Introduction:

Integrated watershed management is not a very new idea. A useful first introduction is available on the net at http://www.epa.gov/OWOW/watershed/wacademy/acad2000/watershedmgt/index.html

In Bill's words, the procedure is inherently fuzzy, and lacks predictably. Never use the term predict for your model output - the results are simply computed. Predicted implies some degree of certainty. Be honest with yourself, and say computed not predicted.

Now go read the requirements for assignment A4.

(Source: NRA 1933a)

[Suggestion for your review: add uses and categories from your experience in your own area.] Of course expected water quality differs for each type of use, and every country has its own water quality objectives. The Province of Ontario's water quality objectives can be downloaded from: http://www.ene.gov.on.ca/envision/gp/3303e.pdf   [For the project chosen for your assignment, suggest a list of relevant water quality constituents, and then check the stated objectives for the area where you live. How do yours compare with ours? What is the reason for the difference, do you think?]

The process of determining which uses require priority intervention is called "scoping". Steps include:

• identify categories of water users in the basin
• determine the desired water uses.
• set short- and long-term targets for each use
• define each problem to be resolved
• specify goals for restoration
• obtain community consensus on the importance of each element in the plan (and resolve conflicts).

Consultation process

Increasingly, public involvment is seen to be necessary, because social change inherent in watershed planning will not occur unless the community affected agrees that such change is necessary. Thus all members of the affected community must be heard, and a consensus built, whereby the majority view is reflected in the proposed change. Principles of public involvment are available in several specialized texts, and the Isobel Heathcote's textbook used for this module provides a good summary. Much is yet to be said (it seems to me) about the use of the web and Internet in the consultation process, however [this might be a good topic for your consideration in your assignment for this module].

Very often participants have a prior position on some issues, and may promote them vigorously, making a fair hearing time-consuming. Many texts are available for such dispute resolution, and recourse to a specialist may reduce tension. In Ontario we are lucky to have a number of specialists with experience in resolving water environment conflicts.

Developing workable management options

In this section we review stormwater best management practices (BMPs). Be careful - there is a semi-infinite number of pollution control BMPs and also there is an overwhelming number of lists of BMPs. Useful lists are probably available for your area, and they make a starting point for your own list of options for your particular study problem. The next module (M6) will have much more to say about this, and further info is also provided in course 661 modules M9 and M11. The Ontario Ministry of Environment and Energy page on stormwater planning and design practices provides their entire Stormwater Management Planning and Design Manual and is almost too long to read (you will need Adobe's pdf software). You can find a draft of the entire manual at: http://www.ene.gov.on.ca/envision/env_reg/er/documents/stormwatermanual/index.htm

The material below is taken from my undergraduate page on this topic (written about 4 or 5 y ago; original is at: http://www.eos.uoguelph.ca/webfiles/james/homepage/Teaching/437/wj437Module11b.htm)

POLLUTION CONTROL OPTIONS

Contents:

1. Introduction.
2. Review selected readings on types of best management practices (BMPs).
3. Review selected readings on particular BMPs.
4. Additional reading.

   1. Introduction:

   These pages describe a comprehensive list of pollution control devices (wrongly called best management practices, or BMPs - a so-called "best management practice" is a structural device that temporarily stores or treats urban stormwater runoff to reduce flooding, remove pollutants, and provide other amenities, or a non-structural idea that will help reduce the flux of pollutants and runoff).

   I suggest that your first task is to consider the various categories of BMPs, and your second to search a little further into the various types of BMPs.

   An good example, is the 2-screen list of highway BMPs posted by Rob at [http://www.chi.on.ca/bmpstructural.html] (structural) and at [http://www.chi.on.ca/bmpnonstructural.html] (non-structural)

   2. Review selected readings on categories of BMPs:

   • infiltration,
   • detention,
   • retention,
   • non-structural (education, political etc.),
   • vegetative,
   • high-rate treatment,
   • source controls.

   To fill in the details (provide you with the background as to why it is convenient to categorize BMPs in some way) read the following stuff on BMPs, their applications, impacts, and use in planning and design):

   • What stormwater management practices are. [http://www.epa.gov/OWOW/NPS/MMGI/Chapter4/ch4-1.html#Practices]
   • BMPs and stormwater quality, APWA task force [http://www.nctcog.dst.tx.us/envir/water/storm/allbmps.html]
   • developing successful stormwater control plans [http://www.bts.gov/smart/cat/RUNOFF.html]

3. Review selected readings on particular BMPs:
Your next task might be to search and read further in those categories of concern to you and create your own list of BMPs. You could search the web on the topic [e.g. (storm and water and best and management and practice) and (porous or permeable and pavement)].

If you do not have time to do the search, here are a few examples of further readings:

1. Source control/street sweeping:
   • City of Almeda study [http://www.schwarze.com/~asforum/Secure/topics/alameda12_94/alameda1.html]
   • American Sweeper magazine [http://www.schwarze.com/~asforum/Secure/v6n1/.www.html]
2. Infiltration:
   • Groundwater pollution and infiltration BMPs [http://www.wsdot.wa.gov/eesc/environmental/InfiltrationBMPs.htm]
3. Vortex separators, sand filters, water quality inlets, and wet detention ponds:
   • Vortex separators, sand filters, water quality inlets, and wet detention ponds as BMPs [http://enviro.nfesc.navy.mil/p2library/section10.html]

4. Additional reading:

Caution: You are NOT required to read all this material! It is offered here as course enrichment - please do not consider that this material is required or necessary. You are invited to dip into it to learn more details: when you click here you get a list of the illustrations and can select individual items, or you can choose to step through any or all of the six entire presentations.

Most of the following presentations are available in a better format at CHI's HongKong seminar site.

• For a non-structural BMP called Stormwater planning (SWP) click here
• For a non-structural BMP called Pollution control planning (PCP) click here
• For a non-structural BMP called Watershed management (WMP) click here
• For a non-structural BMP called Operation and maintenance (O&M) of stormwater management practices (SWMPs) click here
• For a non-structural BMP called Combined sewer overflow (CSO)guidelines click here
• For a review of legal issues in the approval of BMP designs click here
• For my collection of slides of BMP designs and installations click here

Simple assessment methods

Literature on multiobjective decision-making techniques is extensive. You probably applied them in your undergrad curriculum. Many case studies are available in textbooks and government reports. Steps involve: 1. development of constraints (e.g. maximum allowable cost); 2. choosing evaluation criteria (e.g. minimum cost); 3. choosing the weights for each criterium (e.g. 40%); 4. scaling the result (e.g. theoretical maximum of 100). 5. And of course the methodology is subjective and produces lively debate which must be reconciled.

