Abstract

In the present study, five parametric and non-parametric methods: linear regression (LR), conventional Mann–Kendall (MK), modified Mann–Kendall (MMK), Spearman's Rho (SR) and Innovative Trend Method (ITM) were used to identify trends in the groundwater levels of 60 piezometers distributed uniformly across Sirjan plain, Iran, from 2005 to 2018. The LR method was found to be affected by the presence of outliers and autocorrelation. The conventional non-parametric tests (MK and SR) were not able to offset the effects of the autocorrelations between the groundwater level data. The ITM method was also found to be a not so comprehensive and precise statistical tool for trend analysis because it does not provide a quantitative index for identifying trend significance. Therefore, the MMK test was found to be the most appropriate trend analysis method among the five trend identification methods used in this study by eliminating the effect of all significant autocorrelation coefficients. The results of the MMK test showed that the groundwater levels in Sirjan plain had witnessed significant decreasing trends during the study period. In only 24 months (out of a total 10,080 studied months), no significant decreasing trends in groundwater levels were observed.

HIGHLIGHTS

  • Five various parametric and non-parametric methods were used to analyze the trend of groundwater level.

  • The effect of outliers and autocorrelation in time series was investigated.

  • The results indicated that the Mann-Kendall test after elimination of the effect of all significant autocorrelation had the best performance.

  • The effects of human activities on management of critical situation in Sirjan Plain, Iran were investigated.

INTRODUCTION

The main objective of trend analysis is to identify the existence or non-existence of significant increasing or decreasing trends in a data series either by using parametric method, for example, the linear regression test, and non-parametric tests, such as the Spearman's Rho (SR) and Mann–Kendall (MK) methods, the two well-known and most widely used non-parametric tests in identifying the trends in climatic parameters (Hirsch et al. 1982; Bui et al. 2012; Güçlü 2018; Zamani et al. 2018; Ali et al. 2019; Noori et al. 2019; Meshram et al. 2020). Hirsch et al. (1991) explained the reasons for using the non-parametric tests instead of parametric methods in various studies analyzing the trends. The parametric methods are developed based on assumptions, such as the normality, the stationarity and the independency of time series, while these assumptions are most uncommon to be satisfied in a hydrological time series. Further, the parametric tests are very sensitive to the presence of the outliers in the data series, which is not the case with the non-parametric methods. Khalili et al. (2016) reported that the non-parametric methods can be applied to both the linear time series, as well as the non-linear time series. While applying the non-parametric test for trend analysis, it is assumed that there is no significant autocorrelation in the given time series. Hamed & Rao (1998) investigated the effect of existence of autocorrelation in time series on trend detection by using non-parametric tests. They proposed a methodology for eliminating the effect of autocorrelation from a data series and applied the proposed method for identifying trends in precipitation and stream flow time series. On using the MK test after eliminating the autocorrelation effect from time series instead of the classic MK test, an increase in the accuracy of trend detection was observed (Hamed & Rao 1998).

In order to investigate the water table variations, many parametric and non-parametric tests have been developed by various researchers for investigating trends in the groundwater table in different parts of the world. Gehrels et al. (1994) studied the variations in surface water runoff and groundwater in the Netherlands and found that the groundwater levels declined due to the drainage, occurrence of drought and over-exploitation of groundwater by farmers. Almedeij & Al-Ruwaih (2006) reported that the groundwater level has a direct relationship with precipitation and indirect relationship with temperature based on the investigations pertaining to the water table in a residential area in Kuwait. Panda et al. (2007) studied the impacts of drought and the effects of over-exploitation on groundwater levels in Orissa (India) by identifying the trends in groundwater levels of 1,002 piezometric wells by using the MK test during the pre-monsoon and post-monsoon seasons from 1994 to 2003. The results revealed that the groundwater declined due to rainfall reduction during dry years and high temperatures, and over-exploitation of the groundwater did not recover through the recharge in wet years. In the post-monsoon (pre-monsoon) season, about 51% (59%) of the piezometers showed declining trends in the groundwater levels in Orissa. Lee et al. (2007) considered the changes in the groundwater levels at Daegu, Korea and claimed that construction of subway tunnels might have led to decline in the groundwater levels. Shahid & Hazarika (2010) investigated groundwater drought using the data of 85 wells located in the northwestern districts of Bangladesh from 1998 to 2002. The over-exploitation of groundwater for irrigation in the dry season and decrease in the aquifer recharge during dry periods were two main reasons for declining trends in the groundwater levels in the northwestern districts of Bangladesh. Shamsudduha et al. (2009) analyzed trends in groundwater levels in the Ganges–Brahmaputra–Meghna delta of Bangladesh during the period 1985–2005 by applying the non-parametric seasonal-trend decomposition procedure. The seasonality dominated the observed variance in groundwater levels, but groundwater level declined at the rate of about 1 m/year in urban and peri-urban areas, and at the rate of 0.1–0.5 m/year in the areas where there was over-exploitation of groundwater resources for rice cultivation during the dry season. However, groundwater levels increased at the rate of 0.5–2.5 cm/year in the estuarine and southern coastal regions.

Tabari et al. (2012) studied the trends in monthly, seasonal, and annual time series of groundwater levels in Mazandaran province of Iran during the period of 1985–2007 using the MK test and reported a combination of negative and positive trends in the piezometeric wells. However, increasing trends were much more pronounced than the decreasing trends in the groundwater levels. Spatial analysis showed concentration of significant increasing trends in the central parts of the province having irrigated tracts of paddy cultivation. Vousoughi et al. (2013) investigated the variations in the groundwater levels and hydrochemical variables in the Ardebil plain, Iran, in 32 piezometers using the MK test. The groundwater levels in the Ardebil plain declined at the rate of about 18 cm per year. Abdullahi et al. (2015) studied the variations in groundwater levels in seven stations of Terengganu, Malaysia by using the MK test and reported that the groundwater levels had experienced a significant negative trend with occurrence of the highest decline during 2005–2009. Xing et al. (2018) reported that the groundwater levels in the Jinan spring area had experienced a significant downward trend from 1956 to 2013. Fang et al. (2019) analyzed trends in groundwater levels in the Beijing plain during the period 2005 to 2013 using the MK test and found decreases in groundwater levels, which might have created future water stresses in the region. Anand et al. (2019) analyzed the variation of groundwater level in the Lower Bhavani River basin, India using GIS (geographical information system) and MK test. The results of the MK test illustrated that the groundwater fluctuation is very high in the southeastern and northeastern parts of the basin, and it is moderate in the northern and northwestern parts of the study area. Lasagna et al. (2020) investigated the trend of piezometric levels of a shallow unconfined aquifer in Piedmont plain (Italy) through the analysis of recorded time series in 37 piezometers during the period of 2002–2017. They used the MK test and reported that half of the piezometers showed no significant trend.

In arid and semi-arid regions, groundwater plays an important role in the economic and social development of the region. Lack of accurate knowledge and over-exploitation of groundwater resources lead to irreversible environmental damage, such as declining groundwater table, land subsidence, decreasing discharges from wells and Qantas, deteriorating water quality, changing groundwater flow direction, and salt water intrusion (Nayak et al. 2006; Sattari et al. 2018). In order to assess the groundwater resources, the status and optimized water management of aquifers, it is highly necessary to consider the water table oscillations quite accurately. Accurate investigation of variations in water table provides valuable information for water resources managers and policy makers to design appropriate plans and policies for meeting the required water demands reliably. In recent decades, the groundwater level of Sirjan plain has declined remarkably. Since groundwater is the main source of water for various needs in this region, it is very important to consider the fluctuations of the water table for optimal water management. In the present study, the trends in groundwater levels in the Sirjan plain were identified by different parametric and non-parametric methods. The effect of autocorrelation on the performance of trend identifying tests was also investigated and the best method for analyzing the trends in the groundwater levels in the Sirjan plain was determined. To the best of our knowledge, this is the first study analyzing the trends in groundwater levels in the Sirjan plain. Investigation of the causative factors of variation in groundwater levels in Sirjan plain (Iran) is another important objective of this study.

MATERIALS AND METHODS

Study area

Sirjan plain (54° 57′ to 56° 26′ E and 28° 47′ to 29° 58′ N) with a geographical area of 3,982 km2 is located in the Kerman province, Iran (Figure 1). The western area of this plain is formed of quaternary deposits consisting of river alluvium, alluvial terraces, sand deposits with small sand-dunes in the southern part of the plain, and salty sandy-clay flats with salt coverage over 10 cm thick in the Kafeh Namak. The bedrock adjacent to the Kafeh Namak is formed from Miocene Marl and is approximately horizontal. The average thickness of alluvium is about 150 m with a reported maximum thickness of 250 m based on the geophysical experiments’ results. The depth of the bedrocks in the northern and northeastern parts of the plain is rising, and then is found to be decreasing towards the southern and southwestern portions of the plain. The average annual precipitation in this plain is about 177 mm. The main sources of the plain recharge in Kerman are the three rivers of Tanguyeh, Hosseinabad, and Ostor, which are usually dry in the summer season. About 98% of the extracted water from the Sirjan aquifer is used in the irrigation of gardens, especially for Pistachio.

Figure 1

Location map of Sirjan plain and selected piezometers.

Figure 1

Location map of Sirjan plain and selected piezometers.

The region has traditionally been supported by irrigated agriculture, predominantly with groundwater tapped by Qanats and hand-dug wells. In recent decades, the rising population and the agricultural development in this region has led to an increase in the demands of fresh water. Therefore, both quantity and quality of water resources play a dominant role in the availability of drinking water in the region. However, in recent decades, many deep wells have been dug and huge amounts of groundwater extracted to meet the various water demands of the local population. As a result, the shortage of groundwater has intensified, leading to a decline in the groundwater table and in the deterioration of water quality.

In this study, the monthly data of groundwater levels of 60 piezometers distributed uniformly across the Sirjan plain during 2005–2018 were used for trend analysis. The locations of the considered piezometers across Sirjan plain are shown in Figure 1. In Figure 2, the contour lines of average groundwater levels in the Sirjan plain are plotted. The groundwater flow direction in the Sirjan plain is from northwest to east and south (see Figure 2).

Figure 2

The equipotential lines of groundwater level in Srijan plain.

Figure 2

The equipotential lines of groundwater level in Srijan plain.

Regression method

The regression method is used for analyzing the relationship between variables. In this method, the dependent variable is defined as a function of independent variables. The regression method is applied usually for two objectives: determination of relationship among variables and prediction of dependent variables based on the given independent variables.

For a statistical population, the linear regression (LR) relation between dependent variable, Y, and independent variable, X, is expressed as follows (Noori et al. 2010):
formula
(1)
formula
(2)
where, and denote estimated coefficients and is the regression error. The common method for estimating the regression coefficients is the least square error method. The regression coefficients can be estimated by minimizing the SSE function , which is expressed as follows (Noori et al. 2010):
formula
(3)
formula
(4)

where, and are the average of independent and dependent variables, respectively. After determining the linear relation among variables, the null hypothesis states that the coefficient is zero, while the alternative hypothesis states that the value of coefficient is significantly different from zero.

