Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India
Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India
To explore the spatio-temporal dynamics and mechanisms underlying vegetation cover in Haryana State, India, and implications thereof, we obtained MODIS EVI imagery together with CHIRPS rainfall and MODIS LST at annual, seasonal and monthly scales for the period spanning 2000 to 2022. Additionally, MODIS Potential Evapotranspiration (PET), Ground Water Storage (GWS), Soil Moisture (SM) and nighttime light datasets were compiled to explore their spatial relationships with vegetation and other selected environmental parameters. Non-parametric statistics were applied to estimate the magnitude of trends, along with correlation and residual trend analysis to quantify the relative influence of Climate Change (CC) and Human Activities (HA) on vegetation dynamics using Google Earth Engine algorithms. The study reveals regional contrasts in trends that are evidently related to elevation. An annual increasing trend in rainfall (21.3 mm/decade, p < 0.05), together with augmented vegetation cover and slightly cooler (−0.07 °C/decade) LST is revealed in the high-elevation areas. Meanwhile, LST in the plain regions exhibit a warming trend (0.02 °C/decade) and decreased in vegetation and rainfall, accompanied by substantial reductions in GWS and SM related to increased PET. Linear regression demonstrates a strongly significant relationship between rainfall and EVI (R2 = 0.92), although a negative relationship is apparent between LST and vegetation (R2 = −0.83). Additionally, increased LST in the low-elevation parts of the study area impacted PET (R2 = 0.87), which triggered EVI loss (R2 = 0.93). Moreover, increased HA resulted in losses of 25.5 mm GSW and 1.5 mm SM annually. The relative contributions of CC and HA are shown to vary with elevation. At higher elevations, CC and HA contribute respectively 85% and 15% to the increase in EVI. However, at lower elevations, reduced EVI is largely (79%) due to human activities. This needs to be considered in managing the future of vulnerable socio-ecological systems in the state of Haryana.
Climate change, Elevation-dependence, Google earth engine, Human activities, Hydrological variables, Vegetation dynamics
Banerjee, Abhishek
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Kang, Schihang
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Meadows, Michael E.
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Xia, Zilong
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Sengupta, Dhritiraj
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Kumar, Vinod
c27cb1e4-ed1e-4dee-a54d-e65cb53b55da
8 July 2023
Banerjee, Abhishek
1a41bd46-7f51-4715-a335-b240189e0e27
Kang, Schihang
9681fc81-bc93-4b9b-b68c-41cd5f174e78
Meadows, Michael E.
133741cb-d680-4772-a16d-5efabaf2be09
Xia, Zilong
d8d5631b-35f9-4f74-b1d3-ff90c0704a92
Sengupta, Dhritiraj
342ff163-b9b1-4691-a5f9-8dcae7c46032
Kumar, Vinod
c27cb1e4-ed1e-4dee-a54d-e65cb53b55da
Banerjee, Abhishek, Kang, Schihang, Meadows, Michael E., Xia, Zilong, Sengupta, Dhritiraj and Kumar, Vinod
(2023)
Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India.
Environmental Research, 234, [116541].
(doi:10.1016/j.envres.2023.116541).