Your textbooks, research reports and websites give examples for water pollution control planning. Contrariwise, I cite an example here that could hardly cause more argument among this class: ranking the "best" graduate engineering schools (this analysis for the USA). Click on:  http://www.usnews.com/usnews/edu/beyond/gradrank/eng/gdengt1.htm

Note how they use about twelve criteria, and scale the "best" school to produce a score of 100. The methodology is described at: http://www.usnews.com/usnews/edu/beyond/gradrank/gbengmet.htm

Another way of doing this is to reverse the process: first choose whatever school you wish to be your best school, and then try to manipulate the weights and criteria to produce the result that is acceptable to you. Naturally it would then remain for you to convince your readers that the final ranking is reasonable. In our example, US News will have to convince readers that the biggest grad school (PhDs per faculty) with the biggest research grants and the lowest acceptance rate necessarily implies the best grad school experience. Notice that issues important to individuals such as personal safety, local housing, public transit, quality of grad course offerings, are not included in the USNews analysis. Given the statistics,  however, I guess many of us would agree that US grad schools can be impressive.

Detailed assessment methods

This part of this module overlaps with module 7 (course 661), so if you are doing the latter you get a chance to read the complete material.

Here we should cover the various methods of estimating pollutant loads in stormwater. Empirical methods for estimating stormwater quality are of course approximate, being based on field data, which is notoriously sparse. The methods are known as "approximate" or "simple" methods. Isobel's book contains a useful summary of approximate methods based on fitting curves to observed data. Some methods assume correlations with rain as opposed to build up, even though this is widely supposed to be erroneous.  For the purposes of this course, we can afford to eschew simple methods, since they are easy for you to pick up later. We restrict ourselves here to the well-known method used in SWMM RUNOFF. Thus in this section I have cut and paste material originally written by Wayne Huber for the SWMM documentation, but somewhat edited for the version of the manuals which I produce locally for my students. Hence the figure, table and equation numbers. Of course the material is for SWMM simulation and unnecessarily specialized, and should be read selectively. My intention here is to show that even these so-called detailed methods are based on noisy observations, and have more unexplained variance than they have determinism - see for example  Figure 4-34. Non-linear buildup of street solids. Note that the inherent uncertainty is not reported in SWMM computed results.Thus sensitivity, calibration and error analysis (SCEA) ought to be an important part of modelling. Instead of covering SCEA in detail on this page (it already takes too long to load), use this  link to an unfinished, preliminary, draft booklet on SCEA for SWMM modelling.

----excerpts from SWMM manual starts (I have made numerous cuts and simplifications in what follows):

Simulation of urban runoff quality is very inexact. The many difficulties of simulation of urban runoff quality are discussed by Huber (1985, 1986) [*References not yet added to this page   - they are available in Bill's version of the manuals - written 00.01.25]. Very large uncertainties arise both in the representation of the physical, chemical, biological [and sociological] processes and in the acquisition of data and parameters for model algorithms. The true mechanisms of buildup involve factors such as wind, traffic, atmospheric fallout, land surface activities, erosion, street cleaning and other imponderables. Although efforts have been made to include such factors in physically-based equations (James and Boregowda, 1985), it is unrealistic to assume that they can be represented with enough accuracy to determine a priori the amount of pollutants on the surface at the beginning of the storm. Equally naive is the idea that empirical washoff equations truly represent the complex hydrodynamic [and chemical and biological] processes that occur while overland flow moves in random patterns over the land surface.

Such uncertainties can be dealt with in two ways. The first option is to collect enough calibration and verification data to calibrate the model equations used for quality simulation. Given sufficient data, the equations used in SWMM can usually be manipulated to reproduce observed concentrations and loads. This is essentially the option discussed at length in the following sections. The second option is to abandon the notion of detailed quality simulation altogether and use either (a) a constant concentration applied to quantity predictions (i.e., obtain storm loads by multiplying computed volumes by an assumed concentration) (Johansen et al., 1984) or (b) a statistical method (Hydroscience, 1979; Driscoll and Assoc., 1981; EPA, 1983b; DiToro, 1984). Two ways in which constant concentrations can be simulated in SWMM are by using a rating curve with an exponent of 1.0 or by assigning a concentration to rainfall. Statistical methods are based in part upon strong evidence that storm event mean concentrations (EMCs) are lognormally distributed (Driscoll, 1986). The statistical methods recognize the frustrations of physically-based modeling and move directly to a stochastic result (e.g. a frequency distribution of EMCs), but they are even more dependent on available data than methods such as those found in SWMM. That is, statistical parameters such as mean, median and variance must be available from prior information. Furthermore, it is harder to study the effect of controls and catchment modifications using statistical methods.

The main point is that there are alternatives to the approaches used in SWMM; the latter can involve extensive effort at parameter estimation and model calibration to produce quality predictions that may vary greatly from an unknown "reality." Before delving into the arcane methods incorporated in SWMM and other urban runoff quality simulation models, you should try to determine whether or not the effort will be worth it in view of the uncertainties of the process and whether or not simpler alternative methods might suffice. The discussions that follow provide a comprehensive view of the options available in SWMM, which are more than in almost any other comparable model in the public domain, but the extent of the discussion should not be interpreted as a guarantee of success in applying the methods.

Overview of Quality Procedures

Methods for estimation of urban runoff quality constituents are reviewed extensively by Huber (1985, 1986). Many constituents can appear in either dissolved or solid forms (e.g. BOD, nitrogen, phosphorus) and may be adsorbed onto other constituents (e.g. pesticides onto "solids") and thus be generated as a portion of such other constituents. To treat this situation, any constituent may be computed as a fraction ("potency factor") of another. For instance, 5% of the suspended solids load could be added to the (soluble) BOD load. Or several particle size - specific gravity ranges could be generated, with other constituents consisting of fractions of each.

When channel/pipes (links) are included, quality constituents are routed through them assuming complete mixing within each gutter/pipe (link) at each time step. Scour, deposition or decay-interaction during routing is simulated in the TRANS module and not at all in the RUNOFF Module.

Output consists of pollutographs (concentrations versus time) at desired locations along with total loads, and flow-weighted concentration means and standard deviations. In addition, summaries are printed for each constituent describing its overall mass balance for the simulation for the total catchment, i.e., sources, removals, etc. These summaries are the most useful output for continuous simulation runs.

Quality Simulation Credibility

Although the conceptualization of the quality processes is not difficult, the reliability and credibility of quality parameter simulation is very difficult to establish. In fact, quality predictions are almost useless without local data for calibration and validation. If such data are lacking, results may still be used to compare relative effects of changes, but parameter magnitudes (e.g. computed concentrations) will forever be in doubt. This is in marked contrast to quantity prediction for which reasonable estimates of hydrographs may be made in advance of calibration.

