In this study, we investigated the spatio-temporal distribution and performance of seasonal precipitation in all districts of Haryana, India. We analysed the gridded precipitation dataset of the India Meteorological Department (IMD) for a period of 120 years (1901–2020) using different statistical methods. We found that Haryana received a mean precipitation of 37.0, 37.7, 468.3, and 24.8 mm during the winter, pre-monsoon, monsoon, and post-monsoon seasons, respectively. During each season, the eastern districts of Haryana received more precipitation than its western counterparts. Sen's slope results obtained after trend-free pre-whitening (TFPW) showed a statistically significant increasing trend of 0.12 mm (p-value; 0.04) during the pre-monsoon period, whereas decreasing but non-significant trends were observed during the winter, monsoon and post-monsoon seasons at the rate of −0.04 mm (p-value; 0.49), −0.26 mm (p-value; 0.52), −0.05 mm (p-value; 0.33) per year, respectively, for the entire Haryana state. The winter precipitation is expected to increase under the Representative Concentration Pathway 4.5 (RCP4.5) scenario, whereas pre-monsoon precipitation is expected to decrease under the RCP8.5 scenario by the end of the 21st century. The monsoon precipitation is expected to decrease under all RCP scenarios, whereas post-monsoon precipitation is expected to gradually increase under the RCP8.5 scenario by the end of the 21st century.

  • Spatio-temporal distribution, variability, trends, categorization analysis of precipitation for different seasons.

  • Most stable and variable precipitation was observed during monsoon and post-monsoon season respectively.

  • Declining trend in precipitation observed during winter, post-monsoon and summer monsoon season while increasing trend was observed during pre-monsoon season.

  • Future precipitation projections shows increasing/decreasing trend in different seasons under RCP2.6, RCP4.5 and RCP8.5 scenario by the end of 21st century.

Graphical Abstract

Graphical Abstract
Graphical Abstract
CV

coefficient of variation

GFDL

Geophysical Fluid Science Laboratory

IMD

India Meteorological Department

LPA120

long period average precipitation of 120 years (1901–2020)

MK

Mann–Kendall

PDP

percent deviation of precipitation

QDP

quad-decadal projections

QDT

quad-decadal times

RCP

Representative Concentration Pathway

SLR

simple linear regression

TFPW

trend-free pre-whitening

μ

mean precipitation

σ

standard deviation of precipitation

Anthropogenic activities like industrialization, deforestation, burning of fossil fuels, etc. have increased the concentration of greenhouse gases (GHGs) in the atmosphere, resulting in climate change and climate variability. Climate change is the long-term gradual shift in normal weather conditions, whereas climate variability is the variation in the mean state and other characteristics of climate on all spatial and temporal scales beyond those of individual weather events (IPCC 2018). Climate change and variability will adversely affect the world's ecosystem, biodiversity, agriculture, and socio-economic conditions (Handmer et al. 2012). Therefore, a quantification and forecasting of the magnitude of its impact is essential for timely and effective adoption of adaptation and mitigation measures. Climate change and variability has a significant impact on the alteration of precipitation dynamics across the world (Gergis & Henley 2017; Deng et al. 2019; Singh et al. 2019). Precipitation has become a pattern and has become more uncertain owing to climate change, resulting in an increase in the frequency of extreme weather events like drought, flood, heavy rainfall, etc. (Aggarwal 2003; Dore 2005). Since precipitation is the vital component of the hydrological cycle, a quantification and forecasting of the impacts of climate change on precipitation is essential. However, a quantification of its magnitude at the regional level under future climate change scenarios is a challenging task (Bisht et al. 2018). Several climatic indices based on long-term datasets of precipitation and temperature have been developed and used by researchers to identify and quantitatively examine the variations in weather patterns and extreme weather events due to climate change (Duan et al. 2016; de Oliveira Souza et al. 2018; Talchabhadel & Karki 2019; Ferreira et al. 2021; Teixeira et al. 2021).

Several studies have been conducted to understand the variations in precipitation dynamics across different regions of the world due to climate change (Thompson 1973; McQuate & Hayden 1984; Vilar & Burgueño 1995; Partal & Kahya 2006; Zin et al. 2010; Cinco et al. 2014; Tsidu 2017; Akinsanola & Zhou 2019). Many researchers have studied the spatio-temporal dynamics of precipitation at the state or country level in the context of climate change in India (Joshi & Rajeevan 2006; Guhathakurta & Rajeevan 2008; Chowdhury & Beecham 2009; Pal & Al-Tabbaa 2010; Subash et al. 2011; Jena et al. 2014; Pingale et al. 2014; Taxak et al. 2014; Gajbhiye et al. 2016; Dwivedi et al. 2019; Viswambharan 2019). Climate variability and climate change have altered the precipitation dynamics across the globe (Gergis & Henley 2017; Deng et al. 2019; Singh et al. 2019). Recognizing the presence of climate change and quantifying the extent of its magnitude is one of the most challenging climatological tasks. Any shift in the behaviour of this critical hydro-climatic variable is not evenly distributed over all regions; rather, it has its own distinct local component (Bisht et al. 2018). Scientists across different parts of the world have conducted research on climate change and have attempted to decipher the trajectory of various climate indices (Duan et al. 2016; de Oliveira Souza et al. 2018; Talchabhadel & Karki 2019; Ferreira et al. 2021; Teixeira et al. 2021). An analysis of the prevailing trends in climate conditions using long-term meteorological data is very important for climate change studies. Based on the historical records of meteorological stations, a majority of the identified trends are centred on precipitation and temperature indices. Droughts and declining precipitation are likely to continue as a consequence of rising global temperatures over the last century (Cook et al. 2014; Gizaw & Gan 2017; Carrão et al. 2018). Patra et al. (2012) reported a long-term non-significant decrease in annual and monsoonal precipitation but an increase in post-monsoonal precipitation for Odisha state. Krishnakumar et al. (2009) examined Kerala's long-term precipitation data and discovered a marked decline in southwest monsoonal season precipitation and a rise in post-monsoonal season precipitation. Extreme climatic events like heat and cold waves, flooding and drought have serious repercussions on the health of humans, animals, the environment and the economy (Handmer et al. 2012). Changing precipitation volumes and distribution is one of the major consequences of climate change (Dore 2005; Van Wageningen & Du Plessis 2007; Trenberth 2011), which requires urgent consideration and systematic research. Since precipitation is a vital element of the hydrological cycle, an investigation of noticeable climate changes, particularly the vicissitudes in the intensity and distribution patterns of precipitation, is important to understand its key aspects and attributes in climatological, hydrological, industrial, meteorological and agricultural studies globally for sustainable allocation of water resources (Zou et al. 2019), which involves a comprehensive understanding of long-term precipitation dynamics.

Numerous scholars have carried out investigations in India as well as diverse geographical regions in the world to discuss future precipitation projections (Menon et al. 2013; Sudeepkumar et al. 2018; Navarro-Racines et al. 2020; Vijayakumar et al. 2021). Duan et al. (2019) projected an increase of more than 50% in seasonal mean precipitation over the period 2081–2100 relative to the reference period 1981–2000 over China using a high-resolution atmospheric model – the ‘Database for Policy Decision-Making for Future Climate Change’. Woo et al. (2019) projected an increase in seasonal summer precipitation over northwest India, south peninsular India, and west peninsular India in the range of 4–19% from the near-to-far futures, and a decrease of about 5% in precipitation is noted over northeast India during the same period under the Representative Concentration Pathway 4.5 (RCP4.5). However, for RCP8.5, an increase of about 5–50% over south peninsular India and west peninsular India from the near-to-far-future periods is shown, and a decrease of about 15% over northwest India in the near future is shown. Vijayakumar et al. (2021) made future projections for the coastal districts of Odisha under RCP4.5 and RCP8.5 scenarios, respectively. Their analysis suggests an increase in annual mean precipitation at the rate of 0.1–2.2%, –0.3 to 0.7%, and 1.5–3.2%; and 3.6–7.9%, 3.7–6.6%, and 8.5–14% under RCP4.5 and RCP8.5 scenarios during the near (2011–2039), mid (2040–2069), and late (2070–2099) centuries, respectively.

Haryana state, which is situated in the north-western part of India, is widely considered as the breadbasket of India due to ample rice and wheat production (Kumar et al. 2018; Shirsath et al. 2020). Considering its utmost role in feeding a significant population of India, the change in the precipitation dynamics of Haryana due to climate change will significantly affect water availability and agricultural production. Apart from this, Haryana is already facing the problem of decline in the groundwater table, which will be further aggravated by the uncertain rainfall pattern under future climate change scenarios (Kumar et al. 2007; Chaudhuri & Roy 2016). Being one of the major paddy-producing states of the country (Sihmar 2014; Nirmala & Muthuraman 2016; Singh 2018), the groundwater table level in Haryana has drastically dropped several fold in recent decades (Kumar et al. 2007; Chaudhuri & Roy 2016). This indicates a dire need to study the spatio-temporal precipitation dynamics of Haryana under future climate change scenarios for the effective management of water resources at the district level as well as for the adoption of appropriate climate change adaptation and mitigation measures to significantly reduce the impact of climate change on the water resources of the state. The rationale behind selecting the district as an administrative unit in this study is to capture the spatio-temporal patterns of seasonal precipitation because inadequate and irregular precipitation in many parts of Haryana is one of the key limitations to agricultural and other socio-economic activities in the state. However, very limited investigations concentrating on the spatio-temporal dynamics of seasonal precipitation have been conducted across the state at the district level. Therefore, this detailed study was required to give a better understanding of the precipitation dynamics, which can help to assess the changing precipitation pattern over the period of study and, also, to identify the hot spots that are experiencing an increasing frequency of above and below-normal precipitation categories. This may help policymakers to locate the potential drought and flood-prone areas at the micro level for the effective management of the available resources and to take appropriate and timely socio-economic decisions across the state. In addition, the district-level precipitation datasets used for this analysis extend to more than a century, which represents a substantial advancement in relation to the other studies performed in the state. In the light of the discussion made previously, the objectives of this study are to analyse the distribution pattern, trends and performance of seasonal precipitation time series data of 120 years (1901–2020) as well as future seasonal precipitation projections (1921–2100) for Haryana. This paper is divided into five parts. Section 2 offers a description of the study area and data used. Methodology, results and discussions are addressed in Sections 3 and 4, respectively. Section 5 outlines the conclusions derived from the study.

