近40年红河流域（中国部分）水源涵养功能动态演变特征及驱动因素

doi:10.16258/j.cnki.1674-5906.2025.04.006

生态环境学报 ›› 2025, Vol. 34 ›› Issue (4): 556-569.DOI: 10.16258/j.cnki.1674-5906.2025.04.006

• 研究论文【环境科学】 • 上一篇下一篇

近40年红河流域（中国部分）水源涵养功能动态演变特征及驱动因素

张洪波¹(), 尹班¹, 李春勇¹, 崔松云²^,³, 和艳²^,³, 李小红⁴, 邓丽仙²^,³^,^*()

1.昆明理工大学电力工程学院，云南昆明 650500
2.云南省水文水资源局昆明分局，云南昆明 650106
3.滇池湖泊生态系统云南省野外科学观测研究站，云南昆明 650228
4.江苏省水利勘测设计研究院有限公司，江苏南京 210029

收稿日期:2024-11-12 出版日期:2025-04-18 发布日期:2025-04-24
通讯作者: ^*邓丽仙。E-mail: denglixian123@126.com
作者简介:张洪波（1979年生），男，讲师，博士，从事水文学及水资源方向研究。E-mail: zhanghongbo@kust.edu.cn
基金资助:
水灾害防御全国重点实验室“一带一路”水与可持续发展科技基金项目(2022490411)

Characteristics and Drivers of the Dynamic Evolution of Water Conservation Function in the Honghe River Basin (China Section) in the Last 40 Years

ZHANG Hongbo¹(), YIN Ban¹, LI Chunyong¹, CUI Songyun²^,³, HE Yan²^,³, LI Xiaohong⁴, DENG Lixian²^,³^,^*()

1. College of Power Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
2. Kunming Branch of Yunnan Hydrology and Water Resources Bureau, Kunming 650106, P. R. China
3. Dianchi Lake Ecosystem Observation and Research Station of Yunnan Province, Kunming 650228, P. R. China
4. Jiangsu Surveying and Design Institute of Water Resources Co., Ltd., Nanjing 210029, P. R. China

Received:2024-11-12 Online:2025-04-18 Published:2025-04-24

摘要/Abstract

摘要： 水源涵养是生态系统的重要服务功能，对维持区域生态安全和水资源的可持续利用至关重要。明确水源涵养的时空分布特征、变化趋势及驱动因素，对于科学制定水资源保护规划、推进区域生态保护与修复具有重要的理论和实践价值。基于InVEST模型计算红河流域（中国部分）1981－2020年8个时段（每5年为1个时段）的水源涵养功能，采用Sen+Mann-Kendall及自然间断点法分析时空变化特征及其内部差异，并结合地理探测器方法分析其驱动因素。结果表明，1）红河流域（中国部分）多年平均水源涵养深度为68.7 mm，涵养量为5.77×10⁹ m³。近40年间，水源涵养深度以−0.097 mm·a⁻¹的平均速率波动下降、水源涵养量以−7.5×10⁶ m³·a⁻¹的平均速率波动下降。Sen+Mann-Kendall分析结果显示，水源涵养功能变化趋势以基本不变、轻微降低和轻微上升为主。林地、灌木林地和草地在水源涵养中占据主导地位，分别贡献了74.5%、13.8%和7.93%的水源涵养量。主要涵养区域集中在高程400－2400 m和坡度5°－35°的区间，空间分布呈现东南高、西北低的格局。2）自然间断点法分析结果显示，水源涵养重要区面积为8.24×10³ km²，占研究区总面积的10.8%，但其水源涵养量占总量的20.2%，水源涵养率高达1.53×10⁵ m³·km⁻²。3）地理探测器分析结果显示，降水量是水源涵养功能变化的主要驱动因素，土地利用变化则为次要因素。降水量与土地利用类型的交互作用对水源涵养功能空间异质性具有显著影响，超过任何单一因素或其他交互因素。红河流域（中国部分）水源涵养功能呈现出显著的时间演变规律和空间分布特征，降水与土地利用共同构成其时空异质性的关键驱动因素。

关键词: 水源涵养, 红河流域（中国部分）, InVEST模型, Sen+Mann-Kendall, 地理探测器

Abstract:

