Bruno Castanier

Marko Cepin

David Bigaud

Christophe Berenguer

Romain Duponnois
email: romain.duponnois@inrs.fr

Eric Levrat
email: eric.levrat@univ-lorraine.fr

Ali Siadat
email: ali-siadat@ensam.eu

Pascal Lamy
email: pascal.lamy@inrs.fr

Proceedings of the 31th European Safety and Reliability Conference

Keywords: Occupational safety, Dysfunctional analysis, Work situation, Technical drift, Human-Machine interactions, Human error, Hazardous situation analysis, System modeling

 showed the feasibility of identifying this kind of potentially hazardous situation by observing the working situation. Here, we propose a method called Working Situation Health Monitoring (WSHM). The goal of this method is to identify these potentially hazardous situations by analyzing the potential drift of working situations and monitor the advent of potentially hazardous situations using equipment and production data. It consists of three steps: firstly, we model the working situation studied to characterize the nominal working situation; secondly, we analyze cause-and-effect relationships between potential process drifts, potential operator responses and potentially hazardous situations; and thirdly, we construct a health indicator of the working situation based on knowledge of potentially hazardous situations identified in the second step and by equipment data. This paper also presents the application of the method to a case study (an educational automated assembly machine).

Introduction

In France in 2016, nearly ten percent of all work-related accidents occurred on machines (of the 574478 workrelated accidents in France in 2016). Industrial sectors are those most affected, with about a quarter of work-related accidents involving machines. These numbers can be explained by the considerable presence of machines in these sectors [START_REF] Cocheteux | Contribution à la maintenance proactive par la formalisation du processus de pronostic des performances de systèmes industriels[END_REF]).

In the same year, accidents on machines caused more than 4,000 permanent disabilities (12% of all permanent disabilities over the year) and 3 million days of work stoppage (7.5% of all days of work interruption over the year) [START_REF] Brasseur | Le risque machine[END_REF].

To sell a machine on the market of the European Economic Community, all designers and manufacturers must comply with the "essential health and safety requirements" set out in the Machinery Directive 2006/42/EC (CE 2006). This directive is transposed into the French Labor Code by articles R. 4311 and R. 4312). Consequently, confidence is placed in harmonized standards attesting to the conformity of machines with this directive (e.g. ISO12100:2010 (ISO 2010), etc.).

To prevent these accidents, "the solutions to be provided are primarily aimed at intrinsic prevention, in order to eliminate or reduce the risk right from the design stage, by integrating the operating characteristics of the machinery and its maintenance." [2]. The purpose of these solutions is to limit or eliminate the occurrence of hazardous work situations for the operator of work equipment by reducing or eliminating the operator's exposure to a source of hazards.

However, even if risk reduction measures are taken at the design stage, the specificities of the machine's use must also be taken into account. This is done within the framework of the Use Directive 2009/104/EC (CE; 2009) also transposed in the French Labor Code by articles R4321-1 to R4321-5. In this paper, we will look at the impact of the operating context that causes the equipment or the production process to drift. This can lead the machine's operator to react to maintain the performance of their machinery. [START_REF] Pariès | De l'obéissance à la résilience, le nouveau défi de la sécurité[END_REF][START_REF] Pariès | Resilience engineering in practice: a guidebook[END_REF][START_REF] Léger | Contribution à la formalisation unifiée des connaissances fonctionnelles et organisationnelles d'un système industriel en vue d'une évaluation quantitative des risques et de l'impact des barrières envisagées[END_REF].

These drifts can result in modifications of the machine [START_REF] Polet | Human behaviour analysis of barrier deviations using a benefit-cost-deficit model[END_REF][START_REF] Vanderhaegen | A Benefit/Cost/Deficit (BCD) model for learning from human errors[END_REF] or of activities carried out by the operator [START_REF] Pariès | Rule-based vs managed safety : from compliance to resilience[END_REF][START_REF] Lamy | Approach to analyse hazardous situations tied to recurrent production dysfunctions, by observing the work situation[END_REF][START_REF] Hollnagel | Achieving system safety by resilience engineering[END_REF]). These changes are called operator responses to a technical drift (machinery malfunctions, product flow drifts, etc.) and can place the operator in a potentially hazardous situation.

This paper presents an approach for the identification of these potential hazardous situations following an operator response to a technical drift of an automated assembly machine.

This paper is structured as follows:

 Section Two defines the potential hazardous situations following a response by a machine operator to a technical drift.  Section Three gives an overview of the Working Situation Health Monitoring (WSHM) method proposed and explains its purpose.  Section Four presents an overview of the first step of the method and a case study.  Section Five presents an overview of the second step of the method.  Section Six presents the application of these two steps to a case study.  Section Seven concludes this article and proposes perspectives for the continuation of this work and the development of the WSHM method, in particular the third step of the WSHM method.

Operator response leading to a hazardous situation

The link between technical drift, operator response and hazardous situation is defined by Lamy & Perrin: "When using a machine or automated system, dysfunctions such as the jamming of a part can occur and disturb the normal production process. To solve this dysfunction, the operator may place himself in a hazardous situation." [START_REF] Lamy | Approach to analyse hazardous situations tied to recurrent production dysfunctions, by observing the work situation[END_REF]. These "operator responses to technical drift" are similar to what is called in ISO12100:2010: "interactions of person(s) with the machine, including the correction of malfunctions" in the part concerning the "human factor" during risk estimation [START_REF] Iso | ISO 12100 : Safety of machinery --General principles for design --Risk assessment and risk reduction[END_REF].

In order to better understand the causal links between the different events leading to a hazardous situation following a response of a machine operator to a technical drift, we first propose to define what is meant by "operator response" and "hazardous situation".

Operator response

During a technical drift, an operator can make the decision to adapt to the drifting situation. [START_REF] Bot | Suivre la prescription et prendre l'initiative pour être sûr: la résilience en situation[END_REF]. This response contributes to the resilience of the work situation but can also put an operator at risk. An operator's response to a technical drift leading to an accident falls into the category of "human error" and/or "human factors" [START_REF] Reason | Human error[END_REF][START_REF] Van Elslande | L'erreur humaine dans les scénarios d'accident cause ou conséquence?[END_REF][START_REF] Chauvin | Le facteur humain et la sécurité maritime[END_REF][START_REF] Vanderhaegen | A Benefit/Cost/Deficit (BCD) model for learning from human errors[END_REF][START_REF] Larouzée | Le modèle de l'erreur humaine de James Reason[END_REF].

Van Elslande reminds us that "[human] error should not be analyzed as the primary cause of accidents, but rather as the consequence of malfunctions upstream. If man makes a mistake, it is rarely not because he has sought it; it is most often because the external (environmental) as well as internal (human) conditions of his activity did not allow him at a given moment to face the requirements of the [prescribed] task he was confronted with" [START_REF] Van Elslande | L'erreur humaine dans les scénarios d'accident cause ou conséquence?[END_REF].

In this case, the response of an operator to a technical drift is linked to the perception of the situation in which they find themselves [START_REF] Endsley | From here to autonomy: lessons learned from human-automation research[END_REF]). An operator supervising the machine(s) as well as the product flow(s) will be able to perceive drifts [START_REF] Boyd | OODA loop[END_REF][START_REF] Endsley | Theoretical underpinnings of situation awareness: A critical review[END_REF][START_REF] Hoc | La relation homme-machine en situation dynamique[END_REF][START_REF] Boy | On the complexity of situation awareness[END_REF][START_REF] Duponnois | Vers le WSHM (Working Situation's Health Monitoring) pour la prévention des accidents du travail[END_REF].

The perception of the technical drift is perceived by the operator through the drift of interactions between themselves and the technical component of the working situation [START_REF] Duponnois | Working situation reference model for risk assessment on automated assembly lines[END_REF].

