Impact assessment modeling of low-water management policy

HAL Id: hal-02750368

https://hal.inrae.fr/hal-02750368

Submitted on 3 Jun 2020

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Pierre Mazzega, Christophe Sibertin-Blanc, Olivier Therond, Frédéric Amblard, Jean-Paul Arcangeli, Maud Balestrat, Marie-Hélène Charron Moirez, Anne Condamines, Christine Fèvre-Pernet, Benoit Gaudou, et al.

To cite this version:

Pierre Mazzega, Christophe Sibertin-Blanc, Olivier Therond, Frédéric Amblard, Jean-Paul Arcangeli, et al.. Impact assessment modeling of low-water management policy. 4. Workshop of Applied Com- puting for the Management of the Environment and Natural Resources (WCAMA 2013), Jul 2013, Maceió, Alagoas, Brazil. 2173 p. �hal-02750368�

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There are methods to build common ontologies or terminologies using methodologies for extracting knowledge from text corpora which are samples of the actual use of the terms in the disciplines involved (Condamines and Dehaut, 2011; Mazzega et al., 2011). In the MAELIA Project, a common reference terminology with over 250 terms was thus produced (Fèvre-Pernet, 2011) which offers to the actors the project a consensual basis of terms for working together and produce new knowledge.

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The ARDI method (Etienne et al., 2008) allows the participants to collectively build a conceptual model of the functioning of the territory given a studied issue with respect to natural resource management. The construction of the common representation is done by means of four workshops during which Actors, Resources, Dynamics and Interactions

(ARDI) are identified and clarified. Applying the method researchers have to answer to the following questions: Who are the main stakeholders involved directly or indirectly in the management of water resources of the territory during low-water periods? What are the principal resources of these stakeholders? What are the main processes that drive strong changes in water managements and resources states? The last phase of the ARDI method consists in synthesizing answers to the three preceding questions by formalizing the interaction between stakeholders and resources. This core step of the exercise leads to a conceptual model representing all interactions related to low-water management issues. These participatory method allow to involve researchers from a diversity of disciplines in the interdisciplinary modelling process, to start the integration of their disciplinary knowledge and to support an on-going learning and negotiation process known as “social leaning” (Thérond et al., 2010).

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! Figure 1. Organizational frame for abstraction hierarchies. Several conceptual models can be associated with the social-ecological system of reference. One of them is used to derive the concrete model which computer-implementation

(with specific data) is the simulation model driven by scenarios. The comparison of scenario simulations is based on the production and analysis

of a set of indicators.

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We have produced a meta-model that provides guidelines and prescriptions for deriving consistent conceptual models of social-ecological systems (Sibertin-Blanc et al., 2011).

The meta-model involves entities of three kinds – (rational) actors, material resources and cognitive resources- that are the constitutive elements of a system. An entity is characterized by properties whose values define the state of the entity, and operations, which process the properties. Entities are associated through relationships that can be processes (either social, economic or environmental) or activities achieved by an actor in the aim of fulfilling a goal. Those processes or activities external to the representation of the modeled social-ecological system are impacting the state of the entities via spatial or time series of variables involved in the system dynamics.

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N<K7?,! 87L87974:54Y! :M7! AB:<89! <=! :M7! 95N>?A:5<4,! :M7! 879<>8B79! A:! 9:A^7! A4K! :M7! XA;!

74:5:579!<=!J<:M!:;L79!87?A:7!X5:M!7ABM!<:M78,!879>?:54Y!54:<!A!B<M7874:!XM<?7P!+M7!G%.!

:A^79! =<8N! <=! A! #$H! B?A99! K5AY8AN,! XM787! AB:<89! A4K! 879<>8B79! A87! K7L5B:7K! X5:M54!

Joxes containing their respective “0--%&$5-#+” and “/#-.23+”. Relationships are 9;NJ<?5W7K! J;! :M7! 7KY79! J7:X774! :M7! 74:5:579,! A?<4Y! X5:M! :M758! 8<?7! A4K! BA8K54A?5:;!

B<49:8A54:9P!!

!

Figure 2. Normative sub-system of the French low-water management system ! (diagram derived in accordance with the requirements of the meta-model).

Entities are distributed in three types: “actors” (white boxes), “material resources” (blue boxes) and “cognitive resources” (green boxes).