Water conservation is a vital ecosystem service that plays a key role in regional ecological security and sustainable management of water resources. Understanding the spatial and temporal distributions, trends, and drivers of water conservation is of significant theoretical and practical importance for scientific planning and for advancing regional ecological protection and restoration. In this study, the InVEST model was employed to calculate the water conservation function of the Honghe River Basin (China section) over eight time intervals (every five years) from 1981 to 2020. Spatial and temporal variations, along with their internal differences, were analyzed using the Sen+Mann-Kendall test and natural discontinuity method. Subsequently, the GeoDetector model was applied to analyze the driving factors affecting the water conservation function, including precipitation, actual evapotranspiration, land use type, elevation, and slope. The findings of this study provide significant theoretical support for understanding the spatial and temporal heterogeneity of water conservation functions and their drivers in the Honghe River Basin (China). The results of the study indicate that: 1) the average depth of water conservation in the study area is 68.7 mm, and the volume of water conservation is 5.77×10⁹ m³. Over the past 40 years, the depth of water conservation has declined at an average rate of −0.097 mm·a⁻¹, and the volume of water conservation has decreased at an average rate of −7.5×10⁶ m³·a⁻¹. From the second period onwards, the fluctuations in the water conservation function compared to the adjacent previous period were large, with specific fluctuation values of −11.0%, 4.48%, 11.1%, −7.74%, −6.54%, −5.76%, and 8.78%. The results of the Sen+Mann-Kendall analysis showed that the trend of change in water conservation function was primarily characterized by areas with little change, slight decreases, and slight increases, which collectively accounted for nearly 99.25% of the total area. By contrast, areas with significant increases and severe decreases were minimal, accounting for only 0.75% of the total study area. Woodlands, shrublands, and grasslands were the primary contributors to water conservation, accounting for 74.5, 13.8, and 7.93%, respectively. Regression trend analysis revealed a decreasing trend in water conservation in woodland areas over the past 40 years, with a rate of change of −5.18×10⁶ m³·a⁻¹. The overall change in water conservation in wetlands, bare land, and waterbodies was minimal. Cultivated land and grassland exhibited a significant inverse change in water conservation capacity. Although the areas of cultivated land and grassland increased, water conservation decreased substantially, primarily because of increased land-use intensity, soil structure changes, and vegetation degradation. The primary conservation areas were concentrated within an elevation range of 400‒2400 m and on slopes ranging from 5° to 35°. The spatial distribution followed a pattern of high conservation in the southeast, and low conservation in the northwest. In addition, areas with high precipitation and relatively low evapotranspiration exhibited significantly higher water conservation capacities than the other areas. The need to reduce water conservation functional zones is reflected in various aspects, such as revealing spatial heterogeneity, supporting decision making on ecological protection, facilitating the optimal allocation of resources, supporting the ecological compensation mechanism, and addressing ecological and environmental challenges. This process not only helps to comprehensively understand the spatial pattern of regional ecosystem service functions, but also provides a scientific basis for watershed water resource management and ecological protection, and is an important means of achieving sustainable regional ecological development. In this study, the differences in the water-sourcing capacity of each functional area were primarily influenced by multiple factors such as topography, soil type, vegetation cover, and the intensity of human activities. 2) The results of the analysis of functional areas delineated using the natural intermittent point method indicated that the area of the most important water conservation zone was the smallest, covering 8240.27 km² (10.8% of the total study area). However, its water conservation volume accounts for 20.2% of the total volume, with a water conservation rate as high as 1.53×10⁵ m³·km⁻². The area of the generally important water conservation zone is the largest, covering 46049.26 km², which constitutes 60.3% of the total study area, but its water conservation volume is only 41.9% of the total, and the water conservation rate is only 9.91×10⁴ m³·km⁻². For areas with weak water conservation capacity, such as general functional zones, water conservation can be gradually improved through measures, such as returning farmland to forests, vegetation restoration, and scientific land-use planning. For areas with high water conservation capacity such as important functional zones, protective measures should be strengthened to enhance their water conservation capacity. Similarly, for areas with high conservation capacity, such as important functional zones, protection measures should be enhanced to minimize the potential impact of human interference on ecological functions and ensure the stability of their ecosystem service capacity. By implementing differentiated management, the coordinated development of water conservation functions on a regional scale can be promoted, thereby contributing to the sustainable enhancement of ecosystem services. 3) In this study, a GeoDetector approach was employed to systematically analyze the drivers of changes in the water conservation function from 1981 to 2020 in the study area. This approach includes both single-factor and interaction analyses to explore the influence of each driver on the spatial heterogeneity of the water conservation function. In the single-factor analysis, the independent effects of key factors, such as climate, topography, and land use, on water conservation functions were quantified to reveal their contributions to the spatial distribution. In the interaction analysis, the effects of factor interactions were further analyzed to identify the combined influence of these drivers on the spatial heterogeneity of water conservation. The results of the GeoDetector analysis showed that among the single-factor drivers, precipitation was the primary driver of changes in the water conservation function in the study area, and it had significant explanatory power for spatial heterogeneity. Land use type and actual evapotranspiration had the second-largest effects on water conservation, whereas elevation and slope had weaker effects. The results of interaction detection revealed that each driving factor exhibited either a two-way enhancement or a non-linear enhancement in pairwise interactions. In other words, any single factor can significantly enhance the explanatory power of the spatial heterogeneity in water conservation when interacting with other factors. Specifically, the interactions between precipitation and land-use type, as well as between precipitation and actual evapotranspiration, significantly influenced the spatial heterogeneity of regional water conservation. These findings suggest that the synergistic effects of climatic factors and human activities dominate the spatial heterogeneity of the water conservation functions in the study area. These results provide a crucial basis for the further optimization of regional water management, planning, and ecological protection. Conclusion: An in-depth analysis of the main factors influencing water conservation is of great value in land use planning, strengthening water conservation measures, and promoting sustainable development in the region. The water conservation function of the Honghe River Basin (China section) exhibits significant temporal evolution characteristics and spatial distribution differences, with precipitation and land use playing key driving roles in the spatial and temporal heterogeneity. Our findings have important practical implications for regional water management and ecological protection.

