Generation of Synthetic Daily Rainfall Data in Jordan
Keywords:
rainfall, daily rainfall, synthetic data, occurrence model, amounts model, gamma distribution, exponential distribution, JordanAbstract
This study aims to generate synthetic daily rainfall data for 39 meteorological stations in Jordan by estimating the distributional parameters of daily rainfall occurrence and amounts. Daily rainfall occurrence was modeled by the use of rainfall interarrival times, which were fitted to the one-parameter exponential distribution, except zero values which were represented using the ratio of the number of zero interarrival times to the total number of times, which was called zero ratio. Daily rainfall amounts were fitted to the two-parameter gamma distribution. Goodness-of-fit for one of the stations was tested using chi-square test. This test was performed using Microsoft Office Excel. Distributional parameters were calculated for both occurrence and amounts models, and 100 sequences of synthetic daily rainfall data were then generated, of which every sequence included 1000 non-zero daily rainfall data points (1000 wet days) and 1000 interarrival times (1000 dry spells of which some have a length of zero). These sequences were generated using the embedded random number generators in Python, for one-parameter exponential distribution, two-parameter gamma distribution, and uniform distribution. Percent errors were then calculated and found all to be less than 10%, which was considered acceptable.
References
A. M. Nyongesa, G. Zeng and V. Ongoma, "Non-homogeneous hidden Markov model for downscaling of short rains occurrence in Kenya," Theoretical and Applied Climatology, vol. 139, no. 3, pp. 1333-1347, 2020.
R. Mehrotra and A. Sharma, "A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting low-frequency variability," Journal of Hydrology, vol. 335, no. 1–2, pp. 180-193, 2007.
S. Geng, F. W. P. de Vries and I. Supit, "A simple method for generating daily rainfall data," Agricultural and Forest Meteorology, vol. 36, no. 4, pp. 363-376, 1986.
C. Gao, M. J. Booij and Y.-P. Xu, "Development and hydrometeorological evaluation of a new stochastic daily rainfall model: Coupling Markov chain with rainfall event model," Journal of Hydrology, vol. 589, 2020.
C. Gao, Y.-P. Xu, Q. Zhu, Z. Bai and L. Liu, "Stochastic generation of daily rainfall events: A single-site rainfall model with Copula-based joint simulation of rainfall characteristics and classification and simulation of rainfall patterns," Journal of Hydrology, vol. 564, pp. 41-58, 2018.
DSO, "Stochastic Modeling Methods," Dam Safety Office, Department of the Interior, Bureau of Reclamation, Washington D.C., 2003.
F. H. Chiew, R. Srikanthan, A. J. Frost and E. G. Payne, "Reliability of daily and annual stochastic rainfall data generated from different data lengths and data characteristics," in In MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings, Melbourne, Victoria, Australia, 2005.
P. Gogumalla and S. K. Tripathi, "Evaluation of Markov Chain Model for Forecasting Precipitation of Uttarakhand Districts," HCTL Open International Journal of Technology Innovations and Research (IJTIR), vol. 6, no. 5, pp. 142-151, 2018.
A. Mitra, "Bayesian Approach to Spatio-Temporally Consistent Simulation of Daily Monsoon Rainfall over India," New York, NY, USA, 2017.
A. Sharma and U. Lall, "A nonparametric approach for daily rainfall simulation," Mathematics and Computers in Simulation, vol. 48, no. 4–6, pp. 361-371, 1999.
Y. Liu, W. Zhang, Y. Shao and K. Zhang, "A comparison of four precipitation distribution models used in daily stochastic models," Advances in Atmospheric Sciences, vol. 28, no. 4, pp. 809-820, 2011.
J. Piantadosi, J. Boland and P. Howlett, "Generating Synthetic Rainfall on Various Timescales—Daily, Monthly and Yearly," Environmental Modeling & Assessment, vol. 14, no. 4, pp. 431-438, 2009.
A. Azizah, R. Welastika, A. Nur Falah, B. N. Ruchjana and A. S. Abdullah, "An Application of Markov Chain for Predicting Rainfall Data at West Java using Data Mining Approach," Conference Series: Earth and Environmental Science, vol. 303, pp. 12-26, 2019.
R. Leander and T. A. Buishand, "A daily weather generator based on a two-stage resampling algorithm," Journal of Hydrology, vol. 374, no. 3–4, pp. 185-195, 2009.
A. Dahamsheh and H. Aksoy, "Structural Characteristics of Annual Precipitation Data in Jordan," Theoretical and Applied Climatology, vol. 88, pp. 201-212, 2007.
H. Aksoy and A. Dahamsheh, "Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions," Journal of Hydrology, vol. 562, pp. 758-779, 2018.
H. Aksoy and A. Dahamsheh, "Artificial neural network models for forecasting monthly precipitation in Jordan," Stochastic Environmental Research and Risk Assessment, vol. 23, pp. 917-931, 2009.
A. Dahamsheh and H. Aksoy, "Markov Chain-Incorporated Artificial Neural Network Models for Forecasting Monthly Precipitation in Arid Regions," Arabian Journal for Science and Engineering, vol. 39, no. 4, pp. 2513-2524, 2014.
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