Moreover, there is disagreement in the literature as to what are the important and appropriate physical and chemical mechanisms that should be included in a model to generate surface runoff quality. The objective in the RUNOFF Module has been to provide flexibility in mechanisms and the opportunity for calibration. But this places a considerable burden on you to obtain adequate data for model usage and to be familiar with quality mechanisms that may apply to the catchment being studied. This burden is all too often ignored, leading ultimately to model results being discredited.

In the end then, there is no substitute for local data, that is, observed rain, flow and concentrations, with which to calibrate and verify the quality predictions. Without such data, little reliability can be placed in the computed magnitudes of quality parameters.

Required Degree of Temporal Detail

In most applications, detail is unnecessary because the receiving waters cannot respond to rapid changes in concentration or loads. Instead, only the total storm event load is necessary for most studies of receiving water quality. Time scales for the response of various receiving waters are presented in Table 4-17 (Driscoll, 1979; Hydroscience, 1979). Concentration transients occurring within a storm event are unlikely to affect any common quality parameter within the receiving water, with the possible exception of bacteria. The only time that detailed temporal concentration variations might be needed within a storm event is when they might affect control alternatives. For example, a storage device may need to trap the "first flush" of pollutants.

Table 4-17. Required Temporal Detail for Receiving Water Analysis.. Required Temporal Detail, Receiving Water Analysis. (Driscoll, 1979; Hydroscience, 1979)

The significant point is that calibration and verification ordinarily need only be performed on total storm event loads, or on event mean concentrations. This is a much easier task than trying to match detailed concentration transients within a storm event. It is I think central to the approach taken in SLAMM (see module 6).

Quality Constituents

The number and choice of constituents to be simulated must reflect your needs, potential for treatment and receiving water impacts, etc. Almost any constituent measured by common laboratory or field tests can be included in SWMM-RUNOFF, up to a total of ten. 

Land Use Data

Each subcatchment must be assigned only one of up to five user-supplied land uses. The number of the land use is used as a program subscript, so at least one land use data must be entered. Street sweeping is a function of land use and constituent (discussed subsequently). Constituent buildup may be a function of land use depending on the type of buildup calculation specified for each. 

One of the most influential of the early studies of stormwater pollution was conducted in Chicago by the American Public Works Association (1969). As part of this project, street surface accumulation of "dust and dirt" (DD) (anything passing through a quarter inch mesh screen) was measured by sweeping with brooms and vacuum cleaners. The accumulations were measured for different land uses and curb length, and the data were normalized in terms of pounds of dust and dirt per dry day per 100 ft of curb or gutter. These well-known results are shown in Table 4-18 and imply that dust and dirt buildup is a linear function of time. The dust and dirt samples were analyzed chemically, and the fraction of sample consisting of various constituents for each of four land uses was determined, leading to the results shown in Table 4-19.

Table 4-18. Measured Dust and Dirt (DD) Accumulation in Chicago by the APWA in 1969 (APWA, 1969).

Table 4-19. Milligrams of Pollutant Per Gram of Dust and Dirt (Parts Per Thousand By Mass) For Four Chicago Land Uses From 1969 APWA Study Milligrams of Pollutant Per Gram of Dust and Dirt (Parts Per Thousand By Mass) For Four Chicago Land Uses From 1969 APWA Study (APWA, 1969).

From the values shown in Tables 4-18 and 4-19, the buildup of each constituent (also linear with time) can be computed simply by multiplying dust and dirt by the appropriate fraction. 

Of course, the whole buildup idea essentially ignores the physics of generation of pollutants from sources such as street pavement, vehicles, atmospheric fallout, vegetation, land surfaces, litter, spills, anti-skid compounds and chemicals, construction, and drainage networks. Lager et al. (1977a) and James and Boregowda (1985) consider each source in turn and give guidance on buildup rates. But the rates that are (optionally) entered into the RUNOFF Module only reflect the aggregate of all sources.

Available Studies

The 1969 APWA study (APWA, 1969) was followed by several more efforts, notably AVCO (1970) reporting extensive data from Tulsa, Sartor and Boyd (1972) reporting a cross section of data from ten US cities, and Shaheen (1975) reporting data for highways in the Washington, D.C. area. Pitt and Amy (1973) followed the Sartor and Boyd (1972) study with an analysis of heavy metals on street surfaces from the same ten US cities. More recently, Pitt (1979) reports on extensive data gathered both on the street surface and in runoff for San Jose. A drawback of the earlier studies is that it is difficult to draw conclusions from them on the relationship between street surface accumulation and stormwater concentrations since the two were seldom measured simultaneously.

Amy et al. (1975) provide a summary of data available in 1974 while Lager et al. (1977a) provide a similar function as of 1977 without the extensive data tabulations given by Amy et al. Perhaps the most comprehensive summary of surface accumulation and pollutant fraction data is provided by Manning et al. (1977) in which the many problems and facets of sampling and measurements are also discussed. For instance, some data are obtained by sweeping, others by flushing; the particle size characteristics and degree of removal from the street surface differ for each method. Some results of Manning et al. (1977) will be illustrated later. Surface accumulation data may be gleaned, somewhat less directly, from references on loading functions that include McElroy et al. (1976), Heaney et al. (1977) and Huber et al. (1981a).

Ammon (1979) summarized many of these and other studies, specifically in regard to application to SWMM. For instance, there is evidence to suggest several buildup relationships as alternatives to the linear one, and these relationships may change with the constituent being considered. Upper limits for buildup are also likely. Several options for both buildup and washoff are investigated by Ammon, and his results are partially the basis for formulations in this version of SWMM. Jewell et al. (1980) also provide a useful critique of methods available for simulation of surface runoff quality and ultimately suggest statistical analysis as the proper alternative. Many of the problems and weakness with extensive data and present modeling formulations are pointed out by Sonnen (1980) along with guidelines for future research.

To summarize, many studies and voluminous data exist with which to formulate buildup relationships, most of which are purely empirical and data-based, ignoring the underlying physics and chemistry of the generation processes. Nonetheless, they represent what is available, and modeling techniques in SWMM are designed to accommodate them in their heuristic form.

