In recent years, the Subarnarekha River basin has frequently experienced harsh weather and has also observed extreme floods during monsoons and a shortage of water during lean periods. Hence, in this study, the data of extreme values of rainfall and maximum–minimum temperatures from the years 1990 to 2020 in the Subarnarekha River basin have been analysed. The trend analysis of annual extreme rainfall, maximum–minimum temperatures, extreme diurnal temperature range, and number of rainy days have been determined using the Mann–Kendall test and Sen's slope estimator. Further, the analysis has also been carried out to study the shifting of the onset date of monsoon. The results show that extreme rainfall showed a significant increasing trend at a 95% level of confidence in the upper and middle parts of the basin, whereas it had a decreasing trend in the lower part. Annual extreme maximum temperature indicated a decreasing trend of 0.03 and 0.08 °C/year in the upper and middle parts, respectively, and the decrease in extreme minimum temperature in the lower part was 0.03 °C/year. No significant variations in the number of rainy days have been observed; however, a major shift in the onset date of the monsoon was observed during the selected time-period.

The significance of this research is as follows:

  • Analyse the total number of rainy days in the monsoon season and in a year.

  • Shifting of date of onset of monsoon.

  • Significance of trend in the river basins.

  • Effect of climate change on rainfall and temperature.

  • Extreme values of rainfall and temperature.

In the 21st century, the availability of water resources is thought to be significantly influenced by climate change. Climate change has resulted in extreme hydro-climatic phenomena – heavy rainfall, floods, droughts, and extreme maximum and minimum temperatures, which have a negative impact on local people and their socioeconomic environment. Future extremes may be affected by natural variability and anthropogenic changes (IPCC 2012; Abbas et al. 2023a, 2023b). In recent years, it has been observed that the frequency and intensity of extreme rainfall and extreme maximum–minimum temperature events have increased with substantial impacts on society, the economy, and the environment (UN & ESCWA 2017). It is important to know how, and by how much, the frequency and intensity of these extreme occurrences may change in the future for the predicted increase in the concentration of atmospheric greenhouse gases (Tramblay et al. 2014). At a global scale, temperature increases, which changes the rainfall patterns and ultimately impacts the hydrological cycle and human activities involving water. This would have an impact on all facets of the world's water resources. Due to the high reliance of the majority of the population on climate-sensitive sectors like agriculture and forestry, inadequate facilities, and a lack of financial resources, South Asia in general and India in particular are considered vulnerable to climate change and its adverse socioeconomic effects (Gosain et al. 2006; Hao et al. 2008; Arnell & Gosling 2013; Chang et al. 2015; Abbas et al. 2021). The ability to reduce the impacts of climate change is further subjected to regional or local environment change such as changes in land use pattern, deforestation etc. Various studies have shown how extreme climate has impacted different facets of life (Andersson et al. 2011; Narsimlu et al. 2013; Kumar et al. 2014; Amoussou et al. 2020; Naqi et al. 2021).

Various researchers have studied how the climate varies and its trends in different parts of the world (Adib et al. 2017a, 2017b; Almeida et al. 2017; Singh & Singh 2017; Kumar et al. 2018, 2020; Abbas et al. 2022; Ranjan & Singh 2022, 2023). Many studies have also found the trend of extreme rainfall over the world: some of them found increasing trends of extreme rainfall (Easterling et al. 2000; Kunkel 2003; Yaduvanshi et al. 2018; Rao & Bardhan 2020; Abbas et al. 2023b) and some of them found decreasing trends (Manton et al. 2001; Linnenluecke et al. 2011). In India, where most of the economy depends on agriculture, i.e., directly or indirectly on rainfall (Kumar et al. 2010), many researchers have studied to find out how, and by how much, annual and seasonal rainfall changes over time in India. The long-term trend study of Indian monsoon rainfall showed that there is no clear trend of rainfall over the country as a whole, but in small areas, there is a trend of rainfall changes over space and time (Mooley & Parthasarathy 1984; Kumar et al. 1992). The extreme rainfall trend is increasing in the areas where substantial rise in moisture transport has been observed (Bhaskaran et al. 1995). Analysis of annual extreme rainfall in the hilly terrain of Kerala State of south India showed decreasing trends (Soman et al. 1988). Furthermore, some stations in the east of Western Ghats have a significantly increasing trend of extreme rainfall, whereas the southern peninsula and lower Ganga valley show a decreasing trend (Rakhecha & Soman 1994).