Spearman's Rho test

The Spearman's Rho (SR) test is a non-parametric test used in estimating the trend in a time series. Before applying this test, first the data should be sorted in ascending order and a rank number be assigned to every data point. Then the data are sorted in chronological order and the rank of every data is specified. The D statistic for the considered data set can be calculated by using the equation given below (Yue et al. 2002):
formula
(5)
where, denotes the rank of the ith observed data and the n is the sample size (length of dataset). The D statistic follows the normal distribution with zero mean and its variance can be calculated as (Yue et al. 2002):
formula
(6)
The statistic of SR test, , can be calculated as follows:
formula
(7)
If Zsr is less (greater) than zero, the time series have witnessed decreasing (increasing) trend. In fact, the SR test is a special version of Pearson correlation coefficient, in which the data are ranked first and then the correlation coefficients are calculated between the ranks of data. In trend test, if the obtained absolute value of Z is greater than 1.64, the null hypothesis (no significant trend in 10% level) will be rejected and the alternative hypothesis (existence of significant trend) will be accepted. For significance level of 1% (5)%, the corresponding value of Z statistics is 2.33 (1.96).

The standard (classic) Mann–Kendall test (MK)

The standard Mann–Kendall test (MK), also called the classic version of the Mann–Kendall test, has been widely applied to estimate trends in hydro-climatological series. If the sample size is equal to n, the S statistic can be calculated by the following formula:
formula
(8)
where, denotes the jth data, n is the data length and denotes the sign function that is given as:
formula
(9)
For n 8 the S statistic follows the normal distribution, and its mean and variance can be obtained by the following formulae:
formula
(10)
formula
(11)
where, C is a factor for modifying variance. When there are tied data (same consecutive data) in the series, the C is calculated by the formula below and will apply on variance of S.
formula
(12)
where, denotes the number of tied data in the ith group. The Z statistic of the MK test can be computed as:
formula
(13)

In the MK test, the null hypothesis H0, (there is no significant trend in the time series) will be accepted if at the α significance level, , otherwise the H0 will be rejected and the alternative hypothesis (existence of significant trend at the significance level of α) will be accepted (Dinpashoh et al. 2014).

The modified Mann–Kendall test (MMK)

The main assumption of the MK test is that the sample data are not significantly correlated; however, some hydrological series may have significant autocorrelations (Khalili et al. 2016; Sanikhani et al. 2018). If a series has significant positive or negative autocorrelation coefficients, then the MK test will show an unrealistically large value of the Z statistic leading to the rejection of the null hypothesis (there is no trend in data series) instead of the acceptance of the null hypothesis, i.e., there is no trend in hydrological data series in reality (Ahmadi et al. 2018). The modified version of the Mann–Kendall (MMK) test, suggested by Hamed & Rao (1998), has been used by Dinpashoh et al. (2014) for analyzing the trends in precipitation over Iran, and Jhajharia et al. (2012) for identifying trends in reference evapotranspiration over northeast India. In this method, the effect of all significant autocorrelation coefficients is eliminated from the time series before applying the MK test. In the MMK test, the modified variance V(S)* is calculated as follows:
formula
(14)
formula
(15)
where is the i delayed autocorrelation coefficient and is estimated using Equation (11). For calculating the Z statistic in the MMK test (Equation (13)), is substituted by .

Innovative Trend Method (ITM)

The Innovative Trend Method (ITM), proposed by Şen (2012), was used in this study to analyze trend identification in groundwater levels in annual, seasonal, and monthly time scales. In the ITM test, the studied time series should be divided into two equal sub-series. Then, both the sub-series should be separately sorted in the ascending order. A scatter plot is drawn from two sorted sub-series and one sub-series (Xi) lies on the X-axis and the other sub-series (Xj) lies on the Y-axis (Figure 3). If the data points concentrate on the 1:1 (45°) line of the resulting scatter plot, it indicates that there is no significant trend in the studied time series. Otherwise, if data points are accumulated in the upper (below) area of the 1:1 line, it means that there is increasing (decreasing) trend in the considered time series (Şen 2012).

Figure 3

Sen's method graph (Kisi & Ay 2014).

Figure 3

Sen's method graph (Kisi & Ay 2014).

RESULTS AND DISCUSSION

Selection of the best trend analysis method

In the present study, five tests used in identifying trends, namely, linear regression (LR), Spearmen's Rho (SR), classic Mann–Kendall (MK), modified Mann–Kendall (MMK), and Innovative Trend Method (ITM), were applied with the main aim of choosing the best method for analyzing the trends in groundwater levels in the Sirjan plain. The results obtained by using these tests in seasonal and annual time scales are given in Tables 15. The box and whisker plots of trend statistics for 60 studied stations (W1 to W60) in seasonal and annual time scales are given in Figure 4. The lines inside the boxes represent the median of trend test statistics; while the lower and upper lines of the boxes display the 25 and 75% percentiles, respectively (see Figure 4). In addition, the lower and upper parts of the whiskers and vertical lines display the corresponding minimum and maximum values of the trend tests’ statistics of series, respectively. The range of trends in groundwater levels obtained through the t-statistic of the linear regression test is larger than the other four non-parametric tests (see Figure 4). The range of Z statistics of the MK and the SR tests are almost the same, while the Z statistic values obtained by using the MMK test were found to have the lowest variance. Therefore, test statistics’ values from non-parametric methods are largely close to each other, but the calculated t-statistics of the LR test are somewhat higher than the corresponding statistics of non-parametric tests because the parametric tests are sensitive to outlier values and are affected by the extreme values. The non-parametric tests are more robust against the outliers and the extreme data in comparison to the parametric tests.

Table 1

The values of Z statistic of MK test for annual and seasonal series of groundwater level in Sirjan plain (2005–2018)

StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.
W1 − 4.82 − 4.93 − 4.93 − 4.93 − 4.93 W21 − 4.27 − 4.05 − 4.38 − 4.38 − 4.38 W41 − 4.38 − 4.49 − 4.27 − 4.38 − 4.49 
W2 1.42 1.86 1.75 1.75 2.19 W22 − 4.82 − 4.82 − 4.93 − 4.82 − 4.93 W42 − 4.38 − 4.49 − 4.49 − 4.49 − 4.38 
W3 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W23 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W43 − 4.55 − 4.71 − 4.38 − 4.27 − 4.71 
W4 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W24 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W44 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W5 − 2.41 − 2.63 − 2.52 − 2.41 − 2.52 W25 − 4.16 − 4.38 − 4.60 − 3.78 − 4.49 W45 − 4.49 − 4.60 − 4.60 − 4.60 − 4.60 
W6 − 3.83 − 4.27 − 4.38 − 4.00 − 4.27 W26 − 4.60 − 4.60 − 4.71 − 4.71 − 4.71 W46 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W7 − 4.49 − 4.27 -4.16 − 4.16 − 4.27 W27 − 4.00 -4.27 − 3.83 − 3.83 − 3.94 W47 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W8 − 3.61 − 3.50 − 3.28 − 3.39 − 3.50 W28 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W48 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W9 0.33 0.33 0.11 0.11 0.00 W29 − 4.11 − 3.18 − 2.96 − 3.61 − 3.50 W49 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W10 − 3.50 − 3.50 − 3.61 − 3.61 − 3.83 W30 − 4.82 − 4.82 − 4.82 − 4.93 − 4.82 W50 − 3.94 − 3.83 − 4.05 − 4.16 − 4.05 
W11 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W31 − 4.27 − 4.38 − 4.16 − 4.27 − 4.27 W51 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W12 − 4.82 − 4.82 − 4.93 − 4.82 − 4.93 W32 − 3.94 − 3.83 − 3.94 − 3.94 − 3.83 W52 − 4.93 − 4.93 − 4.82 − 4.93 − 4.93 
W13 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W33 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W53 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W14 − 4.38 − 4.38 − 4.16 − 4.27 − 4.38 W34 − 4.82 − 4.93 − 4.93 − 4.93 − 4.93 W54 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W15 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W35 − 2.85 − 2.85 − 2.74 − 3.18 − 2.96 W55 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W16 0.49 0.22 0.44 0.88 0.22 W36 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W56 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W17 − 4.05 − 3.94 − 3.83 − 3.83 − 4.16 W37 − 4.05 − 4.27 − 4.27 − 4.38 − 4.16 W57 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W18 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W38 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W58 1.97 1.75 1.64 1.64 1.75 
W19 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W39 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W59 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W20 − 4.93 − 4.93 − 4.93 − 4.82 − 4.93 W40 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W60 − 4.82 − 4.82 − 4.93 − 4.82 − 4.93 
StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.
W1 − 4.82 − 4.93 − 4.93 − 4.93 − 4.93 W21 − 4.27 − 4.05 − 4.38 − 4.38 − 4.38 W41 − 4.38 − 4.49 − 4.27 − 4.38 − 4.49 
W2 1.42 1.86 1.75 1.75 2.19 W22 − 4.82 − 4.82 − 4.93 − 4.82 − 4.93 W42 − 4.38 − 4.49 − 4.49 − 4.49 − 4.38 
W3 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W23 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W43 − 4.55 − 4.71 − 4.38 − 4.27 − 4.71 
W4 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W24 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W44 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W5 − 2.41 − 2.63 − 2.52 − 2.41 − 2.52 W25 − 4.16 − 4.38 − 4.60 − 3.78 − 4.49 W45 − 4.49 − 4.60 − 4.60 − 4.60 − 4.60 
W6 − 3.83 − 4.27 − 4.38 − 4.00 − 4.27 W26 − 4.60 − 4.60 − 4.71 − 4.71 − 4.71 W46 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W7 − 4.49 − 4.27 -4.16 − 4.16 − 4.27 W27 − 4.00 -4.27 − 3.83 − 3.83 − 3.94 W47 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W8 − 3.61 − 3.50 − 3.28 − 3.39 − 3.50 W28 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W48 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W9 0.33 0.33 0.11 0.11 0.00 W29 − 4.11 − 3.18 − 2.96 − 3.61 − 3.50 W49 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W10 − 3.50 − 3.50 − 3.61 − 3.61 − 3.83 W30 − 4.82 − 4.82 − 4.82 − 4.93 − 4.82 W50 − 3.94 − 3.83 − 4.05 − 4.16 − 4.05 
W11 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W31 − 4.27 − 4.38 − 4.16 − 4.27 − 4.27 W51 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W12 − 4.82 − 4.82 − 4.93 − 4.82 − 4.93 W32 − 3.94 − 3.83 − 3.94 − 3.94 − 3.83 W52 − 4.93 − 4.93 − 4.82 − 4.93 − 4.93 
W13 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W33 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W53 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W14 − 4.38 − 4.38 − 4.16 − 4.27 − 4.38 W34 − 4.82 − 4.93 − 4.93 − 4.93 − 4.93 W54 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W15 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W35 − 2.85 − 2.85 − 2.74 − 3.18 − 2.96 W55 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W16 0.49 0.22 0.44 0.88 0.22 W36 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W56 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W17 − 4.05 − 3.94 − 3.83 − 3.83 − 4.16 W37 − 4.05 − 4.27 − 4.27 − 4.38 − 4.16 W57 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W18 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W38 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W58 1.97 1.75 1.64 1.64 1.75 
W19 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W39 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W59 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 
W20 − 4.93 − 4.93 − 4.93 − 4.82 − 4.93 W40 − 4.93 − 4.93 − 4.93 − 4.93 − 4.93 W60 − 4.82 − 4.82 − 4.93 − 4.82 − 4.93 

The bold numbers denote significant trend at 5% significance level.

