eChapters

In November 1992 a document entitled Warning to Humanity was released, signed by 1500 scientists from around the world, including 99 Nobel laureates, and the Director General of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The document stated that “human beings and the natural world are on a collision course,” which “may so alter the living world that it will be unable to sustain life in the manner that we know.”

9.1 What are Natural Disasters?

The natural disasters are earthquakes, floods, droughts, famines, heat waves, cyclones, wild fire, volcanic, aerosols (small particles), and landslides.

9.2 Disaster Management

Both physical solutions and institutional strategies should be developed to manage natural disasters. For flood disaster control the water flow should be checked. Institutional strategies do not rely on physical barriers and include flood plain management, flood and disaster warning systems, education and preparedness programmes.

To a layman, wasteland is a piece of land, which is lying uninhabited or uncultivated. These lands are also commonly perceived to produce much less than their potential. Depleted of the green cover and productivity, such lands are often in a critical condition. Broadly, wastelands may be defined as land where the production of biomass is less than its optimum ‘productivity. Wasteland also refers to lands, which are economically unproductive, ecologically unstable and subject to environmental deterioration.

7.1 What is Pollution?
 
Pollution is a contamination of the environment by man-made substances or energy that has adverse effects on living or non-living matter. In simple terms, pollution can be seen as the wrong substance in the wrong place in the wrong quantities at the wrong time. This implies that harm is caused to the environment if the same substance is present at levels too low.

Types of pollution
Pollution can be categorized according to the medium in which it occurs such as

6.1 Biodiversity

Biodiversity means biological diversity. It measures the variety of the earth’s animal, plant and microbial species; of genetic difference within species and of the ecosystems that support the species in its widest. Biodiversity is virtually synonymous with “life on earth”, this word was coined in 1985. Biodiversity is the degree of variety in nature and not nature itself.

5.1 Concept of Ecosystem

Ecosystem is the basic functional unit in ecology, since it includes both biotic and abiotic environments influencing each other for maintenance of life.

A.G. Tansky in 1935 first proposed the term ecosystem. According to him, it is defined as the system resulting from the integration of all the living and non-living factors of the environment. In a simplest form, an ecosystem can be defined as a self-sustained community of plants and animals existing in its own environment. The term ecosystem is composed of two terms, “eco” means environment and “system” means an interacting and interdependent complex. It is otherwise known as bio system.

In simple words natural resources can be defined as any part of our environment such as land, air, minerals, forest, rangeland, wild life, fish and human beings.

The sum total of all physical, chemical, biological and social factors which compose the surroundings of man is referred to as environment and each component of these surroundings constitute a “resource”.  The natural resources are land, water, air, minerals, forests, rangeland, wild life or even human beings known as natural resource.

The five basic natural resources are energy, matter, space, time and diversity.

3.1 What is environment ?

The word environment is derived from a French word “environne” means to encircle or surround. Environment is defined as the circumstances or conditions that surround an organism or group of organisms. It is also defined as the complex of social or cultural conditions that affect an individual or community. Since humans inhabit the natural world and they build social, cultural and technological world, all constitute important parts of our environment.

Abiotic Environment

The physical or abiotic environment includes all those physical and non-living chemical aspects, which exert an influence on living organisms. The abiotic components are soil, water, air and energy from various sources. These provide both habitation and raw material for the synthesis of organic food.

1.1 Man’s place in Nature

All religions of the world share the view that man is the supreme being among God’s creations. He is the only one among many creatures to enjoy nature’s beauties and bounties. Nature is given to him so as to make responsible and judicious use of it. No religion justifies the most destructive attitude the modern man has developed towards his environment. All religions urge man to treat nature as a God-given gift, stating that God is omnipresent in the manifestations of the nature. It is irreligious for man to expect miracles and exploit the environment. Man must be confronted with the reality that struggle is essential for life rather than “struggle for existence.”

