Climate is commonly defined as the weather averaged over a long period. The standard averaging period is 30 years, but other periods may be used depending on the purpose. Climate also includes statistics other than the average, such as the magnitudes of day-to-day or year-to-year variations.

Climate in a narrow sense is usually defined as the "average weather," or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period ranging from months to thousands or millions of years. The classical period is 30 years, as defined by the World Meteorological Organization (WMO). These quantities are most often surface variables such as temperature, precipitation, and wind. Climate in a wider sense is the state, including a statistical description, of the climate system.

For a particular place on earth climate variation can be observed over timescales ranging from tens of years to thousands of years it’s because of very slow influence by natural causes such as changes in solar activity and long-term changes in the tilt of the earth and its orbit around the sun. However, the term climate change came in picture since the early 1900s.

The IPCC, 2007 stated that: 'There is very high confidence that the net effect of human activities since 1750 has been one of warming' (IPCC 4th Assessment Report, 2007). IPCC is an Intergovernmental Panel on Climate Change (IPCC) is the leading international body for the assessment of climate change.

1.2 Objective of study

1. To review the literature supporting the notion that LULC changes affects the climate

2. To evaluate and document various responses to climate change for LULC changes

3. Correlating the LULC changes and climate change

Chapter 2 Background theory

2.1 Introduction to Climate & Climate System

2.1.1 Climate

Climate can be defined as long term effect in change in atmospheric variables, over a period of 30 years conventionally described by the World Meteorological Organization (WMO) as a classical period for performing statistics used to define climate. The relevant atmospheric variables which play an important role in climate variability are temperature, precipitation and wind. In other words climate is represented as averaged weather or mean weather for a long period of time.

In general, climate can be defined as a measure of the average pattern of variation in temperature, humidity, atmospheric pressure, wind, precipitation, atmospheric particle count and other meteorological variables in a given region over long periods of time. Basic difference between climate and weather is that weather is defined as short term variation in atmospheric variables, when it studied for a long term basis it terms as climate (Reddy, 1993).

Here it becomes very important to keep in mind that the atmosphere which used to define the climate itself influenced by various processes involving not only the atmosphere but also ocean, sea-ice and vegetation etc.

2.1.2 Climate System

Climate, now defined in broader sense as the statistical description of climate system, which includes the analysis of behavior and interaction of its five major components. These are;

1) The atmosphere (gaseous envelope surrounding the Earth),

2) The hydrosphere (liquid water, i.e. ocean, lakes, underground water, etc),

3) The cryosphere (solid water, i.e. sea ice, glaciers, ice sheets, etc),

4) The land surface and the biosphere (all the living organisms), and

5) The interactions between them (Figure-1).

We will use this wider definition when we use the word climate.

Role of various components of climate system

A The Atmosphere

It is a gaseous envelope surrounding the Earth, mainly responsible for the all the climatic events to take place within its boundaries. The composition Nitrogen 78.08% Oxygen 20.95% and other gases like Orgon 0.93%, Carbon dioxide, water vapor. Atmosphere itself remains in an equilibrium state to facilitate to the all the atmospheric event to occur, this called Hydrostatic Equilibrium (Kesler and Ohmoto, 2006).

Another factor called atmospheric specific humidity also plays a vital role in sustainability of human life over earth. It displays characteristic vertical profile of atmosphere which shows high humidity values at lower levels of atmosphere near earth surface due to large holding of water at that level and a decrease with increasing height from surface.

The change in any of the above mentioned factors of mechanism unbalances the radiative balance as well as the climatic cycle. Atmospheric circulation is related to the movement of all the winds running all over the globe. The motion of winds at equator develops the circulations of wind due to ascendance at equator called Hardy cells, close with a downward branch at a latitude of about 30 degrees. The northern boundary of these cells is denoted by strong westerly winds in the upper troposphere called the tropospheric jets. Earth’s rotation is very much responsible in deflecting the movement of winds due to the strong force called Carlolis force, which deflects the flow coming the mid-latitudes to the equator, towards right in the northern hemisphere and towards left in southern hemisphere which forms the easterly trade winds characteristics over tropics. Westerly winds, facilitate the extra-tropical circulation, whose zonal symmetry is perturbed by large wave like patterns and the continuous succession of disturbances that governs the day to day variation in the weather. Ferrell cells are dominated feature of meridian circulation, weaker than the Hardy cells. This cell is characterized by upward motion in its pole-ward branch, and down-ward motion in the equator branch.

Figure 1: Schematic view of the components of climate system and their potential changes (Based on source IPCC2007).

Both of these two cells transport the heat and moisture from different continents influencing strongly to precipitation which is with temperature very much important in defining the climate.

Figure 2: Representation of the annual mean general atmospheric circulation. The 3-cells transport heat around the planet and, together with the spin of the Earth (the Coriolis Effect), create the dominant surface winds (source: climatica.uk.org).

B The Hydrosphere

Hydrosphere referred to a liquid water envelope which surrounds the earth surface discontinuously, and imparts free movement under the effect of gravity and heat also means that chemically bounded water with minerals are not included in it. Some of the literatures limit its definition to the oceanic bonded water but in actual it has broader sense, and includes lakes, rivers which are also responsible to contribute in world’s hydrology. Water cycle is an importing forcing which serves as a driving force to hydrosphere, and plays vital role in hydrospheric dynamics. It also includes the ground water because of its closed internal connection between both surface and ground water (Vuglinsky, 2001).

The relation among the lithosphere, atmosphere and biosphere is complicated, even becomes more complex with biosphere due to various processes involved. Characterization of hydrosphere if concerned then high water exchange rates become main parameter due to influence of water cycle which also combines all parts of hydrosphere. Approximately ¾ part of earth surface is submersed in water.

According to M.I.L’vovich, the entire volume of the hydrosphere slightly exceeds 1.4billion cubic kilometers. The world ocean is the main contributor to the water cycle, it’s a global phenomenon. Large amount of water undergo through the process of evaporation from the oceanic surface and after getting condense in atmosphere above ocean fall back in it in the form of precipitation.

The ocean and seas accumulate tremendous amount of heat which helps to regulate the temperature regime on the earth’s surface produces the conditions favorable to sustain lives over planet. Although much heat accumulation also imparts a temperature increase phenomena as we will discuss later, due to its low reflectivity and great depths. Composition of ocean constitutes about 96.5% water and 3.5% dissolved salts, gases, and organic matter.

The density of ocean increases as the amount of salinity increases and also with pressure increase, so oceans also remains in hydrostatic equilibrium. With increasing temperature of ocean, density goes down releasing gaseous matter, and CO₂. CO₂ is a leading dissolved gas it’s volume is 11.8*10⁵cubic kilometers which 100 times greater than the volume of CO₂available in the atmosphere, imagine the fact that if this huge amount of CO₂get released into the atmosphere due to increasing temperature (sudden rise of oceanic temperature in the multiples of 10 because; the absorption of sun’s radiation into ocean serves as a heating machine for it.) what amount of CO₂will be there to influence the greenhouse effect. Another thing is that the volume of O₂is much lesser in the dissolved form; it is just 1.4*10⁴cubic kilometers which is approx. 100 times lesser which is in atmosphere. The release of these gases will lead to huge dis-balance in the composition to atmosphere so the climate too. Well, it’s a very slow process so we need not to be worried so much but must keep in mind to deal with future effects.

The main factor which influences the global climate is oceanic circulation, which is driven by the winds. At mid-latitudes the winds called westerlies induces the eastward currents, while trade winds are responsible for westward currents in tropics.

Geography of earth plays an important role in distribution of these oceanic currents. Presence of continental barriers, some currents forms loops named as gyres. The surface current in those gyres are intensified along the western boundaries of the ocean (the east coast of continents) including well known strong currents such as Gulf Stream off the coast of USA and Kurashio off Japan. In the northern hemisphere easterlies permit to formation of weaker sub-polar gyres in the higher latitudes.

