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Drought is one of the most destructive natural disasters, becoming more extreme and less predictable due to climate change. Drought directly affects a region's water resources, leading to inadequate water availability and harming crops, animals, and humans. The objective of the present study is to assess the long-term trends of precipitation and drought characteristics for the Churu district of northeast Rajasthan, India. For this purpose, mean monthly precipitation data are collected through the India-WRIS from 1901 to 2022. The trend analysis uses statistical methods (M-K test & amp; SS estimator), and the results are compared with a graphical method (innovative trend analysis). Drought events are found using precipitation-based drought indices, i.e., standardized precipitation index, Z-score index, and percentage of normal precipitation index. Interestingly, the results of the statistical method are well matched with the results of the graphical method. The study concludes that during the annual precipitation a rising trend is observed, which can help to mitigate the severity of drought. Also, it is found that moderately dry droughts occur most frequently compared with other drought events. This study will help policy-makers and local administrators to take necessary action to mitigate the severity of drought.

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  • To assess the long-term trends of precipitation and drought characteristics.

  • Drought directly affects a region's water resources, leading to inadequate water availability and harming crops, animals, and humans.

Churu, climate change, drought, precipitation, trend analysis
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Globally, climate change receives more attention in relation to weather and intense incidents (Mehta & Yadav 2021a). In recent years, droughts have been occurring more frequently due to climate change and global warming (Surendran et al. 2019). Drought is one of the most destructive catastrophic events, because it directly influences the agricultural sector, environment, heath of the people, and the economy to a great extent (Fung et al. 2020; Mehta & Yadav 2021b). Drought is one of the most complicated water-related disasters because it is a phenomenon that can affect human activities more than other natural disasters (Mahmoudi et al. 2019). Drought is a temporary event which lasts for some duration, but it can destroy the health and wealth of the people living in that area. Generally, drought occurs when there is an absence of water for a long duration usually of months or years in a region (Jonathan & Suvarna Raju 2017). It can also be defined as a natural drop in precipitation over a long time-duration, which is generally a season or more, called meteorological drought (Mishra & Singh 2010; Verma et al. 2024). Generally, drought can take place in any region of the world, but its frequency and effects are more severe in arid and semi-arid regions (Wilhite 2000; Mehta & Yadav 2021c).

Meteorological drought usually occurrs when an area receives less than average precipitation during an extensive period of time (Quiring 2009). According to Burton et al. (2013), compared with other hydrological hazards like flood, it is very difficult to identify when droughts can occur. The development of drought policy to reduce its effects broadly depends on information about its severity and duration (Jain et al. 2015). Therefore, forecasting meteorological drought is necessary to decrease its adverse effects. This can be achieved through drought monitoring and forecasting by using drought indices (Chandrasekara et al. 2021; Verma et al. 2021; Verma et al. 2022a). A drought index is a number which is obtained by using hydrological and meteorological data as an input as well as drought indicators (Chandrasekara et al. 2021; Sahu et al. 2022, 2023). An index basically uses hydro-meteorological parameters like precipitation, temperature, runoff, streamflow, and evapotranspiration and gives a single numerical value as output (Patel et al. 2021; Mehta et al. 2022). The main benefit of employing drought indices is that they are easy to use as well as giving more accurate results to help policy-makers (Zargar et al. 2011; Halder et al. 2022; Verma et al. 2022b).

Also, it is mandatory to determine the past and present trend of precipitation for the study of droughts (Mehta & Yadav 2022a, 2022b). Analysis of precipitation trends helps in better forecasting of future droughts and sustainable water resources management (Perera et al. 2020; Verma et al. 2023a, 2023b). According to Patel & Mehta (2023), it is vital to recognize the trend of precipitation for drought and flood forecasting. Various researchers have analysed long-term precipitation trends and adopted different drought indices for the assessment of droughts based on precipitation data. Thomas & Prasannakumar (2016) applied different precipitation-based drought indices, namely, rainfall anomaly index (RAI), percentage departure of rainfall (D%), and standardized precipitation index (SPI) to determine the severity of droughts in Kerala. Mehta & Yadav (2022a, 2022b) used percentage departure of rainfall (D%), RAI, and SPI to study drought characteristics across Jalore district. Yadav et al. (2021) also utilized various drought indices, i.e., percentage departure (D%), RAI, and SPI to evaluate the severity of droughts (Shaikh et al. 2022; Mehta & Yadav 2023).

