TWOLE , A DECISION SUPPORT SYSTEM FOR INTEGRATED RIVER BASIN PLANNING AND MANAGEMENT

TWOLETWOLE, A DECISION SUPPORT , A DECISION SUPPORT SYSTEM FOR INTEGRATED RIVER SYSTEM FOR INTEGRATED RIVER

BASIN PLANNING AND BASIN PLANNING AND MANAGEMENT MANAGEMENT

Assessment and expert-based Assessment and expert-based prediction of river ecosystem statusprediction of river ecosystem status

CSC - Sheffield, 14 February 2007

Andrea GoltaraAndrea Goltara

[email protected] [email protected] www.cirf.org

Centro Italiano per la Riqualificazione Fluviale

Centro Italianoper la

Riqualificazione Fluviale

CIRF is a private, independent, technical-scientific and non-profit

organisationfounded in 1999 to:

promote river restoration, foster the diffusion of RR culture and related

knowledge, and its application

WHAT IS CIRF

Centro Italianoper la

Riqualificazione Fluviale

MAIN ACTIVITIESMAIN ACTIVITIES

• Training courses

• Seminars

• Study trips

EDUCATION

INFORMATION• Web Site

• Publications

• Meetings

APPLICATION• Pilot Projects

• Studies

EUROPEAN CENTRE FOR RIVER RESTORATION

www.ecrr.org

a network of practitioners of river restoration

2006-2009: CIRF holds the secretariat of the ECRR

4th ECRR RIVER RESTORATION

INTERNATIONAL CONFERENCE

16-21 June 2008

San Servolo Island

• TwoLe: Two-Level Decision Support System for WR planning and management

• Funding: Cariplo Foundation• Duration: 24 months (ongoing)• Partners:

– DEI - Politecnico di Milano

– DIIAR - Politecnico di Milano

– AGR - Istituto di Idraulica Agraria dell’Università degli Studi di Milano

– IIEIT - Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni

– CIRF - Centro Italiano di Riqualificazione Fluviale

– COTI - Consorzio del Ticino

The TwoLe projectsThe TwoLe projects

CENTRO ITALIANO PER LA

RIQUALIFICAZIONE FLUVIALE

Cluster of three projects:

•TwoLe/A: management (application to lake Verbano and Ticino river)

•TwoLe/B: planning (application to lake Lario and Adda river)

•TwoLe/C: software development and management of public participation (STRaRIPa)

www.twole.info

The TwoLe projectsThe TwoLe projects

OBJECTIVE of TwoLe/B:

Test TwoLe in planning of lake Lario and Adda

river basin

OBJECTIVE of TwoLe/A:

Test TwoLe in the management of lake Verbano and Ticino

river basin

TwoLe OBJECTIVESTwoLe OBJECTIVES

• Implement and test a MODSS (TwoLe) to support the definition and implementation of participated River Basin Plans according to the WFD

• Plans have to be developed according to the IWRM paradigm

TwoLe-B: taking into account conflicting objectives in planning at the river basin

scaleCanoeingTourism

Hydropower

Agriculture River

Ecosystem

Fishing

Flooding risk

The PROBLEM:

How to include operationally in a

rational, transparent and

participatory planning scheme and

procedure the objective “improving

fluvial ecosystem status” (WFD) ?

TwoLe-B – CIRF: an index for fluvial ecosystem...and something more

GENERAL OBJECTIVE:

Forecast and assess (ex-ante) the

effects of planning alternatives on fluvial

ecosystems

in order to compare the effects with

those on other sectors/actors at stake


SPECIFIC objectives:

- set-up an operational scheme and tool (index) to evaluate the current and future status of fluvial ecosystem and to forecast (cause-effect model)

the effects of different alternatives

- test the suitability of expert-based modelling in contexts of scarce information


1. CRITERIA to ASSESS the FLUVIAL ECOSYSTEM STATUS (according to WFD): the “VALUE TREE”

2. The REFERENCE STATUS

3. MEASURING the CLOSENESS to REFERENCE STATUS: “CLOSENESS INDICATORS”

4. AGGREGATION of INDICATORS into (sub-)INDICES: the VALUE FUNCTION concept

5. The CAUSE-EFFECT MODEL

a. conceptualization of the causal network

b. Formalization of causal factors

c. determination of cause-effect relationships

STEPS of our METHODOLOGY

WHICH CRITERIA to SELECT the ATTRIBUTES?

