Stéphane Guichard

Frédéric MIRANVILLE b Dimitri Bigot

Bruno Malet-Damour

Harry Boyer

Experimental investigation on a complex roof incorporating phasechange material

Keywords: Thermal resistance, Mean method, Phase change materials, Experimental study

This article deals with a new configuration using a phase change material (PCM) in a complex roof on a dedicated test cell. For the first time and in real conditions, an experimental device using phase change material was set up for a tropical and humid climate such as that in Reunion Island. Results showed that in the configurations tested and for a non-ventilated air layer, the measured temperatures were on either side of the PCM's melting point. It means that the PCM is able to store thermal energy in the daytime and release it at night. Furthermore, this study has made it possible to determine the thermal resistance of the roof via a mean method meeting international standards (ISO 9869) using dynamic data. The test was implemented in a multi-zone building software in order to automatically determine the thermal indicator.

Introduction

To combat global warming, it is necessary to reduce both energy consumption and the emission of greenhouse gases. According to reference [START_REF] Pérez-Lombard | A review on buildings energy consumption information[END_REF], energy-hungry appliances in buildings increasingly contribute to global warming. To reduce energy consumption, a possible solution may be found in the passive design of buildings. This entails using a complete set of chosen materials in building construction as well as specific technical solutions [START_REF] Miranville | Evaluation of the thermal resistance of a roof-mounted multi-reflective radiant barrier for tropical and humid conditions: Experimental study from field measurements[END_REF] to reach high energy efficiency, therefore rendering energy-hungry appliances useless. To achieve this aim, insulation products are used to improve the building's behaviour. Indeed, insulation of the building envelope makes it possible to retain heat during winter and decrease heat loads during summer.

Furthermore, thermal insulation can save energy and improve the comfort of the building because heat flow is reduced.

Additional thermal insulation can be used to help improve the energy efficiency of a building. With this objective in mind, phase change materials (PCMs) have been studied and developed for certain applications [START_REF] Farid | A review on phase change energy storage: materials and applications[END_REF]. These materials have higher thermal energy storage densities than other heat storage materials. During a phase change process at constant temperature, PCMs are able to store and release latent heat. Latent heat storage involves storing energy during melting and energy release during freezing. Organic and inorganic PCMs are often used and solid-liquid phases are chosen. Paraffin is the PCM most frequently used for latent heat thermal energy storage. It has useful thermal properties such absence of super cooling, chemical stability and low vapour pressure [START_REF] Sharma | Review on thermal energy storage with phase change materials and applications[END_REF][START_REF] Zalba | Review on thermal energy storage with phase change: materials, heat transfer analysis and applications[END_REF].

In this paper, the PCM chosen consists of 60% microencapsulated paraffin within a copolymer and laminated with protective aluminium foil [START_REF] Kuznik | Experimental investigation of wallboard containing phase change material: Data for validation of numerical modeling[END_REF]. In addition to the phase change process, the surface of the PCM acts as a reflective insulation. The action of reflective insulation is usually closely linked to the radiative properties of the surface. The reflective surface reduces the radiant heat transfer across an enclosed air space, for example between the metal and the plasterboard in a specific roof configuration. According to reference [START_REF] Miranville | Contribution à l'Etude des[END_REF], the air layer is used to induce heat transfer by infrared radiation. The combination of homogenous and inhomogeneous materials with the air layer can be qualified as a complex wall. A complex wall can be defined as an assembly of materials separated by one or several air layers [START_REF] Miranville | Contribution à l'Etude des[END_REF].

Nevertheless, such an assembly complicates the determination of thermal performance owing to the multiple configurations of the air layer: opened or closed, naturally ventilated or ventilated by force. Generally, studies on the thermal performance of a roof system do not take into account the air layer [START_REF] Pasupathy | Experimental investigation and numerical simulation analysis on the thermal performance of a building roof incorporating phase change material (PCM) for thermal management[END_REF]. It is important to characterize the thermal effects of a complex roof incorporating PCM for the chosen configuration. Therefore, for the specific conditions of Reunion Island (characterized by a tropical and humid climate with strong solar radiation), experimental and numerical approaches were conducted. A methodology was set up to determine both the thermal behaviour and thermal performance of a complex roof with PCM.

In this article, the experimental study is presented. To begin with, it is important to highlight that the methodology presented can be used for all climates, not only tropical and humid. The ventilation of the air layer is an important parameter. The thermal performance of the roof varies depending on whether the air layer is naturally ventilated or force-ventilated [START_REF] Miranville | Evaluation of the thermal resistance of a roof-mounted multi-reflective radiant barrier for tropical and humid conditions: Experimental study from field measurements[END_REF].

Research statement and methodology

Insertion of the PCM into the roof posed two problems for the chosen assembly:

-Will the temperature of the PCM enable phase change?

-What is the thermal performance of a complex roof with PCM?

A methodology was established and presented in order to solve these problems.

Methodology

The methodology is based on both numerical and experimental studies. From a numerical viewpoint, a building simulation code called ISOLAB was developed by Miranville during his thesis and recently a mathematical model dedicated to phase change materials presented in [START_REF] Guichard | A mathematical model of Phase Change Materials (PCMs) used in buildings[END_REF][START_REF] Guichard | A thermal model for phase change materials in a building roof for a tropical and humid climate: Model description and elements of validation[END_REF] was implemented based on the heat apparent capacity method. From an experimental point of view, a specific experimental platform was set up. For more details, refer to [START_REF] Guichard | Contribution à l'étude des parois complexes intégrant des matériaux à changement de phase: Modélisation, Expérimentation et Evaluation de la performance énergétique globale[END_REF].

This article focuses on the determination of thermal performance of a complex roof with PCM and the results of an experimental protocol for a full-scale outdoor PCM test-cell in Reunion Island. Usually, the equivalent thermal resistance is determined in steady-state conditions whereas the focus of this paper is the determination of equivalent thermal resistance in dynamic conditions. To obtain more precise information for the determination of equivalent thermal resistance in these conditions, an international standard mean method [START_REF]Isolation thermique -Elements de construction -Mesures in-situ de la résistance thermique et de la transmittance thermique[END_REF] has been used, and is explained in the following paragraph.

The mean method

To evaluate the thermal resistance from dynamic field measurements, the mean method is used. This method is applied to dynamic data series and can be summarized by the following equation ( 1):

(

With:

This method requires many conditions to validate the results and respect conservation of energy over the entire study period. To validate the results, two criteria must be respected during the calculation in order to determine the thermal resistance in steady-state conditions [START_REF] Miranville | Evaluation of the thermal resistance of a roof-mounted multi-reflective radiant barrier for tropical and humid conditions: Experimental study from field measurements[END_REF]:

1. The percentage difference between resistance calculated from the entire data series and resistance calculated from the database minus one day must be less than 5%.

2. The percentage difference between resistance calculated from the first of the data series and resistance calculated from the last of the database must be less than 5%.