Key words: water conservation, Honghe River Basin (China section), InVEST model, Sen+Mann-Kendall, GeoDetector

中图分类号:

X143

张洪波, 尹班, 李春勇, 崔松云, 和艳, 李小红, 邓丽仙. 近40年红河流域（中国部分）水源涵养功能动态演变特征及驱动因素[J]. 生态环境学报, 2025, 34(4): 556-569.

ZHANG Hongbo, YIN Ban, LI Chunyong, CUI Songyun, HE Yan, LI Xiaohong, DENG Lixian. Characteristics and Drivers of the Dynamic Evolution of Water Conservation Function in the Honghe River Basin (China Section) in the Last 40 Years[J]. Ecology and Environment, 2025, 34(4): 556-569.

图/表 15

图1 研究区地形示意图底图采用自然资源部标准地图制作，审图号为GS[2024]0650号；对底图边界无修改。下同

Figure 1 Topographic map of the studied area

表1 数据来源

Table 1 Data sources

数据名称	数据来源	空间分辨率
研究区边界数据	国家冰川冻土沙漠科学数据中心 http://www.ncdc.ac.cn	‒
高程数据	地理空间数据云 https://www.gscloud.cn/home	30 m分辨率
气象数据	国家青藏高原科学数据中心 https://data.tpdc.ac.cn/	1000 m分辨率
土地利用/覆盖数据	中国科学院空天信息创新研究院 https://data.casearth.cn/	30 m分辨率
土壤数据	国家地球系统科学中心 https://www.geodata.cn/main/	250 m分辨率

表2 地理探测器交互作用类型及判定标准

Table 2 Geographic detector interaction types and judgment criteria

交互作用	判据
非线性减弱	$q\left(X_{1} \cap X_{2}\right)＜\min \left[q\left(X_{1}\right), q\left(X_{2}\right)\right]$
单因子非线性减弱	$\min \left[q\left(X_{1}\right), q\left(X_{2}\right)\right]＜q\left(X_{1} \cap X_{2}\right)＜\max \left[q\left(X_{1}\right), q\left(X_{2}\right)\right]$
$双因子增强$	$q\left(X_{1} \cap X_{2}\right)>\max \left[q\left(X_{1}\right), q\left(X_{2}\right)\right]$
独立	$q\left(X_{1} \cap X_{2}\right)=q\left(X_{1}\right)+q\left(X_{2}\right)$
非线性增强	$q\left(X_{1} \cap X_{2}\right)>q\left(X_{1}\right)+q\left(X_{2}\right)$

表2 地理探测器交互作用类型及判定标准

Table 2 Geographic detector interaction types and judgment criteria

交互作用	判据
非线性减弱	$q\left(X_{1} \cap X_{2}\right)＜\min \left[q\left(X_{1}\right), q\left(X_{2}\right)\right]$
单因子非线性减弱	$\min \left[q\left(X_{1}\right), q\left(X_{2}\right)\right]＜q\left(X_{1} \cap X_{2}\right)＜\max \left[q\left(X_{1}\right), q\left(X_{2}\right)\right]$
$双因子增强$	$q\left(X_{1} \cap X_{2}\right)>\max \left[q\left(X_{1}\right), q\left(X_{2}\right)\right]$
独立	$q\left(X_{1} \cap X_{2}\right)=q\left(X_{1}\right)+q\left(X_{2}\right)$
非线性增强	$q\left(X_{1} \cap X_{2}\right)>q\left(X_{1}\right)+q\left(X_{2}\right)$

