Sonu Kumar, S. R. Yadav, Atul Kumar, T. Baghel, M. Pramanik
{"title":"Evaluation of Temperature and Precipitation Changes under Climate Change Scenarios in Sikkim Himalayan region in India","authors":"Sonu Kumar, S. R. Yadav, Atul Kumar, T. Baghel, M. Pramanik","doi":"10.47884/jweam.v2i3pp89-106","DOIUrl":null,"url":null,"abstract":"Since precipitation and temperature are the major driving factors for the fragility of the Himalayan ecosystem and resources, it is crucial to understand the changes in temperature and precipitation under climate change scenarios to take appropriate adaptation measures. This work, therefore, examined the changes of precipitation and temperature under all Representative Concentration Pathway (RCP2.6, 4.5, 6.0, and 8.5) scenarios of climate change in the Sikkim Himalayan region of India. The datasets from two different global circulation models (GCMs) have been used to analyseSikkim’s;s daily precipitation and temperature for the near, mid, and far future. The linear scaling bias correction method (LCBCM) was employed to remove the bias because of a significant difference between the raw and observed monthly climate data for both GCMs.The predictions based on bias-corrected GCMs data under all RCP scenarios indicated that Tmax and Tmin are projected to increase in the near, mid and far futures. The projection of CSIRO_MK 3.6 model indicated that the increase in Tmax from near to far ranges from 1.0 to 1.5°C, 0.8 to 2.8°C, 0.4 to 2.3°C and 0.5 to 4.2°C under the four scenarios, respectively. Similarly, the projected to increase in Tmin from near to far ranges from 1.5 to 2.0°C, 1.1 to 3.5°C, 0.5 to 3.0°C and 0.8 to 4.5°C in RCP 2.6, 4.5, 6.0 and 8.5 scenarios, correspondingly. The results also showed that in climate change scenarios, the rate of precipitation is expected to increase, which could lead to the rise of snowmelt and flooding in the near future. This study is recommended to increase the number GCMs in future studies to reduce the uncertainty in future prediction and utilize the LCBC method for bias correction.","PeriodicalId":443502,"journal":{"name":"Journal of Water Engineering and Management","volume":"157 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Water Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47884/jweam.v2i3pp89-106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Since precipitation and temperature are the major driving factors for the fragility of the Himalayan ecosystem and resources, it is crucial to understand the changes in temperature and precipitation under climate change scenarios to take appropriate adaptation measures. This work, therefore, examined the changes of precipitation and temperature under all Representative Concentration Pathway (RCP2.6, 4.5, 6.0, and 8.5) scenarios of climate change in the Sikkim Himalayan region of India. The datasets from two different global circulation models (GCMs) have been used to analyseSikkim’s;s daily precipitation and temperature for the near, mid, and far future. The linear scaling bias correction method (LCBCM) was employed to remove the bias because of a significant difference between the raw and observed monthly climate data for both GCMs.The predictions based on bias-corrected GCMs data under all RCP scenarios indicated that Tmax and Tmin are projected to increase in the near, mid and far futures. The projection of CSIRO_MK 3.6 model indicated that the increase in Tmax from near to far ranges from 1.0 to 1.5°C, 0.8 to 2.8°C, 0.4 to 2.3°C and 0.5 to 4.2°C under the four scenarios, respectively. Similarly, the projected to increase in Tmin from near to far ranges from 1.5 to 2.0°C, 1.1 to 3.5°C, 0.5 to 3.0°C and 0.8 to 4.5°C in RCP 2.6, 4.5, 6.0 and 8.5 scenarios, correspondingly. The results also showed that in climate change scenarios, the rate of precipitation is expected to increase, which could lead to the rise of snowmelt and flooding in the near future. This study is recommended to increase the number GCMs in future studies to reduce the uncertainty in future prediction and utilize the LCBC method for bias correction.