Buildup Formulations

Most data, as will be seen, imply linear buildup since they are given in units such as lb/ac-day or lb/100 ft curb-day. The Chicago data that were used in the original SWMM formulation assumed a linear buildup. However, there is ample evidence that buildup can be nonlinear; Sartor and Boyd's (1972) data are most often cited as examples (Figure 4-34). More recent data from Pitt (Figure 4-35) for San Jose indicate almost linear accumulation, although some of the best fit lines indicated in the figure had very poor correlation coefficients, ranging from 0.35 <= r <= 0.9. Even in data collected as carefully as in the San Jose study, the scatter (not shown in the report) is considerable. Thus, the choice of the best functional form is not obvious. Whipple et al. (1977) have criticized the linear buildup formulation included in SWMM, although it is somewhat irrelevant since you may insert your own desired initial loads, calculated by whatever procedure desired. However, this is a useful option only for single-event simulation.

The choice of the functional form must ultimately be your responsibility. The program provides three options for dust and dirt buildup and three for individual constituents, namely: 1. power-linear, 2. exponential, or 3. Michaelis-Menton.

Figure 4-34. Non-linear buildup of street solids. (After Sartor and Boyd, 1972, p. 206.)

Linear buildup is simply a subset of a power function buildup. The shapes of the three functions are compared in Figure 4-36 using the dust and dirt parameters as examples, and a strictly arbitrary assignment of numerical values to the parameters. Exponential and Michaelis-Menton functions have clearly defined asymptotes or upper limits. Upper limits for linear or power function buildup may be imposed if desired. "Instantaneous buildup" may be easily achieved using any of the formulation with appropriate parameter choices. For instance, if it were desired to always have a fixed amount of dust and dirt available, DDLIM, at the beginning of any storm event (i.e., after any dry time step during continuous simulation), then linear buildup could be used with DDPOW = 1.0 and DDFACT equal to a large number ³ DDLIM/DELT. Linear buildup is fastest in terms of computer time.

Figure 4-35. Buildup of street solids in San Jose. (After Pitt, 1979, p. 29.)

  It is apparent in Figure 4-36 that different options may be used to accomplish the same objective (e.g. nonlinear buildup); the choice may well be made on the basis of available data to which one of the other functional forms have been fit. If an asymptotic form is desired, either the exponential or Michaelis-Menton option may be used depending upon ease of comprehension of the parameters. For instance, for exponential buildup the exponent (i.e., DDPOW for dust and dirt of QFACT(2,K) for a constituent) is the familiar exponential decay constant. It may be obtained from the slope of a semi-log plot of buildup versus time. As a numerical example, if its value were 0.4 day^-1, then it would take 5.76 days to reach 90 percent of the maximum buildup (see Figure 4-36).

Figure 4-36. Comparison of linear and three non-linear buildup equations. Dust and dirt, DD is used as an example. Numerical values have been chosen arbitrarily.

For Michaelis-Menton buildup the parameter DDFACT for dust and dirt (or QFACT(3,K) for a constituent) has the interpretation of the half-time constant, that is, the time at which buildup is half of the maximum (asymptotic) value. For instance, DD = 50 lb at t = 0.9 days for curve 4 in Figure 4-36. If the asymptotic value is known or estimated, the half-time constant may be obtained from buildup data from the slope of a plot of DD versus t . (DDLIM- DD), using dust and dirt as an example. Generally, the Michaelis-Menton formulation will rise steeply (in fact, linearly for small t) and then approach the asymptote slowly.

The power function may be easily adjusted to resemble asymptotic behavior, but it must always ultimately exceed the maximum value (if used). The parameters are readily found from a log-log plot of buildup versus time. This is a common way of analyzing data, (e.g. Miller et al., 1978; Ammon, 1979; Smolenyak, 1979; Jewell et al., 1980; Wallace, 1980).

Prior to the beginning of the simulation, buildup occurs over DRYDAY days for both single event and continuous simulation. During the simulation, buildup will occur during dry time steps (runoff less than 0.0005 in./hr or 0.013 mm/hr) only for continuous simulation.

For a given constituent, buildup may be computed: 1. as a fraction of dust and dirt, or 2. individually for the constituent. If the first option is used then the rate of buildup will depend upon the fraction and the functional form used for a given land use. In other words, the functional form could vary with land use for a given constituent. If the second option is used (1 £ KALC £ 3) the buildup function will be the same for all land uses (and subcatchments) for a given constituent. Of course, each constituent may use any of the options. Catchment characteristics (i.e., area or gutter length) may be included through the use of parameters JACGUT or KACGUT.

Buildup Data

Data with which to evaluate buildup parameters are available in most of the references cited earlier under "available studies." Manning et al. (1977) have perhaps the best summary of linear buildup rates; these are presented in tables in the SWMM documentation. It may be noted that dust and dirt buildup varies considerably among three different studies. Individual constituent buildup may be taken conveniently as a fraction of dust and dirt. It is apparent that although a large number of constituents have been sampled, little distinction can be made on the basis of land uses for most of them.

Washoff is the process of erosion or solution of constituents from a subcatchment surface during a period of runoff. It the water depth is more than a few millimeters, processes of erosion may be described by sediment transport theory in which the mass flow rate of sediment is proportional to flow and bottom shear stress, and a critical shear stress can be used to determine incipient motion of a particle resting on the bottom of a stream channel, e.g. Graf (1971), Vanoni (1975). Such a mechanism might apply over pervious areas and in street gutters and larger channels. For thin overland flow, however, rainfall energy can also cause particle detachment and motion. This effect is often incorporated into predictive methods for erosion from pervious areas (Wischmeier and Smith, 1958) and may also apply to washoff from impervious surfaces, although in this latter case, the effect of a limited supply (buildup) of the material must be considered.

Washoff Formulation

Ammon (1979) reviews several theoretical approaches for urban runoff washoff and concludes that although the sediment transport based theory is attractive, it is often insufficient in practice because of lack of data for parameter (e.g. shear stress) evaluation, sensitivity to time step and discretization and because simpler methods usually work as well (still with some theoretical basis) and are usually able to duplicate observed washoff phenomena. Among the latter, the most oft-cited results are those of Sartor and Boyd (1972), in which constituents were flushed from streets using a sprinkler system. It would appear that an exponential relationship could be developed to describe washoff of the form:

(4-133)

• where POFF = cumulative amount washed off at time, t,
• PSHED_o = initial amount of quantity on surface at t = 0, and
• k = coefficient.

Alternatively, since the amount remaining, PSHED(t), equals PSHED_o - POFF, then:

(4-134)

• where PSHED(t) = quantity remaining on surface at time, t,
• PSHED_o = initial amount of quantity, and
• k = coefficient.

It is clear that the coefficient, k, is a function of both particle size and runoff rate. An analysis of the Sartor and Boyd (1972) data by Ammon (1979) indicates that k increases with runoff rate, as would be expected, and decreases with particle size.

