EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 566 (2024) E3S Web Conf., 566 (2024) 01004 References
Open Access
Issue
E3S Web Conf.
Volume 566, 2024
2024 6th International Conference on Environmental Sciences and Renewable Energy (ESRE 2024)
Article Number 01004
Number of page(s) 14
Section Wastewater Treatment and Water Resource Management
DOI https://doi.org/10.1051/e3sconf/202456601004
Published online 06 September 2024
  1. Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An Overview of CMIP5 and the Experiment Design. Bulletin of the American Meteorological Society, 93(4), 485–498. https://doi.org/10.1175/BAMS-D-11-00094.1 [CrossRef] [Google Scholar]
  2. Piani, C., Haerter, J. O., & Coppola, E. (2010). Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology, 99(1–2), 187–192. https://doi.org/10.1007/s00704-009-0134-9 [CrossRef] [Google Scholar]
  3. Sharma, Ojha, Shukla, Pham, Linh, Fai, Loc, & Dung. (2019). Modified Approach to Reduce GCM Bias in Downscaled Precipitation: A Study in Ganga River Basin. Water, 11(10), 2097. https://doi.org/10.3390/w11102097 [CrossRef] [Google Scholar]
  4. Masson, D., & Knutti, R. (2011). Climate model genealogy. Geophysical Research Letters, 38(8). https://doi.org/10.1029/2011GL046864 [Google Scholar]
  5. Mendez, M., Calvo-Valverde, L.-A., Imbach, P., Maathuis, B., Hein-Grigg, D., Hidalgo-Madriz, J.-A., & Alvarado-Gamboa, L.-F. (2022). Hydrological Response of Tropical Catchments to Climate Change as Modeled by the GR2M Model: A Case Study in Costa Rica. Sustainability, 14(24), 16938. https://doi.org/10.3390/su142416938 [CrossRef] [Google Scholar]
  6. Gangrade, S., Kao, S. C. & McManamay, R. A. 2020 Multi-model hydroclimate projections for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. Scientific Reports 10, 1–12. https://doi.org/10.1038/s41598-020-59806-6 [CrossRef] [PubMed] [Google Scholar]
  7. Mendlik, T., & Gobiet, A. (2016). Selecting climate simulations for impact studies based on multivariate patterns of climate change. Climatic Change, 135(3–4), 381–393. https://doi.org/10.1007/s10584-015-1582-0 [CrossRef] [PubMed] [Google Scholar]
  8. Sørland, S. L., Schär, C., Lüthi, D., & Kjellström, E. (2018). Bias patterns and climate change signals in GCM-RCM model chains. Environmental Research Letters, 13(7), 074017. https://doi.org/10.1088/1748-9326/aacc77 [CrossRef] [Google Scholar]
  9. Bishop, C. H., & Abramowitz, G. (2013). Climate model dependence and the replicate Earth paradigm. Climate Dynamics, 41(3), 885–900. https://doi.org/10.1007/s00382-012-1610-y [CrossRef] [Google Scholar]
  10. Cannon, A. J. (2015). Selecting GCM Scenarios that Span the Range of Changes in a Multimodel Ensemble: Application to CMIP5 Climate Extremes Indices. Journal of Climate, 28(3), 1260-1267. https://doi.org/10.1175/JCLI-D-14-00636.1 [CrossRef] [Google Scholar]
  11. Bethere, L., Sennikovs, J., & Bethers, U. (2017). Climate indices for the Baltic states from principal component analysis. Earth System Dynamics, 8(4), 951–962. https://doi.org/10.5194/esd-8-951-2017 [CrossRef] [Google Scholar]
  12. Waylen, P. R., Quesada, M. E., & Caviedes, C. N. (1996). Temporal and spatial variability of annual precipitation in Costa rRica and the southern oscillation. International Journal of Climatology, 16(2), 173–193. https://doi.org/10.1002/(SICI)1097-0088(199602)16:2<173::AID-JOC12>3.0.CO;2-R [CrossRef] [Google Scholar]
  13. Mendez, M., Calvo-Valverde, L.-A., Hidalgo-Madriz, J.-A., & Araya-Obando, J.-A. (2023). A comparison of generalized extreme value, gumbel, and log-pearson distributions. A case study in Costa Rica. BIO Web Conf., 62 (2023) 01002. https://doi.org/10.1051/bioconf/20236201002 [CrossRef] [EDP Sciences] [Google Scholar]
