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.
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 were investigated.
The results indicated that the Mann-Kendall test after elimination 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.