITORING PROGRAMS

The current monitoring program carried out by MSV was developed in 1986 and reflects the recommendations and guidelines of SNV (1986). In the base program, samples are taken at four stations (Bo2, Bo4, M61, Lil3) on the Svart~ River and one station (M62) on the Skena~ River (Figure 3). The frequency of sampling is either monthly (Lil 3) or bi- monthly (Bo2, Bo4, M61, M62). The samples are analysed for 12 variables as shown in Table 1.

A modified monitoring program, that specifically considers non-point source inputs, was carried out concurrently during 1987 (Reinelt et al., 1987). For comparison purposes, the modified monitoring program was designed based on the cost constraints of the ongoing program. Tp address other research questions, additional data were gathered in the basin during 1987, but these data arepresented elsewhere (Reinelt and Grimvall, in press). The modified program consisted of three stations on the Svart~ River (Bo2, M61, Lil3) and three tributary stations (Skenafi. (M62), Lil lfi (Lil8), and Kapell~ (Lil9)) (Figure 3). Samples were taken on 13 occasions, distributed as shown in Figure 4. Table 1 shows the variables measured in the non-point (NP) program.

22 Non-polnt source water pollution

TABLE I. Variables and analysis costs for the two monitoring programs (all costs in 1987 Swedish Kronor, SEK)

MSV NP Variable Cost program program

Temperature 35 X pH 35 X Alkalinity 55 X Conductivity 35 X Oxygen 55 X Oxygen saturation 35 X Chemical oxygen demand 60 X~ Total phosphorus 75 X Orthophosphate-phosphorus 50 Total nitrogen 110 X Nitrate + nitrite- nitrogen 50 X Total suspended solids 70 X Total organic carbon 150 X Flow 100

Total analysis cost per sample

X X

X X

x~ 705/765 245/345

t Chemical oxygen demand measured only at Lil3. ~: Flow measured only at tributary stations.

3/29 4/30 10/26 12/I

i d I F I M I A I M I j I j I A I S i 0 I N I O

Figure 4. Distribution of sampling dates for the NP program during 1987 (month/day).

12119

4. Results and evaluation of the two monitoring programs

4.1. ANNUAL, SPATIAL AND TEMPORAL LOADINGS

A detailed examination of techniques to estimate non-point source loadings is presented in Reinelt and Grimvall (in press). For this study, loadings were estimated using a log load versus log flow regression technique for purposes of comparing the results of the MSV and NP programs (Section 2.2). The log scale reduces the effect o f extreme events. For the data collected in this study, it provides lower loading estimates than other methods (e.g. stepwise constant concentrations, linear interpolation of concentrations, linear relationship between concentration and flow).

Mean daily flow values in the Svartfi River were combined with the concentration data from the two programs to estimate loadings. Flow data were obtained from an outlet gauge at Lake Sommen (near Bo2) and a power station near the mouth at Lake Roxen (Lil3). The daily basin flows (Lil3-Bo2) for 1987 are shown in Figure 5.

Annual loadings and their spatial and temporal distribution were estimated for TSS, total phosphorus (TP) and nitrate + nitrite-- nitrogen (hereafter referred to as NO 3- - N

L . E . R e i n e l t et aL 2 3

50

45

5.5

2 E

25

o •

2O

15

IO

5 i

0 J F M A M J

1987

J A S o N O

Figure 5. Daily flow in the Svart~. River from inputs between Lake Sommen and Lake Roxen (Lil3-Bo2).

in the text). These variables were chosen to meet objective one, and because the variables are indicators of the potential for eutrophication in freshwater lakes and nearshore areas. The variables were also common to both programs. Orthophosphate-phosphorus (ortho-P) was measured by the NP program, but loading estimates are not given for this variable. The relationship between ortho-P and TP is discussed in Section 4.4.

Table 2 shows the annual basin and tributary loading estimates for the two monitoring programs and the three variables. The TSS loading estimates cannot be compared directly, because concentration values less than 5-0 mg/l were not reported in the MSV program. Since most of the values were below this level at Bo2, M62 and Lil3 on the occasions sampled (mostly baseflow conditions), TSS estimates could not be calculated. The annual basin loading estimate for TP was 11% lower for the MSV data, but the NO 3- - N estimate was 4% higher for the MSV data.

