Question

find a research article that uses hypotheses. Identify the hypotheses as null or alternative. Some articles will contain more than one hypothesis. Then, look at the discussion part of the article and see if the p-values were significant and discuss how the article presented the acceptance or rejection of the hypothesis. Include the pdf of the article with your first discussion posting.

Answer 1

1. The Reseach Article is as fallowing

EVALUATION OF TRENDS IN METEOROLOGICAL DATA OF DELHI
AYMAN T. HAMID1, MOHAMMED SHARIF2 & MOHAMMED LATEEF. AHMED3
1Department of Civil Engineering, Mosul University, Mosul, Iraq
2Department of Civil Engineering, Jamia Millia Islamia, Central University, New Delhi, India
3Department of Water Resources and Dams Engineering, Anbar University, Anbar, Iraq

Abstract- Design of hydrological systems is likely to be more reliable if potential impacts of climate change on hydrometeorological
variables are considered. A well accepted method of assessing the impacts of climate change is through the
analysis of hydro-meteorological data. The major objective of this paper is to analyze meteorological data of Delhi for the
detection of climate change impacts. Mean monthly temperature and rainfall data is available for the period 1901-2000. The
trend analysis for the available data has been carried out using the Mann-Kendall non parametric test. The available data
length was partitioned into two different periods of equal length, and the analysis has been carried out for three periods; (a)
1901-2000; (b) 1901-1950; and (c) 1951-2000. A total of four variables have been analyzed for trends. Results of analysis
indicate that the maximum annual temperature for the entire period of analysis has shown an increasing trend whereas the
annual minimum temperature has shown a decreasing trend when the analysis was carried out for the entire period. Annual
rainfall as well the monsoonal rainfall has shown increasing trends. Analysis conducted in the present research clearly
reveals that the impact of warming has been significant in Delhi.
Index Terms- Climate, Delhi, Trend, Mann-Kendall.

I. INTRODUCTION
In recent years the potential impacts of climatic
change and variability have received a lot of attention
from researchers. Anthropogenic activities have
increased the concentration of greenhouse gases in
the atmosphere leading to changes in the climate of
the Earth on both global and regional scales. Many
aspects of the natural environment including the
water resources are significantly impacted due to
climate change. However, the impacts have localized
intensity and must be quantified locally to manage the
natural resources. One of the most important and
immediate effects of global warming would be the
changes in local and regional water availability (Jiang
et al., 2007). Temperature changes are accompanied
by changes in precipitation and runoff amounts.
Reservoir operations, crop production, erosion
processes, runoff production and many other
hydrological processes are likely to be impacted by
climate change. As a result, hydrological systems are
anticipated to experience not only the changes in the
average availability of water but also changes in the
extremes (Simonovic and Li, 2003, Jiang et al.,
2007).
Dracup and Vicuna (2005) reported that increase in
temperature has impacted the timing of snowmelt in
the California Sierra Nevada mountains leading to
earlier system runoff. Other impacts of climate
change that have been identified include changes in
rainfall patterns, extreme weather events, and the
quality of water availability. The IPCC
(Intergovernmental Panel on Climate Change) has
recently completed its Third Assessment Report,
which incorporates new results from the last 5 years
of climate change research. The global average
surface temperature is projected to increase by 1.4° C
to 5.8° C over the period 1990 to 2100. This
projected warming will be greater than that
experienced over the last 10,000 years. It is also
higher than the projected increase in the IPCC Second
Assessment Report, which was 1.0° C to 3.5° C.
Moreover. Recognizing this problem, the United
Nations Framework Convention on Climate Change
(UNFCCC) aims at "stabilization of CO2
concentrations at a level that will prevent dangerous
anthropogenic interference with the climate system.
Revelle and Waggoner, (1983) and Gleick (1987)
have indicated that climate change can adversely
affect the availability of water supply. Patterns of
water demand will need to alter in response to
changes in water supply. Some other impacts of
climate change that have been identified includes
changes in the quantity of runoff produced (Gleick,
1986; Lattenmaier and Gan, 1990), and changes in
the timings of the hydrologic events.
Various studies have been done in different parts in
south Asia for detecting possible climate trends and
changes. (Hingane et al., 1985; Sinha et al., 1997;
Arora et al. 2005; Singh et al., 2008), Bangladesh
(Ahmad and Warrick, 1996), and Nepal (Shrestha et
al., 1999). Hingane et al. (1985) analyzed long-term
mean annual temperature records from 1901 to 1982
over India and detected an increasing trend in mean
surface air temperatures. It was observed that about
0.4°C warming has taken place over India during the
last eight decades mainly due to rise in maximum
temperatures. Sinha et al. (1997), however, showed