Abstract
To explore the spatio-temporal dynamics and mechanisms underlying vegetation cover in Haryana State, India, and implications thereof, we obtained MODIS EVI imagery together with CHIRPS rainfall and MODIS LST at annual, seasonal and monthly scales for the period spanning 2000 to 2022. Additionally, MODIS Potential Evapotranspiration (PET), Ground Water Storage (GWS), Soil Moisture (SM) and nighttime light datasets were compiled to explore their spatial relationships with vegetation and other selected environmental parameters. Non-parametric statistics were applied to estimate the magnitude of trends, along with correlation and residual trend analysis to quantify the relative influence of Climate Change (CC) and Human Activities (HA) on vegetation dynamics using Google Earth Engine algorithms. The study reveals regional contrasts in trends that are evidently related to elevation. An annual increasing trend in rainfall (21.3 mm/decade, p < 0.05), together with augmented vegetation cover and slightly cooler (−0.07 °C/decade) LST is revealed in the high-elevation areas. Meanwhile, LST in the plain regions exhibit a warming trend (0.02 °C/decade) and decreased in vegetation and rainfall, accompanied by substantial reductions in GWS and SM related to increased PET. Linear regression demonstrates a strongly significant relationship between rainfall and EVI (R2 = 0.92), although a negative relationship is apparent between LST and vegetation (R2 = −0.83). Additionally, increased LST in the low-elevation parts of the study area impacted PET (R2 = 0.87), which triggered EVI loss (R2 = 0.93). Moreover, increased HA resulted in losses of 25.5 mm GSW and 1.5 mm SM annually. The relative contributions of CC and HA are shown to vary with elevation. At higher elevations, CC and HA contribute respectively 85% and 15% to the increase in EVI. However, at lower elevations, reduced EVI is largely (79%) due to human activities. This needs to be considered in managing the future of vulnerable socio-ecological systems in the state of Haryana.
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Accepted/In Press date: 1 July 2023
e-pub ahead of print date: 5 July 2023
Published date: 8 July 2023
Additional Information:
Funding Information:
This study was supported by the Second Tibetan Plateau Scientific Expedition and Research Program ( 2019QZKK0605 ), Gansu Provincial Science and Technology Program ( 22ZD6FA005 ), and Gansu Postdoctoral Science Foundation ( E339880202 ). Moreover, we thankfully acknowledge the several remote sensing data agencies (MODIS, CHIRPS, SRTM, DMSP, Sentinel, LISS-III, NASA-GLDAS) to deliver high precision gridded long-term global spatio-temporal information. We also greatly appreciate the Google Earth Engine community and developers to provide a web-based remote-sensing platform with Big data management and less time-consuming computational facilities. The first author (AB) is also thankful to the Haryana Forest Department, Government of Haryana for arranging the field visits and the Chinese Academy of Sciences , Lanzhou, P.R. China for proving space and other research facilities to carry out this study. We are greatly appreciate the insightful and constructive comments from anonymous reviewers which have helped in improving the scientific quality of the manuscript.
Funding Information:
This study was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0605), Gansu Provincial Science and Technology Program (22ZD6FA005), and Gansu Postdoctoral Science Foundation (E339880202). Moreover, we thankfully acknowledge the several remote sensing data agencies (MODIS, CHIRPS, SRTM, DMSP, Sentinel, LISS-III, NASA-GLDAS) to deliver high precision gridded long-term global spatio-temporal information. We also greatly appreciate the Google Earth Engine community and developers to provide a web-based remote-sensing platform with Big data management and less time-consuming computational facilities. The first author (AB) is also thankful to the Haryana Forest Department, Government of Haryana for arranging the field visits and the Chinese Academy of Sciences, Lanzhou, P.R. China for proving space and other research facilities to carry out this study. We are greatly appreciate the insightful and constructive comments from anonymous reviewers which have helped in improving the scientific quality of the manuscript.
Publisher Copyright:
© 2023 Elsevier Inc.
Keywords:
Climate change, Elevation-dependence, Google earth engine, Human activities, Hydrological variables, Vegetation dynamics
Identifiers
Local EPrints ID: 483094
URI: http://eprints.soton.ac.uk/id/eprint/483094
ISSN: 0013-9351
PURE UUID: b310729a-19a2-4fe1-afda-9fa35af96096
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Date deposited: 23 Oct 2023 16:41
Last modified: 17 Mar 2024 04:42
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Contributors
Author:
Abhishek Banerjee
Author:
Schihang Kang
Author:
Michael E. Meadows
Author:
Zilong Xia
Author:
Dhritiraj Sengupta
Author:
Vinod Kumar
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