The NP program included three tributary monitoring stations (M62, Lil8, Lil9), whereas the MSV program included only one station (M62). Therefore, it was possible to obtain a more complete picture of the spatial distribution of loadings using the NP program data. For station M62, the MSV data resulted in a much higher N O a - - N loading estimate. This occurred because there were no data available for snowmeit conditions, when the relative concentration of NO 3- - N in relation to flow was lower. If

TABLE 2. Annual basin and tributary loading estimates for the two programs (all values in tonnes/ year)

"TSS TP NO 3- - N

NP MSV NP MSV NP MSV

Basin 2495 -- 17-7 16"0 194.0 202.2 M62 100 -- 0.9 0.8 23.1 40.3 Lil8 ll4 -- 1-7 -- 6.2 -- Lil9 416 -- 4"2. -- 33"5 - -

24 Non-point source water pollution

the snowmelt data point were removed from the NP analysis, the NO 3- - N estimates would have been comparable, but incorrect.

Estimated annual loadings at Kapellh (Lil9) were much higher than M62 and Lil8 for all three variables (Table 2), according to the information from the NP program. The annual TSS loadings were similar for M62 and Lil8, but the TP loadings were almost twice as high for Lil8. The opposite was true for NO 3- - N loadings, where M62 was nearly four times higher than Lil8. These results indicate that the Kapell~t sub-basin is the greatest contributor of sediment and nutrient loadings to the Svart~ River, and, thus, it should receive more attention than it does under the current MSV program.

The temporal distributions of basin loadings were comparable for the two programs [Figure 6(a-d)]. Generally, higher loadings occurred during the high flow events of the spring and late December snowmelts, and the large June rain. The magnitudes of loadings were the main differences between the two programs.

4.2. RELATIVE INFLUENCE OF POINT AND NON-POINT SOURCES

There are 10 permitted wastewater treatment plants in the Svarth River Basin that constitute the primary point sources of sediment and nutrients. The loading estimates for these point sources were calculated from quarterly or semi-annual mean data available for flow, TP, TSS and TN during 1987. The total annual loading from the point sources was 1.1 tonnes TP, 28 tonnes TSS and 67-3 tonnes TN. Three plants (two at Mj61by and one at Boxholm) discharged greater than 90% of the point source loadings to the Svart~ River. The total annual TP input to the 10 plants was

I I I I I i 2 4 I I i I i

,.8 ' ( o ) 8 ( c )

g 0 . 6

,l,ll,.,, I,[i,, ,,,J,, . . . . , . . . . Ill,, ,all,.,,. ,,J,,...., . . . . . Ih,,ll 0"0 I0 20 30 40 50 0 I0 20 30 40 ~)0

1 .8

1 . 2

0 - 6

O ° 0

I I I I I

( b )

I0 20 50 40 ,50

e 4- ,g z

1 i I I I ( d )

I0 20 30 40

Figure 6(a-d). Basin weekly loading estimates for TP and NO 3- - N for the NP and MSV program data. (a) NP; (b) MSV; (c) NP; (d) MSV.

L. E. Relnelt et aL 25

approximately 51 tonnes. Therefore, the efficiency of phosphorus removal from tertiary treatment was almost 98%.

The point source TP discharges ranged between 6% (NP data) and 7% (MSV data) of the total basin loadings during 1987. This indicates that regardless of which monitoring program data set is used, the current point source discharges are small in comparison to other sources. The dynamics of phosphorus in the environment, however, prevent the estimation of anthropogenic non-point sources by difference (i.e. non-point source loading equals total loading minus point source loading) or adjustment for background levels. Many mechanisms govern phosphorus mobilization and transport from land and its fate in receiving waters (Hegemann and Keenan, 1985; Karlsson and L6wgren, 1990), including pH, dissolved oxygen, organic matter, chemical fractionation and biological uptake, making it difficult to assess the importance of the different factors in the overall phosphorus budget.

4.3. MONITORING PROGRAM COSTS

Monitoring program costs were based on 1987 cost data for the ongoing monitoring program. The overhead, occasion and station costs are shown in Table 3. The total analysis costs for each sample are shown in Tables 1 and 3. The total annual cost of the MSV base program in 1987 was 38 100 Swedish Kronor (SEK) (,,~ $6500 U.S.). The total annual cost of the modified NP program was 39 410 SEK (~$6700 U.S.).

