Assessing future runoff changes with different potential evapotranspiration inputs based on multi-model ensemble of CMIP5 projections

Shi, L; Feng, P; Wang, B; Liu, DL; Zhang, H; Liu, J; Yu, Q

Assessing future runoff changes with different potential evapotranspiration inputs based on multi-model ensemble of CMIP5 projections

Shi, L

Feng, P

Wang, B Liu, DL Zhang, H

Liu, J Yu, Q

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Hydrology, 2022, 612, pp. 128042
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Shi, L https://orcid.org/0000-0003-2290-3983
dc.contributor.author	Feng, P https://orcid.org/0000-0003-4845-9876
dc.contributor.author	Wang, B
dc.contributor.author	Liu, DL
dc.contributor.author	Zhang, H https://orcid.org/0000-0001-9369-7481
dc.contributor.author	Liu, J
dc.contributor.author	Yu, Q
dc.date.accessioned	2023-06-28T06:30:09Z
dc.date.available	2023-06-28T06:30:09Z
dc.date.issued	2022-09-01
dc.identifier.citation	Journal of Hydrology, 2022, 612, pp. 128042
dc.identifier.issn	0022-1694
dc.identifier.uri	http://hdl.handle.net/10453/170952
dc.description.abstract	Runoff projection under future climate scenarios has been widely studied to investigate the impacts of climate change on regional water availability. However, uncertainty in runoff projection due to different ETp inputs has not been fully assessed. This study firstly adopted the physically-based Penman model, temperature-based Hargreaves model, and radiation-based Abtew, Jensen-Haise, and modified Makkink models to drive Xinanjiang (XAJ) model, thus investigating the influence of different potential evapotranspiration (ETp) inputs on runoff simulation. Then, we used the validated XAJ model to project runoff in North Johnstone catchment, northeast Australia. Lastly, we quantified the uncertainty caused by 34 global climate models (GCMs), different representative concentrative pathway (RCP) scenarios (RCP4.5 & RCP8.5), and different ETp models with the technique of three-way analysis of variance (ANOVA). We found that XAJ model performed well (R2 ≥ 0.88, NSE ≥ 0.86) and showed low sensitivity to different ETp inputs in runoff simulation and projection. Under future climate scenarios, spring and winter runoff had a large decrease, which was mainly caused by the decrease in rainfall. The mean decreases in spring and winter runoff were 14.6% – 20.1% and 10.3% – 15.2% respectively by 2090s under RCP8.5. GCMs (50.9% – 67.4%) and their interaction with RCPs (35.4% – 46.6%) were the dominant factors resulting in uncertainty in runoff projection. Our study not only advanced the understanding of the impacts of different ETp inputs on runoff projection but also offered insights on the understanding of the roles different factors played in the uncertainty in runoff projection. We expect such knowledge can provide a way forward to narrow down the uncertainty in runoff projection, thus providing more robust information for policy makers in water management.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Hydrology
dc.relation.isbasedon	10.1016/j.jhydrol.2022.128042
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Engineering
dc.title	Assessing future runoff changes with different potential evapotranspiration inputs based on multi-model ensemble of CMIP5 projections
dc.type	Journal Article
utslib.citation.volume	612
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2023-06-28T06:30:07Z
pubs.publication-status	Published
pubs.volume	612

Abstract:

Runoff projection under future climate scenarios has been widely studied to investigate the impacts of climate change on regional water availability. However, uncertainty in runoff projection due to different ETp inputs has not been fully assessed. This study firstly adopted the physically-based Penman model, temperature-based Hargreaves model, and radiation-based Abtew, Jensen-Haise, and modified Makkink models to drive Xinanjiang (XAJ) model, thus investigating the influence of different potential evapotranspiration (ETp) inputs on runoff simulation. Then, we used the validated XAJ model to project runoff in North Johnstone catchment, northeast Australia. Lastly, we quantified the uncertainty caused by 34 global climate models (GCMs), different representative concentrative pathway (RCP) scenarios (RCP4.5 & RCP8.5), and different ETp models with the technique of three-way analysis of variance (ANOVA). We found that XAJ model performed well (R2 ≥ 0.88, NSE ≥ 0.86) and showed low sensitivity to different ETp inputs in runoff simulation and projection. Under future climate scenarios, spring and winter runoff had a large decrease, which was mainly caused by the decrease in rainfall. The mean decreases in spring and winter runoff were 14.6% – 20.1% and 10.3% – 15.2% respectively by 2090s under RCP8.5. GCMs (50.9% – 67.4%) and their interaction with RCPs (35.4% – 46.6%) were the dominant factors resulting in uncertainty in runoff projection. Our study not only advanced the understanding of the impacts of different ETp inputs on runoff projection but also offered insights on the understanding of the roles different factors played in the uncertainty in runoff projection. We expect such knowledge can provide a way forward to narrow down the uncertainty in runoff projection, thus providing more robust information for policy makers in water management.

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http://hdl.handle.net/10453/170952