1    Introduction to Patent Laws    540

2.    An Overview of the Patenting Process    541

    2.1.    Inventor’s disclosure, 542
    2.2.    Patentability search, 542
    2.3.    Patent application preparation, 542
    2.4.    Patent application amendment, 542
    2.5.    Patent application examination, 543
    2.6    Main parts of a patent, 543
    2.7.    Granting of patent, 544
    2.8.    Date and term of patent, 544
    2.9.    Provisional specification, 544
    2.10.    Complete specification, 545
    2.11.      Cost of patenting, 545

1.    Introduction    496

2.    Strain Development Technologies    496

    2.1.    Screening of natural variants of microorganisms, 496
    2.2.    Genetic manipulation of microorganisms, 498
        2.2.1.    Random mutagenesis and selection, 498
        2.2.2.    Conjugation, 504
        2.2.3.    Transduction, 504
        2.2.4.    Transposition, 506
        2.2.5.      Hybridization, 507
        2.2.6.    Rare Mating, 509
        2.2.7.    Cytoduction, 510
        2.2.8.    Protoplast fusion, 511
        2.2.9.    Transformation, 511
        2.2.10.    Gene cloning and transformation (Recombinant DNA technology), 513

1.    Introduction    468

2.    Biofuel Cell    470

    2.1.    Microbial-based Biofuel Cells, 471
        2.1.1.    Microbial bioreactors producing H2 for conventional fuel  cells, 475
        2.1.2.    Microbial fuel cells producing electrochemically active metabolites in  
                the anodic compartment, 476
        2.1.3.    Microbial fuel cells operating in the presence of artificial electron relays, 477
        2.1.4.    Microbial fuel cells producing electricity during wastewater treatment, 483
        2.1.5.    Microbial fuel cell producing  electricity from acetate or butyrate, 484

1.     Introduction    424

2.    Conventional Methods of Hydrogen Production    426

3.    Biological Methods of Hydrogen Production    429

    3.1.    Hydrogen production by biophotolysis of water, 430
        3.1.1. Photolysis by cyanobacteria (Blue green algae), 430
            3.1.1.1. Nitrogenase dependent hydrogen production, 434
            3.1.1.2.  Hydrogenase-dependent hydrogen production, 437
            3.1.1.3.  Factors affecting  hydrogen production by cyanobacteria, 440
            3.1.1.4.   Bioreactors for cyanobacterial hydrogen production, 443
            3.1.1.5. Future prospects for cyanobacterial hydrogen production, 446

1.    Introduction    392

2.    Landfill Gas    393

3.    Biogas    394

    3.1.    Benefits of biogas, 394
    3.2.    Potential of biogas, 395
    3.3.    Biogas production system, 396
        3.3.1.    Raw materials, 396
        3.3.2.    Biogas technology, 398
        3.3.3.    Process of anaerobic fermentation for methane generation, 400
        3.3.4.    Conditions for anaerobic fermentation, 404
        3.3.5.    Sludge-algae-methane system, 407
        3.3.6.    Biogas plant, 408

1.    Introduction    360
2.    Feedstocks for Biodiesel Production    361

    2.1.    Fats and oils, 362
    2.2.    Alcohols, 363
    2.3.    Catalysts and neutralizers, 367

1.    Introduction    302
2.    Raw Materials for Alcohol Production    304

    2.1.    Sugary substrates, 306
    2.2.    Starchy residues (cereal grains), 309
    2.3.    Cellulosic materials (crop residues), 314

3.    Microorganisms for Alcohol Production    316

    3.1.    Comparision of ethanol production by bacteria and yeasts, 318

1.    Introduction    272
2.    Alcohols    273

    2.1.    Methanol, 274
    2.2.    Ethanol, 277
    2.3.        Butanol, 283

3.    Biodiesel    284
4.    P-series fuels

1.    Introduction    232
2.    Photosynthesis and Biomass Yields    232
3.    Biomass Properties    233
    3.1.     Polymeric and chemical composition, 233
    3.2.     Fuels analysis, 235
    3.3.     Physical properties, 236

