Xiaoran Liu

Xiaoran Liu


Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

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

Representative Publications

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3 February 2019 in Atmosphere
Verified
Journal Article
Open access
PDF file
PJPing Jiang
Ping Jiang
XLXiaoran Liu
Xiaoran Liu
HZHaonan Zhu
Haonan Zhu
YLYonghua Li
Yonghua Li
The spatial and temporal features of urban heat island (UHI) intensity in complex urban terrain are barely investigated. This study examines the UHI intensity variations in mountainous Chongqing using a dense surface monitoring network. The results show that the UHI intensity is closely related to underlying surfaces, and the strongest UHI intensity is confined around the central urban areas. The UHI intensity is most prominent at night and in warm season, and the magnitude could reach ~4.5 °C on summer night. Our quantitative analysis shows a profound contribution of urbanization level to UHI intensity both at night and in summer, with regression coefficient b = 4.31 and 6.65, respectively. At night, the urban extra heat such as reflections of longwave radiation by buildings and release of daytime-stored heat from artificial materials, is added into the boundary layer, which compensates part of urban heat loss and thus leads to stronger UHI intensity. In summer, the urban areas are frequently controlled by oppressively hot weather. Due to increased usage of air conditioning, more anthropogenic heat is released. As a result, the urban temperatures are higher at night. The near-surface wind speed can serve as an indicator predicting UHI intensity variations only in the diurnal cycle. The rural cooling rate during early evening transition, however, is an appropriate factor to estimate the magnitude of UHI intensity both at night and in summer.

18 April 2024 in Remote Sensing
Verified
Journal Article
Open access
PDF file
PZPengguo Zhao
Pengguo Zhao
XLXiaoran Liu
Xiaoran Liu
Chuanfeng ZhaoChuanfeng Zhao
Chuanfeng Zhao
The aerosol–cloud–precipitation correlation has been a significant scientific topic, primarily due to its remarkable uncertainty. However, the possible modulation of aerosol on the precipitation capacity of clouds has received limited attention. In this study, we utilized multi-source data on aerosol, cloud properties, precipitation, and meteorological factors to investigate the impact of aerosols on precipitation efficiency (PE) in the Sichuan Basin (SCB) and Yun-nan-Guizhou Plateau (YGP), where the differences between terrain and meteorological environment conditions were prominent. In the two study regions, there were significant negative correlations between the aerosol index (AI) and PE in spring, especially in the YGP, while the correlations between the AI and PE in other seasons were not as prominent as in spring. In spring, aerosol significantly inhibited both the liquid water path (LWP) and the ice water path (IWP) in the YGP, but negatively correlated with the IWP and had no significant relationship with the LWP in the SCB. Aerosol inhibited precipitation in the two regions mainly by reducing cloud droplet effective radius, indicating that warm clouds contributed more to precipitation in spring. The suppressive impact of aerosols on precipitation serving as the numerator of PE is greater than that of the cloud water path as the denominator of PE, resulting in a negative correlation between aerosol and PE. The AI–PE relationship is significantly dependent on meteorological conditions in the YGP, but not in the SCB, which may be related to the perennial cloud cover and stable atmosphere in the SCB. In the future, as air quality continues to improve, precipitation efficiency may increase due to the decrease in aerosol concentration, and of course, the spatio-temporal heterogeneity of the aerosol–cloud–precipitation relationship may become more significant.

24 February 2025 in Water
Verified
Journal Article
Open access
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Liangliang JiangLiangliang Jiang
Liangliang Jiang
GWGuangming Wu
Guangming Wu
QLQijin Li
Qijin Li
XLXiaoran Liu
Xiaoran Liu
Flash drought events occur frequently in Southwestern China, and a notable upward trend is predicted for the future. Attention should be given to how the severity of flash droughts and vegetation vulnerability hinder vegetation from recovering to their original state, leading to losses. Vegetation resilience and vulnerability to flash droughts was assessed in dry years by adopting a ‘resistance–resilience’ framework from a new perspective, and we measured the significance of various drought characteristics in affecting vegetation reduction by using the boosted regression tree (BRT) model. The results showed that croplands in the Sichuan Basin displayed low resistance to flash droughts, whereas grasslands and forests in mountainous areas had high resistance. Croplands in the Sichuan Basin demonstrated high vegetation resilience, while Guizhou province showed low vegetation resilience. Most regions experienced high vegetation vulnerability to flash droughts, especially in the Sichuan Basin and Yunnan province. We found that croplands and forests in 2006 exhibited a significant decrease in LAI during flash drought events. Croplands experienced a significant decrease in LAI in regions where the drought duration (DD) exceeded 60 days, and the drought interval (DIV) ranged from 30 to 40 days. Forest regions with a DD exceeding 60 days and a DIV below 20 days experienced a high reduction in LAI. Furthermore, croplands and shrubs could recover once their vulnerability fell below thresholds of 0.34 and 0.30, respectively. The impact of species richness on vegetation resilience can be explored in future research. This study reveals the spatial patterns of vegetation vulnerability and provides information on preventing and managing vegetation deterioration in Southwestern China.

1 January 2019 in JOURNAL OF NATURAL RESOURCES
Verified
Journal Article
Closed access
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HZHao-Nan Zhu
Hao-Nan Zhu
XLXiaoran Liu
Xiaoran Liu
JSJia Sun
Jia Sun
YWYin Wang
Yin Wang
DLDai-Qiang Liao
Dai-Qiang Liao
JZJie Zhou
Jie Zhou