• Conceptually robust

• Coherent with the WFD

• Useful discard those that do not change within the Solution Alternatives considered (planning/management)

• Assessable today

• Predictable as a consequence of possible actions to be implemented (solution Alternatives)

• Feasible to assess corresponding REFERENCE conditions

• Can be modelled (computation can be performed automatically in the DSS)

• Can be represented in an intuitive fashion to non experts

1. Status of fluvial ecosystem (WFD) -> the value tree

1. Status of fluvial ecosystem (WFD) -> the value tree

1. Status of fluvial ecosystem -> the value tree: FLEA adapted

1. Status of fluvial ecosystem -> the value tree of TwoLe-B

ECOLOGICAL STATUS

General conditions

Benthic macroinvertebrates

LIM

Biological quality

(terrestrial and aquatic biota)

Fish fauna

Terrestrial flora

Abundance

Biodiversity (EPT)

Community composition

Population structure (key species)

Autochthonous species

Exotic species

Age distribution structure

Abundance

Physico-chemical quality (water quality)

Riparian vegetation

Naturalness

Cover

Longitudinal continuity

Width of riparian strip

Corridor (zonal) vegetation

Hydromorphological quality Hydrological regime

Characteristics of regime (annual, monthly flows; max, min annual flow;

peak and period,…)Mean values

Standard deviations

Biodiversity-spring

Biodiversity-summer

Biodiversity-autumn

Biodiversity-winter

Total exotic species

Presence of Silurus Glanis

Naturalness of structural features

Autochthony

Naturalness (species)

Cover

Indicators not represented for lack of

space




4. AGGREGATION of INDICATORS into (sub-)INDICES : the VALUE FUNCTION concept






5a. Conceptualization of the causal network: fish fauna

FISH FAUNA (f)

Community composition (f1)

Abundance key species (f22)

Longitudinal Continuity

(l)

Prevailing flow during minimum flow quarter (Q)

Minimum daily flow during hatching

period key species (s)

EVALUATION INDEX

Cause-effect model

Making fish-passages / removing

discontinuities

Managing flow released from lake and derived/(released) for

hydropower/irrigation

Stress hydromorphol.

conditions

Prevailing hydromorphol.

conditions during minimum flow

period (same year)

Presence of autochthonous

species (f11)

Presence of exotic species (f12)

Age distribution structure key species (f21)

Population structure (key species) (f2)

Minimum annual

3-days flow (q)

Stress hydromorphol. conditions hatching period key species


conditions during minimum flow period (last 3

years)

Exotic species / tot (f121)

Presence of silurus

(f122)

Actions

Causal factors

Triennial average of prevailing flow during

minimum flow quarter (m)

5a. Conceptualization of the causal network

Which are the main variables?

Statistical analysis

Experts ?

Projec tion of the v ar iables on the f ac tor-plane ( 1 x 2)

A c tiv e and Supplementary v ar iables*Supplementary v ar iable

A c tiv e Suppl.

Num Plec otter i

Num Ef emerotter i

Num Tr ic otter i

*C m in_O2_3m _prec

*C m ediana_C OD _3m _prec

*C m edia_O2_3m _prec

*C m edia_C OD _3m _prec

*Tm edia_3m _prec

*Qm ediana_1m _prec*Qm in_1m _prec

*Q_75°_3m _prec

*Q_75°_1m _prec

-1.0 -0.5 0.0 0.5 1.0

Fac tor 1 : 67.66%

-1.0

-0.5

0.0

0.5

1.0

Factor 2 : 19.54%

Dissolved oxygen previous

3 months (d)

Median flow previous 3 months (Q)

Minimum flow previous month (q)

EVALUATION INDEX

Cause-effect model

Pollutant loads reduction (scenario)




conditions

Prevailing hydromorphol. conditions

Actions

Macroinvertebrates (m)

Biodiversity of the community (m1)

Abundance (of habitat) (m2)

Biodiv. winter (m11)

Biodiv. spring (m12)

Biodiv. summer

(m13)

Biodiv. autumn

(m14)

Causal factors

5a. Conceptualization of the causal network: macroinvertebrates










5b. Formalization of causal factors


3 months (d)



EVALUATION INDEX

Cause-effect model





conditions


Actions






Biodiv. summer

(m13)

Biodiv. autumn

(m14)

Causal factors

hydro-morphological conditions

corresponding to min daily

flow in the preceding

month

“Stress hydro-

morphological conditions”

Min (Qt), t[t-30;t]

5b. Formalization of causal factors

Example 1










5c. Determination of cause-effect relationships


3 months (d)

EVALUATION INDEX

Cause-effect model


Actions






Biodiv. summer

(m13)

Biodiv. autumn

(m14)

Causal factors

?

?

?

?? ?

?

?


conditions


?