Table 2

The values of Z statistic of MMK test for annual and seasonal series of groundwater level in Sirjan plain (2005–2018)

StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.
W1 − 2.56 − 2.62 − 2.61 − 2.61 − 2.61 W21 − 2.54 − 2.41 − 2.59 − 2.57 − 2.58 W41 − 2.55 − 2.46 − 2.30 − 2.34 − 2.43 
W2 0.85 1.16 1.10 1.05 1.31 W22 − 2.87 − 2.70 − 2.75 − 2.69 − 2.75 W42 − 2.75 − 2.88 − 2.88 − 2.82 − 2.78 
W3 − 2.67 − 2.68 − 2.68 − 2.67 − 2.67 W23 − 2.65 − 2.66 − 2.66 − 2.65 − 2.65 W43 − 2.48 − 2.54 − 2.35 − 2.28 − 2.53 
W4 − 2.66 − 2.68 − 2.68 − 2.68 − 2.67 W24 − 2.57 − 2.58 − 2.58 − 2.58 − 2.58 W44 − 2.62 − 2.62 − 2.61 − 2.61 − 2.61 
W5 1.76 1.97 1.92 1.78 1.87 W25 − 2.59 − 2.53 − 2.61 − 2.17 − 2.55 W45 − 2.72 − 2.78 − 2.76 − 2.71 − 2.75 
W6 − 2.70 − 3.02 − 3.11 − 2.83 − 3.01 W26 − 2.50 − 2.50 − 2.55 − 2.54 − 2.55 W46 − 2.65 − 2.65 − 2.63 − 2.64 − 2.64 
W7 − 3.44 − 3.29 − 3.16 − 3.16 − 3.25 W27 − 2.32 − 2.33 − 2.05 − 2.05 − 2.13 W47 − 2.64 − 2.65 − 2.64 − 2.62 − 2.63 
W8 − 2.12 − 2.05 − 1.93 − 2.02 − 2.05 W28 − 2.62 − 2.63 − 2.63 − 2.63 − 2.63 W48 − 2.60 − 2.60 − 2.59 − 2.58 − 2.59 
W9 0.33 0.25 0.08 0.11 0.00 W29 − 2.36 − 1.86 − 1.75 − 2.13 − 2.04 W49 − 2.60 − 2.60 − 2.60 − 2.59 − 2.60 
W10 − 3.50 − 2.72 − 2.68 − 2.69 − 2.87 W30 − 2.59 − 2.59 − 2.57 − 2.62 − 2.57 W50 − 2.53 − 2.75 − 2.87 − 2.65 − 2.86 
W11 − 2.61 − 2.63 − 2.63 − 2.63 − 2.62 W31 − 2.59 − 2.61 − 2.44 − 2.37 − 2.52 W51 − 2.65 − 2.64 − 2.64 − 2.64 − 2.64 
W12 − 2.56 − 2.55 − 2.59 − 2.52 − 2.59 W32 − 2.48 − 2.39 − 2.41 − 2.37 − 2.35 W52 − 2.67 − 2.65 − 2.58 − 2.65 − 2.65 
W13 − 2.68 − 2.70 − 2.71 − 2.70 − 2.70 W33 − 2.63 − 2.62 − 2.62 − 2.61 − 2.62 W53 − 2.58 − 2.58 − 2.58 − 2.57 − 2.58 
W14 − 2.64 − 2.64 − 2.52 − 2.57 − 2.63 W34 − 2.61 − 2.67 − 2.66 − 2.65 − 2.66 W54 − 2.66 − 2.65 − 2.64 − 2.65 − 2.65 
W15 − 2.69 − 2.69 − 2.68 − 2.66 − 2.68 W35 − 2.24 − 2.15 1.94 − 2.28 − 2.12 W55 − 2.58 − 2.57 − 2.57 − 2.57 − 2.57 
W16 0.36 0.16 0.30 0.60 0.15 W36 − 2.65 − 2.66 − 2.64 − 2.63 − 2.64 W56 − 2.57 − 2.56 − 2.56 − 2.57 − 2.57 
W17 − 3.17 − 2.88 − 2.68 − 2.79 − 2.94 W37 − 2.43 − 2.54 − 2.50 − 2.40 − 2.44 W57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 
W18 − 2.57 − 2.58 − 2.58 − 2.57 − 2.57 W38 − 2.57 − 2.58 − 2.57 − 2.57 − 2.57 W58 1.51 1.37 1.24 1.23 1.32 
W19 − 2.59 − 2.59 − 2.58 − 2.57 − 2.58 W39 − 2.67 − 2.67 − 2.67 − 2.66 − 2.67 W59 − 2.61 − 2.61 − 2.60 − 2.60 − 2.61 
W20 − 2.65 − 2.65 − 2.66 − 2.60 − 2.65 W40 − 2.66 − 2.65 − 2.64 − 2.64 − 2.65 W60 − 2.62 − 2.61 − 2.64 − 2.58 − 2.65 
StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.
W1 − 2.56 − 2.62 − 2.61 − 2.61 − 2.61 W21 − 2.54 − 2.41 − 2.59 − 2.57 − 2.58 W41 − 2.55 − 2.46 − 2.30 − 2.34 − 2.43 
W2 0.85 1.16 1.10 1.05 1.31 W22 − 2.87 − 2.70 − 2.75 − 2.69 − 2.75 W42 − 2.75 − 2.88 − 2.88 − 2.82 − 2.78 
W3 − 2.67 − 2.68 − 2.68 − 2.67 − 2.67 W23 − 2.65 − 2.66 − 2.66 − 2.65 − 2.65 W43 − 2.48 − 2.54 − 2.35 − 2.28 − 2.53 
W4 − 2.66 − 2.68 − 2.68 − 2.68 − 2.67 W24 − 2.57 − 2.58 − 2.58 − 2.58 − 2.58 W44 − 2.62 − 2.62 − 2.61 − 2.61 − 2.61 
W5 1.76 1.97 1.92 1.78 1.87 W25 − 2.59 − 2.53 − 2.61 − 2.17 − 2.55 W45 − 2.72 − 2.78 − 2.76 − 2.71 − 2.75 
W6 − 2.70 − 3.02 − 3.11 − 2.83 − 3.01 W26 − 2.50 − 2.50 − 2.55 − 2.54 − 2.55 W46 − 2.65 − 2.65 − 2.63 − 2.64 − 2.64 
W7 − 3.44 − 3.29 − 3.16 − 3.16 − 3.25 W27 − 2.32 − 2.33 − 2.05 − 2.05 − 2.13 W47 − 2.64 − 2.65 − 2.64 − 2.62 − 2.63 
W8 − 2.12 − 2.05 − 1.93 − 2.02 − 2.05 W28 − 2.62 − 2.63 − 2.63 − 2.63 − 2.63 W48 − 2.60 − 2.60 − 2.59 − 2.58 − 2.59 
W9 0.33 0.25 0.08 0.11 0.00 W29 − 2.36 − 1.86 − 1.75 − 2.13 − 2.04 W49 − 2.60 − 2.60 − 2.60 − 2.59 − 2.60 
W10 − 3.50 − 2.72 − 2.68 − 2.69 − 2.87 W30 − 2.59 − 2.59 − 2.57 − 2.62 − 2.57 W50 − 2.53 − 2.75 − 2.87 − 2.65 − 2.86 
W11 − 2.61 − 2.63 − 2.63 − 2.63 − 2.62 W31 − 2.59 − 2.61 − 2.44 − 2.37 − 2.52 W51 − 2.65 − 2.64 − 2.64 − 2.64 − 2.64 
W12 − 2.56 − 2.55 − 2.59 − 2.52 − 2.59 W32 − 2.48 − 2.39 − 2.41 − 2.37 − 2.35 W52 − 2.67 − 2.65 − 2.58 − 2.65 − 2.65 
W13 − 2.68 − 2.70 − 2.71 − 2.70 − 2.70 W33 − 2.63 − 2.62 − 2.62 − 2.61 − 2.62 W53 − 2.58 − 2.58 − 2.58 − 2.57 − 2.58 
W14 − 2.64 − 2.64 − 2.52 − 2.57 − 2.63 W34 − 2.61 − 2.67 − 2.66 − 2.65 − 2.66 W54 − 2.66 − 2.65 − 2.64 − 2.65 − 2.65 
W15 − 2.69 − 2.69 − 2.68 − 2.66 − 2.68 W35 − 2.24 − 2.15 1.94 − 2.28 − 2.12 W55 − 2.58 − 2.57 − 2.57 − 2.57 − 2.57 
W16 0.36 0.16 0.30 0.60 0.15 W36 − 2.65 − 2.66 − 2.64 − 2.63 − 2.64 W56 − 2.57 − 2.56 − 2.56 − 2.57 − 2.57 
W17 − 3.17 − 2.88 − 2.68 − 2.79 − 2.94 W37 − 2.43 − 2.54 − 2.50 − 2.40 − 2.44 W57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 
W18 − 2.57 − 2.58 − 2.58 − 2.57 − 2.57 W38 − 2.57 − 2.58 − 2.57 − 2.57 − 2.57 W58 1.51 1.37 1.24 1.23 1.32 
W19 − 2.59 − 2.59 − 2.58 − 2.57 − 2.58 W39 − 2.67 − 2.67 − 2.67 − 2.66 − 2.67 W59 − 2.61 − 2.61 − 2.60 − 2.60 − 2.61 
W20 − 2.65 − 2.65 − 2.66 − 2.60 − 2.65 W40 − 2.66 − 2.65 − 2.64 − 2.64 − 2.65 W60 − 2.62 − 2.61 − 2.64 − 2.58 − 2.65 

The bold numbers denote significant trend at 5% significance level.

