Environ Monit Assess (2023) 195:1313

https://doi.org/10.1007/s10661-023-11884-5

RESEARCH

Exploring temperature dynamics in Madhya Pradesh:

a spatial‑temporal analysis
Amit Kumar · Siddharth Kumar ·
Kuldeep Singh Rautela · Aksara Kumari ·
Sulochana Shekhar · Mohanasundari Thangavel

Received: 1 July 2023 / Accepted: 13 September 2023 / Published online: 13 October 2023
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023

Abstract Understanding the dynamics of tempera- temperature (Tmin), consistently increase, with the
ture trends is vital for assessing the impacts of climate most pronounced warming observed during winter.
change on a regional scale. In this context, the pre- The trend analysis reveals that the rate of warming
sent study focuses on Madhya Pradesh state in Cen- has increased in the past few years, particularly since
tral Indian region to explore the spatial-temporal dis- the 1990s. However, Pettitt’s test points out significant
tribution patterns of temperature changes from 1951 changes in the temperature, with Tmean rising from
to 2021. Gridded temperature data obtained from the 25.46 °C in 1951–2004 to 25.78 °C in 2005–2021
Indian Meteorological Department (IMD) in 1° × 1° (+0.33 °C), Tmax shifting from 45.77 °C in 1951–2010
across the state are utilised to analyse long-term trends to 46.24 °C in 2011–2021 (+0.47°C), and Tmin increas-
and variations in temperature. The Mann-Kendall ing from 2.65 °C in 1951–1999 to 3.19 °C in 2000–
(MK) test and Sen’s slope (SS) estimator were used 2021 (+0.46 °C). These results, along with spatial-
to detect the trends, and Pettitt’s test was utilised for temporal distribution maps, shed important light on the
change point detection. The analysis reveals signifi- alterations and variations in monthly Tmean, Tmax, and
cant warming trends in Madhya Pradesh during the Tmin across the area, underlining the dynamic charac-
study period during specific time frames. The temper- ter of climate change and highlighting the demand for
ature variables, such as the annual mean temperature methods for adaptation and mitigation.
(Tmean), maximum temperature (Tmax), and minimum

K. S. Rautela
Department of Civil Engineering, Indian
A. Kumar · M. Thangavel (*) Institute of Technology Indore, Simrol, Indore,
School of Humanaties and Social Science, Indian Madhya Pradesh 453552, India
Institute of Technology Indore, Simrol, Indore, e-mail: kuldeeprautela007@gmail.com
Madhya Pradesh 453552, India
e-mail: mohana@iiti.ac.in A. Kumari
A. Kumar M.A. Geography, Nalanda Open University, Patna,
e-mail: geoamit8995@gmail.com Bihar 800001, India
e-mail: aksarakumari123@gmail.com
S. Kumar
Department of Computer Science & Engineering, S. Shekhar
Indian Institute of Information Technology, Ranchi, Department of Geography, Central University of Tamil
Jharkhand 834010, India Nadu, Thiruvarur, Tamil Nadu 610005, India
e-mail: siddharth.rs@iiitranchi.ac.in e-mail: Sulochana@cutn.ac.in

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 1313 Page 2 of 20 Environ Monit Assess (2023) 195:1313

Keywords Climate change · Mann-Kendall test · consequences (Richards et al., 2021). It endangers
Sen’s slope estimator · Pettitt’s test · Spatial-temporal human health, food security, water supply, and eco-
distribution · Central India nomic stability (Dahal et al., 2018). It also wors-
ens social and environmental disparities by making
vulnerable persons and places more sensitive to its
Introduction effects. In order to tackle the impacts of increas-
ing global temperature, globally coordinated efforts
Climate change (CC) is a worldwide phenomenon must be made to minimise emissions of greenhouse
characterised by long-term shifts in weather patterns gases, switch to sustainable and environmentally
and global average temperatures (Kumar et al., 2021; friendly energy sources, conserve forests, support
Kuniyal et al., 2021). CC has severe repercussions sustainable land and water management, and adapt to
for both human societies and natural systems (James existing changes (Beddington et al., 2012). Various
et al., 2019). Human actions, especially the emission international agreements aim to reduce carbon emis-
of greenhouse gases (GHGs) (such as C ­ O2 and C
­ H4), sion rates to keep global warming below 2 °C over
are the primary contributors (Montzka et al., 2011; pre-industrial levels (Gao et al., 2017). India had an
Roshani et al., 2023; Shivam et al., 2017). The green- average temperature increase of 0.7 °C between 1901
house effect occurs when the concentration of GHGs and 2018; however, this increase is predicted to reach
in the atmosphere rises, leading to the trapping of heat 4.4 °C by 2100 (Negi et al., 2022). Over the past few
and an increase in global temperature, clearly indi- years, certain regions of northwest India, specifically
cating climate change (Madhukar et al., 2021). This Rajasthan and Gujrat, have experienced extremely
phenomenon brings about various changes in climate, high temperatures, reaching 50 °C (Dubey et al.,
including extreme hydro-meteorological events (Ajjur 2021). The occurrence, strength, and duration of
& Al-Ghamdi, 2022; Rahmani & Fattahi, 2023) and extreme heat waves have steadily risen over the previ-
modifications in ecosystems and habitats (Pathak et al., ous decades (Mishra et al., 2017; Panda et al., 2017).
2018). In recent years, this temperature increase has In the recent studies, several parametric (linear
become even more pronounced (IPCC-AR4, 2007; regression, T-test, and F-test) and non-paramet-
IPCC-AR5, 2014). The increase in the global aver- ric (MK test, modified MK test, SS estimator, and
age temperatures can initiate the melting of polar and Kruskal-Wallis test) techniques were used to evalu-
Himalayan ice caps and glaciers, resulting in rising ate variability in climate and its extremes (Roshani
sea levels and an increased risk of coastal flooding et al., 2023; Singh et al., 2023). In addition to this,
(Kumar & Pati, 2022). Furthermore, the rising global various studies used trend and homogeneity analysis
mean temperature has also impacted the world’s hydro- approaches to assess temperature patterns and varia-
logical cycle, including precipitation patterns (Mad- tions. Consequently, the research linking the causes
hukar et al., 2021; Rautela et al., 2022, 2023; Syed of changes in time series is developed, and the find-
et al., 2010). Precipitation patterns can impact water ings of the most recent trends are employed in mitiga-
availability (Rahmani & Fattahi, 2023; Todaro et al., tion policies and practices (Asfaw et al., 2018). Trend
2022), agricultural output, and ecosystems. Addition- analysis can be used more effectively to describe
ally, extreme weather events such as storms, droughts, and anticipate changes in the pattern and variability
floods, and heatwaves can cause harm to people’s of temperature. Though parametric approaches are
health, damage infrastructure, and disrupt economic more successful, they are limited to time series with
activities (Jahn, 2015; Madhukar et al., 2021). normally distributed patterns. The non-parametric
Temperature change has numerous far-reaching methods, in contrast, have been favoured by research-
implications (Li et al., 2015). The rising global tem- ers over parametric techniques for selection of fac-
peratures, melting glaciers and ice caps, increasing tors, including their capacities for handling inaccurate
sea levels, changed rainfall patterns, increased fre- data, the requirement for minimum presumptions, and
quency and intensity of extreme weather events (such their independence of data distribution (Jorda et al.,
as droughts, floods, hurricanes, and heatwaves), shifts 2021). Furthermore, non-parametric techniques are
in biodiversity and ecosystems, and disruptions to more resistant to outliers in time series than para-
agriculture and water resources are just a few of the metric techniques. The MK test and the SS estimator