The Sartor and Boyd data lend credibility to the washoff assumption that the rate of washoff (e.g. mg/sec) at any time is proportional to the remaining quantity:

(4-135)

The solution of equation 4-135 is equation 4-134. This was first proposed by Mr. Allen J. Burdoin, a consultant to Metcalf and Eddy, during the original SWMM development. The coefficient k may be evaluated by assuming it is proportional to runoff rate, r:

(4-136)

• where RCOEF = washoff coefficient, in.^-1, and
• r = runoff rate over subcatchment, in./hr.

Burdoin assumed that one-half inch of total runoff in one hour would wash off 90 percent of the initial surface load, leading to the now familiar value of RCOEF of 4.6 in.^-1. (The actual time distribution of intensity does not affect the calculation of RCOEF.)

Sonnen (1980) estimated values for RCOEF from sediment transport theory ranging from 0.052 to 6.6 in.^-1, increasing as particle diameter decreases, rainfall intensity decreases, and as catchment area decreases. He pointed out that 4.6 in.^-1 is relatively large compared to most of his calculated values. Although the exponential washoff formulation of equations 4-135 and 4-136 is not completely satisfactory as explained below, it has been verified experimentally by Nakamura (1984a, 1984b), who also showed the dependence of the coefficient k on slope, runoff rate and cumulative runoff volume.

This exponential formulation did not adequately fit some data, and as a "correction," availability factors of the form

(4-137)

• where AV = availability factor, and
• a,b,c = coefficients,

were multiplied by equation 4-133 in order to match measured suspended solids concentrations in Cincinnati and San Francisco (Metcalf and Eddy et al., 1971a). The primary difficulty is that use of equations 4-135 and 4-136 will always produce decreasing concentrations as a function of time regardless of the time distribution of runoff. This is counter-intuitive, since it is expected that high rates during the middle of a storm might indeed produce higher concentrations than those preceding. This may be explained by observing that concentrations are calculated by dividing the load rate (e.g. mg/sec) to obtain the quantity per volume (e.g. mg/L). Thus,

(4-138)

• where C = concentration, quantity/volume,
• Q = A × r = flow rate, cfs,
• A = subcatchment area, ac, and
• r = runoff rate, in./hr.,

and the constant incorporates conversion factors. Clearly, the concentration will always decrease with time since the runoff rate, r, divides out of the equation and the quantity remaining, PSHED, continues to decrease. This problem is overcome in SWMM by making washoff at each time step, POFF, proportional to runoff rate to a power, WASHPO:

(4-139)

• where POFF = constituent load washed off at time, t, quantity/sec (e.g. mg/sec),
• PSHED = quantity of constituent available for washoff at time, t, (e.g. mg),
• RCOEFX = washoff coefficient = RCOEF/3600, (in/hr^)-WASHPO × sec^-1, and
• r = runoff rate, in./hr.

It may be seen that if equation 4-139 is divided by runoff rate to obtain concentration, then concentration is now proportional to rWASHPO-1. Hence, if the increase in runoff rate is sufficient, concentrations can increase during the middle of a storm even if PSHED is diminished. (Equation 4-139 was first suggested in a 1974 report to the Boston District Corps of Engineers, authorship unknown).

There are two parameters to be determined, RCOEF and WASHPO. Availability factors of the form of equation 4-137 are no longer used since there is sufficient flexibility for calibration using only equation 4-139.

Effects of Parameters

In subroutine QSHED of the RUNOFF Module, washoff load rates (e.g. mg/sec) are computed instantaneously at the end of a time step using equation 4-110. They are subsequently combined with other possible inflow loads to a gutter/pipe (link) or inlet (node) before dividing by the total inflow rate to obtain a concentration. The remaining constituent load on the subcatchment at the end of a time step is determined by using the average power of the runoff rate over the time step,

4-114)

This calculation is done prior to application of equation 4-139. The average (trapezoidal rule) approximates the integral of rWASHPO over the time step.

That the load rate of sediment is proportional to flow rate as in equation 4-139 is supported by both theory and data. For instance, sediment data from streams can usually be described by a sediment rating curve of the form

(4-141)

• where G = sediment load rate, mg/sec,
• Q = flow rate, cfs, and
• a,b = coefficients.

Due to a hysteresis effect, such relationships may vary during the passing of a flood wave, but the functional form is evident in many rivers, e.g. Vanoni (1975), pp. 220-225, Graf (1971), pp.234-241, and Simons and Senturk (1977), p. 602. Of particular relevance to overland flow washoff is the appearance of similar relationships describing sediment yield from a catchment e.g. Vanoni (1975), pp. 472-481. The exponent b in equation 4-141 corresponds to the exponent WASHPO in equation 4-139, and the presence of the quantity PSHED in equations 4-139 reflects the fact that the total quantity of sediment washed off a largely impervious urban area is likely to be limited to the amount built up during dry weather. Natural catchments and rivers from which equation 4-141 is derived generally have no source limitation.

The use of rating curves in their own right is an option in the RUNOFF Module. At this point, however, results from sediment transport theory can be used to provide guidance for the magnitude of parameters WASHPO and RCOEF in equation 4-139. Values of the exponent b in equation 4-141 range between 1.1 and 2.6 for rivers and sediment yield from catchments, with most values near 2.0. Typically, the exponent tends to decrease (approach 1.0) at high flow rates (Vanoni, 1975, p. 476). In the RUNOFF Module, constituent concentrations will follow runoff rates better if WASHPO is higher. A reasonable first guess for WASHPO would appear to be in the range of 1.5-2.5.

Values of RCOEF are much harder to infer from the sediment rating curve data since they vary in nature by almost five orders of magnitude. The issue is further complicated by the fact that equation 4-139 includes the quantity remaining to be washed off, PSHED, which decreases steadily during an event. At this point it will suffice to say that values of RCOEF between 1.0 and 10 appear to give concentrations in the range of most observed values in urban runoff. Both RCOEF and WASHPO may be varied in order to calibrate the model to observed data.

The preceding discussion assumes that urban runoff quality constituents will behave in some manner similar to "sediment" of sediment transport theory. Since many constituents are in particulate form the assumption may not be too bad. If the concentration of a dissolved constituent is observed to decrease strongly with increasing flow rate, a value of WASHPO < 1.0 could be used.

Although the development has ignored the physics of rainfall energy in eroding particles, the runoff rate, r, in equation 4-139 closely follows rainfall intensity. Hence to some degree at least, greater washoff will be experienced with greater rainfall rates. As an option, soil erosion literature could be surveyed to infer a value of WASHPO if erosion is proportional to rainfall intensity to a power.

