Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
권호 표지
DOI QR코드

DOI QR Code

Uncertainty of Simulated Paddy Rice Yield using LARS-WG Derived Climate Data in the Geumho River Basin, Korea

LARS-WG 기후자료를 이용한 금호강 유역 모의발생 벼 생산량의 불확실성

  • Received : 2013.01.14
  • Accepted : 2013.06.13
  • Published : 2013.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study investigates the trends and uncertainty of the impacts of climate change on paddy rice production in the Geumho river basin. The Long Ashton Research Station stochastic Weather Generator (LARS-WG) was used to derive future climate data for the Geumho river basin from 15 General Circulation models (GCMs) for 3 Special Report on Emissions Scenarios (SRES) (A2, A1B and B1) included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. The Food and Agricultural Organization (FAO) AquaCrop, a water-driven crop model, was statistically calibrated for the 1982 to 2010 climate. The index of agreement (IoA), prediction efficiency ($R^2$), percent bias (PBIAS), root mean square error (RMSE) and a visual technique were used to evaluate the adjusted AquaCrop simulated yield values. The adjusted simulated yields showed RMSE, NSE, IoA and PBIAS of 0.40, 0.26, 0.76 and 0.59 respectively. The 5, 9 and 15 year central moving averages showed $R^2$ of 0.78, 0.90 and 0.96 respectively after adjustment. AquaCrop was run for the 2020s (2011-2030), 2050s (2046-2065) and 2090s (2080-2099). Climate change projections for Geumho river basin generally indicate a hotter and wetter future climate with maximum increase in the annual temperature of $4.5^{\circ}C$ in the 2090s A1B, as well as maximum increase in the rainfall of 45 % in the 2090s A2. The means (and ranges) of paddy rice yields are projected to increase by 21 % (17-25 %), 34 % (27-42 %) and 43 % (31-54 %) for the 2020s, 2050s and 2090s, respectively. The A1B shows the largest rice yield uncertainty in all time slices with standard deviation of 0.148, 0.189 and $0.173t{\cdot}ha^{-1}$ for the 2020s, 2050s and 2090s, respectively.

