Investigation of Climate Extremes in Jabalpur District of Madhya Pradesh: Trends and Future Implications

Investigation of Climate Extremes in Jabalpur District of Madhya Pradesh: Trends and Future Implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of Climate Extremes in Jabalpur District of Madhya Pradesh: Trends and Future Implications Pankaj Kumar Thakur, Dheeraj Mohan Gururani, Abhishek Agrawal, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4617217/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is a long-term change in hydro-meteorological conditions in a particular geographical region. In the present study, spatio-temporal long-term change in rainfall during 1961–2020 was analyzed using IMD 0.25°×0.25° resolution gridded dataset in the Jabalpur district. The non-parametric Mann–Kendall trend test and Sen’s slope estimator was applied to determine the trend and its magnitude, respectively in the precipitation time series. Extreme indices were employed to understand the risks and vulnerabilities associated with extreme weather events in the district. Mann-Kendall trend test showed an insignificant trend at 95% confidence interval with Sen’s slope value of -2.98 to 0.73 mm/year. CLIMPACT tool was used to study extreme indices and it revealed that, in the district, CDD, CWD, R10, R20, and RX5 were decreasing, whereas RX1 and SDII were found increasing. The study’s outcomes provide valuable inferences for future water resource planning and management in central India, particularly in Jabalpur district. Mann-Kendall Sen’s slope Trend analysis Extreme Indices Climate Change Future Implication Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The declining worldwide availability of water resources has emerged as a central concern, particularly in the planning and execution of projects geared towards sustainable development, with a specific focus on water sectors (Dungumaro and Madulu, 2003 ; UN, 2020). In this context, there is an increasing emphasis on adopting efficient water resource management techniques and implementing methodologies to estimate and control erosion and floods (Cosgrove and Loucks, 2015 ; Mahmoodzada et al, 2023 ). The significance of these initiatives is underscored by the growing awareness of the interconnectedness of water resources with broader environmental and climatic conditions (Hirji and Davis, 2009 ). The discussion on current and future environmental conditions has prominently illustrated by the evolving global temperature and rainfall patterns (Mondal et al, 2015 ). The Intergovernmental Panel on Climate Change (IPCC, 2001, 2007, 2014, and 2020) has issued reports that highlight the inevitable onset of global climate change in the 21st century. These reports not only underscore the urgency of the situation but also stress the critical importance of implementing mitigation measures aimed at curbing the emission of greenhouse gases. The implications of climate change on rainfall patterns are particularly noteworthy. Alterations in rainfall can result in significant shifts in regional climates, thereby impacting the availability of water resources and exacerbating issues such as floods or droughts (Kusangaya et al , 2015; Sivakumar, 2011 ; García-Ruiz et al, 2011 ). This dynamic relationship between climate change and water resources underscores the need for a comprehensive assessment of likely rainfall patterns (Xu et al , 2004). Such assessments are not only essential for effective water resource management but also play a crucial role in informing disaster mitigation and adaptation strategies (Handmer and Dovers, 2013 ). Furthermore, the increasing global temperatures contribute to the intensification of extreme weather events (Singh et al, 2022 ), such as heatwaves (Maurya et al , 2024) and prolonged periods of high temperatures. These temperature-related challenges further emphasize the complex nature of the environmental changes that societies and ecosystems are currently facing (Ludwig et al, 2014 ). In climate studies, the identification of trends of climatic variables on a global scale within a watershed is a common pursuit. To ascertain these trends with an acceptable level of confidence, statistical approaches frequently employ either parametric or non-parametric trend analysis methods (Vijhani et al, 2021 ). Among the methods used for this purpose, the Mann-Kendall Test and Sen's Slope Test are often popular choices. These tests are particularly applied to historical hydro-meteorological data and extreme climate indices to discern anomalous patterns and changes over time (Hamed and Rao, 1998 ; Tabari and Marofi 2011 ; Sulaiman et al. , 2015; Phuong et al, 2020 ; Trivedi and Gautam, 2022 ; Singh et al., 2023 ;). The Mann-Kendall Test is a non-parametric method that evaluates a monotonic trend in time series data, making it robust to deviations from normality and resistant to outliers. This test assesses the ranks of data points, determining whether a systematic trend, either upward or downward, exists within the sequence of observations. It provides valuable insights into the presence and significance of trends in climatic variables (Hamed and Rao, 1998 ; McLeod, 2005 ). On the other hand, Sen's Slope Test, also a non-parametric method, focuses on estimating the magnitude of trends by calculating the median of all possible slopes between data points. This approach is advantageous when dealing with datasets that might exhibit non-normal distributions or contain outliers and the test is particularly useful for quantifying the direction and strength of trends in historical and extreme climate indices (Yu et al, 1993 ; Bhat et al, 2021 ). The Mann-Kendall test (Mann 1945 ; Kendall 1975) and Sen's slope (Sen, 1968 ) estimator are widely used for trend analysis due to their robustness and simplicity. Unlike some other methods, they are non-parametric, making them suitable for analyzing data with non-normal distributions or outliers. Additionally, they provide reliable estimates of trend direction and magnitude without assuming specific underlying distributional properties, enhancing their applicability across diverse datasets and scenarios. The unique geographical location, topographical features, and land-use patterns of Jabalpur District render it particularly susceptible to climate variability and change. Therefore, an in-depth analysis of trends in climate extremes in this region can offer valuable insights into the underlying regional climate dynamics. Such investigations are essential for accurately assessing associated risks and formulating targeted adaptation and mitigation measures that align with the specific needs of the local community. Additionally, increased urbanization and deforestation significantly alter the water cycle and hydrology of any region and ultimately would lead to a change in climatic conditions (Das et al., 2018 ; Marengo, 2006 ). A significant increase in urbanization has been observed in Madhya Pradesh in India over the last few decades. Particularly, Jabalpur district exhibits a recorded 58.46% urban area, which is more than the state average of 27.63% (Marengo, 2006 ; Gupta, 2014 ). Consequently, in 2009 and 2010, an increase in average annual temperature was observed which exceeded the historical average, while in 2008, the minimum temperature was recorded below the normal minimum temperature. Additionally, in year 2012 a slight increase in rainfall was recorded which exceeds the normal rainfall (Shrivastava et al., 2018 ). Therefore, the district has highest hydro-dynamic vulnerability to climate change. And, in order to understand the hydroclimatic dynamics of the region, the present study was conducted in the district. The current study is dedicated to comprehensively understanding the spatiotemporal variations in climatic and hydrological conditions within Jabalpur district, Madhya Pradesh, from 1960 to 2020. This investigation is motivated by the need to address the hydroclimatic change in the region in the last sixty years, this may due to urbanization, deforestation, and socio-economic changes on local weather patterns (Zhang et al., 2021 ; Jain et al., 2014 ). Leveraging the high-resolution Indian Meteorological Department's (IMD) 0.25°×0.25° grid rainfall dataset ( www.imdpune.gov.in ), the study aims to achieve several specific objectives. Firstly, it endeavors to identify the inflection point of change (year) within the historical climatic dataset, with a particular emphasis on discerning shifts in rainfall dynamics. This involves employing advanced statistical techniques such as change-point analysis to determine the precise temporal occurrences of significant deviations. Secondly, the research seeks to quantitatively assess the changes in historic rainfall trends, including their magnitude and statistical significance at a 95% confidence level. This entails employing methodologies like Mann-Kendall trend tests, and Sen's slope estimator to elucidate the long-term trends and their statistical robustness. Additionally, the study employs climatic extreme indices derived from the dataset to identify and characterize major extreme weather events that have impacted the study area over the specified period. Overall, this study aims to contribute valuable insights into the complex interactions between anthropogenic activities and climatic processes (Kumar and Mohanasundari, 2024 ), thereby informing effective adaptation and mitigation strategies for sustainable environmental management in Jabalpur district and beyond. 2. Study Area In the face of global climate change, Jabalpur geographic location and climatic characteristics may experience discernible shifts, potentially impacting the region ecology, economy, and community resilience (UN, 2020). Nestled along the picturesque Narmada River, the city of Jabalpur, geographical located at 23° 10' 53" N latitude and 79° 59' 11" E longitude as shown in the Fig. 1 , faces the challenges posed by a humid subtropical climate. As of the 2011 census (censusindia.gov.in), it proudly stands as the third-largest urban agglomeration in Madhya Pradesh and the 38th-largest in the country. The region’s critical dependence on the Narmada River for various needs, including agriculture and water supply, adds another layer of vulnerability. Changes in rainfall patterns can significantly impact the river’s flow, potentially affecting the water availability and necessitating adaptive water management strategies. The average annual rainfall of 1386 mm and a mean temperature of 25.5˚C reflect the region’s current climatic norms. However, with climate change, projections indicate potential alteration in these patterns (Gupta and Jain, 2018 ). The rising global temperatures could intensify the already warm summer months in Jabalpur, extending the duration and elevating temperatures further (Yadav et al., 2021 ). May, historically the hottest month with temperatures exceeding 40°C, may witness heightened heat extremes, posing challenges for public health, agriculture, and energy demand. Conversely, changes in winter temperatures, especially in January when the average daily temperature hovers around 15°C, might be observed. Warmer winters could impact ecosystems, affecting flora and fauna accustomed to specific temperature ranges (Williams et al., 2015 ). Such alterations could have severe cascading effects on biodiversity and ecosystem services. 3. Materials and Methods In this study, we begin by gathering precipitation data from the Indian Meteorological Department (IMD) 0.25°×0.25° grid rainfall dataset, extracting it from a Comma-separated value (CSV) file for analytical purposes. The primary objective was to conduct homogeneity tests on this rainfall data to determine whether any significant changes or trends exist in the precipitation pattern over a the 1960–2020 period. Homogeneity testing is crucial in hydrology and climate studies as it ensures the reliability of rainfall data, which is fundamental for various applications including water resource management, flood forecasting, and climate change analysis (Machiwal and Jha, 2006 ; Mohanty et al., 2018 ). XLSTAT, a statistical analysis ( www.xlstat.com ), offers a suite of tools and functions specifically designed for conducting various statistical tests, including homogeneity tests tailored for rainfall data (Kang and Yusof, 2012 ). These tests typically involve scrutinizing the statistical properties of the data series over time to detect any systematic changes or discontinuities. Subsequently, we employ the Mann-Kendall (later discussed in Eq. 1, 2 , 3 & 4 , respectively) (Mann 1945 ; Kendall 1975) and Sen slope (later discussed in Eq. 5, 6, & 7, respectively) (Sen, 1968 ) tests within the R statistical package ( www.r-project.org ) to gain further insights into the behavior of the rainfall data over time. These tests allow us to identify trends, evaluate their significance, and estimate the rate of change in precipitation patterns. R, along with packages like R-ClimDEX ( www.acmad.net ), provides powerful functionalities for calculating a wide range of climate indices, including extreme rainfall indices. These indices offer valuable information for understanding the impact of climate change on precipitation patterns (Thompson, 1984 ). Additionally, in ArcGIS v 10.8.2 , spatial-temporal analysis by working with datasets that encompass both spatial (geographic) and temporal (time) components was performed. This involves tasks such as georeferencing, spatial interpolation, and temporal analysis to comprehend how spatial patterns evolve over time (Ansari et al., 2020 ). ArcGIS serves as a comprehensive platform for managing, visualizing, and analyzing spatial-temporal data, thereby enhancing our understanding of the spatial distribution and temporal dynamics of precipitation (Lafreniere and Gilliland, 2015 ). This study utilizes a multi-faceted approach, integrating statistical analysis software like XLSTAT and R, along with GIS software such as ArcGIS, to comprehensively analyze precipitation data. Through homogeneity testing, trend analysis, and calculation of climate indices, the aim was to gain deeper insights into the behavior of rainfall patterns, which is crucial for informed decision-making in various fields related to hydrology, climate studies, and environmental management. The workflow diagram illustrating the study is presented in Fig. 2 . 