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A8J5:8A854799\! 54! =<>8! 54:78K7L74K74:! 9>J9;9:7N9^Ce! :M7! M;K8<?<Y5BA?! 9>J9;9:7Ng! :M7!

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

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M::LeUUNA7?5A"APX<8KL8799PB<NU!!

AY8<4<N5B!9>J9;9:7Ng!:M7!9>J9;9:7N9!<=!:M7!<:M78!XA:78!>979!ZK854^54Y!XA:78!A4K! XA:78!

=<8!54K>9:8;\g!:M7!4<8NA:567!9>J9;9:7N!Z@5Y>87!C\P!

G:'U9+$"0%)'-2'&@*'3-456$&*7',$+$1*"*+&',-.*/'

+M7! 87L87974:A:5<4! <=! L8<B79979! A4K! AB:565:579! NA^54Y! :M7! 9:A:79! <=! :M7! N<K7?9! 74:5:579!

76<?67! 87?579! <4! A! 6A857:;! <=! :<<?9e! N<K7?! 7_>A:5<49! Z7PYP! M;K8<?<Y;,! L?A4:! Y8<X:M,!

K7N<Y8ALM;,! 7:BP\,! B7??>?A8! A>:<NA:A! Z7PYP! ?A4K! B<678! BMA4Y79\,! L8<B7K>8A?! 8>?79! Z7PYP!

K7B595<4!NA^54Y!AJ<>:!K7B8779!<=!XA:78!>97!879:85B:5<49\,!<8!8A:5<4A?!K7B595<4!NA^54YP!

G:<'U*%0)0-+5"$Z0+1'

I4?;! ^7;! 658:>A?! AB:<89! <=! :M7! ?<X/XA:78! NA4AY7N74:! A87! 74K<X7K! X5:M! A! BALAB5:;! <=!

8A:5<4A?!K7B595<4!NA^54Ye!:M7!=A8N789!54!:M758!L?A4454Y!A4K!NA4AY54Y!<=!9<X54Y,!5885YA:5<4!

A4K! MA8679:54Yg! :<! A! ?799! 7S:74:! 087=7B:! A4K! NA4AY789! <=! KAN9! <8! <:M78! ^54Y! <=! XA:78!

7_>5LN74:P! +M7! K767?<L7K! N<K7?! <=! 8A:5<4A?5:;! 59! 87?;54Y! <4! A! N>?:5/B85:785A! K7B595<4/

NA^54Y!N7:M<K!JA97K!<4!:M7!$#(&#'!-.#2%)!Z1MA=78!"QO'\!A4K!#1&3#,6#!-.#2%)!Z.7NL9:78,!

"Q'O\P!+M59!N<K7?!A9!A??<X7K!:<!87L8<K>B7!]O'a!<=!<J97867K!K7B595<49!B<4B78454Y!B8<L!

8<:A:5<4! <L:5<49! :A^74! J;! 5885YA:54Y! =A8N789! <678! A! &! ;7A89! L785<K! Z+A5??A4K578! 7:! A?P,!

CD"CA\P!

08<B7K>8A?! K7B595<4/NA^54Y! 59! NA54?;! 74B<K7K! <4! :M7! JA959! <=! A4! AB:565:;! K5AY8AN! :MA:!

87L87974:9! :M7! :85YY7854Y! <=! L87K7=547K! AB:5<49! B<495K7854Y! :M7! 9:A:7! <=! 74658<4N74:A?!

6A85AJ?79!ZA::85J>:79!<=!9<N7!NA:785A?!A4K!B<Y45:567!879<>8B79\P!1>BM!ALL8<ABM!59!>97K!54!

LA8:5B>?A8! :<! 87L87974:! A4K! A8:5B>?A:7! :M7! 6A85<>9! 9:7L9! :MA:! Y<79! =8<N! :M7! J7Y54454Y! <=!

:M7! ?<X/XA:78! L785<K! ZA9! K7:7B:7K! =8<N! 85678! 9>867;\,! :M74! LA9954Y! :<!:M7!B<467454Y!<=!

:M7!K8<>YM:!B7??,!7:BP,!:5??!:M7!L<995J?7!74AB:N74:!<=!A!K7B877!<=!XA:78! >97!879:85B:5<4!<=!A!

