DOI: 10.1002/joc.5361
论文题名: Understanding the cascade of GCM and downscaling uncertainties in hydro-climatic projections over India
作者: Sharma T. ; Vittal H. ; Chhabra S. ; Salvi K. ; Ghosh S. ; Karmakar S.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38 起始页码: e178
结束页码: e190
语种: 英语
英文关键词: climate change
; downscaling
; hydro-climatic impact
; hydrological model
; uncertainty propagation
Scopus关键词: Agriculture
; Atmospheric thermodynamics
; Climate change
; Climate models
; Decision making
; Rain
; Water resources
; Climate change impact assessments
; Climatic impact
; Down-scaling
; Hydrological modeling
; Spatiotemporal variability
; Uncertainty propagation
; Variable infiltration capacity models
; Water resources management
; Uncertainty analysis
; climate change
; climate effect
; downscaling
; general circulation model
; hydrological modeling
; monsoon
; spatial variation
; temporal variation
; uncertainty analysis
; India
英文摘要: India is a major agrarian country strongly impacted by spatio-temporal variations in the Indian monsoon. The impact assessment is usually accomplished by implementing projections from general circulation models (GCMs). Unfortunately, these projections cannot capture the dynamicity of the monsoon and require either statistical (SD) or dynamical (DD) downscaling of the GCM projections to a finer resolution. Both downscaling techniques can capture the spatio-temporal variation in climatic variables but are marred by uncertainty in the projections resulting from the choice of the GCM and downscaling method, which affects climate change adaptations. Here, we assessed uncertainties in the projections of hydro-climatic variables over India by considering multiple downscaling techniques, multiple GCMs, and their combined effects (referred as the total uncertainty). Multiple hydrological variables were simulated by implementing the variable infiltration capacity model that considered outputs from DD (derived by the coordinated regional climate downscaling experiment, CORDEX) and SD forced with multiple GCM simulations. Our results showed that the SD projections captured the observed spatio-temporal variability of hydro-climatic variables more efficiently than the DD projections. Importantly, contribution from the downscaled projections to the total uncertainty was significantly smaller compared to the inter-GCM uncertainty. We believe uncertainty analysis is an important component of good scientific practice; however, several researchers appear to be rather reluctant to embrace the concept of uncertainty in making projections, predictions, and forecasting. It remains a common practice to show climate change exercises to decision-makers/stakeholders, without uncertainty bounds. Here, a successful attempt was made to identify the key sources of uncertainty and adequately bracket the uncertainty, indicating a requirement of the code of practice to provide formal guidance, particularly for climate-change impact assessments. This consequently emphasized the importance of follow-up research to understand the inter-GCM uncertainty, which has a significant impact on sustainable agriculture and water resources management in India. © 2017 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116926
Appears in Collections: 气候减缓与适应
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作者单位: Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, India; Centre for Environmental Science and Engineering, Indian Institute of Technology Bombay, India; Civil Engineering Department, Manipal Institute of Technology, India; IIHR-Hydroscience & Engineering, University of IowaIA, United States; Department of Civil Engineering, College of Engineering, Pune, India; Department of Civil Engineering, Indian Institute of Technology Bombay, India; Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, India
Recommended Citation:
Sharma T.,Vittal H.,Chhabra S.,et al. Understanding the cascade of GCM and downscaling uncertainties in hydro-climatic projections over India[J]. International Journal of Climatology,2018-01-01,38