This document discusses modeling watershed dynamics using a cellular automata (CA)-Markov model approach. The objectives are to generate land use/land cover databases from satellite data over time, analyze indicators and drivers of watershed changes, derive transition matrices and suitability images, and project future watershed scenarios to 2024. The study area is a 195 square kilometer watershed in India. Land use/land cover data from 1972-2004 shows changes over time in classes like water, wetland, forest, and agriculture area. Spatial layers of soil, land use, roads, drainage, and slope are generated. The CA-Markov model will integrate Markov transition matrices with CA suitability maps to predict future watershed conditions and

1. Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -Markov Model –A Geoinformation Based Approach
SANTOSH .N. BORATE
Under the Guidance of
Prof. M D BEHERA
SCHOOL OF WATER RESOURCES
IIT KHARAGPUR, KHARAGPUR-721 302
INTRODUCTION
Cellular Automata (CA)
Watersheds are very crucial as they provide water that meets the different water demand ranging from  Spatial component is incorporated
drinking, irrigation, industry, power generation etc. The effects of change in watershed dynamics are leading to a series of  Powerful tool for Dynamic modelling
environmental problems, such as deterioration of water quality, biodiversity loss, extinction of aquatic species, alternation of  Each row represents a single time step of the automaton’s evolution.
river flows, shortage of water resources, and so on.
St+1 = f (St,N,T) where St+1 = State at time t+1
St = State at time t
For sustaining development of watershed there is need of Watershed Modeling which implies the proper use of all land, water
N = Neighbourhood
and natural resources of a watershed for optimum production with minimum hazard to eco-system and natural resources. T = Transition Rule
Modelling of watershed dynamics helps to policymaker and decision maker in making the policies and taking the decisions for
CA-Markov model operation
optimum utilization and sustainable development and management of resources in watershed respectively.
CA-Markov model is used for predicting the land cover changes. This is a holistic approach that integrates the Markov and
A Remote sensing technique and GIS tool is used taking and process the images of watershed of different time periods. CA- CA models in which Markov Model gives the Transition Area Matrix (Area expected to change) while Cellular Automata
Markov model approach is used that integrates the Markov and CA models with the use of a multicriteria decision-making gives the Suitability maps for each class. These both the outputs are used as inputs in CA-Markov Model
technique is used in predicting the future watershed resources information.
OBJECTIVES
The objective of the study to model and analyze the watershed dynamics change using Cellular Automata (CA) -Markov
Model and predict scenarios for next 10 years . The specific objectives are:
• To generate land use / land cover database with uniform classification scheme using satellite data
• To create database on demographic, socioeconomic, Infrastructure parameters
• Analysis of indicators and drivers and their impact on watershed dynamics
• To derive the Transition Area matrix and suitability images based on classification
1972 1990 1999 2004
• To project future watershed dynamics scenarios using CA-Markov Model
• To give the plan of measures for minimize the future watershed dynamics change Fig1. Unsupervised Classification for different time periods
STUDY AREA Area (ha)
Year Water Wet land Marshy land Dense forest Open forest Settle-ment Agricul-ture
River basin map of India 1972 493.52 1013.03 2922.47 7329.09 5405.68 432.766 1926.33
1990 507.08 959.023 1574.05 6353.09 5725.06 653.049 3963.78
• Drainage Area = 195 sq.km 1999 790.88 550.46 1120.17 5709.63 5186.21 1160.62 5403.49
• latitude- 20 29 33.39 to 20 40 21.09 N 2004 472.68 281.88 818.91 5296.32 4763.25 1382.85 6532.56
•Longitude- 85 44 59.33 to 85 54 16.62 E
•Growing Industrial Area
Table 1: Shows the year wise area of different classes in watershed
Spatial Layer Generation of Socioeconomic, Infrastructure parameters
a) Spatial layer of Soil classes b) Spatial layer of Land Use c) Spatial layer of Road d) Spatial layer of slope
in watershed Land Cover of the watershed network, Drainage
Network
Mahanadi River Basin
Land Use/Land Cover Change trends:
DATA AND METHODOLOGY 8000 7000
6000
6000 5000
Data download Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004 Marshy Land
and Layer stack 4000 4000 Settlement
Dense Forest
Area (ha) 3000
Georeferencing and Open Forest Area (ha) 2000
Reprojection 2000 Agriculture
Wetland 1000
Area 0 0
extraction
Multitemporal
1960 1980 2000 2020 1960 1980 2000 2020
image Classification of the satellite data
Classification Year Year
Preparing Road network Drainage Network Soil Type Altitude Decreasing Trend of LULC
Ancillary Data Increasing Trend of LULC
Industrial Structure Population Density
Urban Sprawl Slope
1200
Statistics Calculation of LU/LC area statistics (for different periods) 1000
Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability Images by MCE Area (ha) 800
TAM and
Suitability Images 600 Water
Run CA- Markov model in IDRISI- Andes by giving -1) Basis land Cover Image, 2) TAM and 3) 400 Fallow Land
Simulation Suitability Image as inputs 200
Analysis Analysis of drivers responsible for watershed change 0
Prediction Predict Watershed Dynamics for future 10-Years from the obtained trend 1970 1980 1990 2000 2010
Year
Markov Chain Analysis (MCA)
CONCLUSION
On the basis of observed data between time periods, MCA computes the probability that a cell will change from one
land use type (state) to another within a specified period of time.
From the table and graphs it is observed that Dense forest, open forest, wet lands, Marshy Land are drastically are
The probability of moving from one state to another state is called a transition probability. From which exact area decreasing and transformed in to other classes.
expected to be change is calculated. While the Settlement and Agriculture classes are drastically increasing which obtains the area from above four
classes which are decreasing in area.
A combined use of RS/GIS technology, therefore, can be invaluable to address a wide variety of resource
where P = Markov transition probability matrix management problems including land use and landscape changes in watershed
P i, j = the land type of the first and second time
ACKNOWLEDGEMENTS
period
I express my sincere gratitude to Prof. M D Behera for his proper and timely guidance through out the period of work.
Pij = the probability from land type i to land type j I am thankful to Prof. S N Panda and JRF and SRF in SAL (Spatial Analytical Lab) of CORAL Department
for their help and support.