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Environ Monit Assess (2023) 195:1313 Page 3 of 20 1313

have become the standard techniques for analysing in warming pace. For better visualisation of changing
the trend of various climatic variables (Ashraf et al., or shifting temperature patterns from place to place,
2023). spatial maps are employed for individual months and
Researchers often employ the MK test to detect comparative analyses, highlighting temperature pat-
patterns in time series data, commonly known as the tern shifts. Furthermore, the research calculates the
MK trend test. The MK test defines if a time series overall average temperature rise for Madhya Pradesh
dataset exhibits a monotonic trend (i.e., a consistently across all months and variables. Given the direct
rising or falling pattern). The MK test measures the impact of temperature change on agriculture, the
size and direction of a trend and offers a p-value to findings can guide mitigation strategies and adaptive
indicate whether the trend is statistically significant. measures, which are vital for preventing future crop
A small p-value shows strong evidence of a trend, losses and addressing environmental challenges aris-
whereas no such trend is indicated by a big p-value ing from temperature shifts.
(Thi et al., 2023). SS estimator is a non-parametric
technique for evaluating the trend or slope of time
series data. It is an effective method that performs Materials and methods
well with non-normal or outlier data. SS measures a
line’s slope by taking the mean of all possible pair- Study area
wise slopes. SS estimator has advantages over other
methods, such as its ability to capture the trend’s Madhya Pradesh covers 308,252 ­ km2, is the sec-
overall direction without making assumptions about ond largest state of India, and located in the centre
the data’s distribution and its resistance to outliers of India. It is a landlocked state bordered by Uttar
(Amani & Shafizadeh-Moghadam, 2023). SS estima- Pradesh, Chhattisgarh, Maharashtra, Gujarat, and
tor can be used to estimate the amplitude and direc- Rajasthan. The region extends from latitude 21° 03′ to
tion of a trend. 26° 52′ and from longitude 74° 02′ to 82° 48′ (Fig. 1).
Based on the above literatures, the primary objec- There are 51 districts in Madhya Pradesh. Bhopal is
tive of this research is to examine the shifting patterns the capital city, and Indore is the state’s largest city.
of temperature fluctuations within the state of Mad- Madhya Pradesh is enriched by a multiple rainfed/
hya Pradesh in India by non-parametric approach. spring fed river systems, including the Narmada,
The gridded temperature data for the study area was Mahanadi, Tapti, Tawa, Chambal, Son, and Wain-
collected from the Indian Meteorological Department ganga rivers, which play a vital role in sustaining the
(IMD) between 1951 and 2021. The data were pre- state’s ecological and agricultural landscape. As per
processed before performing descriptive, statistical, Shuttle Radar Topography Mission-Digital Elevation
and spatial analyses of the data. The coefficient of var- Models (SRTM-DEM), the elevation in the Madhya
iance (CV) was calculated to examine the variation of Pradesh ranges from 56 to 1333 m above mean sea
every data point from the mean for temperature vari- level, as indicated in Fig. 1. Notably, Dhupgarh has
ability. MK test and SS estimator were applied to the the the highest elevation at 1333 m.
dataset to detect the study area’s temperature trend. The state has a diverse agricultural sector, which
The Pettitt test was performed on the dataset to find includes cultivating different crops such as cotton,
the temperature change breaking point. The spatial wheat, rice, jowar (sorghum), and soybeans (Nagesh,
maps were created to visualise temperature change in 2020). The area’s subtropical climate has cold winters
the study area better. This study innovatively exam- and scorching, dry summers. Like the rest of India,
ines temperature trends in Madhya Pradesh, Central this region’s climate is distinct by its monsoon sea-
India, using gridded data and statistical tests to reveal son, which provides the most agricultural rainfall.
significant warming patterns, amplified in recent Pre-monsoon season, also known as summer, lasts
years, with Pettitt’s test pinpointing specific tempera- from March to May. The region experiences its mon-
ture shifts, emphasising the dynamic nature of climate soon season, also called the growing season, from
change and the need for adaptation and mitigation June to September. The average annual rainfall in
strategies. It aims to determine recent rates of temper- the region amounts to 1194 mm. During the winter,
ature increase and investigate potential acceleration the average temperatures range between 10 and 25

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Fig. 1  District-wise location of Madhya Pradesh State, including elevation classes

°C, providing a relatively mild and pleasant climate. has the potential to offer valuable insights for the
However, the summer brings scorching temperatures development of policies, urban planning initiatives,
reaching as high as 47 °C or even more, creating hot and agricultural strategies aimed at bolstering resil-
and arid conditions. In contrast, the winter season can ience and mitigating risks associated with tempera-
be quite chilly, with temperatures dropping to 1 °C or ture variations. The presence of data and technologi-
even lower. The state’s average high temperature is cal resources additionally reinforces the viability and
34.6° during summer (March to June). importance of this spatial-temporal analysis.

