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Maximizing existing data and modelling techniques outcomes. Examples related to water issues. Philippe Crouzet i.f.en, Orléans France. Sectoral impact on water composition and trends. Water bodies quality and changes, in relation with measures. Riverine fluxes and apportionments of sources. - PowerPoint PPT Presentation
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BTG Dublin April 2004parallel session 5
Maximizing existing data and modelling techniques
outcomes.Examples related to water issues
Philippe Crouzeti.f.en, Orléans France
BTG Dublin April 2004parallel session 5
Policy questions and data ingredients.
• Seven data sets allow responding to (but not only to):
CORINE L.C.
Quality
Hydrometry
Agriculture
Pollution
Hydrography Administration
Sectoral impact on water composition and trends
Water bodies quality and changes, in relation with
measures
Riverine fluxes and apportionments of sources
BTG Dublin April 2004parallel session 5
Background rationales
Needs Question
Analysis and design
Tool (and data)
Résult
System
BTG Dublin April 2004parallel session 5
Rivers: why improving?
• 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.
Is this representative?
Is this comprehensive?
Is this relevant ?
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
Assessing sector-related vs. water relationships
• 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.
Exam
ple
1
BTG Dublin April 2004parallel session 5
Sector-related vs. water relationships: result
exampleExample related to 6 strata, not representing livestock impact
Example related to 6 strata, not representing livestock impact
Averages per stratum (Nitrate)
0.00
5.00
10.00
15.00
20.00
25.00
30.00
1965 1970 1975 1980 1985 1990 1995 2000 2005
années
mg
/l N
itra
te
Agricultural
Low impact
Urban & Agri.
Urban & Agri.
Very Urban
Moderat. Impacted
Mean stratum values (Ammonium)
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
1965 1970 1975 1980 1985 1990 1995 2000 2005
années
mg
/l A
mm
on
ium Agricultural
Low impacted
Urban and Agricultural
Urban
Densely urban
Moderately impacted
Mean stratum values (Soluble P)
0.00
2.00
4.00
6.00
8.00
10.00
12.00
1965 1970 1975 1980 1985 1990 1995 2000 2005
années
mg
/l S
olu
ble
P
Agricultural
Low impacted
Urban and Agricultural
Urban
Densely urban
Moderately impacted
Impact of 1976 drought, UWW being insufficiently purified
Mean stratum values (Soluble P)
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
1990 1992 1994 1996 1998 2000 2002
années
mg
/l S
olu
ble
P
Agricultural
Low impacted
Urban and Agricultural
Urban
Densely urban
Moderately impacted
Exam
ple
1
BTG Dublin April 2004parallel session 5
Assessment of Responses
• From the stratum average, after filtering off hydrological effects, trends can be derived (hypothesis ("BAA")
• This assessment deals with sectoral policies
NO3 (Agricultural (111 stations))
0
5
10
15
20
25
30
35
0 5 10 15 20 25
Years
Co
nce
ntr
atio
n m
g/l Observed
values
Forecastvalues
OBJ
Series4
Series5
Linear(Observedvalues)
NH4 (Mixed - urban and agriculture (36 stations))
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 5 10 15 20 25
YearsCo
nce
ntr
atio
n m
g/l Observed
values
Forecastvalues
Target
Linear(Observedvalues)
Exam
ple
1
Stratum Agricultural
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1965 1970 1975 1980 1985 1990 1995 2000 2005Year
Rel
ativ
e E
R
0
5
10
15
20
25
30
Nitr
ate
(mg
l-1 N
O3)
Relative effective rainfall
Nitrate (yearly averages)
BTG Dublin April 2004parallel session 5
Are the outcomes satisfactory?
State A
State B
0100%100%
+0%13.5%13.5%
-19%37.5%56.2%
+31%50.0%18.8%
-13%0.0%12.5%
ChangeState BState AQuality class
It is a "broad brush" indication, not comparable.
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?
BTG Dublin April 2004parallel session 5
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.
EuroWaternet Base and Impact networks. Horizontal stratification by diving force.
Water accounts. Vertical stratification by river size class.
Small rivers
Median rivers
Large rivers
Non impacted
Urban
Agricultural
Mixt (U+A)
= Water quality determinands statistics per stratum
= Water quality indicators per class / aggregation
Aggregating together
Exam
ple
2
BTG Dublin April 2004parallel session 5
Water Accounts outcomes…
• 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
Exam
ple
2
BTG Dublin April 2004parallel session 5
WQA: Results exampleFirst: discharge linearization,
Second: quality linearization, (here Nitrate)
Then: results exploitation (catchment / NUTS)
Medium catchments
Large catchments
NUTS3
Exam
ple
2
BTG Dublin April 2004parallel session 5
Pattern mapping (E&W,
indicator FV97-99)
Exam
ple
2
BTG Dublin April 2004parallel session 5
Patterns mapping (Ireland,
biological)
Exam
ple
2
BTG Dublin April 2004parallel session 5
RQGI Largest Big
Medium Small
All together
Exam
ple
2
BTG Dublin April 2004parallel session 5
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
Emissions assessment and validation
• 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.
Restitution point
Downstream sampling point
Upstream sampling and gauging points
Exam
ple
3
BTG Dublin April 2004parallel session 5
Reconciling
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
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
Exam
ple
3CORINE L.C.
Agriculture
Pollution
Hydrography Administration
CORINE L.C.
Agriculture
Pollution
Hydrography Administration
BTG Dublin April 2004parallel session 5
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
The End…
Thanks for your attention