Identifying Effective Factors on Decline in Groundwater Level Using Wavelet-Entropy Index (Case Study: Silakhor Plain Aquifer

Time series analysis of hydrological processes plays an important role in accurate recognition of this process. Wavelet-entropy index is a new indicator to assess the fluctuations of time series. In this paper, the effective factor in groundwater level declining in the Silakhor plain is examined using wavelet-entropy index. Generally, wavelet-entropy index reduction or time series complexity reduction of a phenomenon, indicates the reduction in time series natural fluctuations and thus the occurrence of an unfavorable trend in time series. In this way, to identify the main cause of declining aquifer water table, firstly, monthly time series of precipitation, temperature and rivers flow of this plain divided into shorter time periods and then, each of these time series were decomposed to multiple frequent time series by wavelet transform and then, normalized wavelet energies were calculated for these decomposed time series and finally, wavelet-entropy index was calculated for each three different time periods. The results of wavelet-entropy index analysis reflect the fact that, the complexity reduction of the flow time series about 71% is more effective on groundwater time series complexity reducing compared to the complexity reduction of the precipitation and temperature time series about 13% and 10.5% respectively. This result indicates the primacy of the human factors compared with the climate change factors impacts in declining the groundwater level in this plain.