A Multiscale Simulation Approach for Linking

A Multiscale Simulation Approach for Linking Mangrove Dynamics to Coastal Processes using Remote Sensing Observations Christophe Proisy†, Pascal Degenne‡, Edward J. Anthony††, Uta Berger§, Elodie Blanchard†, François Fromard$, Antoine Gardelɸ, Adewole Olagoke§†, Valdenira Santos#, Romain Walcker!$, Danny Lo Seen‡ †

UMR AMAP, Institut de Recherche pour le Développement, Montpellier, France ‡ UMR TETIS, CIRAD, Montpellier, France †† Institut Universitaire de France, CEREGE, Aix-Marseille Université, Aix en Provence, France § Institute of Forest Growth and Computer Science, Technische Universität, Dresden, Germany

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CNRS-UMR ECOLAB, Toulouse, France Université du Littorale Côte d'Opale-CNRSGuyane, Cayenne, French Guiana # Instituto de Pesquisas Científicas e Tecnológicas do Estado do Amapá (IEPA), Macapá, Amapá, Brazil ! Université Paul Sabatier, Toulouse, France ɸ

Corresponding autor: [email protected]

Citation: Proisy, C., Degenne, P., Anthony, E.J., Berger, U., Blanchard, E., Fromard, F., Gardel, A., Olagoke, A., Santos, V.F., Walcker, R., & Lo Seen, D. (2016). A multiscale simulation approach for linking mangrove dynamics to coastal processes using remote sensing observations. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 810-814. Coconut Creek (Florida), ISSN 0749-0208.

Abstract: We present a new landscape-modelling framework based on a domain-specific language called Ocelet that is used to question our understanding of how mangrove forests cope with fast-changing muddy seashores. For the demonstration, we selected the coast of French Guiana where mangrove physiognomy and extent continuously vary due to successive and recurrent erosion or accretion phases resulting from the alongshore migration of mud banks originating from the Amazon River. We modelled the French Guiana coastal system as a set of ecological and physical processes involving entities (e.g. ocean, mangrove shoreline, mud bank) that are in relation with each other. Interaction functions are written to specify how the entities change when they interact, according to the level of understanding and knowledge available. The scenario then describes what interaction functions are activated at each time step. We applied the approach to explain mangrove shoreline variations from 1986 to 2009 over 45 kilometres, and examined the contribution of alongshore and cross-shore wave energy and current velocities. The model was run with daily ERA-Interim/ECMWF waves and Mercator-Ocean currents as input data, whereas a time series of remote sensing images was used during the initialization and validation phases. We then discuss the flexibility of our approach to integrate existing models of mangrove forest dynamics. Keywords: mangrove coasts, coastal modeling, ERA-Interim, coastal currents.

INTRODUCTION The capability of mangroves to cope with coastal changes will remain a challenging question in the coming decades (e.g. Ellison, 2015). The future of mangroves is now doubly jeopardized by direct human pressures (Duke et al., 2007) that compound effects of sea level rise and coastal erosion induced by changes in hydrodynamic and sedimentary processes or increasingly stronger storms. Among pending difficulties in forecasting changes in the extent and functioning of mangrove ecosystems is describing how multiple biotic and abiotic factors interplay at the local scale (e.g. Alongi, 2009) to regulate mangrove development, knowing that these factors are themselves constrained at larger scales by tides, winds, waves, nearshore currents or sediment transport (e.g. Anthony et al., 2010). Reciprocally, mangrove forests play an important role in the functioning of the coastal zone. Several models have been developed to address the dynamics of these forests (Berger et al., 2007). They simulate growth of mangrove trees under different but stationary environmental conditions that are described in maps of abiotic factors obtained at forest stand or landscape scales. They are not designed to directly tackle the issue of real-world changing coasts. This may be one of the reasons why coastal zone management policies are still far from integrating all the knowledge acquired from scientific studies of mangrove ecology (Lewis, 2009). In our opinion, a considerable potential for groundbreaking research would consist in coupling actual forest dynamics models with hindcast, nowcast and forecast scenarios of coastal changes. Remote sensing observations have proved useful in monitoring decadal-scale changes in mangrove shoreline and extent in many regions (e.g. Nascimento et al., 2013). The knowledge thereby obtained on real case studies by observing decadal changes at a regional scale is not yet exploited sufficiently to forecast mangrove changes. Modeling of forest dynamics does not yet assimilate such pivotal information. Remote sensing data are also at the basis of global and regional climate simulations due to their multiple potential for monitoring oceans and atmosphere parameters. Hindcast data sources such as ERA-Interim wave or oceanic current signals provided by, respectively, the European Centre for Medium-Range Weather Forecasts (ECMWF) and Mercator Ocean provide crucial data to understand changes that have affected mangrove coasts over the last decades. However, to our knowledge, no mangrove studies have exploited this potential yet. One of the reasons that come to mind could be the need for a flexible modelling approach capable of handling the coupling of various types of data and factors at various time and spatial scales in order to go further in the understanding of what has been observed for