Data collection
Rationale of the study area
For this study, the required data were obtained from
The selection of Madhya Pradesh as the study region the IMD, Government of India, from 1951 to 2021.
is driven by its varied geographical characteristics, IMD provides temperature data with a daily basis
vulnerability to climate change, rapid and squeed gridded data (in °C) with a resolution of 1° × 1° for
urban growth, and significant contribution to the agri- the Tmax and Tmin from 1951 to 2021. The current
cultural sector. Theis suitable diverse topography and study uses daily Tmax and Tmin data for 1951–2021,
strategic geographical position make it suitable for totaling 26 grid points. We utilised long-term grid-
studying temperature dynamics. Understanding tem- ded data from the IMD website (https://​www.​imdpu​
perature fluctuations is crucial because of the region’s ne.​gov.​in/). This data series was produced using gauge
susceptibility to extreme weather conditions, urban station records and observations made by weather
heat islands, and reliance on agriculture. This study satellites (INSAT series) (Roshani et al., 2023).

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Environ Monit Assess (2023) 195:1313 Page 5 of 20 1313

Fig. 2  A Comprehensive

Methodology Framework
for assessing the dynamics
of the temperature vari-
ability

Numerous studies have employed the IMD gridded analysis. The spatio-temporal distribution was assessed
dataset as an observed/reference dataset, and multiple by calculating the average value of each grid point
hydro-climatological investigations have assessed the for monthly data. Analysing trends in datasets often
accuracy and reliability of this gridded rainfall dataset requires using both non-parametric and parametric
(Gupta et al., 2020). The collection of this data is of techniques, which provide a comprehensive and robust
utmost importance owing to a scarcity of weather sta- analysis (Punia et al., 2015; Shree & Kumar, 2018).
tions, limitations on observation, uneven distribution, The magnitude and trends of the Tmean, Tmax, and Tmin
and insufficiency of data. were examined in this research using the MK test and
SS estimator. By analysing the fluctuations from 1951
Data analysis techniques to 2021, Pettitt’s test was employed in this research to
identify change points within the temperature time
The daily Tmax and Tmin time series data of all 26 grid series data. Using Inverse distance weighted (IDW), the
points were summed up to observe the descriptive sta- spatial distribution of the temperature pattern in Mad-
tistics (mean, standard deviation, and coefficient of hya Pradesh is demonstrated. The comprehensive meth-
variance), trend, abrupt change, and spatio-temporal odological framework is shown in Fig. 21 for this study.
distribution of the monthly Tmean, Tmax, and Tmin of
Madhya Pradesh. The Tmean was calculated using Tmax
1
and Tmin. The daily temperature in Madhya Pradesh was It is noted that at stage 3 (data analysis), different stationarity
tests have been conducted (ADF, KPSS, etc.), and the annual
calculated by taking the mean value from all grid point series of different rainfall seasons found to be stationary at
data to examine the trend and conduct a change point level, i.e., I (0).

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 1313 Page 6 of 20 Environ Monit Assess (2023) 195:1313

MS Excel was used for the cleaning and conversion of data analysis to identify monotonic trends without
data and descriptive statistical analysis and MATLAB making any assumptions about the normality of the
R2023a for the extraction of data and trend and change data (Pohlert, 2016). The MK test, on the other hand,
point analysis, while ArcGIS 10.8 was employed for employs the plus or minus signs (+ or −) to minimise
GIS analysis. Following is a brief explanation of each the influence of trends (Birsan et al., 2005; Helsel &
technique that was employed. Hirsch, 1992). The MK statistics are computed by
Eq. 1.
Coefficient of variance (CV)
∑ ∑
n−1 n
( )
S= sig xi − xj (2)
This study utilised the coefficient of variance (CV) j=1 i=j+1
to examine the variation of every data point from the
mean for temperature variability, where a more sig- where n = number of data points
nificant value indicates a greater degree of variabil- xi and xj = data values in time series (i > j)
ity (Sarkar et al., 2021). This study uses the statisti- and sig (xi−xj) is the function of the sign as given
cal datasets’ CV for Tmean, Tmax, and Tmin variability below (Eq. 2)
(Landsea & Gray, 1992). It is computed by divid- � �
ing the data’s standard deviation by the mean and � � ⎡ −1, if � xi − xj� < 0 ⎤
expressing the result as a percentage, as shown in sig xi − xj = ⎢ 0, if x�i − xj �= 0 ⎥ (3)
⎢ ⎥
Eq. 10. ⎣ + 1, if xi − xj > 0 ⎦

CV =
𝜎
× 100 The variance of S is calculated by using Eq. 3.
μ (1)
∑ � �� �
n(n − 1)(2n + 5) − Ki=1 pi pi − 1 2pi + 5
where, σ = standard deviation, var(S) =
18
μ = mean precipitation. (4)
The degree of variability of meteorological vari-
where n = no. of data points
ables is categorised by a CV as low (CV < 20%),
K = no. of tied group
moderate (20% > CV < 30%), and high (CV > 30%)
pi = the number of data values in the Kth group
(Bharath et al., 2023; Getahun et al., 2021).
Without connected groupings, this summary
method may be disregarded (Kisi & Ay, 2014). Iden-
Trend analysis
tical sample data form a linked group. When n > 10,
the standard normal test statistic Zs is computed using
This study used non-parametric techniques to analyse
Eq. 4.
annual Tmean, Tmax, and Tmin trends. The MK test and
the SS estimator (magnitude of change) were used to ⎡ √S−1 , if S > o ⎤
accomplish this (Kendall, 1975; Kumar et al., 2023; ⎢ var(S) ⎥
Mann, 1945; Sharif et al., 2013). In order to identify
Zs = ⎢ 0, if S = 0 ⎥ (5)
⎢ √S+1 , if S < 0 ⎥⎦
monotonic trends and sudden shifts, these tests are often ⎣ var(S)
employed in climate research (Chakraborty et al., 2013;
Devi et al., 2018; Kampata et al., 2008). A lower p-value The negative value of Zs indicates a decreasing
denotes a higher degree of significance for the observed trend, while a positive value of Zs shows increasing
difference and indicates the trend’s statistically signifi- trends.
cant (Roshani et al., 2023). It should be emphasised that
these tests need an independent data pattern. Sen’s slope (SS) estimator SS estimator is a non-
parametric technique used to estimate the magnitude
Mann‑Kendall (MK) test The MK test is advised of the trend in a time series (Sen, 1968). It is a robust
for the statistical significance of trends in environ- approach that can provide reliable results even when
mental datasets (Singla et al., 2023; Subash et al., the data is not normally distributed or contains outli-
2011; Zamani et al., 2017). Indeed, the MK (Mann- ers. The slope of the trend, represented by Qi, is com-
Kendall) test is commonly employed in environmental puted as follows:

