Understanding the impact of water availability on vegetation growth in the context of climate change is crucial for assessing the resilience of vegetation to environmental shifts. In this study, the relationship between vegetation growth and water availability was studied using a variety of indicators. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Solar-Induced Chlorophyll Fluorescence (SIF) were utilized as vegetation growth indicators, while the standardized precipitation evapotranspiration index (SPEI) and soil moisture indicators served as water use indices. To investigate the vegetation response to water deficit in the Loess Plateau during the growing season from 2000 to 2020, Spearman’s rank correlation coefficients were calculated using a 5-year sliding window approach. The spatial and temporal heterogeneity of vegetation response to water deficit during the growing seasons were also explored. The results showed that: (1) with the improvement of moisture conditions, vegetation growth recovered significantly, and there was no expansion trend for vegetation water deficit. (2) The most sensitive timescale of vegetation to water deficit was 6–8 months; the response degree and sensitivity of vegetation to water surplus and deficit were the highest from June to August; and broadleaved forest was the vegetation type most sensitive to water deficit in the early growing season, while grass was the vegetation type most sensitive to water deficit during the mid and late growing seasons. (3) Soil moisture emerged as the dominant factor influencing vegetation growth in the Loess Plateau, followed by precipitation, albeit to a lesser extent. These findings contribute to understanding the mechanism and characteristics of the response of vegetation to climate fluctuations induced by global climate change.
Coupled human and natural systems are complex and open giant system with plenty of nonlinear mutual feedback relationships. Although current studies regarding single element and static characteristics in a system are helpful for understanding its state at a certain moment, such study cannot fully express the intricate relationship between different elements inside the system. This study focused on attributing vegetation dynamics (leaf area index, LAI, is applied as an indicator to characterize vegetation dynamics) at the district and county scale in the Yellow River Basin based on multi-source datasets and causal diagnostic methods. A basin-wide complex causal network of LAI is constructed. The spatial distribution of dominant factors influencing LAI variations is finally identified through decomposing the nodes and structural characteristics of the constructed complex network. The results show that: 1) Basin-wide LAI increased at an annual rate of 1.3% during 1990-2018. The growth rate decreased gradually from the southeast to the northwest of the basin. 2) From the perspective of the network, precipitation, temperature, saturated water vapor pressure deficit, agricultural land use, urbanization rate, and grain yield are key factors affecting LAI changes in the basin. 3) Natural elements (for example, precipitation and temperature) dominated LAI changes of 259 districts and counties inside the basin; socioeconomic (for example, urbanization rate and grain yield) and land use (for example, forest and grassland use) elements dominated LAI changes of 76 districts and counties, which are mostly concentrated on the Loess Plateau. The influence intensity of socioeconomic and land use factors on LAI variations is much greater than that of natural factors. In this study, we constructed a multilayer mutual feedback network under the coupled human and natural system framework to comprehensively examine vegetation dynamics and their natural and social drivers in the Yellow River Basin. It provides a new idea for understanding complex mutual feedback relationships in coupled natural-social systems.
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