国际标准期刊号: 2573-458X

环境污染与气候变化

开放获取

我们集团组织了 3000 多个全球系列会议 每年在美国、欧洲和美国举办的活动亚洲得到 1000 多个科学协会的支持 并出版了 700+ 开放获取期刊包含超过50000名知名人士、知名科学家担任编委会成员。

开放获取期刊获得更多读者和引用
700 种期刊 15,000,000 名读者 每份期刊 获得 25,000 多名读者

抽象的

Recent and Future Trend Analysis of Inter-Seasonal to Seasonal Rainfall and Temperature to Climate Change and Variability in Dire Dawa city Administration, Ethiopia

Abdisa Alemu Tolosa, Tasisa Temesgen Tolossa

The trend analysis has been employed to inspect the change of rainfall and temperature in Dire Dawa city administration, using gauged monthly precipitation and Temperature data obtained from National meteorological Agency of Ethiopia from 1980 to 2014 and the future climate data of GCM used by the Intergovernmental Panel on Climate Change fifth generation CMIP5 and World Clim obtained from Agricultural Model inter-comparison and Improvement Project (AgMIP) website. Two models such as HadGEM2-ES and MRI-CGCM3 under rcp4.5and 8.5 have been considered to generate rainfall, maximum and minimum temperature data using AgMIP R script of “run_ agmip_simple_delta.R”in R software 3.5.3 version. Recent and future rainfall variability CV, Standard deviation and rainfall Anomaly have been computed using excel spreadsheet for variability analysis and the Mann-Kendall test was used to detect the time series trend of inter-seasonal to seasonal temperature and rainfall using R software version 3.5.3. The inter-seasonal to seasonal observed and projected rainfall data shown highly variable while the annual rainfall variability shown in moderate range of variability. The declining trend for belg and inclining trend of kiremt mean observed and HadGEM2-ES(CMIP5) under rcp8.5 model rainfall were found to be statistically significant while that of annual and other monthly trend were not significant except July in observed data but the other projected model for future rainfall trend shown no statistical significant. Therefore, the concerned bodies should take in to consideration climate change adaptation strategy and finally, it is recommended to extend farther study with Ensemble GCM model and RCM.

免责声明: 此摘要通过人工智能工具翻译,尚未经过审核或验证。