3.1. Homogeneity Test The homogeneity test for rainfall data serves to evaluate the presence of significant changes or trends in rainfall patterns over time (Kang and Yusof, 2012 ; Das et al., 2018 ; Marengo, 2006 ). This test holds particular importance in hydrological and climatological studies, where it verifies the consistency and reliability of rainfall data for diverse applications such as water resource management, flood forecasting, and climate change analysis (Thompson, 1984 ; Troin et al. 2021). Some common homogeneity tests used for rainfall data analysis include: i. Pettitt test : This non-parametric test is used to detect a single change point in a time series. ii. Buishand range test : This test is based on the range of the data series and detects multiple change points. iii. Standard normal homogeneity test (SNHT) : This test is based on the comparison of the mean of different segments of the data series to detect abrupt changes. iv. Von-Neumann test : This test is used for assesses the equality of variances among groups, critical for valid statistical analyses. 3.2 Mann Kandell Test The Mann-Kendall (MK) test (Mann 1945 ; Kendall 1975) stands as a robust statistical tool, particularly valuable in the analysis of time series (Douglas et al., 2000 ; Partal and Kahya, 2006 Tabari and Marofi, 2011 ; Singh et al., 2023 ; Abdullahi et al., 2015 ) data like rainfall records. Its non-parametric nature makes it versatile, capable of assessing trends without demanding strict adherence to data distribution assumptions (Sulaiman et al. , 2015; Panda and Sahu, 2019 ). Initially, the test assigns rank to each data point, accommodating ties by averaging ranks. Subsequently, it computes Kendall's Tau (τ), measuring correlation across different time lags. With τ determination, the test statistic (S) is derived (Eqs. 1 and 2 ) from the count of concordant and discordant pairs within the dataset, thereby gauging the trend's strength. Following this, the variance (Var(S)) is calculated (Eq. 3) to ascertain the significance of the trend. This, in turn, enables the computation of a Z-score (Eq. 4 ), crucial for assessing statistical significance. By comparing this Z-score against critical values from the standard normal distribution, the test elucidates whether a statistically significant trend exists within the data, thus aiding in discerning patterns in rainfall behavior over time (Hamed and Rao, 1998 ; McLeod, 2005 ; Alhaji et al., 2018 ). The MK test statistic S is calculated using the following formula: S = \(\:\sum\:_{i=1}^{n-1}\sum\:_{j=i+1}^{n}\:sign({x}_{i}-{x}_{j})\) (1) Where, n is the number of data points; x i and x j are the data values in time series i and j , respectively and \(\:sign({x}_{i}-{x}_{j})\) is the sign function as: $$\:sign({x}_{i}-{x}_{j})\:=\:\left\{\begin{array}{c}+1,\:\:\:\:\:\:\:if\:\:({x}_{i}-{x}_{j})>o\\\:0,\:\:\:\:\:\:\:\:\:\:if\:\:\:({x}_{i}-{x}_{j})=0\\\:-1,\:\:\:\:\:\:\:\:if\:\:({x}_{i}-{x}_{j})<0\end{array}\right.$$ 2 The variance is computed as: Var (S) = \(\:\frac{n\left(n-1\right)\left(2n+5\right)-\:\sum\:_{i=1}^{m}{t}_{i}({t}_{i}-1)(2{t}_{i}+5)}{18}\) (3) Where, n is the number of data points; m is the number of tied groups; and \(\:{t}_{i}\) denotes the number of ties of extent. The standard normal test statistic Z s is computed as: $$\:{Z}_{S}=\left\{\begin{array}{c}\frac{S-1}{\surd\:Var\left(S\right)},\:\:\:if\:\:S>0\\\:0,\:\:\:\:\:\:\:\:\:\:\:\:\:if\:\:S=0\\\:\frac{S+1}{\surd\:Var\left(S\right)},\:\:if\:\:S<0\end{array}\right.$$ 4 The assessment of a statistically significant trend relies on the Z-value. A positive Z-value signifies an upward trend, while a negative value indicates a downward trend. 3.3. Sen’s Slope Estimator The Sen's Slope test (Sen, 1968 ) is a valuable tool in the analysis of time series data, including rainfall records, offering a non-parametric approach to detect trends (Trivedi and Gautam, 2022 ). This method is particularly advantageous when working with datasets of varying sizes and distributions. The test involves calculating the differences and slopes between pairs of data points in the time series, ultimately deriving the median slope (Eq. 5) (Sen, 1968 ), which represents the overall trend in the data. Unlike some other trend analysis methods, Sen's Slope test is robust against outliers and can effectively handle irregularly spaced data points. Its significance can be determined through hypothesis testing or by establishing confidence intervals around the median slope. When applied to rainfall data, Sen's Slope test aids in identifying significant trends in precipitation patterns over time (Hollander and Wolfe, 1973, Gilbert, 1987 ), providing insight into whether rainfall is increasing, decreasing, or remaining relatively stable throughout the analyzed period (Lettenmaier et al., 1994 ; Yue and Hashino, 2003 ; Yunling and Yiping, 2005 ; Tabari and Marofi 2011 ). Sen’s Slope = \(\:Median\:\{\:\frac{{x}_{j}-{x}_{k}}{j-k}\::i<j\}\) (5) A, 1– α confidence interval for Sen’s slope can be calculated as (lower, upper) Where, N = C (n, 2) k = Var (S) ⋅ \(\:{Z}_{crit}\) (6) Lower = \(\:{m}_{(\text{N}-\text{k})/2}\) Upper = \(\:{\text{m}}_{(\text{N}+\text{k})/2+1}\) (7) Here, N = the number of pairs of time series elements ( \(\:{x}_{i}\:\) , \(\:\:{x}_{j}\) ) where i < j and Var (S) = the standard error for the Mann-Kendall Test. Also, \(\:{m}_{h}\) = the h th smallest in the set {( \(\:{x}_{j}\) – \(\:{x}_{j}\) )/(j–i): i < j} and \(\:{z}_{\text{c}\text{r}\text{i}\text{t}}\) = the 1–α/2 critical value for the normal distribution. 3.4 Extreme Rainfall Indices The Expert Team on Climate Change Detection and Indices (ETCCDI) has endorsed a compilation of 27 extreme rainfall and temperature indices designed to assess extreme weather events (Singh et al, 2022 ). These indices rely on daily temperature and precipitation data. In recent years, numerous studies have extensively examined these indices at both regional and global levels. (Sharma et al., 2018 ; Pradhan et al., 2019 ; Sillmann et al., 2013 ; Kim et al., 2020 ). This study delves into seven key precipitation indices, selected from a larger set of 27. These indices include Consecutive Dry Days (CDD), Consecutive Wet Days (CWD), R10 (the count of heavy precipitation days), R20 (the count of very heavy precipitation days), RX1 day (maximum precipitation in a single day), RX5 days (maximum precipitation over five consecutive days), and Simple Daily Intensity Index (SDII). CDD measures the uninterrupted duration of days without precipitation, indicating prolonged dry conditions which can lead to droughts impacting agriculture, water availability, and ecosystems. Conversely, CWD quantifies the uninterrupted duration of days with precipitation, signaling prolonged wet periods that heighten risks of flooding, soil erosion, and landslides, affecting infrastructure and agriculture. R10 and R20 signify the frequency of heavy and very heavy precipitation events respectively, indicating increased risks of flash floods and urban drainage issues. RX1 day highlights the intensity of extreme rainfall events, offering insights into potential hazards like flash floods and landslides, while RX5 days assess the persistence of heavy rainfall, affecting agriculture, water management, and ecosystems. SDII provides information on the average daily precipitation intensity, crucial for understanding runoff, erosion, and flooding risks in both urban and rural areas. These indices collectively offer valuable insights into precipitation dynamics and their impacts on various sectors, aiding in risk assessment and management strategies. These indices were computed using Climate Data Operators (CDO) software. Table 1 provides the list of precipitation indices utilized, along with their definitions and units. Table 1 The list of precipitation indices used, and the definition and units. Indices Definition Unit CDD Maximum number of consecutive dry days with RR 1 mm Days R10 Annual count of days when PRCP ≥ 10 mm Days R20 Annual count of days when PRCP ≥ 20 mm Days RX1 Monthly maximum 1-day rainfall mm RX5 Monthly maximum 5-days Rainfall mm SDII Annual total precipitation divided by the number of wet days (defined as PRCP ≥ 1.0 mm in the year) mm/year 4. Result and Discussion A total of 6 rainfall data grids falls under the administrative boundary of the study area, whereas only one grid of temperature data falls under the study area which is not considerable and trivial to perform historical trend analysis therefore, the analysis was performed for rainfall dataset only for 6 grids. 4.1. Homogeneity test to detect the change point In order to detect the inflection points in the time series (1960–2020), the grid-wise standard normal homogeneity test (SNHT) at 5% significance level were applied to the rainfall data viz. monthly, pre-monsoon, monsoon, post-monsoon, winter, and annual (Alexandersson 1986 ; Alexandersson and Moberg 1997 ) and no point of critical change was detected as shown in the Fig. 3 as per the p-values for any of the four tests namely, Pettitt, SNHT test, Buishand, Von-Neumann for any of the grid at the 5% significance level. These results show that the region is not vulnerable to climate change since last sixty years and no serious climate change mitigation strategies has to be adopted in the region. However, the district is urbanizing at the double rate in comparison to state average and urbanization play a greater role in altering the hydrological cycle (Das et al., 2018 ; Marengo, 2006 ). Urbanization and anthropogenic activities severely affect the vegetation dynamics which ultimately affect to the hydroclimatic and environmental conditions of a region. Therefore, climate resilience and climate adaptation strategies must be adopted in development activities in the district to sustain the situation in a longer run. 4.2. Basic historical (1960–2020) statistical characteristics of rainfall The mean, minimum and maximum rainfall received in the Jabalpur district deviate highly in the august month with the standard deviation of 151 mm from the average annual rainfall. During the last six decades, the area has received mean annual rainfall of 1257 mm which is comparatively more than the normal rainfall (1194 mm) of India. However, the area received minimum and maximum rainfall of 654 mm and 2037 mm, respectively during the study period. The area receives its most of the rainfall during monsoon season as shown in the Table 2 . These findings share the fact that, the region is not affected by climate change in the historical period of 1960–2020 even though there is no shift in rainy seasons was observed in the region. Furthermore, the spatial distribution of IMD-derived gridded average annual rainfall over Jabalpur district during 1960–2020 is shown in the Fig. 4 (a and b) which clearly indicates that a decrease in rainfall was observed from east to west in the major portion of the district where western part receives the minimum rainfall (654 mm/year) while middle-east receives the maximum (2037 mm/year) rainfall during the study period. The forest cover greatly affects local climate particularly precipitation of a region (Meher-Homji, 1991 ; Webb et al., 2005 ). The satellite imagery of the district (Fig. 1 ) depicts poor forest cover in the western part in comparison to other part of the district which may be reason for lesser precipitation in the western Jabalpur. Table 2 Basic statistical characteristics of rainfall during (1960–2020). Max Min Mean SD Jan 72 0 17 20 Feb 71 0 16 18 Mar 80 0 12 18 Apr 33 0 5 7 May 31 0 8 9 Jun 520 31 159 109 Jul 753 107 376 130 Aug 780 138 414 151 Sep 595 39 198 126 Oct 113 0 30 29 Nov 104 0 10 21 Dec 124 0 11 23 Pre 101 1 26 23 Monsoon 1839 516 1147 290 Post 192 0 41 41 Winter 186 0 45 43 Annual 2037 654 1257 300 All values are in mm 4.3. Spatio-temporal variability of the long-term rainfall trend There was no significant trend observed during last six-decades in the Jabalpur district ( p-value ≤ 0.05 ). Though, a decrease of 2.24 mm/year in average annual rainfall was observed in the month of August during 1960–2020. Where, a very slight (1.05 mm/year) increase in average annual rainfall was observed in the area. Although, the trend is insignificant but a monsoon month August showed susceptibility to climate change resulting a decrease in magnitude of rainfall. Urbanization affects the local climate of a region in different ways, it alters the rainfall patters and its magnitude (Paul et al., 2018 ). Changing land use pattern and unplanned urbanization may be the reason for climatic vulnerability in the monsoon months particularly in August. The historical temporal change in rainfall during pre, post, winter and monsoon and annual is shown in the Fig. 5 . The z-statistics ( z-value ) and Sen’s slope as shown in the Table 3 do not show any significant change in the rainfall trend. The negative value of the z-statistics indicates