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The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical
Summary
Researchers analyzed climate variation and human activity effects on karst spring discharge using wavelet coherence analysis, finding that anthropogenic factors including land-use changes increasingly influence groundwater dynamics alongside natural climate variability.
This study focused on the impact of anthropogenic activity on magnitude, frequency, and minima of spring discharge. Niangziguan Springs (NS), China, was selected as an example, as its discharge is decreasing due to the combined effects of climate variation and human activity. For exploring the impact of human activity on the spring discharge from climate change, the spring discharges from 1959 to 2015 were divided into two periods: pre-development period (i.e., 1959–1980) and post-development period (i.e., 1981–2015). A polynomial regression model of the spring discharge was developed for the pre-development period. We deduced the model in the post-development period, compared the results with the observed spring discharge, and concluded that the climate variation and human activity caused 6.93% and 32.38% spring discharge decline, respectively. The relationships of spring discharge with Indian Summer Monsoon (ISM), East Asian Summer Monsoon (EASM), E1 Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO) were analyzed by wavelet analysis during the two periods. The results illustrated that the monsoons (i.e., ISM and EASM) were dominated by climate factors that affect the NS discharge versus climate teleconnections (i.e., ENSO and PDO). According to different time scales, human activities have had an impact on the periodicity of NS discharge, which altered the periodicities of the spring discharge at inter-annual time scales, but the periodicities at intra-annual and annual time scales have remained the same between the two periods. Under the effects of human activity, the local parameter of non-stationary general extreme value (NSGEV) distribution varied with time. The predicted spring discharge minimum value is supposed to be 4.53 m3/s with a 95% confidential interval with an upper boundary of 6.06 m3/s and a lower boundary of 2.80 m3/s in 2020. The results of this study would benefit the management of spring discharge and water resources.
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