期刊
JOURNAL OF HYDROLOGY
卷 564, 期 -, 页码 824-835出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jhydrol.2018.07.051
关键词
Water temperature; The Tatra Mts; Vaucluse springs; Groundwaters; The continuous wavelet transform; The principal components analysis
资金
- forest fund of the National Forest Management Agency [EZ/0290.1.2.2015, ZP/371/ 2015, K/KDU/000254]
- Polish Ministry of Science and Higher Education [N 305 081 32/2824]
- Tatra National Park [ZDS/007289]
In this study, we aim to characterise natural variability of water temperature in surface waters of the Tatra Mts. and determine the dominant factors controlling its spatial diversity and seasonal patterns. For this purpose, a total of 33 time series of water temperature representing lakes, vaucluse springs and streams were analysed using the continuous wavelet transform (CWT). The periodicity analysis were conducted with the Morlet wavelet on hourly sampled data covering a period of 5 years. The principal components analysis (PCA) has been applied to describe the relationships among variables and extract potential sources of water temperature variability. The results showed an extremely high heterogeneity in temporal patterns of water temperature fluctuations among streams when compared to lakes and vaucluse springs. Wavelet analysis of water temperature time series revealed the presence of seven different configurations of periodical patterns. The lowest variability was observed among vaucluse springs that are supplied by groundwaters. Temporal fluctuations of water temperature in lakes contained four different types of oscillations. Streams were among the most diversified in terms of water temperature patterns exhibiting low, medium and high frequency behaviour. The PCA analysis confirmed the dependence of water temperature on weather conditions, catchment characteristics and flow rate explaining 88.97% of the total variability in the data. The results obtained from this study emphasize the importance of continuous data collection for capturing the long-term dynamics and consistent temporal patterns in time series. The study also demonstrates that wavelet analysis is helpful to identify cyclical patterns in time series of water temperature, and therefore may be useful in the classification of thermal regimes in surface waters.
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