Miranville implemented the mean method in the building simulation ISOLAB during his post-doctoral degree (authorisation to supervise PhD students). The method used was a specific model able to determine the thermal performance of building materials. This module is very easy to use. It requires either a series of simulation results or measurements from experimental data. The following figure illustrates the procedure: In this case, the complex roof consists of both homogenous and inhomogeneous material with an air layer with different modes of heat transfer. The presence of an air layer can have an impact on the thermal performance depending on convection intensity. The air layer can be ventilated or not and will modify heat transfer. These different conditions are taken into account by the module and ensures conservation of energy during the study period.

Furthermore, the and indicators are verified as illustrated in Figure 1.

For the final calculation on a whole database or a part of it, the following parameters are taken into account:

-Positive heat flux conduction

-Day-time selection -Night-time selection -User-time selection
According to reference [START_REF] Miranville | Evaluation of the thermal resistance of a roof-mounted multi-reflective radiant barrier for tropical and humid conditions: Experimental study from field measurements[END_REF], these criteria allow for a pre-treatment of the database and increase the possibilities of exploitation of the data series. This approach constitutes an alternative option when the validity of the final results is not obtained.

In this part, a brief description of the mean method was presented. See [START_REF] Miranville | Modélisation multizone intégrée et expérimentation in-situ en Physique du Bâtiment: Application aux parois complexes isolantes actives et passives et aux conditions de confort[END_REF] for more details.

Experimental environment

The experimental platform

The experimental platform was set up in the South of Reunion at the University Institute of Technology of Saint-Pierre (Reunion Island) at a low altitude (55 m). The total area of this platform was approximately 600m 2 . Réunion has a tropical climate with strong solar radiation and humidity. These climatic conditions offer ideal conditions for an experimental study. This location was chosen by many researchers from the PIMENT laboratory to observe the phenomena relative to building physics [START_REF] Guichard | Contribution à l'étude des parois complexes intégrant des matériaux à changement de phase: Modélisation, Expérimentation et Evaluation de la performance énergétique globale[END_REF]. On the experimentation site, two experimental buildings were installed (see Figure 2), a small-scale device (named ISOTEST) [START_REF] Miranville | Contribution à l'Etude des[END_REF][START_REF] Bigot | A nodal thermal model for photovoltaic systems: Impact on building temperature fields and elements of validation for tropical and humid climatic conditions[END_REF] and a normal-scale (or unit scale) building (named

LGI) [START_REF] Miranville | Contribution à l'Etude des[END_REF][START_REF] Guichard | Contribution à l'étude des parois complexes intégrant des matériaux à changement de phase: Modélisation, Expérimentation et Evaluation de la performance énergétique globale[END_REF] (see Figure 3). The ISOTEST building corresponds to the LGI building on a smaller scale. The different test cells are oriented north in order to receive symmetrical solar radiation.

The test cells do not shade each other, and thermal interactions between the cells are negligible. Two meteorological stations are installed near the experimental buildings, in order to collect meteorological data. These allows the data obtained by each one to be compared [START_REF] Guichard | Contribution à l'étude des parois complexes intégrant des matériaux à changement de phase: Modélisation, Expérimentation et Evaluation de la performance énergétique globale[END_REF].

LGI test cell design

In order to observe building physics phenomena, an LGI test cell was used in real weather conditions. With dimensions of 3m (height) 3m (weight) 3m (length), it is representative of a typical room in a building encountered in Reunion Island. The modular structure of the roof allowed several configurations to be studied. The roof was inclined at 20° to the horizontal and was equipped with PCM. The suspended ceiling is made of plasterboard and PCM. The components of the LGI test cell are given in Table 1 and Figure 4.

Table 1: Arrangement of the LGI test cell [START_REF] Guichard | A thermal model for phase change materials in a building roof for a tropical and humid climate: Model description and elements of validation[END_REF] Figure 4: Exploded view of the LGI test cell roof

With the aim of obtaining extreme condition input from the roof, a dark colour was chosen for the corrugated covering. In order to have different experimental conditions, a mechanical ventilation and split-system air conditioner were set up. For the experimental sequence, the blind windows and the windowpanes in the door were masked as shown in Figure 5. The geometric details without PCM were provided by [START_REF] Miranville | Contribution à l'Etude des[END_REF]. The LGI test cell was chosen because it represents a typical lightweight construction with a low thermal inertia. -On the inside surface of walls for surface temperature measurements -Inserted in an aluminium cylinder for air temperature measurements -Put inside a black globe for radiant temperature measurements All surfaces of the complex roof were also instrumented. Heat fluxmeters were installed on the roof surfaces, PCM and plasterboard to determinate the heat flux through the complex roof with PCM. In the air layer, radiant and air temperatures were also measured as described above.

To ensure the accuracy of the measured data, protocol dictates that each thermocouple be calibrated on site and that factory sensors be checked. Margin of error from the thermocouples is about 0.5°C. According to the manufacturer's data, the error margin of the heat fluxmeters is approximately 5%. The temperature error margin from the entire data acquisition system is estimated to be 1°C.

A data logger was installed in the LGI test cell to automatically collect the data from the sensors every 15 minutes. All data were saved on a computer.

Description of PCM test

In this paper, the PCM tested is the commercial product from Dupont™ called Energain ® .

It is a flexible sheet 5 mm thick, made of 60% microencapsulated paraffin wax within a copolymer laminated on both sides by an aluminium sheet. The dimensions of the Dupont™Energain ® are 5.26 mm in thickness, 1000 mm in width and 1198 mm in length.

The composite PCM heat capacity was measured using a differential scanning calorimeter. The heating and the cooling rate is 0.05 K.min -1 . The freezing and heating curves are given in [START_REF] Kuznik | Experimental investigation of wallboard containing phase change material: Data for validation of numerical modeling[END_REF].

The latent heat of melting and freezing are 71 kJ.kg-1 and 72.4 kJ.kg-1 respectively, and the phase change temperatures are 23.4°C and 17.7°C respectively. According to reference [START_REF] Kuznik | Experimental investigation of wallboard containing phase change material: Data for validation of numerical modeling[END_REF], the difference between the melting temperature and the freezing temperature characterizes the hysteresis of the material, i.e the fact that the mixture is not a eutectic. In their work, the hysteresis effect is highlighted but in this case it is ignored. From the values given by Dupont™Energain ® , the densities are 750 kg.m -3 in liquid phase and 850 kg.m -3 in solid phase.

Thermal conductivities were measured using the guarded hot-plat test method [START_REF]Standard test method for steady state heat flux measurements and thermal transmission properties by means of the Guarded-Hot-Plate apparatus[END_REF]. The value varies between 0.18 Wm -1 K -1 (liquid phase) to 0.22 Wm -1 K -1 (solid phase). These parameters are dealt with in detail in [START_REF] Kuznik | Experimental investigation of wallboard containing phase change material: Data for validation of numerical modeling[END_REF][START_REF] David | Numerical study of the influence of the convective heat transfer on the dynamical behaviour of a phase change material wall[END_REF].

