Ranghui Wang

Ranghui Wang


Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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18 January 2020 in International Journal of Biometeorology
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Journal Article
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YJYelin Jiang
Yelin Jiang
JGJing Guo
Jing Guo
RWRanghui Wang
Ranghui Wang
YGYanlong Guan
Yanlong Guan
YZYang Zhang
Yang Zhang
RZRenping Zhang
Renping Zhang

17 April 2020 in Land Degradation & Development
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Journal Article
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YWYanwen Wang
Yanwen Wang
HYHuanbi Yue
Huanbi Yue
RWRanghui Wang
Ranghui Wang
CHChao He
Chao He
SHSong Hong
Song Hong
Brett BryanBrett Bryan
Brett Bryan

29 March 2018 in Natural Hazards
Verified
Journal Article
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YJYelin Jiang
Yelin Jiang
RWRanghui Wang
Ranghui Wang
RWRanghui Wang
Ranghui Wang
XWXiaoquan Wu
Xiaoquan Wu
HNHusen Ning
Husen Ning
CLCheng Li
Cheng Li

1 March 2021 in Journal of Climate
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YJYelin Jiang
Yelin Jiang
GWGuiling Wang
Guiling Wang
WLWeiguang Liu
Weiguang Liu
AEAmir Erfanian
Amir Erfanian
RWRanghui Wang
Ranghui Wang
RFRong Fu
Rong Fu
This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.

5 February 2022 in Sustainability
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RWRanghui Wang
Ranghui Wang
RWRanghui Wang
Ranghui Wang
WZWeidong Zhang
Weidong Zhang
WZWenfei Zhao
Wenfei Zhao
Chunwei LiuChunwei Liu
Chunwei Liu
LZLimin Zhou
Limin Zhou
Mountain ecosystems have special ecohydrological services, and the study of water conservation and soil conservation services in the Qilian Mountain Ecosystem (QLME) in China has important theoretical value for scientific understanding of the ecological processes and mechanisms of mountain ecosystems. In this study, we quantitatively estimated the spatial-temporal changes of water conservation and soil conservation services in the QLME based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and estimated the future ecosystem services (ESS) of the QLME under RCP4.5 (Representative Concentration Pathways) and RCP8.5 scenarios using the coupled Geosos-FLUS model. Firstly, the QLME ecohydrological service increased from 1985 to 2018, and its spatial heterogeneity was high in the east and low in the west. Among them, water conservation first decreased and then showed a trend of fluctuating increase, and soil conservation services decreased sharply from 2010 to 2015. Secondly, there are differences in the ecohydrological services of the QLME under different land-use types. The water conservation capacity in descending order is glacier snow, grassland, forest land, wetland, and cultivated land. The soil conservation intensity from strong to weak is woodland, grassland, arable land, glacier snow, and bare land. Thirdly, under different scenarios, QLME water conservation and soil conservation functions will increase to varying degrees over the next 30 years. The water conservation in the RCP4.5 scenario is higher than that in the RCP8.5 scenario, and the higher discharge scenario will lead to the decline of the water conservation service function. The increased rate of soil conservation was greater under the RCP8.5 scenario. With the development of Nationally Determined Contributions (NDCs) and scenarios below 2 °C, the future of QLME ecohydrological services will be further understood.