Figure 3: Schematic representation of the major surface currents. Eq. is an abbreviation for equatorial, C. for current, N. for North, S. for South and E. East Source: Knauss, Introduction to Physical Oceanography. (Long Grove, IL: Waveland Press, Inc, 1997 (reissued 2005)).

Where as in the southern hemisphere due to absence of continental barrier, a strong current which connects all the basin of ocean can be maintained this current known as Antarctic Circumpolar Current (ACC). It is a strongest current in all the oceanic currents on the earth. These currents always flow in parallel to surface winds.

Earth’s rotation very much influence the behavior of ocean, where it is in the form of tidal effect or direct influence to the oceanic currents, the oceanic transport induced by the wind is perpendicular to the wind stress (to the right in the Northern hemisphere and to the left in southern hemisphere), this transport called as Ekman transport. Which is very important in explain the path of winds driven currents. Another large scale circulation, induced by the effect of temperature and salinity(density) are often named as thermohaline circulations.

C The Cryosphere

Cryosphere includes the parts of earth’s surface where water found in solid state, including the sea ice, river ice, snow, glaciers, ice capes, ice sheets and also the frozen grounds called permafrost. So cryosphere overlaps the hydrosphere widely, and affects global climate integrally with hydrosphere; with potential influence to the surface energy balance over surface, and moisture fluxes, clouds, precipitation, hydrology, atmosphere and oceanic circulation.

Having a very large reflectivity properties, snow and ice imparts large albedo; this also denotes its significant influence to the global heat balance of the Earth. It is very complex to explain but true that by storing and releasing latent heat snow and ice affect the seasonal cycle of the surface temperature and they act as good insulators which help to reduce the heat loss from the underlying surface (land or ocean) towards the cold atmosphere in winter (Barry & Gan, 2011).

Movement of ice means sea ice drift act as an important carrier of fresh water, as it is associated with a horizontal freshwater transport. Convergence of the sea-ice transport and intense ice melting in a region, if occurs, this will lead in decrease in the salinity of surface water in that region.

Various studies has estimated the effect of ice sheet melting and documented that, if all the ice sheets melted completely, taking into account the fact that some ice sheets are grounded below sea level, the sea level would rise by more than 60 m. On the other hand, if we neglect the effect of dilution on sea water density and volume, the melting of sea ice and ice shelves does not influence sea levels.

D The land surface and the biosphere

As we have already mentioned above that how geography of land will influence the various climatic parametersand characteristics of the climate by the distribution and topography of land surface. For a little insight again, let’s understand some facts;

Mountain chains such as the Andes or the Rocky mountains form barriers to the westerly winds that influence the climate on a continental scale. Mountains also have an important role at the hemispheric scale, by affecting planetary waves and the global atmospheric circulation.

The distance to the coast influences the temperature and aridity of a region. The presence of land boundaries to the ocean (and more generally the ocean bathymetry) affects the location of the strong western boundary currents and of the straits that allow water exchanges between the different basins. The shape and even the existence of an ice sheet are strongly conditioned by the underlying bedrock.

An additional factor which is very influencing to the land geometry, the type of vegetation present on land, it is critically acted as moderator of climate at all spatial and temporal scales. Very sounding effect of terrestrial vegetation is due to its related albedo. Albedo of vegetation is usually lower than soil, particularly much smaller than that found in deserts. It is the reason behind that why subtropical deserts such as the Sahara appear as regions of particularly high albedo on global maps.

The land cover profile is also an important factor here. The terrestrial biosphere also has a clear impact on the hydrological cycle. Water storage is generally greater in soil covered by vegetation than on bare land where direct runoff often follows precipitation. Stored water can later be taken up by plant roots and transferred back to the atmosphere by evapotranspiration. A third effect of the vegetation cover is related to the surface roughness that influences the stress at the atmosphere-land interface and the turbulent exchanges at the surface.

E Interactions among the components

The interaction between all the components of climate has very complex behavior, as earlier said, these are associated with many physical, chemical and biological interaction processes occurring among these components on a wide range of space and time scales. Although all the components of the climate system has different composition, physical and chemical properties, structure and also the behavior, they can be linked by fluxes of mass, heat and momentum, because they are interrelated.

Atmosphere and ocean, for an example, are strongly connected in exchanging water vapor and heat through evaporation influencing the water cycle. Although it is a part of the hydrological cycle, which leads to condensation, followed by cloud formation, precipitation and runoff, and thus serves as supply energy to weather systems. Reversely, precipitation has an influence on salinity of ocean, and very much alters its distribution and the thermohaline circulation in ocean. Gas exchange between atmosphere and oceans is quite common process, they very much exchanges carbon dioxide, maintaining a balance by dissolving it in cold polar water which sinks into the deep ocean and by outgassing in relatively warm upwelling water near the equator.

Other examples can be listed as below;

[1.] Seaice prevents the exchanges between atmosphere and oceans;

[2.] Biosphere influences the carbon dioxide concentration by photosynthesis and respiration,

[3.] The biosphere also affects the input of water in the atmosphere through evapotranspiration, and alters radiative balance through the amount of sunlight reflected back to the sky (albedo).

2.1.3 Climate Change

As we mentioned earlier that due to the natural causes climate has very slow change rates, we will talk about them later, climate is a long time period change in parameters of weather. Weather, can be expressed by the several meteorological parameters such as temperature, rainfall, wind, humidity, sunshine, radiation, evapotranspiration (Reddy, 1993). For a particular place on earth climate variation can be observed over timescales ranging from tens of years to thousands of years it’s because of very slow influence by natural causes such as changes in solar activity and long-term changes in the tilt of the earth and its orbit around the sun. However, the term climate change came in picture since the early 1900s.

IPCC’s current programs and assessments very much concerned about the influences to climate by human activities, infact IPCC concluded that most of the observed increase in global temperature since the middle of the 20th Century is very likely due to an increase in man-made greenhouse gas emissions. More we will study about it.

To support this notion that Earth’s climate is going under increasing changes, here is evidence;

Figure 4: 5-yearly averages of annual average (mean) air temperatures recorded at Rothamsted (Source: Climate (Science Investigation (CSI): Causes of glaciation)

The graph above shows data from the ECN Roth Amsted site in Hertfordshire, England. The points are 5-yearly averages of annual average (mean) air temperatures recorded at Rothamsted. The data are from 1878 to 2010. Temperatures were relatively stable over the period up to the late 1980s. Since then temperatures have generally been higher than the long-term mean (the red line, which is the mean for 1878-1990). The data suggest progressive warming over the period, and particularly in the last 30 years (Data courtesy of Rothamsted Research Ltd.). Although warming is not only the effect of climate it also has cooling effect. But the effect of warming are more sounding then cooling, because warming of less than 1°C doesn't sound like a lot, but even a 2°C temperature rise could lead to rising sea levels and more extreme events like droughts and heavy rain. In fact, the IPCC reported widespread melting of snow and ice and rising global average sea level (IPCC 4th Assessment Report, 2007). These consequences of climate change will affect many millions of people. Coastal areas are more porn to these consequences in terms of flooding of coastal areas, or parts of the world which are already prone to droughts or flooding could be the worst affected.

2.1.3.1 Factors Responsible for Climate Change

Global climate is influenced by various factors, affecting it directly or indirectly, even the time period associated with their action is more important when we study climate change. In a very systematic manner if we try to list these factors, then we can classify them in two broad classes called internal factors and external factors.

