Hydrological projections of future climate change over the source region of Yellow River and Yangtze River in the Tibetan Plateau: A comprehensive assessment by coupling RegCM4 and VIC model

Lü, Wenjun; Wang, Weiguang; Shao, Quanxi; Yu, Zhongbo; Hao, Zhenchun; Xing, Wanqiu; Yong, Bin; Li, Jinxing

doi:10.1002/hyp.13145

Cited by 52 publications

(28 citation statements)

References 101 publications

(184 reference statements)

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“…Compared with the future runoff simulation results under RCP scenarios in the existing studies [11,12], it is found that the average runoff in the future under the RCP-LUC scenario is greater, which indicates that land use data input into the SWAT model will have a great impact on future runoff simulation results. Compared with the future runoff simulation results under RCP scenarios in the existing studies [11,12], it is found that the average runoff in the future under the RCP-LUC scenario is greater, which indicates that land use data input into the SWAT model will have a great impact on future runoff simulation results.…”

Section: Average Runoff Changementioning

confidence: 89%

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Future Runoff Variation and Flood Disaster Prediction of the Yellow River Basin Based on CA-Markov and SWAT

Lai

Xia

et al. 2021

Land

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The purpose of this paper is to simulate the future runoff change of the Yellow River Basin under the combined effect of land use and climate change based on Cellular automata (CA)-Markov and Soil & Water Assessment Tool (SWAT). The changes in the average runoff, high extreme runoff and intra-annual runoff distribution in the middle of the 21st century are analyzed. The following conclusions are obtained: (1) Compared with the base period (1970–1990), the average runoff of Tangnaihai, Toudaoguai, Sanmenxia and Lijin hydrological stations in the future period (2040–2060) all shows an increasing trend, and the probability of flood disaster also tends to increase; (2) Land use/cover change (LUCC) under the status quo continuation scenario will increase the possibility of future flood disasters; (3) The spring runoff proportion of the four hydrological stations in the future period shows a decreasing trend, which increases the risk of drought in spring. The winter runoff proportion tends to increase; (4) The monthly runoff proportion of the four hydrological stations in the future period tends to decrease in April, May, June, July and October. The monthly runoff proportion tends to increase in January, February, August, September and December.

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Section: Average Runoff Changementioning

confidence: 89%

“…(2) Compared with the future runoff simulation results under the RCP scenario in the existing studies [11,12], it is found that the average runoff and Q95 extreme runoff in the future under the RCP-LUC scenario are greater, which indicates that existing studies have underestimated the risk of flood disasters in the future.…”

Section: Discussionmentioning

confidence: 99%

Future Runoff Variation and Flood Disaster Prediction of the Yellow River Basin Based on CA-Markov and SWAT

Lai

Xia

et al. 2021

Land

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“…Datasets combining remote sensing products, reanalysis datasets, and in situ observation data at weather stations are increasingly important as observed data sources in TP research. For example, many researchers have used the China Meteorological Forcing Dataset (CMFD) to investigate simulations of permafrost changes (Xu et al ., 2017a; 2017b), ground surface soil heat flux (Yang et al ., 2020), the precipitation–runoff process (Cai et al ., 2020), and hydrological projections (Lu et al ., 2018; Nury et al ., 2019). In addition, general circulation models (GCMs) always play a critically important role in climate change projections.…”

Section: Introductionmentioning

confidence: 99%

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Lun

Liu

Cheng

et al. 2021

Intl Journal of Climatology

124

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General circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs for each climate variable, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid‐west and northern edge of the TP. Furthermore, CMIP6 offered a better performance of precipitation in summer and autumn. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1°C and had a smaller bias in spring and winter. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2°C in each elevation zone, with the minimum bias of −0.22°C distributed in the region with altitudes from 3,000 to 4,000 m, while the biases of CMIP5 MMEs in the region of 4,000–5,000 m and over 5,000 m were smaller than those of CMIP6 MMEs. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. GCMs of CMIP6 perform better than those of CMIP5 for precipitation and temperature over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.

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“…Gu et al 29 used the ECHAM5 results under the SRES A1B scenario to drive RegCM4 to project precipitation in the YRB to the end of the twenty-first century and found that precipitation in the north and south of the basin will increase and decrease, respectively. Lu et al 28 used the HadGEM2-ES under three representative concentration pathways (RCPs) scenarios (2.6, 4.5, and 8.5) to drive RegCM4 to project runoff in the source region of YRB for 2041–2060 and found that snowmelt runoff would become more important with increase of 17.5% and 18.3%, respectively, under RCP2.6 and RCP4.5 but decrease of 15.0% under RCP8.5. In general, the above results indicate that the total precipitation and probability of heavy precipitation in the YRB will significantly increase by the end of this century.…”

Section: Introductionmentioning

confidence: 99%

Hydrological projections in the upper reaches of the Yangtze River Basin from 2020 to 2050

Huang

Xiao

Hou

et al. 2021

Sci Rep

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Understanding the impact of climate change on runoff is essential for effective water resource management and planning. In this study, the regional climate model (RCM) RegCM4.5 was used to dynamically downscale near-future climate projections from two global climate models to a 50-km horizontal resolution over the upper reaches of the Yangtze River (UYRB). Based on the bias-corrected climate projection results, the impacts of climate change on mid-twenty-first century precipitation and temperature in the UYRB were assessed. Then, through the coupling of a large-scale hydrological model with RegCM4.5, the impacts of climate change on river flows at the outlets of the UYRB were assessed. According to the projections, the eastern UYRB will tend to be warm-dry in the near-future relative to the reference period, whereas the western UYRB will tend to be warm-humid. Precipitation will decreases at a rate of 19.05–19.25 mm/10 a, and the multiyear average annual precipitation will vary between − 0.5 and 0.5 mm/day. Temperature is projected to increases significantly at a rate of 0.38–0.52 °C/10 a, and the projected multiyear average air temperature increase is approximately 1.3–1.5 ℃. The contribution of snowmelt runoff to the annual runoff in the UYBR is only approximately 4%, whereas that to the spring runoff is approximately 9.2%. Affected by climate warming, the annual average snowmelt runoff in the basin will be reduced by 36–39%, whereas the total annual runoff will be reduced by 4.1–5%, and the extreme runoff will be slightly reduced. Areas of projected decreased runoff depth are mainly concentrated in the southeast region of the basin. The decrease in precipitation is driving this decrease in the southeast, whereas the decreased runoff depth in the northwest is mainly driven by the increase in evaporation.

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Hydrological projections of future climate change over the source region of Yellow River and Yangtze River in the Tibetan Plateau: A comprehensive assessment by coupling RegCM4 and VIC model

Cited by 52 publications

References 101 publications

Future Runoff Variation and Flood Disaster Prediction of the Yellow River Basin Based on CA-Markov and SWAT

Future Runoff Variation and Flood Disaster Prediction of the Yellow River Basin Based on CA-Markov and SWAT

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Hydrological projections in the upper reaches of the Yangtze River Basin from 2020 to 2050

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