1.    Introduction    174
2.    The Thermodynamic and Mechanistic basis of Cellular Metabolism    174

    2.1.      Adenosine triphosphate, 175
    2.2.    Coenzyme A, 176
    2.3.    Modes of ATP synthesis, 177

3.      Components of Electron Transport Chain    181

    3.1.    Electron carriers in ETC, 182
    3.2.    Complex I - NADH dehydrogenase (NADH coenzyme Q reductase), 185
    3.3.    Complex II - Succinate - coenzyme Q reductase, 187
    3.4.    Complex III - Coenzyme Q - cytochrome c reductase, 187
    3.5.    Complex IV - Cytochrome c oxidase, 188
    3.6.    ATP synthese, 188

1.    Introduction    129
2.    Soil Microbiology    129

    2.1.    Bacteria, 130
    2.2.    Fungi, 132
    2.3.    Algae and Cyanobacteria, 132
    2.4.    Protozoa, 132
    2.5.    Viruses, 135

3.    Water Microbiology    133
4.    Microbial Associations    133

    4.1.    Neutral interactions, 133
    4.2.    Positive interactions, 133
    4.3.    Negative interactions, 133

1.    Introduction    71
2.    Microorganisms in Food Industry    72
    2.1.    Microorganisms as the agents of fermentation in  
        traditional fermented foods, 73
    2.2.    Microorganisms as the source of food, 83
    2.3.    Microorganisms as a source of food supplements, 86

3.    Microorganisms in Pharmaceutical Industry    92

    3.1.    Antimicrobial agents from microorganisms, 92
    3.2.     Microorganisms as probiotics, 97
    3.3.    Microbial enzymes in  pharmaceuticals, 98
    3.4.    Mushroom nutriceuticals, 98

1.    Introduction    3
2.    History and Scope of Microbiology    4
    2.1.    Controversy over the origin of microorganisms, 5
    2.2.    Germ theory of fermentation, 6
    2.3.    Germ theory of disease, 6
    2.4.    Development of laboratory techniques and pure cultures, 7
    2.5.    Discovery of immunization, 7
    2.6.    Discovery of protection against infection, 8
    2.7.    Discovery of chemotherapeutic agents, 8
    2.8.    Microorganisms as geochemical agents, 9

Vegetation is a key component in hydrologic modeling. Transpiration, a component in hydrologic cycle, is contributed by vegetation. Most of the hydrologic model thus includes vegetation as a parameter. For accurate and appropriate model output, first requirement is good quality data. Most of the time, the data on vegetation is confusing, as in literature various researcher have used different values for common vegetation parameters. Here some common parameters of some important crops, which yield good quality results, are given.

Kharif crops are generally sown during last week of May and first week of June, while rabi crop are sown during last week of November and first week of December. But this period will vary from region to region as well as depends on variety of crop.

More information, more accurate and timely decision. In present high tech world, the more information you have it is easier to make an accurate decision. The information comes in many different ways, from reports to digital photos. A new way to analyze data is a geographic information system (GIS). GIS allows to bring all types of data together based on the geographic and locational component of the data. GIS is a useful tool for seeking innovative ways to solve the problems.

9.1 What is GIS?

A geographic information system (GIS) is a system designed to capture, store manipulate, analyze, manage and present all types of geographically referenced data. GIS can be defined as a computer-based system for digital entry, storage, transformation, analysis and display of spatial data. A geographic information system integrates hardware, software and geographically referenced data for further analysis. GIS allows users to interpret the georeferenced data in many ways that reveal relationships, patterns and trends.

Remote sensing is emerged as an important application of science in all fields for collection of information from a far. This chapter describes the fundamental concepts of remote sensing, electromagnetic radiation (EMR), the interactions between EMR and earth surface features, satellites etc.

8.1 What is Remote Sensing?

Remote sensing is nothing but acquiring information about an object without touching the object itself. In other words, remote sensing is the acquisition of information about an object or phenomenon, without making physical contact with that object. In present era, the term generally refers to the use of aerial sensor technologies to detect and classify objects on the Earth by means of propagated signals. As remote sensing is obtaining information about objects without making physical contact with them, remote sensing can be described as ‘reconnaissance from a distance’, ‘teledetection’ or a form of the common phrase ‘look but don’t touch’.