Figure 5: Extraterrestrial factors

2.1.3.2 Human induced variation in climate due to human action on nature

It is very important component of climate change, which deals with the human role in climate modification, and their continuous contribution in changing the climatic parameters. These human induced changes due to LULC changes can be divided into two categories;

A Ecological changes associated with LULC changes

In addition to changes in the atmosphere’s composition, changes in the LULC, known as ecological changes, can have important effect on climate. There are several possible forces driving LULC changes, primarily related to population & their lifestyle and technology and extension of basic transport infrastructure. The existence & their accessibility of transportation routes have often dictated patterns of urban growth. Metropolitan areas around the world are growing at unprecedented rates, creating exclusive urban landscapes. Many of the farmlands, forests, wetlands, forests, and deserts have been transformed during the past 100 years in to human settlements, known as ‘concrete jungle’. Thus, the process such as deforestation, reforestation, desertification, changes in topography/orography, urbanization, mining, construction of roads including railways & infrastructure, industry, agriculture- dry land to wetland & vice versa or grazing lands, water resources-construction of dams. etc., that come under ecological changes, play important role at local & regional scales and as well contribute to global scale (Reddy, 1993).

Thus, these ecological changes contributes to changes in meteorological parameter such as

1. Temperature,

2. Humidity,

3. Wind,

4. Precipitation,

5. Evaporation/evapotranspiration etc.

Ecological changes due to LULC changes can be of;

1. Change in weather parameters (trough urban heat-island effect, rural cold-island effect, acid rain, ect.)

2. Changes in greenhouse gas balance in atmosphere both directly and indirectly

a Urban heat island effect

An urban heat island (UHI) is a metropolitan area that is significantly warmer than its surrounding rural areas due to human activities. The phenomenon was first investigated and described by Luke Howard in the 1810s, although he was not the one to name the phenomenon. The temperature difference usually is larger at night than during the day, and is most apparent when winds are weak. UHI is most noticeable during the summer and winter. The main cause of the urban heat island effect is from the modification of land surfaces, which use materials that effectively store short-wave radiation (Willian et. al., 2005). Waste heat generated by energy usage is a secondary contributor (Li and Zhao, 2012). As the population grows it’s tend to expand in area and increase its average temperature.

Causes of UHI effect;

1. Due to release of long wave radiation left captured within the concrete or asphalt and buildings that was absorbed during day time absorption of sort-wave radiation, unlike sub-urban and rural areas; which warms the night, making cooling a slow process (William et. al. 2005).

2. Changed thermal properties of surface material

3. Lack of evapotranspiration (through lack of vegetation in urban areas)

4. Due to geometric effects

Decreasing amounts of vegetation, in cities also modifies the shading and cooling by trees, and albedo values of their leaves, and also affects the CO2 concentration. Materials commonly used in urban areas for pavement and roofs, such as concrete and asphalt, have significantly different thermal bulk properties (including heat capacity and thermal conductivity) and surface radiative properties (albedo and emissivity) than the surrounding rural areas. This causes a change in the energy balance of the urban area, often leading to higher temperatures than surrounding rural areas (Oke, 2006). The tall buildings within many urban areas provide multiple surfaces for the reflection and absorption of sunlight, increasing the efficiency with which urban areas are heated. This is called the "urban canyon effect". Another effect of buildings is the blocking of wind, which also inhibits cooling by convection and pollution from dissipating. Waste heat from automobiles, air conditioning, industry, and other sources also contributes to the UHI (Li and Zhao, 2012; Sailor, 2011).

b Acid rain

Acid rain is a rain or any other form of precipitation that is unusually acidic, meaning that it possesses elevated levels of hydrogen ions (low pH). It can have harmful effects on plants, aquatic animals and infrastructure. Acid rain is caused by emissions of sulfur dioxide and nitrogen oxide, which react with the water molecules in the atmosphere to produce acids. Nitrogen oxides can also be produced naturally by lightning strikes and sulfur dioxide is produced by volcanic eruptions.

The principal cause of acid rain is sulfur and nitrogen compounds from human sources, such as electricity generation, factories, and motor vehicles. Electrical power complexes utilizing coal are among the greatest contributors to gaseous pollutions that are responsible for acidic rain. The gases can be carried hundreds of kilometers in the atmosphere before they are converted to acids and deposited. In the past, factories had short funnels to let out smoke but this caused many problems locally; thus, factories now have taller smoke funnels. However, dispersal from these taller stacks causes pollutants to be carried farther, causing widespread ecological damage ().

A Anthropogenic greenhouse gases change in atmosphere

Contribution to the climate change, Human activities plays a vibrant role, by causing changes in Earth’s atmosphere in the amounts of greenhouse gases, aerosols (small particles), and cloudiness. Major impact on climate came in picture when large amount of fossil fuels burned in the various industrial and domestic activities, releasing carbon dioxide gas, major greenhouse gas.

Greenhouse gases and aerosols changes the earth’s energy budget by altering incoming solar radiation and out-going infrared (thermal) radiation that are part of Earth’s energy balance. Also the changed properties of atmospheric gases and their concentration and particles also exert warming or cooling effect on the climate regional and global basis.

1. Ozone depletion

2. Global warming

Changes in LULC can also affect the greenhouse gases balance in atmosphere, as earlier mentioned the share of different land modifications and constructions. For example they can create ground level ozone which is also acts as greenhouse gas.

Pollutants, which exert direct health hazards on life, may form part of ecological changes.

All over the world it was confirmed beyond the doubt that deforestation not only changed the weather but also greenhouse gas balance (Reddy, 1993), and changes in topography play an important role on precipitation (Rawat et. al., 2013), these facts supports that LULC changes modifies the chemistry of atmosphere and modifies the precipitation patterns in an area.

Human activities mainly contributes to the regional and global climate in two modes, these are listed below:

a Earth’s Energy Balance

In broad sense Earth’s energy balance is a description that accounts the incoming and outgoing sun’s energy and its uses over Earth’s surface. If incoming and outgoing energy over Earth’s surface remains in balance then Earth experience no temperature variation, otherwise there will be cooling or warming effects of this unbalanced energy.

Earth’s energy balance is very big phenomena which start from atmosphere, going through various atmospheric processes (like reflection, scattering, absorption etc.) and ends over Earth’s surface. Atmospheric processes are controlled by the aerosols present in there and their size and properties, human induced various aerosols and metal particles like iron, magnese etc. act as a reflecting agent in atmosphere, and the carbon dioxide absorbs heat. So human activities responsible to increase the concentration of these; modifies the global climate.

Changing surface profile and property is also factor which changes the energy balance of Earth, changed surface property modifies the way of interaction of incoming solar energy by changing its path, smooth profiles reflects more light then rough surface also the darker objects over surface absorbs higher heat then brighter objects. These changes the accumulation of heat over earth surface, more accumulation of heat causes warming effects over Earth.

Figure 6: Earths energy budget (source-http://science-edu.larc.nasa.gov)

b Green-House Gases (CO₂, CH₄, NO_X)

As already been discussed that greenhouse gases has warming effect over climate, the concentration of greenhouse gases at some extent (naturally occurring) in atmosphere is essential for live on Earth, but increased concentration of greenhouse gases has negative impact on climate, which warms the Earth. And human is main builder of this increased concentration in atmosphere from last few centuries, by increasing the use of fossil fuels in various industrial and domestic activities, some of these activities are listed below (source of percentage share http://knowledge.allianz.com/environment/climate_change/?651/fifteen-sources-of-greenhouse-gases-gallery).

1. Power Plant (25-30% share)

2. Residential Buildings (11% share)

3. Road Transport (10.5% share)

4. Deforestation, Forest Degradation, & Land use Changes (10.3% share)

5. Energy Industry processes & losses (8.3% share)

6. Commercial Buildings (7% share)

7. Cement, Ceramics, & Glass Production (6% share)

8. Livestock (5.5% share)

9. Iron & steel manufactures (4.8% share)

10. Agricultural Soils (4.4% share)

11. Chemical & Petrochemical Industries (4.3% share)

12. Oil & Gas Production (3.1% share)

13. Waste & Waste Water (3% share)

14. Coal mining (1.8% share)

15. Aviation (1.5% share)

Principal atmospheric parameters, which controls the evapotranspiration from the water bodies hence the oceanic and atmospheric circulations causing precipitation, moisture distribution over surface; are

1. Solar radiation

The evapotranspiration process is determined by the amount of energy available to vaporize water. Solar radiation is the largest energy source and is able to change large quantities of liquid water into water vapour.

