Chapter 1
Introduction
1.1General
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.
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 CO2 available
in the atmosphere, imagine the fact that if this huge amount of CO2 get
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.
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.
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.
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.
b Green-House Gases (CO2, CH4, NOX)
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.
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
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.
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.
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.
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 Ps,i,t is the observed value for precipitation during season s
for station i at time t, P ˆ s,i,t,U is 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 S2a and S3a
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 S2a and S3a 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 S2a and S3a
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 S2a or S3a
statistic equals zero. Results for the 10- and 20-km buffers reject the null
hypotheses β2 =0 and/or that the S2a and S3a
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 β2 that 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
[1.] Agarwal,
Shivani; Puri, Kanchan; Areendran, G; Raj, Krishna; Govil, Himanshu; Mazumdar,
Sraboni; Munsi, Madhushree, 2010. Forest Change analysis of Jim Corbett
National Park, Uttarakhand: A remote sensing and GIS approach. pp. 2,4,6
[2.] Aggarwal,
S P; Garg, Vaibhav; Gupta, Prasun K; Nikam, Bhaskar R; Thakur, Praveen K, 2012.
Climate and LULC change scenarios to study its Impact on Hydrological regime.
[3.] Alam,
M; Golam, Rabbani M D, 2007.
Vulnerabilities and responses to climate change for Dhaka; Environ.
Urban.19 81–97.
[4.] Arora
A, Goel A K and Singh P, 2005.
Evaluation of temperature trends
over India; Hydrol. Sci. J. 50 81–93.
[5.] Bhutiyani,
M R; Kale, V S; Pawar, N J, 2007. Longterm
trends in maximum, minimum and mean annual air temperatures across the
Northwestern Himalaya during twentieth century; Climatic Change 85 159–177.
[6.] Bhutiyani,
M R; Kale, V S; Pawar, N J, 2010. Climate change and the precipitation
variations in the northwestern Himalaya: 1866–2006; Int. J. Climatol. 30 535–548.
[7.] Bounoua,
L; Defries, R S; Imhoff, M L; Steininger, M K, 2004. Land use and local
climate: A case study near Santa Cruz, Bolivia; Meteorol. Atmos. Phys. 86
73–85.
[8.] Beniston.
Martin,2013. Climatic change in mountain
regions: A review of possible impacts.
[9.] Sharma
Manish, Mishra Sunil K. and Tyagi Shuchi, 2013. The Impact of Torrential
Rainfall in Kedarnath, Uttarakhand, India during June, 2013. pp. 1,3,4
[10.] Bonan,
G.B.; Pollard, D.; Thompson, S.L., 1992, Effects of boreal forest vegetation on
global climate, Nature, 359, 716-718.
[11.] Catherine,
S; Sue, G, 2006. Applied climatology: Urban climate; Progr. Phys.
Geogr. 30 270–279.
[12.] Chung,
Y S; Yoon, M B; Kim, H S, 2004. On
climate variations and changes observed in South Korea; Climatic Change 66
151–161.
[13.]
Dale,
Virginia H., 1997. The Relationship Between Land-Use Change and Climate Change.
Ecological Applications Vol. 7, No. 3 (Aug.,), pp. 753-769
[14.] Dobhal,
D. P.; Gupta, Anil K.; Mehta, Manish; Khandelwal, D. D. 2013. Kedarnath
disaster: facts and plausible causes. pp. 4
[15.] Kattel,
Dambaru Ballab; Yao, Tandong, 2013. Recent
Temperature Trends at Mountain Stations on the Southern Slope of the
Central Himalayas.
[16.] Li,
Y.; Zhao, X. (2012). "An empirical study of the impact of human activity
on long-term temperature change in China: A perspective from energy
consumption". Journal of Geophysical Research 117. doi:10.1029/2012JD018132.
[17.] McGee,
K.A., Doukas, M.P., Kessler, R. and Gerlach, T., 1997, Impacts of volcanic
gases on climate, the environment, and people: U.S. Geological Survey Open-File
Report 97-262, 2 p.
[18.] Oke,
T. R., 1982. The energetic basis of the urban heat island. Quarterly Journal of
the Royal Meteorological Society 108 (455): 1–24
[19.] Peter,
J.bLawrence; Thomas, N. Chase. 2013. Investigating the Climate Impacts of
Global Land Cover Change in the Community Climate System Model (CCSM 3.0).
[20.] Reid,
Robin S.; Kruska, Russell L.; Muthui, Nyawira; Taye, Andualem; Wotton, Sara;
Wilson, Cathleen J.; Mulatu, Woudyalew, 2000. Land-use and land-cover dynamics
in response to changes in climatic, biological and socio-political forces: the
case of southwestern Ethiopia.
[21.] Rao,
K. S.; Pant, Rekha, 2000. Land use dynamics and landscape change pattern in a
typical micro watershed in the mid elevation zone of central Himalaya, India.
pp. 1, 12, 13
[22.] Rawat,
Jiwan; Biswas, Vivekananda; Kumar, Manish, 2013. Quantifying Land Use/Cover Dynamics of
Nainital Town (India) Using Remote
Sensing and GIS Techniques. pp. 1-12
[23.] Rawat,
J. S.; Biswas, Vivekanand; Kumar, Manish, 2013. Changes in land use/cover using
geospatial techniques: A case study of Ramnagar town area, district Nainital,
Uttarakhand, India. pp. 1, 3, 6
[24.] Sailor,
D. J., (2011). A review of methods for estimating anthropogenic heat and
moisture emissions in the urban environment. International Journal of
Climatology 31 (2): 189–199
[25.] Shrestha,
Arun B.; Wake, Cameron P.; Mayewski, Paul a.; Dibb, Jack E., 1998. Maximum
Temperature Trends in the Himalaya and Its Vicinity: An Analysis Based on
Temperature Records from Nepal for the Period 1971–94. pp. 1,2,6,8,10,12,15
[26.] Singh,
Omvir; Arya, Poonam; Chaudhary, Bhagwan Singh, 2007. On rising temperature trends of Dehra Doon
valley. Pp. 1-14
[27.] Vishwa,
BS; Kaur, BK; Simrit, Kahlon, 2013. Land Use/Cover change and its implications
for Kullu District of Himachal Pradesh, India. Pp. 1-1-14
[28.] William
D. Solecki, Cynthia Rosenzweig, Lily Parshall, Greg Pope, Maria Clark, Jennifer
Cox, Mary Wiencke, 2005. Mitigation of the heat island effect in urban New
Jersey, Global Environmental Change Part B: Environmental Hazards, Volume 6,
Issue 1, pp.39-49
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
List of figures
List
of tables
Table 1: Development of built up area in
Dehradun city during 1982–2004……………………………..20
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 ($), random effects (#),
and random coefficient model………………………………………………………………………………………………….…...31
Table 3: The value of Θ2 in Eq.
(11). Coefficients are statistically significantly different from zero at **1%,
*5%, and 10% levels. Equation (11) is estimated using ordinary least squares
($), random effects (#), or the random coefficient model (†)………………………………………………………………..…32