A Stepwise Multifactor Regression Analysis of the Interactive Effects of Multiple Climate Factors on the Response of Vegetation Recovery to Drought
Jingjing Fan,
Yue Zhao,
Dongnan Wang,
Xiong Zhou,
Yunyun Li,
Wenwei Zhang,
Fanfan Xu,
Shibo Wei
Affiliations
Jingjing Fan
Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Resources and Hydropower, Hebei University of Engineering, Handan 056038, China
Yue Zhao
Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Resources and Hydropower, Hebei University of Engineering, Handan 056038, China
Dongnan Wang
Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Resources and Hydropower, Hebei University of Engineering, Handan 056038, China
Xiong Zhou
State Key Joint Laboratory of Environmental Simulation and Pollution Control, China-Canada Center for Energy, Environment and Ecology Research, University of Regina-Beijing Normal University, School of Environment, Beijing Normal University, Beijing 100875, China
Yunyun Li
College of Resources and Environmental Engineering, Mianyang Teachers College, Mianyang 621000, China
Wenwei Zhang
Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Resources and Hydropower, Hebei University of Engineering, Handan 056038, China
Fanfan Xu
Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Resources and Hydropower, Hebei University of Engineering, Handan 056038, China
Shibo Wei
Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Resources and Hydropower, Hebei University of Engineering, Handan 056038, China
In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.