基于地理探测器的张承地区表层土壤湿度变化的驱动因素分析

郑金涛; 金晓媚; 李晴; 殷秀兰; 金爱芳

doi:10.16030/j.cnki.issn.1000-3665.202309007

基于地理探测器的张承地区表层土壤湿度变化的驱动因素分析

1.
中国地质大学（北京）水资源与环境学院，北京　100083

2.
中国地质环境监测院, 北京　100081

3.
中国地质调查局自然资源综合调查指挥中心，北京　100055

基金项目: 国家自然科学基金项目（41372250）；行政事业类专项项目（121201014000150003）

详细信息

作者简介: 郑金涛（1999—），男，硕士研究生，主要从事生态水文地质与水环境遥感等研究。E-mail：870428401@qq.com

通讯作者: 金晓媚（1968—），女，博士，教授，主要从事生态水文地质与水环境遥感等研究。E-mail：jinxm@cugb.edu.cn

中图分类号: S152.7

收稿日期: 2023-09-04

修回日期: 2023-10-16

刊出日期: 2024-03-15

Driving factors analysis of surface soil moisture variation in Zhangjiakou-Chengde district based on geodetector

1.
School of Water Resources and Environment, China University of Geosciences （Beijing）, Beijing　100083, China

2.
China Institute of Geo-Environment Monitoring, Beijing　100081, China

3.
Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing　100055, China

More Information

Corresponding author: JIN Xiaomei, jinxm@cugb.edu.cn

Received Date: 04 September 2023

Revised Date: 16 October 2023

Publish Date: 15 March 2024

摘要

摘要:
河北张家口、承德地区（以下简称“张承地区”）作为京津冀地区的生态屏障、重要的水源保护地和风沙阻隔区，近年来开展了一系列生态治理工程。土壤湿度作为综合反映气候、植被覆盖、土壤性质的重要指标，在大气水—地表水—地下水的循环中至关重要。但张承地区地处半干旱区，水资源相对匮乏，且现有研究忽视了对其土壤湿度空间变化特征及驱动因素的分析。研究以中等分辨率成像光谱仪数据、全球陆地数据同化系统数据集等为基础，基于温度-植被干旱指数模型反演并分析张承地区2001—2021年土壤湿度及时空变化特征，利用地理探测器方法分析8个驱动因子对张承地区土壤湿度时空异质性的解释力。结果表明：（1）2001—2021年张承地区的土壤湿度呈现波动上升趋势，土壤湿度分布为东部高，西部低，张家口低于承德；空间上，研究区83.09%区域的土壤湿度呈现逐年上升的趋势；（2）研究区土壤湿度的驱动因素解释力大小排序为：归一化植被指数>土壤类型>年均降水>土地利用类型>坡度>高程>年均气温>坡向，其中归一化植被指数和土壤类型的解释力均高于30%，为主导驱动因素；（3）在交互作用分析中，多因子共同作用的影响大于单因子作用，归一化植被指数与高程的交互影响作用最强。研究结果对于张承地区水资源的高效利用及生态环境保护具有重要的参考作用。
- 地理探测器 /
- 温度-植被干旱指数模型 /
- 土壤湿度 /
- 驱动因子 /
- 张承地区
Abstract:
As an important water conservation and sand-windy barrier, Zhangjiakou and Chengde (Zhang-Cheng) district of Hebei Province is very important for ecological protection in the Beijing-Tianjin-Hebei region. The soil moisture is an important comprehensive indicator for climate, vegetation cover, and soil property, and is essential in the cycle of atmospheric−surface water−underground water. However, few studies focused on the soil moisture change and its impact factors in the Zhang-Cheng district, an area with semi-arid climate and scarce water resources. Based on moderate-resolution imaging spectroradiometer (MODIS) and global land data assimilation system data (GLDAS), the spatiotemporal variation of surface soil moisture in Zhang-Cheng district was simulated during the period of 2001−2021 using temperature vegetation dryness index (TVDI) model. Moreover, the geodetector method was also employed to identify the contribution of 8 impact factors on soil moisture. The results indicate that the soil moisture is generally fluctuating increase during the period of 2001−2021. Spatially, the soil moisture is higher in the east and lower in the west part of the study area and approximately 83.09% of the the study area experienced a progressive increase in soil moisture. In terms of driving factors, normalized difference vegetation index (NDVI), soil type, annual precipitation, land use type, slope, elevation, annual temperature, and aspect were analyzed in descending order of their effect on soil moisture variation. NDVI and soil type are the dominant drivers, with each contribution exceeding 30%. As to interaction analysis, it indicates that the effect of multiple factors is greater than that of individual factor. The synergistic interaction between NDVI and elevation is the largest influence on soil moisture. This study has great significance for efficient utilization of water resources and eco-environmental protection in the Zhang-Cheng district.
- geodetector /
- TVDI model /
- soil moisture /
- driving factors /
- Zhang-Cheng district

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图 1 张承地区地貌图

Figure 1.

