PROGRESS IN GEOGRAPHY ›› 2022, Vol. 41 ›› Issue (12): 2342-2355.doi: 10.18306/dlkxjz.2022.12.012

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Attribution analysis of vegetation change in the Yellow River Basin based on causal network

LAN Zhiyang1(), LIANG Wei1,*(), FU Bojie2, LV Yihe2, YAN Jianwu1, JI Qiulei2   

  1. 1. School of Geographical Sciences and Tourism, Shaanxi Normal University, Xi'an 710119, China
    2. Research Center for Eco-Environmental Sciences, CAS, Beijing 100085, China
  • Received:2022-03-07 Revised:2022-08-18 Online:2022-12-28 Published:2022-12-31
  • Contact: LIANG Wei;
  • Supported by:
    National Natural Science Foundation of China(42071144)


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.

Key words: leaf area index, causal diagnostics, complex network, Yellow River Basin