Hazardous situation

In standard ISO 12100:2010, a hazardous situation is described as the "circumstance in which a person is exposed to at least one hazard" (ISO 2010).

In the same standard, a hazard is described as a "potential source of harm". Harm is described as a "physical injury or damage to health (ISO 2010).

If a person is in a hazardous situation, a hazardous event can lead to harm ("event that can cause harm" (ISO 2010)). Thus, the condition of occurrence of harm can be represented as in the Figure 1. When a machine operator responds to a technical drift, they can expose themselves to a hazard and thus be in danger (hazardous situation).

To prevent this kind of hazardous situation on automated assembly lines in operation, this paper proposes a method to identify and monitor them: the Working Situation Health Monitoring (WSHM) method.

Proposal of Working Situation Health Monitoring

(WSHM) method The WSHM method proposes results from the following question: can we monitor an initially safe working situation to anticipate the occurrence of a potential hazardous situation following a response of a machine operator to a technical drift? [START_REF] Duponnois | Vers le WSHM (Working Situation's Health Monitoring) pour la prévention des accidents du travail[END_REF].

To allow the monitoring of the working situation in an automated and continuous way, this method is based on existing health management tools used to monitor the "health" of a technical component like Prognostic and Health Management described in [START_REF] Kalgren | Defining PHM, a lexical evolution of maintenance and logistics[END_REF][START_REF] Cocheteux | Contribution à la maintenance proactive par la formalisation du processus de pronostic des performances de systèmes industriels[END_REF].

The goal of the WSHM method is to provide a health indicator of the working situation studied on an automated assembly machine in operation, i.e. the more a hazardous situation is likely to occur, the more the working situation health indicator will be degraded.

To build this health indicator of the working situation, we need to identify a potential hazardous situation following a response by the machine operator to a technical drift. In this part, the dysfunction analysis of the whole situation will be performed. Therefore, in addition to the drift analysis of products (flow and objects) and machines, we propose to analyze the drift and the advent of interactions between operators, machines and products in the working situation.

Prior to the dysfunction analysis, the method will start with the collection of information about the nominal state of the working situation (structure and behavior). Since a working situation is a complex system, we will model it as such. This information collection results in a data model of the working situation studied.

Thus, the WSHM method proposed is divided into three steps:  First, a modeling step where we model the working situation studied to characterize the nominal working situation (R1);  Secondly, an identification step where we analyze cause-and-effect relationships between potential process drifts, potential operator responses and potentially hazardous situations (R2);  Thirdly, an indicator definition step where we construct a health indicator of the working situation based on knowledge of potentially hazardous situations identified in the second step and by equipment data.

The first and second steps will be described in the two following sections. The idea behind the third step will be introduced in the final section.

Modeling the working situation (R1)

The goal of the first step of the WSHM method is to generate a data model containing information about the working situation studied on machines in the operating phase.

In this first step, we will use the Working situation model presented in [START_REF] Duponnois | Working situation reference model for risk assessment on automated assembly lines[END_REF] as a reference to model the working situation studied.

In addition to the reference model, performing this step requires:

 Technical documents: this includes all the documents describing the machines and their components, the raw materials and product descriptions. They can be completed with observations of the working situation studied;  The description of the machine operator's activity (worker): list of activities assigned to each operator in the working situation.

These documents/observations will be used to study the structure and the behavior of the working situation studied, and identify interactions and hazards in its nominal state (without drift). It should be noted that in a nominal working situation on a machine, the operator cannot be in a hazardous situation even if hazards exist.

A single person (who can be a member of the workgroup of the second step) carries out the first step of the WSHM method.

The result of the first step is an instantiated working situation model of the working situation studied (data model).

Sub-steps proposals

The first step of the WSHM method (R1) is composed of eight sub-steps (R1.1 to R1.8) and represented as an activity diagram (SysML) in Figure 2.

The goal of sub-step R1.1 is to model the working situation studied viewed as a "black box" (the interior of the object is not described in its context). This context is composed of: the description of the working space (physical area in which the work is situated), the physical working environment (parameters like temperature, air pressure, air quality, etc.) and flow of raw materials, products and other objects entering and exiting the working situation.

The goal of sub-step R1.2 is to model the activity of the working situation studied viewed as a "black box". This activity description allows describing which flows (products, information, etc.) enter the situation and which flows leave it.

The goal of sub-step R1.3 is to model the structure of the working situation studied, this time viewed as a "white box" (the interior is described, as opposed to the "black box"). Since a working situation is composed of operators, machines and tools (manual tools for the operator to use), this step consists in modeling each one of them viewed as a "black box". It is noteworthy that since the operator is a human being we will not model them more precisely than a "black box": the goal of this method is not to specifically model humans.

The goal of sub-step R1.4 is to model the behavior/activity of each component of the working situation studied. The activities of the machinery and each operator are modeled. The descriptions of the activities allow describing which flows (products, information, etc.) are exchanged between the components of the working situation.

The goal of sub-step R1.5 is to model the structure of the machines in the working situation studied, this time viewed as a "white box". A machine is composed of its components. This decomposition of the machine into its components is useful for the second step of the WSHM method, where the description of the machine components will be analyzed to better understand the causes of its dysfunctions.

The goal of sub-step R1.6 is to model the behavior/activity of the machine's components. The activities of each machine component are modeled. The descriptions of the activities allow describing which flows (products, information, etc.) are exchanged between the machine's components and between the latter and the other components of the working situation.

The goal of sub-step R1.7 is to model all the interactions between the operator, tools, products and machine components. The way these interactions are modeled is presented in [START_REF] Duponnois | Working situation reference model for risk assessment on automated assembly lines[END_REF].

The goal of sub-step R1.8 is to model all the hazards and the resulting hazard zone ("any space within and/or around machinery in which a person can be exposed to a hazard" (ISO 2010)). Information on hazards can be found in previous "hazard identifications" performed on the machinery. If none had been carried out on the machinery, a machine risk expert has to perform one to identify the hazards before they can be modeled.

Conclusion on the first step of the WSHM method

At the end of this step, the working situation studied is modeled in the instantiated model of the working situation. This data model includes information about:

 The structure and behavior of the working situation studied, the structure and behavior of machine's/machines' components. This information is used in the second step of the WSHM method to perform a dysfunction analysis of the technical components;  The description of the interactions between the components of the working situation studied. This information is used in the second step of the WSHM method to analyze the potential perception of a technical drift by an operator and the impact of the operator's response on the rest of the working situation;  The description of the hazards and the hazard zone present in the working space. This information is used in the second step of the WSHM method to estimate the risk associated with each operator's responses to a technical drift. Note that the operators are viewed only as "black boxes" in the instantiated model. This choice is justified by the way the method has been thought out: the method takes into account only responses to a technical drift (not other "human factors").

The instantiated model of the working situation (data model) is the principal input of the next step of the WSHM method: "Identifying potential hazardous situations following an operator response to a technical drift".

Identifying potential hazardous situations following an operator response to a technical drift (R2)

The goal of this step is to generate a list of potential hazardous situations following an operator response and their technical causes (technical drift).

Current risk analysis methods like Process hazard analysis [START_REF] Mortureux | Analyse préliminaire de risques[END_REF][START_REF] Desroches | Les invariants de l'analyse préliminaire des risques[END_REF], FMECA (IEC 2018) and HAZOP [START_REF] Royer | HAZOP: une méthode d'analyse des risques-Principe[END_REF][START_REF] Baybutt | A critique of the Hazard and Operability (HAZOP) study[END_REF] are more focused on the technical part of the working situation to identify hazardous situations. In our case, we want to take into account the whole working situation (actors and interactions between them).