Climatic data and experimental sequences

The tests were carried out between August and October 2012. After studying the data from different meteorological stations on the site, this period was chosen to ensure phase change and regeneration of the PCM. During the experimental sequences, the most important physical variables were measured every day. Variables measured included, solar radiation (global, direct and diffuse, on a horizontal plane), ambient air, exterior relative humidity, wind speed and direction. The data is measured every minute, and every 15 minutes an average value is calculated and saved.

The data from 20 th September to 24 th September 2012 are given in Figure 6.

Figure 6: The climatic conditions during the experimental period [START_REF] Guichard | A thermal model for phase change materials in a building roof for a tropical and humid climate: Model description and elements of validation[END_REF] 4. Results

Introduction

The experimental platform is located at low altitude and near the coast, and is subject to variable weather all year round. The difference between summer and winter for experimental location usually depends on wind conditions. Winter was chosen for the experimental sequence presented here because this season offered ideal conditions for this study. In Figure 6, during the daytime, exterior air temperature varied between 16°C and 27°C, global solar radiation reached 1000 W.m 2 and was unchanged through the study period. The exterior relative humidity rate oscillated between 60% and 80%, the mean of wind speed was generally 4 m.s -1 except on one day when it reached 6 m.s -1 (corresponding to trade winds) (Figure 6(b)). Trade winds occur in winter in Reunion Island. On days where trade winds are observed, the exterior air temperature is relatively low.

In order to obtain accurate measurements from the complex roof components, sensors were placed at three levels (top, middle and bottom) and fully instrumented as shown in Figure 7. To ensure good experimental data, the middle values of the complex roof were used.

Experimental results

To begin with, the curves obtained during the experimental period are in agreement with the physical phenomena observed because from the roof cover to the inside surface of the plasterboard, the temperature decreased. The corrugated iron is the surface where the solar radiation is highest and will have a tendency to heat more than other walls of the complex roof.

In Figure 8, the temperature of a corrugated iron reached 50°C. However, according to reference [START_REF] Miranville | Evaluation of the thermal resistance of a roof-mounted multi-reflective radiant barrier for tropical and humid conditions: Experimental study from field measurements[END_REF], the temperatures of the corrugated iron are often superior to the value obtained. The maximum temperature difference between the inside plasterboard surface and the outside PCM surface can reach 5°C (see Figure 9). These results are very interesting because without using other thermal insulation and with a low PCM thickness, the inside surface temperature of the plasterboard is reduced. However, it is necessary to check that the PCM temperature is on either side of the melting temperature in order to regenerate it. That's why a heat fluxmeter was installed on each surface as shown in Figure 10. During the daytime, the heat flux measured in outside PCM surface is higher than the inside plasterboard surface. At night, the phenomena are reversed. A plausible explanation may be that during the day the energy is stored and during the night the energy is released by the Manuscript submitted to Energy and Buildings S. Guichard, 2015 16 PCM. The temperature curves are consistent with heat flux curves and show that PCM is able to reduce heat transfer from the outside to inside of the test cell. PCM is effective for the given configuration.

ISOLAB has already been validated by the IEA BESTEST protocol (International Energy Agency for Building Energy Simulation Test) for the LGI test cell without PCM. To highlight its impact on the inside air temperature of the test cell, a simulation without PCM was run in order to compare results from experimental data. These results are illustrated in Figure 12.

Without PCM, the inside air temperature is higher than the test cell equipped with PCM.

During the daytime, a peak difference of temperature of 2.4°C is observed. Temperature rise is also delayed as shown by the curves [START_REF] Guichard | A thermal model for phase change materials in a building roof for a tropical and humid climate: Model description and elements of validation[END_REF].

Determination of the R-value

Experimental data and the mean method were used to determine the R-value of the complex roof. The upper air layer is obstructed and no ventilation takes place. In this case, the curve between the two roof boundaries is shown in Figure 13. Evolution of temperatures in the roof covering and inside plasterboard is similar with an average value of 2.15°C, a minimum value of 7.87°C, and a maximum value of 26.10°C. The heat flux through the roof is also periodic, with an average value of 0.69 W.m -1 , the minimum and maximum values being -1.69 W.m -1 and 5.53 W.m -1 respectively. The R-value was calculated by applying the calculation module implemented in ISOLAB code and leads to the following values:

Under steady-state conditions a hypothesis is obtained, the R-value being calculated using the sum of thermal resistance of each wall and taking into account the exchanges of convection and radiation, and applying the Reunion Island thermal rules on the air layer. This gives a value of R=0.61 m 2 .K.W -1 . The value is of the same order as the one determined by the calculation module. It is noticed that the R-value is very small and that the result was expected. This result can be explained by the thickness of the upper air layer and thermal insulators that have not been added to the complex roof. In addition, the air layer was not ventilated and hence heat exchanges by convection are smaller. It should also be noted that the R-value is similar to that obtained by a radiant barrier used on the same test cell. However, the thermal resistance value is significant and shows that PCM alone can not be considered as a thermal insulator but as supplementary insulation. This is consistent with the literature.

Conclusion

This article deals with the first experimental study carried out in Reunion Island on a complex roof with PCM in real conditions. The significance of this article results both from the choice to install PCM and the determination of thermal performance via the R-value in dynamic conditions.

It was necessary to test PCM without using a thermal insulator in order to determine its performance. A PCM was set up in these conditions to highlight the phase change process and to use the reflective properties of its surfaces. The PCM phase change occurred and is guaranteed for the given configuration. It is very interesting because PCM can regenerate without the help of appliances. This is a positive point in terms of both energy consumption and environmental impact because no fossil fuel is used to generate energy, to power appliances to facilitate phase change. Thus, its inclusion in the complex roof reduces heat transfer, in most cases by infrared radiation.

This configuration clearly showed the phase change phenomena. Comparison with the roof without PCM made it possible to highlight the exact phenomena. On either side of the melting temperature, it is observed that energy is stored during daytime and released during at night, implying a delayed energetic demand. By adding PCM, the temperature of test cell was reduced by approximately 2°C. Following these observations, the R-value was calculated in order to evaluate PCM performance.

The mean method was used to asses thermal performance of the LGI test cell equipped with PCM. The principle of conservation of energy was respected and was applied to the ISOLAB code. Experimental measurements in realistic conditions were used. The results give a low Rvalue corresponding to the experimental conditions. Furthermore, the mean method is able to determinate the thermal performance of any building envelopes with PCM, and this value can be improved by adding a layer of thermal insulation. The value obtained is equivalent to the thermal performance of the radiant barrier value determined for the same LGI test cell.

However, other scenarios must be take into account such as a naturally ventilated or mechanically ventilated air layer in order to determinate the R-values for these conditions.

Many experimental studies need to be done.

These particular components of the complex roof were chosen to obtain numerical values. The numerical code developed is able to take into account the thermal behaviour of building envelopes, including PCM, and was incorporated into the ISOLAB code. The results of the numerical simulation and validation of the thermal model will be presented in a future publication.

