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M. Stat. – 508
Environmental Statistics
Full marks – 75
(Examination 60, Tutorial/Terminal 11.25, and Attendance 3.75)
Number of Lectures – Minimum 45
(Duration of Examination: 4 Hours)

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Aim of the Course:
The aim of this course is a broad discipline stretching from how and what to sample, through to modeling impacts on human and ecosystem health and ultimately to providing predictions of what changes might occur in the future. It acts as a bridge between the fundamental methods of the subject and important applications in a wide variety of environmental issues.
Objectives of the Course:
This course will develop the students’ ability to
 
Understand the complex relationships between natural and human systems.
 
Improve knowledge of the environment.
 
Provide quantitative information about the environment’s state and its most important changes over time across territories.
 
Achieve by setting up, strengthening and sustaining environmental issues which already operating in economic and social statistics.
Learning Outcomes:
On successfully completion of this course, the student will be able to:
 
Apply mathematical concepts, including statistical methods, to field and laboratory data to study scientific phenomena.
 
Develop an understanding of current environmental monitoring systems,
Apply knowledge acquired to the process of environmental impact modelling and  prediction as a design tool with application to a number of case studies.
 
Design and execute a scientific project.
 
Adapt skills in GIS to environmental management systems.
 
Develop skill for achieving sustainable development goals.

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Course Contents
Introduction: Environmental Variables – Discrete and continuous; Data collection – primary and secondary; Presentation of data – spatial and non-spatial data.
Design and Analysis of Environmental Data: Conceptual Foundations Methods, Environmental data, Data Exploration, screening and adjustment, Confidence Intervals and More, Deterministic functions, Bestiary of probability distributions, Continuous probability distributions, Discrete probability distributions, Statistical Models – putting it all together, Frameworks for statistical Inference, Bayesian Inference,  Hypothesis testing concepts, Nonparametric Inference: Ordinary least squares and more, Maximum Likelihood inference.
Hazard in the environment: Concept of risk, vulnerability, hazard, and disaster; Types of Natural Hazards and their Global and National perspectives,  Role of Global climatic changes and Global warming. Causes and consequences of Global Warming, Sea level rise in climate.
Study of Agro-meteorological Features: Fundamentals Concept of Meteorology and Climatology. Desertification, Drought and Flood management and Modeling Analysis: Flood hazard and its management: Definition, Causes, nature, frequency of flooding and its impacts.
Desertification and Drought – Causes of desertification; Evaluation of desertification hazard – potential and zoning: Drought – causes, types, distribution and management.
Food Security and Environmental Impact on Health and Agriculture: Pollution and Soil degradation, Deforestation, Land use pattern and regional pattern of productivity.
Case Study of Environmental Data Analysis:
1.
 Applications of probability distributions and Markov chain model,
2.
Drought Identification and Characterization at Local, National and Global level,
3.
Drought indices by Standardized Precipitation Index (SPI),
4.
Drought Prediction Using Markov chains modeling,
5.
Drought indicators: A Stochastic approach to evaluation,
6.
Applications of non-linear and non-stochastic Time series analysis, Wavelets analysis, Spectral analysis,
7.
Study of validity and uncertainty in environmental modeling.
Geographical Information System (GIS): Basic principles, Raster and vector data, Map Projection, Overlay analysis, Data structure and Digital cartography.
Global Positioning System (GPS): Basic principles, Applications to environmental studies.

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Main Books:
1)
G.P. Patil & C.R. Rao (1999): Environmental statistics: analyzing data for environmental policy, John Wiley and Sons.
2)
H. R. Byers (1974): General Meteorology, McGraw-Hill
3)
Isaacson D.L., Madsen R., John (1976): Markov Chains: Theory and Applications. Wiley, New York.
4)
Vic Barnett (2004): Environmental Statistics: Methods and Applications (Wiley Series in Probability and Statistics), John Wiley & Sons.
Books Recommended:
5)
Box, G.E.P. and G.M. Jenkins, (1976): Time Series Analysis Forecasting and Control. San Francisco: Holden-Day.
6)
Bryan F.J. Manly, Statistics for Environmental Science and Management, Second Edition (Chapman & Hall/CRC Applied Environmental Statistics).
7)
G. F. White (ed) (1974): Natural Hazards – Local, National, Global, Oxford University Press
8)
G. F. White (ed): Natural Hazards – Local, National, Global: Oxford University Press.
9)
G. T. Trewartha (1968): An Introduction to Climate; McGraw-Hill
10)
Linda Courtenay Botterill and Geoff Cockfield (2013): Drought, Risk Management, and Policy: Decision-Making Under Uncertainty. Australia
11)
P. Reining (1978): Handbook of Desertification Indicators (Washington D.C.: American Association for the Advancement of Science.
12)
V.T. Chow (1964): Handbook of Applied Hydrology, McGraw-Hill, New York.
13)
Velma I. (2012): Impact of Climate Change on Water and Health, Grover,  Publisher: CRC Press, John Wiley and Sons.
14)
Wayne R. Ott (1979): Environmental Indices: Theory and Practice, Publisher: CRC Press, John Wiley and Sons.