Related Buildup-Washoff Studies

Several studies are directly related to the preceding discussions of the SWMM RUNOFF Module water quality routines. The following discussion is by no means exhaustive but does include several studies that have simulated water quality using buildup-washoff mechanisms, rating curves or both.

The U.S. Geological Survey (USGS) has performed comprehensive urban hydrologic studies from both a data collection and modeling point of view. For example, their South Florida urban runoff data are described and referenced in the EPA Urban Rainfall-Runoff Quality Data Base (Huber et al., 1981a). Urban rainfall-runoff quantity may be simulated with the USGS distributed Routing Rainfall-Runoff Model (Dawdy et al., 1978; Alley et al., 1980a) which includes simulation of water quality. This is accomplished using a separate program that uses the quantity model results as input. These efforts are described by Alley (1980) and Alley et al. (1980b). Alley (1981) also provides a method for optimal estimation of washoff parameters using measured data. The USGS procedures are based in part upon earlier work of Ellis and Sutherland (1979). These four references all discuss the use of the original SWMM buildup-washoff equations. An application of SWMM RUNOFF and TRANSport modules to two Denver catchments during which buildup-washoff parameters were calibrated is described by Ellis (1978) and Alley and Ellis (1979).

Work at the University of Massachusetts has developed procedures for calibration of SWMM RUNOFF Module quality (Jewell et al., 1978a) and for determination of appropriate washoff relationships (Jewell et al., 1978b). Jewell et al. (1980) and Jewell and Adrian (1981) reviewed the supporting data base for buildup-washoff relationships and advocate using local data to develop site specific equations for buildup and washoff. Most of their suggested forms could be simulated using the available functional forms in SWMM.

Since several other models use quality formulations similar to those of SWMM, their documentation provides insight into choosing proper SWMM parameters. In particular, most of the STORM calibration procedures (Roesner et al., 1974, HEC, 1977a,b) can be applied also to SWMM (with WASHPO = 1). Inclusion of water quality simulation in ILLUDAS (Terstriep et al., 1978; Han and Delleur, 1979) also is based on SWMM procedures. Finally, modified SWMM routines have been used to simulate water quality in Houston (Diniz, 1978; Bedient et al., 1978).

Street Cleaning

Street cleaning is performed in most urban areas for control of solids and trash deposited along street gutters. Although it has long been assumed that street cleaning has a beneficial effect upon the quality of urban runoff, until recently (written 1988), few data have been available to quantify this effect. Unless performed on a daily basis, EPA Nationwide Urban Runoff Program (NURP) studies generally found little improvement of runoff quality by street sweeping (EPA, 1983b).

The most elaborate studies are probably those of Pitt (1979, 1985) in which street surface loadings were carefully monitored along with runoff quality in order to determine the effectiveness of street cleaning. In San Jose, California (Pitt, 1979) frequent street cleaning on smooth asphalt surfaces (once or twice per day) can remove up to 50 percent of the total solids and heavy metal yields of urban runoff. Under more typical cleaning programs (once or twice a month), less than 5 percent of the total solids and heavy metals in the runoff are removed. Organics and nutrients in the runoff cannot be effectively controlled by intensive street cleaning -- typically much less than 10 percent removal, even for daily cleaning. This is because the latter originate primarily in runoff and erosion from off-street areas during storms. In Bellevue, Washington (Pitt, 1985) similar conclusions were reached, with a maximum projected effectiveness for pollutant removal from runoff of about 10 percent.

The removal effectiveness of street cleaning depends upon many factors such as the type of sweeper, whether flushing is included, the presence of parked cars, the quantity of total solids, the constituent being considered, and the relative frequency of rainfall events. Obviously, if street sweeping is performed infrequently in relation to rainfall events, it will not be effective. Removal efficiencies for several constituents are available in tables (Pitt, 1979). Clearly, efficiencies are greater for constituents that behave as particulates.

As previously discussed, the original SWMM RUNOFF Module quality routines were based on the 1969 APWA study in Chicago (APWA, 1969). A particular aspect of that study that led to modifications to the first buildup-washoff formulation was that the Chicago quality data (e.g. Table 4-18) were reported for the soluble fraction only, i.e., the samples were filtered prior to chemical analysis. Hence, they could not represent the total content of, say, BOD₅ in the stormwater. In calibration of SWMM in San Francisco and Cincinnati, 5% of computed suspended solids was added to BOD₅ to account for the insoluble fraction. This provided a reasonable BOD₅ calibration in both cities.

The Version II release of SWMM (Huber et al., 1975) followed the STORM model (Roesner et al., 1974) and added to BOD₅, N and PO₄ fractions of both suspended solids and settleable solids. Adding a fraction from settleable solids is double counting, however, since it is no more than a fraction of suspended solids itself. Furthermore, all the fractions in SWMM and STORM were basically just assumed from calibration exercises as opposed to being measured from field samples.

Agricultural models, such as NPS (Donigian and Crawford, 1976), ARM (Donigian et al., 1977) and HSPF (Johanson et al., 1980) also relate other constituent mass load rates and concentrations to that of "solids," usually "sediment" computed by an erosion equation. The ratio of constituent to "solids" is then called a "potency factor" and for some constituents is the only means by which their concentrations are computed. The approach works well when constituents are transported in solid form, either as particulates or by adsorption onto soil particles. This approach can also be used in SWMM. For instance, one constituent could represent "solids" and be computed by any of the means available (i.e., buildup-washoff, rating curve, Universal Soil Loss Equation). Other constituents could then be treated simply as a fraction of "solids." The fractions (potency factors) are input. As a refinement, two or more constituents could represent "solids" in different particle size ranges, and fractions of each summed to predict other constituents. Again, this approach will not work well for constituents that are transported primarily in a dissolved state, e.g. NO₃.

Available Information

In an effort to evaluate potency factors for various constituents in both urban and agricultural runoff, Zison (1980) examined available data and developed regression relationships as a function of suspended solids and other parameters. His only urban catchments were three from Seattle, taken from the Urban Rainfall-Runoff-Quality Data Base (Huber et al., 1981a), for which several water quality and storm event parameters were available. Unfortunately, statistically meaningful results could only be obtained using log-transformed data, and simple fractions of the type required for input are seldom reported. Zison (1980) acknowledged this and suggested that model modifications might be made or piecewise-linear approximations made to the power function relationship. In any event, Zison related the total constituent concentration (not just the nonsoluble portion) to other parameters. Hence, for their use in SWMM the buildup-washoff portion would need to be "zeroed out," (easily accomplished), as suggested earlier.