that the changes in mean annual temperatures are
partly due to the rise in the minimum temperature
caused by rapid urbanization. Pant and Kumar
(1997) have reported an increase in mean annual
temperatures in India at the rate of 0.57°C per 100
years. Arora et al. (2005) investigated temperature
trend all over India using Mann-Kendall non
parametric technique and linear regression method.
The results showed that mean temperature has
increased by 0.94°C per 100 years for the post
monsoon season and 1.1°C per 100 years for the
winter season. Marco et al. (2003) analyzed the
temperature data of 160 climate stations in China
using Mann-Kendall and inverse distance methods.
An increasing temperature trend was detected all over
the country. Fowler and Archer (2005) examined
temperature data of seven climate stations in the
Karakoram and Hindu Kush mountains of the upper
region of UIRB for seasonal and annual trends using
regression techniques. Mean and maximum winter
temperatures showed significant increase while mean
and minimum summer temperatures showed
consistent decline. Zhang et al. (2005) analyzed
monthly temperature and precipitation data for 51
climate stations and three hydrometric stations in the
Yangtze basin, China. Significant positive and
negative trends at 90, 95 and 99% significant level
were detected using Mann-Kendall test. The authors
concluded that the middle and lower regions of the
basin are likely to face more serious flood disasters in
the future.
Several precipitation trend studies have also been
carried out in the south Asia region. Marco et al.
(2003) showed that there is an increasing
precipitation trend throughout the year in southwest
of Xinjiang which is an area adjacent to Northern part
of Pakistan, and in Jammu- Kashmir which is
southwest of Tibet. Archer and Fowler (2004)
analyzed precipitation data of various stations in
Upper Indus River Basin (UIRB) using linear
regression with different record lengths. A significant
increasing trend of precipitation in winter and
summer during the period 1961-1999 was detected in
upper region of UIRB. Zhang et al. (2005) detected
an upward precipitation trend in middle and lower
part of Yangtze basin, China. On the contrary, Raziei
et al. (2005) concluded that precipitation in Iran has a
decreasing trend. Kezer and Matsuyama (2006)
investigated runoff trends for Ili and East rivers in
central Asia. Regression and difference integral curve
techniques were used for long term hydrometeorological
analysis. No statistically significant
change was observed except for runoff. Arora et al.
(2005) detected an increasing trend in mean annual
temperature in industrialized cities of India including
Mumbai where the increase was reported as 0.84°C
per 100 years. Warming trend has also been found for
Bangladesh. Ahmad and Warrick (1996) reported that
the broad region encompassing Bangladesh has
warmed at the rate of 0.5°C per 100 years. Shrestha et
al. (1999) reported increases of 0.61°C, 0.90°C and
1.24°C per decade in winter maximum temperatures
for Nepal, Himalayan and trans-Himalayan climate
stations respectively. CICERO (2000) estimated a
temperature rise of 0.9°C for Pakistan by 2020 and
predicted that the temperature rise could double by
2050. Chen et al. (2007) investigated temporal trends
of annual and seasonal precipitation from 1951 to
2003 in the Hanjiang basin in China using Mann-
Kendall and the linear regression methods. Results
indicated that precipitation has no significant trend
but a significant increasing trend for temperature was
seen in most parts of the basin. Further, decreasing
trend was seen in mean annual, spring, and winter
runoffs in the Danjiangkou reservoir basin. Singh et
al. (2008) have carried out an extensive analysis of
basin-wide temperature trends in northeast and
central India. A warming trend was observed in seven
of the nine river basins studied. The other two basins
showed a cooling trend. Results of several studies
have confirmed that the south Asia region is indeed
warming and the trend of warming is broadly
consistent with the global warming trend. Rana et al.
(2012) determined Long-term trends in rainfall by
Man-Kendall rank statistics and linear regression. In
Delhi and Mumbai, during the period from 1951 to
2004 Precipitation data was studied on basis of
months, seasons and years, and the total period
divided in the two different time periods of 1951–
1980 and 1981–2004 for detailed analysis. Jain and
Kumar (2012) reviewed studies pertaining to trends
in rainfall, rainy days and temperature over India.
Potentially serious climate impacts are likely to be
experienced in the South Asia region. Recent IPCC
report (IPCC, 2007c) clearly indicates likelihood of
considerable warming over sub-regions of south Asia
with greater warming in winter than in summer.
Results of multimodal GCM runs under Special
Report on Emission Scenarios (SRES) scenarios B1
and A1F1 project an increase in average temperature
over whole of South Asia with greatest increase being
projected for winter months. The projected rise in
temperature for winter months is particularly
alarming as it exceeds the limit of global mean
surface temperature rise of 1.8° C to 4° C reported
by IPCC (IPCC, 2007 a).
II. STUDY AREA
India receives 80 per cent of its annual rainfall during
the southwest monsoon season of June to September.
Rainfall over the country during this season shows a
wide range of spatial variation. Delhi, the capital city
of India is a land locked city with an area of
approximately 1500 km2. Delhi is situated at a height
of 235 m above sea level and lies in northern India.
Due to large distance from the sea, Delhi has an
extreme type of continental climate. The summers in
Delhi are very hot and winters very cold. The