Keywords

References

  1. Abedinpoura, M., A. Sarangi, T. B. S. Rajput, Man Singh, H. Pathak and T. Ahmad, 2012. Performance evaluation of AquaCrop model for maize crop in a semi-arid environment. Agricultural Water Management 110: 55-66. https://doi.org/10.1016/j.agwat.2012.04.001
  2. Chung, S.-O., 2010. Simulating evapotranspiration and yield responses of rice to climate change using FAO-AquaCrop. Journal of the Korean Society of Agricultural Engineers 52(3): 57-64 (in Korean). https://doi.org/10.5389/KSAE.2010.52.3.057
  3. Chung, S.-O., and T. Nkomozepi, 2012. Uncertainty of paddy irrigation requirement estimated from climate change projections in the Geumho river basin, Korea. Paddy and Water Environment 10(3): 175-185. https://doi.org/10.1007/s10333-011-0305-z
  4. Craufurd, P. Q., V. Vadeza, S. V. K. Jagadish, P. V. V. Prasad, M. Zaman-Allah, 2013. Crop science experiments designed to inform crop modeling. Agricultural and Forest Meteorology 170: 8-18. https://doi.org/10.1016/j.agrformet.2011.09.003
  5. Dono, G., R. Cortignani, L. Doro, L. Giraldo, L. Ledda, M. Pasqui and P. P. Roggero, 2013. Adapting to uncertainty associated with short-term climate variability changes in irrigated Mediterranean farming systems. Agricultural Systems 117: 1-12. https://doi.org/10.1016/j.agsy.2013.01.005
  6. IPCC (Intergovernmental Panel on Climate Change), 2007. Climate change 2007: the physical science basis. In: Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, H. L. Miller, (Eds.), Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom/New York, NY.
  7. Kassam, A., and M. Smith, 2001. FAO Methodologies on Crop Water Use and Crop Water Productivity. Expert meeting on crop water productivity, Rome.
  8. Kim, H.-Y., J.-H. Ko, S.-C. Kang, and J. Tenhunen, 2013. Impacts of climate change on paddy rice yield in a temperate climate. Global Change Biology 19(2): 548-562. https://doi.org/10.1111/gcb.12047
  9. Kumar, S., D. Gupta, and H. Nayyar, 2012. Comparative response of maize and rice genotypes to heat stress: status of oxidative stress and antioxidants. Acta Physiologiae Plantarum 34(1): 75-86. https://doi.org/10.1007/s11738-011-0806-9
  10. Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, 2006. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50(3): 885-900.
  11. Nkomozepi, T., and S.-O. Chung, 2011. Simulation of the impacts of climate change on yield of maize in Zimbabwe. Journal of the Korean Society of Agricultural Engineers 53(3): 65-73. https://doi.org/10.5389/KSAE.2011.53.3.065
  12. Nkomozepi, T., and S.-O., Chung, 2012. Assessing the trends and uncertainty of maize net irrigation water requirement estimated from climate change projections for Zimbabwe. Agricultural Water Management 111: 60-67. https://doi.org/10.1016/j.agwat.2012.05.004
  13. Raes, D., P. Steduto, T. C. Hsiao, and E. Fereres, 2011. AquaCrop 3.1plus Reference manual, FAO, Rome.
  14. Seck, P. A., A. Diagne, S. Mohanty, and M. C. S. Wopereis, 2012. Crops that feed the world 7: Rice. Food Security 4: 7-24. https://doi.org/10.1007/s12571-012-0168-1
  15. Semenov, M. A., and E. M. Barrow, 1997. Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change 35: 397-414. https://doi.org/10.1023/A:1005342632279
  16. Shah, F., J. Huang, K. Cui, L. Nie, T. Shah, C. Chen, and K. Wang, 2011. Impact of high-temperature stress on rice plant and its traits related to tolerance. Journal of Agricultural Science 149: 545-556. https://doi.org/10.1017/S0021859611000360
  17. Supit, I., C. A. van Diepen, A. J. W. de Wit, J. Wolf, P. Kabat, B. Baruth, and F. Ludwig, 2012. Assessing climate change effects on European crop yields using the Crop Growth Monitoring System and a weather generator, Agricultural and Forest Meteorology 164: 96-111. https://doi.org/10.1016/j.agrformet.2012.05.005
  18. Tallec, T., P. Beziat, N. Jarosz, V. Rivalland, and E. Ceschia, 2013. Crops' water use efficiencies in temperate climate: Comparison of stand, ecosystem and agronomical approaches, Agricultural and Forest Meteorology 168: 69-81. https://doi.org/10.1016/j.agrformet.2012.07.008
  19. Todorovic, M., R. Albrizio, L. Zivotic, M.-T. Abi Saab, C. Stockle, and P. Steduto, 2009. Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes. Agronomy Journal 101: 509-521. https://doi.org/10.2134/agronj2008.0166s
  20. Tragoolram, J., et al., 2011. Climate change impacts and adaptation assessment on rice production in Khon Kaen province of Thailand. The 2nd International Conference on Applied Science (ICAS). http://gsmis.gs.kku.ac.th/publish/get_file?name=788-795_Jamnong%20Tragoolram.pdf accessed on 8 March, 2012.
  21. Wang, Y. P., K. W. Chang, R. K. Chen, J. C. Lo, and Y. Shen, 2010. Large-area rice yield forecasting using satellite imageries. International Journal of Applied Earth Observation and Geo-information 12: 27-35. https://doi.org/10.1016/j.jag.2009.09.009
  22. Ye, L., W. Xiong, Z. Li, P. Yang, W. Wu, G. Yang, Y. Fu, J. Zou, Z. Chen, E. Van Ranst, and H. Tang, 2013. Climate change impact on China food security in 2050. Agronomy for Sustainable Development 33(2): 363-374. https://doi.org/10.1007/s13593-012-0102-0
  23. Yun, J.I., 2003. Predicting regional rice production in South Korea using spatial data and crop-growth modeling. Agricultural Systems 77: 23-38. https://doi.org/10.1016/S0308-521X(02)00084-7
  24. Zhang, T., J. Zhu, and R. Wassmann, 2010. Responses of rice yields to recent climate change in China: An empirical assessment based on long-term observations at different spatial scales (1981-2005). Agricultural and Forest Meteorology 150: 1128-1137. https://doi.org/10.1016/j.agrformet.2010.04.013

Cited by

  1. Statistical Downscaling of Precipitation and Temperature Using Long Ashton Research Station Weather Generator in Zambia: A Case of Mount Makulu Agriculture Research Station vol.06, pp.03, 2017, https://doi.org/10.4236/ajcc.2017.63025