2. Air temperature

The solar radiation absorbed by the atmosphere and the heat emitted by the earth increase the air temperature. The sensible heat of the surrounding air transfers energy to the crop and exerts as such a controlling influence on the rate of evapotranspiration. In sunny, warm weather the loss of water by evapotranspiration is greater than in cloudy and cool weather.

3. Air humidity

While the energy supply from the sun and surrounding air is the main driving force for the vaporization of water, the difference between the water vapour pressure at the evapotranspiring surface and the surrounding air is the determining factor for the vapour removal. Well-watered fields in hot dry arid regions consume large amounts of water due to the abundance of energy and the desiccating power of the atmosphere. In humid tropical regions, notwithstanding the high energy input, the high humidity of the air will reduce the evapotranspiration demand. In such an environment, the air is already close to saturation, so that less additional water can be stored and hence the evapotranspiration rate is lower than in arid regions.

4. Wind speed

The process of vapour removal depends to a large extent on wind and air turbulence which transfers large quantities of air over the evaporating surface. When vaporizing water, the air above the evaporating surface becomes gradually saturated with water vapour. If this air is not continuously replaced with drier air, the driving force for water vapour removal and the evapotranspiration rate decreases. These are the very important factors which are vulnerable to the LULC changes, affecting the weather conditions in an area.

Chapter 3 Recent LULC changes and its response to climate change

3.1 LULC changes

Land cover refers to the physical state of land; an assemblage of biotic and abiotic components; a reflection of climate and geo-physical environment. It denotes the natural presence of different land surfaces such as have forests, grasslands, deserts, etc. The term land use refers to different uses of land by humankind. Land use denotes utilization of land by humans for different purposes and activities that may include settlements or build-up areas, agricultural land, pastures, transport and other infrastructure activities. Land cover/land use and its change reflect certain aspects of changing developmental patterns and environmental dimensions in an area. It defines where and what kind of development is influencing the utilization of land. These changes reflect on the relationship among climate conditions, environmental alterations and disaster vulnerability of the area (Bounoua et al. 2004; Bonan et al. 1992).

The mountain systems are complex ecological entities endowed with a vast resource base for its populace; they also support livelihood and developmental activities in the adjacent lowland areas. These represent very fragile environments that are highly sensitive to changes in hydrological and climatic aspects. Human kind is the most dominant agent of change in earth’s environment at all the geographic scales. Present development in technology and poverty makes it easier to interfere with natural resources and their uses as the wish of mankind. These changes are evident of creation of new climatic era in the Himalayan region.

Increased tourism, vast road network on hill slopes, widening of existing roads and highways, hydro-electric power plants and catchment area changes, deforestation (both planned and unplanned) are the possible regions for disturbing the existing land cover on Himalayan region which to be blame for climatic changes in Himalayan region (Vishwa et al,2013). Mountains also represent unique areas for the detection of climatic change and the assessment of climate-related impacts. One reason for this is that, as climate changes rapidly with height over relatively short horizontal distances, so does vegetation and hydrology.

3.1.1 LULC change: How it affects climate?

a Role of Human in LULC change

Land use/cover dynamics are widespread, accelerating and significant process driven by human action and also produce changes that impact humans (Agarwal et al., 2002). Many socio-economic and environmental factors are involved for the change in land use/cover. Land use/cover change has been reviewed from different perspectives in order to identify the drivers of land use/cover change, their process and consequences. These changes reflect on the relationship among climate conditions, environmental alterations and disaster vulnerability of the area (Bounoua et al, 2004; Bonan et al. 1992).

Following are the some man-made changes in LULC in last few decades:

1. Population growth and declination

2. Establishment of road network and widening of existing roads to facilitate tourism in forested and hilly areas

3. Establishment of new hydro-power stations that changes flow pattern of rivers and affect nearby catchment area and surrounding forest

4. Rehabilitation and settlement of human being in nearby developed area

5. Migration of people, land tenure policies and other policies such as agriculture policy

6. Change in crop pattern, farming and agriculture

7. Industrial development and settlement of new hotels and residences

8. Urbanization and unplanned settlement over river banks

Effect on atmospheric chemistry: Atmospheric chemistry does mean from its composition means aerosols which are very important constituents of chemical composition of atmosphere. The concentration of tropospheric ozone has increased substantially since the pre-industrial era, especially in polluted areas of the world, and has contributed to radiative warming. Emissions of chemical ozone precursors (carbon monoxide, CH4, non-methane hydrocarbons, nitrogen oxides) have increased as a result of larger use of fossil fuel, more frequent biomass burning and more intense agricultural practices.

Atmospheric aerosol particles modify Earth’s radiation budget by absorbing and scattering incoming solar radiation. Even though some particle types may have a warming effect, most aerosol particles, such as sulphate (SO4) aerosol particles, tend to cool the Earth surface by scattering some of the incoming solar radiation back to space.

Effect on atmospheric reflectivity: Aerosols are small particles or liquid droplets in the atmosphere that can absorb or reflect sunlight. The concentration, size and type of aerosols in the atmosphere very much affects its reflective property to sun light. Unlike greenhouse gases (GHGs), the climate effects of aerosols vary depending on what they are made of and where they are emitted. Those aerosols that reflect sunlight, such as particles from volcanic eruptions or sulfur emissions from burning coal, have a cooling effect. Aerosols which absorbs sunlight, such as black carbon (a part of soot), have a warming effect.

When sunlight reaches Earth, it can be reflected or absorbed. The amount that is reflected or absorbed depends on surface cover available over Earth’s surface or property of surface. Light-colored objects and surfaces, like snow and clouds, tend to reflect most sunlight, while darker objects and surfaces, like the ocean, forests, or soil, tend to absorb more sunlight. Also the texture of surface is also responsible for the reflection of light, smooth surfaces reflect more sun light then rough surfaces.

b Forest cover change and effects on climate change

Forest ecosystems have important functions from an ecological perspective and provide services that are essential to maintain the life-support system on a local and global scale. Greenhouse gas regulation, water supplies and regulation, nutrient cycling, genetic and species diversity as well as recreation are only some examples of the services that forest ecosystems provide. The forests of Himalaya not only support millions of residents in the region but also much more people residing in the Indo-Gangetic plains through water cycle regulation (Rao et al, 2000).

Thus the forest cover modification in the any form like deforestation, loss due to forest fires, flooding of forest area, losses due to cultivation area requirements, development of horticulture, establishment of new industries, rehabilitation in forested area, and hydro-power generation, harms the forest ecosystem, thus the global scale changes can be seen in climate such as CO2 increase thus the increase in temperature cause global warming, mosquito breeding and disease and also change in precipitation patterns.

c Snow cover change and effect on climate change

Snow and ice are, for many mountain ranges, a key component of the hydrological cycle, and the seasonal character and amount of runoff is closely linked to cryospheric processes. In addition, because of the sensitivity of mountain glaciers to temperature and precipitation, the behavior of glaciers provides some of the clearest evidence of atmospheric warming and changes in the precipitation regime, both modulated by atmospheric circulation and flow patterns over the past decades. Changes in climate has been shown to result in shifts in seasonal snow pack (Shrestha et al, 1998); glacier melt influences discharge rates and timing in the rivers that originate in mountains.