temperature ranges from 46 degrees in summers to 2
degrees in winters. The winters are marked by mist
and fog in the mornings and often the Sun is seen
afternoon. The cold wave from the Himalayan region
makes winters very chilly. The winter season starts in
November with the peak occurring towards the start
of New Year. Around mid-March the weather begins
to get warmer and becomes hot in April. From April
to June, the weather is extremely hot in Delhi. In
summers the heat wave is immense. The arrival of
monsoon normally takes place around end of June.
The major portion of rainfall occurs in the rainy
season between July to September due to southwest
monsoon.
Delhi, the heart of the country is plagued today by
environmental degradation. Air pollution in Delhi is
particularly alarming because of its harmful effects
on human health. Today Delhi has approximately
three times more vehicles than Mumbai. Delhi is the
metropolitan city where commuters are primarily
dependent on the road transport system. This has led
to an enormous increase in the number of vehicles
with the associated problems of traffic-congestion
and an alarming increase in air pollution. The major
pollutants emitted by motor vehicles include CO,
NOx, sulphur oxides, (SO), HC, lead (Pb) and
suspended particulate matter (SPM). These pollutants
have damaging effects on both human health and
ecology. The human health effects of air pollution
vary in the degree of severity, covering a range of
minor effects to serious illness, as well as premature
death in certain cases. Most of the conventional air
pollutants are believed to directly affect the
respiratory and cardio-vascular systems. In particular,
high levels of SO2 and SPM are associated with
increased mortality, morbidity and impaired
pulmonary function. Lead prevents hemoglobin
synthesis in red blood cells in bone marrow, impairs
liver and kidney function and causes neurological
damage. Given the level of pollution in Delhi, it is
important to analyze its impact on temperature and
rainfall characteristics of the city. The mean annual
maximum and minimum temperatures over India for
the period 1961-1990 are shown in Fig 1 and Fig 2
respectively.
The intent behind choosing Delhi as the study area is
that it is a highly urbanized city with a rapidly
growing population. The present population of Delhi
is estimated to be more than 15 million. It is an uphill
task to cater to the water and energy demands for
such a huge population. Any erratic pattern in the
monsoonal rainfall in the city of Delhi is likely to
further aggravate the water problem in Delhi.
Currently, the water supply in the city of the order of
850 MGD whereas the water demand is 1350 MGD.
In addition to satisfying water demand, it is also
important to satisfy the energy demands which have
grown from 3000 MW in 2008 to around 5500 MW
in 2012. In addition to the well acknowledged global
warming, the temperature in the city of Delhi is
influenced by the huge vehicular traffic which is
highest among all the metropolitan cities. Rising
temperatures and increase in the duration of hot spells
combined with erratic rainfall patterns have led to this
rapid rise in the electricity demand in the city.
Therefore, it is important to analyze the temperature
and rainfall data for the city of Delhi to identify any

trends that might be evident from the available data.