图2 研究区8个时段水源涵养功能年际变化过程

Figure 2 Process of inter-annual changes in water conservation function in the studied area for eight time periods

图3 研究区水源涵养功能Sen+Mann-Kendall趋势分析

Figure 3 Sen+Mann-Kendall trend analysis of water conservation function in the studied area

表3 研究区水源涵养功能变化趋势分级统计

Table 3 Classification statistics on the trends of water conservation function in the studied area

Sen’s slope	Z	变化趋势	面积占比/ %	平均变化趋势/ (mm·a⁻¹)
β>0.1	\|Z\|>1.96	明显增长	0.234	5.46
β>0.1	\|Z\|£1.96	轻微增长	2.74	2.54
\|β\|£0.1	‒	基本不变	67.4	0.01
β£−0.1	\|Z\|£1.96	轻微降低	29.1	−0.888
β£−0.1	\|Z\|>1.96	严重降低	0.517	−4.26

图4 研究区8个时段水源涵养深度空间分布

Figure 4 Spatial distribution of the depth of water conservation in the studied area for eight time periods

图5 1981－2020年降水量、水源涵养量与蒸散发空间分布对比

Figure 5 Comparison of the spatial distribution of precipitation, water retention and evapotranspiration from 1981 to 2020

图6 研究区不同土地利用类型水源涵养深度

Figure 6 Depth of water conservation for different land use types in the studied area

表4 研究区不同土地利用的水源涵养功能及其变化

Table 4 Water conservation functions and changes in different land uses in the studied area

土地利用类型	多年平均水源涵养		1981－2020年变化
土地利用类型	深度/ mm	涵养量/ m³	面积/ km²	涵养量/ m³	回归趋势/ (m³·a⁻¹)
耕地（旱地）	26.7	1.80×10⁸	1.21×10³	−4.66×10⁷	−1.59×10⁶
灌溉水田	24.8	1.14×10⁷	−1.25×10²	−4.42×10⁶	−1.60×10⁵
常绿阔叶林	90.5	1.00×10⁹	−9.20×10²	−1.37×10⁸	−4.84×10⁶
落叶阔叶林	77.2	9.54×10⁸	2.19×10³	1.15×10⁸	4.95×10⁶
常绿针叶林	68.7	1.95×10⁹	−1.81×10³	−2.27×10⁸	−7.39×10⁶
灌木林地	95.2	7.23×10⁸	1.25×10³	7.39×10⁷	2.10×10⁶
草地	46.2	4.17×10⁸	32	−1.95×10⁷	−8.08×10⁵
湿地	11.4	1.20×10⁶	−8	−2.68×10⁵	−3.92×10³
建设用地	13.7	7.68×10⁶	5.69×10²	7.53×10⁶	2.30×10⁵
裸地	12.4	3.68×10³	2	2.45×10⁴	4.55×10²
水域	14.3	2.29×10⁶	40	8.96×10⁵	2.23×10⁴

图7 研究区不同地形水源涵养功能

Figure 7 Water conservation functions of different terrains in the studied area

图8 研究区水源涵养功能区空间分布

Figure 8 Spatial distribution of water conservation function functional areas in the studied area

表5 研究区水源涵养功能区特征统计

Table 5 Characteristics of the water conservation function functional areas in the studied area statistics

重要等级	功能区等级	面积/ km²	面积占比/ %	水源涵养率/ (m³·km⁻²)
一般重要	低功能区	2.67×10⁴	34.9	3.61×10⁴
一般重要	较低功能区	1.94×10⁴	25.4	6.31×10⁴
中等重要	中等功能区	2.21×10⁴	28.9	8.91×10⁴
重要	较高功能区	7.16×10³	9.37	1.19×10⁵
重要	高功能区	1.08×10³	1.43	1.87×10⁵

表6 各驱动因子对水源涵养功能变化的单个效应

Table 6 Individual effects of drivers on changes in water conservation functions

驱动因子	降水量	蒸散发	土地利用	高程	坡度
q	0.539	0.219	0.307	0.088	0.049
p	***	***	***	***	***

图9 各驱动因子交互探测结果 *表示双因子增强，**表示非线性增强

Figure 9 Interaction detection results of driving factors

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近40年红河流域（中国部分）水源涵养功能动态演变特征及驱动因素

Characteristics and Drivers of the Dynamic Evolution of Water Conservation Function in the Honghe River Basin (China Section) in the Last 40 Years

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