Transcript TWOLE, A DECISION SUPPORT SYSTEM FOR INTEGRATED RIVER BASIN PLANNING AND MANAGEMENT
Centro Italiano per la Riqualificazione Fluviale
CSC - Sheffield, 14 February 2007
TWOLE, A DECISION SUPPORT SYSTEM FOR INTEGRATED RIVER BASIN PLANNING AND MANAGEMENT
Assessment and expert-based prediction of river ecosystem status Andrea Goltara
[email protected] www.cirf.org
WHAT IS CIRF
Centro Italiano per la Riqualificazione Fluviale
CIRF is a private, independent, technical scientific and non-profit organisation founded in 1999 to: promote river restoration, foster the diffusion of RR culture and related knowledge, and its application
Centro Italiano per la Riqualificazione Fluviale
MAIN ACTIVITIES
• • •
EDUCATION Training courses Seminars Study trips APPLICATION
• •
Pilot Projects Studies INFORMATION
•
Web Site
• •
Publications Meetings
www.ecrr.org
2006-2009: CIRF holds the secretariat of the
ECRR
EUROPEAN CENTRE FOR RIVER RESTORATION
a network of practitioners of river restoration
4th ECRR RIVER RESTORATION INTERNATIONAL CONFERENCE
16-21 June 2008
San Servolo Island
The TwoLe projects
• TwoLe: Two-Level Decision Support System for WR planning and management • Funding: Cariplo Foundation • Duration: 24 months ( ongoing ) • Partners: – DEI - Politecnico di Milano
C ENTRO I TALIANO PER LA R IQUALIFICAZIONE F LUVIALE
– 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 projects
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
TwoLe 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 OBJECTIVE of TwoLe/A: Test TwoLe in the management of lake Verbano and Ticino river basin OBJECTIVE of TwoLe/B: Test TwoLe in planning of lake Lario and Adda river basin
TwoLe-B: taking into account conflicting objectives in planning at the river basin scale
Tourism Canoeing Hydropower Flooding risk Fishing Agriculture River Ecosystem
TwoLe-B – CIRF: an index for fluvial ecosystem...and something more 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
TwoLe-B – CIRF: an index for fluvial ecosystem...and something more 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
STEPS of our METHODOLOGY
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
1. Status of fluvial ecosystem (WFD) -> the value tree 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 -> the value tree: FLEA adapted
1. Status of fluvial ecosystem -> the value tree of TwoLe-B
ECOLOGICAL STATUS Physico chemical quality (water quality)
General conditions LIM
Biological quality (terrestrial and aquatic biota) Hydromorpholo gical quality
Benthic macroinvertebrates Fish fauna Terrestrial flora Hydrological regime Biodiversity (EPT) Abundance Community composition Population structure (key species) Riparian vegetation Corridor (zonal) vegetation Characteristics of regime (annual, monthly flows; max, min annual flow; peak and period,…) Biodiversity-winter Biodiversity-spring Biodiversity-summer Biodiversity-autumn Autochthonous species Exotic species Age distribution structure Abundance Naturalness Cover Longitudinal continuity Width of riparian strip Naturalness (species) Cover Standard deviations Mean values Total exotic species Presence of Silurus Glanis Naturalness of structural features Autochthony Indicators not represented for lack of space
STEPS of our METHODOLOGY
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
5a. Conceptualization of the causal network: fish fauna
EVALUATION INDEX
FISH FAUNA (f) Community composition (f 1 ) Population structure (key species) (f 2 ) Presence of autochthonous species (f 11 ) Presence of exotic species (f 12 ) Age distribution structure key species (f 21 ) Exotic species / tot (f 121 ) Presence of silurus (f 122 ) Abundance key species (f 22 ) Longitudinal Continuity (l) Prevailing hydromorphol. conditions during minimum flow period (last 3 years) Stress hydromorphol. conditions Prevailing hydromorphol. conditions during minimum flow period (same year) Stress hydromorphol. conditions hatching period key species
Causal factors
Triennial average of prevailing flow during minimum flow quarter (m) Minimum annual 3-days flow (q) Prevailing flow during minimum flow quarter (Q) Minimum daily flow during hatching period key species (s) Making fish-passages / removing discontinuities
Actions Cause-effect model
Managing flow released from lake and derived/(released) for hydropower/irrigation
Which are the main variables?