The artificial neural network (ANN), a data driven model, is inherently suited to the problems that are mathematically difficult to describe. An ANN is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data sets, where explicit knowledge of the internal processes is not available. In this chapter general structure of ANN is described and also explained briefly with a case study.

7.1 What is Artificial Neural Network?

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous system, such as brain process the information. An ANN is an adaptive (Adaptive means that the system parameters are changed during operation i.e. training phase), most often nonlinear system that learns to perform a function (an input/output map) from data. In more practical term, neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find out patterns in the data.

Artificial Intelligence (AI) technology is increasingly used in our everyday lives. It is used in a variety of industries from gaming, journalism/media, to finance, as well as in the research fields from water resources to robotics. In this chapter concept and applications of AI are discussed in brief.

6.1 Historical Background

After World War II, a number of people worked independently on intelligent machines. The English mathematician Alan Turing was the first who explained the concept of intelligent machines through a lecture in 1947. He stated that intelligent machines could be developed by programming computers rather than by building machines. Since late 1950 many researchers are working on intelligent machines, mostly on programming computers

Data mining is a powerful technology which helps in extracting future trends and behaviour from large databases. Thereby it helps decision makers to make knowledge-driven decisions. One common form of real-world data is time series. The suitable models are required to analyze time series data. This chapter provides a short introduction to stochastic system, uncertainty, basic statistics time series analysis and some stochastic models.

In this era of computer, mathematical models become integral part of hydrologic studies due to advancement in soft technology. But question always arises as ‘Why should modeling?’ It was well expressed by Rosenblueth and Wiener (1945) as

No substantial part of the universe is so simple that it can be grasped and controlled without abstraction. Abstraction consists in replacing the parts of the universe under consideration by a model of similar but simpler structure. Models, formal or intellectual on the one hand, or material on the other are thus a central necessity of scientific procedure.

Modeling is heart of hydroinformatics. Simulation modeling became integrated part of modern science to justify feasibility of selected option among various competitive alternatives. Simulation modeling is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time. This chapter describes basic concepts, model simulation, model variables, modeling process, model performance etc. in brief.

Data collection is a tedious and time consuming work, particularly from remote places. So many times the collected data is noisy. But data is the base for future planning and management of water resources. Planning based on noisy data could lead to failure. The modern data collection and subsequent control system i.e. telemetry and SCADA, are introduced in this chapter.

Water is a social requirement. Society now become aware about it’s dependence upon water and hence realized the importance of water in sustaining not only the development but also life. Water: too little, too much, too dirty; is general scenario in the world. But with increase in population water become limiting resource and is becoming scarcer and scarcer day by day. As water become scarcer commodity, the users (stakeholders) become more conscious regarding their share in water and ultimately this situation may lead to water conflicts. Earlier water related problems were generally tackled by hydro-engineers, but now in present climate changed scenario these problems go beyond the hydraulics and hydrology. Now a days politicians, ecologists, Non Government Organizations (NGOs), the media and the stakeholders etc are playing a crucial role in decision making in respect to water management issues. Now water projects are designed in reference to social context (Social context means cultural, economic, political, sociological, legal and other such aspects). When social factors are taken into consideration while tackling the water problems, it could not possible to satisfy all involved. Thus it is required to agree upon a compromise solution acceptable to all. Over recent few decades, information and communication technology (ICT) flourishes with great advances. It provides the ways to search for compromise solutions. In this chapter information, hydroinformatics, evolution of hydroinformatics and role of internet in hydroinformatics are discussed.

