Spatio-temporal Pattern of Surface Albedo in Beijing and Its Driving Factors based on Geographical Detectors

doi:10.5814/j.issn.1674-764x.2021.05.004

Abstract

Abstract:

Surface albedo directly affects the radiation balance and surface heat budget, and is a crucial variable in local and global climate research. In this study, the spatial and temporal distribution of the surface albedo is analysed for Beijing in 2015, and the corresponding individual and interactive driving forces of different explanatory factors are quantitatively assessed based on geographical detectors. The results show that surface albedo is high in the southeast and low in the northwest of Beijing, with the greatest change occurring in winter and the smallest change occurring in spring. The minimum and maximum annual surface albedo values occurred in autumn and winter, respectively, and showed significant spatial and temporal heterogeneity. LULC, NDVI, elevation, slope, temperature, and precipitation each had a significant influence on the spatial pattern of albedo, yielding explanatory power values of 0.537, 0.625, 0.512, 0.531, 0.515 and 0.190, respectively. Some explanatory factors have significant differences in influencing the spatial distribution of albedo, and there is significant interaction between them which shows the bivariate enhancement result. Among them, the interaction between LULC and NDVI was the strongest, with a q-statistic of 0.710, while the interaction between temperature and precipitation was the weakest, with a q-statistic of 0.531. The results of this study provide a scientific basis for understanding the spatial and temporal distribution characteristics of surface albedo in Beijing and the physical processes of energy modules in regional climate and land surface models.

Key words: albedo, spatio-temporal distribution, explanatory factors, geographical detectors

LIU Qinqin, TIAN Yichen, YIN Kai, ZHANG Feifei, YUAN Chao, YANG Guang. Spatio-temporal Pattern of Surface Albedo in Beijing and Its Driving Factors based on Geographical Detectors[J]. Journal of Resources and Ecology, 2021, 12(5): 609-619.

Figures/Tables 9

References 53

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Factors	NDVI		Elevation		Slope		Temperature		Precipitation
Factors	Values	Types	Values (m)	Types	Values (°)	Types	Values (℃)	Types	Values (mm)	Types
1	0.08-0.30	N1	‒129‒158	E1	<2	I	2.5‒7.5	T1	534‒565	C1
2	0.30-0.37	N2	158‒389	E2	2‒6	II	7.5‒9.5	T2	565‒575	C2
3	0.37-0.44	N3	389‒617	E3	6‒15	III	9.5‒10.5	T3	575‒585	C3
4	0.44-0.50	N4	617‒854	E4	15‒25	IV	10.5‒12.0	T4	585‒595	C4
5	0.50-0.56	N5	854‒1187	E5	>25	V	12.0‒13.5	T5	595‒630	C5
6	0.56-0.74	N6	1187‒2270	E6	‒	‒	‒	‒	‒	‒

Factors	NDVI		Elevation		Slope		Temperature		Precipitation
Factors	Values	Types	Values (m)	Types	Values (°)	Types	Values (℃)	Types	Values (mm)	Types
1	0.08-0.30	N1	‒129‒158	E1	<2	I	2.5‒7.5	T1	534‒565	C1
2	0.30-0.37	N2	158‒389	E2	2‒6	II	7.5‒9.5	T2	565‒575	C2
3	0.37-0.44	N3	389‒617	E3	6‒15	III	9.5‒10.5	T3	575‒585	C3
4	0.44-0.50	N4	617‒854	E4	15‒25	IV	10.5‒12.0	T4	585‒595	C4
5	0.50-0.56	N5	854‒1187	E5	>25	V	12.0‒13.5	T5	595‒630	C5
6	0.56-0.74	N6	1187‒2270	E6	‒	‒	‒	‒	‒	‒

	Description	Interaction
1	$q(A\cap B)\text{}\ \text{min}\ \text{(}q(A),\ q(B)\text{)}$	Weaken, nonlinear
2	$min\ (q(A),\ q(B))< q(A\cap B)< \max \ (q(A),\ q(B))$	Weaken, univariate
3	$q(A\cap B)>\max \text{(}q(A),q(B)\text{)}$	Enhance, bivariate
4	$q\left( A\cap B \right)=q\left( A \right)+q\left( B \right)$	Independent
5	$q\left( A\cap B \right)>q\left( A \right)+q\left( B \right)$	Enhance, nonlinear

	Description	Interaction
1	$q(A\cap B)\text{}\ \text{min}\ \text{(}q(A),\ q(B)\text{)}$	Weaken, nonlinear
2	$min\ (q(A),\ q(B))< q(A\cap B)< \max \ (q(A),\ q(B))$	Weaken, univariate
3	$q(A\cap B)>\max \text{(}q(A),q(B)\text{)}$	Enhance, bivariate
4	$q\left( A\cap B \right)=q\left( A \right)+q\left( B \right)$	Independent
5	$q\left( A\cap B \right)>q\left( A \right)+q\left( B \right)$	Enhance, nonlinear

Seasons	Spring (day 30-120)	Summer (day 120-210)	Autumn (day 210-300)	Winter (day 300-30 of next year)
Mean	0.127	0.134	0.122	0.142
Standard deviation	0.001	0.002	0.006	0.030