Maximizing existing data and modelling techniques outcomes. Examples related to water issues

1. Maximizing existing data and modelling techniques outcomes.Examples related to water issues Philippe Crouzet i.f.en, Orléans France BTG Dublin April 2004parallel session 5

2. Sectoral impact on water composition and trends Water bodies quality and changes, in relation with measures Riverine fluxes and apportionments of sources Policy questions and data ingredients. • Seven data sets allow responding to (but not only to): BTG Dublin April 2004parallel session 5

3. Background rationales BTG Dublin April 2004parallel session 5

4. Is this comprehensive? Is this representative? Rivers: why improving? Is this relevant ? • Lessons from the "Dobris" assessment lead to: • Addressing representativity issues, • Considering the scope of classical methods, • Finding appropriate responses, that do not cover all issues related to reporting on rivers. • This outcome could be quite general. Despite accurate questioning, this first work initiated a process of revisiting traditional approaches and lead to more comprehensive addressing river issues through relevant and representative methodologies. BTG Dublin April 2004parallel session 5

5. Assessing sector-related vs. water relationships Example 1 • Question: does sector-related activities (e.g., agriculture, livestock, human settlements, etc. impact water composition? Is the situation improving as a response to sector-related policies? Are quality targets likely to be achieved and when? • Response: • stratification technique applied to sampling networks earmarks each sampling station according to the prominent Driving Forces, that can be defined according to main sectors. • Changes in averages per stratum vs. time capture trends and allow forecasting target achievement (or missing). • Technique: limited needs in ingredients, but rather complex statistics. BTG Dublin April 2004parallel session 5

6. Sector-related vs. water relationships: result example Example 1 Example related to 6 strata, not representing livestock impact BTG Dublin April 2004parallel session 5

7. Assessment of Responses Example 1 • From the stratum average, after filtering off hydrological effects, trends can be derived (hypothesis ("BAA") • This assessment deals with sectoral policies BTG Dublin April 2004parallel session 5

8. Quality class State A State B Change State A 12.5% 0.0% -13% 18.8% 50.0% +31% 56.2% 37.5% -19% 13.5% 13.5% +0% State B 100% 100% 0 Are the outcomes satisfactory? • Stratification gives easy and clear-cut assessment of impact by sectors on water composition, thus complying with certain requirement of the WFD (as reported in the Guidelines for the common implementation strategy), • Data use leads to new concepts related to sound statistics (not developed here), • Assessment of methodology and true meaning of results nevertheless opens new questions: • Is selection needed after stratification? • Can selected stations represent water quality? • could water bodies be assessed accurately using this technique? • Is it possible to address the effectiveness of measures with this technique? It is a "broad brush" indication, not comparable. BTG Dublin April 2004parallel session 5

9. Considering rivers instead of river water • "Quantity of river" is considered instead of "quantity of pressure" as representativity criterion, • Quality index is considered per river reach instead of "concentration statistics". • Consequently: • Stratum becomes explanatory factor (not selection factor), • Sampling point selection becomes useless. • Practical application is carried out using the Water Accounts methodology. Example 2 BTG Dublin April 2004parallel session 5

10. Water Accounts outcomes… Example 2 • Accounts basically yield tables of "quantity of quality", apportioned per reach (or water body…). These outputs are not enough. Indicators aggregated per catchment / river size class were developed: • A 0 (worst)-10 (best) note, called "RQGI" (River Quality Global Index), • A pattern of quality capturing the main features of quality distribution within a catchment / river size class, • An analysis of relative causes of bad / good quality (e.g., comparing nitrate / eutrophication / BOD5) • Latest developments allow aggregating either by catchment or by NUTS BTG Dublin April 2004parallel session 5

11. NUTS3 Then: results exploitation (catchment / NUTS) First: discharge linearization, Second: quality linearization, (here Nitrate) Large catchments Medium catchments WQA: Results example Example 2 BTG Dublin April 2004parallel session 5

12. Pattern mapping (E&W, indicator FV97-99) Example 2 BTG Dublin April 2004parallel session 5

13. Patterns mapping (Ireland, biological) Example 2 BTG Dublin April 2004parallel session 5

14. Big Largest All together Medium Small RQGI Example 2 BTG Dublin April 2004parallel session 5

15. Are the outcomes more satisfactory? • Both dimension of water and river quality are now addressed using simple data sets. • Sector –composition relationship can be computed and forecast carried out. • Water bodies quality is representatively computed and can compare with measure programmes (including monetary . • However, emissions loads and riverine fluxes should be computed and matched together to cross compare with the previous assessments. BTG Dublin April 2004parallel session 5

16. Emissions assessment and validation Example 3 • Riverine fluxes are the sum of emissions minus (retention + self-purification) • Riverine fluxes are computed at ad hoc places, depending on reporting requirements and data acquisition points. BTG Dublin April 2004parallel session 5

17. Results from emissions assessments, itself fuelled by agricultural surplus modelling, all used as well for other purposes ( inc., NAMEA matrix construction, SoE, etc., expressed as emissions Results from monitoring networks, used for other purposes as well (water quality assessment, water quality accounts, water resource, resource accounts, etc. expressed as RI fluxes Reconciling Example 3 BTG Dublin April 2004parallel session 5

18. Concluding… • No unique method can respond to the different reporting requirements, • Most requirements can be consistently fulfiled with existing data ; better accuracy requires optimising monitoring, • Including spatial dimensions, at least Corine LC, links together dramatically improves outcomes made with different data sets (administrative, statistical and instrumented). BTG Dublin April 2004parallel session 5

19. The End… Thanks for your attention BTG Dublin April 2004parallel session 5