decreasing trend whereas positive value shows the increasing trend in the time series data. At the 5% significance level, an increasing or decreasing trend in a dataset is considered significant when its value outfalls the open interval (-1.95 < z < 1.96). In the present study period there was no significant trend observed in the rainfall. The Sen’s slope gives the magnitude of the trend in the dataset. August was found as the month of highest magnitudinal change as 2.24 mm/year decrease in rainfall was observed in the month. The spatial distribution of z-statistics and Sen’s slope values indicates that eastern part of the district is comparatively more prone to experience decrease in rainfall in coming decades. This may be due to deforestation and faster rate of urbanization in the region in comparison to other part of the district. Table 3 The z-statistics and Sen’s slope value. Time Scale Z value Sen's slope January -0.54 -0.15 February -1.19 -0.304 March 1.59 0.221 April -0.33 -0.093 May -1.77 -0.35 June 0.29 0.158 July 1.26 1.048 August -1.82 -2.236 September -0.01 -0.024 October 1.66 0.422 November -0.2 -0.054 December -0.61 -0.107 Annual -0.12 -0.368 Pre monsoon -0.34 -0.234 Monsoon -0.16 -0.172 Post monsoon 0.35 0.388 Winter -0.96 -0.551 at 95% Confidence level or 5% significance level Non-Significant 1.96 4.4. Spatial variability of extreme rainfall indices The spatial analysis of seven extreme rainfall indices were performed to understand the historical spatial change in extreme events during 1960–2020. Consecutive Dry Days (CDD) measures the length of consecutive dry days. As per the Fig. 7 , western Jabalpur has highest number of consecutive dry days (120) in comparison to other part of the district whereas, this part of the district has lowest consecutive wet days (11) during 1960–2020. The Fig. 7 showed the spatial distribution of R10 (10 mm/day) which indicates the western Jabalpur has lower whereas, easter Jabalpur has higher R10 events. Spatial variation of R20 (20 mm/day) rainfall event represents both western and eastern part of the district has lower R20 events. The RX1 and RX5 showed the most intense 1day and 5days rainfall in the region. The spatial variation of RX1 and RX5 indices indicated that the most of the middle—west part of the district faces 1day and 5days intense rainfall events (Fig. 7 ). SDII indicates the simple daily intensity index of rainfall over the region. The spatial variation of SDII showed western part of the district receives higher volume of intense rainfall in comparison to other part of the district as shown in the Fig. 7 . Overall, analysis of extreme indices represents the western part of the Jabalpur district is more vulnerable to climate change, therefore some mitigation measures are required for sustainable management of water resources in the district. 4.5 Long-term trend of extreme rainfall indices The long-term trend of extreme rainfall indices was analyzed at the district boundary scale, and the results are depicted in Fig. 8 . To assess the trend at the district level, the grid values from the whole district were arithmetically averaged. The trend was analyzed using the MK test, and the statistical significance of the trend was assessed at a 5% significance level ( p < 0.05). We found a relatively decreasing trend in most of the extreme rainfall events except for maximum annual one day rainfall (RX1) and SDII. The total no. of days for RX1 and SDII days are found to increase by 0.136 and 0.001 mm respectively (Fig. 8 ). However, the CDD, CWD, R10, R20, and RX5 indices showed a decreasing trend with Sen’s slope values of -0.058, -0.364, -0.065, -0.045, and − 0.364 mm respectively. The p -values were considered for the analysis of significance of the trend. Results reveal the fact that all extreme events indices show a non-significant trend at 95% confidence interval. 5. Conclusions The historical trend of rainfall in the Jabalpur district of Madhya Pradesh is somewhat constant and remained unchanged since last six-decades (1960–2020). This clearly indicate the fact that, despite a faster rate of urbanization, Jabalpur is not so affected with the climate change. This may be because of the richness in forest cover in the district. Inclusively, the change in climate in the district is insignificant however, the western Jabalpur is comparatively more vulnerable to climate change to other part of the district. Homogeneity test results for all p-values (namely, Pettitt, SNHT test, Buishand, Von-Neumann) do not detect any change point in the rainfall (monthly, pre-monsoon, monsoon, post-monsoon, winter, and annual) at any of the grid in the area at 95% confidence interval during 1960–2020. Mann-Kendall’s z-statistics do not show any significant change in the rainfall trend over the years (1960–2020) in the study area at 95% confidence interval. The Sen’s slope values vary between (0.73 to -2.98) mm/year which indicates that, the magnitude of the rainfall trend is negatively skewed and eastern region of the district is prone to show the decreased magnitude of rainfall which must be because of deforestation of the rich forest cover of the eastern Jabalpur. The spatial distribution of extreme indices showed the spatial variability of the extreme events over the area. For example, western region faces the greatest number of consecutive dry days (CDD) over the period of study (1960–2020). Most of the region (particularly middle west) receives 1day and 5day highest rainfall which indicates that duration of rainfall is contracting while its intensity for the short duration is increasing. Jabalpur is one of the megacities of Madhya Pradesh which is urbanizing in a faster rate and unplanned urbanization can affect the local climate in a longer run. The study can be used to better understand the historical change hydroclimatic conditions in the region and take climate change mitigation and adaptation measure, if required. Further, the study can be implemented in the other part of the state to better understand the climate change phenomenon in the state. Declarations Acknowledgements We acknowledge National Institute of Hydrology, Roorkee and scientists working here for their valuable inputs, suggestion and mentorship while preparing the manuscript. Data Availability The authors acknowledge the data support available from the IMD grided dataset. Funding The study didn’t get any funding for carrying out the research. Conflict of Interest The authors declare no competing interests associated to this work. Author Contribution All the author's have contributed to the manuscript. Pankaj Kumar Thakur framed and outlined the research idea, Dheeraj Mohan Gururani prepared the maps and figures, Abhishek Agrawal and Pushpanjali Kumari performed the analysis part, Snehil Dubey was involved in the writing and correction of the manuscript; and Divyesh Varade reviewed the manuscript many times to bring it to its final form. References Abdullahi, M.G., Toriman, M.E., Gasim, M.B. and Garba, I., 2015. Trends analysis of groundwater: using non-parametric methods in Terengganu Malaysia. Journal of Earth Science and Climatic Change, 6(1), 1-3. Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675. Alexandersson H, Moberg A (1997) Homogenization of Swedish temperature data. Part I: a homogeneity test for linear trends. Int J Climatol 17:25–34 Alhaji, U. U., Yusuf, A. S., Edet, C. O., Oche, C. O., & Agbo, E. P., 2018. Trend analysis of temperature in Gombe state using Mann Kendall trend test. Journal of Scientific Research and Reports , 20 (3), 1-9. Ansari, M.Y., Ahmad, A., Khan, S.S., Bhushan, G. and Mainuddin, 2020. Spatiotemporal clustering: a review. Artificial Intelligence Review, 53, 2381-2423. Bhat, M.M., Tali, P.A. and Nanda, A.A., 2021. Seasonal spatio-temporal variability in temperature over north Kashmir Himalayas using sen slope and Mann-Kendall Test. J. Climatol. Weather. Forecast, 9, 288. CHANGE, O.C., 2007. Intergovernmental panel on climate change. World Meteorological Organization , 52 . Cosgrove, W.J. and Loucks, D.P., 2015. Water management: Current and future challenges and research directions. Water Resources Research, 51(6), pp.4823-4839. Das, P., Behera, M.D., Patidar, N., Sahoo, B., Tripathi, P., Behera, P.R., Srivastava, S.K., Roy, P.S., Thakur, P., Agrawal, S.P. and Krishnamurthy, Y.V.N., 2018. Impact of LULC change on the runoff, base flow and evapotranspiration dynamics in eastern Indian river basins during 1985–2005 using variable infiltration capacity approach. Journal of Earth System Science, 127, 1-19. Douglas, E. M., Vogel, R. M., & Kroll, C. N., 2000. Trends in floods and low flows in the United States: impact of spatial correlation. Journal of hydrology , 240 (1-2), 90-105. Dungumaro, E.W. and Madulu, N.F., 2003. Public participation in integrated water resources management: the case of Tanzania. Physics and Chemistry of the Earth, Parts a/b/c, 28(20-27),1009-1014. García-Ruiz, J.M., López-Moreno, J.I., Vicente-Serrano, S.M., Lasanta–Martínez, T. and Beguería, S., 2011. Mediterranean water resources in a global change scenario. Earth-Science Reviews, 105(3-4),121-139. Garg, V., & Sharma, A. (2018). Urban sprawl analysis using GIS applications for Jabalpur City. International Research Journal of Engineering and Technology (IRJET) , 5 (5), 4171-4176. Gilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitoring. John Wiley & Sons, New York. Gupta, N., 2014. Groundwater vulnerability assessment using DRASTIC method in Jabalpur District of Madhya Pradesh. International Journal of recent technology and Engineering , 3 (3), 36-43. Gupta, V. and Jain, M.K., 2018. Investigation of multi-model spatiotemporal mesoscale drought projections over India under climate change scenario. Journal of Hydrology, 567, pp.489-509. Hamed, K.H. and Rao, A.R., 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), pp.182-196. Handmer, J. and Dovers, S., 2013. Handbook of disaster policies and institutions: improving emergency management and climate change adaptation. Routledge. Hirji, R. and Davis, R., 2009. Environmental flows in water resources policies, plans, and projects: findings and recommendations. World Bank Publications. Hollander, M., Wolfe, D. A., & Chicken, E., 2013. Nonparametric statistical methods . John Wiley & Sons. Jain, S., Panda, J. and Kant, S., 2014. Possible socio-scientific issues of land-use and land-cover change impact and associated tools of study with a special reference to Delhi-Mumbai Industrial Corridor Region. International Journal of Earth and Atmospheric Science, 1(2), 58-70. Kang, H.M. and Yusof, F., 2012. Homogeneity tests on daily rainfall series. Int. J. Contemp. Math. Sciences, 7(1), 9-22. Kendall M. G. 1975 Rank Correlation Methods. Griffin, London. Kendall, M., 2015. Trend analysis of pahang river using non-parametric analysis: Mann Kendall’s trend test. Malays. J. Anal. Sci , 19 , 1327-1334. Kesteven, G. L. ,1946. The coefficient of variation. Nature , 158 (4015), 520-521. Kim, Y.; Rocheta, E.; Evans, J.P.; Sharma, A. Impact of bias correction of regional climate model boundary conditions on the simulation of precipitation extremes. Clim. Dyn. 2020, 55, 3507–3526. Kumar, A. and Mohanasundari, T., 2024. Assessing district-level climate vulnerability in Madhya Pradesh, Central India: an integrated environmental and socio-economic approach. Theoretical and Applied Climatology, 1-23. Kusangaya, S., Warburton, M.L., Van Garderen, E.A. and Jewitt, G.P., 2014. Impacts of climate change on water resources in southern Africa: A review. Physics and Chemistry of the Earth, Parts a/b/c, 67, 47-54. Lafreniere, D. and Gilliland, J., 2015. “All the World's a Stage”: A GIS Framework for Recreating Personal Time‐Space from Qualitative and Quantitative Sources. Transactions in GIS, 19(2), 225-246. Lettenmaier, D. P., Wood, E. F., & Wallis, J. R. (1994). Hydro-climatological trends in the continental United States, 1948-88. Journal of Climate , 7 (4), 586-607. Ludwig, F., van Slobbe, E. and Cofino, W., 2014. Climate change adaptation and Integrated Water Resource Management in the water sector. Journal of Hydrology, 518, 235-242. Machiwal, D. and Jha, M.K., 2006. Time series analysis of hydrologic data for water resources planning and management: a review. Journal of Hydrology and Hydromechanics, 54(3), 237-257. Mahmoodzada, A.B., Varade, D., Shimada, S., Okazawa, H., Aryan, S., Gulab, G., Mustafa, A.E.Z.M., Rizwana, H., Ahlawat, Y.K. and Elansary, H.O., 2023. Quantification of Amu River Riverbank Erosion in Balkh Province of Afghanistan during 2004–2020. Land, 12(10), 1890. Mann, H. B., 1945. Nonparametric tests against trend. Econometrica: Journal of the econometric society , 245-259. Marengo, J.A. On the hydrological cycle of the Amazon Basin: A historical review and current state-of-the-art. Rev. Bras. Meteorol. 