decades in satellite images. We present a new landscape-modelling framework based on a domain-specific language called Ocelet that is used here to address the question of mangrove forest dynamics in the context of fast-changing muddy seashores.

METHODS Recently, Walcker et al. (2015) found a significant relationship between multi-decadal fluctuation of mangrove shorelines and wave regimes in French Guiana. In this coastal region influenced by the massive fine-grained discharge from the Amazon River, hydrodynamics and sedimentary processes regulating the extent and physiognomy of mangroves are interwoven at many spatial and time scales (Figure 1). Hindcast, nowcast, and forecast coastal changes are particularly difficult to address.

Figure 1. Schematic representation of the state of the art on knowledge acquired on the French Guiana. Bottom left pictures present a real case study near Kourou. In situ and aerial photographs are laid over a color composite extract of a SPOT-4 multispectral image acquired in 2005 to illustrate mangrove dynamics (M1-3). Top right diagram illustrates how ocean waves and coastal currents impact the mangrove shoreline as a function of the presence and size of two mud banks identified by their subtidal and intertidal parts. The orientation of the coast remains nearly at 131° counter clockwise from the east.

Study area and data used For the demonstration, we focused our work on the coast of French Guiana where mangrove physiognomy and extent continuously vary due to successive and recurrent erosion or accretion phases originating from the alongshore transport of sediments (mud banks) from the mouth of the Amazon River towards the northwest. Previous results suggest a complementary role of wind waves in the 'welding' of mobile mud banks to the shoreline (Gratiot et al., 2007) while geostrophic currents parallel to the coast may control alongshore sediment transport (Bourret et al., 2008). During the accretion phase, mudflats formed on the upper intertidal portions of mud banks undergo continuous consolidation (Gensac et al., 2015) and, generally the latter process is followed by rapid and extensive mangrove colonization over hundreds of hectares (Proisy et al., 2009). Shoreline retreat during erosion phase is mainly attributed to the impact of waves propagating across stretches of coast between banks, called inter-bank zones (Figure 1; Anthony et al., 2010). Massive and rapid removal of mangroves can be observed over several square kilometres in a few years in inter-bank zones. In this work, we used a subset of eleven SPOT-1/2/4/5 multispectral satellite images to obtain mangrove shoreline positions from a simple visual analysis as already done by Fromard et al. (2004) or Walcker et al. (2015). These data imaged the 45 km long coastline between Cayenne and Kourou from 1986 to 2009. Spatial resolutions ranged from 4 m to 20 m. Coastal orientation over the study area remains nearly constant and about 141° w.r.t. east. Additionally, we downloaded ERA-Interim reanalysis data from the European Centre for