Y5674!976785:;!<678!A!X7??/K7=547K!:7885:<8;P!

G:D'(%&0M0&9',-.*/0+1'

[5:M<>:! 74:7854Y! 54:<! K7:A5?9,! :M7!/0&,!actors’ activities modeled A4K! B<K7K! 54! :M7!

L?A:=<8N! A87e! B8<L! NA4AY7N74:! A4K! B8<L! 8<:A:5<4g! 9<X54Y,! 5885YA:54Y! A4K! MA8679:54Yg!

L87LA854Y! A4K! 74AB:54Y! K7B8779! <=! XA:78! >97! 879:85B:5<49g! X5:MK8AX54Y! A4K! K59:85J>:54Y!

8AX! <8! :87A:7K! XA:78g! 87?7A954Y! XA:78! =8<N! 879786<589! ZKAN9,! M5??! 879786<589,! 7:BP\g!

B<4:8<??54Y! XA:78! >979! A4K! 9A4B:5<454Y! XA:78! N59>979g! B<49>N54Y! U! 87f7B:54Y! XA:78!

ZK854^54Y! A4K! 54K>9:85A?! XA:789\P! +M7! NA8^7:/?5^7! Z7PYP! AY85B>?:>8A?! 54L>:9,! AY85B>?:>8A?!

L8<K>B:9,! :A85==9! <=! XA:78!B<49>NL:5<4,!7:BP\!A4K!L<?5B;/?5^7!Z7PYP!AY85B>?:>8A?!L87N5>N\!

K;4AN5B9!are accounted for in the form of time series (“external activities”)!4<:!N<K5=57K!

J;!:M7!:5N7!76<?>:5<4!<=!:M7!9:A:79!<=!:M7!L?A:=<8N!74:5:579P!

G:G'87-%*))',-.*/0+1'

+M7! /0&,! L8<B79979! N<K7?7K! 54! :M7! 95N>?A:5<4! L?A:=<8N! A87e! XA:78! U! 9<5?! U! L?A4:!

54:78AB:5<49! A4K! L?A4:! Y8<X:M! ZNA54?;! B8<L9\g! ?A8Y7/9BA?7! ?<X/879<?>:5<4! M;K8<?<Y5BA?!

L8<B79979!A4K!=?<X9!Z>954Y!A!1[G+/K78567K!A?Y<85:MNg!+Mi8<4K!7:!A?P,!CD"(\g!?A4K!B<678!

A4K! ?A4K! >97! ZH2H#\! BMA4Y79! 54! :M7! GK<>8/)A8<447! JA954! Z7PYP! 3Y>;74! 7:! A?P,! CD"C\g!

76<?>:5<4! <=! :M7! K7N<Y8ALM;! A:! :M7! N>45B5LA?5:;! ?767?! ZK59AYY87YA:54Y! :M7! K7N<Y8ALM5B!

9B74A85<9!<=!:M7!-31**!/!CD""!N<K7?\g!Kiffusion of technological innovation on farmers’

9<B5A?!47:X<8^9P!

+M7! LA9:! A4K! =>:>87! Z9B74A85<9\! 9LA:5<:7NL<8A?! 76<?>:5<4! <=! :M7! L87B5L5:A:5<49,! Y8<>4K/

?767?! A58!:7NL78A:>87!A4K!76AL<:8A49L58A:5<4!A87!:87A:7K!A9!7S:784A?!L8<B79979,!87?;54Y!54!

LA8:5B>?A8!<4!:M7!:5N7!978579!L8<65K7K!J;!2*%@G21!Z0AYi!A4K!+788A;,!CD"D\P!

[7! MA67! A?9<! K767?<L7K! A! N>?:5/9BA?7! 54:7Y8A:5<4! 9BM7N7! A??<X54Y! :M7! :8A49=78! <=!

54=<8NA:5<4! J7:X774! ?<X! 879<?>:5<4! M;K8<?<Y5BA?! L8<B79979! ZM;K8<Y8ALM5B! W<479\,!

N7K5>N!879<?>:5<4!K7N<Y8ALM5B!A4K!XA:78!>97!L8<B79979!ZN>45B5LA?5:579\,!A4K!L8<B79979!

87L87974:7K!A:!:M7!N7:85B!879<?>:5<4!<678!:M7!AY85B>?:>8A?!LA8B7?9!Z@5Y>87!(\P!