Other reports also provide some insight as to potential values for the constituent fractions. For instance, Sartor and Boyd (1972), Shaheen (1975) and Manning et al. (1977) report particle size distributions for several constituents. However, the distributions refer principally to fractions of constituents appearing as "dust and dirt," not to fractions of total concentration, soluble plus nonsoluble. Finally, Pitt and Amy (1973) give fractions (and surface loadings) for heavy metals.

If constituent fractions are used in SWMM, local samples should identify the soluble (filterable) and nonsoluble fractions for the constituents of interest. Alternatively, the fractions may be avoided altogether by treating the buildup-washoff or rating curve approach as one for the total concentration, thus eliminating the need to break constituents into more than one form.

There is now considerable public awareness of the fact that precipitation is by no means "pure" and does not have characteristics of distilled water. Low pH (acid rain) is the best known parameter but many substances can also be found in precipitation, including organics, solids, nutrients, metals and pesticides. Compared to surface sources, rainfall is probably an important contributor mainly of some nutrients, although it may contribute substantially to other constituents as well. In particular, Kluesener and Lee (1974) found ammonia levels in rainfall higher than in runoff in a residential catchment in Madison, Wisconsin; rainfall nitrate accounted for 20% to 90% of the nitrate in stormwater runoff to Lake Wingra. Mattraw and Sherwood (1977) report similar findings for nitrate and total nitrogen for a residential area near Fort Lauderdale, Florida. Data from the latter study are presented in Table 4-28 in which rainfall may be seen to be an important contributor to all nitrogen forms, plus COD, although the instance of a higher COD value in rainfall than in runoff is probably anomalous.

In addition to the two references first cited, Weibel et al. (1964, 1966) report concentrations of constituents in Cincinnati rainfall (Table 4-20), and a summary is also given by Manning et al. (1977). Other data on rainfall chemistry and loadings is given by Betson (1977), Hendry and Brezonik (1980), Novotny and Kincaid (1981) and Randall et al. (1981). A comprehensive summary is presented by Brezonik (1975) from which it may be seen in Table 4-29 that there is a wide range of concentrations observed in rainfall. Again, the most important parameters relative to urban runoff are probably the various nitrogen forms.

Uttormark et al. (1974) provide annual nitrogen (and phosphorus) precipitation loading values (kg/ha-yr) for many cities regionally for the U.S. and Canada. It should be remembered that considerable seasonal variability may exist. These may be easily converted to precipitation concentrations required for SWMM input if the local rainfall is known, since 10 x kg/ha-yr / cm/yr = mg/l. For instance, annual NH₃-N + NO₃-N loadings at Miami are almost 2 kg/ha-yr, and annual rainfall is 60 in. (152 cm). From the above, the inorganic nitrogen concentration is 10 x 2/152 = 0.13 mg/l which compares quite favorably with the sum of NH₃-N and NO₃-N concentrations for two of the three Ft. Lauderdale storms given in Table 4-28. For a better breakdown of nitrogen forms, see Table 17 of Uttormark et al. (1974).

Table 4-29. Representative Concentrations in Rainfall.

aRange for three storms ^bAverage of 35 Storms ^cSum of NH₃-N, NO₂ –N, NO₃-N

Erosion and sedimentation are often cited as a major problem related to urban runoff. They not only contribute to degradation of land surfaces and soil loss but also to adverse receiving water quality and sedimentation in channels and sewer networks. Several ways exist to analyze erosion from the land surface (e.g. Vanoni, 1975), the most sophisticated of which include calculations of the shear stress exerted on soil particles by overland flow and/or the influence of rainfall energy in dislodging them. In keeping with the simplified quality procedures included in the rest of the RUNOFF Module, a widely-used empirical approach, the Universal Soil Loss Equation (USLE), has been adapted for use in SWMM. Full details and further information on the USLE are given by Heaney et al. (1975).

 Universal Soil Loss Equation

The USLE was derived from statistical analyses of soil loss and associated data obtained in 40 years of research by the Agricultural Research Service (ARS) and assembled at the ARS runoff and soil loss data center at Purdue University. The data include more that 250,000 runoff events at 48 research stations in 26 states, representing about 10,000 plot-years of erosion studies under natural rain. It was developed by Wischmeier and Smith (1958) as an estimate of the average annual soil erosion from rainstorms for a given upland area, L, expressed as the average annual soil loss per unit area, (tons per acre per year):

(4-148)

• where R = the rainfall factor,
• K = the soil erodibility factor,
• LS = the slope length gradient ratio,
• C = the cropping management factor or cover index factor, and
• P = the erosion control practice factor.

This equation represents a comprehensive attempt at relating the major factors in soil erosion. It is used in SWMM to predict the average soil loss for a given storm or time period. It is recognized that the USLE was not developed for making predictions based on specific rainfall events. There are many random variables which tend to cancel out when predicting individual storm yields. For example, the initial soil moisture condition, or antecedent moisture condition, is a parameter which cannot routinely be determined directly and used reliably. It should be understood by the SWMM user that equation 4-145 enables land management planners to estimate gross erosion rates for a wide range of rainfall, soil, slope, crop, and management conditions.

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Sensitivity, calibration and error analysis:

Note that the inherent uncertainty is not reported in SWMM RUNOFF computed results. Sensitivity, calibration and error analysis (SCEA) is an important part of modelling. Instead of covering SCEA in detail on this page, I provide here  a link to an unfinished preliminary draft booklet on SCEA for SWMM modelling.

Implementing the plan

This part is provided by Dr Heathcote, and summarises her lecture.

1. Source Data: Historical data on emissions from each major and (if possible) minor source. Aim to match units and time steps: if you want to estimate average annual total phosphorus loads, look for historical data expressed (or expressible) in those terms.
2. Receiver Data: Historical data on the behaviour of physical systems, particularly streamflows, rainfall, and stream cross- sections.

Historical data on the behaviour of chemical constituents in those systems, particularly the parameter(s) you are investigating.

Historical data on the biological systems present in the watershed, including species diversity, annual cycling (e.g. of algal populations), fish spawning patterns and timing, etc.

3. For each gap, determine:

1. If additional data can be collected and analyzed within the scope of work of your study
2. If so, what data is needed, when and where should it be collected, and how should it be analyzed? Who should do each step?
3. If not, can you estimate missing data from literature sources, from adjacent or similar watersheds, or from some other source?

Step 8: What are all the possible solutions to the problem?

1. Brainstorm; create a long list of options:

(a) Do nothing: In some systems (the English-Wabigoon River system, contaminated with mercury in sediments from historical discharges, is one example), just leaving the system alone will eventually result in some improvement. In most systems, it will be useful to model the "do nothing" option as a base case, or foundation, against which other management scenarios can be compared.