2006, 21, 1–19. McLeod, A.I., 2005. Kendall rank correlation and Mann-Kendall trend test. R package Kendall, 602, 1-10. Meher-Homji, V.M., 1991. Probable impact of deforestation on hydrological processes. Climatic Change , 19 (1), pp.163-173. Mohanty, M.P., Sherly, M.A., Karmakar, S. and Ghosh, S., 2018. Regionalized design rainfall estimation: an appraisal of inundation mapping for flood management under data-scarce situations. Water resources management, 32, 4725-4746. Mondal, A., Khare, D. and Kundu, S., 2015. Spatial and temporal analysis of rainfall and temperature trend of India. Theoretical and applied climatology , 122 , 143-158. Panda, A., & Sahu, N., 2019. Trend analysis of seasonal rainfall and temperature pattern in Kalahandi, Bolangir and Koraput districts of Odisha, India. Atmospheric Science Letters , 20 (10), 932. Partal, T., & Kahya, E., 2006. Trend analysis in Turkish rainfall data. Hydrological Processes: An International Journal , 20 (9), 2011-2026. Paul, S., Ghosh, S., Mathew, M., Devanand, A., Karmakar, S. and Niyogi, D., 2018. Increased spatial variability and intensification of extreme monsoon rainfall due to urbanization. Scientific reports , 8 (1), p.3918. Phuong, D.N.D., Tram, V.N.Q., Nhat, T.T., Ly, T.D. and Loi, N.K., 2020. Hydro-meteorological trend analysis using the Mann-Kendall and innovative-Şen methodologies: a case study. International Journal of Global Warming, 20(2), 145-164. Pradhan, R.K., Sharma, D., Panda, S.K., Dubey, S.K. and Sharma, A., 2019. Changes of precipitation regime and its indices over Rajasthan state of India: impact of climate change scenarios experiments. Climate dynamics, 52, 3405-3420. Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association , 63 (324), 1379-1389. Sharma, A., Sharma, D., Panda, S.K., Dubey, S.K. and Pradhan, R.K., 2018. Investigation of temperature and its indices under climate change scenarios over different regions of Rajasthan state in India. Global and Planetary Change, 161, 82-96. Sharma, G., Thomas, T. and Singh, R.M., 2021. Comparison of contrasts in rainfall and drought characteristics in the Chambal basin in Madhya Pradesh and Rajasthan. Journal of Water and Climate Change, 12(8), 3932-3957. Shrivastava, V., Khare, N.K. and Pande, A.K., 2018. Farmers’ perception on climate vulnerability and responsive adaptation measures in district Jabalpur, Madhya Pradesh. Journal of Pharmacognosy and Phytochemistry, 7(4), pp.1238-1243. Singh, H., Varade, D. and Mishra, P.K., 2022. Cloudburst events in the Indian Himalayas: a historical geospatial perspective. In International Handbook of Disaster Research (1-21). Singapore: Springer Nature Singapore. Singh, S., Gururani, D.M., Kumar, A., Kumar, Y., Bohra, M.S. and Mehta, P., 2023. Declining Groundwater Level and Its Impact on Irrigation and Agro-production. In Advancement of GI-Science and Sustainable Agriculture: A Multi-dimensional Approach (217-224). Cham: Springer Nature Switzerland. Sillmann, J., Kharin, V.V., Zhang, X., Zwiers, F.W. and Bronaugh, D., 2013. Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. Journal of geophysical research: atmospheres, 118(4), 1716-1733. Sivakumar, B., 2011. Global climate change and its impacts on water resources planning and management: assessment and challenges. Stochastic Environmental Research and Risk Assessment, 25, 583-600. Tabari, H., & Marofi, S., 2011. Changes of pan evaporation in the west of Iran. Water Resources Management , 25 , 97-111. Tabari, H., & Talaee, P. H., 2011. Analysis of trends in temperature data in arid and semi-arid regions of Iran. Global and Planetary Change , 79 (1-2), 1-10. Thompson, C.S., 1984. Homogeneity analysis of rainfall series: an application of the use of a realistic rainfall model. Journal of climatology, 4(6), pp.609-619. Trivedi, A., & Gautam, V. K., 2022. Decadal analysis of water level fluctuation using GIS in Jabalpur district of Madhya Pradesh. Journal of Soil and Water Conservation , 21 (3), 250-259. Troin, M., Arsenault, R., Wood, A.W., Brissette, F. and Martel, J.L., 2021. Generating ensemble streamflow forecasts: A review of methods and approaches over the past 40 years. Vijhani, A., Sinha, V.S.P. and Govindan, M., 2021. Assessing resource vulnerability quadrants under changing rainfall trends in Uttarakhand, Central Himalayan region. Journal of Mountain Science, 18(10), pp.2722-2741. Water, U.N., 2020. UN World Water Development Report 2020. United Nation: New York, NY, USA . Webb, T.J., Woodward, F.I., Hannah, L. and Gaston, K.J., 2005. Forest cover–rainfall relationships in a biodiversity hotspot: the Atlantic forest of Brazil. Ecological Applications , 15 (6), pp.1968-1983. Williams, C.M., Henry, H.A. and Sinclair, B.J., 2015. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biological Reviews, 90(1), pp.214-235. Xu, C.Y. and Singh, V.P., 2004. Review on regional water resources assessment models under stationary and changing climate. Water resources management, 18, pp.591-612. Yadav, S., Bhattacharya, P., Areendran, G., Sahana, M., Raj, K. and Sajjad, H., 2021. Predicting impact of climate change on geographical distribution of major NTFP species in the Central India Region. Modeling Earth Systems and Environment, pp.1-20. Yu, Y.S., Zou, S. and Whittemore, D., 1993. Non-parametric trend analysis of water quality data of rivers in Kansas. Journal of Hydrology, 150(1), pp.61-80. Yue, S., & Hashino, M., 2003. Temperature trends in Japan: 1900–1996. Theoretical and Applied Climatology , 75 , 15-27. Yunling, H., & Yiping, Z., 2005. Climate change from 1960 to 2000 in the Lancang River Valley, China. Mountain Research and Development , 25 (4), 341-348. Zhang, W., Randall, M., Jensen, M.B., Brandt, M., Wang, Q. and Fensholt, R., 2021. Socio-economic and climatic changes lead to contrasting global urban vegetation trends. Global Environmental Change, 71, p.102385. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4617217","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328938100,"identity":"4b590f8c-0fa5-4940-97c8-8de25877e6bc","order_by":0,"name":"Pankaj Kumar 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selected for investigations in the state of Madhya Pradesh, India.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/4f690d2822eb3408e7feccad.png"},{"id":62040502,"identity":"6c8b51f9-9c08-403f-baf8-b5af1173c06e","added_by":"auto","created_at":"2024-08-08 14:32:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100294,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow-chart of the methodology, with the test for homogeneity, Mann-Kendall test, Sen’s slope estimator and extreme indices used in the study.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/cf037bb955bf0fa0bc82e688.png"},{"id":62041135,"identity":"ca65d4ce-3be6-4277-aa5f-a977c0b509de","added_by":"auto","created_at":"2024-08-08 14:40:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":111610,"visible":true,"origin":"","legend":"\u003cp\u003eGrid-wise \u003cem\u003ep\u003c/em\u003e-values (Pettitt, SNHT test, Buishand, Von-Neumann)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/3b2ea1fb70e7a753f5622753.png"},{"id":62040496,"identity":"573de3d6-4bdb-46b3-aa09-106af6f1daf0","added_by":"auto","created_at":"2024-08-08 14:32:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60161,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Spatial variation of the average annual rainfall over the area. (b) Regionally averaged mean monthly rainfall.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/db8c338b6898dfc60f57b235.png"},{"id":62040503,"identity":"e7263a6f-869b-4ddd-8fa1-07cfe33f3e3e","added_by":"auto","created_at":"2024-08-08 14:32:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85245,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical temporal trend in (a) pre, post, and winter rainfall and (b) monsoon and annual rainfall\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/0e611b33a4a6c3d65f05b85a.png"},{"id":62040500,"identity":"81f92bf9-77e2-4894-9d20-86d21e29b91a","added_by":"auto","created_at":"2024-08-08 14:32:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":121775,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Spatial distribution of MK z-statistics over the area and (b) Spatial distribution of Sen’s slope over the area.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/52c4dec918d54611be4020d0.png"},{"id":62040501,"identity":"1b8b6b01-5aae-4b86-97b9-b6727d66c32e","added_by":"auto","created_at":"2024-08-08 14:32:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":127982,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution of extreme events.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/c3ea1a8a30f0b1c6595b0886.png"},{"id":62040499,"identity":"0398204b-38bd-4305-b45e-562295685095","added_by":"auto","created_at":"2024-08-08 14:32:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":77199,"visible":true,"origin":"","legend":"\u003cp\u003eLong-term trend of extreme rainfall indices.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/4bc83d70103670a06d5ca207.png"},{"id":65196196,"identity":"3dc03de9-03ed-48f5-8aad-499be4263000","added_by":"auto","created_at":"2024-09-24 15:24:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1499861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4617217/v1/f116cec7-6b7a-4c86-8025-c7588aff591e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of Climate Extremes in Jabalpur District of Madhya Pradesh: Trends and Future Implications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe declining worldwide availability of water resources has emerged as a central concern, particularly in the planning and execution of projects geared towards sustainable development, with a specific focus on water sectors (Dungumaro and Madulu, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; UN, 2020). In this context, there is an increasing emphasis on adopting efficient water resource management techniques and implementing methodologies to estimate and control erosion and floods (Cosgrove and Loucks, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mahmoodzada et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The significance of these initiatives is underscored by the growing awareness of the interconnectedness of water resources with broader environmental and climatic conditions (Hirji and Davis, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The discussion on current and future environmental conditions has prominently illustrated by the evolving global temperature and rainfall patterns (Mondal et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The Intergovernmental Panel on Climate Change (IPCC, 2001, 2007, 2014, and 2020) has issued reports that highlight the inevitable onset of global climate change in the 21st century. These reports not only underscore the urgency of the situation but also stress the critical importance of implementing mitigation measures aimed at curbing the emission of greenhouse gases.\u003c/p\u003e \u003cp\u003eThe implications of climate change on rainfall patterns are particularly noteworthy. Alterations in rainfall can result in significant shifts in regional climates, thereby impacting the availability of water resources and exacerbating issues such as floods or droughts (Kusangaya \u003cem\u003eet al\u003c/em\u003e, 2015; Sivakumar, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Garc\u0026iacute;a-Ruiz et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This dynamic relationship between climate change and water resources underscores the need for a comprehensive assessment of likely rainfall patterns (Xu \u003cem\u003eet al\u003c/em\u003e, 2004). Such assessments are not only essential for effective water resource management but also play a crucial role in informing disaster mitigation and adaptation strategies (Handmer and Dovers, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, the increasing global temperatures contribute to the intensification of extreme weather events (Singh et al, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), such as heatwaves (Maurya \u003cem\u003eet al\u003c/em\u003e, 2024) and prolonged periods of high temperatures. These temperature-related challenges further emphasize the complex nature of the environmental changes that societies and ecosystems are currently facing (Ludwig et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn climate studies, the identification of trends of climatic variables on a global scale within a watershed is a common pursuit. To ascertain these trends with an acceptable level of confidence, statistical approaches frequently employ either parametric or non-parametric trend analysis methods (Vijhani et al, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among the methods used for this purpose, the Mann-Kendall Test and Sen's Slope Test are often popular choices. These tests are particularly applied to historical hydro-meteorological data and extreme climate indices to discern anomalous patterns and changes over time (Hamed and Rao, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Tabari and Marofi \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sulaiman \u003cem\u003eet al.\u003c/em\u003e, 2015; Phuong et al, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Trivedi and Gautam, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e;). The Mann-Kendall Test is a non-parametric method that evaluates a monotonic trend in time series data, making it robust to deviations from normality and resistant to outliers. This test assesses the ranks of data points, determining whether a systematic trend, either upward or downward, exists within the sequence of observations. It provides valuable insights into the presence and significance of trends in climatic variables (Hamed and Rao, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; McLeod, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). On the other hand, Sen's Slope Test, also a non-parametric method, focuses on estimating the magnitude of trends by calculating the median of all possible slopes between data points. This approach is advantageous when dealing with datasets that might exhibit non-normal distributions or contain outliers and the test is particularly useful for quantifying the direction and strength of trends in historical and extreme climate indices (Yu et al, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Bhat et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Mann-Kendall test (Mann \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; Kendall 1975) and Sen's slope (Sen, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1968\u003c/span\u003e) estimator are widely used for trend analysis due to their robustness and simplicity. Unlike some other methods, they are non-parametric, making them suitable for analyzing data with non-normal distributions or outliers. Additionally, they provide reliable estimates of trend direction and magnitude without assuming specific underlying distributional properties, enhancing their applicability across diverse datasets and scenarios.