There are numerous drought indices that can be used to analyse the effect of droughts. However, in this study, precipitation-based drought indices (SPI, Z-score index (ZSI), and percentage of normal precipitation index (PNPI)) are used. To assess the trends in precipitation time-series, the widely known M-K test, Sen's slope (SS) estimator, and innovative trend analysis (ITA) method are used. The current research aims (i) to carry out the trend analysis of precipitation across the Churu district of Rajasthan and (ii) to examine the drought characteristics using precipitation-based drought indices.

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Rajasthan is the largest state of India in terms of geographical area. It has 10.41% of total area of India, at 342,239 km2. It is one of the northern states of India, and contains 33 districts, 39,753 populated villages, 249 panchayat samities, and 9,168 gram panchayats. In the present study, Churu district of Rajasthan is selected as the study area. It is also known as the gateway to the Thar Desert of Rajasthan. The district is located between 28° 18′ 0″ N latitude and 74° 57′ 0″ E longitude. The district covers a total geographical area of 11,154.66 km2. The district's climate is semi-arid, and average elevation is 292 m. The district's normal precipitation is 328 mm during July to mid-September. There is a great variation in temperature in the Churu district. The lowest recorded temperature was −4.6 °C. However, the highest recorded temperature was 50.8 °C. Relative humidity is generally below 30%, but in the southeast monsoon, relative humidity rises to 60%. Figure 1 represents the location of the study area in Rajasthan.
Figure 1
Location map of the study area.
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Location map of the study area.

Figure 1
Location map of the study area.
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Location map of the study area.

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In this research, mean monthly precipitation data of the Churu district were gathered through the Indian Water Resource Information System (IWRIS) (indiawris.gov.in) for 1901 to 2022 (122 years).

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The current research work is carried out in two parts:

  • (1) trend analysis of precipitation time series;

  • (2) drought analysis based on precipitation.

The sequencing methodology adopted in this research is elaborated in Figure 2.
Figure 2
Sequencing outline of methodology.
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Sequencing outline of methodology.

Figure 2
Sequencing outline of methodology.
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Sequencing outline of methodology.

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Checking for effects of serial correlation

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While analysing any precipitation time-series data, one of the main problems is the effects of serial correlation, because if the time series contains any kind of negative or positive correlation, the interpretations of results are underestimated (Hamed & Rao 1998). As a result, it is crucial to eliminate the presence of serial correlation from the data. In this research, the lag-1 serial correlation coefficient (r1) was adopted to check whether the precipitation time-series contains any serial correlation. Equations (1) and (2) are used to calculate r1 at the 5% significance level:
(1)
(2)

where implies the mean of sample size and n implies the size of sample.

To indicate the presence of serial correlation, r1 is calculated by Equation (3):
(3)

If r1 is found between the given confidence interval, then the data are free from the effects of serial correlation, and there is no need to perform pre-whitening. Otherwise, pre-whitening should be conducted to terminate the effects of serial correlation (Yue et al. 2002).

M-K test and SS estimator method

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In the present study, the M-K test was employed to detect the trend in precipitation time-series. The World Meteorological Organisation (WMO) suggested that the M-K test is best for identifying trends in time series (Aher & Yadav 2021). The benefit of using this test is that it is simple, it does not consider any assumptions, and it can handle the missing data (Porter et al. 2002). The M-K test was applied at a 5% significance level. To conduct the M-K test, XLSTAT 2022 software was used.

To obtain the values of the M-K test statistic (S), Equations (4) and (5) are used:
(4)
(5)

In the aforementioned equations, xj and xk denote precipitation data in years and n means the number of observations. If the value of S is positive, it means the data follow an increasing trend and vice-versa if it is negative, which shows a decreasing trend.

The variance can be obtained by Equation (6):
(6)

where q and t mean the tied group and number of observations, respectively.

By applying Equation (7), the value of the standard statistic (Z) can be determined:
(7)

If the value of ZMK is more than zero (ZMK > 0), then the data show an increasing trend. Furthermore, the data reveal a decreasing trend when the ZMK value is less than zero (ZMK < 0). On the other hand, the trend is considered as no trend if ZMK is zero (ZMK = 0).

