基于因果网络的黄河流域植被变化归因分析

doi:10.18306/dlkxjz.2022.12.012

Abstract

Abstract:

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

LAN Zhiyang, LIANG Wei, FU Bojie, LV Yihe, YAN Jianwu, JI Qiulei. Attribution analysis of vegetation change in the Yellow River Basin based on causal network[J].PROGRESS IN GEOGRAPHY, 2022, 41(12): 2342-2355.

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节点	入度	出度	接近中心性	中介中心性	出度/入度
土壤温度	856	619	1	2.42	0.72
风速	491	483	1	10.44	0.98
土壤水	844	257	0.95	1.73	0.30
饱和水汽压差	590	780	0.95	11.00	1.32
径流	233	318	1	3.03	1.36
气温	588	975	0.95	1.05	1.66
降水量	422	1114	0.78	1.97	2.64
农业生产总值	158	122	1	0.38	0.77
粮食产量	110	119	1	0.68	1.08
水果产量	123	83	1	0.35	0.67
城镇化率	182	176	1	0.78	0.97
农业机械总动力	92	75	0.90	0.09	0.82
化肥用量	45	34	0.86	0	0.76
农业用地	136	112	0.95	1.60	0.82
果园和梯田	162	121	1	0.55	0.75
草地	133	143	1	0.50	1.08
城镇建设用地	145	167	0.95	0.67	1.15
林地	176	139	0.95	0.71	0.79

节点	节点在所有路径中出现的次数	以该节点为起点、LAI 节点为终点的路径数	节点	节点在所有路径中出现的次数	以该节点为起点、LAI 节点为终点的路径数
饱和水汽压差	740	300	城镇建设用地	36	24
降水量	696	378	粮食产量	36	26
气温	629	339	水果产量	30	14
土壤温度	573	278	林地	28	16
风速	430	224	果园和梯田	24	16
土壤水	274	107	化肥用量	21	7
径流	248	179	草地	20	13
城镇化率	58	35	农业生产总值	12	10
农业用地	39	25	农业机械总动力	11	8

节点	因果强度	节点	因果强度
土壤温度	0.31	水果产量	0.53
风速	0.18	城镇化率	0.54
饱和水汽压差	0.30	农业机械总动力	0.54
径流	0.31	化肥用量	0.59
土壤水	0.34	农业用地	0.35
气温	0.32	果园和梯田	0.44
降水量	0.29	草地	0.68
农业生产总值	0.51	城镇建设用地	0.53
粮食产量	0.57	林地	0.55

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

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