P. Kossieris, D. Koutsoyiannis, C. Onof, H. Tyralis, and A. Efstratiadis, HyetosR: An R package for temporal stochastic simulation of rainfall at fine time scales, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 11718, European Geosciences Union, 2012.
A complete software package for the temporal stochastic simulation of rainfall process at fine time scales is developed in the R programming environment. This includes several functions for sequential simulation or disaggregation. Specifically, it uses the Bartlett-Lewis rectangular pulses rainfall model for rainfall generation and proven disaggregation techniques which adjust the finer scale (hourly) values in order to obtain the required coarser scale (daily) value, without affecting the stochastic structure implied by the model. Additionally, a repetition scheme is incorporated in order to improve the Bartlett-Lewis model performance without significant increase of computational time. Finally, the package includes an enhanced version of the evolutionary annealing-simplex optimization method for the estimation of Bartlett-Lewis parameters. Multiple calibration criteria are introduced, in order to reproduce the statistical characteristics of rainfall at various time scales. This upgraded version of the original HYETOS program (Koutsoyiannis, D., and Onof C., A computer program for temporal stochastic disaggregation using adjusting procedures, European Geophysical Society, 2000) operates on several modes and combinations thereof (depending on data availability), with many options and graphical capabilities. The package, under the name HyetosR, is available free in the CRAN package repository.
Software page: http://itia.ntua.gr/en/softinfo/3/
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|1.||#Montesarchio, V., F. Napolitano, E. Ridolfi and L. Ubertini, A comparison of two rainfall disaggregation models, In Numerical Analysis and Applied Mathematics ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics, AIP Conference Proceedings, Vol. 1479, 1796-1799, 2012.|
|2.||#Villani, V., L. Cattaneo, A. L. Zollo, and P. Mercogliano, Climate data processing with GIS support: Description of bias correction and temporal downscaling tools implemented in Clime software, Euro-Mediterranean Center on Climate Change (RMCC) Research Papers, RP0262, 2015.|
|3.||Förster, K., F. Hanzer, B. Winter, T. Marke, and U. Strasser, An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.1), Geoscientific Model Development, 9, 2315-2333, doi:10.5194/gmd-9-2315-2016, 2016.|
|4.||Devkota, S., N. M. Shakya, K. Sudmeier-Rieux, M. Jaboyedoff, C. J. Van Westen, B. G. Mcadoo, and A. Adhikari, Development of monsoonal rainfall intensity-duration-frequency (IDF) relationship and empirical model for data-scarce situations: The case of the Central-Western Hills (Panchase Region) of Nepal, Hydrology, 5(2), 27, doi:10.3390/hydrology5020027, 2018.|
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|6.||#Thomson, H., and L. Chandler, Tailings storage facility landform evolution modelling, Proceedings of the 13th International Conference on Mine Closure, A. B. Fourie & M. Tibbett (eds.), Australian Centre for Geomechanics, Perth, 385-396, 2019.|
|7.||Sun, Y., D. Wendi, D. E., Kim, and S.-Y. Liong, Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data, Geoscience Letters, 6(17), doi:10.1186/s40562-019-0147-x, 2019.|
|8.||Oruc, S., I. Yücel, and A. Yılmaz, Investigation of the effect of climate change on extreme precipitation: Capital Ankara case, Teknik Dergi, 33(2), doi:10.18400/tekderg.714980, 2021.|
|9.||Hayder, A. M., and M. Al-Mukhtar, Modelling the IDF curves using the temporal stochastic disaggregation BLRP model for precipitation data in Najaf City, Arabian Journal of Geosciences, 14, 1957, doi:10.1007/s12517-021-08314-6, 2021.|
|10.||Diez-Sierra, J., S. Navas, and M. del Jesus, Neoprene: An open-source Python library for spatial rainfall generation based on the Neyman-Scott process, doi:10.2139/ssrn.4092195, 2022.|
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