SAF Cluster Meeting, 13-14 October 2009, Copenhagen SSA 13 : Thau Lagoon “Managing the...

SAF Cluster Meeting, 13-14 October 2009, Copenhagen

SSA 13 : Thau Lagoon

“Managing the microbiological contamination of the

Thau Lagoon”

Science and Policy Integration for COastal System Assessment

José A. Pérez Agúndez, Annie Fiandrino, Johanna Béganton, Rémi Mongruel , Thierry Laugier, Valérie Derolez, Ludovic Cesmat, Ophélie Serais

Hélène Rey-Valette, Sébastien Roussel,François Valette

Gilles Brocard

(NB: GEYSER, in charge of mediation with stakeholders, has retired from SPICOSA)(NB: GEYSER, in charge of mediation with stakeholders, has retired from SPICOSA)

SAF cluster meeting, 20-21 October 2009, Thessaloniki

SAF Cluster Meeting, 20-21 October 2009, Thessalonique

Introduction : policy issue, scenarios, socio-economic dimensions and structure of the system to be modelled

Building the model with Extend (modular use, connections)

Watersheds and the Water treatment system

Microbiological contamination: monitoring and law enforcement

Economic assessment Conclusion : state of work ; interactions with stakeholders

Content


A coastal zone close to thecities of Montpellier and Sète


The detailed structure of the Thau watershed


An overview of the policy issue

The general problem : because of rapid local demographic growth and also the importance of tourism, the watersheds of the Thau lagoon are subject to pollution. In particular, events of microbiological contamination may affect the lagoon, with direct consequences on human activities in the Lagoon and also feedbacks to the overall development pattern of the region.

The microbiological contamination raises four basic questions:1) What are the impacts of microbiological contamination on shellfish farming and other human activities in the lagoon ?2) What are the main contamination sources ?3) What is the efficiency of the current water treatment system as regards microbiological contamination ?4) Which management options would reduce the impacts of MC in a way which would be coherent with the more global local policy objectives ?

Another research project, OMEGATHAU (to be completed by the end of 2010), which has been conducted jointly with the local public organisation in charge of Thau Lagoon water management (SMBT), has addressed the 2 first questions. Our approach for the SPICOSA SSA work in the Thau Lagoon is to rely on OMEGATHAU results in order to explore questions 3 and 4.


A shared diagnosis

SCOT

The territorialmanagement plan,whose perimeter iscoherent with theone defined forthe water policy

SAGEThe local water management plan,which defines the water policyfor the Lagoon and its watersheds

Policy context : two corresponding management plans

Coherence of- perimeters- diagnosis- objectives


The current policy debate and theexpectations from the SPICOSA experiment

The local policy framework, which has been set up during the preparation of the Territorial Management Plan (SCoT) gives the general objectives:- economic development, including tourism and thermal industry- environment preservation, including water quality and ecosystems protection- maintaining of the cultural patrimony, including traditional activities (shellfish farming, professional fisheries in the lagoon, recreational activities, etc.)NB: of course, these objectives generate internal conflicts The local water management plan (SAGE) defines several specific objectives related to the environment policy. One specific objective is the reduction of the water microbiological contamination. The current political debate focuses the ways of translating these objectives into operational management options.

In this context, the main purpose of our SPICOSA experiment is twofold:- to assess the efficiency and the costs of new water treatment systems

Cost-effectiveness analysis (1: restricted)- to estimate the impacts of various operational management options on the traditional activities and the local economy

Multi-criteria analysis (2: extended)


The definition of scenarios and the assessment of the resulting states of the system will contribute to the local political debate in three different ways.

Designing the Water Policy the objective of the MC reduction has to be translated into operational objectives, in particular as regards the sanitary classification of the Lagoon (A or B) and the occurrence of commercial bans for the shellfish farming industry, according to the trends of the system (forcings)

definition of scenarios = trends, operational objectives, technical options

Searching for the more efficient water treatment systemsthe assessment of possible new water treatment systems (technical options), as regards their costs and their ability to reach one particular operational objectives

costs/effectiveness analysis of simple management options

Assessing the impacts of one operational policy objectives- on the regional economy, through the financial analysis of impacted sectors and the induced effects on the other industries and activities- on the broader development concerns (as set up by the SCOT), considering eventually additional management options for traditional activities

selection of indicatorsmulti-criteria analysis of more complex management options

Scenarios definition and system assessment


Economic dimension 1: cost-effectiveness analysis of the water treatment systems, according to different technical options.

