干旱区生态环境知识资源中心(Arid): Simulation of the projected river flow changes using integrated downscaling and Bayesian optimization-tuned kernel-based models

Arid

DOI	10.1007/s13762-023-05322-9
	Simulation of the projected river flow changes using integrated downscaling and Bayesian optimization-tuned kernel-based models
	Roushangar, K.; Abdelzad, S.; Shahnazi, S.
通讯作者	Roushangar, K
来源期刊	INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
ISSN	1735-1472
EISSN	1735-2630
出版年	2023
英文摘要	In terms of having a comprehensive vision toward investigating the impact of future climate changes, LARS-WG, a weather generator model, was employed in the present study to project future changes in precipitation and maximum and minimum temperature. Considering the significant uncertainties linked to future climate projections, three general circulation models (GCMs), HadGEM2-ES, BCC-CSM1, and CanESM2, were utilized to project the future climate depending on three important scenarios of emissions, RCP2.6, RCP4.5, and RCP8.5, covering the period of from 2020 to 2041. Two local synoptic stations in Iran, representing distinct climatic characteristics, were utilized to validate the model, by adopting the historical data spanning twenty years (2001-2020). The results indicated a decrease in mean runoff during spring for semiarid areas (maximum of 19.95 m3/s in May) and in both spring and winter for humid areas (maximum of 8.7 m3/s in May) over the next twenty years. Moreover, under the RCP8.5 scenario, the humid region experienced a maximum rainfall increase of 66.66 mm compared to the base period. Additionally, the semi-arid region observed a maximum temperature increase of 1.18 degrees C under the same RCP8.5 scenario. The next step involved using downscaled parameters as inputs for the Bayesian optimization-based kernel-based models to simulate future runoff considering the impact of climate change. The results obtained demonstrate that the semi-arid region exhibits better forecasting capabilities, achieving evaluation criteria values of R = 0.973, NSE = 0.946, and RMSE = 0.020. The result emphasizes the need for implementing sustainable adaptation strategies to protect future water resources in the basin.
英文关键词	Gaussian process regression Support vector machine Bayesian optimization, LARS-WG Runoff Semi-arid region Temperature
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001104594500003
WOS关键词	EXTREME LEARNING-MACHINE ; CLIMATE-CHANGE ; LARS-WG ; DISCHARGE COEFFICIENT ; IMPACT ; SENSITIVITY ; EVAPORATION ; PREDICTION ; RAINFALL ; NETWORK
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396998
推荐引用方式 GB/T 7714	Roushangar, K.,Abdelzad, S.,Shahnazi, S.. Simulation of the projected river flow changes using integrated downscaling and Bayesian optimization-tuned kernel-based models[J],2023.
APA	Roushangar, K.,Abdelzad, S.,&Shahnazi, S..(2023).Simulation of the projected river flow changes using integrated downscaling and Bayesian optimization-tuned kernel-based models.INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY.
MLA	Roushangar, K.,et al."Simulation of the projected river flow changes using integrated downscaling and Bayesian optimization-tuned kernel-based models".INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (2023).