\u003c/p\u003e \u003cp\u003eThe unique geographical location, topographical features, and land-use patterns of Jabalpur District render it particularly susceptible to climate variability and change. Therefore, an in-depth analysis of trends in climate extremes in this region can offer valuable insights into the underlying regional climate dynamics. Such investigations are essential for accurately assessing associated risks and formulating targeted adaptation and mitigation measures that align with the specific needs of the local community. Additionally, increased urbanization and deforestation significantly alter the water cycle and hydrology of any region and ultimately would lead to a change in climatic conditions (Das et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Marengo, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A significant increase in urbanization has been observed in Madhya Pradesh in India over the last few decades. Particularly, Jabalpur district exhibits a recorded 58.46% urban area, which is more than the state average of 27.63% (Marengo, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Gupta, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, in 2009 and 2010, an increase in average annual temperature was observed which exceeded the historical average, while in 2008, the minimum temperature was recorded below the normal minimum temperature. Additionally, in year 2012 a slight increase in rainfall was recorded which exceeds the normal rainfall (Shrivastava et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, the district has highest hydro-dynamic vulnerability to climate change. And, in order to understand the hydroclimatic dynamics of the region, the present study was conducted in the district.\u003c/p\u003e \u003cp\u003eThe current study is dedicated to comprehensively understanding the spatiotemporal variations in climatic and hydrological conditions within Jabalpur district, Madhya Pradesh, from 1960 to 2020. This investigation is motivated by the need to address the hydroclimatic change in the region in the last sixty years, this may due to urbanization, deforestation, and socio-economic changes on local weather patterns (Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jain et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Leveraging the high-resolution Indian Meteorological Department's (IMD) 0.25\u0026deg;\u0026times;0.25\u0026deg; grid rainfall dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.imdpune.gov.in\" target=\"_blank\"\u003ewww.imdpune.gov.in\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.imdpune.gov.in\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the study aims to achieve several specific objectives. Firstly, it endeavors to identify the inflection point of change (year) within the historical climatic dataset, with a particular emphasis on discerning shifts in rainfall dynamics. This involves employing advanced statistical techniques such as change-point analysis to determine the precise temporal occurrences of significant deviations. Secondly, the research seeks to quantitatively assess the changes in historic rainfall trends, including their magnitude and statistical significance at a 95% confidence level. This entails employing methodologies like Mann-Kendall trend tests, and Sen's slope estimator to elucidate the long-term trends and their statistical robustness. Additionally, the study employs climatic extreme indices derived from the dataset to identify and characterize major extreme weather events that have impacted the study area over the specified period. Overall, this study aims to contribute valuable insights into the complex interactions between anthropogenic activities and climatic processes (Kumar and Mohanasundari, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), thereby informing effective adaptation and mitigation strategies for sustainable environmental management in Jabalpur district and beyond.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eIn the face of global climate change, Jabalpur geographic location and climatic characteristics may experience discernible shifts, potentially impacting the region ecology, economy, and community resilience (UN, 2020). Nestled along the picturesque Narmada River, the city of Jabalpur, geographical located at 23\u0026deg; 10' 53\" N latitude and 79\u0026deg; 59' 11\" E longitude as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, faces the challenges posed by a humid subtropical climate. As of the 2011 census (censusindia.gov.in), it proudly stands as the third-largest urban agglomeration in Madhya Pradesh and the 38th-largest in the country. The region\u0026rsquo;s critical dependence on the Narmada River for various needs, including agriculture and water supply, adds another layer of vulnerability. Changes in rainfall patterns can significantly impact the river\u0026rsquo;s flow, potentially affecting the water availability and necessitating adaptive water management strategies.\u003c/p\u003e \u003cp\u003eThe average annual rainfall of 1386 mm and a mean temperature of 25.5˚C reflect the region\u0026rsquo;s current climatic norms. However, with climate change, projections indicate potential alteration in these patterns (Gupta and Jain, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The rising global temperatures could intensify the already warm summer months in Jabalpur, extending the duration and elevating temperatures further (Yadav et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). May, historically the hottest month with temperatures exceeding 40\u0026deg;C, may witness heightened heat extremes, posing challenges for public health, agriculture, and energy demand. Conversely, changes in winter temperatures, especially in January when the average daily temperature hovers around 15\u0026deg;C, might be observed. Warmer winters could impact ecosystems, affecting flora and fauna accustomed to specific temperature ranges (Williams et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Such alterations could have severe cascading effects on biodiversity and ecosystem services.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Materials and Methods","content":"\u003cp\u003eIn this study, we begin by gathering precipitation data from the Indian Meteorological Department (IMD) 0.25\u0026deg;\u0026times;0.25\u0026deg; grid rainfall dataset, extracting it from a Comma-separated value (CSV) file for analytical purposes. The primary objective was to conduct homogeneity tests on this rainfall data to determine whether any significant changes or trends exist in the precipitation pattern over a the 1960\u0026ndash;2020 period. Homogeneity testing is crucial in hydrology and climate studies as it ensures the reliability of rainfall data, which is fundamental for various applications including water resource management, flood forecasting, and climate change analysis (Machiwal and Jha, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mohanty et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). XLSTAT, a statistical analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.xlstat.com\u003c/span\u003e\u003c/span\u003e), offers a suite of tools and functions specifically designed for conducting various statistical tests, including homogeneity tests tailored for rainfall data (Kang and Yusof, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). These tests typically involve scrutinizing the statistical properties of the data series over time to detect any systematic changes or discontinuities. Subsequently, we employ the Mann-Kendall (later discussed in Eq. 1, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, 3 \u0026amp; \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively) (Mann \u003cspan class=\"CitationRef\"\u003e1945\u003c/span\u003e; Kendall 1975) and Sen slope (later discussed in Eq. 5, 6, \u0026amp; 7, respectively) (Sen, \u003cspan class=\"CitationRef\"\u003e1968\u003c/span\u003e) tests within the R statistical package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003c/span\u003e) to gain further insights into the behavior of the rainfall data over time. These tests allow us to identify trends, evaluate their significance, and estimate the rate of change in precipitation patterns. R, along with packages like R-ClimDEX (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.acmad.net\u003c/span\u003e\u003c/span\u003e), provides powerful functionalities for calculating a wide range of climate indices, including extreme rainfall indices. These indices offer valuable information for understanding the impact of climate change on precipitation patterns (Thompson, \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e). Additionally, in ArcGIS \u003cem\u003ev 10.8.2\u003c/em\u003e, spatial-temporal analysis by working with datasets that encompass both spatial (geographic) and temporal (time) components was performed. This involves tasks such as georeferencing, spatial interpolation, and temporal analysis to comprehend how spatial patterns evolve over time (Ansari et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). ArcGIS serves as a comprehensive platform for managing, visualizing, and analyzing spatial-temporal data, thereby enhancing our understanding of the spatial distribution and temporal dynamics of precipitation (Lafreniere and Gilliland, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThis study utilizes a multi-faceted approach, integrating statistical analysis software like XLSTAT and R, along with GIS software such as ArcGIS, to comprehensively analyze precipitation data. Through homogeneity testing, trend analysis, and calculation of climate indices, the aim was to gain deeper insights into the behavior of rainfall patterns, which is crucial for informed decision-making in various fields related to hydrology, climate studies, and environmental management. The workflow diagram illustrating the study is presented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Homogeneity Test\u003c/h2\u003e\n \u003cp\u003eThe homogeneity test for rainfall data serves to evaluate the presence of significant changes or trends in rainfall patterns over time (Kang and Yusof, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Das et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Marengo, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). This test holds particular importance in hydrological and climatological studies, where it verifies the consistency and reliability of rainfall data for diverse applications such as water resource management, flood forecasting, and climate change analysis (Thompson, \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e; Troin \u003cem\u003eet al.\u003c/em\u003e2021). Some common homogeneity tests used for rainfall data analysis include:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003ei. Pettitt test\u003c/strong\u003e: This non-parametric test is used to detect a single change point in a time series.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eii. Buishand range test\u003c/strong\u003e: This test is based on the range of the data series and detects multiple change points.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eiii. Standard normal homogeneity test (SNHT)\u003c/strong\u003e: This test is based on the comparison of the mean of different segments of the data series to detect abrupt changes.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003eiv. Von-Neumann test\u003c/strong\u003e: This test is used for assesses the equality of variances among groups, critical for valid statistical analyses.\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Mann Kandell Test\u003c/h2\u003e\n \u003cp\u003eThe Mann-Kendall (MK) test (Mann \u003cspan class=\"CitationRef\"\u003e1945\u003c/span\u003e; Kendall 1975) stands as a robust statistical tool, particularly valuable in the analysis of time series (Douglas et al., \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Partal and Kahya, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e Tabari and Marofi, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Singh et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Abdullahi et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) data like rainfall records. Its non-parametric nature makes it versatile, capable of assessing trends without demanding strict adherence to data distribution assumptions (Sulaiman \u003cem\u003eet al.\u003c/em\u003e, 2015; Panda and Sahu, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Initially, the test assigns rank to each data point, accommodating ties by averaging ranks. Subsequently, it computes Kendall\u0026apos;s Tau (\u0026tau;), measuring correlation across different time lags. With \u0026tau; determination, the test statistic (S) is derived (Eqs. 1 and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) from the count of concordant and discordant pairs within the dataset, thereby gauging the trend\u0026apos;s strength. Following this, the variance (Var(S)) is calculated (Eq. 3) to ascertain the significance of the trend. This, in turn, enables the computation of a Z-score (Eq. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), crucial for assessing statistical significance. By comparing this Z-score against critical values from the standard normal distribution, the test elucidates whether a statistically significant trend exists within the data, thus aiding in discerning patterns in rainfall behavior over time (Hamed and Rao, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; McLeod, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Alhaji et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe MK test statistic S is calculated using the following formula:\u003c/p\u003e\n \u003cp\u003eS = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n-1}\\sum\\:_{j=i+1}^{n}\\:sign({x}_{i}-{x}_{j})\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e\n \u003cp\u003eWhere,\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e is the number of data points; \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e are the data values in time series \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, respectively\u003c/p\u003e\n \u003cp\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:sign({x}_{i}-{x}_{j})\\)\u003c/span\u003e\u003c/span\u003e is the sign function as:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:sign({x}_{i}-{x}_{j})\\:=\\:\\left\\{\\begin{array}{c}+1,\\:\\:\\:\\:\\:\\:\\:if\\:\\:({x}_{i}-{x}_{j})\u0026gt;o\\\\\\:0,\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:if\\:\\:\\:({x}_{i}-{x}_{j})=0\\\\\\:-1,\\:\\:\\:\\:\\:\\:\\:\\:if\\:\\:({x}_{i}-{x}_{j})\u0026lt;0\\end{array}\\right.$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe variance is computed as:\u003c/p\u003e\n \u003cp\u003eVar (S) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{n\\left(n-1\\right)\\left(2n+5\\right)-\\:\\sum\\:_{i=1}^{m}{t}_{i}({t}_{i}-1)(2{t}_{i}+5)}{18}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e\n \u003cp\u003eWhere,\u003c/p\u003e\n \u003cp\u003en is the number of data points; m is the number of tied groups; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the number of ties of extent.\u003c/p\u003e\n \u003cp\u003eThe standard normal test statistic Z\u003csub\u003es\u003c/sub\u003e is computed as:\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{Z}_{S}=\\left\\{\\begin{array}{c}\\frac{S-1}{\\surd\\:Var\\left(S\\right)},\\:\\:\\:if\\:\\:S\u0026gt;0\\\\\\:0,\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:if\\:\\:S=0\\\\\\:\\frac{S+1}{\\surd\\:Var\\left(S\\right)},\\:\\:if\\:\\:S\u0026lt;0\\end{array}\\right.$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe assessment of a statistically significant trend relies on the Z-value. A positive Z-value signifies an upward trend, while a negative value indicates a downward trend.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Sen\u0026rsquo;s Slope Estimator\u003c/h2\u003e\n \u003cp\u003eThe Sen\u0026apos;s Slope test (Sen, \u003cspan class=\"CitationRef\"\u003e1968\u003c/span\u003e) is a valuable tool in the analysis of time series data, including rainfall records, offering a non-parametric approach to detect trends (Trivedi and Gautam, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This method is particularly advantageous when working with datasets of varying sizes and distributions. The test involves calculating the differences and slopes between pairs of data points in the time series, ultimately deriving the median slope (Eq. 5) (Sen, \u003cspan class=\"CitationRef\"\u003e1968\u003c/span\u003e), which represents the overall trend in the data. Unlike some other trend analysis methods, Sen\u0026apos;s Slope test is robust against outliers and can effectively handle irregularly spaced data points. Its significance can be determined through hypothesis testing or by establishing confidence intervals around the median slope. When applied to rainfall data, Sen\u0026apos;s Slope test aids in identifying significant trends in precipitation patterns over time (Hollander and Wolfe, 1973, Gilbert, \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e), providing insight into whether rainfall is increasing, decreasing, or remaining relatively stable throughout the analyzed period (Lettenmaier et al., \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e; Yue and Hashino, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Yunling and Yiping, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tabari and Marofi \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSen\u0026rsquo;s Slope = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Median\\:\\{\\:\\frac{{x}_{j}-{x}_{k}}{j-k}\\::i\u0026lt;j\\}\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e\n \u003cp\u003eA, 1\u0026ndash; \u0026alpha; confidence interval for Sen\u0026rsquo;s slope can be calculated as (lower, upper)\u003c/p\u003e\n \u003cp\u003eWhere,\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;C (n, 2) k\u0026thinsp;=\u0026thinsp;Var (S) \u0026sdot; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{crit}\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLower =\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{m}_{(\\text{N}-\\text{k})/2}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eUpper =\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{m}}_{(\\text{N}+\\text{k})/2+1}\\)\u003c/span\u003e\u003c/span\u003e (7)\u003c/p\u003e\n \u003cp\u003eHere, N\u0026thinsp;=\u0026thinsp;the number of pairs of time series elements (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{x}_{j}\\)\u003c/span\u003e\u003c/span\u003e) where i\u0026thinsp;\u0026lt;\u0026thinsp;j and Var (S)\u0026thinsp;=\u0026thinsp;the standard error for the Mann-Kendall Test.\u003c/p\u003e\n \u003cp\u003eAlso,\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{m}_{h}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e = the h\u003csup\u003eth\u003c/sup\u003e smallest in the set {(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{j}\\)\u003c/span\u003e\u003c/span\u003e\u0026ndash;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{j}\\)\u003c/span\u003e\u003c/span\u003e)/(j\u0026ndash;i): i \u0026lt; j} and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{z}_{\\text{c}\\text{r}\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e = the 1\u0026ndash;\u0026alpha;/2 critical value for the normal distribution.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Extreme Rainfall Indices\u003c/h2\u003e\n \u003cp\u003eThe Expert Team on Climate Change Detection and Indices (ETCCDI) has endorsed a compilation of 27 extreme rainfall and temperature indices designed to assess extreme weather events (Singh et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). These indices rely on daily temperature and precipitation data. In recent years, numerous studies have extensively examined these indices at both regional and global levels. (Sharma et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pradhan et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sillmann et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kim et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study delves into seven key precipitation indices, selected from a larger set of 27. These indices include Consecutive Dry Days (CDD), Consecutive Wet Days (CWD), R10 (the count of heavy precipitation days), R20 (the count of very heavy precipitation days), RX1 day (maximum precipitation in a single day), RX5 days (maximum precipitation over five consecutive days), and Simple Daily Intensity Index (SDII). CDD measures the uninterrupted duration of days without precipitation, indicating prolonged dry conditions which can lead to droughts impacting agriculture, water availability, and ecosystems. Conversely, CWD quantifies the uninterrupted duration of days with precipitation, signaling prolonged wet periods that heighten risks of flooding, soil erosion, and landslides, affecting infrastructure and agriculture. R10 and R20 signify the frequency of heavy and very heavy precipitation events respectively, indicating increased risks of flash floods and urban drainage issues. RX1 day highlights the intensity of extreme rainfall events, offering insights into potential hazards like flash floods and landslides, while RX5 days assess the persistence of heavy rainfall, affecting agriculture, water management, and ecosystems. SDII provides information on the average daily precipitation intensity, crucial for understanding runoff, erosion, and flooding risks in both urban and rural areas. These indices collectively offer valuable insights into precipitation dynamics and their impacts on various sectors, aiding in risk assessment and management strategies. These indices were computed using Climate Data Operators (CDO) software. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides the list of precipitation indices utilized, along with their definitions and units.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe list of precipitation indices used, and the definition and units.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndices\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum number of consecutive dry days with RR\u0026thinsp;\u0026lt;\u0026thinsp;1 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum number of consecutive wet days with RR\u0026thinsp;\u0026gt;\u0026thinsp;1 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual count of days when PRCP\u0026thinsp;\u0026ge;\u0026thinsp;10 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual count of days when PRCP\u0026thinsp;\u0026ge;\u0026thinsp;20 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly maximum 1-day rainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRX5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly maximum 5-days Rainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual total precipitation divided by the number of wet days\u003c/p\u003e\n \u003cp\u003e(defined as PRCP\u0026thinsp;\u0026ge;\u0026thinsp;1.0 mm in the year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Result and Discussion","content":"\u003cp\u003eA total of 6 rainfall data grids falls under the administrative boundary of the study area, whereas only one grid of temperature data falls under the study area which is not considerable and trivial to perform historical trend analysis therefore, the analysis was performed for rainfall dataset only for 6 grids.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Homogeneity test to detect the change point\u003c/h2\u003e\n \u003cp\u003eIn order to detect the inflection points in the time series (1960\u0026ndash;2020), the grid-wise standard normal homogeneity test (SNHT) at 5% significance level were applied to the rainfall data viz. monthly, pre-monsoon, monsoon, post-monsoon, winter, and annual (Alexandersson \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e; Alexandersson and Moberg \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e) and no point of critical change was detected as shown in the Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e as per the \u003cem\u003ep-values\u003c/em\u003e for any of the four tests namely, Pettitt, SNHT test, Buishand, Von-Neumann for any of the grid at the 5% significance level. These results show that the region is not vulnerable to climate change since last sixty years and no serious climate change mitigation strategies has to be adopted in the region. However, the district is urbanizing at the double rate in comparison to state average and urbanization play a greater role in altering the hydrological cycle (Das et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Marengo, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Urbanization and anthropogenic activities severely affect the vegetation dynamics which ultimately affect to the hydroclimatic and environmental conditions of a region. Therefore, climate resilience and climate adaptation strategies must be adopted in development activities in the district to sustain the situation in a longer run.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Basic historical (1960\u0026ndash;2020) statistical characteristics of rainfall\u003c/h2\u003e\n \u003cp\u003eThe mean, minimum and maximum rainfall received in the Jabalpur district deviate highly in the august month with the standard deviation of 151 mm from the average annual rainfall. During the last six decades, the area has received mean annual rainfall of 1257 mm which is comparatively more than the normal rainfall (1194 mm) of India. However, the area received minimum and maximum rainfall of 654 mm and 2037 mm, respectively during the study period. The area receives its most of the rainfall during monsoon season as shown in the Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. These findings share the fact that, the region is not affected by climate change in the historical period of 1960\u0026ndash;2020 even though there is no shift in rainy seasons was observed in the region. Furthermore, the spatial distribution of IMD-derived gridded average annual rainfall over Jabalpur district during 1960\u0026ndash;2020 is shown in the Fig. 4 (a and b) which clearly indicates that a decrease in rainfall was observed from east to west in the major portion of the district where western part receives the minimum rainfall (654 mm/year) while middle-east receives the maximum (2037 mm/year) rainfall during the study period. The forest cover greatly affects local climate particularly precipitation of a region (Meher-Homji, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e; Webb et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). The satellite imagery of the district (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) depicts poor forest cover in the western part in comparison to other part of the district which may be reason for lesser precipitation in the western Jabalpur.