Introduction

Famous French postmodern philosopher Jean Baudrillard commented on information once. “Information can tell us everything. It has all the answers. But they are answers to questions we have not asked, and which doubtless don’t even arise.” This seemingly paradox observation fits contemporary Internet age perfectly. Internet is a vast repository of information of all kinds and is bursting on its seams. The seeker needs to search with monumental grit and determination to have a full handle on the range of information available for any particular search. A search string for GIS returns more than 1185000 results, in a mere 0.26 seconds. Recommending a comprehensive resource list is not possible as new material gets continually added by way of additions and updates. However for a beginner the list given below will be handy. The flip side is GIS has innumerable resources and a broad range of content. Software vendors, organizations, individuals, universities and companies have provided a wealth of information. The readers are exhorted to make use of the materials to improve their understanding of the subject.

Introduction

The term geographic information systems (GIS) has come to mean, variously, an industry, a product, a technology, and a science. As such, the term invokes different perceptions dependent on whether the viewpoint is that of the software developer, the system marketer, the data provider, the application specialist, or the academic researcher, among others. A newcomer to the field is likely to be bewildered by the multiple uses of the same term, or, seen alternatively, different definitions of the same term. For instance some find it distasteful to use of the conventional acronym GIS and prefer Geospatial Technology. With all the variety of perceptions predicting the future of GIS is a cumbersome business. The most certain inference we can gain from the available trends is, it is growing and is poised for further growth. Geotechnology along with Nanotechnology and Biotechnology is considered as of the three most promising growth industries for the near future.

Topology building

Boolean operations

Query and model building

Customization

Introduction

At human scales, the world consists of objects, events, processes, and a background environment. Cognition is about knowledge: its acquisition, storage and retrieval, manipulation, and use by humans and other intelligent creatures. Cognition includes sensation and perception, thinking, imagery, reasoning and problem-solving, memory, learning, and language. Cognitive structures and process are part of the mind, which emerges from a brain and nervous system inside of a body that exists in a social and physical world. Spatial cognition deals with the cognition of spatial properties of the world, including location, size, distance, direction, shape, pattern, movement, and inter-object relations. GIS design will be largely influenced by spatial cognition.

Introduction

The GIS database is a model of real world. It goes without saying that there is an inherent discrepancy between GIS database and the real world it represents. This is so because all models are fraught with cognitive issues and remain as approximations. To realize the objectives of a GIS, geographical data collected, entered and processed are to be sufficiently error free and reliable.

Errors can be statistical errors like mistakes, wrong inputs or faulty field surveys or system errors like excessive tolerances or high RMS error. The spatial database has further problems in the sense that the real world phenomena are not always intrinsically discrete. For instance where is the bank of a shallow river? Where is the boundary between two soil types? These are but a few questions revealing the fuzzy nature of geographic data. Many soil scientists, geographers have noticed that carefully drawn boundaries or iso-lines on maps in general give misrepresentations of change that are often gradual, vague or fuzzy. Traditional cartography as a medium of spatial data handling had a solution to such problems. But now when digital data are handled, they need to be stored with certain precision. Moreover, the spatial variation of natural phenomena is not just a local noise function or inaccuracy that can be removed by collecting more data or by increasing the precision measurement but is often a fundamental aspect of nature that occurs at all scales. (Goodchild, 1980; Mark and Aronson, 1984). This means residual unmapped variation is a remarkable problem in geographical information processing. It is therefore a prerequisite to fully understand how errors arise or propagated in GIS and what might be the effects of errors on output.

Introduction

Data input or capture is the operation of encoding the data and incorporating in the database. The creation of a clean error free digital database is a complex and critical task determining the very usefulness of a GIS. GIS data represent not only where the graphic or cartographic features occur, but also the associated attributes of them.

Data input to a geographical information system involves

(a) Entering the spatial data (digitizing);

(b) Entering the non-spatial, associated attributes and

(c) Linking the spatial to the non-spatial data.

At each stage necessary verification and checking procedures shall be deployed to ensure an error free database.

Introduction

The concept of database is the approach to information organization in computer-based data processing. (Date, 1995 and Everest, 1986).

