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How Much Meteorological Information Is Necessary to Achieve Reliable Accuracy for Rainfall Estimations?

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  • Mohammad Valipour

    (Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran)

Abstract

This paper reports the study of the effect of the length of the recorded data used for monthly rainfall forecasting. Monthly rainfall data for three periods of 5, 10, and 49 years were collected from Kermanshah, Mashhad, Ahvaz, and Babolsar stations and used for calibration time series models. Then, the accuracy of the forecasting models was investigated by the following year’s data. The following was concluded: In temperate and semi-arid climates, 60 observation data is sufficient for the following year’s rainfall forecasting. The accuracy of the time series models increased with increasing amounts of observation data of arid and humid climates. Time series models are appropriate tools for forecasting monthly rainfall forecasting in semi-arid climates. Determining the most critical rainfall month in each climate condition for agriculture schedules is a recommended aim for future studies.

Suggested Citation

  • Mohammad Valipour, 2016. "How Much Meteorological Information Is Necessary to Achieve Reliable Accuracy for Rainfall Estimations?," Agriculture, MDPI, vol. 6(4), pages 1-9, October.
    • Handle: RePEc:gam:jagris:v:6:y:2016:i:4:p:53-:d:80523
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    References listed on IDEAS

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    1. Mohammad Valipour, 2014. "Use of average data of 181 synoptic stations for estimation of reference crop evapotranspiration by temperature-based methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4237-4255, September.
    2. Chenoweth, Timothy & Dowling, Karen & Hubata, Robert & St. Louis, Robert, 2004. "Distance and prediction error variance constraints for ARMA model portfolios," International Journal of Forecasting, Elsevier, vol. 20(1), pages 41-52.
    3. Ludlow, Jorge & Enders, Walter, 2000. "Estimating non-linear ARMA models using Fourier coefficients," International Journal of Forecasting, Elsevier, vol. 16(3), pages 333-347.
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    Cited by:

    1. Valipour, Mohammad & Gholami Sefidkouhi, Mohammad Ali & Raeini−Sarjaz, Mahmoud, 2017. "Selecting the best model to estimate potential evapotranspiration with respect to climate change and magnitudes of extreme events," Agricultural Water Management, Elsevier, vol. 180(PA), pages 50-60.
    2. Amaresh Sarkar & Mrinmoy Majumder, 2019. "Real-time monitoring of water requirement in protected farms by using polynomial neural networks and image processing," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1451-1483, June.
    3. Zhao, Xiancong & Bai, Hao & Shi, Qi & Lu, Xin & Zhang, Zhihui, 2017. "Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing," Applied Energy, Elsevier, vol. 195(C), pages 100-113.
    4. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    5. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    6. Jiang, Zhiqiang & Ji, Changming & Qin, Hui & Feng, Zhongkai, 2018. "Multi-stage progressive optimality algorithm and its application in energy storage operation chart optimization of cascade reservoirs," Energy, Elsevier, vol. 148(C), pages 309-323.
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    8. King, Tom & Schoenau, Jeff & Elliott, Jane, 2017. "Relationship between Manure Management Application Practices and Phosphorus and Nitrogen Export in Snowmelt Run-off Water from a Black Chernozem Saskatchewan Soil," Sustainable Agriculture Research, Canadian Center of Science and Education, vol. 6(2), May.

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