The study area comprises 22 districts of Haryana, as shown in Figure 1. Haryana is located in north-western India and occupies about 1.3% of the geographical area of the country. Latitudinal and longitudinal coverage of the state extends from 27°39′ to 30°35′N and 74°28′ to 77°36′E, respectively, with a geographical area of about 44,212 km2 and altitude ranging from 100 to 1,500 m and with a mean altitude of 238 m above the mean sea level. Among districts, Panchkula has the highest mean elevation of 506 m, whereas the lowest mean elevation of 192 m is of Palwal.

Figure 1

Location map of the study area showing the districts of Haryana based on the SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model)-based elevation.

Figure 1

Location map of the study area showing the districts of Haryana based on the SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model)-based elevation.

Close modal

In the present study, the daily precipitation for the period 1901–2020 (120 years) generated by the India Meteorological Department (IMD) at a grid resolution of 0.25°×0.25° was directly projected on the district shapefile of Haryana, and the zonal-statistical average of the bound district region was processed and used for the analysis. This high-resolution gridded dataset was developed using daily precipitation data records collected from a network of 6,955 rain gauge stations over India by Pai et al. (2014). Daily precipitation data simulated by the Geophysical Fluid Science Laboratory (GFDL)'s Earth System Model with Modular Ocean Model version 4 (MOM4) component (GFDL-ESM2M) (Dunne et al. 2012, 2013) for historical conditions (1901–2005) and three projected future RCP scenarios (2006–2021), namely RCP2.6, RCP4.5, and RCP8.5, having a spatial resolution of 2.0225°×2.5° (Latitude×Longitude), was used for projecting the future precipitation of Haryana in different seasons.

Urbanization in India began to accelerate after independence in 1947 (Khan 2011). The Green Revolution in India dates back to the 1960s, which refers to a period when agriculture was converted into an industrial system because of the adoption of modern methods and technology, such as the introduction of high-yielding variety seeds, tractors, irrigation facilities, pesticides, and fertilizers to increase food production in order to alleviate hunger and poverty. Following a clear shift towards economic liberalization in the 1980s, Indian economy has grown remarkably because of increased industrialization (Nomura 2019), but high-intensity agriculture during the Green Revolution was heavily dependent on pesticides and chemical fertilizers, especially those containing nitrogen. Since 1960, the worldwide rate of application of nitrogen fertilizers has increased several times (Tilman 1998; Davidson 2009). Such rapid urbanization, industrialization, and intensive agricultural activities since the Green Revolution are attributed as the potential causes for altering the pattern of precipitation and extreme events. Haryana served as epicentre of the Green Revolution in India (Saha & Loveridge 2020; Kasliwal 2021). Based on this background, we sliced the period of 120 years into three quad-decadal times (QDT) intervals of 40 years each, namely, QDT1, QDT2, and QDT3, for analysing precipitation dynamics, where QDT1 corresponds to the pre-urbanization era (1901–1940), QDT2 corresponds to accelerated industrialization, urbanization, and Green Revolution era (1941–1980), and QDT3 corresponds to recent climate (1980–2020).

IMD has defined four meteorological seasons over India, which are winter (January–February (JF)), pre-monsoon (March–May (MAM)), summer monsoon (June–September (JJAS)), and post-monsoon (October–December (OND)). The daily data of each district were further cumulated over these seasons to obtain the total seasonal precipitation. Basic statistical analysis, including mean, percent deviation of precipitation (PDP), correlation coefficient, Trend-Free Pre-Whitening (TFPW), Mann–Kendall (MK) Test, and the moving average for seasonal precipitation, were used to compare and visualize the time series data (Figure 2).

Figure 2

Schematic flow chart of the methodology.

Figure 2

Schematic flow chart of the methodology.

Close modal

Distribution pattern of precipitation

The normal seasonal precipitation for each district was computed as mean precipitation (μ) by taking the average of precipitation for the whole study period of 120 years. Similarly, μ for each season was calculated for QDT1, QDT2, and QDT3, respectively, for all the districts.

The coefficient of variation (CV) is a statistical measure of the dispersion of data points in a data series around the mean. CV represents the ratio of the standard deviation of precipitation (σ) to the mean precipitation (μ), and it is a useful statistic for comparing the degree of variation from one precipitation time series with another, even if the means are drastically different from each other. The CV of seasonal precipitation was calculated for the whole study period of 120 years as well as for all the QDTs by using the expression
(1)
The seasonal precipitation deviation at a district during each QDT was calculated by expressing the seasonal precipitation in terms of the percent departure from its long-term climatological mean value of 120 years, and is termed PDP. A positive PDP points towards above normal, while a negative PDP depicts below-normal precipitation during the specific QDT.
(2)
where PDP is the percent deviation of precipitation, PQDT is the seasonal precipitation of the district during any QDT, and is the climatological long period average precipitation of 120 (1901–2020) years, referred to as LPA120 hereafter.

Inter-annual variability in the precipitation of the entire Haryana state throughout the course of the study period was calculated for each season using the standardized anomaly index and moving average technique. The concept of computing moving average is based on the idea that any large irregular components of time series at any point in time will have a less significant impact on the trend, so 11 years moving average was computed to evaluate the trends in the seasonal precipitation time series.

The IMD has categorized seasonal precipitation over India into three categories based on the deviation from normal precipitation, (Kothawale & Munot 1998), which are (i) excess (≥+20%), (ii) normal (−19 to +19%), and (iii) deficient (≤−20%). The categorization was done to visualize the spatio-temporal patterns of seasonal precipitation over Haryana during the study period.

Trends of precipitation

Simple linear regression (SLR) was used to estimate the magnitude (slope) of linear trend of the seasonal precipitation data on decadal scale. The positive and negative values of SLR indicate increasing and decreasing trends, respectively. Along with SLR, a rank-based non-parametric MK test (Mann 1945; Kendall 1975) was used for testing the significance of the trend. The advantage of the non-parametric method is that it does not stipulate the sample to follow a certain distribution and is not influenced by a few abnormal values. Therefore, it is suitable for type variables and order variables, and the calculation is relatively simple. The trend detection by the MK test is often subjected to errors when the time series data are serially correlated (Novotny & Stefan 2007). The TFPW-MK test method is a pre-removal-type MK test method to address the autocorrelation problem of the test sequence. To avoid the effect of the autocorrelation on precipitation time series, we used TFPW to adjust the MK test.

Precipitation projections

RCP2.6 represents the low warming scenario of the world. According to RCP2.6, GHG and total radiative force will reach nearly 490 ppm CO2 equivalent and 2.6 Wm−2, respectively, before 2100 (Van Vuuren et al. 2011). RCP4.5 represents the moderate warming scenario of the world. According to RCP4.5, GHG and total radiative force will reach roughly 650 ppm CO2 equivalent and 4.5 Wm−2, respectively, before 2100 (Thomson et al. 2011). While RCP8.5 represents the highest warming scenario, GHG and total radiative force will reach 1,370 ppm CO2 equivalent and 8.5 Wm−2, respectively, before 2100 (Moss et al. 2010). Daily precipitation (mm) data of GFDL-ESM2M for historical conditions (1901–2005) and three projected future RCP scenarios (2005–2021), namely, RCP2.6, RCP4.5, and RCP8.5, were converted to seasonal data. The historical precipitation data of 1901–2005 were used and a part of this historical data (1901–1950) was used as the baseline data for future precipitation projections.

The correlation analysis and percent biases of historical precipitation data was done with gridded datasets of IMD for a time period of 1901–2005. Correlation analysis is used to calculate the Pearson correlation coefficient (r), which is an effective tool for measuring the proximity of two distinct time series. The range of r is −1 to 1. A value greater than 0 indicates a positive correlation, and a value less than 0 indicates a negative correlation. The correlation is greater when the absolute value is close to 1 (Chen et al. 2017). The Pearson correlation coefficient (r) was computed by dividing the sum of the product of deviations from the mean by the square root of the product of sum of the squares of deviations from the respective means of the two variables (Panse & Sukhatme 1985) using the following formula:
(3)
where r is the Pearson correlation coefficient, x is the historical seasonal precipitation time series of GFDL-ESM2M, y is the seasonal precipitation time series of the IMD gridded datasets,, are the mean of x and y, respectively, and n is the sample size.
Biases were computed using the following formula:
(4)
where is the percent biases of GFDL-M2M precipitation historical data () from IMD gridded precipitation data () for the period of 1901–2005.

To project the future precipitation for each RCP, the 21st century was divided into two quad-decadal projections (QDPs), namely, QDP1 (2021–2060) and QDP2 (2061–2100), and the PDP for each scenario was calculated for both QDPs.