The recent reduction of snow and glacier cover in the Himalaya may also be contributing to the higher rates of warming observed in the higher-elevation regions of Nepal. A reduction in snow and glacier cover in the high elevation will change the surface albedo of the region, which in turn will increase the surface air temperature, thereby acting as a positive feedback mechanism (Shrestha et al, 1998). The importance of snow and glacier cover variations is manifested by the effect of the Eurasian snow cover variations on the regional climate, mainly the summer monsoon, as suggested by several empirical as well as model studies.

Recent studies have identified the formation and growth of several glacial lakes, possibly due to fast retreat of glaciers, which could lead to catastrophic outburst floods (Shrestha et al, 1998). It is possible that global warming is responsible for the recent glacial retreat in the Himalayas, although precipitation changes may also be important.

d LULC changes & climate change in hilly regions

Effect of settlement and other nonagricultural land uses: Kedarnath and Rudraparayag region of Himalaya is seismically and ecologically very sensitive and delicate even a minute changes (anthropogenic or natural) can create a dangerous disaster. The fragile nature of oldest crystalline basement of the Himalayan is very sensitive in case of landslides and any disaster. The race between tourism industries, population growth, several hydroelectric projects are in the fast track in Uttarakhand district. After the constitution of Uttarakhand as State there is an increment of approx. 141% in population of Uttarakhand. The development of hotels has been done at the place of river which was left after the flood or some times by changing the path of the rivers. Currently there are 558 hydroelectric power projects are in pipe line those will affect to Bhagirathi (80%) and Alaknanda (65%), (Due to development of roads and Dam in between mountains, the incident of landslides has been inclined. The Rudraprayag district where Kedarnath is situated has already faced the problem of natural disasters 8 times for last 34 years (Sharma et al, 2013).

A study by Sharma et al, 2013, concluded that there was no high signature of Glacier changes in the Chaurabari and Companion, these glaciers are still intact in the valley and only one middle moraine debris has washout by stream due to heavy rainfall. They also illustrated that unplanned settlement over river bank and natural landscapes caused the temperature increase in valley and changed local climate which led to disaster in valley.

Recent disasters and human involvement in creation of LULC changes and thus the climate alteration , it is therefore important to understand the climatic trends in the Himalaya and their relationship with global trends A study of the long-term trend in surface air temperatures in India indicated an increase in mean annual temperature of 0.48C over the past century. The study reveals that the major land use in the Ramnagar town area is built-up area. The built-up area is expanding maximum towards southern direction along the National Highway 121 while it expanded minimum towards the north eastern direction (Rawat et al. 2013).

Effect of forest and grass-land changes: A study revealed the LULC changes for Corbett Tiger Reserve, The study showed that dense forest has increased over the time whereas open forest area has decreased, which can be due to better management strategies/ policies. However, the area under grassland has decrease which may be a threat to tiger habitat. The built up area has also increased due to tourism activities (Agarwal, 2013).

Both LULC changes and its response to climate change are interrelated, both affect each other, drastic changes in land use pattern influence the factors affecting climate change like emission of CO2, local temperature increase thus the global warming. Increase in temperature caused melting of snow and GLOF condition, thus influence the discharge in rivers thus the flooding of river banks and nearby area (Floods). Floods change LULC pattern in area. Increased temperature may cause forest fires, which also changes the LULC pattern, thus adding to the increase in again temperature rise and global changes of climate. Water cycle also changes when LULC pattern changed.

Changes in forested area and grass-lands into the urban settlement or cities modifies the precipitation patterns in that area, minimizing the annual precipitation and accelerating the UHI effect which also affect the natural cooling of nights, and if water exists nearby then increases the humidity. Cultivation patterns change in the agricultural areas also affects the soil moisture, and air humidity in corresponding areas. Another aspect of climate change is change in wetlands, which are water bodies, if changes due to land transfer into urban areas, it will changes the both precipitation and humidity by changing evapotranspiration, and also changes the moisture condition in land area, which affect biosphere of corresponding impact area.

Effect of urbanization: Assessing the impacts of urbanization and land use change on mean surface temperature is a challenging task. Several studies have attempted to assess the effect of urbanization and industrialization on temperature trends (Chung et al. 2004; Singh et al, 2013). However, some studies have tried to establish a link between some of the intense man-made activities of urban industrial areas and temperature trends and found increased size and density of population, land use/land cover changes, reduction in the fraction of vegetative area exclusive use of fossil fuel combination and emission of waste heat from industries, automobiles and building construction activities (roads, buildings, reservoirs, etc.), excessive use of air conditioning, changing level of aerosols, etc., responsible for such urban–rural contrast in temperature trends (Singh et al, 2013).

All over the world urban areas are being affected by urban climate change. Increasing temperatures of Dhaka (Alam and Golam Rabbani, 2007), increase of 2◦C temperature of Sao Paolo since 1993 (Edmilson et al. 2007), increasing tendencies of Beijing temperatures from 1977–2000 and 1.5◦C increase in annual mean temperature of Seoul during last 29 years (Chung et al. 2004) are the global examples of urban climate change.

Effect of Industrialization: Artificially induced climate change and global warming arising from anthropogenic-driven emissions of greenhouse gases and land-use and land cover change have emerged as one of the most important environmental issues among researchers in the last two decades (Arora et al. 2005; Singh et al. 2008). The emission of greenhouse gases has increased considerably since the industrial revolution (1750 onwards), with an increase of 70% between 1970 and 2004 (Singh et al. 2008). The latest fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC 2007) has concluded that the global mean surface temperatures have risen by 0.74 ± 0.18◦C when estimated by a linear trend over the last 100 years (1906–2005). The rate of warming over the recent 50 years is almost double of that over the last 100 years. Weather records from land stations and ships indicate that the global mean surface temperature has warmed up approximately by 0.6 ± 0.2◦C since 1850 and it is expected that, by 2100, the increase in temperature could be 1.4◦–5.8◦C (Singh et al. 2008). Moreover, the world has witnessed change in climatic condition at an unprecedented rate in past few decades. Available records show that the 1990s have been the warmest decade of the millennium in the Northern Hemisphere and 1998 was the warmest year.

Chapter 4 Case study

4.1 Case study 1: On rising temperature trends at Dehradun in Doon valley of Uttarakhand, India

4.1.1 Introduction

The analysis highlighted significantly the role of extreme vulnerability of rising temperatures at Dehradun and urban population will constantly be affected by the change in the temperature which controls the comfort level of the inhabitants and also the resources in the region.

4.1.2 Study area

Includes, Dehradun, belonging to Uttarakhand state of India, is located in a valley at the foothills of Himalayas and lies between 30◦1and 30◦25 north latitudes and 78◦10 and 78◦00 east longitudes. It is surrounded by river Song on the east, river Tons on the west, Himalayan ranges on the north and Sal forests in the south. The high hills in the east and north and Siwaliks in the south offer an interesting topographical setting to the city. During the summer months, the temperature ranges between 36◦C and 16.7◦C. The winter months are colder with the maximum and minimum temperatures touching 23.4◦C and 5.2◦C, respectively. Dehradun experiences heavy to moderate showers during late June to mid -August.

Table 1: Development of built up area in Dehradun city during 1982–2004.

4.1.3 Methodology adopted

The daily temperature and vapour pressure data recorded by the meteorological observatory located at New Forest, Forest Research Institute, Dehradun in Doon valley of Uttarakhand for the period 1967–2007 were collected from the published records. All temperature and vapour pressure measurements were recorded at 0719 hrs and 1419 hrs. The elevation of the meteorological observatory is about 640.08 m above mean sea level. This observatory corresponds to class ‘A’ meteorological observatory of India Meteorological Department

(IMD), Pune. IMD is the only reliable, competent and official source in India for meteorological data.

To find changes, they derived

1. Annual maximum temperature

2. Annual minimum temperature

3. Annual mean temperature

Technique adopted,

5-years moving average technique and linear Regression Method.