Approach = investment and running costs of water treatment settlements, considering local public budget constraints

Economic dimension 2: economic dynamics in the shellfish farming industry.

Approach = financial analysis and the vulnerability of businesses to the closures of the shellfish farming area (period during which sales are forbiden)

Additional management options to be tested in complex scenarios = re-allocation of the production capacity of the farms which stop their activity

Economic dimension 3: a regional economy matrix will be used so as to provide macro-economic drivers and indicators at the site scale.

Approach = incorporating direct (pressure and impacts) and indirect (induced effects) relationships between the environment and the macro-economic dynamics of the Thau Lagoon territory (economic feedback loops)

The economic dimensions


The system and its components

Upstream Uses

Water treatment

Water qualityin the Lagoon

Microbiologicalcontamination

Governance system

Indicators : expectedstate of the system

- Short term :closures- Long term :classification

Recreative uses & amenitiesEcosystem preservation ?Economic developmentTraditional activities

Ecosystem status

Managingthemicrobiologicalcontamination oftheThau Lagoon

Public investmentsRules: sanitary normsAccess regulation ?Settlement: space allocationEconomic incentives (taxes)

Drivers

Pressures

Responses

State

Impacts

Multi-objectiveslocal policiesunderpublic budget constraint

Downstream Uses

Boundaries of the systemfor the formulation step and the defi nition of scenarios


The structure of the Extend Model


The watershed ecological modules


1. Every day permanent (and/or seasonal) populations & activities produce E.coli pollutions2. These E.coli loads can be reduced (or not) by specific treatments3. These reduced (or not) E.coli loads discharge into the lagoon via local streams


What we need to achieve this goal in extend model ?

To identify Main types of E.coli source on watershed

To formulate (in an analytic way) E.coli daily load for each type of source

First results of OMEGA-THAU project Investigations to :1. Identify main E.coli sources (on watershed

& onto the lagoon)

2. Estimate for each source (on watershed) E.coli daily load


In Extend model (in order to be as well generic as possible) we distinguish these 3 steps 3 different modules

E.coli pollutions production blocks

E.coli pollutions treatment blocks

E.coli pollutions transfer blocks



E.coli pollutions production blocks

living in houses connected to WWTP SRC1

living in houses with their own septic tanks SRC2

Seasonal population living in campings with their own WWT systems SRC3

For each town on watershed

Nb. of Perm. Inh.Nb. of tourists

Parameters read in Database

% of pop. connected to WWTPNb. of house with ind. WWT System

Nb. of tourists in campings during summer

Farming (poultry, horse,…) SRC4 Nb. of farms & Nb. of animals in each farm

Permanent population /seasonal population.

Dysfunctioning of sewage network system SRC6

… During rainfall eventsStorm sewers SRC5

For each type of SRC

1 Eq. Inh = 5,7x10 10 E.coli/d.

State variable saved in Database

Daily Produced E.coli load (Cell/d)

.

The watershed modules


E.coli pollutions treatment blocks

SRC1 Daily E.coli load produced by pop. connected to WWTP

For each SRC on watershed

Parameters and state variables read in

Database

Parameters for treatment method

Empirical values forabatement in each WWTP depends on :

.

State variable saved in Database

Daily Produced E.coli loadDaily Produced E.coli load

E.coli Abatement techniques (or not)

Daily Treated E.coli load </=

Daily Produced E.coli load

For instance

• Season (treatment is more efficient in summer)

• Meteorological condition (by-pass during rainfall events)




For each SRC on watershed

Parameters and state variables read in

Database

Abatement in local stream depends on :

.State variable saved

in Database

Daily Treated E.coli loadDaily Treated E.coli load

E.coli Abatement due to natural processes during transfer in the local stream (from point source to the lagoon)

Daily Transferred E.coli load </=

Daily Treated E.coli load

• Distance from point source to the lagoon

• Season (T90 in stream is higher in summer)