4.4. VALIDITY OF THE TWO MONITORING PROGRAMS

The validity of the two monitoring programs was determined by their effectiveness in addressing the objectives (Section 3.4). Flow data for the Svarth River and point source discharge data were available regardless of the chosen monitoring program design.

The more frequent sampling of the NP program during the spring increased the probability of sampling the snowmelt event. Without data for the snowmelt period, the annual TSS and TP loadings would have been under-estimated and the NO 3 - - N loadings would have been over-estimated (Section 4.1). The NP program design did not guarantee sampling of the snowmelt event, but the probability was increased. Future monitoring efforts should include event sampling of the snowmelt period to obtain better information.

TABLE 3. Monitoring program costs, number of occasions, stations and variables for the two monitoring programs (all costs in 1987 Swedish

Kronor, SEK)

NP program MSV program

Cost Number Cost Number

Overhead (Co) 6000 1 6000 1 Occasion (C,, 79 200 13 200 6/12 Station (C,, S) 100 6 100 5 Variables (C,) 245/345 5 705/765 12 Total costt 39 410 38 100

t Total cost, C= 6".+ Tx C,+Sx Tx (C,+ C=)


The MSV program did not provide sufficient information to assess the spatial distribution of loadings. By measuring tributary flows as part of the NP program, it was possible to estimate tributary loadings and better define the spatial distribution of basin loadings. Though tributary loading estimates can be made by difference between two sampling points on the main river, this may sometimes lead to erroneous results (Reinelt and Grimvall, in press).

Similar information about the relationship between point and non-point loadings was obtained by both programs. Results indicated that point sources are no longer a significant source of sediment and nutrients in the Svart~ River. Data from the ongoing monitoring program for additional variables (e.g. pH, alkalinity, conductivity, dissolved oxygen) did not directly contribute information to address the objectives. If specific reasons or objectives do not exist for measuring these variables, they should be dropped from the monitoring program.

A strong linear correlation was found between TP and ortho-P for all sampling occasions. Correlation coefficients ranged from 0.83 to 1.00 for the 13 occasions. The correlation coefficients by station ranged from 0.84 to 0.86 for the three tributary stations and from 0.22 to 0-79 for the Svart~ River. These results suggest that it is possible to measure TP and ortho-P for a few stations on a given occasion, or for individual tributaries on several occasions to establish correlations between these variables. The same is not true, however, for the Svartfi River. Cost savings might be realized by taking advantage of this relationship, if information on both TP and ortho-P is desired.

4.5. RELIABILITY OF THE TWO MONITORING PROGRAMS

As noted in Section 1, there is a desire to reduce annual nutrient loadings to various lakes and nearshore areas of Sweden by as much as 50%. To gauge the success of these programs, it is desirable to have accurate information regarding loading estimates, and some sense of the uncertainty associated with such estimates. The bootstrapping method (Section 2.2) was used in this study to measure the reliability of the annual basin loading estimates.

The bootstrapping method requires that the data used for the analysis be representative of the true population. For this study, 500 estimates of annual loading were estimated for each variable by randomly generating 500 sets of"artificial" sampling data from the actual 13 sampling dates. From these data, 500 sets of coefficients for the regression equation in Section 2.2 were calculated and used to generate 500 annual loading estimates. The results of this analysis were then used to determine how sensitive the annual loading estimates were to individual sampling data.

The distributions of the 500 annual loading estimates for the three variables using the NP program are shown in Figure 7(a-c). Plots of the 13 data records for log load and log flow, and the least squares regressions arc shown in Figure 8(a-c). The annual loading estimates for TSS and TP are sensitive to the snowmelt data point because it falls above the regression fine. This causes the left peak in the distribution plots [Figure 7(a and b)] when the snowmelt data point is missed in the random generation of sampling data. For NO3- -N , the snowmelt data point falls very close to the regression line [Figure 8(c)], resulting in a nearly normal distribution of the loading estimates [Figure 7(c)].