A Geographic Information System is as good as the data contained in it. Accurate and efficiently organized data are critical to the durability of the system. Data collection, capture and organization will occupy much of the time in building a GIS solution. The analysis potential of a GIS will depend on the database. It is essential to understand database concepts to practice GIS. It is erroneous to view these GIS databases as mere stores of information. The database is used to abstract very specific sorts of information about reality and organize it in a way that will prove useful during subsequent analysis. The database should be viewed as a representation or model of the world developed for a very specific application.

Conversion of real world geographical variation into discrete objects is done through data models. They represent the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

Introduction

Data are the most critical element of a GIS. It is reported that more than two thirds of the time and expenditure to develop a GIS is spent on data collection and organization. Understanding and appreciating the criticality of data is of paramount significance to practice GIS. This chapter is complementary to database concepts chapter discussed in Level II and deals with the fundamentals to start with and the concepts of building an information organization.

Data - A Definition and Certain Points to Ponder

“Data” are defined as a body of facts or figures, which have been gathered systematically for one or more specific purposes.”

Introduction

All spatial data stored in a GIS are associated with a map projection, either implicitly (so-called “unprojected” data stored with raw longitude, latitude coordinates) or explicitly (where the data have been transformed into a known map projection). Map projections are a particularly important feature of spatial data because they introduce error and distortion, since a sphere cannot be transformed to a plane without some stretching and twisting of the sphere’s surface.

The typical GIS package supports anywhere from 5 to 50 map projection types, and most projections may be modified or customized by varying their parameters. This flexibility is important for two reasons First, in order to use two or more layers together in a GIS for visualization or analysis, all data must be projected in the same way. Second, certain types of analyses, such as the measurement of area, are only valid when using certain types of projections (and conversely, are completely wrong when using an inappropriate projection).

Introduction

In a technology that consistently maintains a fantastic development curve with capabilities doubling up year-to-year, freezing the hardware specification is not easy or very meaningful. GIS (not geography) owes to its genesis and progress to the developments in information technology front. It may be asserted that any and all upgrade in the hardware industry will only add to its own ability to effectively process more and more data and handle more complex routines. The personal computers of today are seen to be adequate to run typical GIS software. A reference hardware specification is discussed here. GIS software development is an involved process but then the software developers have made good use of hardware capabilities available and have released upgrades consistently. Hardware developments are such that they defy a static spcification. However a reference specification as on 2009. An overview of various software packages used in the past as well as present is attempted. ESRI released a new version in 2002 and claimed that their ArcGIS is an “integrated collection of GIS software products for building a complete GIS that enable users to deploy GIS functionality wherever it is needed, in desktops, servers, or custom applications; over the Web; or in the field.” The details are discussed below. While a number of software overviews are available, this text is not aimed at providing a tutorial to any of the software packages nor do the comparisons made are to promote any particular software. Similarly, it is presumed that the reader is familiar with the hardware elements and functionality. Lot of information about the software packages are available on their respective web sites and the reader is exhorted to look for current, additional and in depth information required in the pages. A brief discussion on Liveware or human resource can be found at the end of the chapter.

Introduction

A system is a simplified version of reality and is defined as a collection of interrelated parts that work together. Most systems share certain common characteristics. These common characteristics include the following:
Systems have a structure that is defined by its parts and processes. Systems are generalizations of reality.
Systems tend to function in the same way as nature. This involves inputs getting changed in a process and realization of new outputs.
The various parts of a system have functional as well as structural relationships between each other.
Owing to the functional relationships that exist between the parts there is a flow and transfer of some type of energy and/or matter. In case of information systems there is a flow of information in the structure.
The parts that make up a system show certain degree of integration. In other words the parts work well together.