In this second step of the WSHM model, we will use the instantiated model of the working situation (data model) produced during the first step of the WSHM method (presented in section 4).

The second step of the WSHM method is carried out by a workgroup composed of:  One or more machine experts who know the machine or similar machines;  One or more machine risk experts to study the risk of each operator response;  One or more ergonomists to study the potential perception capabilities of the operator;

The goal of this workgroup is to identify potential operator responses following a technical drift and the potential hazardous situation resulting from these responses.

The result of the second step is a table listing all the potential hazardous situations following an operator response to a technical drift where the estimated risks are significant (risks that must be reduced). All the sub-step results are reported in the same table (the "analysis table "). This table is a written record of the analysis and is used throughout the second step of applying the WSHM method.

Proposals of sub-steps

The second step of the WSHM method (R2) is composed of six sub-steps (R2.1 to R2.6) and represented as an activity diagram (SysML) in Figure 2. The goal of sub-step R2.1 is to perform a dysfunction analysis on the first level of abstraction: the working situation viewed as a "black box". To carry out this step, an AMDEC-HazOp [START_REF] Reitz | Systematic approach for dysfunctional analysis in radiotherapy[END_REF]) is performed on the information of the working situation activity extracted from the instantiated model of the working situation (data model) to determine:  Failure modes of the activity of the working situation studied;  External causes of these failure modes: causes can be drifts of incoming flows of the activity. The "InFlow" drift can be a drift of product or energy flow characteristics; they can also be drifts of product properties. External causes can be drifts of the work environment;  An Occurrence mark (O): estimation of the frequency of occurrence of each cause.  Local effects of these failure modes on the outgoing flows of the activity. The "OutFlow" drift can be a drift of product flow characteristics; they can also be a drift of product properties.

The goal of sub-step R2.2 is to carry out a dysfunction analysis on the second level of abstraction: the working situation viewed as a "white box" with machines and operators (workers) viewed as "black boxes". To carry out this step, an AMDEC-HazOp is performed on the machine and information on operator activities is extracted from the instantiated model of the working situation (data model) to determine:

 The failure modes of each machine activities and each operator's activities in the working situation studied;  External causes of these failure modes: causes can be drifts of incoming flows of machines or an operator's activities, or drifts of the work environment;  An Occurrence mark (O): estimation of the frequency of occurrence of each cause;  The local effects (drifts) of these failure modes on the outgoing product flows of machine or operator activities;  Effects on the upper level of abstraction: drifts of outgoing product flows in the activity of the working situation and the failure modes of the working situations, i.e. a failure mode of a machine or an operator activity can be an internal cause of the failure mode of the activity of the working situation.

The goal of sub-step R2.3 is to perform a dysfunction analysis on the third level of abstraction: the machines viewed as "white boxes". To perform this sub-step, the same process as in the previous sub-step is carried out but this time on the information of the activities of the machine's components extracted from the instantiated model of the machine's working situation (data model), i.e. a failure mode of a machine component activity, causes, occurrence rate and effects. The results are reported in a table. After this sub-step, the dysfunction analysis is finished. This analysis results in the identification of potential technical drifts (component/machine failure modes and flow drifts) that can occur during the working situation studied. The goal of sub-step R2.4 is to identify drift interactions and new interactions in the working situation following a technical drift potentially visible to the operator. For each technical drift (failure mode or causes), the work group carries out a brainstorming session on how this drift can affect interactions within the working situation and especially interactions where the operator is the "sink" (receiver of information, etc. [START_REF] Bouffaron | Integrative construct for modelbased human-system integration: a case study[END_REF][START_REF] Lieber | IMPROVING HUMAN-MACHINE INTERACTION REQUIREMENTS FOR MAINTENANCE ENABLING SYSTEMS SPECIFICATION[END_REF]). These effects can be drifts in the properties of an existing interaction (drift of an interaction) or occurrences of a new interaction. The group's goal is to identify and characterize the drift and the advent of interactions between an operator and the rest of a working situation.

Existing (nominal) interactions are represented in the instantiated model of the working situation (data model) [START_REF] Duponnois | Working situation reference model for risk assessment on automated assembly lines[END_REF]). An interaction can be seen as a flow; representing an interaction's drift is described as a drift of one of its Time-Space-Shape properties:  Time: duration, frequency, start time.  Space: spatial position/zone of potential interaction.  Shape: list describing all energies (message or raw power) transmitted from a "source" actor to a "well" actor [START_REF] Bouffaron | Integrative construct for modelbased human-system integration: a case study[END_REF][START_REF] Lieber | IMPROVING HUMAN-MACHINE INTERACTION REQUIREMENTS FOR MAINTENANCE ENABLING SYSTEMS SPECIFICATION[END_REF]).

Emerging interactions are new interactions that did not occur in the nominal working situation. These interactions are characterized in the same way as nominal interactions [START_REF] Duponnois | Working situation reference model for risk assessment on automated assembly lines[END_REF]. For each drifting or emerging interaction, the potential perception of this drift by the operator is estimated and is also reported in the table, in the Detection column (D-mark).

The goal of sub-step R2.5 is to identify drifting interactions and new interactions in the working situation following a response of an operator to a perceived technical drift. For each perceived technical drift, the work group carries out a brainstorming session to answer these two questions:

 Is the operator responding to the perceived technical drift?  How can the operator "reasonably" respond to it?

For each potential response identified, the work group identifies the resulting interaction drifts and emerging interactions of these responses (parts of the "reasonably foreseeable behavior" in ISO12100:2010 (ISO 2010)). The description of the interaction drifts and advents are the same as in the previous sub-step (R2.4).

The goal of sub-step R2.6 is to identify operator responses that can potentially lead the operator into a hazardous situation. For each drifting or emerging interaction resulting from an operator's responses to perceived technical drifts, the work group carries out a brainstorming session to estimate the risk, for the operator, related to their responses and the associated interaction drifts and advents. To achieve this analysis, the group uses information about the "hazard zone" and "hazard" presented in the instantiated model of the working situation (data model) and the properties of drifting or new interactions in the table. For each interaction resulting from an operator response, the potential hazard that the operator can be exposed to is estimated and reported in the same table as the previous sub-steps. The severity of the operator's potential injuries for each of the potential hazardous situations identified is estimated and also reported in the table (S-mark in the table). Then a criticality estimation is carried out on each potential hazardous situation identified in this sub-step. This estimation is performed in the same way as in the AMDEC method (IEC 2018) by the mathematical product of O, D and S marking the value present in the table :    The Occurrence mark (O) : four levels (1 to 4), from less frequent to more frequent,  The (non)Detection mark (D) : four levels (1 to 4), from more detectable to less detectable,  The Severity of potential harm for the operator mark (S) : four levels (1 to 4), from less sever to more sever,

The Risk Priority Number (RPN) of each hazardous situation is obtained by using Eq (1).

𝑅𝑃𝑁 = 𝑂 * 𝐷 * 𝑆 (1)

With the value of RPN, the work group chooses the hazardous situation that they consider "significant" to monitor and list its causes (technical drifts).

Conclusion on the second step of the WSHM method

At the end of this step, the table constructed during the second step of the WSHM method will be used as a list of all potential hazardous situations following the operator's responses to a technical drift and its causes (technical drift).

For each "significant" hazardous situation that has to be monitored, causal links between the technical drifts and potential hazardous situations are used to define a health indicator of the working situation in the third step of the WSHM method: "Defining a working situation health indicator".

6. Application of the first and second steps of the WSHM to a case study 6.1. Presentation of the case study: Working situation on the SFP The case study used in this paper was a working situation taking place on a part of the Système Flexible de Production (SFP) of the Lorrain AIP present on the site of the Science and Technology Faculty of Nancy, France (Figure 2). The SFP platform is an educational platform used to train future automation designers and technicians (AIPL 2021). This platform is also used as a research case study, as in this paper.