The results of the bootstrapping analysis are shown in Table 4. The ranges of annual loading estimates for the 90% confidence interval (CI) are 74, 90 and 42% of the annual loading estimate of the actual data for TSS, TP and NO~- - N, respectively. Ranges for

L. E. Reinclt et al. 27

~o ~ i i i I ( o l i l ~OOm I I L t ( c ) ' I I

40 80

3O 60

2 0 ~0

I I 20

o .9 1200 1800 2400 3000 3600 120 160 200 240 280

g

~ I I 1 I (b I)

O I O t5 20 Z5 3 0

Figure 7(a--c). Frequency distributions for the 500 randomly generated annual loading estimates for TSS, TP and NO3- - N for the NP program data (all values in tonnes/year). (a) TSS; (b) TP; (c) NO 3- - N .

G"

o

m-

oo" ._I

5 - 0

(o)

2.0

I-0

0.0

-i-0 -0-5,

0.5

0-0

c - I '0

_? - 2 . 0

..A - 3 . 0

(b)

I I I

./. i

/

• ' ~ I I I C-O 0.5 t .O 1,5 2.0

LC~j flow (mS/s)

I I I

I I I " "

)

! I t -0"5 O.O 0-5 t'O 1"5 2"0

Log fl0w (m3/s)

I-O I I I

o 0.0 I

z

+ -1.0

ff I 1 I .o -2'0-0-5 0.0 o.5 I -0 z.5

Log flow (m3/s)

2 . 0

Figure 8(a-c). Log load, log flow and least squares regression lines for TSS, TP and NO 3- - N using the 13 data points for the NP program. (a) r~=0-91; (b) r2=0.93; (c) 1"2=097.


TABLE 4. Bootstrapping results including the mean, standard deviation (SD) and confidence intervals (CI) for the 500 annual loading estimates using the NP

program data (all values in tonnes/year)

Annual Bootstrapping results loading

Variable estimate Mean SD 90% CI 80% CI

TSS 2495 2025 595 1157-2992 1235-2799 TP 17-7 15.9 5.2 9.4--25-3 9.7-23.0 NO~- - N 194 185 25 145-226 156-218

the 80% CI are 63, 75 and 32%. Annual loading estimates are greatly influenced by high flow events when concentrations are also higher. By carrying out event sampling during several periods of higher flow, it would be possible to tighten these ranges considerably.

The same analysis could be carried out for the MSV data, but because the sampling data are not representative of the true population, the method would yield erroneous results. A high level of confidence would be mistakenly associated with the annual loading estimates when, in fact, it only applies to baseflow conditions. The more frequent sampling of the tributaries (13 versus six occasions) in the NP program and the event data obtained also results in greater confidence and reliability for tributary loading estimates.

5. Relevance of information for decision-making

Ward et al. (1986) noted the importance of the interface between management programs and water quality monitoring efforts. They concluded that a more scientific and systematic approach to water quality monitoring is required if it is to be efficient in meeting the information needs of water quality management.

Eutrophication of Lake Roxen and the Baltic Sea was identified in this study as the primary problem (e.g. impact on beneficial uses). Phosphorus is the limiting nutrient controlling algal growth in Lake Roxen. Phosphorus is also the limiting nutrient in some parts of the Baltic Sea; however, nitrogen is limiting in the open sea areas, and in coastal areas unaffected by local nutrient discharge (Elmgren, 1988). Since most point sources in the Svart~ River Basin receive tertiary treatment for phosphorus removal (Karlsson et al., 1988), control of non-point sources offers the best hope for reducing nutrient loadings further.

Information on the annual, spatial and temporal distribution of non-point nutrient loadings will provide resource managers with more relevant information for decision- making. This information can then be used to target critical areas for application of non- point source control programs so that water quality management programs have a greater chance of success. More reliable loading estimates will also provide decision- makers with information to gauge the success of previous policy decisions (Section 1).

The NP program information facilitates the identification of critical sub-basins on the basis of their contribution to basin loadings. If more detailed information is desired, however, it is necessary to monitor even smaller sub-basins or perform complementary analyses, at a higher level of resolution, on factors that influence non-point source loadings (e.g. GIS analyses, Silvertun et al., 1988).