Abstract

India’s climate policy and its stand in the United Nations Framework Convention on Climate Change (UNFCCC) have developed through a complex and contradictory interactions between global pressures to take actions in the field of climate change, India’s foreign policy orientation, its environmental practices and various other national factors. India has been under growing pressure from the major developed economies and also from many small developing countries to play a leading role in mitigating climate change in keeping with its economic and political importance. National factors, including the very important consideration of maintaining rapid GDP growth, favours a conservative approach, with emphasis on expanding India’s share of global climate space. India ranks high on global climate change vulnerability because of its 7,500 Kilometre-long coastline, high dependence of its agriculture on monsoon, decreasing availability of freshwater and over-exploitation of ground water resources, presence of large population in cyclone-prone areas, low lying river deltas and fragile ecosystems like partially submerged lands, and not least, because of melting of the Himalayan glaciers, which feed close to 70 percent of India’s river waters. In 2008, India formulated a National Action Plan on Climate Change (NAPCC) under international obligations. India’s climate policy has always been the preserve of a small group of individuals protected from societal influence and unaccountable to the public at large. Indian policy makers claim to speak on behalf of those very people who have been consciously excluded from democratic and participatory decision-making. The industrialised economies reluctant to own up to their climate obligation only breeds more cynicism within the Indian society. Whatever the outcome of international conventions on climate change or any kind of global negotiations in the immediate future, India cannot afford to give up on the serious issue of climate change.

Keywords: Climate change, Economics, Policy, Agriculture

Introduction

Urbanization, a vital global phenomenon, refers to the process of city establishment and growth; the term commonly connotes population increase in the city from internal growth and immigration as well as their spatial expansion (Revi, 2007). Recent developments, uncontrolled population growth have been linked with the process of urbanization. Urban areas cover 3% of the global land but it gives shelter to nearly 26% of the global population (U.S. De, 2009). Increase in population growth and industrialization is posing threat to natural resources such as land air and water. Anthropogenic factors have led to change in land use pattern, high energy consumption, high level of generation of waste etc. As per UN survey, by 2050, approximately 70 percent of the global population will be living in cities (UN, World Urbanization Prospects, 2014). Unplanned growth is damaging the existing ecosystems along with that causing climate change and loss of biodiversity.

Introduction

In the recent years, climate change emerged as the single most pressing problem globally, and accordingly received an increased attention (Berrang-Ford et al., 2011). It is evident that human activities are responsible for the climate change. Natural processes may also lead to climate variability and change. The climate change forecasts resulted from simulation models indicate that the future climate is likely to turn into more capricious nature with more occurrences of extreme events (IPCC, 2013b). Future projections according to the World Bank (2010) reveal the need of raising food production by 1.8% per annum between 2005 and 2050 due to climate change rather than 1% per annum if there were no impact of climate change. The effect of climate change can be seen on the quantity and quality of various components in the global hydrologic cycle (Milly et al., 2005; Bates et al., 2008).

In South Asia, vulnerabilities to the climate change are the largest in semi-arid and arid countries, where rainfall is concentrated over a few monsoon months, year to year variations are high, and where water resources are decreasing (Lenton, 2004). Cruz et al. (2007) indicated that climate trends and variability can be characterized by increased air temperatures, which are more pronounced during the winters compared to the summers. Across the entire Asia, both spatial and temporal variability in rainfall trends observed during the past few decades may be attributed to climate change. It is likely to happen that climate change will extend the dry season when negligible or very low flows occur, which particularly affects the water management process due to inability to rely on water storage reservoirs or groundwater resources situated at great depths (Giertz et al., 2006).

Abstract

Women are amoungst one of the most vulnerable groups, being impacted by climate change. Socio- cultural barriers and traditionally ascribed roles make them the key person responsible for arranging primary resources such as fuel, water, food which face a serious threat from environmental stresses such as climate change. Women with the potential in terms of experience and a strong body of indigenous knowledge to combat the increased disaster risks enable their families to cope with climate change. To deal with these stressful situations women both at the local and global level adopt various coping strategies. The coping strategies adopted by them generally involves considerable amount of risk taking as they interfered not only with the household work but also with their income generating activities, education of girls and other opportunities for skill development. This chapter gives a comprehensive view of the gendered impact of climate change, as well as the coping strategies of women. The chapter also highlights the need of developing coping mechanisms into adaptive mechanism and strategies to provide a sustainable solution to deal with the issue of climate change.