Several researchers have studied the spatial and temporal variations of temperature on a large scale (Jones et al. 1999; Christy et al. 2009). Also, studies on long-term annual, mean annual, and seasonal maximum and minimum temperatures in India showed that the maximum temperature is increasing, while the minimum temperature is decreasing (Yadav et al. 2004; Roy & Balling 2005). It is evident from previous studies that the extreme values of rainfall and temperature vary on the regional basin scale as well (Imbulana et al. 2018; Adeyeri et al. 2019; Nontikansak et al. 2022). Hence, this study focuses on the Subarnarekha River basin (SRB) in India, which is a rain-fed river in the state of Jharkhand in eastern India. This basin requires efficient water management for sustainable agriculture and the reduction of natural disasters like floods and droughts (Singh & Giri 2018). In recent years, the study area has experienced an increase in the frequency of harsh weather and has faced a shortage of both surface water and groundwater for domestic, industrial, and agricultural purposes. Extreme values of meteorological data are responsible for the floods and droughts in the SRB, and it varies from year to year. Thus, it is paramount to analyse the extreme values of the rainfall and maximum–minimum temperatures in the current climate-change scenarios, which may lead to floods or drought (Mandal et al. 2021). The main objective of this study is to analyse the trends of extreme values of the rainfall and maximum–minimum temperatures including the number of rainy days in a year. Furthermore, the shifting of the onset date of monsoon in the SRB has been studied for sustainable water resources management.

Since there are many natural disasters in this region and an acute shortage of water for agriculture, industry, and domestic use, the study is crucial for identifying extreme hydro-meteorological factors in climate-change scenarios, such as extreme rainfall and temperature variations. This, in turn, helps in efficient water management for sustainable agriculture and the reduction of natural disasters like floods and droughts.

Study area and data used

The study area, the SRB, is shown in Figure 1, which originates at Nagri village in Ranchi District of Jharkhand State at a latitude of 23°18′N and longitude of 85°11′E at an elevation of 740 m from mean sea-level. The total length of Subarnarekha River is 395 km, of which it flows for 269 km in the state of Jharkhand and 64 km in West Bengal, and the remaining 62 km flows in Orissa State and then empties into the Bay of Bengal in Balasore District of Orissa. The total catchment area is 19,296 km2 (Singh & Giri 2018). The state-wise proportion of the catchment area is presented in Table 1. The maximum percentage of the area (68.4%) is in Jharkhand with 15.5% in the state of West Bengal and the remaining in Orissa. This river is very important for the people of these three states as it satisfies the water demand for irrigation, industries, and domestic use. Rapid urbanization, deforestation, mineral extraction, industry, and agricultural development are increasing day-by-day and pose pressure on the water resources of the basin, which necessitates the critical need for optimal/efficient management of water resources in this interstate river basin under the altered climatic situation.
Table 1

State-wise proportion of the catchment area

S. No.Name of the stateCatchment area (km2)Percentage areaLength of river (km)
Jharkhand 13,193 68.4 269 
West Bengal 2,989 15.5 64 
Orissa 3,114 16.1 62 
Total  19,296 100 395 
S. No.Name of the stateCatchment area (km2)Percentage areaLength of river (km)
Jharkhand 13,193 68.4 269 
West Bengal 2,989 15.5 64 
Orissa 3,114 16.1 62 
Total  19,296 100 395 
Figure 1

Location map of Subarnarekha River basin.

Figure 1

Location map of Subarnarekha River basin.

Close modal

The Subarnarekha River in Peninsular India is fed mostly by rainwater. About 80%–90% of the annual rainfall occurs during monsoon season (June to October). The average annual rainfall of the basin is 1,562 mm. Extreme value is very important as it occurs in the catchment and gives flood to the downstream population. In the upper and middle sections of the river, the flow is almost non-existent during the dry season. The river basin has scorching summers and mild winters typical of the tropics. The hottest month is May, with an average temperature of 40.5 °C, while the coldest is December, with an average temperature of 9 °C. The average extreme annual maximum and minimum temperatures are 41.52 and 7.51 °C, respectively.

Rainfall data for the years 1990–2020 at three rain-gauge stations (Ranchi, Jamshedpur, and Digha) were collected from the India Meteorological Department (IMD) Pune, where IMD Pune is the central agency which provides data for research purposes (https://dsp.imdpune.gov.in/). The temperature data for the same period were downloaded from the NASA Data Power Station (https://power.larc.nasa.gov/). The annual and seasonal extreme values of rainfall and maximum–minimum temperatures have been taken for this study.