Table 3

The values of Zsr statistic of SR test for annual and seasonal series of groundwater level in Sirjan plain (2005–2018)

StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.
W1 − 3.59 − 3.61 − 3.61 − 3.61 − 3.61 W21 − 3.42 − 3.30 − 3.43 − 3.45 − 3.43 W41 − 3.42 − 3.49 − 3.42 − 3.43 − 3.49 
W2 2.12 2.07 1.93 2.07 2.34 W22 − 3.59 − 3.59 − 3.61 − 3.59 − 3.61 W42 − 3.45 − 3.49 − 3.48 − 3.48 − 3.46 
W3 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W23 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W43 − 3.51 − 3.56 − 3.46 − 3.45 − 3.56 
W4 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W24 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W44 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W5 − 2.12 − 2.18 − 2.00 − 1.96 − 2.15 W25 − 3.43 − 3.49 − 3.54 − 3.21 − 3.51 W45 − 3.48 − 3.53 − 3.53 − 3.54 − 3.53 
W6 − 3.13 − 3.37 − 3.46 − 3.24 − 3.37 W26 − 3.53 − 3.53 − 3.56 − 3.56 − 3.56 W46 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W7 − 3.48 − 3.43 − 3.42 − 3.37 − 3.45 W27 − 3.24 − 3.37 − 3.24 − 3.16 − 3.26 W47 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W8 − 3.07 − 2.99 − 2.83 − 2.80 − 3.00 W28 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W48 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W9 − 0.42 − 0.12 0.13 − 0.18 0.02 W29 − 3.36 − 2.80 − 2.59 − 3.07 − 2.99 W49 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W10 − 2.92 − 2.96 − 3.07 − 2.99 − 3.13 W30 − 3.59 − 3.59 − 3.59 − 3.61 − 3.59 W50 − 3.24 − 3.18 − 3.32 − 3.32 − 3.32 
W11 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W31 − 3.37 − 3.43 − 3.35 − 3.38 − 3.38 W51 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W12 − 3.59 − 3.59 − 3.61 − 3.59 − 3.61 W32 − 3.21 − 3.11 − 3.19 − 3.21 − 3.11 W52 − 3.61 − 3.61 − 3.59 − 3.61 − 3.61 
W13 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W33 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W53 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W14 − 3.45 − 3.43 − 3.35 − 3.42 − 3.43 W34 − 3.59 − 3.61 − 3.61 − 3.61 − 3.61 W54 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W15 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W35 − 2.37 − 2.39 − 2.39 − 2.64 − 2.50 W55 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W16 0.54 0.49 0.50 1.01 0.20 W36 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W56 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W17 − 3.23 − 3.18 − 3.19 − 3.16 − 3.35 W37 − 3.30 − 3.37 − 3.42 − 3.43 − 3.35 W57 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W18 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W38 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W58 1.64 1.79 1.66 1.83 1.70 
W19 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W39 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W59 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W20 − 3.61 − 3.61 − 3.61 − 3.59 − 3.61 W40 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W60 − 3.59 − 3.59 − 3.61 − 3.59 − 3.61 
StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.
W1 − 3.59 − 3.61 − 3.61 − 3.61 − 3.61 W21 − 3.42 − 3.30 − 3.43 − 3.45 − 3.43 W41 − 3.42 − 3.49 − 3.42 − 3.43 − 3.49 
W2 2.12 2.07 1.93 2.07 2.34 W22 − 3.59 − 3.59 − 3.61 − 3.59 − 3.61 W42 − 3.45 − 3.49 − 3.48 − 3.48 − 3.46 
W3 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W23 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W43 − 3.51 − 3.56 − 3.46 − 3.45 − 3.56 
W4 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W24 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W44 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W5 − 2.12 − 2.18 − 2.00 − 1.96 − 2.15 W25 − 3.43 − 3.49 − 3.54 − 3.21 − 3.51 W45 − 3.48 − 3.53 − 3.53 − 3.54 − 3.53 
W6 − 3.13 − 3.37 − 3.46 − 3.24 − 3.37 W26 − 3.53 − 3.53 − 3.56 − 3.56 − 3.56 W46 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W7 − 3.48 − 3.43 − 3.42 − 3.37 − 3.45 W27 − 3.24 − 3.37 − 3.24 − 3.16 − 3.26 W47 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W8 − 3.07 − 2.99 − 2.83 − 2.80 − 3.00 W28 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W48 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W9 − 0.42 − 0.12 0.13 − 0.18 0.02 W29 − 3.36 − 2.80 − 2.59 − 3.07 − 2.99 W49 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W10 − 2.92 − 2.96 − 3.07 − 2.99 − 3.13 W30 − 3.59 − 3.59 − 3.59 − 3.61 − 3.59 W50 − 3.24 − 3.18 − 3.32 − 3.32 − 3.32 
W11 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W31 − 3.37 − 3.43 − 3.35 − 3.38 − 3.38 W51 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W12 − 3.59 − 3.59 − 3.61 − 3.59 − 3.61 W32 − 3.21 − 3.11 − 3.19 − 3.21 − 3.11 W52 − 3.61 − 3.61 − 3.59 − 3.61 − 3.61 
W13 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W33 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W53 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W14 − 3.45 − 3.43 − 3.35 − 3.42 − 3.43 W34 − 3.59 − 3.61 − 3.61 − 3.61 − 3.61 W54 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W15 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W35 − 2.37 − 2.39 − 2.39 − 2.64 − 2.50 W55 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W16 0.54 0.49 0.50 1.01 0.20 W36 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W56 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W17 − 3.23 − 3.18 − 3.19 − 3.16 − 3.35 W37 − 3.30 − 3.37 − 3.42 − 3.43 − 3.35 W57 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W18 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W38 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W58 1.64 1.79 1.66 1.83 1.70 
W19 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W39 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W59 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 
W20 − 3.61 − 3.61 − 3.61 − 3.59 − 3.61 W40 − 3.61 − 3.61 − 3.61 − 3.61 − 3.61 W60 − 3.59 − 3.59 − 3.61 − 3.59 − 3.61 

The bold numbers denote significant trend at 5% significance level.

Table 4

The results of LR test for annual and seasonal series of groundwater level in Sirjan plain (2005–2018)

StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.
W1 − 51.51 − 63.55 − 59.70 − 53.75 − 70.13 W21 − 9.84 − 9.45 − 10.40 − 10.36 − 10.43 W41 − 10.28 − 11.02 − 11.15 − 10.85 − 11.31 
W2 2.67 2.43 2.62 3.12 2.82 W22 − 11.63 − 11.56 − 12.71 − 13.80 − 12.66 W42 − 6.11 − 6.13 − 6.09 − 6.81 − 6.29 
W3 − 19.41 − 20.97 − 21.69 − 23.50 − 21.70 W23 − 45.95 − 40.09 − 39.01 − 37.55 − 41.30 W43 − 11.25 − 10.72 − 11.90 − 12.46 − 11.82 
W4 − 15.34 − 16.53 − 18.36 − 16.15 − 16.74 W24 − 51.98 − 54.18 − 50.05 − 47.17 − 52.84 W44 − 49.80 − 73.22 − 83.39 − 38.92 − 61.93 
W5 − 2.53 − 2.96 − 2.94 − 2.61 − 2.87 W25 − 9.33 − 10.85 − 10.99 − 9.28 − 12.10 W45 − 12.41 − 12.60 − 13.99 − 15.42 − 14.06 
W6 − 6.33 − 6.39 − 6.59 − 6.69 − 6.73 W26 − 19.53 − 20.29 − 20.50 − 18.61 − 21.26 W46 − 27.91 − 27.55 − 28.95 − 30.06 − 29.49 
W7 − 4.28 − 4.39 − 4.59 − 5.13 − 4.64 W27 − 8.40 − 9.37 − 9.53 − 8.33 − 9.06 W47 − 40.64 − 40.12 − 49.04 − 63.91 − 50.48 
W8 − 5.89 − 5.94 − 5.50 − 5.52 − 5.85 W28 − 74.48 − 59.79 − 50.84 − 71.13 − 76.04 W48 − 36.33 − 40.09 − 49.77 − 38.23 − 48.72 
W9 0.60 0.04 0.11 0.11 0.19 W29 − 6.72 − 5.19 − 4.47 − 5.06 − 5.36 W49 − 52.59 − 51.73 − 52.38 − 59.19 − 55.49 
W10 − 4.21 − 4.46 − 5.21 − 5.35 − 5.32 W30 − 20.86 − 21.03 − 21.80 − 22.13 − 22.15 W50 − 6.82 − 6.69 − 7.32 − 8.04 − 7.66 
W11 − 31.89 − 30.80 − 29.49 − 31.78 − 32.80 W31 − 7.78 − 9.32 − 9.40 − 8.76 − 9.51 W51 − 24.05 − 25.61 − 25.37 − 26.49 − 25.50 
W12 − 8.90 − 9.19 − 9.49 − 10.01 − 9.46 W32 − 6.77 − 6.75 − 7.01 − 6.99 − 7.09 W52 − 30.71 − 33.02 − 26.89 − 25.28 − 29.80 
W13 − 18.41 − 18.51 − 17.78 − 19.90 − 18.83 W33 − 49.81 − 50.82 − 58.92 − 61.07 − 62.14 W53 − 51.19 − 54.81 − 61.57 − 62.90 − 60.10 
W14 − 11.32 − 11.45 − 9.73 − 10.60 − 11.30 W34 − 34.41 − 33.82 − 35.11 − 38.46 − 37.25 W54 − 49.79 − 60.39 − 57.37 − 44.90 − 55.01 
W15 − 26.19 − 24.86 − 25.88 − 32.50 − 27.73 W35 − 3.01 − 3.28 − 3.36 − 3.59 − 3.57 W55 − 49.97 − 44.62 − 55.01 − 49.14 − 53.76 
W16 0.57 0.62 0.80 0.94 0.76 W36 − 29.65 − 28.32 − 34.83 − 34.89 − 34.22 W56 − 48.01 − 43.78 − 43.07 − 43.83 − 48.65 
W17 − 5.27 − 6.29 − 7.93 − 6.23 − 8.00 W37 − 8.87 − 8.93 − 9.46 − 9.79 − 9.81 W57 − 39.19 − 40.58 − 42.83 − 41.48 − 43.04 
W18 − 56.08 − 67.45 − 65.45 − 58.88 − 68.47 W38 − 40.92 − 39.99 − 38.57 − 35.05 − 40.12 W58 1.82 1.68 1.64 1.96 1.86 
W19 − 31.12 − 34.50 − 36.99 − 36.52 − 35.67 W39 − 28.48 − 25.76 − 23.87 − 23.02 − 25.46 W59 − 44.59 − 40.38 − 37.64 − 39.73 − 41.43 
W20 − 43.81 − 47.12 − 36.03 − 34.41 − 43.12 W40 − 14.05 − 14.32 − 14.22 − 14.34 − 14.31 W60 − 27.40 − 32.27 − 36.72 − 30.30 − 38.43 
StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.StationSpringSummerAutumnWinterAnn.
W1 − 51.51 − 63.55 − 59.70 − 53.75 − 70.13 W21 − 9.84 − 9.45 − 10.40 − 10.36 − 10.43 W41 − 10.28 − 11.02 − 11.15 − 10.85 − 11.31 
W2 2.67 2.43 2.62 3.12 2.82 W22 − 11.63 − 11.56 − 12.71 − 13.80 − 12.66 W42 − 6.11 − 6.13 − 6.09 − 6.81 − 6.29 
W3 − 19.41 − 20.97 − 21.69 − 23.50 − 21.70 W23 − 45.95 − 40.09 − 39.01 − 37.55 − 41.30 W43 − 11.25 − 10.72 − 11.90 − 12.46 − 11.82 
W4 − 15.34 − 16.53 − 18.36 − 16.15 − 16.74 W24 − 51.98 − 54.18 − 50.05 − 47.17 − 52.84 W44 − 49.80 − 73.22 − 83.39 − 38.92 − 61.93 
W5 − 2.53 − 2.96 − 2.94 − 2.61 − 2.87 W25 − 9.33 − 10.85 − 10.99 − 9.28 − 12.10 W45 − 12.41 − 12.60 − 13.99 − 15.42 − 14.06 
W6 − 6.33 − 6.39 − 6.59 − 6.69 − 6.73 W26 − 19.53 − 20.29 − 20.50 − 18.61 − 21.26 W46 − 27.91 − 27.55 − 28.95 − 30.06 − 29.49 
W7 − 4.28 − 4.39 − 4.59 − 5.13 − 4.64 W27 − 8.40 − 9.37 − 9.53 − 8.33 − 9.06 W47 − 40.64 − 40.12 − 49.04 − 63.91 − 50.48 
W8 − 5.89 − 5.94 − 5.50 − 5.52 − 5.85 W28 − 74.48 − 59.79 − 50.84 − 71.13 − 76.04 W48 − 36.33 − 40.09 − 49.77 − 38.23 − 48.72 
W9 0.60 0.04 0.11 0.11 0.19 W29 − 6.72 − 5.19 − 4.47 − 5.06 − 5.36 W49 − 52.59 − 51.73 − 52.38 − 59.19 − 55.49 
W10 − 4.21 − 4.46 − 5.21 − 5.35 − 5.32 W30 − 20.86 − 21.03 − 21.80 − 22.13 − 22.15 W50 − 6.82 − 6.69 − 7.32 − 8.04 − 7.66 
W11 − 31.89 − 30.80 − 29.49 − 31.78 − 32.80 W31 − 7.78 − 9.32 − 9.40 − 8.76 − 9.51 W51 − 24.05 − 25.61 − 25.37 − 26.49 − 25.50 
W12 − 8.90 − 9.19 − 9.49 − 10.01 − 9.46 W32 − 6.77 − 6.75 − 7.01 − 6.99 − 7.09 W52 − 30.71 − 33.02 − 26.89 − 25.28 − 29.80 
W13 − 18.41 − 18.51 − 17.78 − 19.90 − 18.83 W33 − 49.81 − 50.82 − 58.92 − 61.07 − 62.14 W53 − 51.19 − 54.81 − 61.57 − 62.90 − 60.10 
W14 − 11.32 − 11.45 − 9.73 − 10.60 − 11.30 W34 − 34.41 − 33.82 − 35.11 − 38.46 − 37.25 W54 − 49.79 − 60.39 − 57.37 − 44.90 − 55.01 
W15 − 26.19 − 24.86 − 25.88 − 32.50 − 27.73 W35 − 3.01 − 3.28 − 3.36 − 3.59 − 3.57 W55 − 49.97 − 44.62 − 55.01 − 49.14 − 53.76 
W16 0.57 0.62 0.80 0.94 0.76 W36 − 29.65 − 28.32 − 34.83 − 34.89 − 34.22 W56 − 48.01 − 43.78 − 43.07 − 43.83 − 48.65 
W17 − 5.27 − 6.29 − 7.93 − 6.23 − 8.00 W37 − 8.87 − 8.93 − 9.46 − 9.79 − 9.81 W57 − 39.19 − 40.58 − 42.83 − 41.48 − 43.04 
W18 − 56.08 − 67.45 − 65.45 − 58.88 − 68.47 W38 − 40.92 − 39.99 − 38.57 − 35.05 − 40.12 W58 1.82 1.68 1.64 1.96 1.86 
W19 − 31.12 − 34.50 − 36.99 − 36.52 − 35.67 W39 − 28.48 − 25.76 − 23.87 − 23.02 − 25.46 W59 − 44.59 − 40.38 − 37.64 − 39.73 − 41.43 
W20 − 43.81 − 47.12 − 36.03 − 34.41 − 43.12 W40 − 14.05 − 14.32 − 14.22 − 14.34 − 14.31 W60 − 27.40 − 32.27 − 36.72 − 30.30 − 38.43 

