Precipitation projection using a CMIP5 GCM ensemble model : a regional investigation of Syria

The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by −30 to −85.2% for RCPs 4.5, 6.0, and 8.5, while by &lt; 0.0 to −30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by −12 to −93%, which indicated a drier climate for the country in the future.Validerad;2019;Nivå 2;2019-11-11 (johcin)</p

Mutation Breeding of Rice for Sustainable Agriculture in Malaysia

アジア原子力協力フォーラム(FNCA)放射線育種プロジェクトで、主に2013年度から2017年度にかけて実施されたサブプロジェクト「持続可能な農業のためのイネの突然変異育種」に関してマレーシアで実施された研究成果を取りまとめた報告書である。乾燥に強く多収のイネ品種NMR152の開発と普及、及びイオンビームを利用した新奇変異体の選抜等について記載