Methodology

Three rain-gauge stations, namely, Ranchi, Jamshedpur, and Digha, have been selected in the SRB, which represent the upper, middle, and lower regions of the basin, respectively. Rainfall and temperature data have been analysed for these regions during the selected time-period. A homogeneity check has been conducted by the Pettitt test for observed monthly rainfall and temperatures which makes it possible to determine if a series may be considered as homogeneous over time, or if there is a time at which a change occurs. Stationarity has been tested by conducting Dickey–Fuller tests, which provide the common assumption of whether the time series data are stationary or not. Trend analysis has been carried out using the Mann–Kendall (MK) test (Mann 1945; Kendall 1975) and Sen's slope estimator for the extreme values of rainfall and temperatures along with the number of rainy days and diurnal temperature range (DTR). The MK test is used to statistically determine if there is a monotonic upward or downward trend of the variable of interest over time and Sen's slope estimator has been used to perform the task of verifying the statistical linear relationship (Agarwal et al. 2021). The long-term temporal data's trend magnitude is determined using it (Agarwal et al. 2021). These tests have been performed for the observed data of rainfall and maximum–minimum temperatures.

Pettitt test

The Pettitt test is a non-parametric test that does not make any assumptions about how the data are spread out. It is an adaption of the rank-based Mann–Whitney test that identifies the shift's onset time (Pettitt 1979). In this test, the null hypothesis (Ho) is that the series has a unit root and the alternative theory (Ha) is that there is no unit root for the series, which indicates that the series is stationary. The statistic is computed using the rankings r1rn of the Y1Yn (Pettitt 1979):
formula
(1)
If a break occurs in year k, then the statistic is maximal or minimal near the year k:
formula
(2)
where Pk is Pettitt's statistic. If the computed p-value is greater than the significance level, α = 0.05, one cannot reject the null hypothesis Ho, i.e., the series is homogeneous, and if the computed p-value is less than the significance level, α = 0.05, one can reject the null hypothesis Ho and accept the alternative hypothesis Ha, i.e., the series is non-homogeneous.

Augmented Dickey–Fuller test

The equation of the enhanced Dickey–Fuller test to test for stationarity is as follows (Cheung & Lai 1995; Dickey 2011):
formula
(3)
where α and β are constants, yt is the time series being examined for unit root, γ is the coefficient of time trend, t is the time trend, μt is a white-noise error term, and Δ is the difference operator. Based on the parameter's regression t ratio, the test determines whether it is negative. The asymptotic distribution of the statistics was discovered by Dickey and Fuller. The augmented Dickey–Fuller (ADF) calculates the t value of the coefficient, which corresponds to the τ (tau) statistic.

The null hypothesis is rejected (if the computed p-value is lower than the significance level, α = 0.05) if the calculated absolute value of the tau statistic (τ) is smaller than the critical tau values; in this instance, the time series is stationary. In contrast, the null hypothesis is not rejected, if the computed p-value is greater than the significance level, α = 0.05. If the estimated tau statistic is greater than the critical tau value, then the time series is considered nonstationary.

In the present study, the widely adopted non-parametric modified MK test was also used to identify monotonic patterns in various variables. The modified MK test was performed on the data series following autocorrelation pre-whitening. The test was used to determine the trends in the yearly total rainfall data, the extreme daily rainfall in a year, the number of wet days, and maximum and minimum temperatures. In addition, the average rainfall per rainy day and the ratio of maximum rainfall to average rainfall per rainy day were tested. The average rainfall has been determined based on the number of wet days, which is indicative of the intensity of rainfall. Trends have also been determined in monthly statistics for total monthly rainfall, the extreme daily rainfall in a month, and the number of wet days in a month.

Before implementing the modified MK test, it is necessary to confirm that the data series do not exhibit any substantial autocorrelation. The majority of hydro-meteorological time series may, however, exhibit substantial autocorrelation. Even if the null hypothesis of no trend in the time series should not be rejected, the existence of considerable autocorrelation in time series generates falsely significant trends (Dinpashoh et al. 2011). Pre-whitening has been used in the presence of autocorrelation to identify trends in time series (Bayazit & Önöz 2009). The introduction of pre-whitening procedures to eliminate autocorrelation effects may potentially skew the MK test outcome.

For pre-whitening, serial correlation testing is performed on the dataset. If the lag-1 autocorrelation (r1) is non-significant at the 95% confidence level, then the MK test is performed on the original data series (x1, x2………xn). Otherwise, the MK test is applied to the ‘pre-whitened’ series produced as (x2r1x1, x3r1x2,……, xnr1xn–1) (Zhang et al. 2000; Yue et al. 2002):
formula
(4)

Mann–Kendall test

The MK test is one of the most often-used non-parametric tests for finding trends in hydro-meteorological time series. The test examines the relative magnitudes of sample data instead of the actual data values. A feature of this test is that the data do not need to adhere to a certain distribution. The data values are assessed as a time-ordered series. Each data value is compared with all the following data values.