The bold numbers denote significant trend at 5% significance level.

Table 5

The results of ITM test for monthly series of groundwater level in Sirjan plain (2005–2018)

StationSpringSummerAutumnWinterAnnualStationSpringSummerAutumnWinterAnnualSpring
W1 Y(−) Y(−) Y(−) Y(−) Y(−) W31 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W2 Y(+) Y(+) Y(+) Y(+) Y(+) W32 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W3 Y(−) Y(−) Y(−) Y(−) Y(−) W33 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W4 Y(−) Y(−) Y(−) Y(−) Y(−) W34 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W5 Y(−) Y(−) Y(−) Y(−) Y(−) W35 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W6 Y(−) Y(−) Y(−) Y(−) Y(−) W36 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W7 Y(−) Y(−) Y(−) Y(−) Y(−) W37 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W8 Y(−) Y(−) Y(−) Y(−) Y(−) W38 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W9 Y(−) Y(−) Y(−) Y(−) Y(−) W39 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W10 Y(−) Y(− Y(+) Y(−) W40 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W11 Y(−) Y(−) Y(−) Y(−) Y(−) W41 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W12 Y(−) Y(−) Y(−) Y(−) Y(− W42 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W13 Y(−) Y(−) Y(−) Y(−) Y(−) W43 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W14 Y(−) Y(−) Y(−) Y(−) Y(−) W44 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W15 Y(−) Y(−) Y(−) Y(−) Y(−) W45 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W16 Y(+) Y(+) Y(+) Y(+) Y(+) W46 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W17 Y(−) Y(−) Y(−) Y(−) Y(−) W47 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W18 Y(−) Y(−) Y(−) Y(−) Y(−) W48 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W19 Y(−) Y(−) Y(−) Y(−) Y(−) W49 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W20 Y(−) Y(−) Y(−) Y(−) Y(−) W50 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W21 Y(−) Y(−) Y(−) Y(−) Y(−) W51 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W22 Y(−) Y(−) Y(−) Y(−) Y(−) W52 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W23 Y(−) Y(−) Y(−) Y(−) Y(−) W53 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W24 Y(−) Y(−) Y(−) Y(−) Y(−) W54 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W25 Y(−) Y(−) Y(−) Y(−) Y(−) W55 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W26 Y(−) Y(−) Y(−) Y(−) Y(−) W56 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W27 Y(−) Y(−) Y(−) Y(−) Y(−) W57 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W28 Y(−) Y(−) Y(−) Y(−) Y(−) W58 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W29 Y(−) Y(−) Y(−) Y(−) Y(−) W59 Y(− Y(−) Y(− Y(−) Y(−) Y(−) 
W30 Y(−) Y(−) Y(−) Y(−) Y(−) W60 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
StationSpringSummerAutumnWinterAnnualStationSpringSummerAutumnWinterAnnualSpring
W1 Y(−) Y(−) Y(−) Y(−) Y(−) W31 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W2 Y(+) Y(+) Y(+) Y(+) Y(+) W32 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W3 Y(−) Y(−) Y(−) Y(−) Y(−) W33 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W4 Y(−) Y(−) Y(−) Y(−) Y(−) W34 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W5 Y(−) Y(−) Y(−) Y(−) Y(−) W35 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W6 Y(−) Y(−) Y(−) Y(−) Y(−) W36 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W7 Y(−) Y(−) Y(−) Y(−) Y(−) W37 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W8 Y(−) Y(−) Y(−) Y(−) Y(−) W38 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W9 Y(−) Y(−) Y(−) Y(−) Y(−) W39 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W10 Y(−) Y(− Y(+) Y(−) W40 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W11 Y(−) Y(−) Y(−) Y(−) Y(−) W41 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W12 Y(−) Y(−) Y(−) Y(−) Y(− W42 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W13 Y(−) Y(−) Y(−) Y(−) Y(−) W43 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W14 Y(−) Y(−) Y(−) Y(−) Y(−) W44 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W15 Y(−) Y(−) Y(−) Y(−) Y(−) W45 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W16 Y(+) Y(+) Y(+) Y(+) Y(+) W46 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W17 Y(−) Y(−) Y(−) Y(−) Y(−) W47 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W18 Y(−) Y(−) Y(−) Y(−) Y(−) W48 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W19 Y(−) Y(−) Y(−) Y(−) Y(−) W49 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W20 Y(−) Y(−) Y(−) Y(−) Y(−) W50 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W21 Y(−) Y(−) Y(−) Y(−) Y(−) W51 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W22 Y(−) Y(−) Y(−) Y(−) Y(−) W52 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W23 Y(−) Y(−) Y(−) Y(−) Y(−) W53 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W24 Y(−) Y(−) Y(−) Y(−) Y(−) W54 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W25 Y(−) Y(−) Y(−) Y(−) Y(−) W55 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W26 Y(−) Y(−) Y(−) Y(−) Y(−) W56 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W27 Y(−) Y(−) Y(−) Y(−) Y(−) W57 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W28 Y(−) Y(−) Y(−) Y(−) Y(−) W58 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
W29 Y(−) Y(−) Y(−) Y(−) Y(−) W59 Y(− Y(−) Y(− Y(−) Y(−) Y(−) 
W30 Y(−) Y(−) Y(−) Y(−) Y(−) W60 Y(−) Y(−) Y(−) Y(−) Y(−) Y(−) 
Figure 4

The box and whiskers plot of trend statistics of LR, SR, MK, and MMK tests for 60 studied wells (W1 to W60) in seasonal and annual time scales.

Figure 4

The box and whiskers plot of trend statistics of LR, SR, MK, and MMK tests for 60 studied wells (W1 to W60) in seasonal and annual time scales.

The autocorrelation function (ACF) plots of groundwater levels at the W1 station in different time scales: in the month of January and annual timescale are shown in Figure 5. The lag 1 and lag 2 correlation coefficients in all the considered timescales were found to be significant, and both correlation coefficients became zero after reduction (Figure 5). The existence of autocorrelation in any time series may lead to incorrect detection of significant trend by the test (Hamed & Rao 1998; Dinpashoh et al. 2014). The LR method overestimated trends in groundwater levels in the Sirjan plain because parametric tests have high sensitivity to the outliers. This same issue was observed at the W2 station as no significant trends in groundwater level were witnessed by using all four non-parametric tests in the spring season. On the other hand, the LR test detected statistically significant increasing trends in the groundwater levels because of the effect of autocorrelation coefficients.

Figure 5

The autocorrelation function (ACF) plot of groundwater level in the W1 well for the month of January and seasonal and annual timescales.

Figure 5

The autocorrelation function (ACF) plot of groundwater level in the W1 well for the month of January and seasonal and annual timescales.

The results obtained by using the SR and MK tests, both being non-parametric tests, are not affected by the outliers and the distribution of the data. However, it is still a very important determinant of the autocorrelation coefficients in these tests that leads to unrealistic results. The results obtained through these two tests (the MK and the SR) were found to be close to each other in the case of most of the stations and the same trends at the same level of significance (see Tables 1 and 3). Therefore, the power of these two tests was found to be almost identical.