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic statistical characteristics of rainfall during (1960\u0026ndash;2020).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNov\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonsoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eAll values are in mm\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3. Spatio-temporal variability of the long-term rainfall trend\u003c/h2\u003e\n \u003cp\u003eThere was no significant trend observed during last six-decades in the Jabalpur district (\u003cem\u003ep-value\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003c/em\u003e). Though, a decrease of 2.24 mm/year in average annual rainfall was observed in the month of August during 1960\u0026ndash;2020. Where, a very slight (1.05 mm/year) increase in average annual rainfall was observed in the area. Although, the trend is insignificant but a monsoon month August showed susceptibility to climate change resulting a decrease in magnitude of rainfall. Urbanization affects the local climate of a region in different ways, it alters the rainfall patters and its magnitude (Paul et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Changing land use pattern and unplanned urbanization may be the reason for climatic vulnerability in the monsoon months particularly in August. The historical temporal change in rainfall during pre, post, winter and monsoon and annual is shown in the Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe z-statistics (\u003cem\u003ez-value\u003c/em\u003e) and Sen\u0026rsquo;s slope as shown in the Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e do not show any significant change in the rainfall trend. The negative value of the z-statistics indicates decreasing trend whereas positive value shows the increasing trend in the time series data. At the 5% significance level, an increasing or decreasing trend in a dataset is considered significant when its value outfalls the open interval (-1.95\u0026thinsp;\u0026lt;\u0026thinsp;z\u0026thinsp;\u0026lt;\u0026thinsp;1.96). In the present study period there was no significant trend observed in the rainfall. The Sen\u0026rsquo;s slope gives the magnitude of the trend in the dataset. August was found as the month of highest magnitudinal change as 2.24 mm/year decrease in rainfall was observed in the month. The spatial distribution of z-statistics and Sen\u0026rsquo;s slope values indicates that eastern part of the district is comparatively more prone to experience decrease in rainfall in coming decades. This may be due to deforestation and faster rate of urbanization in the region in comparison to other part of the district.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe z-statistics and Sen\u0026rsquo;s slope value.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Scale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSen\u0026apos;s slope\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFebruary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre monsoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonsoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost monsoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eat 95% Confidence level or 5% significance level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Significant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4. Spatial variability of extreme rainfall indices\u003c/h2\u003e\n \u003cp\u003eThe spatial analysis of seven extreme rainfall indices were performed to understand the historical spatial change in extreme events during 1960\u0026ndash;2020. Consecutive Dry Days (CDD) measures the length of consecutive dry days. As per the Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, western Jabalpur has highest number of consecutive dry days (120) in comparison to other part of the district whereas, this part of the district has lowest consecutive wet days (11) during 1960\u0026ndash;2020. The Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e showed the spatial distribution of R10 (10 mm/day) which indicates the western Jabalpur has lower whereas, easter Jabalpur has higher R10 events. Spatial variation of R20 (20 mm/day) rainfall event represents both western and eastern part of the district has lower R20 events. The RX1 and RX5 showed the most intense 1day and 5days rainfall in the region. The spatial variation of RX1 and RX5 indices indicated that the most of the middle\u0026mdash;west part of the district faces 1day and 5days intense rainfall events (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). SDII indicates the simple daily intensity index of rainfall over the region. The spatial variation of SDII showed western part of the district receives higher volume of intense rainfall in comparison to other part of the district as shown in the Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Overall, analysis of extreme indices represents the western part of the Jabalpur district is more vulnerable to climate change, therefore some mitigation measures are required for sustainable management of water resources in the district.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Long-term trend of extreme rainfall indices\u003c/h2\u003e\n \u003cp\u003eThe long-term trend of extreme rainfall indices was analyzed at the district boundary scale, and the results are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. To assess the trend at the district level, the grid values from the whole district were arithmetically averaged. The trend was analyzed using the MK test, and the statistical significance of the trend was assessed at a 5% significance level (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We found a relatively decreasing trend in most of the extreme rainfall events except for maximum annual one day rainfall (RX1) and SDII. The total no. of days for RX1 and SDII days are found to increase by 0.136 and 0.001 mm respectively (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). However, the CDD, CWD, R10, R20, and RX5 indices showed a decreasing trend with Sen\u0026rsquo;s slope values of -0.058, -0.364, -0.065, -0.045, and \u0026minus;\u0026thinsp;0.364 mm respectively. The \u003cem\u003ep\u003c/em\u003e-values were considered for the analysis of significance of the trend. Results reveal the fact that all extreme events indices show a non-significant trend at 95% confidence interval.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe historical trend of rainfall in the Jabalpur district of Madhya Pradesh is somewhat constant and remained unchanged since last six-decades (1960\u0026ndash;2020). This clearly indicate the fact that, despite a faster rate of urbanization, Jabalpur is not so affected with the climate change. This may be because of the richness in forest cover in the district. Inclusively, the change in climate in the district is insignificant however, the western Jabalpur is comparatively more vulnerable to climate change to other part of the district. Homogeneity test results for all \u003cem\u003ep-values\u003c/em\u003e (namely, Pettitt, SNHT test, Buishand, Von-Neumann) do not detect any change point in the rainfall (monthly, pre-monsoon, monsoon, post-monsoon, winter, and annual) at any of the grid in the area at 95% confidence interval during 1960\u0026ndash;2020. Mann-Kendall\u0026rsquo;s z-statistics do not show any significant change in the rainfall trend over the years (1960\u0026ndash;2020) in the study area at 95% confidence interval. The Sen\u0026rsquo;s slope values vary between (0.73 to -2.98) mm/year which indicates that, the magnitude of the rainfall trend is negatively skewed and eastern region of the district is prone to show the decreased magnitude of rainfall which must be because of deforestation of the rich forest cover of the eastern Jabalpur. The spatial distribution of extreme indices showed the spatial variability of the extreme events over the area. For example, western region faces the greatest number of consecutive dry days (CDD) over the period of study (1960\u0026ndash;2020). Most of the region (particularly middle west) receives 1day and 5day highest rainfall which indicates that duration of rainfall is contracting while its intensity for the short duration is increasing. Jabalpur is one of the megacities of Madhya Pradesh which is urbanizing in a faster rate and unplanned urbanization can affect the local climate in a longer run. The study can be used to better understand the historical change hydroclimatic conditions in the region and take climate change mitigation and adaptation measure, if required. Further, the study can be implemented in the other part of the state to better understand the climate change phenomenon in the state.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge National Institute of Hydrology, Roorkee and scientists working here for their valuable inputs, suggestion and mentorship while preparing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the data support available from the IMD grided dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study didn\u0026rsquo;t get any funding for carrying out the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests associated to this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll the author's have contributed to the manuscript. Pankaj Kumar Thakur framed and outlined the research idea, Dheeraj Mohan Gururani prepared the maps and figures, Abhishek Agrawal and Pushpanjali Kumari performed the analysis part, Snehil Dubey was involved in the writing and correction of the manuscript; and Divyesh Varade reviewed the manuscript many times to bring it to its final form.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdullahi, M.G., Toriman, M.E., Gasim, M.B. and Garba, I., 2015. Trends analysis of groundwater: using non-parametric methods in Terengganu Malaysia. Journal of Earth Science and Climatic Change, 6(1), 1-3.\u003c/li\u003e\n\u003cli\u003eAlexandersson H (1986) A homogeneity test applied to precipitation data. \u003cem\u003eJ Climatol\u003c/em\u003e 6:661\u0026ndash;675.\u003c/li\u003e\n\u003cli\u003eAlexandersson H, Moberg A (1997) Homogenization of Swedish temperature data. Part I: a homogeneity test for linear trends. \u003cem\u003eInt J Climatol\u003c/em\u003e 17:25\u0026ndash;34\u003c/li\u003e\n\u003cli\u003eAlhaji, U. U., Yusuf, A. S., Edet, C. O., Oche, C. O., \u0026amp; Agbo, E. P., 2018. Trend analysis of temperature in Gombe state using Mann Kendall trend test. \u003cem\u003eJournal of Scientific Research and Reports\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(3), 1-9. \u003c/li\u003e\n\u003cli\u003eAnsari, M.Y., Ahmad, A., Khan, S.S., Bhushan, G. and Mainuddin, 2020. Spatiotemporal clustering: a review. Artificial Intelligence Review, 53, 2381-2423.\u003c/li\u003e\n\u003cli\u003eBhat, M.M., Tali, P.A. and Nanda, A.A., 2021. Seasonal spatio-temporal variability in temperature over north Kashmir Himalayas using sen slope and Mann-Kendall Test. J. Climatol. Weather. Forecast, 9, 288.\u003c/li\u003e\n\u003cli\u003eCHANGE, O.C., 2007. Intergovernmental panel on climate change. \u003cem\u003eWorld Meteorological Organization\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eCosgrove, W.J. and Loucks, D.P., 2015. Water management: Current and future challenges and research directions. Water Resources Research, 51(6), pp.4823-4839.\u003c/li\u003e\n\u003cli\u003eDas, P., Behera, M.D., Patidar, N., Sahoo, B., Tripathi, P., Behera, P.R., Srivastava, S.K., Roy, P.S., Thakur, P., Agrawal, S.P. and Krishnamurthy, Y.V.N., 2018. Impact of LULC change on the runoff, base flow and evapotranspiration dynamics in eastern Indian river basins during 1985\u0026ndash;2005 using variable infiltration capacity approach. Journal of Earth System Science, 127, 1-19.\u003c/li\u003e\n\u003cli\u003eDouglas, E. M., Vogel, R. M., \u0026amp; Kroll, C. N., 2000. Trends in floods and low flows in the United States: impact of spatial correlation. \u003cem\u003eJournal of hydrology\u003c/em\u003e, \u003cem\u003e240\u003c/em\u003e(1-2), 90-105.\u003c/li\u003e\n\u003cli\u003eDungumaro, E.W. and Madulu, N.F., 2003. Public participation in integrated water resources management: the case of Tanzania. Physics and Chemistry of the Earth, Parts a/b/c, 28(20-27),1009-1014.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Ruiz, J.M., L\u0026oacute;pez-Moreno, J.I., Vicente-Serrano, S.M., Lasanta\u0026ndash;Mart\u0026iacute;nez, T. and Beguer\u0026iacute;a, S., 2011. Mediterranean water resources in a global change scenario. Earth-Science Reviews, 105(3-4),121-139.\u003c/li\u003e\n\u003cli\u003eGarg, V., \u0026amp; Sharma, A. (2018). Urban sprawl analysis using GIS applications for Jabalpur City. \u003cem\u003eInternational Research Journal of Engineering and Technology (IRJET)\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(5), 4171-4176.\u003c/li\u003e\n\u003cli\u003eGilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitoring. John Wiley \u0026amp; Sons, New York.\u003c/li\u003e\n\u003cli\u003eGupta, N., 2014. Groundwater vulnerability assessment using DRASTIC method in Jabalpur District of Madhya Pradesh. \u003cem\u003eInternational Journal of recent technology and Engineering\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 36-43.\u003c/li\u003e\n\u003cli\u003eGupta, V. and Jain, M.K., 2018. Investigation of multi-model spatiotemporal mesoscale drought projections over India under climate change scenario. Journal of Hydrology, 567, pp.489-509.