The work situation simulated was composed of a member of the work group who played the role of the "operator", a part of the SFP machinery -"SFP machine". The goal of the "SFP situation" was to assemble raw materials "Parts 01" and "Parts 09" into final products "Products 0109".

Workgroup composition

The work group that carried out the application of the WSHM method on the "SFP situation" case study was comprised of the Ph.D. student developing the WSHM method, a system-modeling expert, several industrial machine experts, and several risk analysis experts.

Application of the first step of the WSHM method

To carry out this step, members of the workgroup were given access to technical descriptions of the machine and process in the working situation studied and made several observations on the "SFP machinery" in and out of use.

The "SFP situation" case was modeled using the working situation model (reference model). This model was built as a block definition diagram with the SysML modeling language (OMG 2019) using the Modelio (4.0.1) (Modelio 2021) Software.

During this modeling phase, several Activity diagrams (SysML) were produced to help the identification and characterization of the several activities taking place in the "SFP situation" case. Likewise, for several sequence diagrams (SysML) that were produced to help the identification and characterization of interactions between "SFP situation" components.

The resulting data model describes:

 Two raw material flows ("Parts 01" and "Parts 09");  One production support flow ("Sled");  One final product flow ("Product 0109");  One worker/ operator ("Operator");  Four activities performed by the "Operator";  One machine ("SFP machine") composed of 13 machine components. The machine is powered by electrical energy and pneumatic energy.  10 activities performed by the components of the "SFP machine";  77 interactions between actors (actors are "Operator", "SFP machine" components and products flow);  3 types of hazards, 12 hazards and 12 hazard zones.

With all the information in the instantiated model of the working situation, namely the "SFP situation model" (data model), the work group concluded the first step of application to the case study.

Application of the second step of the WSHM method

A dysfunction analysis was carried out using the information extracted from the instantiated model of the working situation, namely the "SFP situation model" (data model). The results were discussed in a brainstorming session with a risk analysis expert to identify:  Technical drifts that can be perceived by the operator and the associated drifts/advents of the interaction;  Potential operator responses to perceived technical drifts and the associated drifts/advents of the interaction;  Potential risks for the operator in the event of such responses. At the end of applying the second step to the "SFP situation" case study, two hazardous situations were identified as significant:

(i) Potential electrical or mechanical injuries: (a) Technical drift: dysfunction of the "piece 01 distributor" can result in a "Piece 01 position" drift by being "other than" in the expected position (i.e. piece 01 is ejected and can be either on the ground or stuck somewhere in the SFP machine); (b) Perception: a new sensorial interaction between piece 01 and the operator: "the operator sees piece 01 outside the process"; (c) Operator responses: the operator can try to extract the piece, leading to physical interaction with another machine component; (d) Hazard: physical interactions with sharp edges can lead to cuts while physical interaction with electrical parts can lead to electrification. (ii) Potential musculoskeletal disorder:

(a) Technical drift: the dysfunction of the "Sled conveyor" leads to the degradation of "Moving the sled from the work zone to the exit" activity. The post condition of this "Sled's position" activity drifts by being "other than" in the expected position (i.e. the sled is not at the end of the conveyor of the "SFP machine"); (b) Perception: the message "Sled position" (i.e. information transmitted) of the sensorial interaction between the sled and the operator drifts by being "other than" in the expected position (i.e. the operator sees that the sled is not at the end of the conveyor). (c) Operator response: "The operator grabs and moves the sled" potential zone of operating drift by being "other than" in the expected situation (i.e. the operator performs a motion different from normal to grab and move the sled). (d) Hazard: this operator response can lead to more effort and bad posture during the interaction between the output sled and the operator.

Discussions and Conclusion

7.1. Discussion on the results of application to the case study

The application of the WSHM method to the "SFP situation" case study proved the feasibility of the first and second steps but also the need to improve it. It allowed improving the modeling part in the first step and of the identification of interactions drifts/advents part in the second part.

During the application of the WSHM method to the case study, the "potential hazard" analysis showed it can be used not only to identify potential injuries to the machine operator, but also hazardous situations for the operator's health (i.e. bad posture, excessive effort, etc.).

Defining a working situation health's indicators

The result of the second step will be used in the last step of the WSHM method to define a working situation health's indicator to allow monitoring the working situation studied. This indicator will be based on indicators of degradation of the machine's components, interactions and/or activities.

Conclusion

The application of the WSHM method to a case study allowed improving the method. It also provided feedback on its applicability.

The main negative feedback on the method was that its use required more time than the previous risk analysis method used by the members of the workgroup.

The positive negative feedback on the method confirmed the original contribution of the WSHM:

 The use of a model and documents in all the steps allowed good re-usability of the results;  The fact that the method relies on both theoretical expertise and user experiences led to finding more hazardous operator responses than by using user experience alone (especially in the part dedicated to "operation perception capabilities" and "operator responses";  The use of a model in the first part also allowed integrating the method in design and not just in an already existing working situation.

Future work will take into account these feedbacks to continue improving the method. More case studies will be carried out in order to verify that the method is applicable to all work situations on automated assembly lines.

Fig. 1 .

 1 Fig. 1. Condition of occurrence of harm based on ISO/TR 14121-2 (ISO; 2012).

Fig. 2 .

 2 Fig. 2. Activity diagram of the WSHM method proposed.


Biarnès Anne

Colin François

François Colin

Methodology to assess the hydrological impact of weed control practices with a view to management of Mediterranean winegrowing catchments

Keywords: vineyard, weed control practices, soil surface characteristics, hydrological modelling, catchment, parameterisation

This paper proposes a methodology to assist water resources managers in assessing the hydrological impact of weed control practices in Mediterranean winegrowing catchments. The methodology is based on a spatial representation of practices and its integration in a distributed hydrologic model. The representation is based on the search for indicators that can be used to attribute a distribution of practices to each hydrological unit of the model and a classification of practices according to their effect on soil surface hydraulic conductivity. The observed diversity is integrated in the hydrologic modelling running an existing physical hydrologic model on an elementary experimental catchment.

Introduction

Assessment of the risks of non-point water pollution associated with cropping practices is a topic of current concern in many countries owing to the increased use of pesticides and fertilizers since the 1950s [START_REF] Katerji | L'eau, l'agriculture et l'environnement : analyse introductive à une réflexion sur la contribution de la recherche agronomique[END_REF][START_REF] Novotny | Diffuse pollution from agriculture -a worldwide outlook[END_REF]. In France, pesticide contamination is over European drinking water standards at many water quality monitoring sites. According to the French Institute of the Environment, in 1999 and 2000, only 56% of samples of surface water for drinking water were of sufficiently high quality to be distributed without prior treatment (IFEN, 2002). In southern France, regional water quality inventories conducted by the Rhône-Mediterranean-Corsica water authority over the same period showed that 65% of surface water and 80% of groundwater sources are contaminated by pesticides (Agence de l'eau RMC, 2000 and2002). More than 50% of the active substances identified are herbicides. Studies of water contamination processes indicate that this pollution should be seen in relationship to the importance of winegrowing and the high risks of herbicide leaching in a Mediterranean climate [START_REF] Lennartz | Diuron and simazine losses to runoff water in mediterranean vineyards[END_REF]Louchard et al., 1999;Louchard et al., 2001). The studies showed that the water surface pollution was related to the transport of the active matter by runoff during heavy rainfall events. The authors pointed to a crucial role by the weed control practices used in vineyards.