L. E. Relnelt et aL 29

6. Conclusions

Effective monitoring of water quality requires that programs be designed to address specific objectives. The objectives should be developed from the information needs of decision-makers so that there is a link between the results o f the monitoring program and subsequent decisions. Many routine monitoring programs include the collection of data for which there is no identified purpose. I f the money and effort expended to collect these data were directed, for example, towards specific variables, or the collection of samples at more stations, or on more frequent occasions, then the result would be more relevant information available for decision-makers.

The amount and reliability of the information obtained from the two monitoring programs of this study were significantly different. For similar costs, the NP program resulted in apparently more reliable information that was targeted directly towards the issues o f concern in the study area. By targeting specific objectives, the NP program produced information with greater validity. The tributary flow and loading information obtained by the NP program also facilitated the identification of significant sub-basin loadings within the study area. Information from both programs indicated that point sources in the basin are insignificant in comparison to non-point sources.

When estimating annual non-point source loadings in a river basin, the reliability of the estimates can be increased by obtaining water quality information for a variety of different flow conditions. This information can be obtained by increasing the frequency of sampling or by targeting specific events. For optimal cost effectiveness, event sampling is recommended. I f information about the spatial distribution of loadings is desirable, then sampling of tributary inputs is necessary. This facilitates the targeting of critical sub-basins for implementation of non-point source control programs. Finally, each variable measured should yield useful information for decision-making.

This research was partially supported by the Valle Scandinavian Exchange Program at the University of Washington, Seattle and the National Swedish Council for Building Research.

References

Castensson, R., Falkenmark, M. and Gustafsson, J. E. (1990). Land and Water in 1990s Municipal Natural Resource Planning. Report No. G23. Solna, Sweden: Swedish Council for Building Research (Byggforsk- ningsr:~det). (In Swedish).

Efron, B. (1982). The Jackknife, the Bootstrap, and Other Resampling Plans. SIAM, CBMS-NSF Regional Conference Series in Applied Mathematics, No. 38, Philadelphia, Pennsylvania.

Elmgren, R. (1988). The eutrophication of the Baltic Sea" input of nitrogen and phosphorus, and their availability for plant production. In The Health of the Seas. Fourth Conference in Karlskrona, Sweden, 7-9 June 1988, pp. 29-41.

Fleischer, S., Andr6asson, I-M., Holmgren, G., Joelsson, A., Kindt, T., Ryber, L. and Stibe, L. (1989). Land Use and Water Quality: A Stud), of Lahohn Bay's Drainage Area. Sweden: Lfnsstyrelsen i Hallands l~n. (In Swedish).

Forsberg, C. and Ryding, S.-O. (1985). Research on recovery of polluted lakes. Vatten 41, 3-19. (In Swedish). Goulter, I. C. and Castensson, R. (1985). Multi-objective model for allocation ofwater shortage in the Svart~i

river, Sweden. IVater Resources ,Bulletin 24, 761-793. Hegemann, D. A. and Keenan, J. D. (1985). Measurement of watershed phosphorus: A review. Toxicological

and Environmental Chemistry 9, 265-289. Helsinki Commission. (1988). Ministerial Declaration of the Helsinki Commission. Henderson-Sellers, B. and Markland, H. R. (1987). Decaying Lakes: The Orighzs and Control of Cultural

Eutrophication. Chichester: John Wiley & Sons. Homer, R. R., Mar, B. W., Reinelt, L. E., Richey, J. S. and Lee, J. M. (1986). Design of Monitoring Programs

for Determination of Ecological Change Resulting from Non-point Source Water Pollution hz IVashblgton State. Final Report. Olympia, Washington: Washington State Department of Ecology.

30 Non-point source ~ater pollution

Karlsson, G. (I989). Dynamics of Nutrient AIass Transport: A River Basin Evaluation. Vol. 40. Linkrping Studies in Arts and Science. Linkrping University, Linkrping, Sweden.

Karlsson, G. and L6wgren, M. (1990). River transport of phosphorus as controlled by large scale land use changes. Acta Agriculture Scandinavia 40, 149-162.

Karlsson, G., Grimvall, A. and Lfwgren, M. (1988). River basin perspective on long-term changes in the transport of nitrogen and phosphorus. Water Research 22, 139-149.