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Environ Monit Assess (2023) 195:1313 Page 7 of 20 1313

tj − tk Inverse distance weighted (IDW)

Qi = For i = 1, 2, 3 … , N (6)
j−k
To illustrate the spatial and temporal distribution
where tj and tk are the data values at j and k time (j > of temperature across the study area, the research-
k) ers calculated the average temperature at each grid.
The N values of Qi are listed from least to greatest, This data was then used to create a geographical map
and Sen’s slope estimator, or the median slope, is cal- depicting the temperature distribution patterns. This
culated as follows: geographical map was created in ArcGIS using the
[ ] IDW interpolation method. This method believes that
Q N+1 , if N is odd
data points closer together are more comparable than
(7)
2
Qmed = Q N +Q N+2
2 2
, if N is even those closer apart (Kumar et al., 2023).
2

The negative value of Qmed shows a downward

trend and a positive value of Qmed indicates an upward Results
trend in the time series.
Statistical characteristics of temperature

Change point detection: Pettitt test It is a non- Table 1 displays descriptive information for tempera-
parametric technique created by Pettitt (1979) and ture in Madhya Pradesh, India, from 1951 to 2021.
may be used to assess the occurrence of rapid changes The table shows the yearly fluctuation of Tmean, Tmax,
in climate data (Dhorde & Zarenistanak, 2013; Smadi and Tmin. For each variable, the following data are
& Zghoul, 2006). One of the reasons for using this provided: mean, standard deviation, coefficient of
test is its heightened sensitivity to identifying disrup- variation (CV), and maximum and lowest temperature
tions or abrupt changes that occur in the centre of records. The annual Tmean is 25.5 °C, which means that
the time series (Dhorde & Zarenistanak, 2013; Ming the average temperature in Madhya Pradesh for the last
Kang & Yusof, 2012), and other researchers have 71 years has been 25.5 °C. Tmax is 45.8 °C on average,
already explained the statistical computation used in which is substantially higher than Tmean. It implies that
the Pettitt test. The first step involves computing the the Tmax was much greater than the Tmean for the year.
Uk statistic using Eq. 7. Tmin is 2.8 °C on average, which is substantially lower
than Tmean. It indicates that the Tmin during the year was
∑
n
considerably lower than the annual Tmean.
Uk = 2 pi − k(n + 1) (8)
i=0
The standard deviation quantifies how far the data
deviates from the mean. The standard deviation for
Pi is the rank of the ith observation when the values yearly Tmean is 0.361, indicating that the temperature
x1, x2…...xn in the series are arranged in ascending for each year was consistent with the average temper-
order. The next step is to define the statistical change ature. The standard deviation for Tmax is 1.105, show-
point test (SCP) as follows: ing more variability in Tmax over 71 years. Similarly,
Kn = max ||Uk || (9)
Table 1  Descriptive statistics of the temperature of Madhya
when Uk attains the maximum value of K in a series, Pradesh (1951–2021)
a change point will occur. The critical value is
Variables Mean Standard CV (%) Max (°C) Min (°C)
acquired by: deviation
[ ( ) ]1∕2
K𝛼 = −1n𝛼 n3 + n2 ∕6 (10) Annual 25.5 0.361 1.41 26.5 24.5
(Tmean)
where n is the number of observations and α is the Maximum 45.8 1.105 2.41 47.7 42.7
level of significance which determines the critical (Tmax)
value. Minimum 2.8 1.116 39.92 5.8 0.4
(Tmin)