!

! !

Figure 3. Multi-scale interlocking of territorial entities. Left: low and medium resolution: hydrology + land cover-land use; Right: high resolution:

positioning of water resources, water uses and agricultural parcels (land use class #2; spatial positioning from LPIS, 2010; this example on a small area).

+M59! N>?:5/9BA?7! 9BM7N7! ?5N5:9! :M7! 9LA:5A?! 879<?>:5<4! <=! M;K8<?<Y5B! K;4AN5B9! ZA4K!

A99<B5A:7K! B<NL>:A:5<4! :5N7\! XM5BM! =547! K7:A5?! 59! 4<:! 87_>587K! =<8! :M7! 95N>?A:5<4! <=!

9B74A85<9!<=!XA:78!NA4AY7N74:!<4!:M7!M<85W<4!<=!97678A?!K7BAK79,!XM5?7!87L87974:54Y!A:!

:M7! N7:85B! ?767?! :M7! L<95:5<454Y! A4K! K;4AN5B9! <=! J<:M! XA:78! 879<>8B79! <4! :7885:<8579,!

=A8N9,!:M758!5885YA:7K!LA8B7?9!A4K!B8<L9P!

K:'U$&$'N$&@*70+1='J$7"-+0;$&0-+'$+.'!+&*17$&0-+'

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54:78657X7K! ^7;/AB:<89,! 7:BP\! MA67! :<! J7! YA:M787K! A4K! 54:7Y8A:7K! 54:<! A! B<M7874:!

95N>?A:5<4! N<K7?P! +M7;! A87! <=! K5==7874:! :;L79! ?5^7! _>A4:5:A:567! 7NL585BA?! <J9786A:5<49,!

4>N785BA?! N<K7?! <>:L>:,! NAL9! <8! :7S:9,! 7:BP! `7:78<Y747<>9! KA:A! 9LAB7! <8! :5N7!

resolution, or “definition”!<=:74!87_>587!:<!?54^!KA:A!97:9!:M8<>YM!95NL?7!J>:!7SL?5B5:!ZA4K!

:M>9! 87659AJ?7\! 8>?79! Z7PYP! A??<BA:54Y! :M7! 6<?>N7! <=! X5:MK8AX4! XA:78! AYY87YA:7K! A:! :M7!

municipality level to the most likely “reservoir” –!XA:78!B<>897,!?A^7,!KAN,!Y8<>4KXA:78,!

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:M7! ?<BA?5WA:5<4! <=! XA:78! 879<>8B79! A4K! X5:MK8AX4! 6<?>N79,! L<L>?A:5<49! A4K! H2H#!

NAL9,!L87B5L5:A:5<4!KA:A,!=A8N9!A4K!:M758!5885YA:7K!LA8B7?9,!7:BP!-:!54B?>K79!54!LA8:5B>?A8!:M7!

?<BA?5WA:5<4! ZA4K! 6A?>79\! <=! :M7! 97B:5<49! <=! XA:78! B<>8979! A4K! 9>8=AB7! XA:789! Z-)3,!

CD"(\,! 47A8/9>8=AB7! Y8<>4KXA:78,! >4K78/XA:78! 9>8=AB79! Z?A8Y7! XA:78B<>8979,! NA9<48;!

JA9549,! =?<<KL?A549,! 7:BPg! -)3,! CD"(\,! 87L<8:7K! X5:MK8AX4! XA:78! 6<?>N79! A4K! L<54:9! <=!

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B<NLA87K! :<! :M7! 87Y>?A:<8;! =?<X9! ZG)[G,! CD"(\,! ?5^7?;! 7S:7495<4! <=!K7B8779!<=!XA:78!

>97!879:85B:5<49,!2<8547!HA4K!2<678!NAL9!<=!:M7!H#H2!54!CDDD!A4K!CDD'!Z2H2@,!CD"(\,!

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?5N5:9!ZN>45B5LA?5:579,!K7LA8:N74:9,!M;K8<Y8ALM5B!W<479,!XA:78!JA9549,!7:BP\P!

%7Y>?A:<8;!NAL9!<=!:M7!A>:M<85W7K!AJ9:8AB:5<4!XA:78!6<?>N79!=<8!5885YA:5<4!Z_><:A\!A4K!