5a. Conceptualization of the causal network Projection of the variables on the factor-plane ( 1 x 2) Active and Supplementary variables *Supplementary variable 1.0
Statistical analysis 0.5
Num Tricotteri *Q_75°_3m_prec 0.0
Num Plecotteri *Cmedia_O2_3m_prec *Cmin_O2_3m_prec -0.5
Num Efemerotteri -1.0
-1.0
-0.5
0.0
Factor 1 : 67.66% 0.5
1.0
Active Suppl.
Experts ?
5a. Conceptualization of the causal network: macroinvertebrates
EVALUATION INDEX
Macroinvertebrates (
m
) Biodiversity of the community (
m 1
) Biodiv. winter (
m 11
) Biodiv. spring (
m 12
) Biodiv. summer (
m 13
) Biodiv. autumn (
m 14
) Abundance (of habitat) (
m 2
) Dissolved oxygen previous 3 months (d)
Causal factors
Stress hydromorphol. conditions Prevailing hydromorphol. conditions Minimum flow previous month (q) Median flow previous 3 months (Q) Pollutant loads reduction
Actions Cause-effect model
(scenario) Managing flow released from lake and derived/(released) for hydropower/irrigation
STEPS of our METHODOLOGY
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
5b. Formalization of causal factors
EVALUATION INDEX
Macroinvertebrates (
m
) Biodiversity of the community (
m 1
) Biodiv. winter (
m 11
) Biodiv. spring (
m 12
) Biodiv. summer (
m 13
) Biodiv. autumn (
m 14
) Abundance (of habitat) (
m 2
) Dissolved oxygen previous 3 months (d)
Causal factors
Stress hydromorphol. conditions Prevailing hydromorphol. conditions Minimum flow previous month (q) Median flow previous 3 months (Q) Pollutant loads reduction
Actions Cause-effect model
(scenario) Managing flow released from lake and derived/(released) for hydropower/irrigation
Example 1 “Stress hydro morphological conditions” 5b. Formalization of causal factors hydro morphological conditions corresponding to min daily flow in the preceding month Min (Q t ), t [t-30;t]
STEPS of our METHODOLOGY
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
5c. Determination of cause-effect relationships
EVALUATION INDEX
Macroinvertebrates (
m
) Biodiversity of the community (
m 1
) Biodiv. winter (
m 11
) Biodiv. spring (
m 12
) Biodiv. summer (
m 13
) Biodiv. autumn (
m 14
) Abundance (of habitat) (
m 2
)
Causal factors
?
Dissolved oxygen previous 3 months (d)
?
?
?
Stress hydromorphol. conditions Prevailing hydromorphol. conditions
?
Minimum flow previous month (q)
?
?
Median flow previous 3 months (Q) Pollutant loads reduction (scenario)
Actions Cause-effect model
?
?
Managing flow released from lake and derived/(released) for hydropower/irrigation
5c. Determination of cause-effect relationships 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 ( questionnaires deterministic) or a Bayesian Belief Network (BBN) ( stochastic), calibrated through answers of experts to ad hoc
5c. Determination of cause-effect relationships
EVALUATION INDEX
Macroinvertebrates (
m
) Abundance (of habitat) (
m 2
) Example 1 – empirical,
deterministic
model based on experimental data
?
