PREDICTION OF PADDY IRRIGATION REQUIREMENTS BY USING STATISTICAL DOWNSCALING AND CROPWAT MODELS: A CASE STUDY FROM THE KERIAN IRRIGATION SCHEME IN MALAYSIA
DOI:
https://doi.org/10.11113/jt.v76.4038Keywords:
Climate change, irrigation, water requirement, water stressAbstract
With an average rainfall of 2500mm per year, Malaysia has abundant water resources but climate change coupled with drought, urbanisation and pollution sometimes causes water stress. Global warming has changed the local climate, threatening agricultural activities with particular impact on paddy production systems. To ensure availability of sufficient irrigation water for growing crops, there is a need to estimate future irrigation water requirements in the face of the complex dynamic resulting from global warming. The current study was therefore carried out to estimate paddy irrigation water requirements based on future climate trends by using SDSM and CROPWAT Models at the Kerian Irrigation Scheme, Perak, Malaysia. The application of the SDSM model revealed that both temperature and rainfall will increase in the future. Meanwhile the CROPWAT model predicted that the annual irrigation requirement will slightly decrease for period between 2010-2069 and increase for years 2070-2099 even though crop evapotranspiration (ETcrop) is predicted to increase in future for rise in temperature for year 2010 to 2099. This integration of SDSM and CROPWAT models produced better simulations of crop water requirement and irrigation requirement. Therefore, it can assist the reservoir’s operating management team in giving effective and proficient response to climate changes in the future.
References
Toriman M. E. and M. Mokhtar. 2012. Irrigation: Types, Sources and Problems in Malaysia, Irrigation Systems and Practices in Challenging Environments, Dr. Teang Shui Lee (Ed.), ISBN: 978-953-51-0420-9, InTech.
Murad, M.W., R. I. Molla, M. B. Mokhtar, and M. A. Raquib. 2010. Climate Change and Agricultural Growth: An examination of the link in Malaysia. International. Journal of Climate Change Strategic Management. 2: 403-417.
Li, X., T. Takahashi, N. Suzuki, and H. M. Kaiser. 2011. The Impact of Climate Change on Maize Yields in the United States and China. Agricultural System. 104: 348-353.
UNEP, 2011. Global Environment Outlook 3: Past, Present and Future Perspectives. Earthscan Publication, London.
Rosegrant M. W., C. Ringler, and R. V. Gerpacio, 1997. Water and Land Resources and Global Food Supply. 23rd International Conference of Agriculture Economist on Food Security, Diversification and Resource Management: Refocusing the role of Agriculture. Sacramento, California.
Lee, T. S, M. A. Haque, and M. M. M. Najim. 2005. Scheduling the Cropping Calendar in Wet-seeded Rice Schemes in Malaysia. Agricultural Water Management. 71: 71-84.
Kuo, S. F., B. J. Lin, and H. J. Shieh. 2001. CROPWAT Model to Evaluate Crop Water Requirements In Taiwan. International Commission on Irrigation and Drainage. 1st Asian Regional Conference Seoul, Korea.
Banik, P., N. K. Tiwari, and S. Ranjan. 2014 Comparative Crop Water Assessment Using CROPWAT: Crop Water Assessment of Plain and Hilly Region Using CROPWAT Model. International Journal of Sustainable Materials, Processes & ECO-Efficient – IJSMPE. 1: (3). [ISSN 2374 –1651].
Neelima, T. L., K. V. Ramana, M. D. Reddy, M. U. Devi, V. Ramulu, and A. V. Ramanjaneyulu. 2014. Supply Demand Ratio and Water Utilization Index in Jurala Project Command Area, Andhra Pradesh, India. International Journal of Bio-resource and Stress Management. 5(1): 068-073.
Shahid, S. 2010. Impact of Climate Change on Irrigation Water Demand of Dry Season Boro Rice in Northwest Bangladesh. Journal of Climatic Change. DOI 10.1007/s10584-010-9895-5.
Shen, Y., S. Li, Y. Chen, Y. Qi and S. Zhang, 2013. Estimation of Regional Irrigation Water Requirement and Water Supply Risk in the Arid Region of Northwestern China 1989–2010. Agricultural Water Management. 128: 55-64.