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the Tmin standard deviation is 1.116, indicating more implications for the development of adaptation strate-
fluctuation in Tmin over 71 years. The highest annual gies and the formulation of policy decisions, thereby
Tmean temperature recorded in the 71 years was 26.5 emphasising the importance for stakeholders to take
°C in 2010, while the lowest was 24.5 °C in 1971. The them into account when addressing sectors that are
highest recorded temperature for Tmax was 47.7 °C in susceptible to climate change.
1994, while the lowest recorded temperature was 42.7
°C in 1951. It appears that exceptional temperature Trend analysis of temperature
occurrences have been reported in Madhya Pradesh
during the past 71 years. Tmin had a high temperature Table 2 shows the trend analysis results for tempera-
of 5.8 °C in 2009 and a low temperature of 0.4 °C in ture data in Madhya Pradesh from 1951 to 2021. The
2013, showing that the temperature might drop sig- analysis was carried out with a significant level of 5%
nificantly during specific seasons. The table offers a to evaluate the importance of the observed trends in
complete picture of Madhya Pradesh’s temperature the data. The table presents the results of the MK test,
trends during the last 71 years. The data may be used a statistical test that does not rely on specific assump-
to discover patterns and analyse the temperature vari- tions and is employed to identify trends in time series
ations in the region. This data is useful for assessing data. The results of the MK test are reported using
the impact of climate change, agriculture, and disaster Z-statistics, which are measures of the deviation of
management planning, among other things. the observed trend from what would be expected
under the null hypothesis, assuming no trend exists. A
Temperature variability pattern large Z-statistic indicates a strong trend, while a small
Z-statistic suggests a weak or no trend. The table also
Table 1 also includes coefficient of variation (CV) shows Sen’s slope (Q-statistics), which measures
data for three temperature variables in Madhya the trend’s magnitude. SS represents the change in
Pradesh. CV is a statistical metric that depicts a data- the variable per unit of time and estimates the trend
set’s relative variability. The Tmean exhibits minimal line slope. A positive Q-statistic indicates a rising
variability, indicated by a low CV of 1.41%. It sug- trend, and a negative slope shows a falling trend. The
gests a consistent trend over time rather than signifi- p-value, which assumes no underlying trend in the
cant fluctuations. In contrast, the Tmax shows slightly data, shows the likelihood of finding the observed
higher variability, with a CV of 2.41%. However, trend by chance. If the p-value is below the selected
the most significant variability is observed in the significance level (5%), the trend is likely statistically
Tmin, which has a CV of 39.92%. It means there is a significant and not random.
lot of variation in the Tmin over the 71 years due to The trend analysis results in Table 2 show
geographical characteristics and rapid urban growth that the Tmean, Tmax, and Tmin in Madhya Pradesh
(Duhan et al., 2013). have increased over the past 71 years. The Tmean
These findings have significant implications. The (Z = 2.919) shows a significant decreasing trend
observed regular fluctuations in the Tmean indicate (Fig. 3a), indicating a change of 0.006 °C per year.
that the yearly average temperature exhibits a rela- On the other hand, the Tmax (Z = 1.772) shows an
tively consistent pattern over some time, character-
ised by a reduced occurrence of extreme deviations.
A slightly elevated level of variability in the Tmax Table 2  Result of trend analysis for temperature (at 5% sig-
nificance level) of Madhya Pradesh (1951–2021)
suggests a somewhat reduced level of predictability
in the pattern, although it remains within acceptable Categories MK test Sen’s slope p-value Trend
(Z-statis- (Q-statis-
boundaries. Conversely, the high variability in Tmin
tics) tics)
indicates that the temperature can drop significantly
yearly. It can substantially impact various sectors like Annual (Tmean) 2.919 0.006 0.003 Increasing
agriculture, health, and energy. For example, sudden Maximum 1.772 0.011 0.076 Increasing
temperature drops could lead to frost and damage (Tmax)
to crops and could also lead to health problems like Minimum 2.452 0.017 0.014 Increasing
(Tmin)
hypothermia. These variations possess noteworthy

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Environ Monit Assess (2023) 195:1313 Page 9 of 20 1313

Fig. 3  Temporal variation of temperature Tmean (a), Tmax (b), and Tmin (c) for Madhya Pradesh (1951–2021)

insignificant increasing trend (Fig. 3b), suggesting were identified as 2004, 2010, and 1999, respectively.
a change of 0.011 °C per year. The Tmin (Z = 2.452) These breakpoints may be linked to regional or global
shows a significant increasing trend (Fig. 3c) with events, policy modifications, technological advance-
0.017 °C magnitude of change per year. The slope of ments, or natural variability, which can influence tem-
the trend lines is 0.006, 0.012, and 0.013 for Tmean, perature patterns in the study area.
Tmax, and Tmin, respectively, indicating that the slope Moreover, the sudden shifts observed after the
for Tmean<Tmax<Tmin. However, the R2 square values identified points of change possess many implica-
are also 0.117, 0.046, and 0.061, respectively, which tions. It is possible to infer that Madhya Pradesh
depicts the explanatory power of the time trend com- underwent significant changes in temperature pat-
ponent in explaining the temperature variations. terns after these years. The potential implications of
these alterations extend to ecosystems, agriculture,
Change point analysis water resources, human health, and the overall capac-
ity of society to adapt to shifting climatic conditions.
The researchers applied Pettit’s test to detect a A comprehensive comprehension of the implications
change point in the time series temperature data set arising from the outcomes of the Pettitt test, concern-
of Madhya Pradesh. This change point indicates a ing particular change points is of utmost importance
potential abrupt change in the underlying process, for making well-informed decisions and develop-
which denotes a shift in the data’s mean or distribu- ing effective policies. These insights have the poten-
tion. The test results are presented in graphical form tial to provide valuable guidance for policymakers,
in Fig. 4 (a–c). Based on the analysis of Pettit’s test, researchers, and stakeholders in formulating specific
the change points for the Tmean, Tmax, and Tmin trends strategies aimed at mitigating the adverse effects of

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Fig. 4  Graphical representation of Pettitt’s test used for detecting change points in temperature trend of Tmean (a), Tmax (b), and Tmin
(c) for Madhya Pradesh

temperature fluctuations and bolstering the region’s month’s Tmean, Tmax, and Tmin. The maps were colour-
ability to adapt. Furthermore, it emphasises the coded, with dark red representing the highest value
necessity of ongoing monitoring and evaluation of range and grey showing the lowest value range. These
climate patterns to confront the difficulties presented maps clearly demonstrate the spatial distribution of
by climate variability and change. temperature patterns in Madhya Pradesh.

Spatial change analysis of temperature Mean temperature (Tmean)

To understand the spatial changes in temperature in Figure 5 depicts the spatio-temporal fluctuations of
Madhya Pradesh, the IDW technique was employed. the Tmean across the region of Madhya Pradesh from
The monthly Tmean, Tmax, and Tmin of all 26 grid points January to December. This analysis was conducted
were analysed to capture the spatial changes. The using the IDW technique. Significant observations
long-term time series data was divided into two parts arise from the study. The distribution of the green
based on Pettitt’s test results, with one part spanning and orange shaded areas in January shows a slight
from 1951 to 2004 and the other from 2005 to 2021 change, which indicates a subtle shift. February
for Tmean, 1951–2010 and 2011–2021 for Tmax, and exhibits a discernible change, as noted in the alter-
1951–1999 and 2000–2021 for Tmin. After dividing ing distribution of red, blue, and green regions, cor-
the data, IDW maps were created to represent each responding to a decrease in the presence of orange

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Environ Monit Assess (2023) 195:1313 Page 11 of 20 1313