Causal factors
Median flow previous 3 months (Q) Managing flow released from lake and derived/(released) for hydropower/irrigation
Actions Cause-effect model
5c. Determination of cause-effect relationships Example 1 – empirical, deterministic model based on experimental data Step 1 – Analysis of satellite images (Landsat TM 7) 3 4 5 6 7
LandSat TM 7
Banda TM Range (Micron)
1 2 0.45 – 0.52
0.52 – 0.60
0.63 – 0.69
0.76 – 0.90
1.55 – 1.75
10.4 – 12.5
2.08 – 2.35
Posizione nello Spettro
Visibile (blu) Visibile (verde) Visibile (rosso) Infrarosso vicino Infrarosso medio Infrarosso termico Infrarosso medio
Risoluzione Spaziale (metri)
30 30 30 30 30 120 30
5c. Determination of cause-effect relationships Example 1 – empirical, deterministic model based on experimental data "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 200 150 100 50 0 0 250 Step 2 - Classification and assignment of pixel “water”
Bande: Infrarosso Vicino - Rosso
50 100 150
Infrarosso Vicino
200 250
5c. Determination of cause-effect relationships Example 1 – empirical, deterministic model based on experimental data Step 3 – Estimation of the relationship “flow rate-wet area” 100 90 80 70 60 50 40 30 20 10 0 0,00 Serie1 Lineare (Serie1) 20,00 40,00 60,00
Portata [m 3/s]
80,00 100,00 y = 0,3397x + 30,406 R 2 = 0,8746
5c. Determination of cause-effect relationships Example 2 - empirical, statistical model based on experimental data 2.5
Projection of the cases on the factor-plane ( 1 x 2) Cases w ith sum of cosine square >= 0.00
1.0
Projection of the variables on the factor-plane ( 1 x 2) Active and Supplementary variables *Supplementary variable 2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
Cal00_3 In many cases INSUFFICIENT amount of DATA Cor03_2 Cor04_3 Cor02_1 Cal01_4 and/or NOT SUITABLE because of the METHODOLOGY adopted -0.5
*Cmediana_O2_3m_prec Cor04_1 Riv02_1 0.5
Num "Altri Taxa" Num Taxa "Rari" *Qmediana_3m_prec *Qmin_12m_prec -1.5
-2.0
-2.5
-6 -5 -4 -3 -2 -1 0 Factor 1: 83.62% 1 2 3 4 5 Active -1.0
-1.0
-0.5
0.0
Factor 1 : 83.62% 0.5
1.0
Active Suppl.
r (p<0.01, n=41)
Num Plecotteri Num Efemerotteri Num Tricotteri Cmin_O2 Cmediana_C _3m_prec OD_3m_pre Cmedia_O2_ 3m_prec Cmedia_CO D_3m_prec Tmedia_3 m_prec -0.38 -0.09 -0.37 0.49 0.11 0.22 -0.43 -0.14 -0.35 0.46 0.18 0.20 0.37 0.09 0.34 Qmediana _1m_prec Qmin_1m _prec Q_75°_3m _prec Q_75°_1 m_prec -0.27 -0.18 0.02 -0.21 -0.20 0.11 -0.27 -0.34 0.03 -0.27 -0.13 -0.01
5c. Determination of cause-effect relationships RENOUNCING to EXPRESS a JUDGEMENT OR TRYING a DIFFERENT APPROACH?
?
5c. Determination of cause-effect relationships Example 3 - models based on expert judgement
5c. Determination of cause-effect relationships: fish fauna Example 3 - models based on expert judgment Depend on available data and on direct experience of experts on the case study considered Hybrid fario/marmorata 7% Trota marmorata 93% 250 Vairone 200 196 150 108 100 50 25 0 45 60 75 87 90 49 33 11 105 120 135
Classi di lunghe zza (mm)
23 150 9 165 3 180 2 195
5c. Determination of cause-effect relationships: fish fauna
EVALUATION INDEX
FISH FAUNA (f) Community composition (f 1 ) Example 3 - models based on expert judgment Presence of autochthonous species (f 11 ) Longitudinal Continuity (l) Prevailing hydromorphol. conditions during minimum flow period (last 3 years)
Causal factors
Triennial average of prevailing flow during minimum flow quarter (m) Making fish-passages / removing discontinuities
Actions Cause-effect model
Managing flow released from lake and derived/(released) for hydropower/irrigation
5c. Determination of cause-effect relationships: fish fauna Example 3 - models based on expert judgment For a given alternative of longitudinal (dis)continuity...
Soglia di Maccastorna Briglia di Pizzighettone
900 800 700 600 500 400 300 200 100 0 5c. Determination of cause-effect relationships: fish fauna Example 3 - models based on expert judgment
CORNATE - reali Q min flow quarter
1990 reale 1991 reale 1992 reale 1993 reale 1994 reale 1995 reale
?
?
?
?
Hydromorphol. conditions (v, h,
t
...) ?
