Zhong Liang’s scientific contributions

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (2)


Pearson correlation coefficient between annual SM in 0–7 cm (upper panel) and 28–100 cm (lower panel) depths with (a, i) NDVI, (b, j) TMP, (c, k) PCP, (d, l) AET, (e, m) PET, (f, n) SWR, (g, o) LWR, (h, p) SHF during 1981–2019. The dotted regions indicate the 95% significant confidence level.
Global coherence (GWC) between monthly SM (0-7 cm) in (a-f) karst (upper panel) and (g-l) non-karst (lower panel) regions with NDVI, TMP, PCP, AET, LWR and SHF in the KRSC during 1981–2019.
Pearson correlation coefficient between regionally averaged seasonal, annual, wet and dry season SM in karst (a, c) and non-karst (b, d) region at 0-7 cm (a-b) and 28-100 cm (c-d) soil depth with climatic factors and teleconnections in the KRSC. The asterisk sign indicates significant positive correlation coefficient at 5% confidence level.
Relative importance of variables by the Lindeman, Merenda, and Gold (LMG) importance plot (Lindeman et al., 1980) of monthly SM in karst (a, c) and non-karst (b, d) region at 0-7 cm (a-b) and 28-100 cm (c-d) soil depth with climatic factors in the KRSC. The SM values in the two soil layers are the two independent variables in the regression. The values with bars in each subplot show the mean variances explained by models.
Recent increase in soil moisture levels concerning climate variability in the karst region of southwest China using wavelet coherence and multi-linear regression
  • Article

February 2025

·

54 Reads

Gondwana Research

·

Huizeng Liu

·

Jianhua Cao

·

[...]

·

Zhong Liang

(a) Location of study area with topographic details, whereas different color arrow direction indicates moisture transport from the Indian, Pacific and Atlantic Oceans, (b) spatial distribution of limestone, dolomite, mix (limestone/ dolomite) and carbonate rocks, (c) box plots of monthly NDVI in the KRSC and its sub-regions I-VIII, which are Chongqing, Guangdong, Guangxi, Guizhou, Hubei, Hunan, Sichuan and Yunnan, respectively, and (d) monthly NDVI variations over Karst, non-karst, limestone, dolomite, mix (limestone/ dolomite) and carbonate in the KRSC.
Correlation of NDVI with (a) temperature, (b) precipitation, (c) potential evapotranspiration, (d) soil temperature, (e) soil moisture and (f) short-wave radiation flux in the KRSC. Black dots indicate significant trends at 5% confidence level as of Mann-Kendall test.
MWC of NDVI over the KRSC with large-scale CIs (a) AMO-AO-NAO, (b) ENSO-PDO-PNA, (c) ENSO-AMO, (d) ENSO-PDO-NAO, (e) ENDO-PDO-AMO, (f) NP-WP-WHWP, (g) AMO-NP-WHWP, (h) PCP-SM- NP-WP-WHWP, and (i) TMP-ST-NP-WP-WHWP.
Pearson correlation coefficient between seasonal, annual, wet and dry season NDVI (regionally averaged) with regional climatic factors and individual large-scale indices in the KRSC. The asterisk “*” sign indicates the positive significant trends at 5% confidence bounds.
The time lagged correlation coefficients between spring and summer NDVI with (a) regional climatic factors, and (b) teleconnections in the KRSC.
Characterizing the local and global climatic factors associated with vegetation dynamics in the karst region of southwest China

September 2024

·

199 Reads

·

1 Citation

Journal of Hydrology

Abstract Understanding the relationship between vegetation and climatic drivers is essential for assessing terrestrial ecosystem patterns and managing future vegetation dynamics. This study examines the effects of local climatic factors and remote large-scale ocean–atmosphere circulations from the Pacific, Atlantic, and Arctic Oceans, as well as the East Asian and Indian summer monsoons, on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) in the karst region of southwest China (KRSC) using Mann-Kendall test, Sen’s slope, cross-correlation, and wavelet analysis. We observed a significant increase in NDVI over karst and non-karst regions from 1981 to 2019, with a notable abrupt shift from 2001 onwards, underscoring the importance of understanding the underlying drivers. The significant correlation and coherence of surface air (TMP) and soil temperatures (ST) with NDVI, especially when analyzed using wavelet methods, indicate their crucial role in vegetation dynamics. Additionally, the broad coherence patterns of AMO and WHWP with NDVI at annual and decadal cycles suggest that ocean–atmosphere interactions also play a significant part. At interannual periodicities, most large-scale indices displayed significant coherence with NDVI. These findings highlight the complexity of NDVI variability, which is better explained by the integration of multiple local and global factors rather than by single variables. The integrated local–global drivers, particularly TMP-ST-AMO-NP-WHWP and PCP-SM-AMO-NP-WHWP with mean coherence of 0.90 and 0.89, respectively, showed the highest mean coherence, emphasizing the need for a multifaceted approach in understanding vegetation changes rather than a single local variable or atmospheric circulation index. These findings have significant implications for policymakers, aiding in better planning and policy formulations considering climate change and atmospheric variability.

Citations (1)


... The Wavelet Coherence (WTC) introduced by Torrence and Compo (1998) is used to assess the coherences between the de-seasonalized time series of SM and NDVI, TMP, PCP, AET, PET, SHF and LWR (Abbas et al., 2024;Hussain et al., 2024a). The monthly SM and local/global climatic factors in the karst and non-karst regions of the KRSC are used to assess the WTC. ...

Reference:

Recent increase in soil moisture levels concerning climate variability in the karst region of southwest China using wavelet coherence and multi-linear regression
Characterizing the local and global climatic factors associated with vegetation dynamics in the karst region of southwest China
  • Citing Article
  • September 2024

Journal of Hydrology