• Meteorological condition (speed of stream higher during rainfall event)




In Watershed Database Number of tables (containing fields associated

to Daily Produced,Treated and Transferred E.coli load)

= numbers of SRC on the watershed

Custom blockRe-arrange daily E.coli load & create one Dynamic_Database per outlet that contains

Total Daily E.coli load (sum of all E.coli load of all SRC on this watershed) and each component of this total load



There are infinite technical ways for reaching the objectives

fixed by stakeholders

creating new water treatment structures

modifying existing structures (size, technology)

improving the collection nets

management of punctual treatment under-capacity

(storms)

But the aim of the model is to explore

prospective scenarios, not to optimize a

public policy

The water treatment module


v

The cost – efficient analysis


The water treatment module, associated with the hydrologic component, is integrated in the model following two complementary steps

1st → Restrictive model : comparative assessment of water treatment options (several runs)

Selection of best possibilities considering general constraints : total budget (operational and investment costs), land

availability, opportunity costs, etc. Comparative assessment of water treatment options Hierarchical classification of each option by considering

costs and abatement efficiency

2nd → Global model: integration of the selected water treatment options in the prospective model

Strategic planning at long run in terms of investments Estimation on global impacts in the model depending of

selected scenarios

Operational use of the water treatment module by Extend


INTO THE LAGOON

9 monitoring stationsare sampling every first Monday of the month

National monitoring network (REMI) controls

microbiological quality of oysters and mussels to determine category of

shellfish production areas.

20 local streams on the watershed

?j=1

20

What is the impact of daily E.coli load discharges by each local

stream on lagoon water quality ?

[E.coli] i = Fctn ( Daily E.coli load,

Meteorological conditions)

At each time step, at each REMI station (i = 1,9)

First step : Simulate impact of E.coli discharge into the lagoon

If E.coli concentration in shellfish passes a legal level, administration can

decide to temporarily forbid shellfish harvesting

Second step : Simulate surveillance procedure and regulation rules

Lagoon contamination module


Lagoon contamination simulation using 3D model

First step : Simulate impact of E.coli load discharge into the lagoon by each local streamsusing results of 3D-hydrodynamics model coupled to a biological module that simulate transport, dispersion, mortality of E.coli cells in water lagoon

100 m x 100 m grid10 level along vertical direction

Calibration and validation of this 3D model achieve in the framework of OMEGA-THAU project

Vent faible de secteur S-SE


Sensitivity studies with 3D- model shown


1) [E.coli] in water can be summed

[E.coli]c1 + [E.coli]c1 = [E.coli]c1 load 1 load 2 load 1 & 2

Load 1

Load 2

2) High impact of wind conditions on [E.coli] in water

No wind

Wind conditions O-NO, 36 km/h

Daily E.coli load = 13 Log

5 characteristics wind conditions are retained : 315N, 330N, 90N, 150N / 10m/s, No wind)


Sensitivity studies with 3D- model shown


3) High impact of temporal distribution of stream flow on [E.coli] in water

E.coli discharge during dry weather

Daily E.coli load = Stream flow x [E.coli]stream

0

5

10

15

20

25

0 5 10 15 20 25

Pal

las

stre

am f

low

(m

3/s

)

hours

[E.coli] x Stream Flow = [E.coli] x StreamHigh x Low Low x High

E.coli discharge during rainfall eventDaily E.coli load = 13 Log

Wind conditions N-NO, 36 km/h

2 characteristics meteorological conditions are retained :dry period, rainfall event : 70 mm in one day


Lagoon contamination module


The governance module : regulation of sanitary risks due to the microbiologic water charge


The governance module : monitoring and regulation

MONITORING

J = 1er Monday of the month

9 sampling (REMI)

If all values < Threshold (9/9)

If 1 value > Threshold (1/9)

ALERTLevel 1

MONITORING J+2

J = J+ 2

9 sampling (REMI)

Risk of contamination(rainfall, incident, etc..) ?