TYPES of MODELS to BUILD CAUSE-EFFECT RELATIONSHIPS


1. Mechanistic (deterministic or stochastic)

2. Empirical (based on experimental data) : deterministic (multiple regression, neural network, ...) or stochastic (ex. ARX, PARMAX)

3. Expert-based, based on value judgement of experts, formalized through a multi-attribute VALUE FUNCTION ( deterministic) or a Bayesian Belief Network (BBN) ( stochastic), calibrated through answers of experts to ad hoc questionnaires

Example 1 – empirical,

deterministic model based on

experimental data

5c. Determination of cause-effect relationshipsEVALUATION INDEX

Cause-effect model

Actions



?




Causal factors

LandSat TM 7

Banda TM

Range (Micron)

Posizione nello Spettro Risoluzione Spaziale (metri)

1 0.45 – 0.52 Visibile (blu) 30

2 0.52 – 0.60 Visibile (verde) 30

3 0.63 – 0.69 Visibile (rosso) 30

4 0.76 – 0.90 Infrarosso vicino 30

5 1.55 – 1.75 Infrarosso medio 30

6 10.4 – 12.5 Infrarosso termico 120

7 2.08 – 2.35 Infrarosso medio 30

Step 1 – Analysis of satellite images (Landsat TM 7)


Example 1 – empirical, deterministic model based on

experimental data

Bande: Infrarosso Vicino - Rosso

0

50

100

150

200

250

0 50 100 150 200 250

Infrarosso Vicino

Ro

ss

o

"Sup. Bagnata"

Serie2

Serie3

Serie4

Serie5

Serie6

Serie7

Serie8

Serie9

Serie10

Serie11

Serie12

Serie13

Serie14

Serie15

Serie16

Serie17

Serie18

Serie19

Serie20

Serie21

Serie22

Serie23

Serie24

Serie25

Step 2 - Classification and assignment of pixel “water”


experimental data


Step 3 – Estimation of the relationship “flow rate-wet area”

y = 0,3397x + 30,406R2 = 0,8746

0

10

20

30

40

50

60

70

80

90

100

0,00 20,00 40,00 60,00 80,00 100,00

Portata [m3/s]

% S

up

Bag

nat

a

Serie1

Lineare (Serie1)


experimental data


r (p<0.01, n=41)

Cmin_O2_3m_prec

Cmediana_COD_3m_pre

Cmedia_O2_3m_prec

Cmedia_COD_3m_prec

Tmedia_3m_prec

Qmediana_1m_prec

Qmin_1m_prec

Q_75°_3m_prec

Q_75°_1m_prec

Num Plecotteri

-0.38 0.49 -0.43 0.46 0.37 -0.27 -0.21 -0.27 -0.27

Num Efemerotteri

-0.09 0.11 -0.14 0.18 0.09 -0.18 -0.20 -0.34 -0.13

Num Tricotteri

-0.37 0.22 -0.35 0.20 0.34 0.02 0.11 0.03 -0.01

Pro jec tion of the c as es on the f ac tor -p lane ( 1 x 2 )

Cas es w ith s um of c os ine s quare >= 0 .00

A c tiv e

Cal00_1

Cal00_2

Cal00_3

Cal00_4

Cal01_1

Cal01_2

Cal01_3

Cal01_4

Cal02_1

Cal02_2

Cal02_3Cal03_1

Cal03_1Cal03_3

Cal03_4Cal04_1

Cal04_2Cor02_1 Cor02_2

Cor02_3

Cor03_4Cor03_1

Cor03_2

Cor03_3

Cor04_1

Cor04_2

Cor0 4_3

Riv 00_1

Riv 00_2

Riv 01_1

Riv 01_2Riv 02_1

Riv 02_1

Riv 02_2

Riv 02_3

Riv 03_1

Riv 03_2Riv 03_3

Riv 04_1

Riv 04_2

Riv 04_2

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Fac tor 1 : 83.62%

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

Factor 2: 16.38%

Projec tion of the v ar iables on the f ac tor-p lane ( 1 x 2)A c tiv e and Supplementary v ar iab les

*Supplementary v ar iab le

A c tiv e Suppl.

Num TOT Tax a

Num Tax a "Rar i"

Num "A ltr i Tax a"

*Cmediana_O2_3m_prec

*Tmediana_3m_prec

*Qmediana_3m_prec*Qmin_3m_prec*Qmin_12m_prec

-1.0 -0.5 0.0 0.5 1.0

Fac tor 1 : 83.62%

-1.0

-0.5

0.0

0.5

1.0

Factor 2 : 16.38%

In many cases INSUFFICIENT amount of DATA and/or NOT SUITABLE because of

the METHODOLOGY adopted

Example 2 - empirical, statistical model based on experimental

data


?RENOUNCING to EXPRESS a JUDGEMENT

OR TRYING a DIFFERENT APPROACH?