5c. Determination of cause-effect relationships: fish fauna Example 3 - models based on expert judgment
X
f 11 (sc.A)
X
24 20 16 12 8 4 0 5 24 43 62
m [m 3 /s]
81 100 IF 5 < m ≤ 25
→
IF 25 < m ≤75
→
IF m > 75
→
f 11 f 11 = 6 + (8/20)*(f 11 -5)*m; f 11 = 19 = 14 + (5/50)*(f 11 -25) *m;
5c. Determination of cause-effect relationships Example 3 - models based on expert judgment How was the consultation/questionnaire to experts conducted?
Example: biodiversity indicators for macroinvertebrates
5c. Determination of cause-effect relationships: macroinvertebrates
EVALUATION INDEX
Macroinvertebrates (
m
) Example 3 - models based on expert judgment Biodiversity of the community (
m 1
) Biodiv. winter (
m 11
) Biodiv. spring (
m 12
) Biodiv. summer (
m 13
) Biodiv. autumn (
m 14
) Dissolved oxygen previous 3 months (d)
Causal factors
Stress hydromorphol. conditions Prevailing hydromorphol. conditions Minimum flow previous month (q) Median flow previous 3 months (Q) Pollutant loads reduction
Actions Cause-effect model
(scenario) Managing flow released from lake and derived/(released) for hydropower/irrigation
900 800 700 600 500 400 300 200 100 0 5c. Determination of cause-effect relationships: macroinvertebrates Example 3 - models based on expert judgment 1.
How was the consultation/questionnaire to experts conducted?
Flow Q hydro-morphological condition (state) For each reach we got several couples [Q, image]
CORNATE - reali
1990 reale 1991 reale 1992 reale 1993 reale 1994 reale 1995 reale
5c. Determination of cause-effect relationships: macroinvertebrates Example 3 - models based on expert judgment How was the consultation/questionnaire to experts conducted?
2.
We showed the experts data of samplings from representative stations and corresponding value of causal factors (for Q: corresponding images)
Q=15 m 3 /s
5c. Determination of cause-effect relationships: macroinvertebrates Example 3 - models based on expert judgment How was the consultation/questionnaire to experts conducted?
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 N max N min X min X best X max X
5c. Determination of cause-effect relationships: macroinvertebrates Example 3 - models based on expert judgment How was the consultation/questionnaire to experts conducted?
4.
Constructions with the experts of the mono-dimensional “Value Functions” (VF) related to each causal factor v Q (Q) 1 0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 5 55 105
Q [m 3/s]
155 205 1 0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 5 v q (q) 55 105
q [m 3/s]
155 v d (d) 1 0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 4 6 8
d [m g/L]
10 12
5c. Determination of cause-effect relationships: macroinvertebrates Example 3 - models based on expert judgment How was the consultation/questionnaire to experts conducted?
5. Aggregation of the single VF in a multi-dimensional Value Function, asking the experts about the relative importance of each single causal factor m 13 = m 13,min + D *[ l Q v Q (Q)+ l q v q (q)+ l d v d (d)] l Q l q l d = 0.27
= 0.20
= 0.53
m 13 = 1+ 15 *[ 0.27
v Q (Q)+ 0.20
v q (q)+ 0.53
v d (d)]
5c. Determination of cause-effect relationships: macroinvertebrates Example 3 - models based on expert judgment How was the consultation/questionnaire to experts conducted?
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 d 12 d R = 10 8 a. Indifference Curves 6 4 5 9 35 77 100 220 Q R = 128 Q
d Q 4 6 8 10 12 5
30 24 20 18 22 29 23 19 17 21
9
b. Ranking
35 77 100 220
28 16 11 10 12 27 15 8 7 9 26 14 4 2 6 25 13 3 1 5
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 filling the information gaps (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
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
PROJECT CONCLUSIONS 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
Centro Italiano per la Riqualificazione Fluviale
CSC - Sheffield, 14 February 2007
GRAZIE PER L’ATTENZIONE!
Andrea Nardini, Andrea Goltara, Bruno Boz, Marco Monaci, Ileana Schipani, Simone Bizzi, Daniele Lenzi, Anna Polazzo
[email protected] www.cirf.org
Key QUESTIONS
• 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?
Immagini sat (Google Earth) Ticino e Adda
Immagini sat (Google Earth) Ticino e Adda
Immagini sat (Google Earth) Ticino e Adda
Immagini sat (Google Earth) Ticino e Adda
Immagini sat (Google Earth) Ticino e Adda