Implications of CMIP6 Models-Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India

IF 3.5 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Shoobhangi Tyagi, Sandeep Sahany, Dharmendra Saraswat, Saroj Kanta Mishra, Amlendu Dubey, Dev Niyogi
{"title":"Implications of CMIP6 Models-Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India","authors":"Shoobhangi Tyagi,&nbsp;Sandeep Sahany,&nbsp;Dharmendra Saraswat,&nbsp;Saroj Kanta Mishra,&nbsp;Amlendu Dubey,&nbsp;Dev Niyogi","doi":"10.1002/joc.8661","DOIUrl":null,"url":null,"abstract":"<p>Accurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these uncertainties rely on the stationarity of climate biases and runoff sensitivity, which may not hold for climate-sensitive regions (e.g., semi-arid regions). This study investigates the validity of the stationarity assumption across 29 CMIP6 models, encompassing diverse climate biases (Dry Warm, Wet Warm, Dry Cold, and Wet Cold), utilising a semi-arid region in central India as a testbed. The implications of this assumption on runoff projection uncertainties were comprehensively assessed across the runoff modelling chain for three time periods (the 2030s, 2060s and 2090s) based on the Soil and Water Assessment Tool (SWAT) simulations. The results highlight the non-stationary nature of climate biases and runoff sensitivity under future scenarios, challenging the widespread applicability of common uncertainty-constraining approaches. Moreover, the impact of non-stationarity on runoff projection uncertainty was found to be strongly influenced by the choice of GCMs, preprocessing methods and climate change scenarios. In the 2030s, GCMs dominate runoff uncertainty, with dry models exhibiting ~10%–15% higher uncertainty compared to warm models, which is further amplified when interacting with warm biases. However, from the mid-century onwards, the bias-adjustment approaches and climate change scenarios significantly shape runoff projection uncertainties under non-stationary conditions. These findings emphasise the potential of climate bias and runoff sensitivity-based GCM selection for reducing runoff uncertainty in near-future assessment (2030s). For mid-term and long-term runoff projections, addressing diverse climate biases through bias-adjustment approaches is more viable. This study offers critical insights to prioritise the development of a non-stationarity-based approach for reliable runoff projections in climate-sensitive regions.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 16","pages":"5727-5744"},"PeriodicalIF":3.5000,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8661","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Climatology","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/joc.8661","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Accurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these uncertainties rely on the stationarity of climate biases and runoff sensitivity, which may not hold for climate-sensitive regions (e.g., semi-arid regions). This study investigates the validity of the stationarity assumption across 29 CMIP6 models, encompassing diverse climate biases (Dry Warm, Wet Warm, Dry Cold, and Wet Cold), utilising a semi-arid region in central India as a testbed. The implications of this assumption on runoff projection uncertainties were comprehensively assessed across the runoff modelling chain for three time periods (the 2030s, 2060s and 2090s) based on the Soil and Water Assessment Tool (SWAT) simulations. The results highlight the non-stationary nature of climate biases and runoff sensitivity under future scenarios, challenging the widespread applicability of common uncertainty-constraining approaches. Moreover, the impact of non-stationarity on runoff projection uncertainty was found to be strongly influenced by the choice of GCMs, preprocessing methods and climate change scenarios. In the 2030s, GCMs dominate runoff uncertainty, with dry models exhibiting ~10%–15% higher uncertainty compared to warm models, which is further amplified when interacting with warm biases. However, from the mid-century onwards, the bias-adjustment approaches and climate change scenarios significantly shape runoff projection uncertainties under non-stationary conditions. These findings emphasise the potential of climate bias and runoff sensitivity-based GCM selection for reducing runoff uncertainty in near-future assessment (2030s). For mid-term and long-term runoff projections, addressing diverse climate biases through bias-adjustment approaches is more viable. This study offers critical insights to prioritise the development of a non-stationarity-based approach for reliable runoff projections in climate-sensitive regions.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
International Journal of Climatology
International Journal of Climatology 地学-气象与大气科学
CiteScore
7.50
自引率
7.70%
发文量
417
审稿时长
4 months
期刊介绍: The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信