If all values < Threshold (9/9)


Alert Level 2

MONITORING

J = every monday

9 sampling (REMI)


All values (9/9), Must have 2 values

< threshold (9/9)

AlertMONITORING J+1

9 sampling (REMI)

J = J détection du risque

ALERTLevel 0


The Shellfish farming module (1/3)

- Figuring the shellfish farming sector

Thau lagoon is the principal shellfish farming production area of the French

Mediterranean

There are globally 540 companies that represent locally a source of direct

employment for 2,000 workers (fulltime equivalent estimates). The global

production can be estimated slightly higher than 20,000 tons by year and

distributed into 60% of oysters and 40% of mussels

Companies are segmented into three categories depending on their production

capacity. This capacity is directly linked to the number of concessions they own:

- Small companies (owning until 4 tables)

- Medium companies (owning 4-8 tables)

- Big companies (owning more than 8 tables)



- Rationale of the approach

. No bioeconomic model (such as in SSA10)

. Each commercial ban potential economic losses

. Linking the production capacity by type of company, their representative market

distribution structure and the selling prices practiced, it is possible to estimate

the global revenue obtained by class of company in a “normal” year.

. A normal year can be considered as an annual economic period with no

sanitary closure events. Consequently, it translates the maximum revenue which

can be obtained by shellfish companies in the absence of sanitary events.

Growth : dWij/dt = F° (Wij, T, Phyto, Gij) …Population : dNij/dt = - k x Nij - Hrij ± N( purchases/sales to other farmers)Production : dPij/dt = Hij x Wij

W

t

Cohort 13

Cohort 12

Cohort 11

Cohort 23Cohort 22

Cohort 21

OysterWeigh

Cohort 33

Cohort 32

Cohort 31

0 1 32

Biomass dynamic model (structured by cohorts and sub-cohorts)



- Expected output of the shellfish farming module

. The shellfish farming box of the model evaluates the economic

impacts directly supported by companies due to microbiological water

overcharge

. Estimation of the economic losses associated to commercial bans

due to microbiologic peaks

. Effects at short-run: determined by intra-annual economic

dynamics and sanitary rules in the governance box

. Effects at mid and long-un: image impacts, economic

sensibility of companies


SOL et ROUTES LOCAUX et CONSTRUCTIONS etc…

Noms PS. NON CONST.

PS.CONSTR AMNGT.SOL RES.ROUTES AMNGT.RTES HABIT CONC HABIT LOT. P.HAB.ISOL

Unités 100HA 100HA 100HA 100KM 100KM 100LGT 100LGT 100LGT

1Us.SOL.CONSTR. 100HA 1.00 1.00 -0.010 -0.020 -0.060

2Us.SOL NON CONSTR. 100HA 1.00 -1.00 -0.46 -0.46 0.00 0.00

3HAB.CONC. LOGT 100.00 0.00

4HAB.LOTISST. LOGT 100.00

5HAB.DISPERS. LOGT 0.00 100.00

6RESID.SEC LOGT 0.00 0.00

7SEJ.TOUR. KJTRS 0.00 0.00

8LOC.PROF. 1000M2 0.00 0.00

9LOC.TECH. 1000M2 0.00 0.00

10Us.MACH.AGR. KHUSM -1.20 -2.00 -0.55 -0.55 0.00 0.00

11Us.RES.ROUTES 100KM 0.00 1.00 1.00 -0.01 -0.01 -0.01

12RES.EAU.POT. 100KM

13RES.EAU.NON POT. 100KM

14RES.COL.EAU.US 100KM

etc

The macro-economic module


The tourism industry

There are two ways to integrate the tourism industry in the model

(this question is actually under discusion)

- Endogenously (feed back): the site attractiveness generates a tourism

demand

. The objective is to calibrate a function comparing data series of tourism

frequentation and an indicator of water quality (sanitary closures as a

proxy)

. Problem : in water quality other environmental components are included.

Separability question

- Exogenously : if problems to assess an attractiveness function, the

dynamics of the sector can be forced in the scenarios box


Conclusion: state of work

The modeling activities are going on

The model is not complete at the whole but some associated blocks are

starting to run

Some modules of the model need to be quickly developed

High efforts have been made in restructuring the model, programming

and integrating

We have some incertitude about additional information required for some

processes but globally the structure of the model seems enough robust

Interaction with local stakeholders will be assured by our Spicosa

colleagues participating to the expertise requirements of “Scot” and “Sage”


Thank you for your attentionThank you for your attention

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