Data analysis was done in three phases,

1. Whole period 1967-2007

2. Period 1967-1987

3. Period 1988-2007

Trend line of graph was used to get clear picture of results. Temperature is treated as dependent variable, and time period as an independent.

Temperature = t (time) and Y = (α+βX +μ).

Where Y = mean temperature (minimum, maximum),

X = time period and μ = error/random term.

The t-value of each parameter is calculated to check the statistical significance that null hypothesis Ho: β = 0. The student’s t statistics is calculated as:

Where, β is the coefficient (i.e., slope) and S.E. is the standard error of β which shows the rate of change in temperature per unit time. It is calculated as:

Where, n is the sample size.

For better understanding, anomalies are also calculated. Anomalies are more accurate than absolute temperature to describe climatic variability. To analyze anomalies in maximum, minimum and mean temperature, the average annual maximum, annual minimum and annual mean temperatures were calculated for the entire 1967–2007 period and subsequently, it was subtracted from yearly average maximum, minimum and mean temperature. Folland et al. (1999) suggested 30 years as a standard period for calculating the average used to analyze the anomalies.

Figure 7: Evolution of the Dehradun city population according to national census of India.

4.1.4 Results and analysis

The results are summarized below and presented in fig-10, fig-11, fig-12 and fig-13. For

1. Trends and anomalies in annual maximum temperature

2. Trends and anomalies in annual minimum temperature

3. Trends and anomalies in annual mean temperature

4. Vapor pressure and anomalies in Dehra Doon

Figure 8: (a) Annual maximum temperature variations and (b) anomalies at Dehradun during 1967–2007.

The long term annual maximum temperature at Dehradun was found to be 28◦C during the study period. Remarkable cooling period was observed during 1972–1989 (fig-11). The warmest year was 1967 with annual maximum temperature of 28.9◦C which was 0.9◦C warmer than the normal. The coolest year was 1997 with the annual maximum temperature 26.5◦C, when the drop was 1.5◦C below the normal.

The linear trend in annual maximum temperature from 1967–2007 at Dehradun indicated the increasing trend and the observed increase was found to be about 0.43◦C. The increasing annual maximum temperature results observed at Dehradun are in good agreement with the findings of other urban studies on the climate change in the Himalayas (Bhutiyani et al. 2007, 2010). This rise in annual maximum temperature has been attributed to increasing anthropogenic activities in the Himalayan region (Bhutiyani et al. 2007). Positive anomaly of about 0.85◦C in annual maximum temperature was observed during 1967 whereas negative anomaly of about 1.5◦C in annual maximum temperature was observed in the year 1997. It was also observed from the analysis that positive anomalies are more prominent in annual maximum temperature (fig-10). Moreover, the analysis also demonstrated that change in annual maximum temperature is not uniform during the whole study period.

Figure 9: (a) Annual minimum temperature variations and (b) Anomalies at Dehradun during 1967–2007.

The long term annual minimum temperature at Dehradun was observed to be 13.5◦C during 1967–2007. The annual minimum temperature ranged between 12.2◦C in 1989 and 14.5◦C in 1985. Therefore, the warmest year during the study period in terms of annual minimum temperature was 1985 when it was found 0.9◦C warmer than the normal whereas the coolest year was 1989 when The linear trend in annual minimum temperature from 1967–2007 at Dehradun also indicated the increasing trend and the observed increase was found to be about 0.38◦C. The increase in magnitude of annual minimum temperature was observed to be 0.007◦C/year and 0.98◦C/100 year. Positive anomaly of about 0.9◦C in annual minimum temperature was observed during 1985.

Figure 10: (a) Annual mean temperature variations and (b) anomalies at Dehradun during 1967–2007.

Observed annual mean temperature is affected both by maximum and minimum temperature. The long term annual mean temperature at Dehradun was observed to be 20.8◦C during the study period. As revealed from figure 12(a) annual mean temperature witnessed a continuous period of warming since the year 2000 at Dehradun. Over the period under observation, 1999 was the warmest year with annual mean temperature 22.3◦C when temperature rose 1.5◦C more than the normal. The year 1997 on the other hand was the coolest year with annual mean temperature 19.6◦C and the temperature was 1.2◦C lower than the normal. Likewise, the linear trend in annual mean temperature at Dehradun also indicated the increasing trend and the observed increase were found to be about 0.47◦C during the whole study period but this change in first phase was not found to be more pronounced than the phase two. During 1967–1987, annual mean temperature increased only 0.12◦C which is only 25% of the total change.

Figure 11: (a) Annual vapour pressure variations and (b) anomalies at Dehradun during 1967–2007.

4.1.5 Conclusion

Study concluded that with the growth in urbanization and industrialization in Dehra Don (About 114% growth in urban population since 1991 and 230% growth in urban built up area have been observed since 1982). Therefore, Dehradun city of Uttarakhand state in India is highly vulnerable to urban climate change mostly due to anthropogenic activities.

The annual maximum, annual minimum and annual mean temperatures at Dehradun city have positive trends of change. Overall it has warmed significantly and annual mean temperature has increased about 0.47◦C during the 41-year period (1967–2007). Per decade increase in annual mean temperature was found to be 0.12◦C which is about four times more than the global increase of temperature.

4.2 Case study 2: Climate Response to Rapid Urban Growth: Evidence of a Human-Induced Precipitation Deficit

4.2.1 Introduction

The authors establish the effect of urbanization on precipitation in the Pearl River Delta of China with data from an annual land use map (1988–96) derived from Landsat images and monthly climate data from 16 local meteorological stations. A statistical analysis of the relationship between climate and urban land use in concentric buffers around the stations indicates that there is a causal relationship from temporal and spatial patterns of urbanization to temporal and spatial patterns of precipitation during the dry season. Results suggest an urban precipitation deficit in which urbanization reduces local precipitation.

Urban land use change has been and will continue to be one of the biggest human impacts on the terrestrial environment. At the start of the 1900s, there were only 16 cities with populations over 1 million; by 2000, there were 417. Building cities on previously vegetated surfaces modifies the exchange of heat, water, trace gases, aerosols, and momentum between the land surface and overlying atmosphere. In addition, the composition of the atmosphere over urban areas differs from undeveloped areas (Pataki et al. 2003). These changes imply that urbanization can affect local, regional, and possibly global climate at diurnal, seasonal, and long-term scales (Zhou et al. 2004; Zhang et al. 2005).

4.2.2 Urban growth and its impacts on local climate

There is now a coherent understanding of urban surface energy balance dynamics, with a well-established urban heat island effect that appears stronger during the night than the day (Lo et al. 1997; Banta et al. 1998). This effect is thought to be generated by the interaction among building geometry, land use, and urban materials (Oke 1976; Wang et al. 1990; Arnfield 2003).

Numerous studies evaluate the relationship between urban areas and precipitation (Shepherd 2005). These studies are based on static comparisons between metropolitan regions and their rural surroundings. They have generated a general consensus that urbanization affects precipitation, but the mechanism(s) by which urbanization affects precipitation is poorly understood (Lowry 1998).

Mechanisms discussed include 1) enhanced convergence due to increased surface roughness in the urban environment (Thielen et al. 2000), 2) destabilization due to urban heat island (UHI)-thermal perturbation of the boundary layer and the resulting downstream translation of the UHI circulation or

UHI-generated convective clouds (Shepard et al. 2002), 3) enhanced aerosols in the urban environment for cloud condensation nuclei sources (Molders and Olson 2004), or 4) bifurcating or diverting precipitating systems by the urban canopy or related processes (Bornstein and Lin 2000). Others have hypothesized that urban areas serve as moisture sources needed for convective development (Dixon and Mote 2003).

Even less understood is the relationship between urban growth—or land conversion—and local climate.

While numerous studies focus on urban climate, few examine urban growth explicitly.