The magnitude of trend changes cannot be evaluated by the M-K test. Hence, to determine the slope of a linear trend in the times-series data, the SS estimator method suggested by Sen (1968) is employed. The Sen's slope can be determined by Equation (8):
(8)
where βi, x, and i and j are slope, variables, and indices, respectively.

If the value of βi > 0, it demonstrates upward trends in the data. Vice-versa, it reveals a downward trend if the value of βi > 0.

ITA method

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To find trends in precipitation time-series, Şen (2012) discovered an ITA method. The main merit of the ITA method is that it is free from serial correlation, normality, or record length, compared with the frequently used trend analysis techniques, such as the M-K test and SS estimator (Pastagia & Mehta 2022). In this approach, the precipitation data are split into two equivalent parts from the start date to the last date (Caloiero 2020). Then both subseries are listed in either an increasing or a decreasing order. Further, the first part of subseries is plotted on the x-axis, while the second part of the subseries is plotted on the y-axis (Ali & Abubaker 2019). If every data point is obtained on a 1:1 line, it is interpreted as no trend existing in the data. On the other hand, data points that are acquired above the 1:1 line indicate a positive trend. However, a negative trend exists in the data when it is gathered below the 1:1 line (Besha et al. 2022).

Standardized precipitation index

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SPI was found by McKee et al. (1993), which is one of the most popular drought indices to assess meteorological drought. Generally, it is used worldwide due to its simplicity as well as accuracy. The major benefit of using this index is that it requires only precipitation data as an input variable. Also, it can be used over multiple timescales to show the consequences of drought on water resources. In this study, three, six, and 12 months SPI are utilized to assess the intensity of drought over multiple time-scales. To calculate the SPI, DrinC software is used.

Table 1 illustrates the distribution of drought situations as per the SPI value.

Table 1

Distribution of drought severity based on SPI

SPI valueCategory
More than 2.0 Extremely wet 
1.5 to 1.99 Severely wet 
1.0 to 1.49 Moderately wet 
−0.99 to 0.99 Near normal 
−1.0 to −1.49 Moderately dry 
−1.5 to −1.99 Severely dry 
Less than −2.0 Extremely dry 
SPI valueCategory
More than 2.0 Extremely wet 
1.5 to 1.99 Severely wet 
1.0 to 1.49 Moderately wet 
−0.99 to 0.99 Near normal 
−1.0 to −1.49 Moderately dry 
−1.5 to −1.99 Severely dry 
Less than −2.0 Extremely dry 
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Z-score index

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ZSI calculation is easy as well as effective. The advantage of this index is that, unlike SPI, it does not require the transformation of fit data for distributions like gamma or Pearson type III (Mahmoudi et al. 2019a). It can be calculated by applying Equation (9):
(9)
where x, σ, and xij are mean, standard deviation, and precipitation for the jth month and the ith length of precipitation time-series.

Table 2 illustrates the distribution of drought situations as per the ZSI value.

Table 2

Distribution of drought severity based on ZSI

ZSI valueCategory
More than 2.0 Extremely wet 
1.5 to 1.99 Severely wet 
1.0 to 1.49 Moderately wet 
−0.99 to 0.99 Near normal 
−1.0 to −1.49 Moderately dry 
−1.5 to −1.99 Severely dry 
Less than −2.0 Extremely dry 
ZSI valueCategory
More than 2.0 Extremely wet 
1.5 to 1.99 Severely wet 
1.0 to 1.49 Moderately wet 
−0.99 to 0.99 Near normal 
−1.0 to −1.49 Moderately dry 
−1.5 to −1.99 Severely dry 
Less than −2.0 Extremely dry 
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Percentage of normal precipitation index

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PNPI is the simple drought index found by Willeke et al. (1994). It is calculated by dividing the actual precipitation with its average value during a particular time period and expressed as percentage. The main benefit of using PNPI is that only precipitation data are needed for its computation, and it is used on the monthly timescale (Willeke et al. 1994).

This index can be calculated by applying Equation (10):
(10)
where Pi represents precipitation during i time period and P means the normal precipitation during the study period.