The long-term average rainfall in Delhi is 714 mm.

The bar plots of longterm average monthly rainfall is
shown in Fig 3. It can be seen from Fig. 3 that the
major portion of annual rainfall occurs during the
monsoon period (June to September). Monsoon
rainfall over Delhi during the months from June to
September exhibits interesting oscillations. The
monsoon generally sets in the last week of June and
withdraws towards the second week of September.

Some winter rainfall is experienced in the months of
December, January and February.

temperature. As expected, May and June are the
hottest months. The longterm monthly average
minimum temperature is shown in
III. METHODOLOGY
The major objective of the present study is to analyze
the long term trends of monthly, annual, seasonal
TMX, TMN, and rainfall data for the city of Delhi.
The data is available for the period 1901-2000. The
analysis for trends has been carried out four variables.
The list of variables is provided in Table I. Analysis
of data has been carried out for three different time
slices. First, the time series with the data for the entire
period is analyzed. Second, the time series with the
data for the period 1901-1950 has been analyzed.
Finally, the time series for the more recent data
spanning from 1951 – 2000 has been analyzed. The
analysis of trends for each of the time series has been
carried out using Mann-Kendall non parametric test
(Mann, 1945; Kendall, 1975.
TABLE I – LIST OF VARIABLES
S.No
. Variable Abbreviation
1 Total annual rainfall ARAIN
2 Total monsoonal rainfall MRAIN
3 Mean annual maximum
temperature
ATMX
4 Mean annual minimum
temperature
ATMN
IV. MANN-KENDALL TEST
Many parametric and non-parametric methods have
been applied for detection of trends (Kundzewicz &
Robson, 2004; Zhang et al., 2006). Mann-Kendall test
is one of the most widely used non-parametric tests
for detecting a trend in the hydro-meteorological time
series. Parametric tests are more powerful than the
non-parametric ones, but the assumption regarding
the normality of data must be satisfied. Hydrometeorological
time series are often characterized by
data that is not normally distributed, and therefore
nonparametric tests are considered more robust
compared to their parametric counterparts. Several
researchers have employed Mann-Kendall test to
identify trends in the hydro-meteorological variables
due to climate change ((Burn, 1994; Douglas et al.,
2000; Yue et al., 2002; Burn et al., 2004b; Aziz &
Burn 2006; Chen et al., 2007; Burns et al., 2007;
Singh et al., 2008; Burn, 2008; Burn and Sharif,
2010, Khattak et al. 2011).
Mann Kendall test is a ranked based approach that
consists of comparing each value of the time series
with the remaining in a sequential order. The statistic
S is the sum of all the counting as given in equation
1(Hirsch et al., 1982)
and xj and xk are the sequential data values, n is the
length of the data set. A positive value of S indicates
an upward and a negative value indicates a downward
trend. For samples greater than10, the test is
conducted using normal distribution (Helsel &
Hirsch, 1992) with the mean and variance as follows.

