Quantifying the contribution of climate change and human activities to biophysical parameters in an arid region

Abstract

Quantifying the variation of biophysical parameters and their driving mechanisms is essential for monitoring land surface environmental changes and for understanding the land–atmosphere interaction in the arid region. Due to the complexity of human activities, most researches are limited to climate change, whereas the response analysis of human activities to changes in biophysical parameters are still lacking or not comprehensively considered. Therefore, large biases and uncertainties still exist in the estimates of regional responses to global change. Firstly, we specifically quantified the main human activities related to land use/land cover change (LULCC) in the northern Tianshan Mountains (NTM), and identified the spatiotemporal changes of primary biophysical parameters, including Albedo, leaf area index (LAI), land surface temperature (LST), and Normalized Difference Vegetation Index (NDVI). Then, we tested the performance of the five models used, including multiple linear regression (MLR), random forest (RF), support vector regression (SVR), multi-layer perceptron (MLP), and K-nearest neighbor (KNN). RF outperformed others and was used to quantify and disaggregate the contribution of climate change and human activities to land surface parameters in the NTM. We found a strong spatial heterogeneity in the spatial variation of all biophysical parameters. Except for LST, the annual maximum Albedo, LAI, and NDVI showed a significant increasing trend in the NTM from 2000 to 2019 (p < 0.05). Generally, climate change contributed more to the biophysical parameters than human activities. However, the contribution of human activities to NDVI was 0.51, which was greater than that of climate change during 2000–2015. This study provides new insight on the impact of climate change and human activities on biophysical parameters and a scientific basis for model parameterization in the arid region.

Keywords

• Climate change
• Human activities
• Biophysical parameters
• The Northern slope of the Tianshan Mountains
• Random Forest model
• Machine learning