Table 3 illustrates the distribution of drought situations as per the PNPI value.

Table 3

Distribution of drought severity based on PNPI

PNPI value (%)Category
70–80 Normal 
55–70 Moderately dry 
40–55 Severely dry 
Less than 40 Extremely dry 
PNPI value (%)Category
70–80 Normal 
55–70 Moderately dry 
40–55 Severely dry 
Less than 40 Extremely dry 
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Effects of serial correlation in precipitation time-series

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To investigate the effects of serial correlation the lag-1 autocorrelation coefficient (r1) was applied at a 5% significance level to the seasonal (winter, pre-monsoon, southwest (SW) monsoon, and post-monsoon) and annual precipitation time-series of the Churu district. The test revealed that the seasonal (winter, pre-monsoon, SW monsoon, and post-monsoon) time-series had the following values of the autocorrelation coefficient: −0.055, 0.235, 0.126, and 0.307, respectively. Furthermore, the annual time series was observed with r1 = 0.115. As the results of serial correlation were found within the permitted range (−1.178 to 1.178), it can be concluded that none of the time series (either seasonal or annual) has any significant serial correlation. Hence, in this research, the M-K test is conducted on the original precipitation data excluding the pre-whitening approach.

Results of precipitation trend analysis using M-K test and SS estimator

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Table 4 shows the outcomes of precipitation trend analysis result for Churu district under the M-K test and SS estimator. From the findings, it is discovered that the M-K test exhibits an increasing trend for winter, pre-monsoon, SW monsoon, and annual seasons with values of ZMK (0.01, 0.201, 0.158, and 0.199), respectively. However, it reveals a decreasing trend for the post-monsoon season with ZMK value −0.079. According to the findings of the M-K test, the majority of months has a rising trend, followed by a falling trend. Ten of the 12 months are observed with increasing trends as February (0.005), March (0.114), April (0.147), May (0.215), June (0.209), July (0.162), August (0.035), September (0.053), October (0.072), and November (0.135). However, the remaining two months (January and December) indicate a decreasing trend with the ZMK values (−0.003 and −0.104), respectively. The SS estimator revealed the same result which was acquired from the M-K test. Results of the SS estimator show that a significant increasing trend is observed for April, May June, and July. However, an insignificant increasing trend is noticed for March, August, September, and October. Furthermore, the January, February, November, and December months discovered no trend. Figure 3(a)–3(e) represents a times series of precipitation during different seasons as well as the annual timescale for 122 years (1901–2022). The trend line of the precipitation time-series is shown by the orange line.
Table 4

Summary of the M-K test and SS estimator results from 1901 to 2022 across Churu district

Precipitation durationKendall's tauTrend InterpretationM-K test
p-value		Sen's slopeTest interpretation
January −0.003 Falling 0.966 0 No trend 
February 0.005 Rising 0.931 0 No trend 
March 0.114 Rising 0.065 0.009 Insignificant increasing trend 
April 0.147 Rising 0.017 0.015 Significant increasing trend 
May 0.215 Rising 0 0.107 Significant increasing trend 
June 0.209 Rising 0.001 0.233 Significant increasing trend 
July 0.162 Rising 0.008 0.416 Significant increasing trend 
August 0.035 Rising 0.571 0.097 Insignificant increasing trend 
September 0.053 Rising 0.392 0.068 Insignificant increasing trend 
October 0.072 Rising 0.246 0.001 Insignificant increasing trend 
November 0.135 Rising 0.042 0 No trend 
December −0.104 Falling 0.107 0 No trend 
Winter 0.01 Rising 0.873 0.004 Insignificant increasing trend 
Pre-monsoon 0.201 Rising 0.001 0.152 Significant increasing trend 
SW monsoon 0.158 Rising 0.01 0.758 Significant increasing trend 
Post-monsoon −0.079 Falling 0.197 −0.02 Insignificant decreasing trend 
Annual 0.199 Rising 0.001 1.041 Significant increasing trend 
Precipitation durationKendall's tauTrend InterpretationM-K test
p-value		Sen's slopeTest interpretation
January −0.003 Falling 0.966 0 No trend 
February 0.005 Rising 0.931 0 No trend 
March 0.114 Rising 0.065 0.009 Insignificant increasing trend 
April 0.147 Rising 0.017 0.015 Significant increasing trend 
May 0.215 Rising 0 0.107 Significant increasing trend 
June 0.209 Rising 0.001 0.233 Significant increasing trend 
July 0.162 Rising 0.008 0.416 Significant increasing trend 
August 0.035 Rising 0.571 0.097 Insignificant increasing trend 
September 0.053 Rising 0.392 0.068 Insignificant increasing trend 
October 0.072 Rising 0.246 0.001 Insignificant increasing trend 
November 0.135 Rising 0.042 0 No trend 
December −0.104 Falling 0.107 0 No trend 
Winter 0.01 Rising 0.873 0.004 Insignificant increasing trend 
Pre-monsoon 0.201 Rising 0.001 0.152 Significant increasing trend 
SW monsoon 0.158 Rising 0.01 0.758 Significant increasing trend 
Post-monsoon −0.079 Falling 0.197 −0.02 Insignificant decreasing trend 
Annual 0.199 Rising 0.001 1.041 Significant increasing trend 
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Figure 3
Time series of precipitation during (a) winter, (b) pre-monsoon, (c) SW monsoon, and (d) post-monsoon, and (e) annually over the Churu district.
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Time series of precipitation during (a) winter, (b) pre-monsoon, (c) SW monsoon, and (d) post-monsoon, and (e) annually over the Churu district.