Distribution pattern of precipitation

Descriptive statistics of precipitation

Descriptive statistical parameters, including mean precipitation (μ), standard deviation of precipitation (σ), maximum seasonal precipitation, and percent normal precipitation in seasonal and annual time series precipitation data of 22 districts of Haryana through the course of 120 years, are summarized in Table 1. The μ for the state during the entire study period was 37.0, 37.7, 468.3, and 24.8 mm, whereas σ was found to be 24.8, 28.8, 136.9, and 29.4 mm during the winter, (JF), pre-monsoon (MAM), summer monsoon (JJAS), and post-monsoon (OND) seasons, respectively. Haryana received the maximum seasonal precipitation of 117.5 mm (1954), 167.8 mm (1982), 815.3 mm (1917), and 165.8 mm (1956) during the winter, pre-monsoon, summer monsoon, and post-monsoon seasons, respectively, in the entire study period. The values of percent normal precipitation varied from 10.0% (Mahendragarh) to 24.2% (Panchkula) during the winter season, 9.2% (Palwal) to 24.2% (Ambala) during the pre-monsoon season, 29.2% (Faridabad) to 51.7% (Panchkula) during the summer monsoon season, and 5.8% (Rohtak) to 15.0% (Palwal) during the post-monsoon season. Among different districts, the values of μ and σ for winter precipitation events ranged from 21.0 mm (Mahendragarh) to 125.0 mm (Panchkula) and 20.1 mm (Sirsa) to 76.0 mm (Panchkula), respectively. The values of μ and σ for the pre-monsoon precipitation ranged from 22.9 mm (Faridabad) to 118.6 mm (Panchkula) and 23.7 mm (Sirsa) to 81.5 mm (Panchkula), respectively. The μ of the summer monsoon season varied from 259.8 mm (Sirsa) to 1,061.0 mm (Panchkula), with σ varying between 118.4 mm (Sirsa) and 318.3 mm (Panchkula). In the post-monsoon season, μ varied from 15.5 mm (Sirsa) to 74.4 mm (Panchkula), with σ varying between 22.6 mm (Charkhi Dadri) and 87.8 mm (Panchkula).

Table 1

Descriptive statistics of seasonal rainfall in different districts of Haryana, India, during 1901–2020

DistrictWinter
Pre-monsoon
Monsoon
Post-monsoon
μσMXPPNPμσMXPPNPμσMXPPNPμσMXPPNP
Ambala 79.8 52.3 289.9 (1961) 20.0 65.8 50.8 329.6 (1982) 24.2 863.0 262.2 1754.5 (1995) 45.0 47.9 54.0 282.8 (1955) 12.5 
Bhiwani 25.5 22.1 99.5 (1977) 11.7 30.7 25.9 147.7 (2008) 11.7 352.9 136.4 733.6 (1917) 39.2 17.7 24.0 132.5 (1997) 9.2 
Charkhi Dadri 22.0 21.3 100.6 (1970) 15.0 27.9 27.8 146.7 (1982) 15.8 343.7 158.5 839.6 (1995) 30.8 16.1 22.6 108.5 (1956) 11.7 
Faridabad 27.5 28.1 113.2 (1928) 12.5 22.9 27.2 138.0 (1944) 10.8 520.0 265.2 1424.5 (1923) 29.2 24.7 41.1 231.4 (1956) 6.7 
Fatehabad 26.9 21.0 102.5 (1962) 13.3 33.2 28.0 151.1 (1983) 16.7 307.3 135.3 670.8 (1917) 33.3 18.2 28.3 201.6 (1955) 11.7 
Gurgaon 29.8 23.3 89.9 (1915) 13.3 37.5 37.7 204.7 (1983) 13.3 514.4 182.9 934.6 (1933) 38.3 24.0 35.2 227.6 (1956) 5.8 
Hisar 27.6 23.3 112.9 (1954) 17.5 35.5 27.9 126.5 (1982) 17.5 336.7 135.3 724.8 (1917) 38.3 18.5 27.5 170.0 (1917) 10.8 
Jhajjar 26.0 21.6 102.4 (2013) 18.3 31.7 31.3 160.0 (1982) 9.2 429.0 168.5 905.2 (1977) 40.0 19.0 27.4 170.5 (1956) 10.8 
Jind 33.2 29.2 148.7 (2013) 16.7 34.7 30.4 145.6 (1982) 16.7 388.4 150.8 832.7 (1933) 45.0 19.6 26.9 137.4 (1955) 10.8 
Kaithal 43.4 32.5 137.0 (1937) 15.8 41.6 36.0 174.5 (1983) 17.5 441.9 166.9 1159.7 (1988) 40.8 24.0 31.8 173.5 (1917) 12.5 
Karnal 46.3 34.8 168.4 (1954) 17.5 38.4 33.6 173.6 (2020) 16.7 529.1 173.2 1030.8 (1942) 37.5 28.6 37.9 219.3 (1955) 6.7 
Kurukshetra 56.1 38.0 174.2 (1954) 20.8 48.3 40.9 233.9 (1982) 19.2 591.4 181.6 1251.3 (1988) 50.0 34.7 42.3 223.6 (1955) 13.3 
Mahendragarh 21.0 22.2 101.2 (1948) 10.0 29.3 27.9 137.4 (1913) 15.0 396.5 164.3 932.2 (1908) 37.5 16.8 22.9 103.0 (1956) 12.5 
Mewat 22.9 20.9 105.3 (1948) 20.0 25.8 27.5 136.2 (1982) 14.2 480.5 172.2 1064.0 (1917) 46.7 22.8 36.5 249.3 (1910) 13.3 
Palwal 26.0 22.8 93.4 (1954) 16.7 25.9 29.3 131.6 (1982) 9.2 487.9 185.6 1052.7 (1933) 40.8 26.7 45.8 334.6 (1956) 15.0 
Panchkula 125.0 76.0 432.5 (1961) 24.2 118.6 81.5 536.4 (1982) 23.3 1061.1 318.3 1929.2 (1964) 51.7 74.4 87.8 531.0 (1956) 13.3 
Panipat 40.5 32.8 166.1 (1954) 14.2 35.7 36.0 228.7 (1982) 18.3 549.9 219.8 1075.1 (1964) 30.8 29.1 44.3 297.9 (1956) 10.0 
Rewari 25.6 23.7 125.9 (1948) 14.2 33.3 34.6 161.3 (1913) 14.2 456.4 172.3 1085.0 (1917) 45.0 20.5 27.2 130.2 (1997) 10.0 
Rohtak 31.0 27.0 159.9 (2013) 15.0 36.9 33.7 192.5 (1982) 15.0 417.8 163.0 989.2 (1995) 36.7 21.1 25.2 114.6 (1997) 5.8 
Sirsa 23.3 20.1 87.9 (1954) 15.0 27.7 23.7 116.5 (1982) 13.3 259.8 118.4 811.3 (1917) 31.7 15.5 26.2 221.4 (1955) 10.0 
Sonipat 37.1 28.2 148.6 (2013) 18.3 39.2 35.9 230.1 (1982) 20.8 488.1 173.5 1072.0 (1964) 39.2 24.6 32.5 220.9 (1956) 9.2 
Yamunanagar 78.1 54.5 258.7 (2013) 19.2 62.3 47.0 235.8 (1982) 21.7 935.0 267.8 1536.1 (1966) 45.8 48.3 58.2 410.1 (1956) 10.8 
Haryana 37.0 24.8 117.5 (1954) 20.0 37.7 28.8 167.8 (1982) 17.5 468.3 136.9 815.3 (1917) 43.3 24.8 29.4 165.8 (1956) 12.5 
DistrictWinter
Pre-monsoon
Monsoon
Post-monsoon
μσMXPPNPμσMXPPNPμσMXPPNPμσMXPPNP
Ambala 79.8 52.3 289.9 (1961) 20.0 65.8 50.8 329.6 (1982) 24.2 863.0 262.2 1754.5 (1995) 45.0 47.9 54.0 282.8 (1955) 12.5 
Bhiwani 25.5 22.1 99.5 (1977) 11.7 30.7 25.9 147.7 (2008) 11.7 352.9 136.4 733.6 (1917) 39.2 17.7 24.0 132.5 (1997) 9.2 
Charkhi Dadri 22.0 21.3 100.6 (1970) 15.0 27.9 27.8 146.7 (1982) 15.8 343.7 158.5 839.6 (1995) 30.8 16.1 22.6 108.5 (1956) 11.7 
Faridabad 27.5 28.1 113.2 (1928) 12.5 22.9 27.2 138.0 (1944) 10.8 520.0 265.2 1424.5 (1923) 29.2 24.7 41.1 231.4 (1956) 6.7 
Fatehabad 26.9 21.0 102.5 (1962) 13.3 33.2 28.0 151.1 (1983) 16.7 307.3 135.3 670.8 (1917) 33.3 18.2 28.3 201.6 (1955) 11.7 
Gurgaon 29.8 23.3 89.9 (1915) 13.3 37.5 37.7 204.7 (1983) 13.3 514.4 182.9 934.6 (1933) 38.3 24.0 35.2 227.6 (1956) 5.8 
Hisar 27.6 23.3 112.9 (1954) 17.5 35.5 27.9 126.5 (1982) 17.5 336.7 135.3 724.8 (1917) 38.3 18.5 27.5 170.0 (1917) 10.8 
Jhajjar 26.0 21.6 102.4 (2013) 18.3 31.7 31.3 160.0 (1982) 9.2 429.0 168.5 905.2 (1977) 40.0 19.0 27.4 170.5 (1956) 10.8 
Jind 33.2 29.2 148.7 (2013) 16.7 34.7 30.4 145.6 (1982) 16.7 388.4 150.8 832.7 (1933) 45.0 19.6 26.9 137.4 (1955) 10.8 
Kaithal 43.4 32.5 137.0 (1937) 15.8 41.6 36.0 174.5 (1983) 17.5 441.9 166.9 1159.7 (1988) 40.8 24.0 31.8 173.5 (1917) 12.5 
Karnal 46.3 34.8 168.4 (1954) 17.5 38.4 33.6 173.6 (2020) 16.7 529.1 173.2 1030.8 (1942) 37.5 28.6 37.9 219.3 (1955) 6.7 
Kurukshetra 56.1 38.0 174.2 (1954) 20.8 48.3 40.9 233.9 (1982) 19.2 591.4 181.6 1251.3 (1988) 50.0 34.7 42.3 223.6 (1955) 13.3 
Mahendragarh 21.0 22.2 101.2 (1948) 10.0 29.3 27.9 137.4 (1913) 15.0 396.5 164.3 932.2 (1908) 37.5 16.8 22.9 103.0 (1956) 12.5 
Mewat 22.9 20.9 105.3 (1948) 20.0 25.8 27.5 136.2 (1982) 14.2 480.5 172.2 1064.0 (1917) 46.7 22.8 36.5 249.3 (1910) 13.3 
Palwal 26.0 22.8 93.4 (1954) 16.7 25.9 29.3 131.6 (1982) 9.2 487.9 185.6 1052.7 (1933) 40.8 26.7 45.8 334.6 (1956) 15.0 
Panchkula 125.0 76.0 432.5 (1961) 24.2 118.6 81.5 536.4 (1982) 23.3 1061.1 318.3 1929.2 (1964) 51.7 74.4 87.8 531.0 (1956) 13.3 
Panipat 40.5 32.8 166.1 (1954) 14.2 35.7 36.0 228.7 (1982) 18.3 549.9 219.8 1075.1 (1964) 30.8 29.1 44.3 297.9 (1956) 10.0 
Rewari 25.6 23.7 125.9 (1948) 14.2 33.3 34.6 161.3 (1913) 14.2 456.4 172.3 1085.0 (1917) 45.0 20.5 27.2 130.2 (1997) 10.0 
Rohtak 31.0 27.0 159.9 (2013) 15.0 36.9 33.7 192.5 (1982) 15.0 417.8 163.0 989.2 (1995) 36.7 21.1 25.2 114.6 (1997) 5.8 
Sirsa 23.3 20.1 87.9 (1954) 15.0 27.7 23.7 116.5 (1982) 13.3 259.8 118.4 811.3 (1917) 31.7 15.5 26.2 221.4 (1955) 10.0 
Sonipat 37.1 28.2 148.6 (2013) 18.3 39.2 35.9 230.1 (1982) 20.8 488.1 173.5 1072.0 (1964) 39.2 24.6 32.5 220.9 (1956) 9.2 
Yamunanagar 78.1 54.5 258.7 (2013) 19.2 62.3 47.0 235.8 (1982) 21.7 935.0 267.8 1536.1 (1966) 45.8 48.3 58.2 410.1 (1956) 10.8 
Haryana 37.0 24.8 117.5 (1954) 20.0 37.7 28.8 167.8 (1982) 17.5 468.3 136.9 815.3 (1917) 43.3 24.8 29.4 165.8 (1956) 12.5 