_________________________________________________________________________________________________ Medium-Range Weather Forecasts reanalysis products (ECMWF) website and focused on 1° daily ocean wave data available since 1986. We analyzed ocean wave data provided at the node 6°N, 53°W, which is located about 60 km north of Sinnamary. We considered this node the most suitable to study ocean waves that predominantly propagate southwestward (Figure 1). We combined wave height H and period T to produce the ratio H3/T², a relevant parameter for describing wave forcing (Gratiot et al., 2007). The average wave energy from 1986 to 2009 was about 0.066 ±0.04 m3.s-2 and maximum values regularly observed between January and March reach 0.15 m3.s-2. The high-energy season from October to May is characterized by wave incidence nearly orthogonal to the coastline. During the lowenergy season from June to September, the incidence angle of ocean waves decreases to 70°. Data on coastal currents since 1986 were freely provided by Mercator-Ocean at 0.5° from the PSY2G2R1 analysis system. The node closest to the coast was at 6°N, 51.5°W and corresponded approximately to a distance of 140 km northeast of Cayenne. Coastal currents are continuously oriented alongshore with a direction remaining nearly constant around 115° w.r.t. east. Seasonal variations in coastal currents are observed with the highest velocities of 0.3 m.s-1 occurring between February and June. The average velocity rate between 1986 and 2009 was 0.1 ± 0.07 m.s-1. We centered and scaled the ocean wave and current signals in order to have them on an equal basis relative to their variation and, to enable evaluation of their respective contributions to changes in the mangrove shoreline. Modelling with Ocelet We used Ocelet (Degenne et al, 2009; 2010), which is a domain-specific language for modelling spatial dynamics within a model-building environment called the Ocelet Modelling Platform (OMP). Compared to other modelling approaches such as Systems Dynamics or AgentBased systems, Ocelet proposes a more flexible way of handling space within processes, due to usage of interaction graphs. Ocelet introduced an extension to the concept of graph (a set of vertices connected with edges) wherein appropriate semantics are attached to the vertices and edges so that they can be defined to represent a dynamic system in space and time. For example, a landscape can be represented by landscape elements (vertices) that are linked (through edges) to each other. During a simulation, interaction functions can be applied on the edges to change the state of the entities connected. Interaction graphs are thus dynamic (vertices changing state), with dynamic structure, as vertices and edges can be added or removed during a simulation. Among the properties included by the modeller to describe an entity, one is usually dedicated to its spatial representation, with points, lines, polygons and cells as possible data types. In certain models, it may be necessary to use vector and raster entities together, and interaction graphs in Ocelet appropriately embrace both types. Once the main processes at work in the system studied are identified, they can be modeled with Ocelet one after the other, starting with the process expected to be the most relevant. Other processes can then be added in turn, and interactions between processes included whenever necessary. For a given process, the different types of entities involved are first identified and described. Then, relations are built by stating which entity interacts with which other entity (the graph structure), and describing each of the functions that can be applied when the entities interact (the interaction functions). These functions are used in the scenario, which comprises an initialization part, to define the initial state of the system, and the simulation part that describes, for each time step, which functions are executed, and in what sequence. Ocelet uses a different datafacer to interface with the different types of data sources possible (GIS database, text file, shapefile, kml file, raster image). No specific mechanism has yet been included to track the properties of the entities or other state variables. Therefore, the scenarios must also contain instructions to save the required variables into a file for display and analysis. The OMP environment groups within the same interface all the tasks required when building a model with Ocelet: model creation and maintenance, Ocelet code edition, compilation, simulation run, display and export of simulation results. Ocelet and the Ocelet Modelling Platform are released under the CECILL V2.1 license and can be downloaded from http://www.ocelet.org.The sources are for now available on demand. They are currently being properly formatted and will be openly available on GitHub soon. Application to the French Guiana coast We limited the spatial extent of the modelling study to the 45 km long coastal area between Cayenne and Kourou for which the maximum number of satellite images was available. With the benefit of hindsight of observations and analysis of changes that occurred over the last 60 years (Walcker et al., 2015), we decided to start modelling using only three main interactions noted f1-3 (Figure 2) and empirical equations given in table 1. • f1: Mud bank displacement along the coast. The relation f1 between Ocean and Mud bank entities describes how mud banks move under current and wave forcing. The displacement of the mud bank Dm is assumed to be linearly related to the alongshore components of ocean waves Wal and currents Cal by the coefficients kcc and kcw. From the observation of oceanic water areas displaying high turbidity in the 1986 satellite image, we roughly delineated the contour of two mud banks as semi-ellipses (Figure 2). The one near Kourou was larger than the second near Cayenne. We assumed that their size and direction of movement remained unchanged from 1986 to 2009. • f2: Mangrove shoreline exposure to wave-induced erosion depending on the degree of protection by a mud bank. Mud banks are expected to offer some protection to mangroves by attenuating the erosive forces of waves. The exposure factor Fexp is computed as a function dependent on the distance rattn a wave has to travel over a mud bank to reach a given point of the shoreline. This point is expected to be totally protected by the mud bank when rattn>20 km. The exposure equation also includes a scale factor Sf and a shape parameter n of the attenuation function. • f3: Mud bank colonization by mangroves. Mangrove seaward progression is due to complex colonization processes occurring at microtopography level (Proisy et al., 2009) that could not be taken into account in the model at this time (Figure 1). Instead, we used a very simple constant rate of progression of the mangrove shoreline perpendicularly to the overall coastal orientation. This function is set proportional to (Sf – Fexp) and weighted by a scale factor βcol. When and where (Sf – Fexp) is greater zero, the mangrove shoreline is expected to shift seaward.