Figure 3
Time series of precipitation during (a) winter, (b) pre-monsoon, (c) SW monsoon, and (d) post-monsoon, and (e) annually over the Churu district.
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Time series of precipitation during (a) winter, (b) pre-monsoon, (c) SW monsoon, and (d) post-monsoon, and (e) annually over the Churu district.

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Results of precipitation trend analysis using ITA method

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Figure 4 displays a graph that compares the first part of the times series with the second part of the time series. The ITA plots of different seasonal and annual precipitation time-series are represented in Figure 4(a)–4(e). From the results of the ITA plot, it is observed that the winter season precipitation trends are decreasing in medium and high regimes, whereas pre-monsoon, SW monsoon, as well as annual precipitation trends are increasing in all three regimes. The findings also indicate that for the post-monsoon season, the trend is decreasing in the low regime, while it is increasing in the medium regime. However, it shows no trend in the high regime. Table 5 illustrates the nature of the trend in all regimes for seasonal as well as annual precipitation time-series. From Table 5, it is also concluded that in contrast to the high regime, the low and medium regimes are observed with the most positive trend.
Table 5

Results of innovative trend analysis plot for seasonal and annual precipitation over Churu district

SeasonLowMediumHigh
Winter ↑ ↓ ↓ 
Pre-monsoon ↑ ↑ ↑ 
SW monsoon ↑ ↑ ↑ 
Post-monsoon ↓ ↑ O 
Annual ↑ ↑ ↑ 
SeasonLowMediumHigh
Winter ↑ ↓ ↓ 
Pre-monsoon ↑ ↑ ↑ 
SW monsoon ↑ ↑ ↑ 
Post-monsoon ↓ ↑ O 
Annual ↑ ↑ ↑ 
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Figure 4
Innovative trend analysis plot during (a) winter, (b) pre-monsoon, (c) SW monsoon, and (d) post-monsoon, and (e) annually over the Churu district.
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Innovative trend analysis plot during (a) winter, (b) pre-monsoon, (c) SW monsoon, and (d) post-monsoon, and (e) annually over the Churu district.

Figure 4
Innovative trend analysis plot during (a) winter, (b) pre-monsoon, (c) SW monsoon, and (d) post-monsoon, and (e) annually over the Churu district.
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Innovative trend analysis plot during (a) winter, (b) pre-monsoon, (c) SW monsoon, and (d) post-monsoon, and (e) annually over the Churu district.

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Comparative trend analysis of precipitation between M-K test, SS estimator, and ITA method

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In this research, trends in monthly, seasonal, and annual time-series of precipitation are obtained employing the M-K test and SS estimator, and compared with the ITA method. Table 6 shows the comparative trend analysis of precipitation results between these three methods. By comparing results among these methods, it is found that, for the annual precipitation, all three methods (M-K test, SS estimator, and ITA) show an increasing trend. Also, pre-monsoon and SW monsoon seasons are observed with an increasing trend for all three methods (M-K test, SS estimator, and ITA). Moreover, the winter season reveals an increasing trend for statistical methods, while the graphical method represents the decreasing trend, which is highlighted in Table 6. Only the post-monsoon season is found with a decreasing trend in all three methods (M-K test, SS estimator, and ITA).