However in the case of the MMK test, the effect of all autocorrelation coefficients was omitted leading to comparatively smaller values of the Z statistic obtained from this test in comparison to the Z statistics values obtained from both the MK and the SR tests. The effect of autocorrelation coefficients on these tests is clearly visible in the case of station W2. Both the MK and the SR tests showed significant increasing trends at 5% significance level in different timescales (annual and seasonal: summer, autumn, and winter). On the other hand, no significant trends were observed by using the MMK test in these timescales after eliminating the effect of autocorrelation coefficients at station W2. In the cases of stations such as W9 and W16, the three non-parametric tests (MK, SR, and MMK) yielded similar results. After examining the autocorrelation coefficients at both stations (W9 and W16), it was observed that the autocorrelation in the data series of these stations was found to be too weak to influence the amount of computational statistics. Hamed & Rao (1998) showed that application of the MK test after eliminating the effect of all autocorrelation coefficients was more accurate than the classical MK test.

In addition to the MK, the SR, the LR, and the MMK tests, the results obtained by using the ITM method are also presented in Table 5. The results using the ITM method also confirmed the existence of decreasing trends in the groundwater levels in the study area, which is similar to the results of the MMK and the SR methods (Table 5). One of the important advantages of this method is to provide good initial results for decision-making so that it can be used for cognitive stage studies. However, the biggest disadvantage of this method is the failure to provide a decision index and to determine the range of significant and non-significant changes, unlike the MMK and the SR methods that have an index for judging the significance of trend. Therefore, according to the explanations presented in this section, it can be concluded that the MMK test has a better performance than other examined tests in analyzing the trends in groundwater levels, which is the main reason to use the MMK test in this study for analyzing changes in groundwater levels in the Sirjan plain.

Trends in groundwater levels in Sirjan plain by the MMK test

Trends in groundwater levels in monthly timescale

Table 6 presents the results of trends obtained through the MMK test in the groundwater levels of 60 wells in the Sirjan plain on a monthly timescale. Results indicate that groundwater levels decreased in most parts of the Sirjan plain. Significant decreasing trends were observed in 56 out of 60 studied wells in all 12 months in the Sirjan plain. Two stations (W9 and W58) experienced an insignificant reduction in the groundwater level in Sirjan plain as well. However, increasing trends, although statistically insignificant, were observed in groundwater levels at W2 and W16 stations in the Sirjan plain. Among the four stations that did not witness significant changes in the groundwater levels, three stations W2, W9, and W16 are located in the eastern parts of the plain, where there is no agricultural activity and over-exploitation for irrigation purposes due to soil salinity and desert connectivity (Rahnama & Mirabbasi 2010). The W58 station, a high-elevation site, also has less agricultural activity in the surrounding areas of the well.

Table 6

The values of Z statistic of the MMK test for monthly series of groundwater level in the Sirjan plain (2005–2018)

