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Water Management

Updated: August 12, 2015                                   

Contents:

• Lake Management- Quality objectives as a function of its intended use

• Predictive Models:

  • OECD Management Model (Vollenweider and Kerekes, 1982)
    • Table-4: Management Model- yearly averages
    • Figure-7: OECD Management Model together with the long term correlation equations

  • Vollenweider 1976 Model (Vollenweider, 1976)
    • Figure-8: An example of the Vollenweider (1976) Model with the OECD (1982) Management Model trophic categories superimposed

  • Sensitive areas and exceptions in modelling

Lake Management- Quality objectives as a function of its intended use

These objectives may vary considerably between different regions, depending on local conditions and on the expectation of the population concerning water quality. Depending on the intended use (e.g., drinking water, industrial water, power plant cooling water, recreation, multiple use, etc.), the stringency of objectives could be set at various levels, considering algal biomass (chlorophyll a) as a trophic response indicator, i.e.:

1. Mean algal biomass (annual, ice-free season, summer) to be kept below a certain level;

2. Mean annual peak algal biomass to be kept below a certain level;

3. Exceptional (highest possible) annual peak biomass to be kept below a certain level.

Predictive Models

• Although predictive models are not perfect, neither are measurements.

• Actual biological conditions in a lake vary from year to year, as a result of climatic and other environmental variations. Field sampling and, in the case of TP and Cha, laboratory analysis are not perfect processes and involve a degree of error.

• For these reasons, when used as a basis for lake management decisions, measured data should represent the means of large numbers of samples over long periods of time; therefore the estimates generated by a well calibrated model based on current use and development conditions can be taken as representing the long term means around which measurements, if available, would vary.
  • This is ofcourse an ideal world, and in the real world of large and complex systems, this claim of accuracy cannot be made in all cases.
  • Even if there is a significant gap between the predicted and measured trophic state indicators for a particular lake, and some doubt surrounds the absolute value of the predicted indicators, the models will still indicate the relative change in trophic state that would result from a given change in conditions.

• The planning agency may pick any intermediate level of TP or Cha from the predictive models should it so desire subsequent to intensive public consultations. Based on the predicted values, the agency could set the densities as follows:

  • In the case of areas served with on-site sewage disposal systems, a maximum number of allowable systems within 300 metres of lake shores could be ascertained. 300 metres is an approximate compromise value and has withstood the test of time in Ontario, Nova Scotia and elsewhere in North America.

  • In the case of areas served with sewered systems, controls could be placed on the amount of impervious areas as well as the agency could require total stormwater systems, for e.g., specially constructed (or engineered) wetlands together, not either/or, with a pre-detention basin. The latter will remove larger particles, and the wetland would act as a polishing device for removal/reduction of smaller particles as well as dissolved pollutants.
    • The pollutants are not just total phosphorus alone, but are a whole range of post-development post-human-occupation inevitable generation of pollutants.

OECD Management Model (Vollenweider and Kerekes, 1982):

Table-4: Management Model- yearly averages

Figure-7: OECD Management Model together with the long term correlation equations

Vollenweider 1976 Model (Vollenweider, 1976):

The model is: L_c (mg/m².y) = P_c^sp (z/τ_w + 10), where

L_c is the critical loading of phosphorus,
P_c^sp is the critical concentration of total phosphorus (mg/m³) for simplicity taken at spring overturn,
z is the mean depth, and
τ_w the water residence time

In this model, z/τ_w = q_s represents the hydraulic load which is independant of mean depth. It can be argued however that the model ignores, or at least underestimates, the effect of mean depth, i.e., the dilution function.

Figure-8: An example of the Vollenweider (1976) Model with the OECD (1982) Management Model trophic categories superimposed (Mandaville, 2000):

Some sensitive areas and exceptions in modelling:

1. The prediction of lake concentration should be given with statistical confidence limits to reflect more realistically the uncertainties in the model prediction.
   1. The models assume steady state conditions in a completely "mixed reactor".
   2. It is unlikely that individual loading estimates are more accurate than ± 35%.
   3. The nutrient loading models give an estimation of average conditions; local conditions may deviate considerably, temporally and/or spatially.
   4. The empirical phosphorus loading models should be used only for water bodies within the range of conditions (hydrologic, morphometric, trophic and climatic) represented by each of the model development data set.
   5. A steady state on an annual basis is assumed in the models. This is seldom the case, even in undisturbed natural systems, considerable year to year variations in nutrient load are detected due to fluctuations in annual runoff as well as in the nutrient loads.
   6. The models assume that the basin is open and that there is an annual water surplus or outflow from the lake (the models cannot be applied without modification to closed basins or arid and semi-arid regions).

2. In large water bodies or those of complex morphometry, localized imbalances in nutrient concentration and trophic response may develop ("local reactors"). In extreme cases localized symptoms of eutrophication may develop near point sources of nutrients, in an otherwise oligotrophic water body.

3. The relationship between phosphorus and algal chlorophyll (i.e. algal biomass) is a log-log plot of the data, and there is considerable scatter to the linear data, indicating effects from other factors in the pelagic environment, such as light, nitrogen, or zooplankton grazing, in limiting algal biomass. This also indicates that there can be wide variation in the expected chlorophyll from any given phosphorus concentration.

4. The chlorophyll a - total phosphorus ratio, an index of the efficiency of algae to utilize phosphorus varies greatly among lakes (ca. 0.05 to 0.7 in the OECD programme). A shift in algal composition and the efficiency of phosphorus use can profoundly affect the trophic response in a given water body.

5. Macrophytes and filamentous algae are ignored in the models, macrophytes may contain large amounts of phosphorus which is ignored in the determination of in-lake P concentration.

6. Macrophytes often act as nutrient pumps and may cause appreciable internal loading.

7. Biological activity such as by bottom feeding fish and emerging bottom dwelling invertebrate fauna often produces considerable internal loading of nutrients.

8. The presence or absence of fish and types present in a lake can profoundly affect the apparent trophic response; in the absence of predation highly abundant zooplankton reduce phytoplankton by grazing which results in lower than expected chlorophyll a concentrations.

9. The type of algal community can profoundly affect the sedimentation rate of phosphorus; lakes dominated by diatoms remove phosphorus by sedimentation at a much faster rate than lakes dominated by more buoyant green or blue-green algae.

10. In shallow unstratified lakes, the recycling and utilization of nutrients is more efficient than in deep stratified lakes.

11. In reservoirs, peculiar flow regimes and hypolimnetic water withdrawal (rich in nutrients) should be taken into account.

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