μ, mean seasonal rainfall; σ, standard deviation; MXP, maximum precipitation (year); PNP, percent normal precipitation.

The spatio-temporal variation of μ at the district level in the state of Haryana during different seasons is depicted in Figure 3. The highest winter μ was observed in QDT1, followed by QDT2 and QDT3 in the entire state. Among districts, a higher amount of precipitation was observed in the districts lying in the north-eastern region of Haryana; that is, Ambala, Kurukshetra, Panchkula, and Yamunanagar observed the highest μ during all the QDTs, whereas the lowest amount of μ during all the QDTs was observed in the districts lying in the south-western region of the state, namely Bhiwani, Charkhi Dadri, Hisar, Mahendragarh, and Mewat. A decreasing trend in precipitation was observed at Faridabad, Hisar, Mahendragarh, and Mewat over the years during the winter season. The maximum μ in Haryana during the pre-monsoon season was observed in QDT3, followed by QDT1 and QDT2, respectively. Ambala, Panchkula, and Yamunanagar observed the highest μ, while the lowest μ was observed at Faridabad, Charkhi Dadri, and Palwal in the pre-monsoon season. The highest μ during the summer monsoon season in Haryana was observed in QDT2, followed by QDT1 and QDT3, respectively. Among districts, Panchkula observed the highest μ; however, Sirsa received the lowest μ in the summer monsoon season. Similarly, the highest μ during the post-monsoon season in Haryana was observed in QDT2, followed by QDT1 and QDT3, respectively. Among districts, Panchkula and Yamunanagar observed the highest μ; however, Sirsa and Fatehabad received the lowest μ in the post-monsoon season. Overall, it can be noticed that the amount of μ was higher in the districts lying in eastern Haryana, prominently north-eastern ones, as compared to its western counterparts. The comparatively high precipitation in the north-eastern and eastern parts of Haryana may be attributed to the higher elevation of these parts compared with the western parts (Singh & Kumar 1997; Kuraji et al. 2001; Shrestha et al. 2012).

Figure 3

Long-term distribution of mean precipitation (mm per season) presented in (a) QDT1, (b) QDT2, (c) QDT3, and (d) LPA120 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana state, India.

Figure 3

Long-term distribution of mean precipitation (mm per season) presented in (a) QDT1, (b) QDT2, (c) QDT3, and (d) LPA120 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana state, India.

Close modal

Precipitation deviation

Figure 4 portrays the visuals of the spatio-temporal variation of PDP during different seasons at the district level in different QDTs against LPA120. The positive PDP depicts above-normal precipitation and the negative PDP depicts below-normal precipitation. It can be observed that most of the districts received above-normal precipitation during QDT1 in the winter and post-monsoon seasons; however, a below-normal precipitation trend was apparent in most of the districts during the pre-monsoon and monsoon seasons. The below-normal precipitation trend was observed during QDT2 and QDT3 in the winter season. Pre-monsoon season precipitation showed an increasing trend during the last QDT; however, the most negative deviations were observed in QDT2. Negative PDP during the QDT3 depicts the decreasing trend in precipitation in most of the districts in the winter, summer monsoon, and post-monsoon seasons in recent times. Overall, no synchronized trend of deviation in any particular season was observed in all three QDTs. Precipitation during the winter and pre-monsoon seasons showed a positive precipitation deviation in all districts during QDT1 and QDT3, respectively. However, both monsoon and post-monsoon seasons showed a positive trend in precipitation deviation in all districts during QDT2. Overall, we can conclude that precipitation was decreasing during the winter, monsoon, and post-monsoon seasons while it was increasing in the pre-monsoon season in the last QDT, i.e. during the recent climate trends. These shifts in precipitation patterns might be attributed to the high pace of climate change and climate variability during recent times in India (Dore 2005; Patra et al. 2012; Krishnan et al. 2020; Praveen et al. 2020).

Figure 4

Long-term distribution of PDP (%) presented in (a) QDT1, (b) QDT2, and (c) QDT3 from LPA120 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana.

Figure 4

Long-term distribution of PDP (%) presented in (a) QDT1, (b) QDT2, and (c) QDT3 from LPA120 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana.

Close modal

Precipitation variability

The CV depicted in Figure 5 is used to indicate the spatial variability in the seasonal precipitation of Haryana over the course of the study period at the district level in different QDTs. A large CV is indicative of large spatial variability and vice-versa. The highest variability in precipitation was observed during QDT2 and QDT1 for the winter and pre-monsoon seasons, whereas maximum CV was observed during QDT3 for both monsoon and post-monsoon seasons, respectively. Among different seasons, the overall value of CV was observed to be the highest during post-monsoon, while it was the lowest for the monsoon season. This is indicative of the most stable precipitation during monsoon and the most variable precipitation during the post-monsoon season. Among districts, the variability in precipitation was higher in the districts lying in western Haryana as compared to the ones lying in eastern Haryana.

Figure 5

Long-term distribution of CV (%) presented in (a) QDT1, (b) QDT2, (c) QDT3, and (d) LPA120 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana.

Figure 5

Long-term distribution of CV (%) presented in (a) QDT1, (b) QDT2, (c) QDT3, and (d) LPA120 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana.

Close modal

Inter-annual variability in the seasonal precipitation of Haryana over the course of the study period is shown in Figure 6. The blue histogram bars show the standardized anomaly of seasonal precipitation from LPA120, and the moving average depicted by the black line shows the 11 years moving average of the seasonal precipitation of the state. An increase in winter precipitation was witnessed during the transition from QDT1 to QDT2 during the years 1935–1946 and 1948–1959 in QDT2. Thereafter, a fall was observed in the winter season precipitation till 1990 in QDT3. The recent decade 2010–2020 also showed a decline in winter season precipitation. A diminution in seasonal precipitation was observed from 1918 till 1975 and 1988–1998, whereas an increase from 1977 to 1987 and post-1999 till today was observed during the pre-monsoon season. Precipitation during the summer monsoon season increased from 1950 to 1975 and then declined in the last decade of the study period. Post-monsoon precipitation rose from 1950 to 1964 in QDT2 and dropped from 1933 to 1949 and 2003 to the end of the study period. However, precipitation during each season in the entire state showed epochal variations during the entire investigation span of 1901–2020, but the magnitude and range of fluctuation was the widest during the monsoon and the narrowest during the winter season. Overall, the most consistent precipitation was observed during the monsoon season in the districts lying in the north-eastern region of Haryana.