StationJanFebMarAprMayJunStationJanFebMarAprMayJun
W1 − 2.61 − 2.59 − 2.56 − 2.56 − 2.56 − 2.62 W31 − 2.37 − 2.37 − 2.58 − 2.60 − 2.59 − 2.57 
W2 1.05 1.03 0.98 0.85 1.00 0.93 W32 − 2.37 − 2.30 − 2.42 − 2.49 − 2.48 − 2.41 
W3 − 2.67 − 2.66 − 2.68 − 2.66 − 2.67 − 2.69 W33 − 2.61 − 2.61 − 2.63 − 2.57 − 2.63 − 2.62 
W4 − 2.69 − 2.61 − 2.65 − 2.67 − 2.67 − 2.68 W34 − 2.65 − 2.65 − 2.62 − 2.61 − 2.61 − 2.61 
W5 1.74 1.92 1.71 1.68 1.62 − 1.79 W35 − 2.32 − 2.33 − 2.25 − 2.17 − 2.14 − 2.12 
W6 − 2.86 − 2.78 − 2.61 − 2.79 − 2.76 − 2.94 W36 − 2.63 − 2.63 − 2.64 − 2.65 − 2.65 − 2.66 
W7 − 3.00 − 3.03 − 3.33 − 3.20 − 3.38 − 3.30 W37 − 2.41 − 2.26 − 2.43 − 2.46 − 2.43 − 2.46 
W8 − 2.02 − 2.03 − 2.12 − 2.06 − 1.99 − 2.05 W38 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.58 
W9 0.22 0.00 0.22 0.44 0.17 0.17 W39 − 2.66 − 2.67 − 2.67 − 2.67 − 2.67 − 2.67 
W10 − 2.77 − 2.80 − 3.61 − 3.50 − 3.50 − 2.69 W40 − 2.64 − 2.64 − 2.67 − 2.66 − 2.66 − 2.66 
W11 − 2.63 − 2.62 − 2.61 − 2.61 − 2.62 − 2.62 W41 − 2.34 − 2.34 − 2.49 − 2.55 − 2.41 − 2.47 
W12 − 2.51 − 2.53 − 2.56 − 2.56 − 2.55 − 2.55 W42 − 2.81 − 2.80 − 2.73 − 2.68 − 2.78 − 2.87 
W13 − 2.70 − 2.70 − 2.68 − 2.68 − 2.69 − 2.70 W43 − 2.28 − 2.27 − 2.43 − 2.42 − 2.50 − 2.54 
W14 − 2.57 − 2.64 − 2.64 − 2.64 − 2.64 − 2.56 W44 − 2.61 − 2.61 − 2.63 − 2.62 − 2.62 − 2.62 
W15 − 2.66 − 2.65 − 2.69 − 2.69 − 2.68 − 2.69 W45 − 2.64 − 2.54 − 2.78 − 2.72 − 2.72 − 2.79 
W16 0.60 0.53 0.40 0.36 0.44 0.16 W46 − 2.64 − 2.65 − 2.65 − 2.65 − 2.65 − 2.65 
W17 − 2.80 − 2.75 − 2.93 − 3.21 − 3.05 − 3.04 W47 − 2.62 − 2.61 − 2.64 − 2.64 − 2.64 − 2.64 
W18 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 W48 − 2.58 − 2.58 − 2.60 − 2.60 − 2.60 − 2.60 
W19 − 2.58 − 2.57 − 2.59 − 2.59 − 2.59 − 2.59 W49 − 2.59 − 2.59 − 2.60 − 2.60 − 2.60 − 2.60 
W20 − 2.60 − 2.60 − 2.65 − 2.65 − 2.65 − 2.65 W50 − 2.66 − 2.58 − 2.63 − 2.54 − 2.54 − 2.48 
W21 − 2.57 − 2.54 − 2.54 − 2.55 − 2.54 − 2.47 W51 − 2.64 − 2.63 − 2.65 − 2.64 − 2.64 − 2.64 
W22 − 2.69 − 2.68 − 2.80 − 2.80 − 2.86 − 2.86 W52 − 2.64 − 2.64 − 2.67 − 2.67 − 2.66 − 2.66 
W23 − 2.65 − 2.64 − 2.65 − 2.65 − 2.65 − 2.66 W53 − 2.57 − 2.58 − 2.58 − 2.58 − 2.58 − 2.58 
W24 − 2.58 − 2.58 − 2.57 − 2.57 − 2.58 − 2.58 W54 − 2.65 − 2.65 − 2.66 − 2.66 − 2.67 − 2.66 
W25 − 2.21 − 2.16 − 2.47 − 2.67 − 2.57 − 2.57 W55 − 2.57 − 2.57 − 2.58 − 2.58 − 2.58 − 2.57 
W26 − 2.54 − 2.48 − 2.50 − 2.50 − 2.50 − 2.50 W56 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 
W27 − 2.05 − 2.04 − 2.30 − 2.29 − 2.40 − 2.52 W57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 
W28 − 2.64 − 2.63 − 2.62 − 2.62 − 2.62 − 2.63 W58 1.40 1.32 1.51 1.43 1.44 1.36 
W29 − 2.39 − 2.13 − 2.17 − 2.42 − 2.58 − 2.22 W59 − 2.60 − 2.60 − 2.62 − 2.61 − 2.61 − 2.61 
W30 − 2.62 − 2.61 − 2.58 − 2.59 − 2.53 − 2.60 W60 − 2.58 − 2.58 − 2.62 − 2.62 − 2.59 − 2.63 
StationJulAugSepOctNovDecStationJulAugSepOctNovDec
W1 − 2.62 − 2.62 − 2.61 − 2.61 − 2.62 − 2.61 W31 − 2.61 − 2.60 − 2.45 − 2.42 − 2.50 − 2.36 
W2 1.09 1.02 0.91 1.10 1.01 1.01 W32 − 2.39 − 2.38 − 2.36 − 2.41 − 2.39 − 2.31 
W3 − 2.68 − 2.68 − 2.68 − 2.68 − 2.68 − 2.67 W33 − 2.62 − 2.62 − 2.62 − 2.62 − 2.61 − 2.61 
W4 − 2.67 − 2.68 − 2.68 − 2.68 − 2.68 − 2.68 W34 − 2.67 − 2.67 − 2.66 − 2.66 − 2.66 − 2.65 
W5 − 2.07 − 2.00 − 2.02 − 1.92 − 1.92 − 1.88 W35 − 2.14 − 2.02 − 1.88 − 1.94 − 1.93 − 2.17 
W6 − 3.10 − 3.19 − 3.19 − 3.04 − 3.04 − 3.03 W36 − 2.66 − 2.66 − 2.64 − 2.64 − 2.63 − 2.63 
W7 − 3.29 − 3.32 − 3.16 − 3.15 − 3.25 − 3.11 W37 − 2.54 − 2.53 − 2.45 − 2.44 − 2.35 − 2.41 
W8 − 2.05 − 2.05 1.92 − 1.99 − 2.00 − 2.01 W38 − 2.58 − 2.58 − 2.57 − 2.57 − 2.57 − 2.57 
W9 − 0.41 0.00 0.17 0.00 0.00 0.22 W39 − 2.67 − 2.67 − 2.67 − 2.67 − 2.67 − 2.66 
W10 − 2.80 − 2.82 − 2.63 − 2.68 − 2.85 − 2.77 W40 − 2.65 − 2.65 − 2.65 − 2.64 − 2.64 − 2.58 
W11 − 2.63 − 2.64 − 2.63 − 2.63 − 2.63 − 2.63 W41 − 2.46 − 2.45 − 2.38 − 2.30 − 2.35 − 2.34 
W12 − 2.55 − 2.54 − 2.54 − 2.59 − 2.58 − 2.52 W42 − 2.87 − 2.90 − 2.88 − 2.88 − 2.93 − 2.85 
W13 − 2.71 − 2.71 − 2.71 − 2.72 − 2.71 − 2.71 W43 − 2.52 − 2.42 − 2.35 − 2.38 − 2.32 − 2.28 
W14 − 2.63 − 2.66 − 2.59 − 2.52 − 2.45 − 2.61 W44 − 2.62 − 2.62 − 2.62 − 2.62 − 2.61 − 2.61 
W15 − 2.70 − 2.69 − 2.69 − 2.68 − 2.68 − 2.67 W45 − 2.79 − 2.78 − 2.77 − 2.76 − 2.81 − 2.72 
W16 0.12 0.00 0.00 0.15 0.52 0.60 W46 − 2.65 − 2.64 − 2.63 − 2.63 − 2.63 − 2.63 
W17 − 2.89 − 2.90 − 2.69 − 2.69 − 2.75 − 2.84 W47 − 2.65 − 2.66 − 2.65 − 2.63 − 2.62 − 2.62 
W18 − 2.58 − 2.58 − 2.58 − 2.58 − 2.58 − 2.57 W48 − 2.60 − 2.60 − 2.60 − 2.59 − 2.59 − 2.58 
W19 − 2.59 − 2.59 − 2.58 − 2.58 − 2.58 − 2.58 W49 − 2.60 − 2.60 − 2.60 − 2.60 − 2.59 − 2.59 
W20 − 2.65 − 2.66 − 2.60 − 2.66 − 2.64 − 2.59 W50 − 2.49 − 2.75 − 2.88 − 2.87 − 2.78 − 2.85 
W21 − 2.48 − 2.47 − 2.59 − 2.53 − 2.51 − 2.57 W51 − 2.64 − 2.65 − 2.65 − 2.65 − 2.64 − 2.64 
W22 − 2.86 − 2.70 − 2.75 − 2.70 − 2.75 − 2.69 W52 − 2.65 − 2.65 − 2.59 − 2.59 − 2.58 − 2.65 
W23 − 2.66 − 2.66 − 2.66 − 2.66 − 2.65 − 2.65 W53 − 2.58 − 2.57 − 2.58 − 2.58 − 2.58 − 2.58 
W24 − 2.58 − 2.59 − 2.58 − 2.58 − 2.58 − 2.58 W54 − 2.64 − 2.64 − 2.64 − 2.64 − 2.65 − 2.65 
W25 − 2.52 − 2.43 − 2.61 − 2.54 − 2.49 − 2.28 W55 − 2.57 − 2.58 − 2.58 − 2.57 − 2.57 − 2.57 
W26 − 2.50 − 2.53 − 2.55 − 2.55 − 2.55 − 2.48 W56 − 2.56 − 2.56 − 2.56 − 2.56 − 2.56 − 2.57 
W27 − 2.33 − 2.32 − 2.24 − 2.08 − 2.11 − 2.05 W57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 
W28 − 2.63 − 2.64 − 2.64 − 2.63 − 2.63 − 2.63 W58 1.36 1.21 1.33 1.24 1.22 1.15 
W29 − 1.93 − 1.94 − 1.85 − 1.81 − 1.78 − 2.06 W59 − 2.60 − 2.61 − 2.61 − 2.60 − 2.60 − 2.60 
W30 − -2.59 − 2.58 − 2.57 − 2.57 − 2.62 − 2.62 W60 − 2.67 − 2.67 − 2.65 − 2.64 − 2.63 − 2.57 
StationJanFebMarAprMayJunStationJanFebMarAprMayJun
W1 − 2.61 − 2.59 − 2.56 − 2.56 − 2.56 − 2.62 W31 − 2.37 − 2.37 − 2.58 − 2.60 − 2.59 − 2.57 
W2 1.05 1.03 0.98 0.85 1.00 0.93 W32 − 2.37 − 2.30 − 2.42 − 2.49 − 2.48 − 2.41 
W3 − 2.67 − 2.66 − 2.68 − 2.66 − 2.67 − 2.69 W33 − 2.61 − 2.61 − 2.63 − 2.57 − 2.63 − 2.62 
W4 − 2.69 − 2.61 − 2.65 − 2.67 − 2.67 − 2.68 W34 − 2.65 − 2.65 − 2.62 − 2.61 − 2.61 − 2.61 
W5 1.74 1.92 1.71 1.68 1.62 − 1.79 W35 − 2.32 − 2.33 − 2.25 − 2.17 − 2.14 − 2.12 
W6 − 2.86 − 2.78 − 2.61 − 2.79 − 2.76 − 2.94 W36 − 2.63 − 2.63 − 2.64 − 2.65 − 2.65 − 2.66 
W7 − 3.00 − 3.03 − 3.33 − 3.20 − 3.38 − 3.30 W37 − 2.41 − 2.26 − 2.43 − 2.46 − 2.43 − 2.46 
W8 − 2.02 − 2.03 − 2.12 − 2.06 − 1.99 − 2.05 W38 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.58 
W9 0.22 0.00 0.22 0.44 0.17 0.17 W39 − 2.66 − 2.67 − 2.67 − 2.67 − 2.67 − 2.67 
W10 − 2.77 − 2.80 − 3.61 − 3.50 − 3.50 − 2.69 W40 − 2.64 − 2.64 − 2.67 − 2.66 − 2.66 − 2.66 
W11 − 2.63 − 2.62 − 2.61 − 2.61 − 2.62 − 2.62 W41 − 2.34 − 2.34 − 2.49 − 2.55 − 2.41 − 2.47 
W12 − 2.51 − 2.53 − 2.56 − 2.56 − 2.55 − 2.55 W42 − 2.81 − 2.80 − 2.73 − 2.68 − 2.78 − 2.87 
W13 − 2.70 − 2.70 − 2.68 − 2.68 − 2.69 − 2.70 W43 − 2.28 − 2.27 − 2.43 − 2.42 − 2.50 − 2.54 
W14 − 2.57 − 2.64 − 2.64 − 2.64 − 2.64 − 2.56 W44 − 2.61 − 2.61 − 2.63 − 2.62 − 2.62 − 2.62 
W15 − 2.66 − 2.65 − 2.69 − 2.69 − 2.68 − 2.69 W45 − 2.64 − 2.54 − 2.78 − 2.72 − 2.72 − 2.79 
W16 0.60 0.53 0.40 0.36 0.44 0.16 W46 − 2.64 − 2.65 − 2.65 − 2.65 − 2.65 − 2.65 
W17 − 2.80 − 2.75 − 2.93 − 3.21 − 3.05 − 3.04 W47 − 2.62 − 2.61 − 2.64 − 2.64 − 2.64 − 2.64 
W18 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 W48 − 2.58 − 2.58 − 2.60 − 2.60 − 2.60 − 2.60 
W19 − 2.58 − 2.57 − 2.59 − 2.59 − 2.59 − 2.59 W49 − 2.59 − 2.59 − 2.60 − 2.60 − 2.60 − 2.60 
W20 − 2.60 − 2.60 − 2.65 − 2.65 − 2.65 − 2.65 W50 − 2.66 − 2.58 − 2.63 − 2.54 − 2.54 − 2.48 
W21 − 2.57 − 2.54 − 2.54 − 2.55 − 2.54 − 2.47 W51 − 2.64 − 2.63 − 2.65 − 2.64 − 2.64 − 2.64 
W22 − 2.69 − 2.68 − 2.80 − 2.80 − 2.86 − 2.86 W52 − 2.64 − 2.64 − 2.67 − 2.67 − 2.66 − 2.66 
W23 − 2.65 − 2.64 − 2.65 − 2.65 − 2.65 − 2.66 W53 − 2.57 − 2.58 − 2.58 − 2.58 − 2.58 − 2.58 
W24 − 2.58 − 2.58 − 2.57 − 2.57 − 2.58 − 2.58 W54 − 2.65 − 2.65 − 2.66 − 2.66 − 2.67 − 2.66 
W25 − 2.21 − 2.16 − 2.47 − 2.67 − 2.57 − 2.57 W55 − 2.57 − 2.57 − 2.58 − 2.58 − 2.58 − 2.57 
W26 − 2.54 − 2.48 − 2.50 − 2.50 − 2.50 − 2.50 W56 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 
W27 − 2.05 − 2.04 − 2.30 − 2.29 − 2.40 − 2.52 W57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 
W28 − 2.64 − 2.63 − 2.62 − 2.62 − 2.62 − 2.63 W58 1.40 1.32 1.51 1.43 1.44 1.36 
W29 − 2.39 − 2.13 − 2.17 − 2.42 − 2.58 − 2.22 W59 − 2.60 − 2.60 − 2.62 − 2.61 − 2.61 − 2.61 
W30 − 2.62 − 2.61 − 2.58 − 2.59 − 2.53 − 2.60 W60 − 2.58 − 2.58 − 2.62 − 2.62 − 2.59 − 2.63 
StationJulAugSepOctNovDecStationJulAugSepOctNovDec
W1 − 2.62 − 2.62 − 2.61 − 2.61 − 2.62 − 2.61 W31 − 2.61 − 2.60 − 2.45 − 2.42 − 2.50 − 2.36 
W2 1.09 1.02 0.91 1.10 1.01 1.01 W32 − 2.39 − 2.38 − 2.36 − 2.41 − 2.39 − 2.31 
W3 − 2.68 − 2.68 − 2.68 − 2.68 − 2.68 − 2.67 W33 − 2.62 − 2.62 − 2.62 − 2.62 − 2.61 − 2.61 
W4 − 2.67 − 2.68 − 2.68 − 2.68 − 2.68 − 2.68 W34 − 2.67 − 2.67 − 2.66 − 2.66 − 2.66 − 2.65 
W5 − 2.07 − 2.00 − 2.02 − 1.92 − 1.92 − 1.88 W35 − 2.14 − 2.02 − 1.88 − 1.94 − 1.93 − 2.17 
W6 − 3.10 − 3.19 − 3.19 − 3.04 − 3.04 − 3.03 W36 − 2.66 − 2.66 − 2.64 − 2.64 − 2.63 − 2.63 
W7 − 3.29 − 3.32 − 3.16 − 3.15 − 3.25 − 3.11 W37 − 2.54 − 2.53 − 2.45 − 2.44 − 2.35 − 2.41 
W8 − 2.05 − 2.05 1.92 − 1.99 − 2.00 − 2.01 W38 − 2.58 − 2.58 − 2.57 − 2.57 − 2.57 − 2.57 
W9 − 0.41 0.00 0.17 0.00 0.00 0.22 W39 − 2.67 − 2.67 − 2.67 − 2.67 − 2.67 − 2.66 
W10 − 2.80 − 2.82 − 2.63 − 2.68 − 2.85 − 2.77 W40 − 2.65 − 2.65 − 2.65 − 2.64 − 2.64 − 2.58 
W11 − 2.63 − 2.64 − 2.63 − 2.63 − 2.63 − 2.63 W41 − 2.46 − 2.45 − 2.38 − 2.30 − 2.35 − 2.34 
W12 − 2.55 − 2.54 − 2.54 − 2.59 − 2.58 − 2.52 W42 − 2.87 − 2.90 − 2.88 − 2.88 − 2.93 − 2.85 
W13 − 2.71 − 2.71 − 2.71 − 2.72 − 2.71 − 2.71 W43 − 2.52 − 2.42 − 2.35 − 2.38 − 2.32 − 2.28 
W14 − 2.63 − 2.66 − 2.59 − 2.52 − 2.45 − 2.61 W44 − 2.62 − 2.62 − 2.62 − 2.62 − 2.61 − 2.61 
W15 − 2.70 − 2.69 − 2.69 − 2.68 − 2.68 − 2.67 W45 − 2.79 − 2.78 − 2.77 − 2.76 − 2.81 − 2.72 
W16 0.12 0.00 0.00 0.15 0.52 0.60 W46 − 2.65 − 2.64 − 2.63 − 2.63 − 2.63 − 2.63 
W17 − 2.89 − 2.90 − 2.69 − 2.69 − 2.75 − 2.84 W47 − 2.65 − 2.66 − 2.65 − 2.63 − 2.62 − 2.62 
W18 − 2.58 − 2.58 − 2.58 − 2.58 − 2.58 − 2.57 W48 − 2.60 − 2.60 − 2.60 − 2.59 − 2.59 − 2.58 
W19 − 2.59 − 2.59 − 2.58 − 2.58 − 2.58 − 2.58 W49 − 2.60 − 2.60 − 2.60 − 2.60 − 2.59 − 2.59 
W20 − 2.65 − 2.66 − 2.60 − 2.66 − 2.64 − 2.59 W50 − 2.49 − 2.75 − 2.88 − 2.87 − 2.78 − 2.85 
W21 − 2.48 − 2.47 − 2.59 − 2.53 − 2.51 − 2.57 W51 − 2.64 − 2.65 − 2.65 − 2.65 − 2.64 − 2.64 
W22 − 2.86 − 2.70 − 2.75 − 2.70 − 2.75 − 2.69 W52 − 2.65 − 2.65 − 2.59 − 2.59 − 2.58 − 2.65 
W23 − 2.66 − 2.66 − 2.66 − 2.66 − 2.65 − 2.65 W53 − 2.58 − 2.57 − 2.58 − 2.58 − 2.58 − 2.58 
W24 − 2.58 − 2.59 − 2.58 − 2.58 − 2.58 − 2.58 W54 − 2.64 − 2.64 − 2.64 − 2.64 − 2.65 − 2.65 
W25 − 2.52 − 2.43 − 2.61 − 2.54 − 2.49 − 2.28 W55 − 2.57 − 2.58 − 2.58 − 2.57 − 2.57 − 2.57 
W26 − 2.50 − 2.53 − 2.55 − 2.55 − 2.55 − 2.48 W56 − 2.56 − 2.56 − 2.56 − 2.56 − 2.56 − 2.57 
W27 − 2.33 − 2.32 − 2.24 − 2.08 − 2.11 − 2.05 W57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 − 2.57 
W28 − 2.63 − 2.64 − 2.64 − 2.63 − 2.63 − 2.63 W58 1.36 1.21 1.33 1.24 1.22 1.15 
W29 − 1.93 − 1.94 − 1.85 − 1.81 − 1.78 − 2.06 W59 − 2.60 − 2.61 − 2.61 − 2.60 − 2.60 − 2.60 
W30 − -2.59 − 2.58 − 2.57 − 2.57 − 2.62 − 2.62 W60 − 2.67 − 2.67 − 2.65 − 2.64 − 2.63 − 2.57 