• Initial state: The simulation process starts from the 1986 observation. Two polylines were drawn: one for the chenier (the boundary inland that is considered stable) and the other for the mangrove shoreline that is subject to wave erosion.

Figure 2. Entities and relation functions described, and sketch of the modeling approach in its present state. The mangrove shoreline is subjected to the energy of waves and coastal currents propagating along different directions. At a given time, the energy of waves that induce erosion is dampened by intertidal and subtidal areas that the forcing signal must go through following a given approach angle (αw or αc). Point A at t1 is less protected than point B at t2. For the moment, a mud bank is considered without influence on the neighboring mud bank to the northwest. The chenier (sand ridge) is considered as the landward limit of the mangrove area.

In the equations below, five parameters had to be estimated: the linear relation coefficients kcc, kcw and the shape and scale factors n, Sf and βcol. These were obtained by minimization of the differences between the simulated mangrove shoreline between Cayenne and Kourou and the same shoreline observed in the satellite image acquired in 2001, this year lying close to the middle of the 1986-2010 period of study. Initial values for these five parameters (Table 1) were rounded estimates obtained using average values of observed shoreline displacements and ocean wave and current data (not shown for reasons of conciseness). The coast function used was defined as the root mean square error between simulated and observed shoreline positions along transect lines spaced 500 m and all directed perpendicular to the coast. The downhill simplex method (Nelder and Mead, 1965) was used to search for the optimal parameter values. Simulations were then carried out for the whole 45 km long studied coastal area between 1986 and 2009 with this parameter set. Table 1. Equations used in the main processes modelled. Process and function Mud bank displacement along coast (f1) Mangrove shoreline exposure to erosion (f2) Mud bank colonization by mangroves (f3)

Initial state

Equations -1

Dm (m.day ) = kcc.Cal + kcw.Wal �����⃗ ���⃗ with 𝑭𝒆𝒆𝒆 = 𝑺𝒇 . (𝟏 − 𝒅𝒘 = 𝑭𝒆𝒆𝒆 . �𝑾

𝒓𝒂𝒂𝒂𝒂 𝒏

)