  Abbreviations

Figure 1 :

 1 Figure 1: Calculation of thermal resistance in ISOLAB

Figure 2 :

 2 Figure 2: Experimental devices

Figure 3 :

 3 Figure 3: LGI and ISOTEST test cells

Figure 5 :

 5 Figure 5: Experimental conditions

Figure 7 :

 7 Figure 7: Instrumentation of the complex roof

Figure 8 :

 8 Figure 8: Surface temperature of each roof component

Figure 9 :Figure 10 :

 910 Figure 9: Difference between exterior surface PCM temperature and inside plasterboard surface temperature

Figure 11 :

 11 Figure 11: Phase change on 21th September 2012 (day)

Figure 12 :

 12 Figure12: Comparison of test cell air temperature with and without PCM[START_REF] Guichard | A thermal model for phase change materials in a building roof for a tropical and humid climate: Model description and elements of validation[END_REF]

Figure 13 :

 13 Figure 13: Heat flux through the roof and temperature difference between corrugated iron exterior and plasterboard interior.

Figure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :Figure 7 :Figure 8 :Figure 9 :Figure 10 :Figure 11 :Figure 12 :Figure 13 :•

 12345678910111213 Figure 1: Calculation of thermal resistance in ISOLAB

FigureFigure 1 :Figure 3 :

 13 Figure(s)

Figure 4 :

 4 Figure 4: Exploded view of the LGI test cell roof

Figure 5 :

 5 Figure 5: Experimental conditions

Figure 6 :

 6 Figure6: The climatic conditions during the experimental period[START_REF] Guichard | A thermal model for phase change materials in a building roof for a tropical and humid climate: Model description and elements of validation[END_REF]

Figure 7 :Figure 8 :

 78 Figure 7: Instrumentation of the complex roof

Figure 9 :Figure 10 :Figure 11 : 13 Figure 12 :

 910111312 Figure 9: Difference between exterior surface PCM temperature and inside plasterboard surface

Figure 13 :•

 13 Figure 13: Heat flux through the roof and temperature difference between corrugated iron

  In Reunion Island, depending on the building materials used, buildings have different thermal performance. Mainly wood and concrete have been used in construction. Recently, a new type of residential building has appeared using concrete and traditional construction materials. The roof consists of a corrugated sheet and a false ceiling. The false ceiling is

	generally plywood or plasterboard. However, thermal insulation used in the roofing are not
	effective. Usually, the roofs are inclined at 20°. The problem with these types of roof is the large
	heat gain due to the roof coverings. The roof is the part with highest energy losses ( 20% to
	30%) [9]. Furthermore, radiative effects must not be disregarded.
	Thermal regulations have been implemented in Reunion Island since 2009 with
	PERENE [10] and since 1 st May 2010 with RTAA DOM to avoid construction of buildings with
	different thermal performance. Current regulations make insulation and other technical
	solutions compulsory to minimise the energy consumption of buildings. This major step in
	building design reduces the need for air conditioning or heating systems and the harmful
	effects they have on the environment.
	2.2. Research statement
	PCM incorporated in a typical roof in Reunion Island will result in complex physical
	phenomena. PCM is inserted into an enclosed air space between the corrugated iron and
	2.1. Introduction
	plasterboard. Once installed, an air layer is present between the corrugated iron, the surface of
	Located in the Indian Ocean, Reunion Island is a French overseas department the PCM and the metal. All heat transfers are fully combined and coupled. In this way, heat
	characterized by a tropical and humid climate. Microclimates make it an ideal location for transfer by conduction, convection and radiation has to be taken into account on the PCM
	experimental investigation. During the summer, temperatures are generally above 30°C, with surface. Between the plasterboard and the PCM, only heat transferred by conduction is
	wind speed oscillating between 0 and 1.5 m.s -1 , and the mean relative humidity is usually above considered.

80%. It should be made clear that sometimes Reunion Island is subject to powerful and destructive cyclones

[START_REF] Miranville | Evaluation of the thermal resistance of a roof-mounted multi-reflective radiant barrier for tropical and humid conditions: Experimental study from field measurements[END_REF]

.

Table ( s) with Caption(s)

 (

Acknowledgment

The authors wish to thank Fonds Social Européen and La Région Réunion for their support and their funding of the first author's thesis.

Submission of manuscript to Energy and Buildings

Experimental investigation on a complex roof incorporating phasechange material.

List of tables

Submission of manuscript to Energy and Buildings

Experimental investigation on a complex roof incorporating phasechange material.


Annalisa Minelli
email: annalisa.minelli@univ-brest.fr

Cyril Tissot
email: cyril.tissot@univ-brest.fr

Mathias Rouan
email: mathias.rouan@univ-brest.fr

Matthieu Le Tixerant
email: matthieu.letixerant@terramaris.fr

Multi-scale modelling for simulating marine activities under heterogeneous environmental constraints

Keywords: multi-scale modelling, agent-based model, marine activities, spatio-temporal constraints, human-environment

This paper describes the concepts behind the implementation of a multi-agents model aimed to explore how marine activities respond to various environmental constraints. The methodology takes advantage on a responsive agent-based structure, and treats the environment as a set of forcing variables (biophysical, socio-economic and regulatory data). A first experiment in the Iroise Sea area shows a great potential in assessing the intensity and the variability of marine activities at different scales level. The whole methodology is presented in this paper in order to completely analyze the contributions and limitations concerning the SIMARIS prototype.

RESUME. Cette contribution décrit les concepts associés à la mise en oeuvre d'un modèle multiagents simulant le déroulement d'activités marines sous contraintes d'environnement. La méthodologie proposée se base sur un environnement de simulation permettant d'intégrer des données spatio-temporelles multi-sources et multi-échelles au sein d'un modèle à base d'agents contraints. Un premier prototype développé en mer d'Iroise montre les apports possibles de cette démarche pour évaluer la variabilité des activités de pêche à différentes niveaux scalaires.

Introduction

A review of the various scientific contributions concerning the modelling of human-environment interactions shows how difficult it is to integrate spatial and temporal dimensions into a multi agent based approach [START_REF] Gould | Time's Arrow,Time's Cycle: Myth and Metaphor in the Discovery of Geological Time[END_REF][START_REF] Stonebraker | The implementation of POSTGRES[END_REF][START_REF] Allen | Time and time again: the many ways to represent time[END_REF][START_REF] Muxart | Hétérogénéité du temps et de l'espace : niveaux d'organisation et échelles spatio-temporelles[END_REF][START_REF] Snodgrass | Temporal Databases[END_REF][START_REF] Cheylan | Toward a conceptual model for the analysis of spatiotemporal processes[END_REF][START_REF] Claramunt | Database Modelling for Environmental and Land Use Changes[END_REF]Parent, 1999;Legay, 2000;[START_REF] Pelekis | Literature Review of Spatio-Temporal Database Models[END_REF][START_REF] Tang | Simulating complex adaptive geographic systems: A geographically aware intelligent agent approach[END_REF]. The main limitation results from the variability of these interactions. In fact, ecosystem dynamics are not always synchronised with the evolution of human activities, which respond to far more complex cycles that are particularly difficult to model. Although this human-environment "desynchronization" may seem obvious, most multi agent based models run in environments where all inputs and/or outputs are considered known and all the events hold the same rhythm.