Let x1, x2xn represent n data points, where xj represents the data point at j. The MK statistics (S) are given by:
formula
(5)
where n is the length of the dataset, xj and xk are two generic sequential data values, and function sign (xj − xk) assumes the following values:
formula
(6)

The value of S > 0 means an increasing trend and S < 0 means a decreasing trend.

The S statistic therefore represents the number of positive differences minus the number of negative differences found in the analysed time-series. Under the null of that, there is no trend in the data, there is no correlation between the considered variable and time, and each ordering of the dataset is equally likely. Under this hypothesis, the statistic S is normally distributed with the mean E(S) and the variance Var(S) as follows:
formula
(7)
formula
(8)
where n is the length of the time series, tp is the number of ties for the pth value, and q is the number of tied values, i.e., equal values. The second term represents an adjustment for tied or censored data. The standardized test statistic Z is given by the following equation.
Computation of normalized test statistic Z:
formula
(9)

The presence of a statistically significant trend is evaluated using the Z value. This statistic is used to test the null hypothesis such that no trend exists. A positive Z indicates an increasing trend in the time series, while a negative Z indicates a decreasing trend. To test for either increasing or decreasing monotonic trend at p significance level, the null hypothesis is rejected if the absolute value of Z is greater than Z(1−p/2), where Z(1−p/2) is obtained from the standard normal cumulative distribution tables. In this work, the significance level of 0.05 was applied, and the significant level p-value was obtained for each analysed time-series. If P < 0.05, then there is a statistically significant trend, and if P > 0.05, then there is a statistically insignificant trend.

Sen's slope estimator

The magnitude of the trend in a time series can be determined using a non-parametric method known as Sen's estimator. This method assumes a linear trend in the time series, and the slope (Ti) of all data pairs is computed as follows:
formula
(10)
where Xj and Xk are data values at times j and k (j > k), respectively. The median of these N values of Ti gives Sen's estimator of slope (β). A positive value of β indicates an increasing trend, and a negative value indicates a decreasing trend in the time series (Mondal et al. 2015). The levels of significance of Sen's slope estimator and the MK test are equivalent.

Shifting of monsoon

A total number of rainy days (daily rainfall >2.5 mm) in the year and in the monsoon has been computed from daily rainfall data from the years 1990 to 2020.

The date of onset of monsoon in Jharkhand is 31 May. The actual date of onset of monsoon has been computed for all the years and at all stations from 1990 to 2020, and based on these data, an analysis of monsoon shifts has been computed.

Diurnal temperature range

The DTR (difference of daily maximum and minimum temperature) is an important meteorological indicator associated with global climate change, which describes within-day temperature variability. Its variations can have significant impacts on many sectors like public health, agricultural productivity, and the carbon cycle in terrestrial ecosystems (Adekanmbi & Sizmur 2022; Shelton et al. 2022). In this study, daily DTR values have been computed using daily maximum and minimum temperatures, and the extreme annual value has been selected for the analysis.

Homogeneity and stationarity tests

The homogeneity and stationarity tests for monthly rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) at all three stations in the SRB were tested by the Pettitt's and Dickey–Fuller tests, respectively, and results are presented in Table 2 for Ranchi, Jamshedpur, and Digha gauging stations. It was found that the acceptance of the null hypothesis for rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) data varies from 95% to 98%, 21% to 70%, and 55 to 88%, respectively, which shows that all data were homogeneous. The stationarity test shows that the computed p-value at all the stations is less than 0.0001, which is lower than the significance level, α = 0.05. It also shows the null hypothesis is rejected, and thus, all data were found to be stationary.

Table 2

Results of homogeneity and stationarity tests of rainfall, Tmax and Tmin data at Ranchi, Jamshedpur, and Digha