Table 6

Comparative trend analysis of precipitation between M-K test, SS estimator and ITA method

SeasonM-K testSS estimatorITA method
Winter Increasing Increasing Decreasing 
Pre-monsoon Increasing Increasing Increasing 
SW monsoon Increasing Increasing Increasing 
Post-monsoon Decreasing Decreasing Decreasing 
Annual Increasing Increasing Increasing 
SeasonM-K testSS estimatorITA method
Winter Increasing Increasing Decreasing 
Pre-monsoon Increasing Increasing Increasing 
SW monsoon Increasing Increasing Increasing 
Post-monsoon Decreasing Decreasing Decreasing 
Annual Increasing Increasing Increasing 
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Drought analysis using SPI

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In this research, SPI is computed for three different timescales (three, six and 12 months). Short-term drought events are analysed using a three-month SPI, and to analyse intermediate drought events, six-month SPI is employed. Twelve-month SPI is utilized to analyse long-term drought events. The calculation of SPI is done for the hydrological year October to September by using DrinC software.

Figure 5 represents the three-month SPI time series during October–December, January–March, April–June, and July–September. The results of three-month SPI show that 74 drought events were observed during October–December, January–March, April–June, and July–September. From the 74 years of drought events, 12 years are observed as extremely dry, 22 years are observed as severely dry, and 40 years are observed as moderately dry events. It is also observed that the April–June show 17 years (minimum) of drought events and January–March months show 21 years (maximum) of drought events, while October–December and July–September show 18 and 19 years of drought events, respectively. During the 122 years of the study period, 39.34% of years are observed as wet years. Also, 32.79%, 18.03%, and 9.84% of years are observed as moderately dry, severely dry, and extremely dry years, subsequently. Drought years with extremely dry, severely dry, and moderately dry events are shown in Figure 6.
Figure 5
Three-month SPI for (a) October–December, (b) January–March, (c) April–June, and (d) July–September over the Churu district.
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Three-month SPI for (a) October–December, (b) January–March, (c) April–June, and (d) July–September over the Churu district.

Figure 5
Three-month SPI for (a) October–December, (b) January–March, (c) April–June, and (d) July–September over the Churu district.
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Three-month SPI for (a) October–December, (b) January–March, (c) April–June, and (d) July–September over the Churu district.

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Figure 6
Classification of drought years using three month SPI from 1901 to 2022 over Churu district.
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Classification of drought years using three month SPI from 1901 to 2022 over Churu district.

Figure 6
Classification of drought years using three month SPI from 1901 to 2022 over Churu district.
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Classification of drought years using three month SPI from 1901 to 2022 over Churu district.

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Figure 7 represents the six-month SPI time series during October–March and April–September. The results of six-month SPI show that 34 years of drought events were observed during October–March and April–September. From 34 years of drought events, three years are observed as extremely dry, 16 years are observed as severely dry, and 15 years are observed as moderately dry events. It is also observed that the October–March months show 16 years (minimum) of drought events and the April–September months show 18 years (maximum) of drought events. During the 122 years of the study period, 72.14% of years are observed as wet years. However, 12.29%, 13.11%, and 2.46% of years are observed as moderately dry, severely dry, and extremely dry years, respectively. Drought years with extremely dry, severely dry, and moderately dry events are shown in Figure 8.
Figure 7
Six-month SPI for (a) October–March and (b) April–September over the Churu district.
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Six-month SPI for (a) October–March and (b) April–September over the Churu district.

Figure 7
Six-month SPI for (a) October–March and (b) April–September over the Churu district.
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Six-month SPI for (a) October–March and (b) April–September over the Churu district.

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Figure 8
Classification of drought years using six-month SPI from 1901 to 2022 over Churu district.
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Classification of drought years using six-month SPI from 1901 to 2022 over Churu district.