Figure 6

Long-term distribution of inter-annual variability of seasonal precipitation depicted as standardized anomaly by histograms and an 11-year moving average as a dark black line during the winter season (JF; in a), pre-monsoon season (MAM; in b), monsoon season (JJAS; in c), and post-monsoon season (OND; in d) in Haryana. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2022.005.

Figure 6

Long-term distribution of inter-annual variability of seasonal precipitation depicted as standardized anomaly by histograms and an 11-year moving average as a dark black line during the winter season (JF; in a), pre-monsoon season (MAM; in b), monsoon season (JJAS; in c), and post-monsoon season (OND; in d) in Haryana. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2022.005.

Close modal

Precipitation categorization

Figure 7 shows the categorization of precipitation based on the IMD norms, over different seasons in Haryana, which was done to visualize the spatio-temporal patterns of precipitation during the course of the study period. Haryana received normal precipitation in 20% of the events, whereas precipitation was deficient and excess in 45.8 and 34.2% of the events during the winter season of the study period. All of the locations in the year 1902, 1918, 1925, 1932, 1946, 1958, 1960, 1963, 1964, 1967, 1969, 1974, 1980, 1997, 2006, 2008, 2012, 2016, and 2018 received deficient precipitation. Similarly, 1911, 1919, 1924, 1954, 1990, and 2013 were the years during which all districts received excess precipitation, and the winter season witnessed no single year during which all the districts received normal precipitation. During the pre-monsoon season, Haryana received normal precipitation in 17.5% of the events, whereas the deficient and excess category of precipitation was received in 52.5 and 30.0% of the events, respectively. All districts of Haryana received deficient precipitation in the years 1903, 1910, 1921, 1922, 1924, 1925, 1928, 1929, 1930, 1949, 1953, 1954, 1958, 1961, 1974, 1975, 1984, 1985, 1992, 1996, and 2010, whereas 1907, 1913, 1926, 1982, 1983, 2015, and 2020 were the years during which all districts received excess precipitation in the pre-monsoon season. Haryana received normal monsoon precipitation in 42.5% of the events, though deficient and excess precipitation was observed in 31.7 and 25.8% of precipitation events of the monsoon season, respectively. The years 1905, 1918, 1929, 1979, 1987, and 2014 were among the years where all districts received deficient precipitation, whereas years 1917 and 1964 observed excess precipitation in all districts. Normal precipitation for post-monsoon season precipitation events for Haryana was found only in 11.7% of the events, whereas deficient and excess precipitation was witnessed in 60 and 28.3% of the precipitation occasions. All districts of Haryana during the years 1901, 1905, 1907, 1908, 1918, 1920, 1926, 1930, 1939, 1940, 1941, 1943, 1949, 1950, 1952, 1953, 1969, 1976, 1978, 1984, 1993, 1994, 1995, 2000, 2005, 2007, 2011, 2012, 2015, and 2017 received deficient precipitation, whereas the years 1910, 1917, 1928, 1955, 1956, 1957, 1967, and 1997 received excess precipitation in all districts. Among districts, highest excess precipitation events were observed at Kurukshetra and Gurgaon (43.0%) during the winter season, Bhiwani (41.0%) during the pre-monsoon season, Panipat (40.0%) during the monsoon season, and Rohtak (33.3%) during the post-monsoon season, respectively. Similarly, maximum cases of deficient categories of precipitation events were observed at Mahendragarh (73.0%) during the winter season, Jhajjar and Palwal (75.0%) during the pre-monsoon season, Sirsa (50.0%) during the monsoon season, and Faridabad (83.0%) during the post-monsoon season, respectively. Overall, the most normal precipitation events in the different districts of Haryana were noticed during the monsoon season, followed by the winter, pre-monsoon, and post-monsoon seasons. Among districts, the districts lying in eastern Haryana received the most normal precipitation as compared to the ones lying in western Haryana. This can be due to the presence of eastern Haryana near the foothills of Himalayas and the comparatively high altitude which facilitates higher and most normal precipitation.

Figure 7

Categorization of seasonal precipitation during the winter season (JF), pre-monsoon season (MAM), monsoon season (JJAS), and post-monsoon season (OND) as per the IMD classification presented in blocks a–d, respectively, in the districts of Haryana, where excess (≥20% from LPA120) is denoted by blue, normal (−19 to +19% from CLM120) is donated by grey, and deficient (≤−20% from LPA120) is denoted by orange. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2022.005.

Figure 7

Categorization of seasonal precipitation during the winter season (JF), pre-monsoon season (MAM), monsoon season (JJAS), and post-monsoon season (OND) as per the IMD classification presented in blocks a–d, respectively, in the districts of Haryana, where excess (≥20% from LPA120) is denoted by blue, normal (−19 to +19% from CLM120) is donated by grey, and deficient (≤−20% from LPA120) is denoted by orange. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2022.005.

Close modal

Performance of precipitation

Precipitation trends

The SLR and TFPW-MK test methods were applied to detect the trends in the time series of precipitation from 1901 to 2020 in different seasons. The precipitation trends in the winter season detected by the TFPW-MK method are given in Table 2, and the spatio-temporal results are presented in Figure 8. According to SLR, all districts showed a decreasing trend in winter season precipitation. A total of 20 of 22 districts showed a decreasing trend as per the Sen's slope after TFPW, whereas only Mewat district showed a statistically significant decreasing trend at 95% confidence level during the winter season. During the pre-monsoon season, the increasing trend in precipitation was depicted by both SLR and for all the districts of Haryana, but a statistically significant increasing trend as per the TFPW-MK test were observed at Ambala, Mahendragarh, Panchkula, and Yamunanagar at 99% confidence level and at Charkhi Dadri, Rewari, Kurukshetra, and Rohtak at 95% confidence level (Table 3). A decreasing trend in monsoon precipitation was observed in 16 of 22 districts as per SLR as well as Sen's slope method after TFPW, but a statistically significant decrease at 99% confidence level was observed only at Panchkula, Faridabad, and Panipat, while Palwal showed a statistically significant decrease at 95% confidence level (Table 4). An increasing but statistically insignificant trend in precipitation during the monsoon season was observed at Kurukshetra, Kaithal, Yamunanagar, Gurgaon, Rewari, and Ambala. According to SLR, as well as Sen's slope method after TFPW, all districts have a statistically insignificant and declining trend in post-monsoon season precipitation (Table 5). A synchronous behaviour was observed for trend detection between SLR and Sen's slope method after TFPW, but SLR had values with a slightly higher magnitude.

Table 2

Results of TFPW-MK test of winter season precipitation from 1901 to 2020 in different districts of Haryana, India

DistrictzSLP#SLPp-valueSτ
Ambala −0.45 −0.06 −0.10 0.65 −197.00 −0.03 
Bhiwani −0.85 −0.04 −0.05 0.40 −369.00 −0.05 
Charkhi Dadri −0.69 −0.03 −0.03 0.49 −303.00 −0.04 
Faridabad −1.09 −0.05 −0.05 0.28 −475.00 −0.07 
Fatehabad −0.53 −0.03 −0.03 0.60 −231.00 −0.03 
Gurgaon −0.73 −0.04 −0.05 0.46 −321.00 −0.05 
Hisar −1.27 −0.06 −0.07 0.20 −555.00 −0.08 
Jhajjar −1.11 −0.05 −0.06 0.27 −485.00 −0.07 
Jind −0.51 −0.03 −0.04 0.61 −225.00 −0.03 
Kaithal −0.06 0.00 −0.02 0.96 −25.00 0.00 
Karnal −0.46 −0.04 −0.05 0.64 −203.00 −0.03 
Kurukshetra −1.11 −0.10 −0.12 0.27 −483.00 −0.07 
Mahendragarh −0.64 −0.02 −0.03 0.52 −279.00 −0.04 
Mewat −2.03 −0.09 −0.10 0.04 −885.00 −0.13 
Palwal −1.24 −0.06 −0.07 0.21 −541.00 −0.08 
Panchkula 0.25 0.04 −0.01 0.80 111.00 0.02 
Panipat −0.97 −0.08 −0.09 0.33 −425.00 −0.06 
Rewari −0.51 −0.02 −0.03 0.61 −225.00 −0.03 
Rohtak −1.28 −0.07 −0.08 0.20 −559.00 −0.08 
Sirsa −0.26 −0.01 −0.01 0.79 −115.00 −0.02 
Sonipat −0.67 −0.04 −0.05 0.50 −293.00 −0.04 
Yamunanagar −0.63 −0.08 −0.11 0.53 −275.00 −0.04 
Haryana −0.68 −0.04 −0.06 0.49 −299.00 −0.04 
DistrictzSLP#SLPp-valueSτ
Ambala −0.45 −0.06 −0.10 0.65 −197.00 −0.03 
Bhiwani −0.85 −0.04 −0.05 0.40 −369.00 −0.05 
Charkhi Dadri −0.69 −0.03 −0.03 0.49 −303.00 −0.04 
Faridabad −1.09 −0.05 −0.05 0.28 −475.00 −0.07 
Fatehabad −0.53 −0.03 −0.03 0.60 −231.00 −0.03 
Gurgaon −0.73 −0.04 −0.05 0.46 −321.00 −0.05 
Hisar −1.27 −0.06 −0.07 0.20 −555.00 −0.08 
Jhajjar −1.11 −0.05 −0.06 0.27 −485.00 −0.07 
Jind −0.51 −0.03 −0.04 0.61 −225.00 −0.03 
Kaithal −0.06 0.00 −0.02 0.96 −25.00 0.00 
Karnal −0.46 −0.04 −0.05 0.64 −203.00 −0.03 
Kurukshetra −1.11 −0.10 −0.12 0.27 −483.00 −0.07 
Mahendragarh −0.64 −0.02 −0.03 0.52 −279.00 −0.04 
Mewat −2.03 −0.09 −0.10 0.04 −885.00 −0.13 
Palwal −1.24 −0.06 −0.07 0.21 −541.00 −0.08 
Panchkula 0.25 0.04 −0.01 0.80 111.00 0.02 
Panipat −0.97 −0.08 −0.09 0.33 −425.00 −0.06 
Rewari −0.51 −0.02 −0.03 0.61 −225.00 −0.03 
Rohtak −1.28 −0.07 −0.08 0.20 −559.00 −0.08 
Sirsa −0.26 −0.01 −0.01 0.79 −115.00 −0.02 
Sonipat −0.67 −0.04 −0.05 0.50 −293.00 −0.04 
Yamunanagar −0.63 −0.08 −0.11 0.53 −275.00 −0.04 
Haryana −0.68 −0.04 −0.06 0.49 −299.00 −0.04 