MAS-based approaches are, therefore, inapplicable when the system encounters a situation unexpected by the model designer, i.e. when problem specification is incomplete or the environment undergoes changes affecting human activities. This limit is underlined by the complexity of multi-scale interaction between human activities and environment. This article proposes a new methodological approache that can simulate marine activities at different scales in order to take into account these complex interactions.

Conceptual framework

Our methodology is based on a cross-cutting approach focused on combining multi-agents model with multi-scale spatio-temporal databases with the aim to model interactions between human activities and their environment. This approach has a two-pronged goals: the simulation of the impact of human activity on a given environment and the assessment of the capacity of activities to adapt to environmental changes. Moreover in the paper we will point out how, using these modeling techniques, the representation of the interactions between activities and between activities and environment, is easily accomplished and the individuation of potential pressure zones (in terms of resources exploitation) and conflict zones (between activities) is possible.

Environmental Modelling

Environment modelling is a critical point in multi-agent models if it involves natural phenomena. In computing terms, the environment is considered as the support system component of simulations (Ferber and Müller, 1996). It thereby manages access to resources. In ecological modelling terms, it refers to the formalisation of how a natural environment operates, leading to the closest possible abstraction of reality (an interesting example could be find in [START_REF] Matthews | Agent-based land-use models: a review of applications[END_REF]. Computer modelling is restricted by natural environment features whereas ecological modelling is restricted by programming language structures and methods.

In agent-based systems, environmental modelling is an extremely important task yet paradoxically often disregarded [START_REF] Weyns | Environment as a first class abstraction in multiagent systems[END_REF]. According to [START_REF] Weyns | Environment as a first class abstraction in multiagent systems[END_REF], the environment represents a first-class abstraction.

MAS-related studies that give less importance to the environment focus mainly on the role, capacities, interactions and modelling of agents. In contrast, ecological modelling-related studies specifically involve the regulations and processes that govern ecosystems in order to study their impacts on ecosystem balance [START_REF] Pereira | Different modelling tools of aquatic ecosystems: A proposal for a unified approach[END_REF][START_REF] Pereira | EcoSimNet: A Multi-Agent System for Ecological Simulation and Optimization[END_REF], Wang et al., 2008). [START_REF] Odell | Modeling agents and their environment.Agent-oriented software engineering III[END_REF] define a MAS environment as the constraints under which agents exist.

In our approach, the environment consists of a set of spatial data. In particular, it includes constraints that influence the development of an activity, so it influences directly or indirectly almost all the decisions and/or actions taken by agents.

Integration of spatio-temporal variables

Time constitutes a core element in any dynamic modelling approach. The temporal dimension can be comprehended linearly (occurrences with chronological development) or discontinuously (cyclical or random occurrences) [START_REF] Moellering | The potential uses of a computer animated film in the analysis of geographical patterns of traffic crashes[END_REF][START_REF] Muxart | Hétérogénéité du temps et de l'espace : niveaux d'organisation et échelles spatio-temporelles[END_REF][START_REF] Peuquet | Representations of Space and Time[END_REF][START_REF] Edsall | Applications of a cognitively informed framework for the design of interactive spatio-temporal representations[END_REF]. For what concerns human activities, time is generally associated with a [given] state of the environment (landuse, landcover, resources etc.). In addition to exploring the chronological progression of environmental change, the analysis focuses on the space-to-time ratio. Concerning spatial data integration, recent models often ignore the interactions underlying geographical objects [START_REF] Berjak | An improved cellular automata model for simulating fire in a spatially heterogeneous Savanna system[END_REF][START_REF] Jantz | Analysis of scale dependencies in an urban land-use change model[END_REF][START_REF] Marceau | Les automates cellulaires appliqués à la simulation des changements d'utilisation du sol: Sensibilité à l'échelle spatiale et temporelle[END_REF]. Moreover, the great part of modelling approaches considering the analysis of the dynamics of human-environment interactions, integrate space only as a structural element. It is clear that this assumption, drastically limits the spatial resolution and possible applications of the model itself because it requires that all the rules, governing the evolution of the simulation domain, must be specified in an explicit or probabilistic way. So, due to the intrinsic variability of both natural and human systems, this type of approach cannot adequately take into account the complexity of human-environment interactions. The impact analysis of human activities on the environment requires a close connection between the notions of time and space. This relationship determines the various states of the space observed, by considering the physical or human factors guiding the changes of that space. The space-time relationship, in this context, include the spatio-temporal entity concept [START_REF] Cheylan | Toward a conceptual model for the analysis of spatiotemporal processes[END_REF][START_REF] Hornsby | Event-oriented approaches in geographic information science[END_REF][START_REF] Cheylan | Les processus spatio-temporels: quelques notions et concepts préalables à leur représentation[END_REF]. In conceptual terms, several authors have suggested that the orthogonality of the spatio-temporal entities is a key criterion to the integration of hierarchies in a modelling approach [START_REF] Claramunt | Database Modelling for Environmental and Land Use Changes[END_REF][START_REF] Parent | Spatio-Temporal Conceptual Models : data structures + space + time[END_REF]. These studies show also that the evolution of the nature and structure of a spatial entity is associated with a time scale that determines state changes. In a human-environment relationship analysis, this time and space scale variability has a direct effect on the intensity and nature of interactions. Orthogonality therefore allows the joint consideration of two different scales:

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• a temporal scale, where the various cycles can be decomposed into as many time steps of variable duration as necessary; • a spatial scale, where the evolution of geographic entities at a given aggregation level is determined by the conjunction of human and natural events (each of which is conversely constrained by specific temporalities). In this context, the integration of geomatics tools (as digital geographical data processing, spatio-temporal databases etc.) in model design can facilitate the development of a modelling platform that can determine a complex simulation space by combining a set of human (socioeconomic, regulatory forcings) and environmental (physical and environmental forcings) factors.

The SIMARIS prototype

The SIMARIS (Simulation of marine activities under heterogeneous environmental constraints) prototype is designed to describe human activities and their spatiotemporal distribution, modelled in the form of responsive agents constrained by exogenous variables (biophysical, socioeconomic and regulatory constraints). It considers the ecosystem studied as a resource potentially used by humans according to the techniques available and to their social organisation at a given time. This use is not always associated with natural resources exploitation; it can also refer to land allocation for a specific use.

For example, manure-spreading practices are associated with a farming area for which the potential use of the practice itself has a set of spatial and temporal constraints. Each activity thus responds to a specific operating cycle, adjusted accordingly to technical, economic, regulatory, environmental and social filters.

In parallel, the analysis of the variability of environmental components entails time fragmentation into a series of intervals more or less long according to the processes they are associated with.

General principle of SIMARIS

The SIMARIS model provides a simulation environment that can integrate multi-source and multi-scale spatio-temporal constraints as forcing variables within constrained agent models (Tissot et al., 2013, Le Tixerant et al., 2012). This approach meets the requirement of explicitly formalize spatio-temporal relationships between spatial entities, environmental processes and human activities. The aim is to set up a methodological framework that can run without a complete system specification, i.e. considering the modelling environment as the result of a set of multi-scale constraints. SIMARIS has been implemented in GAMA simulation platform.

