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E-raamat: Impact of Urbanization on Water Shortage in Face of Climatic Aberrations

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Impact of Urbanization on Water Shortage in Face of Climatic AberrationsSuurem pilt
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The uncontrolled utilization of natural resources to supply to the water demands of the ever-growing population has brought about worldwide scarcity. The supply shortage has resulted in conflicts between countries, created prolonged drought, closing of industrial units, shifting of local inhabitants etc. The abnormality in climatic patterns due to global warming has only enhanced the uncertainties. Unregulated discharge of waste water into fresh water resources is also polluting the available water resources and making them non-utilizable. That is why the discrepancy between water supply and demand is slowly but steadily becoming a problem, which may lead to conflict and inequality all over the world. The present investigation is an attempt to find the impact of urbanization in the face of climatic uncertainties on water shortage or scarcity. How is climate responsible? What urbanization factors have an influence on the extent of shortages? What is the role of the socio-economic status of the inhabitants? Industrialization? Consumption pattern? Each of the causes and effects were analyzed with the help of data from a climate model, which was then fed into a hydrologic model. The hydrologic output data was then put into various other novel simulation platforms to predict the uncertainties that can be caused by urbanization in various sectors of the regions of interest. The impact was calculated based on IPCC recommended climatic and five distinct urbanization scenarios. The study results will help to predict what is in store of those living in the developing countries. Possible mitigation measures are also discussed.

1 Introduction 1(34)
1.1 Definition of Water Shortage
1(1)
1.2 Causes
2(26)
1.2.1 Urbanization
2(9)
1.2.2 Water Pollution
11(9)
1.2.3 Climate Change
20(7)
1.2.4 Mismanagement of Available Water Resources
27(1)
1.3 Objective of the Present Study
28(2)
1.3.1 To Develop a Weighted Cognitive Indicator for Representing Status of the Water Shortage
29(1)
1.3.2 To Identify the Priorities of the Related Parameters of Water Availability
30(1)
1.4 Brief Methodology
30(3)
1.4.1 MCDM
31(1)
1.4.2 Artificial Neural Network
32(1)
1.4.3 Validation of the Indicators by Sensitivity Analysis
32(1)
1.4.4 Application and Validation of the Indicator with Respect to Climate Change Scenarios in Three Different Study Areas
32(1)
References
33(2)
2 Multi Criteria Decision Making 35(14)
2.1 Definition
35(1)
2.2 Steps of Decision Making
36(1)
2.3 Working Principle
36(1)
2.4 Types of MCDM
37(1)
2.4.1 Compensatory Method
37(1)
2.4.2 Outranking Methods
37(1)
2.5 Examples
38(8)
2.5.1 Analytical Hierarchal Process (AHP)
38(4)
2.5.2 Fuzzy Logic Decision Making (FLDM)
42(4)
2.6 Limitations
46(1)
References
46(3)
3 Artificial Neural Network 49(6)
3.1 Definition
49(1)
3.2 Limitations
50(1)
3.3 Working Principle
51(1)
3.4 Types of ANN
51(1)
3.5 Applications
52(2)
3.5.1 Drought Management
52(1)
3.5.2 Water Supply System
52(1)
3.5.3 Landuse and Landcover Change
53(1)
3.5.4 Groundwater Quality
53(1)
References
54(1)
4 Climate Change and Climate Models 55(12)
4.1 Climate Change
55(6)
4.1.1 Causes
56(1)
4.1.2 Impacts
57(4)
4.2 Climate Models
61(5)
4.2.1 Definition
61(1)
4.2.2 Types
61(3)
4.2.3 Intergovernmental Panel on Climate Change
64(2)
References
66(1)
5 Detail Methodology 67(12)
5.1 MCDM
67(5)
5.1.1 Selection of Criteria
68(2)
5.1.2 Selection of Alternative
70(1)
5.1.3 Aggregation Methods
70(1)
5.1.4 Determination of Priority Values
71(1)
5.2 Water Limitation Index
72(1)
5.3 ANN
72(2)
5.3.1 Input and Output
72(1)
5.3.2 Topology
73(1)
5.3.3 Training
73(1)
5.3.4 Performance Metrics
73(1)
5.4 Sensitivity Analysis
74(1)
5.5 Scenario Analysis
74(5)
5.5.1 Farakka Township
74(2)
5.5.2 Mahi Dam
76(1)
5.5.3 Vaigai Dam
77(2)
6 Results and Discussions 79(16)
6.1 Results from MCDM Applications
80(1)
6.2 Results from ANN Applications
80(4)
6.3 Results from the Sensitivity Analysis
84(1)
6.4 Scenario Analysis
85(7)
6.5 Discussions
92(3)
7 Conclusion 95
7.1 Summary
96(1)
7.2 Drawbacks
97(1)
7.2.1 Source of Abstraction
97(1)
7.2.2 Location Specific
97(1)
7.2.3 Estimation Based on Extreme Condition
97(1)
7.3 Future Scope
98
Dr. Mrinmoy Majumder is an Assistant Professor in the School of Hydro-Informatics Engineering at the National Institute of Technology Agartala, India. He completed his PhD in 2010 from Jadavpur University. His current research interests are on Hydro-informatics, Natural resource management and Nature based algorithms. He has published more than 25 papers in various national and international journals and has authored 5 books.
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