where, tp is the number of data points in the pth tied
group and q is the number of tied groups in the data
set. The standardized test statistic (Zmk) is calculated
by
where the value of Zmk is the Mann- Kendall test
statistics that follows standard normal distribution
with mean of zero and variance of one. Thus, in a two
sided test for trend, the null hypothesis Ho is accepted
if –Z1-α/2 ≤ Zmk ≤ Z1-α/2 , where α is the significance
level that indicates the trend strength.
V. ANALYSIS OF MONTHLY RAINFALL
The variability of annual rainfall for Delhi has been
presented using box plots. Box plots are a favored
method of data analysis in many hydrometeorological
applications as they show the range of
variation in statistics of simulations and provide a
straightforward method of comparing the statistics of
simulations with historical data. The bottom and top
horizontal lines in the box in a box plot indicate the
25th and 75th percentile, respectively, of the statistics
computed from the simulated data. The horizontal
line within the box represents the median. The
whiskers are lines extending from each end of the box
to show the extent of the rest of the data.
The whisker extends to the most extreme data value
within 1.5 times the inter-quartile range of the data.
The values beyond the ends of the whiskers are called
outliers and are shown by dots.
The total monthly rainfall for each month has been
summed up to obtain the total annual rainfall for each
year whereas the monthly rainfall for June to
September has been summed up to obtain the total
monsoonal rainfall for each year of the data. Fig. 6
presents the box plots of monthly rainfall observed at
Delhi from 1901-2000. The box plots of total annual
rainfall and monsoonal rainfall are shown in Fig 7.
Large inter-annual variability is evident from the
boxplots shown in Fig. 6 and Fig. 7.
VI. ANALYSIS OF MONTHLY
TEMPERATURE
The analysis for variability of TMX was carried out
for the monthly, seasonal and annual data. The
seasonal analysis has been carried out for four 3-
monthly periods. The inter-annual variability in
temperature data is much less than in the rainfall data.

VII. MEASURES OF VARIABILITY
To study the variability of rainfall, several measures
of variability have been computed and studied. The
precipitation variability can be expressed both in
absolute as well as in relative terms. From among
various absolute measures of variability the standard
deviation (SD), absolute mean deviation (AMD) and
mean absolute inter-annual variability (MAIV) are
used in this paper.
where P is the daily maximum precipitation or
monthly precipitation or their ratio, and is the
temporal mean for N years.
Table II shows the absolute measures of variability
(SD, AMD and MAIV) for the monthly rainfall data.
The highest values of these parameters are observed
for the months of July and August that are associated
with high values of rainfall. The highest values of
coefficient of variability (CV), relative variability
(RV) and percentage of variability (PIV) occurred in
the low rainfall months of June and September. When
these three absolute measures of variability are
divided by the mean and multiplied by 100 they give
rise to three relative measures of variability. These
are the coefficient of variability (CV), relative
variability (RV) and percentage internannual
variability (PIV), i.e.
which are useful measures of variability widely used
in hydro-climatological studies.
TABLE II- SD, AMD, MAIV, CV, RV AND PIV FOR
THE MONTHLY RAINFALL AT NEW DELHI DURING
THE PERIOD 1901-2000
Month June July August September Mean
SD
(mm)
69.76 120.52 134.81 103.06 107.04
AMD
(mm)
48.00 96.86 104.22 88.11 84.30
MAIV
(mm)
68.81 141.56 148.73 115.45 118.64
CV (%) 105.26 60.75 65.92 79.08 77.75
RV (%) 72.43 48.83 50.96 67.61 59.96
PIV (%) 103.82 71.36 72.72 88.59 84.12
Table III and Table IV show the results of SD, AMD,
MAIV, CV, RV and PIV for the seasonal maximum
temperature and seasonal minimum temperature
respectively. For the maximum temperature the
parameters SD, AMD and MAIV have the highest
values in the spring season (May - March). But the
highest values of CV, RV and PIV occurred in winter
season (December – February). For the minimum
temperature the highest values of SD and AMD are
observed in autumn season (September – November).
TABLE III- SD, AMD, MAIV, CV, RV, PIV FOR
THE SEASONAL MAXIMUM TEMPERATURE AT NEW
DELHI DURING THE PERIOD 1901-2000
Month DJF MAM JJA SON Mean
SD (mm) 1.02 1.25 1.06 1.08 1.10
AMD (mm) 0.78 0.97 0.80 0.83 0.85
MAIV (mm) 1.26 1.69 1.64 1.24 1.45
CV (%) 4.55 3.55 2.94 3.86 3.72
RV (%) 3.50 2.76 2.22 2.96 2.86
PIV (%) 5.60 4.79 4.55 4.44 4.85

