Modelling groundwater-dependent vegetation index using Entropy theory

Gengxi Zhang, Xiaoling Su and Vijay P. Singh

Ecological Modelling, 2020, vol. 416, issue C

Abstract: An ecosystem is vulnerable to the scarcity of water resources and sparse vegetation cover in arid regions. Groundwater plays an important role in maintaining ecological environment and strongly impacts the ecosystem through influencing vegetation structure and species distribution. It is therefore important to clearly understand the relationship between vegetation patterns and groundwater depth(GWD). In this paper, Tsallis entropy theory was applied to derive a functional relationship between GWD and vegetation distribution. The theory was tested using observed data from arid regions in northwestern China. Results showed that higher vegetation coverage exist at places of shallow GWD. The values of NDVI gradually increase with increasing GWD until reaching a maximum at the optimum depth, after which they decrease with increasing groundwater depth when GWD is less than approximately 10 m. Beyond that depth, a low level of vegetation coverage is maintained. The correlation coefficients between measured and simulated values of NDVI were above 0.9 (p < 0.01) in the Ejina, Qaidam and Hailiutu basins. The theory is applicable to different regions and vegetation types and may improve our ability to sustainably manage land and groundwater resources in arid regions, especially where the vegetation is groundwater-dependent.

Keywords: Arid ecosystems; Tsallis entropy; Vegetation index; Groundwater depth (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304380019304247
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:416:y:2020:i:c:s0304380019304247

DOI: 10.1016/j.ecolmodel.2019.108916

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().