Example 3 - models based on expert judgement


25

108

196

87

11

49

3323

93 2

0

50

100

150

200

250

45 60 75 90 105 120 135 150 165 180 195

Classi di lunghezza (mm)

Fre

quen

za (

n° in

divi

dui)

Vairone

Depend on available data and on direct experience of experts on the case study considered

Trota marmorata

93%

Hybridfario/marmorata

7%

5c. Determination of cause-effect relationships: fish fauna

Example 3 - models based on expert judgment

5c. Determination of cause-effect relationships: fish faunaFISH FAUNA (f)

Community composition (f1)

Longitudinal Continuity

(l)

EVALUATION INDEX

Cause-effect model

Making fish-passages / removing

discontinuities



Presence of autochthonous

species (f11)


conditions during minimum flow period (last 3

years)

Actions

Causal factors

Triennial average of prevailing flow during

minimum flow quarter (m)


For a given alternative of longitudinal (dis)continuity...

Briglia diBriglia di RivoltaRivolta

Presa Canale VacchelliPresa Canale Vacchelli

Briglia diBriglia di SpinoSpino

Briglia di LodiBriglia di Lodi

Briglia di Briglia di PizzighettonePizzighettone

Soglia di Soglia di MaccastornaMaccastorna

Sbarramento con passaggio per pesci non funzionanteSbarramento con passaggio per pesci non funzionante

Sbarramento sprovvisto di passaggio per pesciSbarramento sprovvisto di passaggio per pesci

Non valicabile in condizioni di portata di magra

Briglia diBriglia di RivoltaRivolta

Presa Canale VacchelliPresa Canale Vacchelli

Briglia diBriglia di SpinoSpino

Briglia di LodiBriglia di Lodi

Briglia di Briglia di PizzighettonePizzighettone

Soglia di Soglia di MaccastornaMaccastorna

Sbarramento con passaggio per pesci non funzionanteSbarramento con passaggio per pesci non funzionante

Sbarramento sprovvisto di passaggio per pesciSbarramento sprovvisto di passaggio per pesci

Non valicabile in condizioni di portata di magra




?

?

?

?

?

CORNATE - reali

0

100

200

300

400

500

600

700

800

900

23/12

/1989

07/01

/1990

22/01

/1990

06/02

/1990

21/02

/1990

08/03

/1990

23/03

/1990

07/04

/1990

22/04

/1990

07/05

/1990

22/05

/1990

06/06

/1990

21/06

/1990

06/07

/1990

21/07

/1990

05/08

/1990

20/08

/1990

04/09

/1990

19/09

/1990

04/10

/1990

19/10

/1990

03/11

/1990

18/11

/1990

03/12

/1990

18/12

/1990

02/01

/1991

1990 reale

1991 reale

1992 reale

1993 reale

1994 reale

1995 realeQmin flow quarter

Hydromorphol. conditions (v, h, ...)


f11(sc.A)

0

4

8

12

16

20

24

5 24 43 62 81 100

m [m 3/s]

IF 5 < m ≤ 25 → f11 = 6 + (8/20)*(f11-5)*m;IF 25 < m ≤75 → f11= 14 + (5/50)*(f11-25) *m;IF m > 75 → f11 = 19



X

X

Example: biodiversity indicators for

macroinvertebrates

How was the consultation/questionnaire to experts conducted?



5c. Determination of cause-effect relationships: macroinvertebrates



3 months (d)



EVALUATION INDEX

Cause-effect model





conditions


Actions





Biodiv. summer

(m13)

Biodiv. autumn

(m14)

Causal factors

1. Flow Q hydro-morphological condition (state)

For each reach we got several couples [Q, image]




CORNATE - reali

0

100

200

300

400

500

600

700

800

900

23/1

2/1

989

07/0

1/1

990

22/0

1/1

990

06/0

2/1

990

21/0

2/1

990

08/0

3/1

990

23/0

3/1

990

07/0

4/1

990

22/0

4/1

990

07/0

5/1

990

22/0

5/1

990

06/0

6/1

990

21/0

6/1

990

06/0

7/1

990

21/0

7/1

990

05/0

8/1

990

20/0

8/1

990

04/0

9/1

990

19/0

9/1

990

04/1

0/1

990

19/1

0/1

990

03/1

1/1

990

18/1

1/1

990

03/1

2/1

990

18/1

2/1

990

02/0

1/1

991

1990 reale

1991 reale

1992 reale

1993 reale

1994 reale

1995 reale

2. We showed the experts data of samplings from representative stations and corresponding value of causal factors (for Q: corresponding images)




