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Prediction of Agricultural Water Demand and Uncertainties Under Climate Change(Case Study: Wheat in Cities of Dezful and Andimeshk)

Memarzadeh, Ali | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 50829 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Abrishamchi, Ahmad
  7. Abstract:
  8. Climate change impacts especially on temperature and precipitation, may have various effects on agriculture and water demand of agriculture sector in different regions around the world. Based on numerous researches, these effects may be positive in a region while another region might endure negative effects. Therefore carrying out researches and developing methods to predict these impacts as well as mitigating strategies seem to be essential. The most worldwide accepted method is using global circulation models (GCMs) outputs under different greenhouse gases emission scenarios, and according to Intergovernmental Panel on Climate Change (IPCC) assessments. IPCC claims that the most recent assessment report which is called AR5, is also the most valid and verified report comparing preceding versions.However, this new report is less applied in researches compared to previous reports. In this study, the uncertainty of agricultural water demand of irrigated wheat in the regions of Dezful and Andimeshk located in southern Iran, will be estimated during next three decades (until 2050), using 4 GCMs based on 4 RCP scenarios and employing a plant growth model named AquaCrop. Moreover two sample agricultural management scenarios will be tested as mitigating strategies. Results show that climate change impacts on this specific plant and in this specific region will be positive, so that in the base scenario (no management strategies applied), in order to maintain the wheat production equal to present day, the irrigation demand will decrease somewhere between 75 to 92 millimeters, which is mainly because of increase in winter precipitation during next three decades. This decrease will be 124 to 144 millimeters if sowing date moves to one week earlier and also 169 to 182 millimeters if sowing occurs in two weeks earlier. Moreover a less used uncertainty analysis method, called REA method will be exploited which is suitable for researches that use few number of GCMs, and also a less used downscaling method – Knn method – will be utilized to scale down climatic outputs of GCMs to the scale of the studied region
  9. Keywords:
  10. Uncertainty ; Climate Change ; Irrigation ; Wheat Grains ; Downscaling ; Khuzestan Province ; Reliability Ensemble Averaging (REA) Method

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