The bold numbers denote significant trend at 5% significance level.

Figure 6 shows the spatial variations of the MMK test statistics on the monthly scale over the studied plain. The Z statistics were evaluated by using the Kriging, the Cokriging, the IDW, and the Spline methods. Most parts of the plain lie in the class of significant decreases in groundwater levels. In the month of January, much of the plain has undergone a significant decrease in groundwater levels, and only small portions in the east, north, and west of the plain have undergone insignificant changes in the groundwater levels. The Spline method also estimated the Z statistic values greater than 1.64 for a small area in the west of the plain. The situation of groundwater levels in the month of February is similar to the situation witnessed in January. Some parts in the north and west of the plains witnessed insignificant trend in February that could be due to precipitation and aquifer recharge. However, still large parts of the plain witnessed significant downward trends in groundwater levels in February. In the month of March, the northwestern part of the plain witnessed insignificant decreasing trends in groundwater levels due to occurrence of rainfall in this month leading to comparatively less groundwater extraction required for irrigating the pistachio gardens. This situation expanded through June, but still large portions of the plain in central areas were experiencing a decline in groundwater levels. With the end of spring and winter rains and the start of the warm season since July, the area with the insignificant groundwater level trends gradually declined to a minimum in the month of December. During the second half of the water year, large parts of the plain experienced significant downward trends in groundwater levels due to the exploitation of groundwater for irrigation and the lack of aquifer recharge.

Figure 6

Spatial variation of Z statistic of MMK test for groundwater level of Sirjan plain in monthly scale.

Figure 6

Spatial variation of Z statistic of MMK test for groundwater level of Sirjan plain in monthly scale.

Trends in groundwater levels in seasonal and annual timescales

Results indicated sharp decline in the groundwater levels observed in most parts of the Sirjan plain similar to trends in the groundwater levels witnessed in monthly timescale. About 47 and 45% of the total rainfall received at Sirjan station occurs in spring and winter seasons, respectively. However, in spite of the aquifer recharge in these two seasons in view of the occurrence of half of total rainfall, statistically significant decreasing trends in groundwater levels were found over most of the plain with only four stations (W2, W9, W16, and W58) witnessing no significant trends in groundwater levels. In the summer and autumn seasons, a large area of the Sirjan plain is under cultivation and groundwater plays an important role in supplying agricultural water demands of the resource-crunched farmers of the plain region. In recent years, aquifer harvesting has intensified leading to the sharp decline in groundwater levels due to the occurrence of droughts (Abarghouei et al. 2011; Ghahremanzadeh et al. 2017). Trends in groundwater levels in annual scale over the plain were also found to be almost similar to the kind of trends witnessed in the groundwater levels in monthly and seasonal timescales, i.e., statistically significant decreasing trends in the groundwater levels were witnessed at 56 stations out of 60 stations in the Sirjan plain.

Figure 7 shows the spatial variations of the Z statistics of the MMK test on a seasonal scale for Sirjan plain. The trends in the groundwater levels in all seasons are significantly decreased in large parts of the plain. In spring, the northwest part of the aquifer has witnessed a significant downward trend and showed non-significant decreasing trend due to occurrence of rainfall and proper groundwater recharge in this part of the plain.

Figure 7

Spatial variation of Z statistic of MMK test for groundwater level of Sirjan plain in seasonal scale.

Figure 7

Spatial variation of Z statistic of MMK test for groundwater level of Sirjan plain in seasonal scale.

The groundwater harvesting intensified significantly with the onset of the warm season and increasing water demands for agricultural and other activities in this region. As the Sirjan plain receives little rainfall in the autumn (Khalili et al. 2016) and harvesting of the aquifer for drinking and industrial use continued, so the areas with non-significant decreasing trend are moving lower. In winter season, the non-significant decreasing trend in the northwest of the plain has generally disappeared, and only small portions in the east and west of the plain witnessed a non-significant trend in groundwater levels. All these developments appear to be repeating a cycle, with each year in the Sirjan plain, which is losing a large amount of its aquifer water without the opportunity to recover.

Figure 8 shows the spatial variation of the Z statistic obtained by the MMK test in the groundwater levels in the Sirjan plain in annual scale. The overall condition of the groundwater levels in the plain has been critical on an annual timescale and a large part of it has experienced significant downward trends at the significance level of 5% in the groundwater levels. However, small portions of the plain are located in the areas where agricultural activities are restricted because of desertification and groundwater salinity (Mirabbasi & Eslamian 2010), and they witnessed non-significant decreasing trends in groundwater levels. In the eastern part of the plain connecting to the desert, the Spline method estimated statistically significant increase in the Z statistic of the MMK test also. This indicates that the agricultural and human activities are the main causes of the downward trends in the groundwater levels in the Sirjan plain.

Figure 8

Spatial variation of Z statistic of MMK test for groundwater level of Sirjan plain in annual scale.

Figure 8

Spatial variation of Z statistic of MMK test for groundwater level of Sirjan plain in annual scale.

In recent years, in addition to human activities, climatic factors, such as droughts and rise in temperature have exacerbated the reduction of groundwater levels in the Sirjan plain. Rising trends in temperature are reported by various researchers (Tabari & Talaee 2011; Kousari et al. 2013; Zarenistanak et al. 2014; Ahmadi et al. 2018) in different regions of Iran, including Kerman province, especially during the cold months. In addition to increasing temperature, decreasing trend in precipitation was also reported at Sirjan synoptic station (Khalili et al. 2016). The increase in drought severity in this region was also confirmed by Abarghouei et al. (2011). Rising temperature with increases in the crop water requirements and reduced rainfall and increased drought events would put additional pressures on groundwater resources in the Sirjan plain. Significant downward trends in the groundwater levels over large parts of the plain were observed due to the expansion of human activities, rainfall reduction, and rising drought occurrences. The continuation of the current trends in the near future may cause salt water intrusion, water quality deterioration, and land subsidence and will present many challenges to the region's agriculture and industry.

CONCLUSIONS

In this study, trends in the groundwater levels in the Sirjan plain were investigated by using parametric and non-parametric methods. The values of the Z statistic variations were the same in the MK and the SR tests, and the MMK test witnessed the least value of the Z statistics. However, in parametric LR test, the t-statistics demonstrated the most variance, confirming the effect of autocorrelation and outliers on the results of parametric tests. In the case of the three non-parametric tests used in this study, non-significant increasing trend in groundwater level is observed in the spring season in the Sirjan plain. However, due to the effect of autocorrelation, the t-statistics obtained by using the linear regression test were found to be unrealistically significant leading to erroneous results, which may create many hazards in different stages of groundwater exploitation in the Sirjan plain and may lead to poor decision-making by the policy makers involved in the groundwater resources management.

The SR and the conventional MK tests (elementary non-parametric tests) were used to identify trends in the groundwater levels without application of any correction to eliminate the effect of autocorrelation coefficients. The results showed that these two tests were close to each other at most of the stations and determined a similar kind of trend at the same level of significance. The effect of all the significant autocorrelation coefficients was omitted in the case of the MMK test and the results indicated that the Z statistic of this method was found to be smaller than both the MK and SR tests. Both the MK and SR tests showed significant trends in groundwater level at 5% significance level at the W2 station in different seasons (summer, autumn, winter) and annual time scale. However, after removing the effect of autocorrelation coefficients before using the MMK test, the trend was found to be non-significant in seasonal and annual timescales. The results of the ITM method are purely qualitative and do not provide any numerical criteria for judgment. Therefore, the use of non-parametric MMK test is recommended to investigate the trends of time series having significant autocorrelation coefficients.

On monthly, seasonal, and annual timescales, the MMK test results indicated that the groundwater levels in the Sirjan plain experienced severe declining trends as 56 out of 60 studied stations witnessed statistically significant decreasing trends in the groundwater levels. Large portions of the plain experienced significant decreasing trends in the groundwater level in January with only small portions in the east, north, and west of the plain witnessing non-significant changes in the groundwater levels. From March, due to the increase of rainfall and fewer agricultural activities in the northwestern part of the region, non-significant downward trend in groundwater levels were observed. The necessary arrangements to restore the aquifer and harvest management should be implemented to improve the negative aquifer balance in order to emerge victorious from the current crisis in the medium term in the Sirjan plain and other parts of Iran. Agricultural scientists and extension workers should encourage progressive farmers to plant less hydrophilic crops along with significant changes in the region's agricultural activities for better management of precious water resources under rising water demands for agricultural and other activities and changing climate scenarios in plains.

ACKNOWLEDGEMENTS

We are grateful to the Research Council of Shahid Chamran University of Ahvaz for their distinguished support. We are also grateful to the anonymous reviewers for their critical comments, which helped us to improve the manuscript.

DATA AVAILABILITY STATEMENT

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

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