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图 2 地理探测器驱动因子空间分布

Figure 2.

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图 3 张承地区地表10 cm土壤含水率实测数据与TVDI值的关系

Figure 3.

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图 4 张承地区多年平均TVDI值空间分布

Figure 4.

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图 5 张承地区 2001—2021 年各土壤湿度等级面积占比及TVDI值年际变化

Figure 5.

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图 6 TVDI值空间变化趋势分级图

Figure 6.

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表 1 土壤湿度等级划分标准

Table 1. Standard for classification of soil moisture levels

I_TVDI值	土壤湿度等级	实测土壤含水率区间/%
0<I_TVDI≤0.2	极湿润	30～32
0.2<I_TVDI≤0.4	湿润	24～31
0.4<I_TVDI≤0.6	正常	16～25
0.6<I_TVDI≤0.8	干旱	10～18
0.8<I_TVDI≤1	极干旱
注：表中“I_TVDI”为TVDI值；空白为无数据，是由于实测土壤湿度的测量季节为夏季，受季节和测量位置影响，测量位置的I_TVDI值均<0.8，因此无极干旱等级实测土壤含水率数据。

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表 2 Z绝对值的显著性检验表

Table 2. Significance test table of absolute value of Z

\|Z\|	≥1.64	≥1.96	≥2.58
信度/%	90	95	99

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表 3 交互作用类型

Table 3. Interaction types

判据	交互作用
q(X1∩X2)<Min(q(X1),q(X2))	非线性减弱
Min(q(X1),q(X2))<q(X1∩X2)<Max(q(X1),q(X2))	单因子非线性减弱
q(X1∩X2)>Max(q(X1),q(X2))	双因子增强
q(X1∩X2)=q(X1)+q(X2)	独立
q(X1∩X2)>q(X1)+q(X2)	非线性增强
注：表中“Min”为取最小值；“Max”为取最大值；“X1”、“X2”分别为2个不同的驱动因子。

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表 4 TVDI值空间变化趋势面积统计

Table 4. Areal statistics of spatial change in I_TVDI

TVDI值变化趋势	等级	面积占比/%
β<0.001,\|Z\|>1.96	显著下降	45.16
β<0.001, \|Z\|≤1.96	轻微下降	37.93
−0.001≤β≤0.001	基本不变	7.62
β>0.001, \|Z\|≤1.96	轻微上升	6.92
β>0.001, \|Z\|>1.96	显著上升	2.38

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表 5 分异及因子探测器探测结果

Table 5. Detection results of differentiation and factor detectors

驱动因子	p值	q值	q值排序
NDVI	0.000	0.417	1
年均气温	0.000	0.098	7
年均降水	0.000	0.297	3
高程	0.000	0.146	6
坡度	0.000	0.154	5
坡向	0.506	0.006	8
土壤类型	0.000	0.369	2
土地利用类型	0.000	0.274	4

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表 6 交互作用探测器探测结果

Table 6. Detection results of interaction detector

驱动因子	NDVI	年均气温	年均降水	高程	坡度	土壤类型	土地利用类型
NDVI	0.417
年均气温	0.584	0.098
年均降水	0.470	0.534	0.297
高程	0.615	0.239	0.529	0.146
坡度	0.427	0.304	0.339	0.294	0.154
土壤类型	0.509	0.512	0.453	0.580	0.409	0.369
土地利用类型	0.472	0.397	0.422	0.403	0.301	0.466	0.274
注：绿色代表非线性增强，黄色代表双因子增强。

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基于地理探测器的张承地区表层土壤湿度变化的驱动因素分析

1. 中国地质大学（北京）水资源与环境学院，北京 100083 2. 中国地质环境监测院, 北京 100081 3. 中国地质调查局自然资源综合调查指挥中心，北京 100055

作者简介: 郑金涛（1999—），男，硕士研究生，主要从事生态水文地质与水环境遥感等研究。E-mail：870428401@qq.com

通讯作者: 金晓媚（1968—），女，博士，教授，主要从事生态水文地质与水环境遥感等研究。E-mail：jinxm@cugb.edu.cn

Driving factors analysis of surface soil moisture variation in Zhangjiakou-Chengde district based on geodetector

1. School of Water Resources and Environment, China University of Geosciences （Beijing）, Beijing 100083, China 2. China Institute of Geo-Environment Monitoring, Beijing 100081, China 3. Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing 100055, China

Corresponding author: JIN Xiaomei, jinxm@cugb.edu.cn

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1.
中国地质大学（北京）水资源与环境学院，北京　100083

2.
中国地质环境监测院, 北京　100081

3.
中国地质调查局自然资源综合调查指挥中心，北京　100055

1.
School of Water Resources and Environment, China University of Geosciences （Beijing）, Beijing　100083, China

2.
China Institute of Geo-Environment Monitoring, Beijing　100081, China

3.
Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing　100055, China