One requirement for addressing these concerns is to evaluate the efficacy of public policy instruments for pollutant emission control based on technical proposals and/or economic mechanisms such as taxes, pollution rights, etc. Within this perspective, there is a need to assist water resources managers to assess the environmental impact of agricultural practices and to define pertinent corrective actions. As the legislation does not explicitly define an authorized catchment management authority, managers may be farmers' organizations, mayors, river basin authorities, etc., freely chosen or imposed. Corrective actions may concern the catchment considered as a whole. As recommended by the CORPEN (French Advisory Comity for Environment-friendly Practices), corrective actions can also be focused on priority zones of action selected according to their particular characteristics (e.g. geomorphological characteristics or user's action on the environment) and their contribution to transfers of the pollutant (CORPEN, 2003). The latter option is particularly relevant in Mediterranean winegrowing regions because of the heterogeneity and the high number of holdings, which makes the choice of pertinent actions difficult. However, the choice of such an option should be based on a credible representation of the hydrological functioning of the local environments and a good representation of user's actions on the usable environment concerned: o risk diagnosis should be implemented to enable identification of priority zones o the environmental impact of realistic scenarios of changes in practices should be simulated.

When dealing with water resources, the spatial field is a catchment area: a surface catchment area for direct intake of drinking water from a river, or the catchment area of the aquifer. The aim is thus to construct representations of a spatial field ranging from 100 to 1000 square kilometers.

Distributed hydrologic models are available at this scale. Most of them require catchment segmentation based on subcatchments, also called Hydrologic Response Units [START_REF] Uhlenbrook | Hydrological process representation at the meso-scale : the potential of a distributed, conceptual catchment model[END_REF]. Research into changes of scale in hydrology suggest using subcatchments of about 1 to 5 km² [START_REF] Wood | Effects of spatial variability and scale with implications to hydrologic modelling[END_REF][START_REF] Woods | Investigating the representative elementary area concept: an approach based on field data[END_REF]. The hydrological behaviour of these subcatchments is then globally defined with respect to relief, soil types and land use, as described in Soil and Water Assessment Tools [START_REF] Arnold | A comprehensive surface-groundwater flow model[END_REF]. River reaches interconnect subcatchments to simulate the whole catchment behaviour. Such models are used to evaluate hydrologic modifications linked with evolutions such as global change. As they are spatially distributed, users can run them to define priority zones of action within a water resources catchment. But difficulties still exist when these models are to be used in the context of a vineyard, one being the impossibility to account for the impact of the range of different weed control practices that are used within the subcatchments, whereas according to [START_REF] Moussa | Hydrological modelling of flood events in a farmed Mediterranean catchment[END_REF]2002), this diversity is one determining factor of hydrological functioning. Weed control practices are based on three main methods: chemical weeding, mechanical weeding and grass cover. These methods results in changes on the soil surface characteristics, on which the soil surface hydraulic conductivity and the volume of runoff water at the field outlet depend. They also determine the concentration of herbicides in the runoff water.

Furthermore, acquiring data on cropping practices to run a model on a large area is difficult since costly exhaustive surveys cannot be envisaged. This explains researchers' use of averaged data based on the literature, on experts' evaluations or on technical recommendations [START_REF] Giupponi | A multicriteria approach for mapping risks of agricultural pollution for water ressources: The Venice Lagoon watershed case study[END_REF][START_REF] Bioteau | Evaluation des risques de pollution diffuse par l'azote d'origine agricole à l'échelle de bassins versants : intérêts d'une approche par modélisation avec SWAT®[END_REF][START_REF] Mignolet | Spatial dynamics of agricultural practices on a basin territory: a retrospective study to simulate nitrate flow. The case of the Seine basin[END_REF] or the use of scenarios of uniformly distributed agricultural practices [START_REF] Knox | Mapping the spatial distribution of volumetric irrigation water requirements for maincrop potatoes in England and Wales[END_REF][START_REF] Hartkamp | Regional application of a cropping systems simulation model: crop residue retention in maize production systems of Jalisco, Mexico[END_REF] to assess the impact of agricultural practices. Such indirect means cannot be used in our case since the focus is on the range of different weed control practices used within vineyards. Remote sensing has long proven its value for acquiring spatialized data on land use. More recently, it has also been used for mapping tillage practices in intensive agricultural regions of the United States [START_REF] Van Deventer | Using thematic mapper data to identify contrasting soil plains and tillage practices[END_REF][START_REF] Gowda | Mapping tillage practices with landstat thematic mapper based logistic regression models[END_REF][START_REF] South | Optimal classification methods for mapping agricultural tillage practices[END_REF], using Landsat images. However, in the case of winegrowing catchments in southern France, [START_REF] Wassenar | Sunlit soil surface extraction from remotely sensed imagery of perennial, discontinuous crop areas; the case of Mediterranean vineyards[END_REF] and Corban (ongoing research) showed that different weed control practices cannot be distinguished even with high spatial resolution images (less than 1 m spatial resolution). The results obtained proved to inaccurate in the majority of cases. Moreover, tools of this kind can only provide partial knowledge of practices because some technical options, particularly those involving the use of pesticides, cannot be detected through remote sensing. No other method for mapping agricultural practices over large areas is mentioned in the literature.

After this state of the art, it was clear that to assist water resources managers manage a vineyard catchment, it would be necessary to develop a modelling approach able to account for the diversity of practices. On the other hand, no relevant method was available to obtain precise information over large areas on cropping practices. This paper thus proposes a methodology based on an integrated approach to the water resources catchment to create a spatialized representation of the weed control practices and to integrate it in a distributed hydrologic model. The methodology presented here only concerns water fluxes. First we describe some specifications for an integrated modelling approach and the resulting methodological choices. We then apply the proposed methodology to the case of a Mediterranean catchment in the department of Hérault in southern France. Our results are discussed in the last section.

Methodological approach

Specifications for an integrated modelling methodology

With a view to reducing non-point pollution in Mediterranean winegrowing catchments, we assumed that water resources managers need a methodology for spatial and temporal evaluation of water quality. In order to be useful for managers, the specifications for the methodology need to be detailed.

Such a methodology must be able to predict the impact of alternative scenarios. The selected hydrological model must be sensitive to the diversity of soil surface hydraulic conductivity created by the range of weed control practices at the subcatchment level. This implies first constructing a representation of these practices based on a functional classification of practices and on being able to provide plausible distributions of values of soil surface hydraulic conductivity for areas which fullfill the hydrological modelling need.

Finally, the number of parameters must be limited to easy available variables.

For this first integrated approach we chose to work at the flood event scale. This decision was justified because the most important water management problems occur at this temporal scale in Mediterranean winegrowing catchments.

Resulting methodological choices

Distribution of weed control practices and of resulting soil surface hydraulic conductivity within the hydrological units of the model

The method proposed to characterize weed control practices and the resulting soil surface hydraulic conductivity is based on:

(i) an inventory of the range of the different practices used in the study area and the classification of these practices according to their effect on changes in soil surface characteristics and consequently on changes in hydraulic conductivity;

(ii) the search for spatialized or spatializable indicators of the practices that can be used to attribute a distribution of types of practices in each hydrological unit;

(iii) the calculation of plausible distributions of values of soil surface hydraulic conductivity at any given date.

To this end, spatially explicit data on the practices and the factors that may explain their distribution must be collected in the study area. As existing agricultural censuses do not provide these data, they have to be gathered through surveys. The selection of potential indicators of practices should be based on a review of the literature and on knowledge of the specific situation in the study area, so as to take different levels of organisation into account. In the case of winegrowing catchments, a previous work (Biarnes et al., 2004) showed that a minimum of three possible levels have to be considered: the field, the holding and the commune of location of the field, which roughly corresponds to a cooperative winery's supply basin. In the Languedoc Region of southern France, the winery supply basin roughly covers the vineyards of one commune or two neighbouring communes. The first two levels are intended to take into account the specific constraints of the field and the operating constraints of the holding, which limit the technical options. The third is intended to take into account the growers' socio-professional environment, including relations between neighbours and the influence of the cooperative wineries in the organization of the industry.