B<8879L<4K54Y!K7?5N5:A:5<4!<=!:M7!9>J/JA954!>4K78!:M7!A>:M<85:;!<=!:M7!954Y?7!<8YA459N!=<8!

B<??7B:567! NA4AY7N74:! 59! 7SL7B:7K! 9<<4P! I4! @5Y>87! E,! A! M;K8<Y8ALM5B! A87A! 59! B<6787K!

X5:M!LA:BM79!<=!K5==7874:!?A4K!B?A9979!Z>8JA4,!9>J>8JA4,!=<879:,!A8AJ?7!?A4K,!LA9:>87,!7:BP\!

7ABM!X5:M!K5==7874:!XA:78!=?<X!BMA8AB:7859:5B9P!

!

!

!

!

!

!

!

!

!

!

!

!

Figure 4. GIS layer in the Gama-based environment of the simulation platform here showing the distribution of land use classes over a hydrographic zone,

and the list (right column) of the entities involved in its state evolution.

.5==7874:!:;L79!<=!B8<L9!A87!L?A4:7K!<4!:M7!LA8B7?9,!7ABM!B8<L!K7=5454Y!9L7B5=5B!477K9!54!

:78N9!<=!5885YA:5<4!XA:78,!XM5BM!59!X5:MK8AX4!=8<N!9L7B5=5B!L<54:9!<4!:M7!XA:78!87978679!

<=! :M7! A87AP! +M7! XM<?7! 95N>?A:5<4! L?A:=<8N! 59! K767?<L7K! <4! A! )ANA/74658<4N74:!

Z+A5??A4K578!7:!A?P,!CD"CJ\P!

R:'3-456$&*7',$+$1*"*+&'A%*+$70-)'

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)A8<447! JA954,! :M7! N<9:! 5NL<8:A4:! K85654Y! =<8B79! A==7B:54Y! :M7! =>:>87! <=! :M7! )A8<447!

85678! A4K! XM5BM! 54K5BA:<89! NA;! M7?L! :<! N<45:<8! 9>BM! 5NLAB:9! MA67! J774! 5K74:5=57K! A4K!

B<NLA87K!:<!:M7!7S59:54Y!?5:78A:>87!<4!XA:78!9B74A85<9!Z$A8BM!7:!A?P,!CD"C\P![7!L8<L<97!

545:5A??;!:<!L8<K>B7!=<>8!NA54!N<K7?!:8Af7B:<8579!Z97:!<=!6A?>79!:A^74!J;!A??!9:A:7!6A85AJ?79!

<8!A::85J>:79!<=!74:5:579!<=!:M7!L?A:=<8N!:M8<>YM<>:!:M7!95N>?A:7K!:5N7!L785<K\!/!:M>9!=<>8!

9B74A85<9!Z1""!:<!1CC\!/!XM<97!5NLAB:9!BA4!J7!A997997K!A4K!B<NLA87K!Z+AJ?7!"\P!

*6A?>A:5<4! <=! 9<B5A?! A4K! 74658<4N74:A?! 5NLAB:9! <=! 6A85<>9! 9B74A85<9! 59! K<47! J;!

B<NLA854Y!:M7!B<8879L<4K54Y!:8Af7B:<8579P!+M7!_>A?5:A:567!A99799N74:!<=!5NLAB:9!87?579!<4!

54K5BA:<89! :MA:! 9>NNA85W7! :M7! 79974:5A?! 54=<8NA:5<4! L8<K>B7K! J;! :M7! 95N>?A:5<4,! A4K!

87?76A4:!:<!:M7!L87K7=547K! >979!Z54K5BA:<89!=<8!XA:78!NA4AY789,!=<8!=A8N789,!L<995J?;!=<8!

8797A8BM789,! 7:BP\P! 1<N7! <=! :M7! NA54! 87?76A4:! 54K5BA:<89! 79:5NA:7K! J;! :M7! L?A:=<8N! A87e!

XA:78! =?<X! A:! 4<KA?! L<54:9! <=! <Jf7B:567! =?<Xg! ! 9>8L?>9! U! K7=5B5:! <=! XA:78! Z7%-! <Jf7B:567!

=?<X,! 977! 17BP! "PC\g! =87_>74B;! <=! XA:78! B85959! =87_>74B;g! ;57?K! <=! 5885YA:7K! B<8L9g!