\u003c/li\u003e\n\u003cli\u003eHamed, K.H. and Rao, A.R., 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), pp.182-196.\u003c/li\u003e\n\u003cli\u003eHandmer, J. and Dovers, S., 2013. Handbook of disaster policies and institutions: improving emergency management and climate change adaptation. Routledge.\u003c/li\u003e\n\u003cli\u003eHirji, R. and Davis, R., 2009. Environmental flows in water resources policies, plans, and projects: findings and recommendations. World Bank Publications.\u003c/li\u003e\n\u003cli\u003eHollander, M., Wolfe, D. A., \u0026amp; Chicken, E., 2013. \u003cem\u003eNonparametric statistical methods\u003c/em\u003e. John Wiley \u0026amp; Sons.\u003c/li\u003e\n\u003cli\u003eJain, S., Panda, J. and Kant, S., 2014. Possible socio-scientific issues of land-use and land-cover change impact and associated tools of study with a special reference to Delhi-Mumbai Industrial Corridor Region. International Journal of Earth and Atmospheric Science, 1(2), 58-70.\u003c/li\u003e\n\u003cli\u003eKang, H.M. and Yusof, F., 2012. Homogeneity tests on daily rainfall series. Int. J. Contemp. Math. Sciences, 7(1), 9-22.\u003c/li\u003e\n\u003cli\u003eKendall M. G. 1975 Rank Correlation Methods. Griffin, London.\u003c/li\u003e\n\u003cli\u003eKendall, M., 2015. Trend analysis of pahang river using non-parametric analysis: Mann Kendall\u0026rsquo;s trend test. \u003cem\u003eMalays. J. Anal. Sci\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 1327-1334.\u003c/li\u003e\n\u003cli\u003eKesteven, G. L. ,1946. The coefficient of variation. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e158\u003c/em\u003e(4015), 520-521.\u003c/li\u003e\n\u003cli\u003eKim, Y.; Rocheta, E.; Evans, J.P.; Sharma, A. Impact of bias correction of regional climate model boundary conditions on the simulation of precipitation extremes. Clim. Dyn. 2020, 55, 3507\u0026ndash;3526.\u003c/li\u003e\n\u003cli\u003eKumar, A. and Mohanasundari, T., 2024. Assessing district-level climate vulnerability in Madhya Pradesh, Central India: an integrated environmental and socio-economic approach. Theoretical and Applied Climatology, 1-23.\u003c/li\u003e\n\u003cli\u003eKusangaya, S., Warburton, M.L., Van Garderen, E.A. and Jewitt, G.P., 2014. Impacts of climate change on water resources in southern Africa: A review. Physics and Chemistry of the Earth, Parts a/b/c, 67, 47-54.\u003c/li\u003e\n\u003cli\u003eLafreniere, D. and Gilliland, J., 2015. \u0026ldquo;All the World\u0026apos;s a Stage\u0026rdquo;: A GIS Framework for Recreating Personal Time‐Space from Qualitative and Quantitative Sources. Transactions in GIS, 19(2), 225-246.\u003c/li\u003e\n\u003cli\u003eLettenmaier, D. P., Wood, E. F., \u0026amp; Wallis, J. R. (1994). Hydro-climatological trends in the continental United States, 1948-88. \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(4), 586-607.\u003c/li\u003e\n\u003cli\u003eLudwig, F., van Slobbe, E. and Cofino, W., 2014. Climate change adaptation and Integrated Water Resource Management in the water sector. Journal of Hydrology, 518, 235-242.\u003c/li\u003e\n\u003cli\u003eMachiwal, D. and Jha, M.K., 2006. Time series analysis of hydrologic data for water resources planning and management: a review. Journal of Hydrology and Hydromechanics, 54(3), 237-257.\u003c/li\u003e\n\u003cli\u003eMahmoodzada, A.B., Varade, D., Shimada, S., Okazawa, H., Aryan, S., Gulab, G., Mustafa, A.E.Z.M., Rizwana, H., Ahlawat, Y.K. and Elansary, H.O., 2023. Quantification of Amu River Riverbank Erosion in Balkh Province of Afghanistan during 2004\u0026ndash;2020. Land, 12(10), 1890.\u003c/li\u003e\n\u003cli\u003eMann, H. B., 1945. Nonparametric tests against trend. \u003cem\u003eEconometrica: Journal of the econometric society\u003c/em\u003e, 245-259.\u003c/li\u003e\n\u003cli\u003eMarengo, J.A. On the hydrological cycle of the Amazon Basin: A historical review and current state-of-the-art. Rev. Bras. Meteorol. 2006, 21, 1\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eMcLeod, A.I., 2005. Kendall rank correlation and Mann-Kendall trend test. R package Kendall, 602, 1-10.\u003c/li\u003e\n\u003cli\u003eMeher-Homji, V.M., 1991. Probable impact of deforestation on hydrological processes. \u003cem\u003eClimatic Change\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), pp.163-173.\u003c/li\u003e\n\u003cli\u003eMohanty, M.P., Sherly, M.A., Karmakar, S. and Ghosh, S., 2018. Regionalized design rainfall estimation: an appraisal of inundation mapping for flood management under data-scarce situations. Water resources management, 32, 4725-4746.\u003c/li\u003e\n\u003cli\u003eMondal, A., Khare, D. and Kundu, S., 2015. Spatial and temporal analysis of rainfall and temperature trend of India. \u003cem\u003eTheoretical and applied climatology\u003c/em\u003e, \u003cem\u003e122\u003c/em\u003e, 143-158.\u003c/li\u003e\n\u003cli\u003ePanda, A., \u0026amp; Sahu, N., 2019. Trend analysis of seasonal rainfall and temperature pattern in Kalahandi, Bolangir and Koraput districts of Odisha, India. \u003cem\u003eAtmospheric Science Letters\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(10), 932.\u003c/li\u003e\n\u003cli\u003ePartal, T., \u0026amp; Kahya, E., 2006. Trend analysis in Turkish rainfall data. \u003cem\u003eHydrological Processes: An International Journal\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(9), 2011-2026.\u003c/li\u003e\n\u003cli\u003ePaul, S., Ghosh, S., Mathew, M., Devanand, A., Karmakar, S. and Niyogi, D., 2018. Increased spatial variability and intensification of extreme monsoon rainfall due to urbanization. \u003cem\u003eScientific reports\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), p.3918.\u003c/li\u003e\n\u003cli\u003ePhuong, D.N.D., Tram, V.N.Q., Nhat, T.T., Ly, T.D. and Loi, N.K., 2020. Hydro-meteorological trend analysis using the Mann-Kendall and innovative-Şen methodologies: a case study. International Journal of Global Warming, 20(2), 145-164.\u003c/li\u003e\n\u003cli\u003ePradhan, R.K., Sharma, D., Panda, S.K., Dubey, S.K. and Sharma, A., 2019. Changes of precipitation regime and its indices over Rajasthan state of India: impact of climate change scenarios experiments. Climate dynamics, 52, 3405-3420.\u003c/li\u003e\n\u003cli\u003eSen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u0026apos;s tau. \u003cem\u003eJournal of the American statistical association\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(324), 1379-1389.\u003c/li\u003e\n\u003cli\u003eSharma, A., Sharma, D., Panda, S.K., Dubey, S.K. and Pradhan, R.K., 2018. Investigation of temperature and its indices under climate change scenarios over different regions of Rajasthan state in India. Global and Planetary Change, 161, 82-96.\u003c/li\u003e\n\u003cli\u003eSharma, G., Thomas, T. and Singh, R.M., 2021. Comparison of contrasts in rainfall and drought characteristics in the Chambal basin in Madhya Pradesh and Rajasthan. Journal of Water and Climate Change, 12(8), 3932-3957.\u003c/li\u003e\n\u003cli\u003eShrivastava, V., Khare, N.K. and Pande, A.K., 2018. Farmers\u0026rsquo; perception on climate vulnerability and responsive adaptation measures in district Jabalpur, Madhya Pradesh. Journal of Pharmacognosy and Phytochemistry, 7(4), pp.1238-1243.\u003c/li\u003e\n\u003cli\u003eSingh, H., Varade, D. and Mishra, P.K., 2022. Cloudburst events in the Indian Himalayas: a historical geospatial perspective. In International Handbook of Disaster Research (1-21). Singapore: Springer Nature Singapore.\u003c/li\u003e\n\u003cli\u003eSingh, S., Gururani, D.M., Kumar, A., Kumar, Y., Bohra, M.S. and Mehta, P., 2023. Declining Groundwater Level and Its Impact on Irrigation and Agro-production. In Advancement of GI-Science and Sustainable Agriculture: A Multi-dimensional Approach (217-224). Cham: Springer Nature Switzerland.\u003c/li\u003e\n\u003cli\u003eSillmann, J., Kharin, V.V., Zhang, X., Zwiers, F.W. and Bronaugh, D., 2013. Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. Journal of geophysical research: atmospheres, 118(4), 1716-1733.\u003c/li\u003e\n\u003cli\u003eSivakumar, B., 2011. Global climate change and its impacts on water resources planning and management: assessment and challenges. Stochastic Environmental Research and Risk Assessment, 25, 583-600.\u003c/li\u003e\n\u003cli\u003eTabari, H., \u0026amp; Marofi, S., 2011. Changes of pan evaporation in the west of Iran. \u003cem\u003eWater Resources Management\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e, 97-111.\u003c/li\u003e\n\u003cli\u003eTabari, H., \u0026amp; Talaee, P. H., 2011. Analysis of trends in temperature data in arid and semi-arid regions of Iran. \u003cem\u003eGlobal and Planetary Change\u003c/em\u003e, \u003cem\u003e79 \u003c/em\u003e(1-2), 1-10.\u003c/li\u003e\n\u003cli\u003eThompson, C.S., 1984. Homogeneity analysis of rainfall series: an application of the use of a realistic rainfall model. Journal of climatology, 4(6), pp.609-619.\u003c/li\u003e\n\u003cli\u003eTrivedi, A., \u0026amp; Gautam, V. K., 2022. Decadal analysis of water level fluctuation using GIS in Jabalpur district of Madhya Pradesh. \u003cem\u003eJournal of Soil and Water Conservation\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 250-259.\u003c/li\u003e\n\u003cli\u003eTroin, M., Arsenault, R., Wood, A.W., Brissette, F. and Martel, J.L., 2021. Generating ensemble streamflow forecasts: A review of methods and approaches over the past 40 years.\u003c/li\u003e\n\u003cli\u003eVijhani, A., Sinha, V.S.P. and Govindan, M., 2021. Assessing resource vulnerability quadrants under changing rainfall trends in Uttarakhand, Central Himalayan region. Journal of Mountain Science, 18(10), pp.2722-2741.\u003c/li\u003e\n\u003cli\u003eWater, U.N., 2020. UN World Water Development Report 2020. \u003cem\u003eUnited Nation: New York, NY, USA\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWebb, T.J., Woodward, F.I., Hannah, L. and Gaston, K.J., 2005. Forest cover\u0026ndash;rainfall relationships in a biodiversity hotspot: the Atlantic forest of Brazil. \u003cem\u003eEcological Applications\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(6), pp.1968-1983.\u003c/li\u003e\n\u003cli\u003eWilliams, C.M., Henry, H.A. and Sinclair, B.J., 2015. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biological Reviews, 90(1), pp.214-235.\u003c/li\u003e\n\u003cli\u003eXu, C.Y. and Singh, V.P., 2004. Review on regional water resources assessment models under stationary and changing climate. Water resources management, 18, pp.591-612.\u003c/li\u003e\n\u003cli\u003eYadav, S., Bhattacharya, P., Areendran, G., Sahana, M., Raj, K. and Sajjad, H., 2021. Predicting impact of climate change on geographical distribution of major NTFP species in the Central India Region. Modeling Earth Systems and Environment, pp.1-20.\u003c/li\u003e\n\u003cli\u003eYu, Y.S., Zou, S. and Whittemore, D., 1993. Non-parametric trend analysis of water quality data of rivers in Kansas. Journal of Hydrology, 150(1), pp.61-80.\u003c/li\u003e\n\u003cli\u003eYue, S., \u0026amp; Hashino, M., 2003. Temperature trends in Japan: 1900\u0026ndash;1996. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e, 15-27.\u003c/li\u003e\n\u003cli\u003eYunling, H., \u0026amp; Yiping, Z., 2005. Climate change from 1960 to 2000 in the Lancang River Valley, China. \u003cem\u003eMountain Research and Development\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(4), 341-348.\u003c/li\u003e\n\u003cli\u003eZhang, W., Randall, M., Jensen, M.B., Brandt, M., Wang, Q. and Fensholt, R., 2021. Socio-economic and climatic changes lead to contrasting global urban vegetation trends. Global Environmental Change, 71, p.102385.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mann-Kendall, Sen’s slope, Trend analysis, Extreme Indices, Climate Change, Future Implication","lastPublishedDoi":"10.21203/rs.3.rs-4617217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4617217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is a long-term change in hydro-meteorological conditions in a particular geographical region. In the present study, spatio-temporal long-term change in rainfall during 1961\u0026ndash;2020 was analyzed using IMD 0.25\u0026deg;\u0026times;0.25\u0026deg; resolution gridded dataset in the Jabalpur district. The non-parametric Mann\u0026ndash;Kendall trend test and Sen\u0026rsquo;s slope estimator was applied to determine the trend and its magnitude, respectively in the precipitation time series. Extreme indices were employed to understand the risks and vulnerabilities associated with extreme weather events in the district. Mann-Kendall trend test showed an insignificant trend at 95% confidence interval with Sen\u0026rsquo;s slope value of -2.98 to 0.73 mm/year. CLIMPACT tool was used to study extreme indices and it revealed that, in the district, CDD, CWD, R10, R20, and RX5 were decreasing, whereas RX1 and SDII were found increasing. The study\u0026rsquo;s outcomes provide valuable inferences for future water resource planning and management in central India, particularly in Jabalpur district.\u003c/p\u003e","manuscriptTitle":"Investigation of Climate Extremes in Jabalpur District of Madhya Pradesh: Trends and Future Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-08 14:32:32","doi":"10.21203/rs.3.rs-4617217/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"acb2bd85-d8c3-4272-af34-a63d2daa944e","owner":[],"postedDate":"August 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-24T15:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-08 14:32:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4617217","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4617217","identity":"rs-4617217","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}