Winegrowers have three main methods available to control the development of weeds in theirs fields: chemical weeding, mechanical weeding, which consists in repeated surface tillage during the year, and grass cover. These different methods may be combined in different ways in the same field : the rows of vines may be treated differently to the alleys and some alleys may be treated differently to others, leading to a wide range of possible types of weed control practices depending on the percentage of area of individual field that can be attributed to each method.

A review of the literature and ongoing research in winegrowing catchments in southern France showed that changes in soil surface characteristics and in the resulting soil surface hydraulic conductivity depends on the methods used [START_REF] Leonard | Infiltration characteristics of soils in Mediterranean vineyards in Southern France[END_REF][START_REF] Hébrard | Spatio-temporal distribution of soil surface moisture in a heterogeneously farmed Mediterranean catchment[END_REF][START_REF] Andrieux | Soil surface characteristics and infiltration in Mediterranean cultivated areas[END_REF]. Thus the spatial distribution of weed control methods within a field determines the percentage of area that can be attributed to soil surface characteristics that may correspond to eachmethod.

To obtain a spatial representation of weed control practices, the data are processed by multivariate analysis. The aim is to highlight the variables and modalities of variables that best explain the choice between the different types of practices, in order to use them as pertinent keys to represent the spatial distribution of the practices and to attribute a percentage distribution of the different types of practices to each hydrological unit of the model.

In order to allow this representation to be coupled with the hydrological model, these percentage distributions of types of practices are then converted into percentage distributions of weed control methods from which, on the basis of experts' evaluations, plausible percentage distributions of types of soil surface characteristics can be deduced for a given date. Each type of soil surface characteristic is then attributed to a value of hydraulic conductivity deduced from the literature.

Hydrological modelling structure

The selected hydrological model is a distributed model based on a functional spatial segmentation of the catchment in terms of both hydrological functioning and land management. The segmentation procedure is based on the division of the catchment into subcatchments of about 1 to 5 km² as recommended by [START_REF] Wood | Effects of spatial variability and scale with implications to hydrologic modelling[END_REF] and [START_REF] Woods | Investigating the representative elementary area concept: an approach based on field data[END_REF]. A Geographical Information System can be used to obtain these hydrologic spatial units from morphologic (relief and hydrography) and soil layers.

The hydrological model works with a production function dividing rainfall into infiltration and surface runoff, a transfer function and an exchange function (Figure 1).

Figure 1: Schematic hydrological modelling structure

Aquifer level

Hydrological Unit

Rainfall

Exchange function between aquifer and hydrological network T r a n s f e r f u n c t i o n Production function

Runoff Infiltration

Aquifer level

Hydrological Unit

Rainfall

Exchange function between aquifer and hydrological network T r a n s f e r f u n c t i o n Production function

Runoff Infiltration

For each subcatchment, the production function operates as a reservoir whose filling depends on the depth of the superficial aquifer, the hydrological network (including manmade ditches) and the diversity of surface hydraulic conductivity due to the impact on the soil surface characteristics of land use and, in the case of a vineyard, of the different weed control practices. This diversity of surface hydraulic conductivity is represented using a single parameter that we called Kuh and whose value varied throughout the year. The transfer function carries water through the hydrological units to their outlets and from there to the outlet of the main drainage basin. This function requires only measurable variables such as lengh, depth, slope and bank roughness of the stream reaches. The exchange function allows the representation of the fluxes between stream reaches and aquifer compartments according to a Darcian flow hypothesis. This is a quite simple function which uses two conceptual parameters: permeability between the aquifer and the network and permeability between the network and the aquifer. The value parameters have to be fixed during the calibration phase.

Parameterisation of the hydrological units and coupling with the spatial representation of practices

The coupling of the hydrological model with the spatial representation of practices and the resulting distributions of soil surface hydraulic conductivities for a given date is based on the calculation of the values of the Kuh parameter. These values must be calculated for each hydrological unit before simulating the impact of a rainfall event. We propose a 2-step procedure to parameterise the resource catchment hydrologic model and on this basis, to calculate the required values of Kuh for each simulated event:

-in the first step, hydrological units are classified according to a functionnal typology;

-in the second step, nomogram systems are performed, which enable the value of the parameter Kuh to be calculated for each hydrological unit type according to a given percentage area distribution of soil surface hydraulic conductivities.

Pedological expertise enables subcatchments to be classified according to the probability of the presence of a superficial aquifer. This criterion is particularly important because it determines the exchange conditions between the hydrographic network and the aquifer. Consequently, we propose that three subcatchments types : type (a) with a temporary suspended aquifer; type (b) without an aquifer; and type (c) with a permanent superficial aquifer.

The second step is based on existing experimental references for each type of subcatchment in an elementary representative catchment, and on the use of a physically elementary catchment model, the MHYDAS model [START_REF] Moussa | Effects of the spatial organization of agricultural management on the hydrological behavior of a farmed catchment during flood events[END_REF] which is a powerful tool to simulate streamflows for Mediterranean catchments affected by agricultural activities. For each subcatchment type within the experimental catchment, streamflows are simulated with the MHYDAS model considering various possible distributions of values of soil surface hydraulic conductivity induced by land use and practices and, when a superficial aquifer exists (types a and b subcatchments), by a range of different initial depths of the aquifer.

Next, streamflows at the subcatchment outlet are simulated using the reservoir production function for various possible values of the Kuh parameter. Finally, on this basis, we associate a Kuh value with the distribution of hydraulic conductivities which enables calculation of the same streamflows for a subcatchment type and for the initial depth of the aquifer.

Case study

Study site

The catchment used for our study was the Peyne river basin in the mid Hérault valley. It is 75 km² in area, about the same size as catchments in the region that are used for water resources. It was chosen with a view to developing an integrated modelling methodology that can be used in comparable situations. The Peyne river basin presents a succession of clearly differentiated geomorphological units which, according to [START_REF] Bonfils | Analyse des déterminants de la diversité et de la localisation des pratiques d'entretien du sol en vue de la spatialisation de ces pratiques dans un bassin versant viticole[END_REF], strongly determine the distribution of soils within the landscape (Figure 2). Altitude ranges from 20 m to 340 m. There are sharp contrasts in landscape between the northwestrugged and mainly scrub-covered, with little arable landand the rest of the valley, which has gentler landforms and is almost entirely under vines. This catchment includes an experimental elementary catchment (the Roujan catchment), established in 1992 and which is 91 ha in area. It was chosen because it is made of the main geomorphological units found in the Peyne catchment (Figure 3). The region's climate is sub-humid Mediterranean with a long dry season. Annual rainfall is about 700 mm, but varies widely from year to year. There are sharp seasonal contrasts, with rainy autumns and springs and hot, dry summers with heavy downpours. Potential evapotranspiration is high, with an average of 1000 mm, owing to high temperatures, strong sunshine and often strong winds.

The Peyne basin incorporates most of the territory of six different communes and is farmed by some 800 winegrowers. According to the data from the last farm census carried out in 2000 by the Regional Direction of Agriculture and Forestry, 93% of these holdings supply their grapes to cooperative wineries, the remainder supplying private wineries. Four commune-based cooperative wineries collect most of the output.