!

AY85B>?:>8A?!L8<K>B:5<4g!9>9:A54AJ5?5:;!<=!=A8N9g!BMA4Y79!54!LA::7849!<=!B8<L!8<:A:5<4g!:<:A?!

A4K! 5885YA:7K! 9>8=AB79! <=!=A8N9g!4>NJ78!<=!=A8N9g!B<9:!<=!5NL?7N74:A:5<4!U! <L78A:5<4!<=!

A?:784A:567! NA4AY7N74:! L<?5B;! Z54!€)g! ABB7L:AJ5?5:;! Z4<8N! B<NL?5A4B7,! 9A:59=AB:5<4! ?767?!

<=!6A85<>9!AB:<89\!<=!:M7!5NL?7N74:7K!87Y>?A:5<49!<8!NA4AY7N74:!K765B79P!

!

Table 1. The four main scenarios to be simulated.

! [A:78!$A4AY7N74:!J;!

[A:78!!

@?<X9!

GJ9:8AB:5<4!

j<?>N79!

2?5NA:7!

2MA4Y7!

1B74A85<9!

!T>954799!!A9!>9>A?!

A<<' A<D'

%7K>B:5<4!<=!L87B5L5:A:5<49!!

54!?<X/XA:78!L785<K9!

AD<' ADD'

!

087974:?;! 97678A?! L8<B799! K;4AN5B9! and activities have been “encapsulated” in sub/

9;9:7N9!<=!:M7!L?A:=<8N!Z?A8Y7!9BA?7!M;K8<?<Y5BA?!K;4AN5B9!=<8B7K!J;!B?5NA:7!9B74A85<!<4!

L87B5L5:A:5<49g! H#2H! K;4AN5B9! 54B?>K54Y!>8JA45WA:5<4!A4K!=A??<X!K767?<LN74:!X5:M!:M7!

?<99! <=! 9<N7! =A8N9g! L?A4:! Y8<X:M! A4K! 9<5?! 54:78AB:5<49! =<8! 6A85<>9! ^54K9! <=! B8<L9g!

implementation of farmers’ B8<L! NA4AY7N74:! A4K! AB:565:;! K767?<LN74:! <4! :M758!

879L7B:567!LA8B7?9g!7:BP\P!`<X7678!:M7!L8<K>B:5<4!<=!:M7!=>??!N<K7?!:8Af7B:<8579!59!4<:!;7:!

L78=<8N7KP! 3<:7! :MA:! A! =>??! K79B85L:5<4! <=! :M7! L?A:=<8N! B<4B7L:5<4,! K795Y4! A4K!

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A simple approach to account for norms is sufficient to simulate satisfactorily basin-scale low-water management in France: in fact these norms mainly define the framework of procedural decisions (Sec. 3.1), or the volume of water available at a given time and place for a given community (see Figure 2). Agents’ rationality applies primarily for decision making about actions (irrigation, water release, crop rotation, etc.) constrained by the state of resources (crop growth, rainfall, weather forecast, agricultural calendar, etc.). Another part of the “system control” is external to its evolution, such as the level of annual premium to agriculture or the rules of agricultural land allocation, and play like a kind of top-down constraints. Thus the representation of truly "normative" rational agents is not necessary given our objectives.

The simulation of the dynamics of the state variables (attributes of entities) produces the information required to estimate and represent the spatial (maps) or temporal (time series and curves) evolution of useful indicators for decision-making regarding management options desirable considering their likely impacts. These indicators are first developed with managers and policy-makers to ensure their relevance and usefulness.

Thus the project quickly presented here has several advantages which integration in a simulation platform is probably an innovation: the design of a methodology for sharing knowledge from various disciplines involved in the analysis of the management of water resources; the design and operation of a rigorous framework for integrating this knowledge that leads from the formalization of the knowledge on the reference social- ecological system to the implementation of a model for the simulation of scenarios; the inter-operability of various simulation tools (equations, cellular automata, multi-agent models of rationality, GIS). All together they allow the multi-spatial / temporal scales

and multi-level transfer of information between entities linked dynamically. They produce simulated scenarios that fit closer to the territorial dynamics of the real system and indicators that make sense to the resource-manager or decision-maker.

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