Karlsson, G. and L6wgren, M. (1985). Water-course monitoring in a Swedish river basin: growth, development and future. In Strategies for River Basin Management (J. Lungqvist, U. Lohm and M. Falkenmark, eds), pp. 179-187. Dordrecht: Reidel.

Logan, T. J. (1987). Diffuse (non-point) source loading of chemicals to Lake Erie. Journal Great Lakes Research 13, 649-658.

Lfinsstyrelsen i OstergStland. (1988). Vattenmiljrn i Osterg6tland (Water Environment in Osterg6tland). l_.finsstyrelsen i Ostergftland l~n. Linkrping, Sweden. (In Swedish).

Lrwgren, M., Hitlmo, T. and Lohm, U. (1989). Water pollution perspectives; problem conceptualizations and abatement strategies in Sweden during the 20th century. GeoJournal 19, 161-171.

Lrwgren, M. and Karlsson, G. (1987). Effectiveness of tertiary was~ewater treatment in river-basin scale. Journal of Environmental Management 25, 13-26.

Mar, B. W., Horner, R. R., Richey, J. S., Palmer, R. N. and Lettenmaier, D. P. (1986a). Data acquisition: cost- effective methods for obtaining data on water quality. Environmental Science and Technology 20, 545-551.

Mar, B. W., Lettenmaier, D. P., Horner, R. R., Richley, J. S., Palmer, R. N., Millard, S. P., MacKenzie, M. C. and Lund, J. R. (1986). Sampling Design for Aquatic EcologicaIMonitorh~g, Vol. !-5. Final Report, Project No. RP1729-I. Palo Alto, California: Electric Power Research Institute.

Motala Str6ms Vattertv~rdsfrrbund (MSV). (1989). Vattendragskontrollen 1986-1988 Water Course Control Program 1986-1988). MSV. (In Swedish).

North Sea Conference. (1987). Ministerial Declaration of the Second International Conference on the Protection of the North Sea. London, 24-25 November.

Reckhow, K. H. and Chapra, S. C. (1983). Engineering Approaches for Lake Management VoL 1: Data Analysis and Empirical Modeling. Boston, Massachusetts: Butterworth.

Reinelt, L. E., Castensson, R. and Hornmer, R. R. (1987). Modification of an existing monitoring program to address nonpoint source pollution: a case study of the Svarth river basin. Patten 3, 199-208.

Reinelt, L. E., Homer, R. R. and Mar, B. W. (1988). Nonpoint source pollution monitoring program design. Journal Water Resources Planning and Management, 114, 335-352.

Reinelt, L. E. and Grimvall, G. (in press). Estimation of nonpoint source loadings with data obtained from limited sampling programs. Environmental Monitoring and Assessment.

Romhs, A. (1985). Vattenkvalitet och Regional Utveekling (Water Quality and Regional Development). Tema V Report 10. Linkfping, Sweden: Linkfping University.

Ryding, S. O. (1984). Water monitoring programmes--perspectives and prospects. Vatten 40, 216-226. (In Swedish, English abstract).

Sheridan, J. M. and Hubbard, R. K. (1987). Transport of solids in streamflow from Coastal Plain watersheds. Journal of Environmental Quality 16, 131-136.

Sivertun A., Reinelt, L. E. and Castensson, R. (1988). A GIS method to aid in non-point source critical area analysis. International Journal of Geographical Information Systems 2, 365-378.

Smith, R. V. and Stewart, D. A. (1977). Statistical models of river loadings of nitrogen and phosphorus in the Lough Neagh System. Water Research 11,631-636.

Statens Naturv~rdsverk (SNV). (1986). Recipient Water Control Guidelines. Allm~nna R~d 86:3; Method descriptions, Part I. Investigation methods for base programs. Rapport 3108. (In Swedish, English abstract).

Sfissmann, W. (1983). Comparison of water quality in drainage basins under agricultural and forest land use. In Dissolved Loads of Rivers and Surface Water Quantity[Quality Relationships. Proceedings of the Hamburg Symposium, August 1983. IAHS Publ. No. 14I.

Ward, R. C., Loftis, J. C. and McBride, G. B. (1986). The "data-rich but information-poor" syndrome in water quality monitoring. Environmental Management 10, 291-297.

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