  14. Leander, R., Buishand, T. A., & Tank, A. M. G. K. (2014). An Alternative Index for the Contribution of Precipitation on Very Wet Days to the Total Precipitation. Journal of Climate, 27(4), 1365–1378. https://doi.org/10.1175/JCLI-D-13-00144.1 [CrossRef] [Google Scholar]
  15. Kraemer, G., Reichstein, M., & Mahecha, M., D. (2018). dimRed and coRanking—Unifying Dimensionality Reduction in R. The R Journal, 10(1), 342. https://doi.org/10.32614/RJ-2018-039 [CrossRef] [Google Scholar]
  16. Benestad, R., Parding, K., Dobler, A., & Mezghani, A. (2017). A strategy to effectively make use of large volumes of climate data for climate change adaptation. Climate Services, 6, 48–54. https://doi.org/10.1016/j.cliser.2017.06.013 [CrossRef] [Google Scholar]
  17. Peres, D. J., Senatore, A., Nanni, P., Cancelliere, A., Mendicino, G., & Bonaccorso, B. (2020). Evaluation of EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) historical simulations by high-quality observational datasets in southern Italy: Insights on drought assessment. Natural Hazards and Earth System Sciences, 20(11), 3057–3082. https://doi.org/10.5194/nhess-20-3057-2020 [CrossRef] [Google Scholar]
  18. Xu, R., Chen, N., Chen, Y., & Chen, Z. (2020). Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin. Advances in Meteorology, 2020, 1–17. https://doi.org/10.1155/2020/8680436 [Google Scholar]
  19. Singh, S. K., Lo, E. Y.-M., & Qin, X. (2017). Cluster Analysis of Monthly Precipitation over the Western Maritime Continent under Climate Change. Climate, 5(4), 84. https://doi.org/10.3390/cli5040084 [CrossRef] [Google Scholar]
  20. Murtagh, F., & Legendre, P. (2014). Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? Journal of Classification, 31(3), 274–295. https://doi.org/10.1007/s00357-014-9161-z [CrossRef] [Google Scholar]
  21. Mendez, M., Calvo-Valverde, L.-A. (2016). Development of the HBV-TEC Hydrological Model. Procedia Engineering, 154, 1116-1123. https://doi.org/10.1016/j.proeng.2016.07.521 [CrossRef] [Google Scholar]
  22. Reiter, P.; Gutjahr, O.; Schefczyk, L.; Heinemann GCasper, M. Does applying quantile mapping to subsamples improve the bias correction of daily precipitation? Int. J. Climatol. 2018, 38, 1623–1633. https://doi.org/10.1002/joc.5283 [CrossRef] [Google Scholar]
  23. Jose, D. M., Vincent, A. M., & Dwarakish, G. S. (2022). Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning. Scientific Reports, 12(1), 4678. https://doi.org/10.1038/s41598-022-08786-w [CrossRef] [PubMed] [Google Scholar]
  24. R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ [Google Scholar]
  25. Almazroui, M., Islam, M. N., Saeed, F., Saeed, S. (2021). Projected Changes in Temperature and Precipitation Over the United States, Central America, and the Caribbean in CMIP6 GCMs. Earth Systems and Environment, 5(1), 1–24. https://doi.org/10.1007/s41748-021-00199-5 [CrossRef] [Google Scholar]
  26. Oyerinde, G. T., Lawin, A. E., & Anthony, T. (2022). Multiscale assessments of hydroclimatic modelling uncertainties under a changing climate. Journal of Water and Climate Change, 13(3), 1534–1547. https://doi.org/10.2166/wcc.2022.266 [CrossRef] [Google Scholar]
  27. Mendez, M., Calvo-Valverde, L.-A. (2020). Comparison performance of machine learning and geostatistical methods for the interpolation of monthly air temperature over Costa Rica. IOP Conf. Ser.: Earth Environ. Sci., 432, 012011. https://doi.org/10.1088/1755-1315/432/1/012011 [CrossRef] [Google Scholar]
  28. Pereira, H. R., Meschiatti, M. C., Pires, R. C. D. M., & Blain, G. C. (2018). On the performance of three indices of agreement: An easy-to-use r-code for calculating the Willmott indices. Bragantia, 77(2), 394–403. https://doi.org/10.1590/1678-4499.2017054 [CrossRef] [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.