Outing and Classification of Effective Factors in the Groundwater Level Decline Using Cross and Coherence Wavelet Transforms:Case Study Silakhor Plain Aquifer

Identification of factors affecting hydrological processes plays an important role in water resources management. In this study, the effects of hydrological parameter variations such as rainfall, temperature and runoff time series are prioritized on the groundwater level fluctuations for the Silakhor plain using cross and coherence wavelet transforms. Cross wavelet transform and coherence wavelet transform estimate the interaction and time lag between two time series relatively. These transformations indicate that two time series have depended on period and phase lag. For this purpose, the time series of groundwater level- runoff, groundwater levelrainfall and groundwater level-temperature have entered into the cross wavelet transform and coherence wavelet transform algorithm mutually to determine the effects and interaction between these time series. The results show that runoff time series as the human activity factors with the mean wavelet correlation coefficient estimated as 0.83 is more effective than rainfall and temperature time series whereas the climate change factors are with the mean wavelet correlation coefficients of 0.52 and 0.58, respectively concerning groundwater level decline of the Silakhor plain. Thus, the human activity factors are more effective than the climate factors in declining the groundwater level in this plain.