Coupling Cellular Automata with MEDALUS Assessment for Desertification Issue

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This study explores the connection between cellular automata and MEDALUS assessment in addressing desertification issues. Desertification, a severe form of land degradation affecting arid and semi-arid regions, poses significant challenges to the environment and human well-being. By examining factors such as soil sensitivity, climate, and vegetation, the research aims to develop a comprehensive understanding of desertification processes. The use of cellular automata modeling provides insights into the dynamics of desertification, offering valuable information for sustainable land management strategies.

  • Desertification
  • Cellular Automata
  • MEDALUS Assessment
  • Land Degradation
  • Environmental Sustainability
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  1. 1 COUPLING CELLULAR AUTOMATA WITH MEDALUS ASSESSMENT FOR DESERTIFICATION ISSUE A. KONE, A. FONTAINE, S. EL YACOUBI IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 1

  2. INTRODUCTION 2 Figure 1: Desertified area Source : Secretariat of the United Nations Convention to Combat Desertification (UNCCD) IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 2

  3. INTRODUCTION 3 Desertification [UNCCD]: land degradation in arid, semi-arid and dry sub-humid areas due to various factors, including climatic variations and human activities Consequences to : environment, human well-being and its life, to 1.5 million people worldwide and quarter of the land in more than 100 countries through : - a reduction in soil potential, in cultivable land in worldwide, - a decrease in surface and groundwater, - damages to crops, livestock, electricity productivity, - insufficient food production, - poverty, upheavals social, rural exodus IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago - 3

  4. OBJECTIVES 4 Figure 2: Objectives of the study IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 4

  5. METHODOLOGY 5 MEDALUS assessment Soil ?? Desertification Sensitivity Index (DSI) Management ?? Climate ??? Vegetation ?? Figure 3 : Desertification factors IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 5

  6. METHODOLOGY 6 MEDALUS assessment Class of DSI State of DSI I5= I4= I3= I2= I1= [1.78; 2[ Very- degraded [1.53;1.78[ Degraded [1.38;1.53[ High [1.22;1.38[ Moderate [1; 1.22[ Low Table 1 IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 6

  7. METHODOLOGY 7 CA model : A cellular automaton (CA) is defined by a tuple L;S;V;f : - L is cellular space : - S is a finite set of cells states; - V is the neighbourhood set : contains all the cells whose states have an impact on the evolution of a chosen cell c : V = c1; c2; ,cn - The transition function f calculates the state of each cell c at time t + 1 using its neighbourhood at time t f: Sn S (sc1t ;sc2t ; ;scn(t)) sc(t + 1) IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 7

  8. METHODOLOGY 8 Built model : Coupling CA approach and MEDALUS assessment Evolution of the desertification phenomenon using a cellular automaton model (L;S;?c;f) whose transition function f is based on the MEDALUS assessment where : - L two-dimensional network of cells: L = 0;1;..;W 1 0;1; ;H 1 . - S=[1;2] a set of cell states A state of a cell c denoted by s?= DSI is continuous -The neighborhood is Moore s order 1 : ?c= (c1;c2; ;c8) The neighborhood state of a cell c : 1 8 Figure 4 : Moore s Neighbourhood s?c= (sc1 sc2 sc8) IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 8

  9. METHODOLOGY 9 Built model : Coupling CA approach and MEDALUS assessment The transition function ? takes into account three important properties of land evolution : (1) Irreversibility : Desertified or very degraded state is almost irreversible in degradation situation (2) Stress condition: Bad climate conditions and human pressures lead to a stress condition in the exposed land. (3) Normal evolution : through interactions with neighbouring lands IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 9

  10. METHODOLOGY 10 Built model : Coupling CA approach and MEDALUS assessment - The transition function ? maps the state sc of each cell c at time t to its state sc f:[1;2] [1;2] [1;2] (sc,s?c) sc Id sc if sc I5 and s?c I3 or s?c I4 s?c if sc I4 and s?c I5 min I5 if sc and s?c verifiy stress condition 1 min I5 if sc and s?cverifiy stress condition 2 1 2 if not where Id is identity function, I3 is range of high state, I4 range of degraded state and I5 range of very degraded state +at time t + 1 according to its neighbourhood state s?c. + += sc (sc s?c) IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 10

  11. METHODOLOGY 11 Built model : Coupling CA approach and MEDALUS assessment - Stress condition : Case 1: The states of cell c and its neighbourhood reach the upper part of the range of degraded state such as: min I4 + min ?5 2 s? < min I5and min I4 + min ?5 s??< min I5 2 Case 2: The state of cell c reaches the upper part of the range of high state and its neighbourhood reaches the upper part of the range of verydegraded state: min I3 + min I4 2 sc < min(I4) and min I5 + max I5 s?c 2 IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 11

  12. METHODOLOGY 12 Built model : Coupling CA approach and MEDALUS assessment A cell c is in the process of : - Degradation if its state sc is in a range inferior to the range of its neighbourhood state s?cexcepted the stress case i.e. sc< sc - Regeneration if its state sc is in a range superior to the range of its neighbourhood state s?c i.e. sc> sc - Stability or Conservation if its state sc is in the same range with its neighbourhood state s?cexcepted the stress case. + + IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 12

  13. RESULTS 13 Grid evolution : Figure 4: Evolution of the number of cells in various sensitivity levels to the desertification during the time interval [0; 230] in the considered study area W=Low, M=Moderate, H=High, D=Degraded and DE=Very degraded. Figure 4: Evolution of an area initially distributed by the sensitivity level to the desertification IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 13

  14. CONCLUSION 14 -Model based on a continuous cellular automaton whose state set and transition function were extracted from the MEDALUS method. -This model allowed to predict the evolution of an area with moderate slope and an annual time step. -The used of Moore neighbourhood of radius 1 allows to consider the influence of all the surrounding neighbouring cells -This work is validated by geographers specializing in the study of desertification It has been improved by adding additional parameters and served as a basis for the development of software for monitoring land degradation. The work of this software constitutes our next publication IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago - - 14

  15. REFERENCES 15 [1] P. Tchakerian, V. Desertification: Exploding the myth by David S. G. Thomas and Nicholas J. Middleton, Wiley, Hichester. Earth Surface Processes and Landforms, 21(8):780 780, 1994. [2] Secr tariat de la Convention des Nations Unies sur la Lutte contre la D sertification (CNULD). D sertification: Une synth se visuelle. Centre International UNISFERA, Jul 2011. [3] F. Boudjemline and A. Semar. Assessment and mapping of Desertification Sensitivity with MEDALUS model and GIS - case study: Basin of Hodna, Algeria. Journal of Water and Land Development, 36:17 26, 03 2018. IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 15

  16. REFERENCES 16 [4] S. El Yacoubi and A. El Jai. Cellular automata modelling and spreadability. Mathematical and Computer Modelling, 36(9):1059 1074, 2002 [5] P. Shoba and S. S. Ramakrishnan. Modeling the contributing factors of desertification and evaluating their relationships to the soil degradation process through geomatic techniques. Solid Earth, 7:341 354, mars 2013 [6] P. D dorico, A. Bhattachan, F. Davis, K., S. Ravi, and W. Runyan, C. Global desertification: Drivers and feedbacks. Advances in Water Resources, 51:326 344, January 2013 IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 16

  17. THANK YOU! IConETech-2020, Faculty of Engineering, The UWI, St. Augustine, Trinidad and Tobago 17

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