z, Z statistic after TFPW; SLP#, Sen's slope after TFPW; SLP, Sen's slope before TFPW; p-value, p-value after TFPW; S, Mann–Kendall S statistic; τ, Mann–Kendall's Tau.

Table 3

Results of TFPW-MK test of pre-monsoon season precipitation from 1901 to 2020 in different districts of Haryana, India

DistrictzSLP#SLPp-valueSτ
Ambala 2.81 0.26 0.24 0.00 1,225.00 0.17 
Bhiwani 1.60 0.08 0.10 0.11 697.00 0.10 
Charkhi Dadri 2.43 0.11 0.11 0.02 1,057.00 0.15 
Faridabad 0.69 0.02 0.02 0.49 301.00 0.04 
Fatehabad 0.76 0.04 0.05 0.45 333.00 0.05 
Gurgaon 1.59 0.09 0.10 0.11 693.00 0.10 
Hisar 0.91 0.07 0.07 0.36 397.00 0.06 
Jhajjar 1.53 0.08 0.08 0.13 667.00 0.10 
Jind 1.63 0.11 0.09 0.10 711.00 0.10 
Kaithal 0.99 0.07 0.07 0.32 433.00 0.06 
Karnal 1.10 0.07 0.07 0.27 481.00 0.07 
Kurukshetra 2.14 0.16 0.16 0.03 931.00 0.13 
Mahendragarh 3.53 0.20 0.19 0.00 1,539.00 0.22 
Mewat 1.31 0.07 0.06 0.19 573.00 0.08 
Palwal 1.36 0.07 0.06 0.17 595.00 0.08 
Panchkula 2.48 0.39 0.37 0.01 1,079.00 0.15 
Panipat 0.78 0.05 0.05 0.43 341.00 0.05 
Rewari 2.40 0.14 0.12 0.02 1,047.00 0.15 
Rohtak 1.99 0.13 0.12 0.05 867.00 0.12 
Sirsa 0.96 0.05 0.05 0.33 421.00 0.06 
Sonipat 1.80 0.13 0.11 0.07 783.00 0.11 
Yamunanagar 2.93 0.26 0.25 0.00 1,277.00 0.18 
Haryana 2.07 0.12 0.11 0.04 903.00 0.13 
DistrictzSLP#SLPp-valueSτ
Ambala 2.81 0.26 0.24 0.00 1,225.00 0.17 
Bhiwani 1.60 0.08 0.10 0.11 697.00 0.10 
Charkhi Dadri 2.43 0.11 0.11 0.02 1,057.00 0.15 
Faridabad 0.69 0.02 0.02 0.49 301.00 0.04 
Fatehabad 0.76 0.04 0.05 0.45 333.00 0.05 
Gurgaon 1.59 0.09 0.10 0.11 693.00 0.10 
Hisar 0.91 0.07 0.07 0.36 397.00 0.06 
Jhajjar 1.53 0.08 0.08 0.13 667.00 0.10 
Jind 1.63 0.11 0.09 0.10 711.00 0.10 
Kaithal 0.99 0.07 0.07 0.32 433.00 0.06 
Karnal 1.10 0.07 0.07 0.27 481.00 0.07 
Kurukshetra 2.14 0.16 0.16 0.03 931.00 0.13 
Mahendragarh 3.53 0.20 0.19 0.00 1,539.00 0.22 
Mewat 1.31 0.07 0.06 0.19 573.00 0.08 
Palwal 1.36 0.07 0.06 0.17 595.00 0.08 
Panchkula 2.48 0.39 0.37 0.01 1,079.00 0.15 
Panipat 0.78 0.05 0.05 0.43 341.00 0.05 
Rewari 2.40 0.14 0.12 0.02 1,047.00 0.15 
Rohtak 1.99 0.13 0.12 0.05 867.00 0.12 
Sirsa 0.96 0.05 0.05 0.33 421.00 0.06 
Sonipat 1.80 0.13 0.11 0.07 783.00 0.11 
Yamunanagar 2.93 0.26 0.25 0.00 1,277.00 0.18 
Haryana 2.07 0.12 0.11 0.04 903.00 0.13 

z, Z statistic after TFPW; SLP#, Sen's slope after TFPW; SLP, Sen's slope before TFPW; p-value, p-value after TFPW; S, Mann–Kendall S statistic; τ, Mann–Kendall's Tau.

Table 4

Results of TFPW-MK test of monsoon season precipitation from 1901 to 2020 in different districts of Haryana, India

DistrictzSLP#SLPp-valueSτ
Ambala 1.84 1.40 1.40 0.07 803.00 0.11 
Bhiwani −0.58 −0.18 −0.09 0.56 −255.00 −0.04 
Charkhi Dadri 0.00 0.00 0.10 1.00 1.00 0.00 
Faridabad −2.83 −1.99 −1.87 0.00 −1,231.00 −0.18 
Fatehabad −0.59 −0.24 −0.18 0.56 −257.00 −0.04 
Gurgaon 0.78 0.37 0.48 0.43 341.00 0.05 
Hisar −1.13 −0.41 −0.34 0.26 −491.00 −0.07 
Jhajjar −0.23 −0.10 −0.02 0.82 −99.00 −0.01 
Jind −1.41 −0.57 −0.50 0.16 −613.00 −0.09 
Kaithal 0.45 0.18 0.21 0.65 197.00 0.03 
Karnal −1.77 −0.82 −0.77 0.08 −771.00 −0.11 
Kurukshetra 0.21 0.10 0.11 0.83 93.00 0.01 
Mahendragarh −0.64 −0.29 −0.19 0.52 −279.00 −0.04 
Mewat −1.54 −0.70 −0.64 0.12 −671.00 −0.10 
Palwal −2.13 −1.07 −0.99 0.03 −927.00 −0.13 
Panchkula −2.92 −2.60 −2.49 0.00 −1,273.00 −0.18 
Panipat −2.82 −1.65 −1.51 0.00 −1,229.00 −0.18 
Rewari 1.09 0.50 0.56 0.28 475.00 0.07 
Rohtak −0.16 −0.09 0.00 0.87 −71.00 −0.01 
Sirsa −0.89 −0.25 −0.19 0.38 −387.00 −0.06 
Sonipat −1.02 −0.44 −0.37 0.31 −445.00 −0.06 
Yamunanagar 0.40 0.32 0.30 0.69 175.00 0.02 
Haryana −0.64 −0.26 −0.19 0.52 −281.00 −0.04 
DistrictzSLP#SLPp-valueSτ
Ambala 1.84 1.40 1.40 0.07 803.00 0.11 
Bhiwani −0.58 −0.18 −0.09 0.56 −255.00 −0.04 
Charkhi Dadri 0.00 0.00 0.10 1.00 1.00 0.00 
Faridabad −2.83 −1.99 −1.87 0.00 −1,231.00 −0.18 
Fatehabad −0.59 −0.24 −0.18 0.56 −257.00 −0.04 
Gurgaon 0.78 0.37 0.48 0.43 341.00 0.05 
Hisar −1.13 −0.41 −0.34 0.26 −491.00 −0.07 
Jhajjar −0.23 −0.10 −0.02 0.82 −99.00 −0.01 
Jind −1.41 −0.57 −0.50 0.16 −613.00 −0.09 
Kaithal 0.45 0.18 0.21 0.65 197.00 0.03 
Karnal −1.77 −0.82 −0.77 0.08 −771.00 −0.11 
Kurukshetra 0.21 0.10 0.11 0.83 93.00 0.01 
Mahendragarh −0.64 −0.29 −0.19 0.52 −279.00 −0.04 
Mewat −1.54 −0.70 −0.64 0.12 −671.00 −0.10 
Palwal −2.13 −1.07 −0.99 0.03 −927.00 −0.13 
Panchkula −2.92 −2.60 −2.49 0.00 −1,273.00 −0.18 
Panipat −2.82 −1.65 −1.51 0.00 −1,229.00 −0.18 
Rewari 1.09 0.50 0.56 0.28 475.00 0.07 
Rohtak −0.16 −0.09 0.00 0.87 −71.00 −0.01 
Sirsa −0.89 −0.25 −0.19 0.38 −387.00 −0.06 
Sonipat −1.02 −0.44 −0.37 0.31 −445.00 −0.06 
Yamunanagar 0.40 0.32 0.30 0.69 175.00 0.02 
Haryana −0.64 −0.26 −0.19 0.52 −281.00 −0.04 

z, Z statistic after TFPW; SLP#, Sen's slope after TFPW; SLP, Sen's slope before TFPW; p-value, p-value after TFPW; S, Mann–Kendall S statistic; τ, Mann–Kendall's Tau.