4.2.3 Data and methodology

Estimates for urban growth are extracted from Landsat TM images for each of 9 yr, 1988 to 1996. Around each station, we establish three concentric buffers that have a radius of 3, 10, and 20 km. For each of 48 buffers (3 sizes 16 stations), we calculate the fraction of total area that is urban (Frac) for each year (1988–96). To determine whether the pattern of urbanization affects precipitation, we also calculate two spatial metrics for urbanization (McGarigal and Marks 1995). Urban edge density (ED) measures the total edge of urban areas relative to the total landscape and is calculated as follows:

In which E is the total length (m) of edge in the buffer and A is the total buffer area (m2). The ratio is multiplied by 10 000 to convert square meters to hectares.

The landscape shape index (LSI) provides a standardized measure of perimeter length of all patches of a given land cover type. The landscape shape index is calculated as follows:

In which E is the total length of edge in landscape in terms of number of cell surfaces and includes all landscape boundary and background edge segments, and min E is the minimum total length of edge in landscape in terms of number of cell surfaces.

To determine whether urbanization affects precipitation, we use the notion of Granger causality (Granger 1969, 1980). Although Granger causality does not imply a physical causal relationship, the methodology is used to investigate physical systems, including the relationship between surface features and local climate (Kaufmann et al. 2003; Mosedale et al. 2006). A causal relationship from the urbanization variable to precipitation is estimated from Eq. (3):

In which P is observed precipitation during season s for station i at time t, Year is the year in which the image is acquired, Urban is either the fraction urban (Frac) or one of the spatial metrics for urbanization (e.g., ED or LSI), and T is temperature. Temperature is included to represent any cotemporaneous correlation between temperature (the urban heat island effect or other change) and precipitation. Current values of temperature and precipitation at the other 15 meteorological stations are included to represent conditions at a regional scale. If the region is warming or drying due to changes other than local urbanization, including regional averages will reduce the likelihood that the statistical methodology will mistakenly attribute them to local urbanization. From a statistical perspective, their inclusion reduces any cross correlation of the regression errors due to large-scale events, and this will increase the efficiency of the estimation.

Equation (1) can be specified and estimated using a variety of assumptions about variations in the intercept (α) and slopes (β) among the 16 stations. Specifically, Eq. (3) can assume that (a) the intercept and slopes are the same across all 16 stations; (b) the intercept varies across the 16 stations, but the slopes are the same; and (c) the intercept and slopes vary across stations. Each assumption requires a different estimation technique. If the intercept and slopes are the same across stations, Eq. (3) is estimated using ordinary least squares. If the intercept varies across the 16 stations, but the slopes are the same, Eq. (3) is estimated using either a fixed or random effect estimator. If the intercept and slopes vary across stations, Eq. (3) is estimated using a random coefficient model, which assumes that coefficients for individual stations vary randomly around a constant mean.

Figure 12: The 16 meteorological stations and the 10-km buffers. Urban areas are shown in magenta. Meteorological stations (1) Fogang, (2) Sanshui, (3) Qingyuan, (4) Huadu, (5) Conghua, (6) Guangzhou, (7) Nanhai, (8) Dongguan, (9) Longmen, (10) Zengcheng, (11) oluo, (12) Heshan, (13) Xinhui, (14) Shunde, (15) Zongshan, and (16) Shenzhen.

There is no a priori justification for choosing an assumption about spatial variations in the regression coefficients; therefore, we chose among specifications and estimation techniques using test statistics (Mundlak 1978; Hsiao 1986). In summary, we start with the least restrictive assumption, the slope and/or intercepts vary among stations (random coefficient model) and test whether restrictions that equalize the intercept and slopes across stations increase the residual sum of squares in a statistically meaningful fashion. If they do, the less restrictive assumption is used to estimate Eq. (3).

The number of lags, one, is the maximum value that allows us to perform these tests on the nine observations per station. A value of one lag implies that the version of Eq. (3) used to estimate summer precipitation specifies springtime values for temperature and precipitation.

Granger causality from the urbanization variable to precipitation is indicated by the statistical significance of β₂ in Eq. (3). Rejecting the null hypothesis β₂= 0 indicates that the lagged value of the urbanization variable has information about the current value of precipitation beyond that contained in the lagged values of precipitation, temperature, time, and average values for current temperature and precipitation in the other 15 stations. This would provide statistical evidence that the urbanization variable “Granger causes” precipitation. We extend the analysis of Granger causality by testing whether Eq. (3) (unrestricted model) generates a more accurate out-of-sample forecast than a restricted version of Eq. (3) (restricted model), in which the lagged value of the urbanization variable is eliminated by imposing β₂= 0 (Granger and Huang 1997):

The out-of-sample forecast generated from Eqs. (3) and (4) can be calculated using two methods. One method eliminates observations for a single year from the sample data, estimates Eqs. (3) and (4), and uses those equations to generate an out-of-sample forecast for the year omitted from the sample. This process is repeated for all years for which lagged values are available. The other method eliminates the nine observations from a single station from the sample, estimates Eqs. (3) and (4) from the observations for the remaining 15 stations, and uses these equations to generate an out-of-sample forecast for the nine observations for the station that is excluded from the sample. This process is repeated for each of the 16 stations. Of these two methods, we generate the out-of sample forecast by eliminating a single station from the sample and repeating this process 16 times so that we have an out-of-sample forecast for every year and station. This generates 144 observations. Fewer observations would be available using the alternative method because the lagged values in Eqs. (3) and (4) would prevent us from estimating those equations for the year excluded from the sample and the year that follows. Furthermore, generating the out-of-sample forecast by eliminating observations from a single meteorological station increases the power of analysis (Granger and Huang 1997). Granger and Huang (1997) argue that generating the out-of-sample forecast by excluding individuals is a more powerful method of testing Granger causality than generating an out-of-sample forecast by excluding observations across individuals for a given period. They warn that this power is lost if the test statistic used to compare forecasts is affected by errors that covary or are heteroscedastic.

To avoid the effects of covariance and/or heteroscedasticity, we evaluate the out-of-sample forecasts generated by Eqs. (3) and (4) using two parametric tests, the sign test and the signed rank test (Lehmann 1975). To calculate these test statistics, we use the following loss function:

in which P_s,i,tis the observed value for precipitation during season s for station i at time t, P ˆ _s,i,t,Uis the out of- sample forecast for precipitation generated by the unrestricted model [Eq. (3)], and P ˆ _s,i,t,R is the out-of sample forecast generated by the restricted model [Eq. (4)]. Values of d are used to calculate the S2a [Eq. (6)] and S3a [Eq. (7)] statistics (Lehmann 1975) as follows:

In which N is the number of observations (9*16 =144). The S_2a and S_3a statistics test the null hypothesis that the accuracy of the out-of-sample forecasts is equal (i.e., d=0). A negative value for the S2a or S3a statistic that exceeds the p=0.05 threshold (-1.96) indicates that eliminating the urbanization variable from Eq. (3) reduces the accuracy of the out-of-sample forecast generated by Eq. (4). Such a result would imply that lagged values of urbanization have information about current values of precipitation that extends beyond the other variables in Eq. (3). Under these conditions, we would conclude that urbanization Granger causes precipitation (Granger and Huang 1997).

4.2.4 Results and discussion

Restrictions that equalize the slopes and/or intercept among meteorological stations generally are not rejected. The slopes and intercept are the same among stations for 22 equations, and these equations are estimated using ordinary least squares (Table 1). The random effects estimator is used to estimate six equations, for which the intercept varies among stations and the slopes are the same. Finally, test statistics reject restrictions that equalize the slopes or intercept for eight equations; therefore, these equations are estimated using the random coefficient model.