TABLE IV- SD, AMD, MAIV, CV, RV, PIV FOR
THE SEASONAL MINIMUM TEMPERATURE AT NEW
DELHI DURING THE PERIOD 1901-2000
Month DJF MAM JJA SON Mean
SD (mm) 1.02 1.10 0.69 1.23 1.01
AMD
(mm)
0.84 0.88 0.53 0.99 0.81
MAIV
(mm)
0.75 1.29 1.02 0.95 1.00
CV (%) 11.60 5.22 2.54 9.06 7.11
RV (%) 9.55 4.17 1.93 7.32 5.74
PIV (%) 8.56 6.13 3.75 6.99 6.36
VIII. TREND ANALYSIS RESULTS
The trend analysis for the four variables has been
carried out using the Mann-Kendall non parametric
test. The available data length was partitioned into
two different periods of equal length, and the analysis
has been carried out for three periods; (a) 1901-2000;
(b) 1901-1950; and (b) 1951-2000. Trend evaluation
using Mann-Kendall test relies on two important
statistical metrics -- the trend significance level or the
p-value, and the trend slope β. The p-value is an
indicator of the trend strength and β provides the rate
of change in the variable allowing determination of
the total change during the analysis period. The
results of analysis are presented in Table V. Results
of analysis have indicated
Table V Mann-Kendall test results
ARAIN slope p-value
1901-2000 1.7566 0.048
1901-1950 1.1889 0.651
1951-2000 0.2185 0.907
MRAIN slope p-value
1901-2000 1.4856 0.088
1901-1950 1.2774 0.676
1951-2000 0.625 0.828
ATMX slope p-value
1901-2000 0.001 0.926
1901-1950 0.0105 0.121
1951-2000 0.01 0.081
ATMN slope p-value
1901-2000 -0.009 0.001
1901-1950 -0.038 0.001
1951-2000 0.001 0.906
Note: Bold values indicate trends statistically
significant trends at 10% significance level
It can be seen from Table V that the annual rainfall
has a statistically significant trend with a p-value of
0.048 thereby indicating a very strong trend.
However, when the analysis was carried for the
period 1901-1950, and 1951-2000 no statistically
significant trend was observed. For all the three
analysis, the trends were found to be increasing. The
monsoonal rainfall showed a statistically significant
increasing trend with a p-value of 0.088. It is
important to note from Table V that the annual
maximum temperature (ATMX) has an increasing
trend during the last 50 year period (1951-2000) that
is statistically significant also. The annual minimum
temperature has a very week increasing trend for the
1951-2000 period. This clearly shows that during
recent years, the impact of warming has been
significant in Delhi.
IX. CONCLUSIONS
The objective of the present research was to conduct
analysis of the meteorological data for the city of
Delhi. The analysis of the time series was carried out
using Mann-Kendall nonparametric test which is the
most widely used test for conducting trend analysis of
hydro-meteorological data. The main advantage of
the Mann-Kendall test is that is free from the
assumption of normality of data and can tolerate
outliers in the data series. The variability
characteristics of the rainfall and temperature have
been presented using boxplots. A total of four
variables have been analyzed for trends. Results of
analysis indicate that the maximum annual
temperature for the entire period of analysis has
shown an increasing trend whereas the annual
minimum temperature has shown a decreasing trend
when the analysis was carried out for the entire
period. Annual rainfall as well as the monsoonal
rainfall has shown increasing trends. It can be
concluded that there are signs of warming in the data
analysed in this research. The present research is
likely to provide impetus to climate change modeling
efforts being undertaken in India.

2. Hypothesis

The Null hypothesis for the above article is

H₀ : There is no significant trend in temporal and precipitation data of Delhi city for the period 1901-2000.

The Alternate hypothesis is

H₁ : There is significant trend in temporal and precipitation data of Delhi city for the period 1901-2000.

3. Discussion

The analysis was carried out by using Mann-Kendall non parametric trend test.

The trend was thought as a significant if the P.Value is calculated below 0.05 otherwise the trend was thought to be insignificant or non significant.