Q=15 m3/s

3. Definition of the range of variation of the causal factors;

Definition of the values min and max of each indicator, in correspondence with the worst and best values assumed by the causal factors in the range




N. of EPT taxa

XXmaxXmin

Nmax

Nmin

Xbest

4. Constructions with the experts of the mono-dimensional “Value Functions” (VF) related to each causal factor

vQ(Q)

0

0.1

0.20.3

0.4

0.5

0.6

0.70.8

0.9

1

5 55 105 155 205

Q [m3/s]

vq(q)

00.1

0.20.3

0.40.50.6

0.70.8

0.91

5 55 105 155

q [m3/s]

vd(d)

00.1

0.20.3

0.40.50.6

0.70.8

0.91

4 6 8 10 12

d [mg/L]




5. Aggregation of the single VF in a multi-dimensional Value Function, asking the experts about the relative importance of each single causal factor

Q = 0.27q = 0.20d = 0.53

m13= m13,min+*[QvQ(Q)+qvq(q)+ dvd(d)]

m13= 1+*[vQ(Q)+vq(q)+ vd(d)]




6. Validation of the function obtained, asking the experts to rank situations corresponding to several different combinations of the value assumed by the causal factors

dR= 10

5 220 Q

d

4

12

8

100 35

6

QR= 128

779

5 9 35 77 100 220

30 29 28 27 26 25

24 23 16 15 14 13

20 19 11 8 4 3

18 17 10 7 2 1

22 21 12 9 6 5

Q

d

4

6

8

10 12

a. Indifference Curves b. Ranking




CONCLUSIONS about our CASE STUDY

1. Coherence of the indicators and indices with WFD: partially satisfied, but definitely not provable

2. Dramatic gaps in available data, particularly Q! Low reliability of models (reconstruction of Q in some reach with high uncertainty; lack of images of some reach to represent hydro- morphological situations; models developed for some reach and extended to others)

For a real use (evaluation of management alternatives and negotiation) needs to refine the results based on the same methodology, but after filling the information gaps

PROJECT CONCLUSIONS

1. When abundant and reliable data is available, the empirical –statistical or mechanistic- approach is more likely to give reliable and convincing results

2. Nevertheless, the most frequent situation is just that of extreme scarcity of useful data and of impossibility (due to available resources and time, but also due to physical and operational difficulties) to collect necessary data to develop empirical or mechanistic models

One needs to choose whether to give up, for the sake of scientific rigour, to use a rational tool for decision-making, or rather accept a more approximate tool, but conceptually robust

3. It is sensible to articulate the evaluation INDEX and cause-effect network according to the case at hand

At the extreme, one might proceed in “one shot” by building the final INDEX with no intermediate attributes/indicators. BUT: i) Lower accomplishment of WFD scheme; ii) Less representable and understandable by non-experts (stakeholders);

In any case the conceptualization exercise is recommendable not to lose internal understanding and agreement.

PROJECT CONCLUSIONS

4. Expert based approach implies big conceptualization and inter-disciplinary effort

shared, agreed scheme of reasoning full identification and focussing of key factors

and interconnected relationships decision maker is lead to applying a real multi-

objective approach

PROJECT CONCLUSIONS

CSC - Sheffield, 14 February 2007

[email protected] [email protected] www.cirf.org

Centro Italiano per la Riqualificazione Fluviale

GRAZIE PER L’ATTENZIONE!

Andrea Nardini, Andrea Goltara, Andrea Nardini, Andrea Goltara, Bruno Boz, Marco Monaci, Ileana Bruno Boz, Marco Monaci, Ileana Schipani, Simone Bizzi, Daniele Schipani, Simone Bizzi, Daniele

Lenzi, Anna PolazzoLenzi, Anna Polazzo

• Which CRITERIA are relevant/suitable to assess “how is” the fluvial ecosystem, coherently with the WFD (Dir.2000/60/CE)? Is it possible to measure, through an INDEX, the status of a fluvial ecosystem?

• Which information is relevant to a non-expert to elicit a value judgement on how important is the improvement/worsening (value change) of the fluvial ecosystem, compared with other objectives?

• Which are the EFFECTS of different solution alternatives (actions) on the fluvial ecosystem (i.e. on the INDEX)?

• How can we PREDICT such effects while just disposing of scarce information?

Key QUESTIONS

Immagini sat (Google Earth) Ticino e Adda





Documents

TWOLE , A DECISION SUPPORT SYSTEM FOR INTEGRATED RIVER BASIN PLANNING AND MANAGEMENT