Spatial distribution of weed control practices

Data collection

The required data were gathered by a survey of a sample of 64 holdings cultivating 1004 fields in the Peyne basin which represented 1017 ha, i.e. about 20% of the area under vines within the basin (Gal, 2004;[START_REF] Boissieux | Analyse de la variabilité spatiale des pratique d'entretien du sol dans un bassin versant viticole du sud de la France[END_REF]. The holdings covered by the survey were selected by drawing fields by lot along seven transects perpendicular to the river Peyne, spread so as to traverse the toposequence, the range of soil types and the communes. It was assumed that the appropriate selection of fields would imply the correct selection of holdings to provide a sample of fields that were representative of the range of practices and the proportionate use of each.

The survey questionnaire was in two complementary parts. The first part focused on the weed control practices used and their distribution among the holding's vineyard fields, which were precisely located on the land register map. The second part was designed to provide data for the variables assumed to explain the choice of practices, identifying o the physical characteristics of the fields on which each practice was implemented, o the commune the vineyard field belonged to,

Bassin de la Peyne

Station climatique

• VAILHAN

Roujan experimental catchment

Terraced hillside

Peyne bassin

Weather station o the holdings' structural characteristics and their production priorities.

Locating the vineyard field on the land register map made it possible to add information such as the commune in which the field was located, soil type and slope class, which were taken, respectively, from the land register, the 1:100,000 soil map by [START_REF] Bonfils | Analyse des déterminants de la diversité et de la localisation des pratiques d'entretien du sol en vue de la spatialisation de ces pratiques dans un bassin versant viticole[END_REF] and the digital terrain model (DTM) based on the IGN (French Institute of Geography) topographical database with a spatial resolution of 50 m.

The data were processed by correspondence discriminant analysis and a classification tree.

To this end, the values taken by each explanatory variable were divided into classes using thresholds that took into account our hypotheses on the determinants of the practices while ensuring a sufficient number of fields per class. Within each set of explanatory variables, correspondence discriminant analysis was used to select those best correlated with the choice of weed control practices. The selected variables were then used in a classification tree to establish a hierarchy between the discriminant variables and to evaluate their discrimination power.

Diversity of weed control practices in the Peyne valley

Four types of weed control practice were differentiated: M1, M2a, M2b and M2c. Practice M1 was based on chemical weeding in the rows of vines and alleys alike. The other three practices also used chemical weeding in the rows, but differed in the methods used for the alleys. In practice M2a, the alleys were repeatedly shallow-tilled. Practices M2b and M2c both managed some alleys by tillage but alternated these at regular intervals within the field with alleys managed using a different method. In practice M2b, tillage alternated with chemical herbicide, and in M2c, tillage alternated with alleys under permanent grass, natural or sown, and controlled by mower or rotary cutter. In both M2b and M2c, the untilled alleys were those where the tractor passed to spray the crop in spring and summer; the reason for not tilling was to ensure a good load-bearing capacity. The corresponding repartition of weed control methods is shown in Table 1.

Indicators of practices

The classification tree presented in Figure 4 summarizes the results of the data analysis.

Only four variables (Commune of location, Alley width, Pruning method, Availability of tillage and traction equipment for closely spaced rows) were necessary to reach a satisfactory performance in classifying the practices. With these variables, 68 % of the parcels were assigned to their true practice. This percentage reached 77% when practices M2b and M2c, whose discrimination was not good, were combined. However, the most striking result revealed the importance of the variable "Commune of location", which roughly corresponded to the supply basin of a winery cooperative, as first discriminant variable. On the basis of these results, we chose "Commune of location" as a single key to spatialize the practices. Assuming that the distributions of practices can be considered as homogeneous within a commune, distribution of the types of weed control practice were calculated per commune by extrapolating from the data survey. This choice resulted from o high fragmentation and dispersion of the land of the farm holdings in the sample of holdings o the lack of preferential localization of the determining factors of practices within the communes.

These results are shown in Table 2. They underline the contrast between the communes of Alignan-du-Vent and Roujan, where M2b and M2c were predominant practices, and the communes of Caux-Pezenas and Neffiès-Vailhan, where the main method was tillage (M2a).

Hydrological segmentation and subcatchment typology

A preliminary division into surface drainage subcatchments was obtained from the relief (the DTM) and the hydrographic network. For groundwater, we assumed that soil types were indicative of homogeneous aquifer conditions [START_REF] Tassinari | Caractérisation de leurs nappes et des indicateurs morphologiques associés[END_REF]. From the 1:100,000 soil map data [START_REF] Bonfils | Analyse des déterminants de la diversité et de la localisation des pratiques d'entretien du sol en vue de la spatialisation de ces pratiques dans un bassin versant viticole[END_REF], we grouped soil units according a groundwater criterion:

1. molasse soils with a temporary shallow aquifer, soils 2. limestone plateau, basalt soils, molasse soils and urban areas with no groundwater, soils 3. soils on alluvial terraces, recent soils and alluvia with stream-connected groundwater with a shallow aquifer.

The divisions into surface subcatchments and units with homogeneous groundwater features were then superimposed to define the hydrological units for the resource-catchment as shown in Figure 5.

Three types of hydrological units were defined according the groundwater criterion:

o type a with a temporary suspended aquifer on soils (1), o type b without an aquifer on soils (2), o type c with a permanent superficial aquifer on soils (3).

Nomogram system used to parameterise the hydrologic model

For each of the three hydrological unit types, an average ditch network density was adopted based on observations made in the Peyne Valley. The hydrological impact of the distribution of the different weed control practices was simulated by running the MHYDAS model in the experimental Roujan catchment.

For the study area, several studies describe the diversity of soil surface characteristics (0-10 cm) that are induced by different weed control methods [START_REF] Leonard | Infiltration characteristics of soils in Mediterranean vineyards in Southern France[END_REF][START_REF] Hébrard | Spatio-temporal distribution of soil surface moisture in a heterogeneously farmed Mediterranean catchment[END_REF][START_REF] Andrieux | Soil surface characteristics and infiltration in Mediterranean cultivated areas[END_REF]. For each set of soil surface characteristics and different type of soil, the authors measured hydraulic conductivity using rainfall simulation. They showed that hydraulic conductivity was more influenced by soil surface characteristics than by soil type. They consequently proposed a typology of soil surface characteristics in association with hydraulic conductivity. We selected three types of soil surface characteristics that we assumed were representative of the scope of impact of weed control methods in vinegrowing fields (Table 3). Each of these three types corresponds to a specific value of soil surface hydraulic conductivity which was measured. Moreover, we attributed the same value of soil surface hydraulic conductivity to land-use areas other than vineyard (forest, scrub cover, fallow land and cereal), the value we selected is the one that was measured with a grass cover. On this basis, to test the methodology, nomogram systems were constructed for each type of hydrological unit (Figure 6). This nomogram system enabled calculation of the values of the Kuh parameter according to a wide range of possible distributions of the three selected types of soil surface characteristics. The procedure was based on the calculation of the values of the runoff coefficient ratio which is the ratio between the total depth of runoff at the catchment outlet and the total depth of rainfall. This was done using the rainfall event of the 20 th October 2002. The runoff coefficient simulated by the MHYDAS model is given for 11 distributions of the three values of hydraulic conductivity as a function of the initial depth of the aquifer (Figure 6, sub-figure I). In Figure 6 sub-figure II, the runoff coefficient simulated by the reservoir production function is given for various Kuh values as a function of the initial depth of the aquifer. Finally, in Figure 6 sub-figure III, Kuh is given for the selected distributions of hydraulic conductivity as a function of the initial depth of the aquifer.