The presence/absence of a causal relationship from urbanization to precipitation varies by season and buffer size (Table 1). Results reject the null hypotheses that β₂ =0 and that the S_2a and S_3a statistics equal zero for winter. For spring and fall, results generally reject β₂ =0 but fail to reject the null hypothesis that the S2a and S3a statistics are zero. For summer, results fail to reject the null hypothesis β₂ =0 (except for two cases) and always fail to reject the null hypothesis that the S_2a and S_3a statistics are zero. These results indicate that urbanization Granger causes precipitation during winter, with suggestions of a weaker effect during spring and fall. For all seasons, results for the 3-km buffer are less likely to reject the null hypotheses β₂ =0 and/or that the S_2a or S_3a statistic equals zero. Results for the 10- and 20-km buffers reject the null hypotheses β₂ =0 and/or that the S_2a and S_3a statistics equal zero for winter, spring, and fall. Results do not differ between the 10- and 20-km buffers. Nor do the results differ among the measures of urbanization metrics. These results suggest that the effect of urbanization on precipitation is generated at scales of hundreds of square kilometers (the 3-km buffers are less than 30 km², and the 10-km buffers are more than 300 km²). Furthermore, this effect is not generated solely by the pattern of urbanization; the causal relationship appears when Frac is used as the urbanization variable.

For seasons and buffer sizes that show a causal relationship between urbanization and precipitation, β₂ generally is negative. This would seem to imply that increasing urbanization reduces precipitation. But this result has to be interpreted with care. Equation (3) is a reduced form of a structural equation from a system of three equations, which is given by (we omit the current temperature and precipitation variables for the other stations to save space)

In which the null hypothesis β₂ =0 in Eq. (3) is an indirect test of γ₁₃=0 in Eq. (8). Recovering the value of γ₁₃ from the regression coefficients estimated for Eq. (3) requires identifying restrictions. Due to feedback effects of urbanization on temperature (γ₂₃≠ 0), which measures the urban heat island effect, and a contemporaneous relationship between temperature and precipitation (γ₁₄≠0 and γ₂₄≠0), we cannot identify the system and therefore cannot recover the value of γ₁₃. Instead, we explore the nature (positive or negative) of the relationship between urbanization and precipitation by estimating the following equation:

And use the sign on Θ₂ to proxy the sign of the effect of urbanization on precipitation (γ₁₃). As indicated in Table 2, the sign of Θ₂ is consistent with the negative value of β₂that is estimated from Eq. (3). This too suggests that urbanization reduces local precipitation. The seasonal nature of the causal relationship between urbanization and precipitation indicates that the results are not a statistical artifact and suggests several possible mechanisms for an urban precipitation deficit. There is no causal relationship between urbanization and precipitation during the summer. Summer coincides with the rainy season, when the East Asian monsoon has a dominant effect at spatial scales far beyond urban areas. As such, the magnitude of this effect may overwhelm local urban impacts. During the dry season, cold fronts from northern China brings some rainfall but with a much smaller magnitude and thus local urban effects may be more visible.

Table 2: Regression results for Eq. (3) and tests of predictive accuracy for out-of-sample forecasts. Coefficients are statistically significantly different from zero at **1%, *5%, and 10% levels. Equation (3) is estimated using ordinary least squares ($), random effects (#), and random coefficient model.

This may explain why the UHI is most visible in winter (Zhou et al. 2004). The causal relationship indicates that the level of urbanization within the 10- and 20-km buffers influences local synoptic events. Within buffers, urbanization may reduce precipitation by changing surface properties, such as vegetation cover, roughness, and albedo, energy flows, and/or water flows in ways that reduce water supplies to the local atmosphere. Structures associated with urban areas may change surface hydrology in ways that accelerate runoff via storm water management, which would reduce surface storage and ultimately the water that is available for evaporation. This effect may be exacerbated by a reduction in vegetative cover, which would slow the transfer of water from the soil to the atmosphere via evapotranspiration. This notion is supported by empirical analyses that indicate urbanization reduces the fraction of net radiation that is used for evaporative processes (Carlson and Arthur 2000; Arthur-Hartranft et al. 2003).

Table 3: The value of Θ₂ in Eq. (11). Coefficients are statistically significantly different from zero at **1%, *5%, and 10% levels. Equation (11) is estimated using ordinary least squares ($), random effects (#), or the random coefficient model (†).

Our results also are consistent with previous studies that show a tight coupling between air pollution and precipitation (Cerveny and Balling 1998). The reduction in precipitation may be amplified by increased emissions of air pollutants. A significant increase in aerosol concentrations could increase cloud condensation nuclei and thereby reduce precipitation (Rosenfeld 2000; Crutzen 2004). However, other studies indicate that the net effect of aerosols is to cool the climate system (Kaufman et al. 2002), suggesting that the heat island effect could be partially offset by the cooling caused by aerosols (Chen et al. 2006).

These negative effects on precipitation may be larger than urbanization’s other effects, which could boost precipitation by increasing surface roughness and/or convection. Increased convection associated with the urban heat island effect probably does not generate the causal effect of urbanization on precipitation that is estimated from Eq. (3). Because this equation includes temperature, eliminating the urbanization variable from Eq. (3) does not eliminate the effect of urbanization on convection via the urban heat island effect. The potential for conflicting effects implies that the relationship between urbanization and precipitation may vary by location. The Pearl River Delta has one of the highest rates of urbanization in the world: during the 9-yr period, urban areas increased 300% (Seto et al. 2000). Although these high rates may make the effect easier to detect, the effect of urbanization on precipitation probably is not restricted to the Pearl River Delta of China. Large areas of the United States have been paved or “built up” such that they are now considered “impervious surface areas.” This implies that anthropogenic changes in land use could have significant effects on local precipitation throughout the world.

Chapter 5 Conclusion

Change in LULC and climate change are interrelated, they both affect each other, change in climate modifies land use and land cover and change Land use and cover becomes a reason to change the climate. But human induced changes are very significant in changing the land use and land cover, so both mankind and climate plays a drastic role in modification of land use and land cover. Change in local climate creates a way to drastic damages to the nature and human in the form of disasters, droughts and forest fires, tsunamis etc. So becomes a very important task to understand the correlation among both LULC change and climate.

By having the insight of recent studies dealing with understanding the impacts of LULC changes and relation with climate change, we observed that, 1) in recent centuries, LULC changes and climate change are interrelated trough the ecological variable changes, 2) vast majority of LULC changes have little to do with climate change, 3) human induced LULC changes and land management made climate to adjust and change, these adaptation with LULC change will have ecological effects.

There is a need of development of such an estimation technique by which surface profile parameter α, β can be effectively taken care in account, (with including variation in trend and pattern of α, β) to estimate the relationship between human influenced changes and precipitation to avoid complexity in estimation.

Recently, the risk of natural disasters has increased in the area as a result of increasing anthropogenic activities. This trend is likely to increase in future as the activities like pilgrimage, tourism, etc. will increase Himalayan region. The natural flow paths of the channels get obstructed due to the construction of man-made structures that results in deviation of the flow from its natural course (Dobhal et al, 2013). The knowledge of land use/land cover is instrumental in understanding the positive and negative aspects of change, controlling haphazard growth and degradation of environment (Vishwa et al,2013).

References

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Reference of Online books:

Agroclimatic Agrometeological Tecnicques by S. J. Reddy, 1993.

Hydrosphere Structure and its Relation to The Global Hydrological Cycle by V.S. Vuglinsky

Evolution of Early Earth's Atmosphere, Hydrosphere, and Biosphere By Stephen E. Kesler, Hiroshi Ohmoto, 2006

Atmosphere, Ocean and Climate Dynamics: An Introductory Text by John Marshall, R. Alan Plumb, 2007

The Global Cryosphere: Past, Present and Future by Roger Barry, Thian Yew Gan, 2011

Contents

Chapter 1

Introduction

1.1General

1.2 Objective of study

Chapter 2