Fig. 5  Spatial distribution of Tmean in Madhya Pradesh from January to December

and grey-shaded areas. A noticeable transforma- in the shaded areas during June and July. Signifi-
tion occurred after 2004, marked by the emergence cantly, August experiences the occurrence of dark
of dark red areas. The month of March exhibits a red zones, accompanied by a heightened prevalence
comparable pattern, wherein there is an increase in of green, blue, and red areas, while observing a
regions classified as red and blue, while there is a decline in regions shaded in orange.
decrease in areas categorised as green and orange September reveals a significant spatial transition in
after 2004. April is characterised by notable trans- the northern and eastern regions of Madhya Pradesh.
formations, wherein entire regions experiences These regions undergo an increase in the presence of
increase of 0.5 °C in Tmean. May indicates a sig- red and blue regions, accompanied by the emergence
nificant transformation characterised by a nota- of dark red zones after 2004 in October. This month,
ble decrease in areas coloured blue, accompanied a uniform temperature increase of 0.5 °C is observed
by an increase in regions coloured red and dark throughout the entire state, with a corresponding shift
red after 2004. Minor variations can be observed occurring in all shaded regions. In December, there is

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a noticeable similarity in the fluctuation pattern of the to December. It is worth noting that January exhibits a
Tmean. It is characterised by an increase in the extent noticeable downward trend in the Tmax, evident through
of red, blue, and green regions, while a decrease is the increasing prevalence of colour-shaded areas rep-
observed in the areas shaded in orange and grey after resenting lower values, particularly after 2010. Febru-
2004. These results highlight the fluctuating tempo- ary and March demonstrate nuanced variations in the
ral progression of temperature patterns in Madhya distribution of Tmax, while significant changes charac-
Pradesh, which holds significant implications for terise April and May. These shifts are observed as an
monitoring climate conditions and developing adap- increase in the size of colour-shaded areas represent-
tation strategies. ing high-value regions and a decrease in colour-shaded
areas representing low-value regions after 2010. Dur-
Maximum temperature (Tmax) ing this period, there has been a notable increase in the
prevalence of regions exhibiting shades of red and dark
Figure 6 demonstrates the spatial distribution of the red. However, May observed an increase of 0.5 °C in
Tmax across the Madhya Pradesh region from January almost the whole state after 2010.

Fig. 6  Spatial distribution of ­Tmax in Madhya Pradesh from January to December

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Environ Monit Assess (2023) 195:1313 Page 13 of 20 1313

In contrast, June and July demonstrate consistent following 1999. During June, a decrease in the Tmin
patterns in the distribution of Tmax. The shifting pat- is observed, accompanied by noticeable changes in
tern of Tmax persists during August and September the regions shaded in red and orange. The distribu-
as the prevalence of regions exhibiting blue and red tion patterns of Tmin in July exhibit a notable degree
colouration increases, coinciding with the emergence of stability in the period following 1999.
of dark red shading after 2010. The above pattern On the contrary, slight modifications become evi-
remains consistent during October and November, dent in the months of August and September, which
indicating notable changes and an increased occur- are distinguished by the presence of an expanded area
rence of regions shaded in red and dark red. Fur- exhibiting shades of red and dark red after 1999. Sig-
thermore, there is an increase of 0.5 °C in the entire nificant alterations become apparent during October
Madhya Pradesh. Comparatively, December shows a (increase of 0.5 °C) and November, as regions shaded
slight shift in the Tmax pattern after this period. in blue replace those previously shaded in green
The implications of these findings are of utmost in October, and green areas replace those coloured
importance in enhancing our comprehension of the in orange in November, following the year 1999. A
temporal and spatial fluctuations in Tmax within the noticeable parallel trend can be observed in Decem-
region of Madhya Pradesh. The shifts and trends that ber, as indicated by changes in the sectors coloured in
have been identified can provide valuable insights green and orange after 1999.
for developing climate change adaptation strategies, The results of this analysis highlight notable
allocating resources, and formulating policies. It changes in the distribution of Tmin across various
is particularly relevant in regions with a noticeable months in Madhya Pradesh since 1999. The shifts
increase in temperature patterns. Visual representa- that have been observed serve as indicators of evolv-
tions of these patterns assist individuals in positions ing climatic dynamics, thus necessitating the atten-
of authority in preparing specific strategies to allevi- tion of policymakers, researchers, and stakeholders.
ate the potential consequences of shifting tempera- The heightened fluctuations in temperature could
ture trends on ecosystems, agriculture, and nearby potentially have diverse consequences for nearby eco-
population. systems, agricultural practices, water availability, and
human well-being. Future inquiries may explore the
Minimum temperature (Tmin) underlying factors contributing to these shifting tem-
perature patterns and their potential ramifications for
The spatio-temporal distribution of Tmin across Mad- the socio-economic and environmental contexts of
hya Pradesh throughout the months of the year is the area.
illustrated in Fig. 7. In January, a noticeable change
in the Tmin pattern is observed, characterised explic-
itly by an enlarged region shaded in green after 1999. Discussion
The following months, namely February and March,
demonstrate a shifting pattern in the Tmin. It is evident This study utilizes gridded temperature data and
through expanded regions displaying red and dark employs statistical tools such as the MK test, Sen
red hues, which signify higher temperatures post- slope, and Pettitt’s test to detect notable temperature
1999. Simultaneously, there is a decrease in coverage shifts in Central India’s Madhya Pradesh region. It
in regions previously represented as areas shaded in also investigates recent warming rates and poten-
green or orange. Notably, a significant transformation tial acceleration. However, spatio-temporal map-
occurs in April, as a considerable change is observed ping illustrates changing temperature patterns across
wherein regions previously coloured in blue are now months. The study calculates average tempera-
predominantly covered in red hues, while the blue ture rise, crucial for understanding climate change
areas transition to green. The results suggest a sig- impacts in tropical to semi-arid regions in central
nificant rise in statewide temperatures of around 1 India. These insights guide adaptation strategies
°C in April after 1999. The state’s northern region for agriculture, mitigating crop losses and environ-
experienced a similar transformation, characterised mental challenges tied to temperature fluctuations.
by an increase in the extent of red and dark red areas It is crucial to analyse climate variability, detect

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 1313 Page 14 of 20 Environ Monit Assess (2023) 195:1313

