Long-term Estimates of Reservoir Evaporation Using ARIMA Model and Impact on Water Supply: A Case Study of Erinle Dam, Osun State, Nigeria

Long-term Estimates of Reservoir Evaporation Using ARIMA Model and Impact on Water Supply: A Case Study of Erinle Dam, Osun State, Nigeria

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Author(s)

Author(s): B. F. Sule, O. F. Ajala

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DOI: 10.18483/ijSci.1331 244 698 29-38 Volume 6 - Sep 2017

Abstract

It is common practice in water resource management to estimate evaporation of water from reservoirs using nearby measurements of pan evaporation. With the emergence of water supply and food security issues as a result of increasing population and climate change pressures, the need for efficient use of available water supplies is paramount. Management of available resources and improved efficiency require accurate knowledge of evaporation, which is a major water loss pathway. This study used Autoregressive Integrated Moving Average (ARIMA) models to forecast pan-evaporation data. The historical data on pan evaporation (1982 – 2012) at Osogbo, southwest Nigeria, was initially subjected to a regression analysis which showed that the data has an increasing trend, while the plot of autocorrelation function indicated that the data is not stationary. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), as well as diagnostics of residuals confirmed that ARIMA(3,4,3) is a good fit for both short term data forecast and data generation for pan-evaporation. Estimated long term reservoir evaporation series (2013 – 2062) was applied to the reservoir capacity curve and compared to the water demand curve. The results showed that with the increasing evaporation trend the reservoir will not be able to serve the various benefitting towns after year 2038. This implies that new water sources would be needed to meet the increasing water demand due to increasing population.

Keywords

Evaporation, ARIMA, Water Supply, Forecasting, Reservoir, Management

References

  1. Adeboye, O. B., Jimmy A.O., Kenneth O. A., and David A. O. (2009) Evaluation of FAO-56 Penman-Monteith and Temperature Based Models in Estimating Reference Evapotranspiration Using Complete and Limited Data, Application to Nigeria, Agricultural Engineering International: the CIGR E-journal, Manuscript Number 1291. Volume XI, Pg. 1 – 25
  2. Amadi, I. U. and Aboko, S. I. (2013); The Fitting of ARIMA Model in Forecasting Nigeria Gross Domestic Product (GDP), Journal of Physical Science and Innovation, ISSN: 2277-0119, Volume 5, No. 2, pg. 79 – 90.
  3. Arora KR (2004) Irrigation, Water Power and Water Resources Engineering, 4th Edition, Standard Publishers Distributors, Delhi.
  4. Babu, S.K., Karthikeyan, K., Ramanaiah, M.V., and Ramanah, D. 2011. “Prediction of Rain-fall Flow Time Series using Auto-Regressive Models”. Advances in Applied Science Research. Vol. 2, Issue 2, pp 128-133.
  5. Clarvis, M. H, Fatichi, S., Allan, A., Fuhrer, J., Stoffel, M., Romerio, F., Gaudard, L., Burlando, P., Beniston, M., Xoplaki, E., and Toreti, A. (2013), Governing and managing water resources under changing hydro-climatic contexts: The case of the upper Rhone basin, Environ. Sci. Policy. http://dx.doi.org/10.1016/j.envsci.2013.11.005
  6. Douville H, Chauvin F, Planton S, Royer J, Salas-Melia D, Tyteca S (2001); Sensitivity of the hydrological cycle to increasing amounts of greenhouse gases aerosols, Climate Dynamics 20: Page 45 - 68
  7. Finch J. W. and Hall R. L. (2001); Estimation of Open Water Evaporation – A Review of Methods, Environment Agency, Rio House Waterside Drive, Aztec West, Almondsbury, Bristol, BS32 4UD
  8. Jarabi, B. (2012); Population Projections – Background & First Steps, Population Studies and Research Institute, University of Nairobi, Nairobi, Kenya
  9. Marvin E. J. (2010); Estimating Evaporation from Water Surfaces, CSU/ARS Evapotranspiration Workshop, Fort Collins.
  10. Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J. (2008), “Stationarity Is Dead: Whither Water Management?” Policy Forum, Science Vol. 319, pg. 573 – 574.
  11. Mohammad K., Ferenc S., and Banafsheh Z. (2003), Water Resources System Analysis, a CRC Press Company, London.
  12. Naill P. E., and Momani, M. (2009), Time Series Analysis Model for Rainfall Data in Jordan: Case Study for using Time Series Analysis, American Journal of Environmental Sciences 5 (5) ISSN 1553-345X: pg. 599-604
  13. National Population Commission, NPC (2016), Osogbo, Nigeria
  14. Okunlola O. A., and Folorunso S. (2015), Modelling Rainfall Series in the Geo-Political Zones of Nigeria, Journal of Environment and Earth Science, ISSN 2225-0948, Vol.5, No.2, Pg. 100 – 111
  15. Osun (state). (2016, February 14). In Wikipedia, The Free Encyclopedia. Retrieved 13:09, April 2, 2016, from https://en.wikipedia.org/w/index.php?title=Osun_(state)&oldid=704863518
  16. Pereira, L. S. (2005); Water and Agriculture: Facing Water Scarcity and Environmental Challenges. Agricultural Engineering International: the CIGR E-journal, Vol. 7, Pg. 1-26.
  17. Sadoff, C. W., and Muller, M. (2009), Better water resources management – Greater resilience today, more effective adaptation tomorrow, A Perspective Paper contributed by the Global Water Partnership through its Technical Committee.
  18. Woudenberg, D. L., (2002); "The Role of Climate in Modern Water Planning and Related Decisions: Nebraska Case Study". OpenAccess* Master's Theses from the University of Nebraska-Lincoln, Paper 7

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International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

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