Table 5

Results of TFPW-MK test of post-monsoon season precipitation from 1901 to 2020 in different districts of Haryana, India

DistrictzSLP#SLPp-valueSτ
Ambala −0.48 −0.04 −0.04 0.63 −209.00 −0.03 
Bhiwani −1.22 −0.03 −0.03 0.22 −531.00 −0.08 
Charkhi Dadri −0.92 −0.02 −0.01 0.36 −403.00 −0.06 
Faridabad −0.38 −0.01 −0.02 0.70 −167.00 −0.02 
Fatehabad −0.63 −0.02 −0.02 0.53 −277.00 −0.04 
Gurgaon −0.37 −0.02 −0.01 0.71 −163.00 −0.02 
Hisar −0.90 −0.03 −0.02 0.37 −393.00 −0.06 
Jhajjar −1.11 −0.03 −0.03 0.27 −485.00 −0.07 
Jind −1.02 −0.03 −0.02 0.31 −443.00 −0.06 
Kaithal −0.28 −0.01 0.00 0.78 −125.00 −0.02 
Karnal −0.74 −0.04 −0.04 0.46 −325.00 −0.05 
Kurukshetra −0.52 −0.03 −0.02 0.60 −229.00 −0.03 
Mahendragarh −1.21 −0.05 −0.02 0.23 −527.00 −0.08 
Mewat −1.45 −0.04 −0.04 0.15 −633.00 −0.09 
Palwal −0.48 −0.02 −0.03 0.63 −211.00 −0.03 
Panchkula −0.50 −0.05 −0.07 0.62 −217.00 −0.03 
Panipat −1.28 −0.08 −0.04 0.20 −559.00 −0.08 
Rewari −1.75 −0.07 −0.03 0.08 −763.00 −0.11 
Rohtak −0.69 −0.03 −0.03 0.49 −303.00 −0.04 
Sirsa −1.25 −0.03 −0.02 0.21 −547.00 −0.08 
Sonipat −1.43 −0.06 −0.05 0.15 −623.00 −0.09 
Yamunanagar −0.59 −0.04 −0.06 0.55 −259.00 −0.04 
Haryana −0.97 −0.05 −0.04 0.33 −423.00 −0.06 
DistrictzSLP#SLPp-valueSτ
Ambala −0.48 −0.04 −0.04 0.63 −209.00 −0.03 
Bhiwani −1.22 −0.03 −0.03 0.22 −531.00 −0.08 
Charkhi Dadri −0.92 −0.02 −0.01 0.36 −403.00 −0.06 
Faridabad −0.38 −0.01 −0.02 0.70 −167.00 −0.02 
Fatehabad −0.63 −0.02 −0.02 0.53 −277.00 −0.04 
Gurgaon −0.37 −0.02 −0.01 0.71 −163.00 −0.02 
Hisar −0.90 −0.03 −0.02 0.37 −393.00 −0.06 
Jhajjar −1.11 −0.03 −0.03 0.27 −485.00 −0.07 
Jind −1.02 −0.03 −0.02 0.31 −443.00 −0.06 
Kaithal −0.28 −0.01 0.00 0.78 −125.00 −0.02 
Karnal −0.74 −0.04 −0.04 0.46 −325.00 −0.05 
Kurukshetra −0.52 −0.03 −0.02 0.60 −229.00 −0.03 
Mahendragarh −1.21 −0.05 −0.02 0.23 −527.00 −0.08 
Mewat −1.45 −0.04 −0.04 0.15 −633.00 −0.09 
Palwal −0.48 −0.02 −0.03 0.63 −211.00 −0.03 
Panchkula −0.50 −0.05 −0.07 0.62 −217.00 −0.03 
Panipat −1.28 −0.08 −0.04 0.20 −559.00 −0.08 
Rewari −1.75 −0.07 −0.03 0.08 −763.00 −0.11 
Rohtak −0.69 −0.03 −0.03 0.49 −303.00 −0.04 
Sirsa −1.25 −0.03 −0.02 0.21 −547.00 −0.08 
Sonipat −1.43 −0.06 −0.05 0.15 −623.00 −0.09 
Yamunanagar −0.59 −0.04 −0.06 0.55 −259.00 −0.04 
Haryana −0.97 −0.05 −0.04 0.33 −423.00 −0.06 

z, Z statistic after TFPW; SLP#, Sen's slope after TFPW; SLP, Sen's slope before TFPW; p-value, p-value after TFPW; S, Mann–Kendall S statistic; τ, Mann–Kendall's Tau.

Figure 8

Slope of SLR trend in precipitation (mm per year) presented in (a), Sen's Slope (mm per year) after TFPW presented in (b), and p-value after TFPW presented in (c) from 1901 to 2020 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana.

Figure 8

Slope of SLR trend in precipitation (mm per year) presented in (a), Sen's Slope (mm per year) after TFPW presented in (b), and p-value after TFPW presented in (c) from 1901 to 2020 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana.

Close modal

Overall, all districts showed a decreasing trend in winters and post-monsoon precipitation. The monsoon season showed mixed behaviour, with some districts showing a rising trend, while others showing a falling precipitation pattern. However, all districts showed an upsurge in precipitation during pre-monsoon precipitation.

We compared the results of our study with many similar analyses performed by different researchers. The monthly, seasonal, and annual data series of 30 years (1989–2018) were examined by Guhathakurta et al. (2020) for all districts of Haryana where they found a significantly decreasing trend in Ambala, Bhiwani, Charkhi Dadri, Kaithal, Panchkula, and Panipat. Trend analysis performed by Nain & Hooda (2019) at a monthly scale for 27 rain gauge stations scattered in all districts of Haryana State using the datasets of IMD, Pune, for a period of 42 years (1970–2011) showed mixed trends for stations. Malik and Singh also (2019) explored precipitation pattern characteristics in Haryana region during (1997–2014) at daily and seasonal scales and found positive trends in monthly maximum and total precipitation. Anurag et al. (2018) performed monthly analysis of precipitation and reported variable results with somewhat increasing trends in HI and decreasing trends in SI in Haryana. Many other studies were conducted in different parts of India to detect trends in precipitation. Kumar et al. (2010) performed an analysis on monthly data series of 135 years (1871–2005) for 30 meteorological sub-divisions of India and found that pre-monsoon rainfall increased over 23 meteorological sub-divisions; monsoon rainfall increased over 10 sub-divisions; post-monsoon rainfall increased over 27 sub-divisions; and winter rainfall increased over 20 sub-divisions. Pre-monsoon, post-monsoon, and winter rainfall increased and monsoon rainfall decreased on an all-India basis. Kumar et al. (2017) analysed precipitation data of 115 years (1901–2015) rainfall for the five districts of south Gujarat using MK trend and found an increasing trend in Valsad, Dang, and Surat, while they found a declining trend in Navsari and Bharuch in annual and monsoon rainfall. Prabhakar et al. (2018) observed a decreasing trend in annual and monsoon precipitation during 1901–2013 in most of the districts of Odisha state.

Precipitation projections

In order to determine the behaviour of precipitation in future, two sets of climate projections were assessed using three different RCPs, one focusing on the near term (2021–2060) as QDP1 and the other extending beyond 2061 up to 2100 as QDP2 across Haryana. The correlation analysis performed between historical data of GFDL-ESM2M and IMD gridded data of precipitation for a period of 105 years (1901–2005) shown in Figure 9 depicts a positive correlation ranging from 0 to 0.4; however, negative biases were observed in the historical data across different seasons. The magnitude of negative biases was observed to be minimum during the monsoon season, followed by pre-monsoon and post-monsoon seasons, while the magnitude was observed to be maximum during the winter season.

Figure 9

Correlation presented in (a) and percent biases presented in (b) between GFDL-ESM2M historical precipitation and IMD gridded precipitation for a time period of 1901–2005 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in Haryana.

Figure 9

Correlation presented in (a) and percent biases presented in (b) between GFDL-ESM2M historical precipitation and IMD gridded precipitation for a time period of 1901–2005 during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in Haryana.