StationParametersPettitt test
ADF
Pkp-value (two-tailed)TauTau (critical)p-value (one-tailed)
Ranchi Rainfall 2,010 0.958 −16.79 −0.85 <0.0001 
Tmax 1,460 0.692 −18.23 −3.40 <0.0001 
Tmin 1,445 0.816 −21.38 −3.40 <0.0001 
Jamshedpur Rainfall 1,828 0.980 −13.51 −0.85 <0.0001 
Tmax 1,766 0.716 −18.95 −3.40 <0.0001 
Tmin 1,531 0.883 −22.50 −3.40 <0.0001 
Digha Rainfall 1,902 0.974 −12.68 −0.85 <0.0001 
Tmax 2,299 0.214 −22.04 −3.40 <0.0001 
Tmin 2,769 0.558 −23.04 −3.40 <0.0001 
StationParametersPettitt test
ADF
Pkp-value (two-tailed)TauTau (critical)p-value (one-tailed)
Ranchi Rainfall 2,010 0.958 −16.79 −0.85 <0.0001 
Tmax 1,460 0.692 −18.23 −3.40 <0.0001 
Tmin 1,445 0.816 −21.38 −3.40 <0.0001 
Jamshedpur Rainfall 1,828 0.980 −13.51 −0.85 <0.0001 
Tmax 1,766 0.716 −18.95 −3.40 <0.0001 
Tmin 1,531 0.883 −22.50 −3.40 <0.0001 
Digha Rainfall 1,902 0.974 −12.68 −0.85 <0.0001 
Tmax 2,299 0.214 −22.04 −3.40 <0.0001 
Tmin 2,769 0.558 −23.04 −3.40 <0.0001 

Trend of annual and seasonal extreme rainfall and extreme temperatures

Trend analysis was carried out for three rain-gauge stations for extreme values of annual and seasonal rainfall and maximum–minimum temperatures from the years 1990 and 2020 using MK tests and Sen's slope estimator.

Annual extreme data

Figure 2 shows the variation of annual extreme rainfall data at (a) Ranchi, (b) Jamshedpur, and (c) Digha rain-gauge stations. The trend of extreme rainfall was increasing at Ranchi and Jamshedpur, whereas it was decreasing at Digha. The maximum and minimum extreme rainfall at Ranchi, Jamshedpur, and Digha were in the years 1994 and 2005, 2008 and 1998, 1996 and 2020, respectively.
Figure 2

Time series of annual extreme rainfall with the graphical representation of trend at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Figure 2

Time series of annual extreme rainfall with the graphical representation of trend at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Close modal
Figure 3 shows the variation of extreme values of annual maximum and minimum temperatures at (a) Ranchi, (b) Jamshedpur, and (c) Digha. The time series of the extreme values of maximum and minimum temperatures show a slight decreasing trend at all three gauging stations.
Figure 3

Time series of annual extreme maximum and minimum temperatures with graphical representation of trend at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Figure 3

Time series of annual extreme maximum and minimum temperatures with graphical representation of trend at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Close modal

Trend analysis of annual extreme values of rainfall and maximum and minimum temperatures have also been performed using the MK test and Sen's slope estimator for all three rain-gauge stations, and results are presented in Table 3, which shows similar trends as indicated in Figures 2 and 3, i.e., trend of extreme rainfall was increasing from 7.7% to 12.9% and 15.6% to 58.8% in the upper and middle regions, respectively, whereas it was decreasing from 51.1% to 17.8% in the lower region, but it was not significant in annual extreme rainfall at any station during 1990–2020. Mandal et al. (2021) analysed the trend in annual rainfall data in the Subarnarekha basin for the period from 1993 to 2012. A similar significant increasing trend of 14%–36% annual rainfall was observed for the study period. The annual extreme maximum temperatures of the basin indicate a significant decreasing trend of 0.03 °C/year and 0.08 °C/year in the upper and middle regions, respectively, and extreme minimum temperature shows a significant decreasing trend of 0.03 °C/year in the lower region.

Table 3

Results of the Mann–Kendall test and estimated Sen's slope for trends of annual extreme data of rainfall and maximum–minimum temperatures at all stations (1990–2020)

StationMK test P-value
Sen's slope
Trend
RainfallTmaxTminRainfallTmaxTminRainfallTmaxTmin
Ranchi 0.97 0.03 0.07 0.01 −0.03 −0.05 I(Ins) D(Sig) D(Ins) 
Jamshedpur 0.22 0.01 0.14 0.9 −0.08 −0.03 I(Ins) D(Sig) D(Ins) 
Digha 0.23 0.88 0.04 −1.68 −0.01 −0.03 D(Ins) D(Ins) D(Sig) 
StationMK test P-value
Sen's slope
Trend
RainfallTmaxTminRainfallTmaxTminRainfallTmaxTmin
Ranchi 0.97 0.03 0.07 0.01 −0.03 −0.05 I(Ins) D(Sig) D(Ins) 
Jamshedpur 0.22 0.01 0.14 0.9 −0.08 −0.03 I(Ins) D(Sig) D(Ins) 
Digha 0.23 0.88 0.04 −1.68 −0.01 −0.03 D(Ins) D(Ins) D(Sig) 

Note: Ins, insignificant; Sig, significant; I, increasing; D, decreasing.