Tukimat N. A., and S. Harun. 2012. Comparative Methods in Measuring the Irrigation Water Needs at Muda Irrigation Scheme, Kedah. ULTRA Engineer. 1(1): 29-33.
Yoshikawa S., J. Cho, H. G. Yamada, N. Hanasaki, and S. Kanae 2014. An Assessment of Global Net Irrigation Water Requirements from Various Water Supply Sources to Sustain Irrigation: Rivers and Reservoirs (1960–2050). Hydrology Earth System Science. 18: 4289-4310.
Wada, Y., D. Wisser, S. Eisner., M. Flörke, D. Gerten, I. Haddeland, N. Hanasaki, Y. Masaki, F. T. Portmann, T. Stacke, Z. Tessler, and J. Schewe. 2014. Multi-model Projections and Uncertainties of Irrigation Water Demand Under Climate Change. Geophysical Research Letter. 40: 4626-4632. doi:10.1002/grl.50686.
Zainal, Z., M. N. Shamsudin, Z. A. Mohamed, S. U Adam, and S. Kaffashi 2014. Assessing the Impacts of Climate Change on Paddy Production in Malaysia. Research Journal of Environmental Sciences. 8: 331-341.
Chakrabarti, B. 2013. Crop Simulation Model. Articles from Indian Agricultural Research Institute. New Delhi. 225-229.
Rowshon, M. K., M. S. M. Amin, M. Mojid, M. Yaji. 2013 Estimated Evapotranspiration of Rice Based on Pan Evaporation as a Surrogate to Lysimeter Measurement. Paddy and Water Environment. 13(4). doi:10.1007/s10333- 013-0356-4.
Wilby, R. L., C. W. Dawson, and E. M. Barrow. 2002 SDSM - A Decision Support Tool for the Assessment of Regional Climate Change Impacts. Environmental Modelling & Software. 17(2): 147-159.
Busuioc, A., D. Chen, and C. Hellstrom. 2001 Performance of Statistical Downscaling Models in GCM Validation and Regional Climate Change Estimates: Application For Swedish Precipitation. International Journal of Climatology.
Dibike, Y. B., and P. Coulibaly. 2005 Hydrologic Impact of Climate Change in the Saguenay Watershed: Comparison of Downscaling Methods and Hydrologic Models. Journal of Hydrology. 307(1-4): 145-163.
McCarthy, J., O. Canziani, Leary, N., D. Dokken, and K. White, 2001. Climate Change 2001: Impacts, Adaptation, and Vulnerability. Cambridge University Press, New York. 105-110.
Hessami, M., P. Gachon, T. B. M. J. Ouarda, and A St-Hilaire. 2008. Automated Regression Based Statistical Downscaling Tool. Environmental Modeling and Software. 23(6): June 813-834.
Wilby, R. L., and C. W. Dawson. 2007. SDSM 4.2 — A Decision Support Tool For The Assessment Of Regional Climate Change Impacts. User Manual.
Malaysian Meteorological Department, Scientific Report. 2009. Climate Change Scenarios for Malaysia 2001-2099, Ministry of Science, Technology and Innovation, Malaysia.
Kabiri R, V. R. Bai, A. Chan. 2014 Assessment of Hydrologic Impacts of Climate Change on the Runoff Trend in Klang Watershed. Malaysia. Environment Earth Science. DOI 10.1007/s12665-014-3392-5.
Hassan, Z., S. Shamsudin, S. Harun. 2014. Application of SDSM and LARS-WG for Simulating and Downscaling of Rainfall and Temperature. Theoretical and Applied Climatology. 116(1-2): 243–257.doi:10.1007/s00704-013-0951-8.
Karanja, F. K. 2013. CROPWAT Model Analysis of Crop Water Use in Six Districts in Kenya. Reported from Department of Meteorology, University of Nairobi.
Khan, M. I. 2013. Optimal Water Allocation for Rice Production under Climate Change. School of Economics, La Trobe University: Thesis for Doctor Philosophy.
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