Assessment of Trend in Groundwater Level using Hybrid Mann-Kendall and Wavelet Transform Method (Case Study: Ardabil Plain)

Study of changes in groundwater resources has great importance on planning and management of sustainable water resources in any region. The goal of this study was trends and dominant period investigation in groundwater level data at monthly timescales in fifteen piezometers of Ardabil plain using non-parametric Mann–Kendall (MK), temporal pre-processing (discrete wavelet transform) and spatial pre-processing (self-organizing map) methods. In first step, a Self-Organizing-Map (SOM)-based clustering technique was used to identify spatially homogeneous clusters of groundwater level (GWL) data. At second step, the wavelet transform (WT) was also used to extract dynamic and multi-scale features of the non-stationary GWL for central piezometers at 3 level. At last step, The MK test were applied to different combinations of DWT after removing the effect of significant lag-1 serial correlation to calculate components responsible for trend of the time series. The results showed that negative trend is prevalent in the case study; generally, wavelet-based detail at level 3 plus the approximations time series was conceded as the dominant periodic component.