Figure 8
Classification of drought years using six-month SPI from 1901 to 2022 over Churu district.
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Classification of drought years using six-month SPI from 1901 to 2022 over Churu district.

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Figure 9 represents the 12-month SPI time series during October–September. The results of 12-month SPI show that 19 years of drought events were observed during the October–September timescale. From the 19 years of drought events, two years are observed as extremely dry, six years are observed as severely dry, and 11 years are observed as moderately dry events. During the 122 years of the study period, 84.42% of years are observed as wet years, and 9.02%, 4.92% and 1.64% of years are observed as moderately dry, severely dry, and extremely dry years, respectively. Drought years with extremely dry, severely dry, and moderately dry events are shown in Figure 10.
Figure 9
Twelve-month SPI for October–September over the Churu district.
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Twelve-month SPI for October–September over the Churu district.

Figure 9
Twelve-month SPI for October–September over the Churu district.
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Twelve-month SPI for October–September over the Churu district.

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Figure 10
Classification of drought years using 12-month SPI from 1901 to 2022 over Churu district.
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Classification of drought years using 12-month SPI from 1901 to 2022 over Churu district.

Figure 10
Classification of drought years using 12-month SPI from 1901 to 2022 over Churu district.
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Classification of drought years using 12-month SPI from 1901 to 2022 over Churu district.

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Drought analysis using Z-score index

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In this study, ZSI is also calculated for the three timescales, i.e., three, six and 12 months.

Figure 11 represents the three-month ZSI time series during October–December, January–March, April–June, and July–September. From the results of three-month ZSI, it is found that 47 years of drought events occurred during all four timescales (October–December, January–March, April–June, and July–September). Of the 47 years of drought events, six years are observed as severely dry and 41 years are observed as moderately dry events. It was also found that during the October–December months, there were no drought events. However, July–September months show 18 years of drought events. Furthermore, January–March and April–June showed 16 and 13 years of drought events, respectively. During the 122 years of the study period, 61.47% of years are observed with wet years. Also, 33.61% and 4.92% of years are observed as moderately dry and severely dry years, respectively. Drought years with severely dry and moderately dry events are shown in Figure 12.
Figure 11
Three-month ZSI for (a) October–December, (b) January–March, (c) April–June, and (d) July–September over the Churu district.
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Three-month ZSI for (a) October–December, (b) January–March, (c) April–June, and (d) July–September over the Churu district.

Figure 11
Three-month ZSI for (a) October–December, (b) January–March, (c) April–June, and (d) July–September over the Churu district.
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Three-month ZSI for (a) October–December, (b) January–March, (c) April–June, and (d) July–September over the Churu district.

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Figure 12
Classification of drought years using three month ZSI from 1901 to 2022 over Churu district.
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Classification of drought years using three month ZSI from 1901 to 2022 over Churu district.

Figure 12
Classification of drought years using three month ZSI from 1901 to 2022 over Churu district.
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Classification of drought years using three month ZSI from 1901 to 2022 over Churu district.

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Figure 13 represents the six-month ZSI time-series during October–March and April–September. From the results of six-month ZSI, it is found that 32 years of drought events occurred during the two timescales (October–March and April–September). Of the 32 years of drought events, four years are observed as severely dry and 28 years are observed as moderately dry events. It is also found that October–March months show 14 years (minimum) of drought events and April–September months show 18 years (maximum) of drought events. During the 122 years of the study period, 73.77% of years are observed as wet years. Also, 22.95% and 3.28% of years are observed as moderately dry and severely dry years, respectively. Drought years with severely dry and moderately dry events are shown in Figure 14.
Figure 13
Six-month ZSI for (a) October–March and (b) April–September over the Churu district.
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Six-month ZSI for (a) October–March and (b) April–September over the Churu district.

Figure 13
Six-month ZSI for (a) October–March and (b) April–September over the Churu district.
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Six-month ZSI for (a) October–March and (b) April–September over the Churu district.

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Figure 14
Classification of drought years using six-month ZSI from 1901 to 2022 over Churu district.
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Classification of drought years using six-month ZSI from 1901 to 2022 over Churu district.

Figure 14
Classification of drought years using six-month ZSI from 1901 to 2022 over Churu district.
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Classification of drought years using six-month ZSI from 1901 to 2022 over Churu district.

