Comparison of six trend detection methods and forecasting for monthly groundwater levels – a case study

ABSTRACT

In the present study, groundwater level (GWL) trends were analysed using six statistical trend techniques on seasonal time scale and forecasted monthly using time series analysis (TSA). Groundwater data (piezometric level) of 14 observation wells are considered from 1996–2017 for Warangal district of Telangana State. Results showed a significant declining trend in five observation wells out of 14. Notably, two observation wells exhibited increasing trends. Out of four seasons considered, variation in trends was observed in post-monsoon kharif and rabi. Change points in trend were detected using sequential Mann–Kendall (SMK) test which showed a good agreement with trend variations obtained from Mann–Kendall test. Innovative trend analysis technique (ITAT) is applied to derive monotonic and non-monotonic trend series plots. Compared to ITAT, SMK test showed good agreement with other trend tests. Considering the piezometric data of 1996–2017, GWLs for the year 2017 were forecasted using autoregressive integrated moving average model (ARIMA). Results concluded that ARIMA (4,1,2) model exhibited the best forecasting of GWLs for observation well 1 with coefficient of determination (R²) as 0.86, root mean square error as 0.28 and Akaike information criteria as 377.5 that showed a good correlation with observed and forecasted GWLs.

KEYWORDS:

• Groundwater levels
• ARIMA
• Spearman’s Rank correlation
• sequential Mann–Kendall test
• ITAT

Disclosure statement

No potential conflict of interest was reported by the authors.