The GAMA multi agent platform [START_REF] Grignard | GAMA 1.6: Advancing the art of complex agent-based modeling and simulation[END_REF] is a quite recent multi agent protocol able to fully integrate geographical data in a multi agent system Copyright © by the paper's authors. Copying permitted for private and academic purposes. Proceedings of the Spatial Analysis and GEOmatics conference, SAGEO 2015.

( Taillander et al., 2014). It is written in Java and can be parametrized in .gaml language. This specific platform has been chosen for this work in reason of its large compatibility with geographical data. In fact it allows the reading of georeferenced files by their projected coordinate systems specification and their automatic reprojection from different coordinate reference systems if necessary. This is quite unusual for a MAS, especially considering that MAS execute operations in a synthetic world, measured by the (X, Y) cartesian coordinates instead of (East, West) or (Lat, Long) coordinates, as Geographical Informative Systems do.

Moreover it allows the multi-level modelling, which means that different analyses can be executed contemporarily in the same environment but with a different spacetime resolution and this is different from a mere parallel computing, since these "levels" of analyses can influence each other. In case of multi-level analysis the agents can be organized in super-agents: a sort of macro-entities which generalize the characteristics of a group of affine agents and, conversely, the single agent inherits the characteristics of the macro-agent.

All these features makes GAMA suitable for the SIMARIS prototype implementation.

Spatio-temporal granularity

Defining a spatial granularity, necessary to model human or environmental events, is one of the key issues and an essential precondition to build a model with spatio-temporal constraints [START_REF] Pereira | Agent-Based Simulation of Ecological Models[END_REF]. A relatively intuitive, commonly used practice is to consider the smallest common denominator of the objects involved as the model's spatial unit. A similar practice is used for the simulation time step. While this practice provides good results in many applications, its drawback is that it tends to involve objects with unknown dynamics or no tangible reality. For instance, in the case of marine activities, the knowledge of a procedure performed on a daily scale does not necessarily reveal its hourly course.

In this context, the SIMARIS model use spatial aggregation and disaggregation procedures that access data to match the level scale of the various sub-models used in the simulation. This issue raises important methodological questions like carrying out scale transfers within models. Scientific contributions dealing with this type of approach are rare and highlight the complexity of spatial aggregation and disaggregation procedures [START_REF] Gotway | Combining incompatible spatial data[END_REF][START_REF] Duboz | Intégration de modèles hétérogènes pour la modélisation et la simulation de systèmes complexes. Application à la modélisation multi-échelles en écologie marine[END_REF]. The main restrictions is the loss of consistency among data (which can be homogeneous at a given scale, but heterogeneous at different scale). It is therefore necessary to control small-scale variability in the models used, either explicitly or in a parameterised form.

Given the diversity of the data representing constraints for the models developed within the SIMARIS model (statistical data, digital geographic information, e.g. raster and vector data, temporal data) and the heterogeneity of the spatial organisation levels involved, the methodology developed is based on a bottom-up approach. The aim is to hook several abstraction levels in the model based on the higher resolution one.

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Regarding the multi-scale approach, in GAMA it is possible to define different spatio-temporal resolution, both in the same level of analysis and through different levels. In fact, different spatial resolutions can be specified for different grids and the cells can have squared, rectangular and hexagonal shape. Regarding time, different temporal granularities can also be specified for different agents in the way that any possible desynchronization between agents' activities can be fully represented.

Once the first and higher resolution level analysis is simulated, GAMA can aggregate agents in super-agents, as hinted before. This means that if, for example, the SIMARIS model simulates different agents for different types of fishing boats in the first level (for example "algae fishing boat", "king scallop dredge fishing boat", etc.), at the second level (more aggregate) it could appear a super-agent "fishing boat" grouping all types of fishing boats. And informations coming from the first level simulation can be remounted to the second level of simulation performing an aggregation operation too. Since GAMA is fully configurable, it is then possible to choose for each level the informations to be remounted and the better aggregation method to be chosen.

Application and results in the Iroise Sea

The Studied Zone

A first implementation of SIMARIS model in GAMA platform applies the principles described above on the Iroise Sea. In fact, this zone is a Natural Marine Parc and a Marine Protected Area (Figure 1) hosting concurrent (in space and time) activities, potentially generating pressure on natural environment, exploiting natural resources and interacting one with each other.

Input data

Following the bottom all the single activities in detail. At the first level of simulation (higher resolution), two different types of fishing activities are taken into account: the algae and the king scallop dredge fishing. A brief summary of input data and it table below.

Comingfromfishingcommittees

Comingfromother sources

Species implemented in GAMA

In SIMARIS model, a set of agents is implemented in order to simulate fishing activities. Each element implemented as agent is supposed to be able to interact with the environment and other agents following internal and external constraints and factors (weather conditions, regulation, fish market price…).

Regarding the limits and the shape of the "world" (here intended as working zone), they are defined by the bathymetry Multi-scale modelling for simulating marine activities Copyright © by the paper's authors. Copying permitted for private and academic purposes. Proceedings of the Spatial Analysis and GEOmatics conference, SAGEO 2015. Following the bottom-up approach, we choose to start the simulation by defining all the single activities in detail. At the first level of simulation (higher resolution), two different types of fishing activities are taken into account: the algae and the king scallop dredge fishing. A brief summary of input data and its origin is reported in the

Table 1. Type and origin of input data

Geographical Data Not Geographical Data

Comingfromfishingcommittees

Fishing Zones Maximum capture rates, fishing calendars

Comingfromother sources

Bathymetry, ports location Tide, king scallop growing rules

Species implemented in GAMA

In SIMARIS model, a set of agents is implemented in order to simulate fishing activities. Each element implemented as agent is supposed to be able to interact with environment and other agents following internal and external constraints and factors (weather conditions, regulation, fish market price…).

Regarding the limits and the shape of the "world" (here intended as working zone), they are defined by the bathymetry. So, since bathymetry represents the In SIMARIS model, a set of agents is implemented in order to simulate fishing activities. Each element implemented as agent is supposed to be able to interact with environment and other agents following internal and external constraints and

Regarding the limits and the shape of the "world" (here intended as working . So, since bathymetry represents the

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environment, it means that automatically it cannot be influenced by the behavior of all other agents or by external occurrences. This assumption fits quite well the real behavior of the earth surface, since, unless of severe and rapid natural changes (as big earthquakes, for example), it can be considered in "stable" condition. All the elements acting on the environmental scene are defined as agents.

Since the simulation regards the algae and king scallop dredge fishing activities, two different agents "boat" are defined: one for each type of activity. Both the agents implement a set of rules in order to simulate moving and fishing acts:

• the first action (implemented in GAMA as "reflex") is the movement from a starting port to a random point of the fishing zone. This reflex is activated by the conditions for the agent to be initialized, to be in a fishing period and to have all its fishing potential still intact (the boat has enough space to accommodate the catch). Once this first action is executed, the boat changes state from 0 (inactive) to 1 (active).