Seasonal extreme data

Seasonal variations of extreme rainfall and maximum–minimum temperatures have also been analysed for all the seasons – winter (December–February), pre-monsoon (March–May), monsoon (June–September), and post-monsoon (October–November) at all the stations.

Variations of seasonal extreme rainfall for all the seasons are presented in Figures 46 for the upper (Ranchi), middle (Jamshedpur), and lower (Digha) regions in graphical forms. It shows that the trends of seasonal extreme rainfall in all the seasons and at all the stations are increasing, except at Digha in the monsoon season, where it is decreasing.
Figure 4

Variation of extreme rainfall with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Ranchi.

Figure 4

Variation of extreme rainfall with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Ranchi.

Close modal
Figure 5

Variation of extreme rainfall with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Jamshedpur.

Figure 5

Variation of extreme rainfall with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Jamshedpur.

Close modal
Figure 6

Variations of extreme rainfall with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Digha.

Figure 6

Variations of extreme rainfall with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Digha.

Close modal
Figures 79 show the variation of seasonal extreme maximum and minimum temperatures for all the seasons and at all the stations. Variation of the seasonal extreme values of maximum and minimum temperatures does not show a clear trend, but the trends in all the seasons and at all the stations are more or less decreasing, except in post-monsoon season, which shows an increasing trend at all three rain-gauge stations.
Figure 7

Variation of seasonal extreme of Tmax and Tmin with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Ranchi.

Figure 7

Variation of seasonal extreme of Tmax and Tmin with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Ranchi.

Close modal
Figure 8

Variation of seasonal extreme of Tmax and Tmin with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Jamshedpur.

Figure 8

Variation of seasonal extreme of Tmax and Tmin with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Jamshedpur.

Close modal
Figure 9

Variation of seasonal extreme of Tmax and Tmin with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Digha.

Figure 9

Variation of seasonal extreme of Tmax and Tmin with the graphical representation of trend in (a) winter, (b) pre-monsoon, (c) monsoon, and (d) post-monsoon seasons at Digha.

Close modal

Trend analysis of seasonal extreme values of rainfall and maximum–minimum temperatures have also been performed using the MK test and Sen slope estimator for all the seasons and at all three rain gauge stations, and results are presented in Table 4.

Table 4

Result of trend analysis for seasonal extreme rainfall, maximum–minimum temperatures at Ranchi, Jamshedpur, and Digha

StationSeasonP-value
Sen's slope
Trend
RainfallTmaxTminRainfallTmaxTminRainfallTmaxTmin
Ranchi 0.54 0.50 0.06 0.114 −0.030 −0.058 I(Ins) D(Ins) D(Ins) 
Pr-M 0.01 0.04 0.52 0.647 −0.039 −0.024 I(Sig) D(Sig) D(Ins) 
0.86 0.97 1.00 −0.120 0.003 0.000 D(Ins) I(Ins) I(Ins) 
Po-M 0.25 0.13 0.87 0.606 0.023 0.008 I(Ins) I(Ins) I(Ins) 
Jamshedpur 0.73 0.49 0.13 0.082 −0.025 −0.034 I(Ins) D(Ins) D(Ins) 
Pr-M 0.99 0.00 0.52 0.029 −0.085 −0.028 I(Ins) D(Sig) D(Ins) 
0.59 0.85 0.89 0.508 −0.010 −0.002 I(Ins) D(Ins) D(Ins) 
Po-M 0.97 0.10 0.28 0.013 0.022 0.034 I(Ins) I(Ins) I(Ins) 
Digha 0.75 0.56 0.04 0.103 −0.014 −0.035 I(Ins) D(Ins) D(Sig) 
Pr-M 0.89 0.39 0.65 0.093 −0.023 −0.016 I(Ins) D(Ins) D(Ins) 
0.21 0.40 0.12 −1.420 0.029 0.015 D(Ins) I(Ins) I(Ins) 
Po-M 0.39 0.04 0.29 0.608 0.024 0.024 I(Ins) I(Sig) I(Ins) 
StationSeasonP-value
Sen's slope
Trend
RainfallTmaxTminRainfallTmaxTminRainfallTmaxTmin
Ranchi 0.54 0.50 0.06 0.114 −0.030 −0.058 I(Ins) D(Ins) D(Ins) 
Pr-M 0.01 0.04 0.52 0.647 −0.039 −0.024 I(Sig) D(Sig) D(Ins) 
0.86 0.97 1.00 −0.120 0.003 0.000 D(Ins) I(Ins) I(Ins) 
Po-M 0.25 0.13 0.87 0.606 0.023 0.008 I(Ins) I(Ins) I(Ins) 
Jamshedpur 0.73 0.49 0.13 0.082 −0.025 −0.034 I(Ins) D(Ins) D(Ins) 
Pr-M 0.99 0.00 0.52 0.029 −0.085 −0.028 I(Ins) D(Sig) D(Ins) 
0.59 0.85 0.89 0.508 −0.010 −0.002 I(Ins) D(Ins) D(Ins) 
Po-M 0.97 0.10 0.28 0.013 0.022 0.034 I(Ins) I(Ins) I(Ins) 
Digha 0.75 0.56 0.04 0.103 −0.014 −0.035 I(Ins) D(Ins) D(Sig) 
Pr-M 0.89 0.39 0.65 0.093 −0.023 −0.016 I(Ins) D(Ins) D(Ins) 
0.21 0.40 0.12 −1.420 0.029 0.015 D(Ins) I(Ins) I(Ins) 
Po-M 0.39 0.04 0.29 0.608 0.024 0.024 I(Ins) I(Sig) I(Ins) 