Keywords: Coping strategies, Climate change, Gendered impact, Women

Abstract

Climatic variability with respect to total annual precipitation, mean maximum and mean minimum temperature as well as phytosociological characteristics of various herbaceous plant species of two distinct landscapes, ie, BudhaPushkar (BP) as sandy and Nasirabad Valley (NV) as rocky gravel, of Central Aravalli region was compared over a long period of time (1957-2012). Analysis of climatic data indicates reduction of total precipitation of 50 years by 1226.2 mm. There has been a ‘shift’ in monsoon season. However, a significant increase (36.8%) in precipitation was noticed during the winter season of last five decades. Data on total mean maximum temperature also showed significant variation. A rise of 2.5⁰C was recorded while compared the periods 1971-1990 and 1991-2011. Study showed a notable increase in the mean maximum and mean minimum temperature in the region. An assessment of changes in plant diversity at two landscapes was carried out over a period of 35 years (1976 and 2012). The phytosociological characteristics were compared for these selected years. It was observed that climatic variability have changed the order of dominants and other species with respect to frequency, density and IVI of herbaceous species at both BP and NV landscapes. Total number of species increase for BP landscape while decreased for NV stands, while numbers of invasive species are increased for both the landscapes over a period of 35 years.

Keywords: Climatic variability, Plant diversity, Central Aravalli region, Landscapes, Phytosociological characteristics

Abstract

While multiple species interact with each other in variable environments, they employ a variety of interaction strategies to withstand variation in environmental conditions. Phenotypic plasticity is one such mean allowing species to cope with environmental challenges often posed by the variation in consumer pressure, disturbances, and predation risks. In response to these and other environmental cues, expressed phenotypes can alter the sign and strength of interactions among species as these responses are often reflected in increased or decreased species competitive or facilitative abilities. Besides phenotypic plasticity, species interactions also depend on continued variation in abundances or densities of interacting species commonly termed as density-dependent interactions. Such density-dependence follows the law of diminishing returns, which means that while an individual species adds a density-dependent weight to the performance of other species, with increasing accounts, at a certain level of interaction it provides diminishing returns resulting in decreased population growth of the species. Thus, species interactions in variable environments hold two interdependent opposing forces: the growth-escalating and the growth-diminishing forces. The first one is resulted from adaptive phenotypic responses to variable environments and the second one is resulted from dynamic changes of abundances or densities of interacting species. Based on this conceptual framework, we describe community dynamics that are shaped by the balance of these two forces in variable environment. By analyzing these dynamics, we then seek underlying mechanism of Grim’s classic humped-back model of productivity-diversity relationship and also provide an alternative explanation for the intermediate disturbance hypothesis.

Keywords: Competition, Facilitation, Functional niche, Interaction continuum, Phenotypic plasticity.

Abstract

Climate variability/change is one of the very significant concerns discussed in the recent two decades. Its impact on air temperature and precipitation has received massive attention worldwide as they affect both the spatial and temporal patterns of water availability. Hence, it is becoming clearer that analysis of trends in air temperature and rainfall is significant in studying the impacts of climate variability/change for water resources planning and management. Therefore, the aim of the study was to detect theannual, seasonal and monthly trends in air temperature and rainfall in the Râmgangâ river basin. Daily air temperature (Bareilly and Moradabad) and rainfall data (Shahajahanpur, Bareilly, Bareilly BPO and Moradabad)of 40 years (1971 to 2010) were used for the study. Non-parametric Mann-Kendall test and theTheil-Sen’s Slope Estimator was used for determination of estimating the trend and magnitude of the trend. Findings revealed that the basin is experiencing a warming trend because of the rise in themaximum and minimum air temperature. In case of rainfall, Moradabad showna significant high positive trend during the monsoon season. This would alter the river run-off in long run if the existing trend continues to persist like this.

Keywords: Climate change/Variability, Mann-Kendall test, Theil-Sen’s Slope estimator, Water resource planning