Fig. 7  Spatial distribution of ­Tmin in Madhya Pradesh from January to December

trends, and assess spatial distribution to compre- is also vital in climate research as it aids in under-
hend climate change’s consequences and enhance standing regional variations in climate variables
resource management and planning. Climatic vari- and their temporal changes. The CV values provide
ables serve as fundamental data sources for evaluat- helpful information about the temperature variabil-
ing these trends and understanding changes occur- ity in Madhya Pradesh. The low CV values for Tmean
ring in the climate system (Moratiel et al., 2011). and Tmax indicate stability and consistency. In con-
Non-parametric tests are frequently employed in trast, the high CV value for Tmin indicates significant
climate research (Ampofo et al., 2023) because variability that should be considered when analys-
they can identify trends in time series data without ing the region’s temperature patterns. The findings
assuming a specific distribution. These tests exam- of a recent study on temperature variability concur
ine multiple meteorological variables at different with those of other studies, such as Pal and Al-Tab-
scales, including temperature, precipitation, humid- baa (2010), Punia et al. (2015), Radhakrishnan et al.
ity, and wind speed. Spatial distribution analysis (2017), Roshani et al. (2023).

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Environ Monit Assess (2023) 195:1313 Page 15 of 20 1313

The annual Tmean, Tmax, and Tmin trends in Madhya transition points in the temperature data of Madhya
Pradesh have been thoroughly examined from 1951 to Pradesh. By employing Pettitt’s test, the researchers
2021 using the MK test and SS estimator. The analy- aimed to pinpoint significant changes in tempera-
sis indicated a non-significant upward trend in Tmax, ture patterns, thereby offering valuable insights into
whereas significant upward trends were observed in the temporal dynamics of temperature in this study
Tmean and Tmin. These findings hold immense signifi- area. Pettitt’s test (presented in Fig. 4) revealed that
cance for the region’s ecology, agriculture, and econ- the Tmean, Tmax, and Tmin trends underwent abrupt
omy, as a sustained rise in temperature can profoundly changes. Specifically, the annual Tmean shifted from
affect the local climate. These effects may include an 25.46 °C in 1951–2004 to 25.77 °C in 2005–2021,
escalation in drought conditions, alteration in crop the Tmax shifted from 45.77 °C in 1951–2010 to 46.24
growing seasons, and the proliferation of disease vec- °C in 2011–2021, and the Tmin shifted from 2.65 °C
tors. As reported in Table 2, the trend analysis pro- in 1951–1999 to 3.11 °C in 2000–2021. These find-
vides invaluable information for policymakers and ings highlight the occurrence of notable changes in
researchers devising region-specific climate change temperature dynamics within specific periods. Simi-
adaptation and mitigation strategies. While prior lar research has been conducted in other geographi-
research focusing on the same time period and study cal locations to identify significant transition points in
location is lacking, our findings align with numer- climate variables. For example, Shukla et al. (2017)
ous studies conducted in Central and Central West identified 1963 as the change point for the Tmean time
India. For instance, Duhan et al. (2013) reported an series spanning from 1901 to 2005 for the entirety
increased temperature of 0.60 °C for the annual Tmean, of Madhya Pradesh. Chandole and Joshi (2023)
Tmax, and Tmin over 102 years (1901–2002). Shukla observed a positive trend in Tmin, which underwent a
et al. (2017) and Shukla and Khare (2013) identified coherent change after 1986 in two districts of Guja-
a significant increasing trend in the mean temperature rat. In a study by Zarenistanak et al. (2014) focus-
of Madhya Pradesh over 105 years (1901–2005). ing on Iran, annual and seasonal temperature data
Similarly, Devi et al. (2020) observed a noteworthy from 1950 to 2007 were analysed, revealing mutation
increasing trend in Tmean, Tmax, and Tmin over 45 years points occurring between the 1980s and 1990s. How-
(1971–2015) in Central India. Kundu et al. (2017), ever, Srilakshmi et al. (2022) applied Pettitt’s test to
using 105 years (1901–2005) of Tmax and Tmin data the pan coefficient in the northeastern region of India
from Madhya Pradesh, demonstrated an increasing and observed that abrupt change occurred after 1990
trend in both variables, with the highest temperature in four stations. Ahmadi et al. (2018) investigated
rise occurring during the winters and post-monsoon the long-term temperature patterns in Iran by ana-
seasons. Furthermore, Punia et al. (2015) and Singla lysing data from 34 synoptic stations over 50 years
et al. (2023) noted an upward trend in Tmax and Tmin in (1961–2010) at both seasonal and annual time scales.
the northwestern part of India after 1970. The results The period of highest change point occurrence was
of this study align with previous studies conducted by observed from 1986 to 1994.
Dubey et al. (2021), Pal and Al-Tabbaa (2010), Rad- As the world continues to grapple with the effects
hakrishnan et al. (2017), and Srivastava et al. (2017), of climate change, it is more important than ever
which also reported positive trends in Tmean, Tmax, to understand how our environment is evolving. In
and Tmin across various regions in India. However, Madhya Pradesh, India, the comparative spatial
Jhajharia et al. (2014) analysed trends in temperature distribution of mean monthly temperature provides
over the Godavari River basin in Southern Peninsu- valuable insights into climate change. The spatial-
lar India using 35 stations. They also reported that temporal distribution of monthly Tmean, Tmax, and
the specific number of stations presents an increasing Tmin maps were constructed based on the average
trend in Tmean, Tmax, and Tmin. for each grid point’s temperature data. Time series
Detecting breaking points in climatic time series data was divided into two sections for comparison
data is a critical task that helps uncover sudden shifts analysis based on Pettitt’s test result. These maps
in data trends. Within the realm of change point detec- (Figs. 5–7) represent how monthly Tmean, Tmax, and
tion, various methods are available. In this particu- Tmin changed and shifted from one place to another.
lar study, Pettitt’s test was utilised to identify sharp Similar studies conducted by Duhan et al. (2013)