Close modal

The spatial distribution of seasonal precipitation against the historical mean precipitation of GFDL-ESM2M is presented in Figures 10 and 11, while Figure 12 illustrates the temporal fluctuations in the precipitation of Haryana for different RCP scenarios. PDP in the case of RCPs was calculated as per Equation (1) for both QDPs, but the historical mean seasonal precipitation of 105 (1901–2005) years was taken in place of LPA120 for the respective RCPs. Figure 10 shows the PDP in different RCP scenarios across various seasons in QDP1 (2021–2060). During the winter season, a positive PDP was observed in all scenarios, which is suggestive of an increase in precipitation during QDP1, except RCP8.5, where a negative PDP was observed in north-eastern Haryana. During the pre-monsoon season, a positive PDP was observed for RCP2.6, whereas a mixed behaviour of rise and fall in PDP was observed for RCP4.5 and RCP8.5 across the study area. During the monsoon season, overall, a negative PDP was observed in all scenarios, suggesting a decrease in the amount of precipitation in QDP1; however, the magnitude of decrease in the amount of precipitation is higher for RCP4.5, followed by RCP4.5 and RCP2.5, respectively. During the post-monsoon season, a decrease in PDP was observed for RCP2.6, and it was positive for RCP4.5, whereas RCP8.5 showed a mixed behaviour. Figure 11 shows the PDP in different RCP scenarios across various seasons in QDP2 (2061–2100). During the winter season, a positive PDP was observed in all scenarios, which is suggestive of an increase in precipitation during QDP2, except for RCP2.6, where a slightly negative PDP was observed in the eastern part of the study area. During the pre-monsoon season, a positive PDP was observed for RCP4.5, whereas a negative PDP was observed for RCP2.6 and RCP8.5 across most of the regions of Haryana. During the monsoon season, overall, a negative PDP was observed in all scenarios, suggesting a decrease in the amount of precipitation in QDP2. During the post-monsoon season, a mixed behaviour in PDP was observed for RCP2.6, and a decreasing PDP was observed for RCP4.5, while it was positive for RCP8.5, indicating an increase in precipitation during QDP2.

Figure 10

Long-term distribution of PDP (%) from a historical mean precipitation of 105 years (1901–2005) of GFDL-M2M for (a) RCP2.6, (b) RCP4.5, and (c) RCP8.5 in QDP1 (1921–1960) during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in Haryana.

Figure 10

Long-term distribution of PDP (%) from a historical mean precipitation of 105 years (1901–2005) of GFDL-M2M for (a) RCP2.6, (b) RCP4.5, and (c) RCP8.5 in QDP1 (1921–1960) during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in Haryana.

Close modal
Figure 11

Long-term distribution of PDP (%) from a historical mean precipitation of 105 years (1901–2005) of GFDL-M2M for (a) RCP2.6, (b) RCP4.5, and (c) RCP8.5 in QDP2 (1961–2100) during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana.

Figure 11

Long-term distribution of PDP (%) from a historical mean precipitation of 105 years (1901–2005) of GFDL-M2M for (a) RCP2.6, (b) RCP4.5, and (c) RCP8.5 in QDP2 (1961–2100) during the winter season (JF; first row), pre-monsoon season (MAM; second row), monsoon season (JJAS; third row), and post-monsoon season (OND; fourth row) in the districts of Haryana.

Close modal
Figure 12

Trends in historical data (1950–2005) and projected precipitation (2006–2100) with time presented in (a) the winter season (JF), (b) pre-monsoon season (MAM), (c) monsoon season (JJAS), and (d) post-monsoon season (OND) in Haryana. Trends in historical data and RCP2.6, RCP4.5 and RCP8.5 scenarios are shown by the black, green, blue, and red lines, respectively. The historical precipitation data of 1901–1950 were used as baseline data for future precipitation projections. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2022.005.

Figure 12

Trends in historical data (1950–2005) and projected precipitation (2006–2100) with time presented in (a) the winter season (JF), (b) pre-monsoon season (MAM), (c) monsoon season (JJAS), and (d) post-monsoon season (OND) in Haryana. Trends in historical data and RCP2.6, RCP4.5 and RCP8.5 scenarios are shown by the black, green, blue, and red lines, respectively. The historical precipitation data of 1901–1950 were used as baseline data for future precipitation projections. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2022.005.

Close modal

Figure 12 shows the variation in projected precipitation over time. The historical precipitation data of 1901–1950 were used as baseline data for these future precipitation projections. During the winter season, the precipitation is projected to be higher than the baseline precipitation, but a declining trend is expected in all RCP scenarios by the end of QDP1. The precipitation is expected to increase during winters by the end of the 21st century under RCP4.5 scenario by around 10 mm. During the pre-monsoon season, fluctuating precipitation trends are anticipated under RCP2.6 scenario in QDP1, with an increase in precipitation by nearly 7 mm above baseline from 2030 to 2040, followed by a decline of up to 3 mm per season till 2050, whereas a gradual decline in precipitation is expected throughout QDP2. A declining trend in precipitation is expected under the RCP4.5 scenario throughout QDP1 and the first decade of QDP2, thereafter, an increase of nearly 4 mm from baseline is expected during the pre-monsoon season by the 2080s, followed by a decline in precipitation by 1 mm by the end of the 21st century. The pre-monsoon precipitation is expected to decline by the year 2035; thereafter, an increase of around 5 mm is likely to happen by the end of QDP1, followed by a gradual decline in precipitation till the end of QDP2 under the RCP8.5 scenario. The monsoon precipitation is projected to reveal a systematic change over most of Haryana, whose magnitude of gradual decline increases with time by 150–200 mm from baseline by the end of the 21st century. The post-monsoon precipitation has shown an inconsistent shift under RCP2.6 and RCP4.5 scenarios during both QDPs, but a gradual increase in precipitation by 30 mm above baseline is expected under RCP8.5 scenario by the year 2100.

Overall, winter precipitation is expected to increase under the RCP4.5 scenario, while pre-monsoon precipitation is expected to decrease under the RCP8.5 scenario by the end of the 21st century. The monsoon precipitation is expected to decrease under all RCP scenarios, while post-monsoon precipitation is expected to gradually increase under the RCP8.5 scenario by the end of the 21st century. Chaturvedi et al. (2012) projected all-India precipitation to increase by 6, 10, and 14% under the scenarios RCP2.6, RCP4.5, and RCP8.5, respectively, by the 2080s’ relative to the 1961–1990 base, while much larger variability was seen in the spatial distribution of precipitation. Duan et al. (2017) predicted a decreasing trend in average annual precipitation by 5.78, 8.08, and 10.18%; and 12.89, 17.92, and 11.23% in the future 2030s, 2060s, and 2090 for A2a and B2a emission scenarios in the upper Ishikari River basin area. Dash et al. (2015) also projected the weakening of monsoon precipitation over most of the Indian land mass and the northern and equatorial Indian Ocean. They projected a decrease in monsoon precipitation over the central, eastern, and peninsular India by the end of the 21st century in the range of 15–25% and 30–40% from their mean reference period values under the RCP4.5 and RCP8.5 scenarios, respectively.

In the present study, the dynamics of seasonal precipitation time series data of 120 years (1901–2020) for 22 districts of Haryana, India, were analysed using the spatio-temporal patterns of mean precipitation, PDP, CV, standardized anomaly, moving average, precipitation categorization, precipitation trend, correlation, and percent bias analysis. Districts lying in eastern Haryana received a higher precipitation in each season than the ones lying in the western region. Precipitation during the pre-monsoon season showed a positive precipitation deviation, while it was negative for all other seasons during QDT3, which is indicative of decreasing precipitation at most of the districts of Haryana during the winter, monsoon and post-monsoon seasons in recent times. Among different precipitation categories, normal precipitation events were observed to be relatively high during the summer monsoon season, whereas it was the lowest during the post-monsoon season. The magnitude of SLR depicts all districts during the winter and post-monsoon season, while most of the districts during the summer monsoon season showed a decrease in precipitation. However, all districts showed an escalation in precipitation during the pre-monsoon season over the study period. Similarly, TFPW-MK analysis showed a statistically significant decreasing trend in Mewat during the winter season and in Panchkula, Faridabad, Panipat, and Palwal during the monsoon season. Ambala, Mahendragarh, Panchkula, Yamunanagar, Charkhi Dadri, Rewari, Kurukshetra, and Rohtak showed a statistically significant decreasing trend during the pre-monsoon season. The presence of these trends depicts the impact of climate change on precipitation. The winter precipitation is expected to increase under the RCP4.5 scenario, while pre-monsoon precipitation is expected to decrease under the RCP8.5 scenario by the end of the 21st century. Monsoon precipitation is expected to decrease under all RCP scenarios, while post-monsoon precipitation is expected to gradually increase under the RCP8.5 scenario by the end of the 21st century. Our results have quantified the qualitative and quantitative aspects of precipitation dynamics for seasonal precipitation in the districts of Haryana. Such analysis, along with the spatio-temporal maps, would be useful for planning a well-organized use of water resources and also for district-level water management in a sustainable manner considering the impact of climate change on the changing precipitation pattern of Haryana. Agricultural or other socio-economic activities can also be managed by taking into account the precipitation dynamics and projections discussed in this paper. Further, the scope of such analysis can also be replicated and expanded to understand the regional precipitation dynamics in other states of India.

The author(s) would like to thank the India Meteorological Department (IMD), Pune, for providing the daily precipitation time series data for this study.

The corresponding author is thankful to CCS Haryana Agricultural University for providing scholarship and the necessary facilities during the course of research.

A.S.C. was involved in preparing the methodology, investigation, data analysis, preparing figures, and writing the original draft. R.K.S.M. performed GFDL climatological data analysis for future projections. A.R., A.D. and S.S. were involved in conceptualization, preparing the methodology, visualization, writing the review, and editing.

The gridded precipitation data were extracted, and the figures were plotted using python libraries like Pandas, Matplotlib, and NumPy. GFDL data analysis was done using Grads, and spatial plotting was done using ArcGIS. The codes for conducting trend analysis were derived from R packages ‘trend’ (https://CRAN.R-project.org/package=trend) and ‘modifiedmk’ (https://CRAN.R-project.org/package=modifiedmk).

The authors declare that they have no conflict of interest.

All authors give consent for the publication of identifiable details, which include figures within the text to be published in this manuscript.

All relevant data are included in the paper or its Supplementary Information.

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