1. 1 - COURSE 4.4 - OPTIMIZATION PROGRAMMES

2. Peter Kelderman UNESCO-IHE Institute for Water Education Online Module Water Quality Assessment

3. A general view of information The “Data-rich but Information-poor” syndrome in water quality monitoring (Ward, R.C., J.C. Loftis and G.B. McBride, 1986) “Research reports are lying in dusty drawers of policy makers or elsewhere and never used” (Boogerd, A., P. Groenewegen and M. Hisschemöller, 1997) 3

4. Refresher Course Rwanda, 22-26 October 2012 4

5. Monitoring cycle Watermanagement Information utilisation Information needs Information strategy Assessment and reporting Monitoring programmes Data collection Data handling Data analysis 5

6. Improved quality 6

7. Sources of information 7

8. 8 OPTIMIZATION/MODERNIZATION PROGRAMMES: “Optimization”: less stations, frequency, variables; guideline: still possible to fulfill requirements of the network? Introducing modern techniques (remote sensing, ferryboxes, smart phones, etc.) “Modernization” of decades-old programmes A “driver” for optimization may be an ever increasing number of variables, to be monitored because of legislation/(inter)national agreements, etc.

9. 9 The Netherlands: increase from <20 to about 250 variables between 1952 and 2002: (however much less stations,..)  optimizations ( )

10. 10 Criteria for optimization of monitoring networks: Measuring frequency?  according to “relevant margins” Trends can still be detected? (e.g. over 5 years) With two stations A and B: leave out A or B, in case of certain minimum level of correlation between A and B; the same holds for correlation between two variables (see before: NH 4 -N vs. N tot. ) Optimized network still gives reliable overview of the water quality in the area? Network still according to (inter)national agreements and legislation? OPTIMIZATION PROGRAMMES

11. 11 Example: It can be decided to leave out station B if for > 90% of the variables, the correlation coefficient r between A and B is larger than 0.8 (= recommendation in the Netherlands; “governmental waters”)

12. 12 Example: number of monitoring stations in “governmental waters”, the Netherlands, 1972-1996 E.g. Markermeer lake: >10  2 stations

13. 13 You have to keep in mind factors such as: Seasonal trends? Often have to be “filtered out” using tests: Kruskal-Wallis or ANOVA Using the relevant margins, monitoring frequencies may be unrealistically high  make compromises (see EU-WFD) Data dependence between stations (e.g. in rivers); “autocorrelation” How to deal with “outliers” in a data set? (Dixon Q test? + 3 or 4 standard deviations away from average?)

14. 14 Relevant margins VariableRelevant margin Dissolved oxygen0.5 mg/L Phosphate-P0.01-0.05 mg/L Nitrate0.5 mg N/L Ammonium0.1 mg N/L Chloride5 mg/L Cadmium0.05 µg/L Pb1 µg/L Benzo (a) pyrene0.1 mg/kg suspended matter.. Is the margin which is relevant for the information that is needed; the level of precision needed for a variable: x avg. + relevant margin For example: for dissolved oxygen, a relevant margin of 0.01 mg O 2 /L is too strict; of 3 mg/L irrelevant. In the Netherlands:

15. 15 Example: For indicating the risk for eutrophication, phosphate levels in surface waters will be classified into different classes, e.g.: 0 – 0.05 mg PO 4 -P/L : Class 1 0.05- 0.10 mg PO 4 -P/L : Class 2 0.10 – 0.15 mg PO 4 -P/L :Class 3 0.15 - 0.25 mg PO 4 -P/L :Class 4 > 0.25 mg PO 4 -P/L : Class 5 It will be clear that the relevant margin must be maximally 0.05 mg P/L, since with this “error” you may already decide on classifying into a wrong class, for example, into Class 3 instead of Class 2. No, more interested if it could be 5.5. or 6.5

16. 16 In an annual water quality monitoring programme measuring phosphate (average = 0.20 mg P/L; s x = 0.05), it is required that the average is known, with 95% confidence, within + 0.03 mg P/L distance from this average. How many samples/year must be taken to fulfil this requirement? Assume a normal distribution of data. “Relevant margin”  n = (2.2 * 0.05/0.03) 2 = 13 (: monthly intervals) Important: the smaller the relevant margin RM chosen, the higher the sampling frequency needed; this works with (RM) 2 ! Remember..?

17. 17 STEP-BY-STEP OPTIMIZATION OF WATER QUALITY MONITORING NETWORKS

18. 18 Feasibility Study Modifying the goals Optimization Study Monitoring programme Budget Quantification of the goals Data collection Historical data Information needs

19. Feasibility Study Modifying the goals Optimization Study Monitoring programme Budget Setting the goals Data collection Historical data Information needs 19 “Primary information needs”: Monitoring goals Monitoring stations (pre-selection) Variables (pre-selection) Level of (statistical) confidence Minimum frequencies (e.g. by law, or by expertise (e.g. once per season))

20. 20 Feasibility Study Modifying the goals Optimization Study Monitoring programme Budget Setting the goals Data collection Historical data Information needs Making use of existing data E.g. for finding (seasonal) trends If not present: try to make use of comparable studies; of models

21. 21 Feasibility Study Modifying the goals Optimization Study Monitoring programme Budget Quantification of the goals Data collection Historical data Information needs Relevant margins Mimimum information needs Maximum information needs* * “no need to go beyond certain “maximum” information needs”

22. 22 Feasibility Study Modifying the goals Optimization Study Monitoring programme Budget Quantification of the goals Data collection Historical data Information needs Leaving out “unrealistic” variables (too complicated/costly..), unrealistic frequencies,.. Modifying relevant margins, if necessary

23. 23 Feasibility Study Modifying the goals Optimization Study Monitoring programme Budget Setting the goals Data collection Historical data Information needs Check cost per (set of) variable(s), including running cost, maintenance Possible cancelling of variable(s) on basis of cost/benefit analysis Come to a practical combination of monitoring routines for different variables (e.g. 1x, 2x, 4x, 6x per year) Optimize also with other monitoring programmes in same region

24. 24 Measuring stations: Representative for their surrounding Not too high variability Can give clear indication of “trends”, e.g. after sanitation Variables; “indicator variables” are most suitable; be careful with: Variables with hardly any added values With extreme variability in time and space (e.g. susp. solids) With excessive cost (like organic micropollutants)

25. 25 A WQ monitoring network is “optimal” if: “Everywhere” and at any time, the water quality is known, with sufficient reliability Each station is representative for a certain type of water, or area There is no overlap in info between stations The programme fits well with other monitoring programmes and monitoring objectives.

26. 26 COST ASPECTS: Highest cost: sampling and analysis If cost are too high for budget: Ask for higher budget Reduce the number of variables, and/or frequency and/or stations (see e.g. Course 3.9.) Give much attention to relatively “inexpensive” factors