Assessing the intensity of the water cycle utilizing a Bayesian estimator algorithm and wavelet coherence analysis in the Issyk-Kul Basin of Central Asia

Abstract

Study region: The Issyk-Kul Basin, Central Asia. Study focus: Currently, there is a scarcity of in-depth evaluations of the progression and factors that affect the intensification of the water cycle at the basin level, given the increased fluctuations in the global hydrological cycle. In this study, a water cycle intensity (WCI) indicator based on the terrestrial water balance equation was used to assess basin-scale water cycle characteristics from 1982 to 2017, and the Bayesian estimator algorithm (BEAST) and wavelet transform coherence (WTC) were used to analyse the temporal evolution characteristics and factors influencing the intensity of the water cycle. New hydrological insights for the region: The WCI of the Issyk-Kul Basin (ISKB) undergoes an abrupt change and displays an intensified trend with a clear time-scale effect in the trend change following the global warming hiatus. In terms of forcing factors, the interannual variability in the WCI was driven by the Tibetan Plateau Index_B (TPI) and the North Atlantic Oscillation (NAO) until 1998, while afterwards it was driven by the Antarctic Oscillation (AAO). The alteration in the forcing relationship was a significant contributor to the sudden shift observed in the WCI in 1998. This study improves the comprehension of basin-scale alterations in the intensity of the water cycle and their driving mechanisms under climate change, with practical implications for the management of water resources in arid Central Asia.

Keywords

• Global warming hiatus
• Water cycle intensity
• Bayesian ensemble algorithm
• Wavelet coherence analysis
• Central Asia
• Issyk-Kul Basin