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focused on Madhya Pradesh, where they examined However, there are certain limitations to the cur-
temperature data to analyse the spatial-temporal rent approach. It primarily relies on historical tem-
distribution of seasonal Tmean, Tmax, and Tmin over a perature data and does not consider other important
study period of 102 years (1901–2002). Their find- climate factors like the range of temperature changes
ings highlighted increasing temperature trends that within a day (diurnal temperature range) and the
were spatially diverse. Sengupta and Thangavel number of hours of sunlight (sunshine hours). Addi-
(2023) also utilised meteorological data to examine tionally, a relatively low level of detail regarding
temperature, rainfall, and drought severity (Stand- geographic accuracy does not explore the underlying
ardised precipitation index-SPI) patterns across the reasons for shifts in temperature. These limitations
research period (1990–2015). Based on multiple lin- will help in future to develop a more comprehensive
ear regression analyses, their findings indicated that approach that considers a wider range of variables,
Maharashtra’s shifting precipitation patterns were conducts a thorough analysis of the causes behind
the primary driver behind intensifying drought con- temperature shifts, and looks at the bigger picture in
ditions, posing a risk to cotton crop yields. Further- terms of space and time. It will ultimately help us cre-
more, Nabi et al. (2023) reported changes in temper- ate more effective strategies to deal with the impacts
ature patterns across different locations, with higher of climate change.
elevation zones consistently experiencing lower
temperatures, while lower elevation zones exhibited
higher temperature profiles. Conclusion
The effects of climate change and variability pre-
sent significant obstacles to human livelihoods. Our In order to understand the ramifications of climate
research findings reveal a distinct change in tem- change and enhance resource management and
perature patterns after identifying a change point. planning, this study emphasises the critical impor-
This change in temperature trends emphasises how tance of analysing variability in the climate, iden-
dynamic climate change is and how it may affect tifying trends, and assessing regional distribution.
several facets of human life. It highlights the critical The Tmean’s CV is 1.41%, indicating low variabil-
need to comprehend and adapt to these changes, fos- ity. It implies a long-term trend rather than signifi-
tering sustainable and resilient practices while miti- cant fluctuations. The Tmax variability is slightly
gating the adverse effects. The graphs presented in higher, with a CV of 2.41%. The Tmin has the most
our study vividly depict the detection of trends and variability, with a CV of 39.92%. The observed
the spatial-temporal distribution of Tmean, Tmax, and wide range of Tmin suggests a notable annual fluc-
Tmin across Madhya Pradesh. These visual represen- tuation in temperature, with the potential for large
tations offer valuable insights into temperature pat- decreases. The potential effects of this phenomenon
terns throughout the region. The colour-shaded areas are significant across several domains, including
effectively capture the month-to-month and year-to- agriculture, health, and energy. Without establish-
year changes, making these graphs a treasure trove ing any assumptions about the spatial distribution
of information for researchers, policymakers, and of the data, non-parametric tests like the MK test
individuals interested in understanding temperature make it possible to determine trends in meteoro-
variations in Madhya Pradesh. The observed changes logical variables. The study investigated tempera-
serve as a poignant reminder of the importance of ture data from 1951 to 2021 in Madhya Pradesh,
monitoring and addressing climate change to miti- India, and revealed significant increasing patterns
gate its impacts on our environment and lives. Over- for the annual Tmean, Tmax, and Tmin. The Tmean (Z
all, the data unequivocally illustrates the alterations = 2.919) decreases by 0.006 °C per year. However,
in temperature distribution within Madhya Pradesh, Tmax (Z = 1.772) shows an insignificant increasing
highlighting the profound effect of climate change on trend of 0.011 °C per year. The Tmin (Z = 2.452)
the region. This knowledge serves as a foundation for increases by 0.017 °C per year. These discoveries
informed decision-making and implementing strat- might affect the region’s drought conditions, crop
egies to manage and adapt to the changing climate growth seasons, disease vectors, and ecological,
effectively. agricultural, and economic variables. Significant

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changes in temperature patterns during certain time Kuldeep Singh Rautela: Methodology, statistical analysis,
frames were discovered utilising Pettitt’s technique and critical review of the manuscript.
Aksara Kumari: Data interpretation, literature review, and
for change point identification. Based on Pettitt’s manuscript editing.
test result, the annual Tmean increased by 0.33 °C, Sulochana Shekhar: Conceptualization, Suggestions, and
the mean Tmax increased by 0.47 °C, and the mean manuscript review.
Tmin increased by 0.46 °C. Based on this result, it Mohanasundari Thangavel: Supervision, Conceptualization,
methodology, and final manuscript review.
is inferred that the average temperature of Mad- All authors contributed significantly to the research and
hya Pradesh state had increased by 0.42 °C for manuscript preparation.
all months on average. The maps of the spatial-
temporal distribution shed important light on the Data availability The data used in this study was obtained from
alterations and variations in monthly Tmean, Tmax, the Indian Meteorological Department (IMD) Gridded Tempera-
ture Data (https://​www.​imdpu​ne.​gov.​in/). The availability and
and Tmin across the area. These results underline access to the data used in this research are subject to the poli-
the dynamic character of climate change and high- cies and regulations of the Indian Meteorological Department.
light the demand for methods for adaptation and Researchers interested in accessing the data are advised to contact
mitigation. the Indian Meteorological Department directly for further infor-
mation on data availability, access, and any required permissions.
The study’s results are consistent with earlier
studies in Central and Central West India, which Declarations
revealed rising trends in Tmean, Tmax, and Tmin. Pet-
titt’s test’s change point analysis revealed signifi- Ethics approval All authors have read, understood, and
cant changes in temperature dynamics within time have complied as applicable with the statement on “Ethical
frames, supporting research of a similar nature done Responsibilities of Authors” as found in the Instructions for
Authors.
in other places. Understanding how temperature var-
iations vary worldwide is essential for understanding
Competing interest All authors certify that they have no affil-
climate change. The spatial comparison of monthly iations with or involvement in any organisation or entity with
temperature maps offered substantial details about any financial interest or non-financial interest in the subject mat-
climate change in Madhya Pradesh. The maps illus- ter or materials discussed in this manuscript.
trated how the region’s monthly Tmean, Tmax, and Tmin
varied and changed. Similar studies have examined
variations in the spatial-temporal temperature dis-
tribution, indicating spatially diverse rising tem- References
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