maximizing existing data and modelling techniques outcomes. examples related to water issues

BTG Dublin April 2004parallel session 5

Maximizing existing data and modelling techniques

outcomes.Examples related to water issues

Philippe Crouzeti.f.en, Orléans France


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


Background rationales

Needs Question

Analysis and design

Tool (and data)

Résult

System


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.


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


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

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

Densely urban

Moderately impacted

Exam

ple

1


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)


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?


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


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


WQA: Results exampleFirst: discharge linearization,

Second: quality linearization, (here Nitrate)

Then: results exploitation (catchment / NUTS)

Medium catchments

Large catchments

NUTS3

Exam

ple

2


Pattern mapping (E&W,

indicator FV97-99)

Exam

ple

2


Patterns mapping (Ireland,

biological)

Exam

ple

2


RQGI Largest Big

Medium Small

All together

Exam

ple

2


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.


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


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


CORINE L.C.

Agriculture

Pollution



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).


The End…

Thanks for your attention

maximizing existing data and modelling techniques outcomes. examples related to water issues

Documents

water quality

water relationshipsquestion

water body

river waterquantity

water accounts methodology

impact water composition

quality index

sectorrelated activities