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Figure 15 represents the time series of 12-month ZSI for the October–September timescale. From the results of 12-month ZSI, it is found that 19 years of drought events occurred during the October–September timescale. Of the 19 years of drought events, one year is observed as extremely dry, two years are observed as severely dry, and 28 years are observed as moderately dry events. During the 122 years of the study period, 84.43% of years are observed as wet years. Also, 13.11%, 1.64%, and 0.82% of years are observed as moderately dry, severely dry, and extremely dry years, respectively. Drought years with extremely dry, severely dry, and moderately dry events are presented in Figure 16.
Figure 15
Twelve-month ZSI for October–September over the Churu district.
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Twelve-month ZSI for October–September over the Churu district.

Figure 15
Twelve-month ZSI for October–September over the Churu district.
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Twelve-month ZSI for October–September over the Churu district.

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Figure 16
Classification of drought years using 12-month ZSI from 1901 to 2022 over Churu district.
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Classification of drought years using 12-month ZSI from 1901 to 2022 over Churu district.

Figure 16
Classification of drought years using 12-month ZSI from 1901 to 2022 over Churu district.
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Classification of drought years using 12-month ZSI from 1901 to 2022 over Churu district.

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Drought analysis using percentage of normal precipitation index

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In this study, PNPI is calculated for 12 months for the hydrological year from October to September.

Figure 17 represents the 12-month PNPI time-series during October–September. The results of 12-month PNPI show that 27 years of drought events occurred during the October–September timescale. From the 27 years of drought events, two years are observed as extremely dry, nine years are observed as severely dry, and 16 years are observed as moderately dry events. During the 122 years of the study period, 77.87% of years are observed as wet years. Also, 13.11%, 7.38%, and 1.64% of years are observed as moderately dry, severely dry, and extremely dry years, respectively. Drought years with extremely dry, severely dry, and moderately dry events are shown in Figure 18.
Figure 17
Twelve-month PNPI for October–September over the Churu district.
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Twelve-month PNPI for October–September over the Churu district.

Figure 17
Twelve-month PNPI for October–September over the Churu district.
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Twelve-month PNPI for October–September over the Churu district.

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Figure 18
Classification of drought years using 12-month PNPI from 1901 to 2022 over Churu district.
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Classification of drought years using 12-month PNPI from 1901 to 2022 over Churu district.

Figure 18
Classification of drought years using 12-month PNPI from 1901 to 2022 over Churu district.
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Classification of drought years using 12-month PNPI from 1901 to 2022 over Churu district.

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In this study, the semi-arid district of northeast Rajasthan (Churu district) is selected and trend analysis is carried out for monthly, seasonal, and annual precipitation. To carry out the trend analysis, statistical methods (M-K test and SS estimator) for the time period of 1901–2022 (122 years) are used. However, the graphical method (ITA) is also utilized to detect a trend in the precipitation time-series. Also, to evaluate the severity of drought in the study area, various drought indices such as SPI, ZSI, and PNPI are utilized. The study concludes the following points:

  • The winter season shows a decreasing trend for the M-K test and SS estimator. On the other hand, the ITA method shows an increasing trend.

  • For pre-monsoon, SW monsoon, and annual seasons, an increasing trend is noticed by applying all three methods (M-K test, SS estimator, and ITA method).

  • Only the post-monsoon season revealed a decreasing trend under all three methods (M-K test, SS estimator, and ITA method).

  • Except only for the winter season, the results of M-K test and SS estimator match the results of the ITA method.

  • The graphical method (ITA method) is more accurate in detecting the trend because it does not consider any assumptions like the M-K test.

  • During the annual season, the rising trend of precipitation can help to mitigate the severity of drought.

  • From the results of drought analysis, it can be said that moderately dry events take place more frequently compared with other drought events.

  • The results of 12-month SPI and 12-month ZSI show that an extremely dry event occurred during 1918 with the severity of −3.21 and −2.11, respectively.

  • This study will help policy-makers and local administrators to take necessary action to mitigate the severity of drought as well as better management of water resources in the Churu district.

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All authors have read, understood, and have complied as applicable with the statement on ‘Ethical responsibilities of Authors’ as found in the instructions for authors.

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This research received no external funding.

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

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The authors declare there is no conflict.

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