Note: W, winter; Pr-M, pre-monsoon; M, monsoon; Po-M, post-monsoon; Ins, insignificant; Sig, significant; I, increasing; D, decreasing.

The trends of seasonal extreme rainfall in all the seasons and at all the stations are insignificantly increasing as the values of Sen's slope are positive and p values are greater than 0.05, except at Ranchi and Digha in monsoon, where they are insignificantly decreasing with negative Sen's slope and p values greater than 0.05. These results are similar to the earlier graphical representation of trends. Further, at Ranchi in pre-monsoon, the trend is significantly increasing with the p-value being less than 0.05. It was found that there was a significant positive trend in seasonal extreme rainfall with an increase of 0.647 mm/year in pre-monsoon at the Ranchi rain-gauge station.

The trends of maximum extreme temperature in all the seasons and at all the stations are insignificantly decreasing as the values of Sen's slope are negative and p values are greater than 0.05, except in monsoon and post-monsoon at Ranchi and Digha, where they are increasing. The trends are significant in pre-monsoon at Ranchi and Jamshedpur and in post-monsoon at Digha. The trends of minimum extreme temperature in all the seasons and at all the stations are insignificantly decreasing as the values of Sen's slope are negative and p values are greater than 0.05, except in monsoon and post-monsoon at Ranchi and Digha and post-monsoon at Jamshedpur, where they are increasing. The trend of extreme minimum temperature in winter at Digha is significant.

Trends in the number of rainy days

Variations of the total number of rainy days in a year and in the monsoon season at all three stations (Ranchi, Jamshedpur, and Digha) have been analysed for the years 1990 to 2020 and are presented in Figures 10 and 11.
Figure 10

Variation of the total number of rainy days in the year at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Figure 10

Variation of the total number of rainy days in the year at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Close modal
Figure 11

Variation of the total number of rainy days in monsoon at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Figure 11

Variation of the total number of rainy days in monsoon at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Close modal

The trend of the number of rainy days shows that it is decreasing at Ranchi and Digha, whereas it is increasing slightly at Jamshedpur.

Figure 11 shows the variation of the total number of rainy days in the monsoon season at all the stations and for all the years. A dashed line shows the mean values of the number of rainy days in monsoon, which are 60, 65, and 60 at Ranchi, Jamshedpur, and Digha, respectively. Results show that the number of rainy days in the monsoon season is more or less the same as the mean number of rainy days at each station with slight variation at Ranchi and Digha.

Shifting of monsoon

The shift of the onset date of monsoon rainfall has been studied for the years 1990 to 2020 at all three rain-gauge stations in the SRB and is presented in Figure 12. The solid line plotted through the points represents the actual date of onset of monsoon in a year, whereas the dotted line shows the probable date of onset of monsoon (i.e., 31 May in every year) in Jharkhand State of India for reference.
Figure 12

Onset of monsoon date in Subarnarekha River basin: (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Figure 12

Onset of monsoon date in Subarnarekha River basin: (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Close modal

It was observed that the onset of monsoon was shifted towards pre-monsoon, i.e., onset of monsoon starts in the first week of May and the trends show that the onset of monsoon date shifted towards the pre-monsoon side in the upper and middle regions. However, in the lower region, it shifted towards the monsoon side but the trend is more or less the same. The result of the study shows that the date of onset of monsoon in the whole region of the SRB is shifting towards the pre-monsoon side, which means the monsoon rains start earlier. This shows global climate change and its impacts on the extreme hydro-meteorological parameters.