• the second reflex simulates the fishing activity. This reflex is activated if the boat is active (state = 1) and it has the possibility to accommodate fish.

Starting from the random point of the fishing zone, the boat tries to catch all that it can; a calculation of the catch is made basing on the quantity of fish present in the specific cell of the fishing zone so that the boat starts to load.

If the boat fulls after the fish, it modifies the state parameter to 3 (discharge) and activates the discharge reflex. If the boat is not full after have visiting a specific cell of the fishing zone, it selects the neighbour cells and it chooses one of them as fishing target. The state value is turned to 2 (fishing). The process continues since the boat is not full.

• the third reflex simulates the action of discharge for the boats and it is activated when the boat is full and its state is set to "discharge". In this reflex the boat goes automatically to the nearest discharge point, it discharges the catch and modifies its state to 1 (inactive) so that the moving/fishing loop can continue.

Another particular characteristic of the agent "boat" is that the captain has a memory. In fact, once he catched all the possible from a fishing zone cell, he stores the visited cell in a list in the way that, during the fishing movement, he drops the visited (and empty) cells from the list of neighbours, excluding them from the possible target cells.

The agents "fishing zones" are differentiated in reason of the type of activity. The resource is stored in a regular square cell grid, perfectly sized on the input vector area of fishing zones. The resolution of this grid is automatically connected to the extension of the studied area. Each cell carries information regarding the quantity (nb. of individuals), size and weight of each individual. The exemplaries are differentiated in reason of the stage of growth too. At the beginning of the simulation a random quantity and distribution of individuals are simulated, size and total weight are calculated consequently for each cell.

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For example, for the king scallop, there are three different stages of life: young, growing and mature. The stages have a respective duration of 180, 365 and 365 days [START_REF] Buestel | Croissance de la coquille Saint-Jacques (Pecten maximus L.) en rade de Brest et en baie de Saint-Brieuc[END_REF][START_REF] Buestel | Croissance de différents lots de coquille Saint-Jacques Pecten maximus en culture sur le fond dans la rade de Brest[END_REF]Chavaud et al., 1998). The boat catches as much as it can. and if the boat catches all the resource in the cell (all the exemplaries) the cell becomes empty, if the boat catches less than the cell content, the remaining part of resource is left in the cell. The regeneration of the resource is periodically executed once or twice a year (seeding of juvenile in some fishing area).

The agent "tide" is a regular grid covering all the bathymetry extension with the same spatial resolution. In reason of the zone examined, the grid is appropriately resized and, accordingly to the nearest located port, the value of tide is read from the database Postgres. Thanks to a specific set of functions, called "SQLSKILL", GAMA is able to connect with the database and read values from tables, executing SQL queries. For each time step, a request is formulated to the database Postgres and the corresponding value for tide is read from the table, the value is then stored in each cell of the tide grid and it changes at each time step. If the zone studied is quite large and more than one port is included into the zone itself, the values of tide for different ports are read and the obtained values are interpolated in order to simulate, by means of a continuous surface, the spatial variability of the tide. Tide values infer fishing activities, in fact, depending on the fishing type, there is a range for the height of water which maximize the catchment. This threshold value is used if fishing zones are not provided or available in the working area, in joint with the bathymetry grid and an eventual seafloor bed grid, in order to individuate potential fishing zones.

Moreover the model contains an agent "traffic" which has the shape of a regular grid. This grid too is resized once the working zone is individuated and its task is to record the passage of a boat adding a "+1" in each single cell visited by a boat. This agent is useful in order to quantify the effective pressure on the environment (due to frequentation), once a specific scenario is simulated.

Procedure description

The SIMARIS model is dynamic and multilevel. This means that the first thing asked to be specified is the spatial and temporal limits of simulation. A start and end datetime must be inserted into the graphical interface, and the working area is described by two couple of coordinates, belonging to two points, delimiting a rectangular zone.

On the basis of spatial extension (area of the working zone) is assigned a level to the analysis, considering that, for each different level, different type of informations will be returned from the model. For example, if the spatial extent of the analysis is the fishing zone (or a part of it) and the period is one week, probably the interest of the simulation is to monitor the single catchment act, so the spatio-temporal resolution of the analysis must be appropriate, and the information obtained will be quite detailed (e. g. catchment rate for each single fishing session, view of the boat movement during the catchment etc. etc.). As opposite, if the simulation covers one

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year and it is performed over all the Iroise Sea, the interest could be to monitor the maritime traffic and the global catchment rates each month, for example, and it makes no sense to view the movement of boats during a single fishing session.

Another model peculiarity is the possibility to represent desynchronization between agents. In fact it exists (and must be specified) a global "step" parameter, which represents the temporal granularity and the time step corresponds to a cycle. But it is also possible to define an optional "step" parameter for each single agent. This means that if, for example, our aim is to simulate the growth of species (which is regularly a quite long process) and the fishing activities at a different velocity, we can do it. Although this instrument is potentially very powerful, it must be used carefully since an unnecessary desynchronization could invalidate the results of an entire simulation: for example it is fundamental to synchronize fishing operations (which should be represented at hourly or half-an-hour rate) with fishing calendar (which can be represented at hourly but also at daily or monthly rate). So, different values for time and space granularity are automatically defined.

The models' input layers (spatial data) are: a general vector map of the studied zone; the raster file of bathymetry; the vector point map of ports; the vector maps of fishing zones (optional); the vector map of seafloor bed (optional).

Other models' input data are: the database of the tide table; the name of the port(s) to be used to calculate tide level(s); a starting and ending datetime of simulation; the coordinates of two points defining the working zone.

Once all the inputs are specified, the model guides the user, by using messages on the console, through a step by step procedure. For atypicalrun:

1. the model initializes both the environment and the agents; a general skeleton map of the zone reporting starting and discharge points of ports appear. The user is invited to specify the zoom level by reading two couple of coordinates on the map (Figure 2). In this case the presence of two types of agents «boats» are examined: three algae and two king scallop fishing boats. The time period examied is one week and the extension of the selected zone allows the most detailed level of analysis, so, the movement of the single boat and resource consumption will be evidenced during the routing; purposes. Proceedings of the Spatial Analysis and GEOmatic Figure 2.from the top: the "world" map with zoom on the working zone, the GAMA 2. all the input layers and the entire world are resized on the specified zoom and the agents boats, tide and traffic are created; 3. the species are randomized over the fishing zone grid and they start growing. The boats start moving and fishing, if the calendar allows it (Figure 3 a, b). A typical situation after some steps is the one evidenced in Figure 3c. On the higher part is reported an extract from the king scallop fishing zones attribute table and it is possible to see different parameters belonging to each single cell (the number of individuals in each cell, their age, their stage of growth and the weight of the single individual); on the bottom part is reported the fishing zone situation as it appears after some cycles: in light orange the cells containing some resource, in blue the empty cells. From this figure it is possible to evidence that there are some parts of the fishing zone most frequented than others (e. g. the ones circled in red).