Hydrological simulations for the Peyne basin

To conclude the methodology, we ran the model on the Peyne basin for two rainfall events, 20 th October 2002 and 1 st October 2003. Percentage distributions of the values of soil surface hydraulic conductivity at the dates of the two simulated events were first calculated for each commune of the Peyne valley. To this end, the average distribution of the different types of land use was calculated on the basis of the last agricultural census. We then attributed to the area under vine the corresponding percentage distribution of weed control practices (see Table 2) and, consequently, of the three available methods. Assuming that at the dates of the two simulated events all the area under mechanical weeding had have recently been tilled, the percentage distribution of each of the three values of soil surface hydraulic conductivity within each commune was deduced on the basis of Table 3. Finally, the average percentage distribution of hydraulic conductivity found in a given commune was attributed to each hydrological unit located within that commune.

Simulations

For the two simulated rainfall events, a piezometric survey provided the initial depth of the aquifer. Using the nomogram system (Figure 6) and the previously calculated distributions of soil surface characteristics, we set the infiltration capacity parameter Kuh for each hydrological unit.

The parameters of the Peyne basin's hydrographic network were partly measured (crosssections, slopes, roughness) and partly fitted (coefficients of flood wave celerity and diffusivity, groundwater-river exchange coefficients) from the rainfall event of 20 th October 2002. These parameters were kept for validation by the rainfall event of 1 st October 2003.

The rainfall measured at the Roujan catchment weather station was assumed to be uniform across the Peyne basin. The results of the simulations were compared with the outflows measured at the basin outlet (Figure 7). These two simulations (calibration and validation) showed that the model reproduced autumn flood events satisfactorily using the parameterisation approach adopted for the two different rainfall events.

Discussion -conclusion

We initiated this study under the assumption that integrated tools were not available to manage non-point pollution at the scale of water resources, particularly in winegrowing catchments. Needs require diagnosis and forecasting tools. Diagnostic approaches aim to simulate the catchment's current situation to highlight either the areas whose contributions to the transfer of the pollutants are the greatest, or areas resulting in the same contributions but with very different geomorphological characteristics or agricultural practices. Forecasting focuses on the evaluation of the impacts of plausible alternative scenarios of practices. Within theses perspectives, our goal was to develop a methodology enabling the diversity of weed control practices to be taken into account in order to parameterise a distributed hydrological model that would work at the flood event scale. To this end, we constructed a hydrologic model that is sensitive to hydraulic conductivity which, in vineyard catchments, is mainly controlled by the weed control practices. Developing such a methodology is a long and exacting task which requires a good knowledge of the biophysical processes and of the decisional ones leading to the selection of practices by the farmers. The results presented here only concern stream flows, but research is currently underway to enable us to add a pesticide module. These results are based on the winegrowing Peyne catchment in the department of Hérault in Southern France. They underline the advantages of simultaneously developing a model representing the practices and the hydrological model that allows assessment of the environmental impact of these practices. However, at this stage, they represent a first approach which will continue to be developed.

The results presented here highlight the importance of spatial coupling of the representation of weed control practices and hydrological modelling. As far as the practices are concerned, the results of our case study show that these were not evenly distributed, and that consequently the question of spatialisation of practices is relevant. The diversity of soil surface hydraulic conductivity which results from the spatial distribution of practices within each hydrological unit was integrated in the hydrologic model through a nomogram system. This nomogram system allowed calculation of the simulated rainfall events as the value of a parameter we called Kuh, which represents the hydraulic conductivity of each hydrological unit. It was run in an existing physical hydrologic model on an elementary experimental catchment that is representative of the observed diversity of hydrological units in the study area.

In the case study presented here, the proposed grid to spatialize the practices was coarse grain as it was based on the administrative boundaries of the neighbouring communes which roughly correspond to the supply basin of a same winery cooperative. The same distribution of practices was thus attributed to each of the hydrological units located in the same commune or group of communes. To couple it with the hydrological model, a finer grain to spatialize the practices was considered unnecessary because of the very probable homogeneous distribution of practices within the communes. However, such a choice will probably not be valid in situations where other determining factors of practices than the commune exist. Further studies are thus required to be able to produce distributions of practices on a fine grid when necessary.

In the same way, further efforts are needed to take better account of temporal aspects in the representation of the practices and of their impact on soil surface hydraulic conductivity. This is particularly important when a more precise diagnosis is required. In the case study presented here, we assumed that, for the simulation dates, only one given distribution of soil surface hydraulic conductivity could be attributed to a given distribution of weed control practices. In so doing, we did not account for possible staggering of the dates of cultivation operations for a given method and its effect on changes in soil surface characteristics and consequently in hydraulic conductivity. Further studies should associate the distribution of weed control practices with different probable distributions of values of soil surface hydraulic conductivity. Taking this uncertainty into account in the hydrological modelling will enable us to build fuzzy nomogram systems.

To be useful for water resources managers, the hydrological modelling approach should be applicable at different temporal and spatial scales. In the Mediterranean context, we proposed the flood event scale as the most pertinent temporal scale to manage crisis situations. In the results presented here, the simulated events concerned two autumn rainfall events. Tests remain to be done: similar simulations should be extended to different typical rainfall events during the year. Other nomogram systems should be calculated for different climatic conditions to parameterise the hydrologic model. In this way, we will obtain nomogram systems for several types of climatic conditions. Extrapolation of the modelling approach to any water resource catchments of the Languedoc winegrowing plain (which represents 297.118 ha, equal to 33% of total French vineyards) is partially possible on the basis of the representativeness of the elementary experimental Roujan catchment and of the typology of subcatchments chosen. However, this typology will probably have to be extended. To obtain a nomogram system suitable for other types of subcachement, new experimental experiments will thus be necessary.

The use of the proposed methodology by water resources managers will always require a considerable financial investment given the amount of data needed on practices and hydrological behaviour even if the investment will always be much lower than that required for integral physical modelling like the SHE model [START_REF] Abbott | An Introduction to the European Hydrological System -"SHE[END_REF]. The quality of the results of the hydrological simulations depends on the investment in the collection of the necessary data. Therefore, it may be that the water resources managers will have to decide on the level of quality they expect from the simulations and, consequently, how much money to spend on data collection. Finally, as suggested by [START_REF] Power | Predictive validation of ecological and environmental models[END_REF], such an application by managers would provide global validation of the methodology by its use.

Figure 3 :

 3 Figure 2: Geomorphological and soil units of the lower Peyne valley (from Lagacherie et al. (2001), adapted by Bonfils (1993))

Figure

  Figure 4: Classification tree

Figure

  Figure 5: Construction of hydrological units: (a) Digital Elevation Model, (b) hydrographic network, (c) topographical subcatchments, (d) soil map, (e) final hydrological units

  of soil surface hydraulic conductivity at the dates of the two simulated events

Table 1 : Average percentage distribution of each weed control method as a function of the type of practice

 1

		% of area of field under each weed control method
	Type of practice	Chemical	Mechanical	Permanent grass
		weeding	weeding	cover
	M1	100	0	0
	M2a	30	70	0
	M2b	65	35	0
	M2c	30	35	35

Table 2 : Distribution of practices between communes

 2

	Commune	M1	% area per type of practice M2a M2b	M2c

Table 3 : Types of soil surface characteristics and corresponding hydraulic conductivity values as a function of weed control methods

 3

	Type of soil	Average value of	Weed control methods
	surface	hydraulic
	feature	conductivity (mm/h)
	Csd	12	Chemical weeding or
			mechanical weeding (not
			recently tilled field)
	T	28	Mechanical weeding
			(recently tilled field)
	Vst	35	Permanent grass cover

Published inInt. J. Sustainable Development, Vol. 9, No. 2, 161-179, 2006

Acknowledgements:

This work was partly supported by the French INRA programme PSDR (Pour et Sur le Développement Régional) and was originally presented at the INRA symposium "Territoires et Enjeux du Développement Régional", Lyon, France, 9-11 March 2005.