(b) Structural Treatment Measures: In most systems, it is possible to build something--a treatment plant, a stormwater pond, a grassed waterway, a sedimentation basin, etc.--that will result in some water quality improvement. Create a long list of structural measures ("technologies") that may be useful in reducing loads of the pollutant(s) you are examining. Manuals such as Tom Schueler's BMP manual and the Ontario Ministry of Environment's BMP manual may be helpful in this. Planting shade trees could be considered a structural measure even though it doesn't involve "building" a structure or installing a mechanical technology.

(c) Non-structural or Management Measures: In some systems, so- called "management" measures may be as or more effective than structural measures; they are, however, often harder to implement because they require behavioural changes. Such measures might include "stoop and scoop" by-laws (and enforcement) for control of pet excrement in urban systems. In agricultural systems, non- structural measures could include tillage and cropping practices such as contour plowing and intercropping.

2. Consult with stakeholders to check, refine and expand your list.

Step 9: Which solutions work best?

1. Reduce your "long list" to a short list of feasible options for your site.

(a) Rule out any options that are inappropriate for the physical conditions (soils, climate, infrastructure, development, etc.) of your site. Document and explain your decisions.

(b) Rule out any options that are clearly too costly for your client (but document and explain this decision; it could change at a later time). Such a decision should be made in consultation with your client.

(c) Try to rule out options that are unlikely to have a significant effect on loadings (unless they are very low in cost). Don't guess: use a simple predictive model, spreadsheet, or literature values to estimate impacts. Document and explain your decisions.

2. Test your "short list" of options singly:

(a) Decide whether you want to model only present conditions or also one or more future development scenarios. If the latter, obtain any available land-use planning forecasts, for example from the municipality's Official Plan, to give you an idea of future population densities and land uses.

(b) Develop a list of management scenarios to be modelled. These should include:

(i) "Do nothing" cases for the present and also for any separate future scenarios.

(ii) Scenarios that examine the impact of a single management option applied to the greatest possible effect ("best case" for that option). Model both present and future conditions, if applicable.

(iii) Scenarios that examine the impact of a single option applied to a portion of the basin (e.g. a portion of the total stream length; or 50% of farms; or some similar scenario). This type of scenario might be used to model various levels of stakeholder (e.g. farmer) willingness to implement a given management practice. Model both present and future conditions, if applicable.

3. Rule out any ineffective options:

(a) Decide with your client and stakeholder group what level of performance constitutes adequate effectiveness (e.g. 20% removal of phosphorus; 10% reduction in instream temperature). This is essentially an arbitrary decision but again should be documented and explained.

(b) Perform your model runs and examine the results of your single- option runs and eliminate any options that do not meet your desired performance targets. Document and explain your decisions.

4. Conduct final testing:

(a) Decide which options you will include in final testing and combine them into appropriate scenarios. For example, you might want to group all the agricultural tillage/cropping measures into one scenario; all the urban stormwater management options into a second; and so on. Finally, construct some "cadillac" scenarios combining all possible actions into a single plan. Don't forget to model both present case and future land use cases, and always document and explain why you have included/excluded options in each scenario.

(b) Perform your model runs and summarize the results of all modelling (single and multi-option scenarios) in a table, showing the effectiveness and cost of each "plan."

 

Step 10: Which solution will be easiest to implement?

1. Consult your client and stakeholder group to determine any obstacles to implementation:

(a) If you have included your stakeholder group meaningfully in your decision making, they will have advised you of potential obstacles to implementation early in your planning. Nevertheless, it is important to present the results of your final testing to the decision-making group. Obstacles may become apparent at this stage that have not been identified previously.

(b) Do NOT present a single option to your decision-making group for approval. Instead, present them with the top 3-5 or even more plans, with advantages, disadvantages, costs and performance. Let them make the decision. If requested by your client, you may of course identify a single option that in your view is preferred, but you must provide enough information on alternative approaches so that your audience can participate in the final decision making.

(c) Eliminate any options, however effective, that will present significant obstacles to implementation. It's better to have a less effective plan fully implemented than a very effective plan that never sees the light of day.

(d) Where you have identified obstacles, determine (with your client and stakeholder group) whether there is any way to surmount them, for instance through financial compensation, additional structures or technologies, etc. Note that some obstacles will remain no matter what you do: for instance, farmers are not usually keen to give up their land for a dam project; neither are aboriginal groups going to be happy about use of heritage/spiritual lands for pollution-control purposes. But some obstacles (e.g. property value concerns) may be manageable.

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Assignment A4:

Allow up to 6 h for reading, and up to 12 h for writing your web page. This means perhaps that you will have to read selectively, and spend considerable time digging up data for your local problem. Spend a few minutes at the outset ranking the assigned reading according to your own priorities. It's OK if you are not able to complete all the suggested reading. Feel free to criticize our notes; there are lots of textbooks with different viewpoints. You are expected to apply ideas in this module to a local problem in the area where you live. Start by naming and describing a potential water pollution control problem of interest to you, one with good chance of getting a handle on the facts (try to get a problem with reports available form a local authority or consultant). You are required to apply the main points as you see them in our above notes. As I say, what would interest us most, is your focus on possible applications to problems in your area. Present the discussion as a brief summary on your web page, covering as many of the following suggested points as you can (these thanks to Isobel Heathcote):

1. What is the problem you are trying to fix ("pollution" isn’t a problem. But a use impairment is a problem - e.g. can’t drink the water, can’t swim, can’t fish).
2. How do you know it’s a problem - what indicators are there that a problem exists? Choose variables on which to concentrate.
3. For whom is it a problem? Who are the stakeholders? How do their views of the situation differ?
4. What are all the sources of the problem?
5. What physical, biological, or chemical systems affect or are affected by the problem? i.e. advection in a river, biomagnification, etc.
6. How do the sources and receivers behave in space and time - i.e. what are the sources of variability?
7. What are all the possible solutions to the problem?
8. Which solutions are feasible (i.e. meet cost, space, time, etc. constraints)?
9. How will you evaluate the remaining feasible solutions (e.g., which model(s) will you use)?
10. What criteria will you use to evaluate "best" performance of the various solutions?
11. How does each feasible solution perform on each decision criterion?
12. What is the preferred solution?
13. What implementation obstacles might exist? How might you overcome these obstacles?

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Costing and financing

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Legal, institutional and administrative concerns

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Environmental and social impact assessment

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Choosing the best plan

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Frequently asked questions

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Reading

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Reminders

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