These first results could be used, for example, in order to extract some important information about frequentation and resource exploitation, or they can be crossed with other data (map extrapolate potential pressures on natural environment. Moreover an unforeseen event could occur (a modification on fishing calendar or the interdiction of a part of all the input layers and the entire world are resized on the specified zoom and the agents boats, tide and traffic are created; the species are randomized over the fishing zone grid and they start growing. The boats start moving and fishing, if the calendar allows it (Figure 3 a, b). A typical situation after some steps is the one evidenced in Figure 3c. On the orted an extract from the king scallop fishing zones attribute table and it is possible to see different parameters belonging to each single cell (the number of individuals in each cell, their age, their stage of growth and the weight of ual); on the bottom part is reported the fishing zone situation as it appears after some cycles: in light orange the cells containing some resource, in blue the empty cells. From this figure it is possible to evidence that there are some parts ng zone most frequented than others (e. g. the ones circled in red).

These first results could be used, for example, in order to extract some important information about frequentation and resource exploitation, or they can be crossed with other data (map of more sensible zones -at naturalistic level) in order to extrapolate potential pressures on natural environment. Moreover an unforeseen event could occur (a modification on fishing calendar or the interdiction of a part of scale modelling for simulating marine activities Copyright © by the paper's authors. Copying permitted for private and academic s conference, SAGEO A typical situation after some steps is the one evidenced in Figure 3c. On the orted an extract from the king scallop fishing zones attribute table and it is possible to see different parameters belonging to each single cell (the number of individuals in each cell, their age, their stage of growth and the weight of ual); on the bottom part is reported the fishing zone situation as it appears after some cycles: in light orange the cells containing some resource, in blue the empty cells. From this figure it is possible to evidence that there are some parts ng zone most frequented than others (e. g. the ones circled in red).

These first results could be used, for example, in order to extract some important information about frequentation and resource exploitation, or they can be crossed at naturalistic level) in order to extrapolate potential pressures on natural environment. Moreover an unforeseen event could occur (a modification on fishing calendar or the interdiction of a part of It must be said that in this case, since the example is aimed to individuate the more exploited zones (into a previously defined fishing zone) and to obtain a resource balance, a small geographical zone is selected and the analysis level is the most refined. If a longer period and a wider zone is selected, the model gives aggregate information as requested from specifications. For example, if the zone contains more fishing zones we will not see any boat moving but we will obtain in output a resource exploitation graphic which will synthesize the fishing activity for all the boats over the specified period for the specific zone. So, detailed data are It must be said that in this case, since the example is aimed to individuate the more exploited zones (into a previously defined fishing zone) and to obtain a resource balance, a small geographical zone is selected and the analysis level is the ost refined. If a longer period and a wider zone is selected, the model gives aggregate information as requested from specifications. For example, if the zone contains more fishing zones we will not see any boat moving but we will obtain in ce exploitation graphic which will synthesize the fishing activity for all the boats over the specified period for the specific zone. So, detailed data are permitted for private and academic purposes. Proceedings of the Spatial Analysis and GEOmatics conference, SAGEO enario configuration could lead to sensibly different It must be said that in this case, since the example is aimed to individuate the more exploited zones (into a previously defined fishing zone) and to obtain a resource balance, a small geographical zone is selected and the analysis level is the ost refined. If a longer period and a wider zone is selected, the model gives aggregate information as requested from specifications. For example, if the zone contains more fishing zones we will not see any boat moving but we will obtain in ce exploitation graphic which will synthesize the fishing activity for all the boats over the specified period for the specific zone. So, detailed data are Copyright © by the paper's authors. Copying permitted for private and academic purposes. Proceedings of the Spatial Analysis and GEOmatics conference, SAGEO 2015.

«hidden» since a high level of refinement is not required, synthesis data is produced instead.

Conclusions

The approach presented in this paper explore new methodological framework in multi-scale modelling. Developed in GAMA simulation platform, the SIMARIS prototype explicitly formalizespatio-temporal relationships between spatial entities, environmental processes and human activities. The complexity of the prototype is still limited, since the model takes into account for the moment only fishing activity (super-agent) and details some of them (agents), so there are two different analysis levels.

In thematic terms, the applications to coastal area situations, provide encouraging results for assessing complex interactions between marine activities and coastal ecosystems. Changes in activity intensity will be particularly interesting to study because they illustrate the variability of these interactions.

Since it involves human activities under heterogeneous spatio-temporal constraints, the approach described here require the development of models integrating many stochastic processes. The validation of an approach combining deterministic and stochastic modelling is therefore a key issue in the implementation of the SIMARIS model. Finally, the approach described here raises numerous questions as, for example, how to structure a multi-scale model like SIMARIS. The applications mentioned above indicate that there is a relationship between the granularity of the spatial and temporal data and the abstraction level of the model. The formalisation of this relationship remains complex. Extensive exploration of model structure may help to describe this relationship.

Figure 1 .

 1 Figure 1.the Natural Marine Parc area in the Iroise Sea.

Figure 1 .

 1 Figure 1.the Natural Marine Parc area in the Iroise Sea.

  scale modelling for simulating marine activities Copyright © by the paper's authors. Copying permitted for private and academic s conference, SAGEO ion by defining all the single activities in detail. At the first level of simulation (higher resolution), two different types of fishing activities are taken into account: the algae and the king s origin is reported in the Not Geographical Data Maximum capture rates, fishing calendars king scallop growing rules

  Multi-scale modelling for simulating marine activitiesCopyright © by the paper's authors. Copying permitted for private and academic purposes. Proceedings of the Spatial Analysis and GEOmatics conference, SAGEO 2015.

Figure 2 .

 2 Figure2.from the top: the "world" map with zoom on the working zone, the GAMA console and the "Parameters" section.

Figure 2 .

 2 Figure 2.from the top: the "world" map with zoom on the working zone, the GAMA

  the paper's authors. Copying purposes. Proceedings of the Spatial Analysis and GEOmatics conference, SAGEO the fishing zone) and a new sc results.

Figure 3 .

 3 Figure 3.on the top: an extract from the fishing zone area attributes: the idividuals present in each cell have all the same growt stage (lifedays and stage) but they are numerically are evidenced visited and empty cells; (b) a boat at discharge place; (c) the resized fishing zones (light orange) accordingly to the specified zoom. Circled in red: an example of most exploit

  Figure 3.on the top: an extract from the fishing zone area attributes: the idividuals present in each cell have all the same growt stage (lifedays and stage) but they are numerically different (nbIndiv); on the bottom: (a) a boat fishing. In blue are evidenced visited and empty cells; (b) a boat at discharge place; (c) the resized fishing zones (light orange) accordingly to the specified zoom. Circled in red: an example of most exploited zones.

Figure 3 .

 3 Figure 3.on the top: an extract from the fishing zone area attributes: the idividuals present in each cell have all the same growt stage (lifedays and stage) but different (nbIndiv); on the bottom: (a) a boat fishing. In blue are evidenced visited and empty cells; (b) a boat at discharge place; (c) the resized fishing zones (light orange) accordingly to the specified zoom. Circled in red: an