Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India

Abhishek Banerjee; Shichang Kang; Michael E Meadows; Zilong Xia; Dhritiraj Sengupta; Vinod Kumar

doi:10.1016/j.envres.2023.116541

Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India

Environ Res. 2023 Oct 1:234:116541. doi: 10.1016/j.envres.2023.116541. Epub 2023 Jul 5.

Authors

Abhishek Banerjee¹, Shichang Kang², Michael E Meadows³, Zilong Xia⁴, Dhritiraj Sengupta⁵, Vinod Kumar⁶

Affiliations

¹ State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou, 730000, China; Haryana Forest Department (HFD), Government of Haryana, Panchkula, 134109, India. Electronic address: banerjee.abhishek@nieer.ac.cn.
² State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd. 318, Lanzhou, 730000, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
³ School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China; Department of Environmental and Geographical Science, University of Cape Town, Cape Town, 7701, South Africa.
⁴ School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu, 210023, China.
⁵ School of Geography and Environmental Sciences, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK.
⁶ Haryana Forest Department (HFD), Government of Haryana, Panchkula, 134109, India.

PMID: 37419198
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.

Keywords: Climate change; Elevation-dependence; Google earth engine; Human activities; Hydrological variables; Vegetation dynamics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Climate Change
Ecosystem*
Humans
India
Soil*

Substances

Soil