𝟐𝟐𝟐𝟐𝟐

𝒅𝒄𝒄𝒄 (m.day-1) = (𝑺𝒇 − 𝑭𝒆𝒆𝒆 ). 𝜷𝒄𝒄𝒄

kcc=30.0 kcw=30.0 Sf = 6.0 n=3 βcol = 0.1

Final state

kcc=21.7 kcw=35.1 Sf = 2.5 n = 4.75 βcol = 0.13

RESULTS The most salient result obtained from our modeling work concerns the confirmation that the iteration procedure converges, this meaning that our equations and thus the description of the underlying processes were suitable enough to explain some of the fluctuations in the mangrove shorelines. It is to be noted that the minimum was reached after 240 iterations of 2.8 min of computing time each (under Windows 7, 64 bits, 8 GB of RAM). Resulting simulated mangrove shorelines for the ten images acquired after 1986 have been recorded in a kml format and displayed directly in Google Earth®. A relatively good visual agreement between satellite-observed and simulated fluctuations in mangrove shorelines was

noted for each of the 14 years between 1987 and 2009, as shown in part in Figure 3 for only 1987, 1999 and 2009. Overall, our model makes the mangrove shoreline retreat in interbank zones during erosion phases and prograde when a mud bank protects a given area. The values obtained for the 5 parameters (table 1, right column) describe mangrove coast dynamics over 90 points of observation (every 500 m) with an averaged root mean square error increasing, except in 2001, and reaching a maximum of 360 m in 2006, 20 years after (Figure 3, bottom left). DISCUSSION The flexibility of the Ocelet language allowed us to intuitively establish a relationship between entities, such as the ocean, the mud banks and the mangrove areas. To our knowledge, these entities have remained unlinked in most of the studies dealing with mangrove changes. Our preliminary results suggest that the impact of waves on mangrove shorelines is almost two times more (kcw=35.1; ~62% of the total contribution) than that of coastal currents do (kcc=21.7; ~38%). These findings are in support of statistical evidence of a clear relationship between the extent of mangroves and large-scale, low-frequency changes in North Atlantic waves (Walcker et al., 2015). We have therefore started developing a tool capable of steering and spatializing mangrove forest dynamics models from the simulated impacts of large-scale oceanic processes that should yield, for example, the timing and location of mangrove colonization and erosion processes. For these purposes, high spatial resolution images could be used to detect the initial time (Proisy et al., 2009), the mangrove growth stage (Proisy et al., 2007) and mangrove destruction. The convergence of the modelling approach based on simple equations proves that ocean wave and coastal current signals 'force' the mangrove shoreline to adapt itself to different levels and types of processes, such as erosion associated with sediment remobilization by ocean waves (Gratiot et al., 2007) or mud bank colonization by mangroves following substrate consolidation (Proisy et al., 2009). A next step would be to integrate a forest dynamics model in the modelling approach, which can also be tested in other regions of the French Guiana coast, especially in the extreme western sector of the coast where shoreline orientation approaches 180° and mud banks become very elongated. CONCLUSION In this paper, we have presented preliminary milestones towards the simulation of the dynamics of mangrove coasts. The Ocelet modelling approach highlighted a good potential for addressing coastal processes involved in decadal changes in mangrove extent. We have succeeded, to a certain degree, in tracking and explaining mangrove shoreline variations using both widely available satellite and ocean data. Rapidly changing pristine mangrove coasts demonstrate fascinating capacities to cope with coastal changes. These capacities need to be properly addressed through interdisciplinary research in order to anticipate the highly variable future of coastal areas. ACKNOWLEDGMENTS This work was conducted in the framework of the 'STAMP', 'INFOLITTORAL-1' and 'MANGWATCH' projects funded, respectively, by the ANR Blanc, the French "Unique Inter-ministerial Fund", certified by the “Aerospace Valley competitiveness cluster" and CNRS 'Incubator for interdisciplinary research projects in French Guiana' research programmes. Mr. Olagoke acknowledges the "Erasmus Mundus" Forest for Nature programme (FONASO). We thank Mercator Ocean (http://www.mercator-ocean.fr/) for providing ocean current data. Mercator Ocean cannot be held responsible for the results nor for the use to which they are put.

Simulated mangrove shorelines (yellow line) overlain on SPOT satellite images, Kourou-Cayenne region from 1987 to 2009.

Figure 3.

A Multiscale Simulation Approach for Linking Mangrove Dynamics to Coastal Processes

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