Trends in annual diurnal temperature range

The extreme value of the DTR has also been studied for the years 1990 to 2020 at all three stations of the SRB. Figure 13 shows the variation of the annual extreme DTR at (a) Ranchi, (b) Jamshedpur, and (c) Digha. The trend line of the DTR shows a decrease at all the stations. The decreasing trend of DTR is attributed to a decrease in sunshine duration and an increase in the amount of cloud, precipitation, and water vapour at Ranchi and Jamshedpur, but is slight at Digha.
Figure 13

Time series of annual DTR with the graphical representation of trend at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Figure 13

Time series of annual DTR with the graphical representation of trend at (a) Ranchi, (b) Jamshedpur, and (c) Digha.

Close modal

The trend of the total number of rainy days and extreme annual DTR has also been studied using the MK test and Sen's slope estimator, and results are shown in Table 5 for all the stations. The total number of rainy days in the year and in monsoon is almost the same, which means no increase or decrease in the total number of rainy days has been observed at any stations. Extreme annual DTR is decreasing in the whole basin. However, the decrease is significant in the upper and middle regions, whereas it is insignificant in the lower region. A similar significant decreasing trend of 0.31 °C/decade in annual DTR was reported by Shree et al. (2021). Thus, the validity of the current study is confirmed by these comparisons with earlier literature.

Table 5

Results of the Mann–Kendall test and estimated Sen's slope for trends of the total number of rainy days and DTR at all stations

P-value
Sen's slope
Trend
StationTNRD in the yearTNRD in monsoonAnnual DTRTNRD in the yearTNRD in monsoonAnnual DTRTNRD in the yearTNRD in monsoonAnnual DTR
Ranchi 0.61 0.77 0.03 −0.09 −0.05 −0.03 D(Ins) D(Ins) D(Sig) 
Jamshedpur 0.94 0.96 0.01 0.02 −0.06 I(Ins) I(Ins) D(Sig) 
Digha 0.32 0.16 0.11 −0.25 −0.31 −0.02 D(Ins) D(Ins) D(Ins) 
P-value
Sen's slope
Trend
StationTNRD in the yearTNRD in monsoonAnnual DTRTNRD in the yearTNRD in monsoonAnnual DTRTNRD in the yearTNRD in monsoonAnnual DTR
Ranchi 0.61 0.77 0.03 −0.09 −0.05 −0.03 D(Ins) D(Ins) D(Sig) 
Jamshedpur 0.94 0.96 0.01 0.02 −0.06 I(Ins) I(Ins) D(Sig) 
Digha 0.32 0.16 0.11 −0.25 −0.31 −0.02 D(Ins) D(Ins) D(Ins) 

Note: TNRD, total number of rainy days; DTR, diurnal temperature range; Sig, significant; Ins, insignificant; I, increasing; D, decreasing.

The variation of the extreme rainfall and extreme maximum–minimum temperatures in the SRB during 1990–2020 was analysed using the MK test and Sen's slope methods. It was found that the trend of annual extreme rainfall was increasing from 7.7% to 12.9% and 15.6% to 58.8% in the upper and middle regions, respectively, whereas it was decreasing from 51.1% to 17.8% in the lower region of the basin during the selected time-period. The trend of annual extreme maximum temperature indicates a significant decreasing trend of 0.03 and 0.08 °C/year in the upper and middle regions, respectively, and extreme minimum temperature shows a significant decreasing trend of 0.03 °C/year in the lower region of the basin. The seasonal maximum extreme temperature trends were decreasing in all seasons and in all regions, except for the monsoon and post-monsoon in the upper and lower regions. Similar trends have been observed for seasonal extreme minimum temperatures in all the seasons and at all the stations as in the seasonal extreme maximum temperature. It was also observed that the date of onset of monsoon was shifted towards the pre-monsoon side, which means the monsoon rains start earlier in the SRB. The trend of the total number of rainy days in a year and in monsoon was almost similar in all regions. Extreme annual DTR was decreasing in the upper and middle regions by 0.03 and 0.06 °C/year, respectively, whereas in the lower region of the basin, the decrease was insignificant.

The authors would like to thank the India Meteorological Department, Pune, for providing daily rainfall data (1990–2020) and other online sources such as NASA Prediction of Worldwide Energy Resources (NASA POWER), website https://power.larc.nasa.gov, for providing daily maximum and minimum temperature data (1990–2020), that are used for carrying out this research work. The authors would also like to thank the reviewers for providing detailed feedback, which helped to improve the quality and clarity of the research.

Santosh Kumar: literature review, conceptualization, methodology, modelling, results analysis, conclusion and writing original manuscript. Vivekanand Singh: conceptualization, supervision/guidance, and reviewed original manuscript.

No funding was obtained for this study.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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