Open Access
Issue
MATEC Web Conf.
Volume 385, 2023
The 15th International Scientific Conference of Civil and Environmental Engineering for the PhD. Students and Young Scientists – Young Scientist 2023 (YS23)
Article Number 01006
Number of page(s) 11
DOI https://doi.org/10.1051/matecconf/202338501006
Published online 30 October 2023
  1. M. Fendeková, O. Horvát, L. Blaškovičová, Z. Danáčová, M. Fendek & O. Bochníček, Prognosis of climate change driven drought in the Poprad, Torysa and Topľa river basins. Acta Hydrologica Slovaca, 19(2), 234-243. (2018). [Google Scholar]
  2. Y. Velísková, R. Dulovičová, R. Schügerl, Impact of vegetation on flow in a lowland stream during the growing season. Biologia, Vol. 72, No. 8, 840-846. (2017). [CrossRef] [Google Scholar]
  3. W. Almikaeel, L. Čubanová, A. Šoltész, Comparison of mean daily discharge data for under-mountain and highland-lowland types of rivers. Acta Hydrologica Slovaca, 23(1), 73-81. (2022) [CrossRef] [Google Scholar]
  4. S. Wang, Freezing Temperature Controls Winter Water Discharge for Cold Region Watershed. Water Resources Research, 55, 10479-10493. (2019). [Google Scholar]
  5. A. Dráb, J. Říha, An approach to the implementation of European Directive 2007/60/EC on flood risk management in the Czech Republic. Natural Hazards and Earth System Science, Volume 10, Issue 9, 1977-1987. (2010) [CrossRef] [Google Scholar]
  6. L. O. Serrano, R. B. Ribeiro, A. C. Borges, F. F. Pruski, Low-Flow Seasonality and Effects on Water Availability throughout the River Network. Water Resources Management, 34, 1289-1304. (2020). [Google Scholar]
  7. V. Sýs, P. Fošumpaur, T. Kašpar, The impact of climate change on the reliability of Water Resources. Climate, 9(11), 153, 1-15. (2021) [Google Scholar]
  8. A. F. van Loon, E. Tijdeman, N. Wanders, H. A. J. Van Lanen, A. J. Teuling, R. Uijlenhoet, How climate seasonality modifies drought duration and deficit. Journal of Geophysical Research: Atmospheres, 119, 4640-4656. (2014). [CrossRef] [Google Scholar]
  9. B. Kandra, A. Tall, M. Gomboš, D. Pavelková, Quantification of Evapotranspiration by Calculations and Measurements Using a Lysimeter. Water 15(2), 373, 1-18. (2023) [CrossRef] [Google Scholar]
  10. M. Sivapalan, G. Blöschl, R. Merz, D. Gutknecht, Linking flood frequency to long-term water balance: Incorporating effects of seasonality. Water Resources Research, 41. (2005). [Google Scholar]
  11. G. Y. Abawi, S. Dutta, T. R. Harris, J. W. Ritchie, D. Rattray, A. J. Crane, The Use of Seasonal Climate Forecasts in Water Resources Management. In Proceedings of the 3rd International Conference on Water Resources and Environment Research, Part I (pp. 129-136). (2000). [Google Scholar]
  12. Y. F. Sang, A review on the applications of wavelet transform in hydrology time series analysis. Atmospheric Research, 122, 8-15. (2013). [CrossRef] [Google Scholar]
  13. S. Lv, Multi-scale analysis of hydrological series using ensemble empirical mode decomposition. Proceedings of the 2nd International Conference on Mechanic Automation and Control Engineering, 974-977. (2011). [Google Scholar]
  14. D. Zhihong, EMD Method for Multiple Time-scale Analysis on Fluctuation Characteristic of Natural Annual Runoff Time Series of Fen River. Water Resources and Power. (2008). [Google Scholar]
  15. P. Pathak, A. Kaira, S. Ahmad, M. Bernárdez, Wavelet-Aided Analysis to Estimate Seasonal Variability and Dominant Periodicities in Temperature, Precipitation, and Streamflow in the Midwestern United States. Water Resources Management, 30, 4649-4665. (2016). [CrossRef] [Google Scholar]
  16. B. V. Mitková, Effect of the data length and seasonality on the accuracy of T-year discharges estimation: Case study on the Topľa River. Proceedings of the IAHR World Congress, 28, 1692-1699. (2020). [Google Scholar]
  17. L. Čubanová, W. Almikaeel, Drought assessment and prediction for Gidra river, Slovakia. IOP Conf. Ser.: Mater. Sci. Eng., 1209. (2021). [Google Scholar]
  18. A. Dokumentov, R. J. Hyndman, STR: A seasonal-trend decomposition procedure based on regression. arXiv: Methodology, (1506.03805). (2015). [Google Scholar]
  19. M. Theodosiou, Forecasting monthly and quarterly time series using STL decomposition. International Journal of Forecasting, 27, 1178-1195. (2011). [CrossRef] [Google Scholar]
  20. Statsmodels. Statsmodels: Time Series Analysis in Python. Retrieved September 10, 2021, from [https://www.statsmodels.org/stable/tsa.html] (https://www.statsmodels.org/stable/tsa.html) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.