海南“三江源”水源涵养量时空演变特征及驱动因素分析

何倩倩 ,  任明迅

热带生物学报 ›› 2026, Vol. 17 ›› Issue (5) : 748 -759.

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热带生物学报 ›› 2026, Vol. 17 ›› Issue (5) : 748 -759. DOI: 10.15886/j.cnki.rdswxb.20250206
海南热带雨林国家公园

海南“三江源”水源涵养量时空演变特征及驱动因素分析

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Spatio-temporal dynamics and driving factors of water conservation in the “Sanjiangyuan” area of Hainan Island

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摘要

海南“三江源”区域是海南 3 大河流南渡江、昌化江和万泉河的共同发源地和最重要的汇水区,位于海南热带雨林国家公园的中央地带。本研究综合考虑水文、地形地貌、植被条件及国家公园管理边界等,科学划定了海南“三江源”区域。采用 2013、2018 和 2023 年共 3 期数据,利用 InVEST 模型评估水源涵养量及其动态变化趋势,并利用最优参数地理探测器(OPGD)探究水源涵养量时空变化的主要驱动因素。结果表明:1)2013、2018 和 2023 年的海南“三江源”水源涵养量分别为 19.4×108、8.97×108、10.66×108 m3,呈现先下降后小幅回升的阶段性变化特征,体现出筹建于 2019 年的海南热带雨林国家公园对海南“三江源”水源涵养量的积极作用;2)水源涵养量高值区主要集中在五指山、鹦哥岭和黎母山中高海拔山区;3)降水量是水源涵养空间分异的主导因素,q 值为 0.525~0.654。高程次之,潜在蒸散发和气温也具有较高解释力。降水量与高程交互作用的解释力最强,q 值为 0.779~0.804。本研究阐明了海南“三江源”水源涵养量的时空演变特征及其影响因素,有助于海南水资源管理和海南热带雨林国家公园水源涵养量优化提升策略的科学制定。

Abstract

The “ Sanjiangyuan” area (Sources of three major rivers) of Hainan Island constitutes the shared source area and primary catchment of the three major rivers, the Nandu, Changhua, and Wanquan rivers. As the central core of the National Park of Hainan Tropical Rainforest, this area plays a vital role in maintaining ecological security, sustaining ecosystem services, and supporting socio-economic development across Hainan Island. The “Sanjiangyuan” area of Hainan Island was delineated through an integrated framework that incorporates hydrological processes, topographic and geomorphological characteristics, natural environmental conditions, ecosystem service functions, and national park management boundaries. The data of three periods in 2013, 2018, and 2023 were selected for analysis of their water conservation and its temporal evolution and spatial distribution pattern conservation by using the InVEST model, and the Optimal Parameters-based Geographical Detector was used to explore the main driving factors of temporal and spatial changes of water conservation. The results showed that water conservation from 2013 to 2023 was 19.4×108, 8.97×108, 10.66×108m3, respectively, and exhibited a non-linear temporal trajectory, characterized by a pronounced decline followed by a modest recovery. In terms of spatial scale, high-value areas were predominantly concentrated in the middle-to high-elevation mountainous headwaters of the three major river systems, reflecting the combined effects of higher elevations, favorable precipitation regimes, and relatively low evapotranspiration demand. Precipitation emerged as the primary determinant of spatial variation in water conservation (q = 0.525−0.654), followed by elevation, while potential evapotranspiration and temperature also exerted substantial explanatory influence. Interactions among driving factors consistently enhanced their explanatory power, with the precipitation–elevation interaction exerting the strongest effect (q = 0.779−0.804). These findings clarify the spatiotemporal dynamics and driving forces of water conservation in a critical tropical headwater region and provide robust scientific evidence to improve water resource management and ecosystem-based strategies for enhancing water security on Hainan Island.

关键词

海南 / 水源涵养 / 时空演变 / 驱动因素

Key words

Hainan Island / water conservation / spatial variations / driving factors

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何倩倩,任明迅. 海南“三江源”水源涵养量时空演变特征及驱动因素分析[J]. 热带生物学报, 2026, 17(5): 748-759 DOI:10.15886/j.cnki.rdswxb.20250206

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参考文献

[1]

高红凯, 刘俊国, 高光耀, 等. 水源涵养功能概念的生态和水文视角辨析[J]. 地理学报, 2023, 78(1): 139-148. https://doi.org/10.11821/dlxb202301009

[2]

龚诗涵, 肖洋, 郑华, 等. 中国生态系统水源涵养空间特征及其影响因素[J]. 生态学报, 2017, 37(7): 2455-2462. https://doi.org/10.5846/stxb201512012406

[3]

杜乐山, 刘海鸥, 刘文慧, 等. 热带雨林区水源涵养服务时空演变及驱动因素分析: 以海南热带雨林国家公园为例[J]. 环境科学研究, 2023, 36(9): 1716-1727. https://doi.org/10.13198/j.issn.1001-6929.2023.07.06

[4]

韩念龙, 刘子荣, 贾培宏, 等. 1996-2020 年海南岛水源涵养量时空变化及地理探测[J]. 水土保持通报, 2022, 42(5): 193-201. https://doi.org/10.13961/j.cnki.stbctb.2022.05.025

[5]

白婷婷, 徐栋, 武少腾. 生态系统服务时空交互特征及其驱动力: 以海南岛为例[J]. 中国环境科学, 2023, 43(11): 5961-5973. https://doi.org/10.19674/j.cnki.issn1000-6923.20230606.003

[6]

Li L M, Tang H N, Lei J R, et al. Spatial autocorrelation in land use type and ecosystem service value in Hainan Tropical Rain Forest National Park[J]. Ecological Indicators, 2022, 137: 108727. https://doi.org/10.1016/j.ecolind.2022.108727

[7]

Lei J R, Zhang L, Wu T T, et al. Spatial-temporal evolution and driving factors of water yield in three major drainage basins of Hainan Island based on land use change[J]. Frontiers in Forests and Global Change, 2023, 6: 1131264. https://doi.org/10.3389/FFGC.2023.1131264

[8]

Yao X L, Zhou L, Wu T X, et al. Ecosystem services in National Park of Hainan Tropical Rainforest of China: spatiotemporal dynamics and conservation implications[J]. Journal for Nature Conservation, 2024, 80: 126649. https://doi.org/10.1016/j.jnc.2024.126649

[9]

邓雅文, 侯鹏, 蒋卫国, 等. 基于多特征指标和层次聚类分析的河源区范围自动划分方法研究[J]. 地球信息科学学报, 2022, 24(3): 469-482.

[10]

王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017, 72(1): 116-134. https://doi.org/10.11821/dlxb201701010

[11]

Song Y Z, Wang J F, Ge Y, et al. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data[J]. GIScience & Remote Sensing, 2020, 57(5): 593-610. https://doi.org/10.1080/15481603.2020.1760434

[12]

刘纪远. 中国资源环境遥感宏观调查与动态研究[M]. 北京: 中国科学技术出版社, 1996: 144-158

[13]

李昂, 叶长青, 朱丽蓉, 等. 土地利用/覆被变化对产水服务功能的影响: 以海南热带雨林国家公园为例[J]. 水利水电技术 (中英文), 2022, 53(5): 36-45. https://doi.org/10.13928/j.cnki.wrahe.2022.05.004

[14]

Zhang L, Dawes W R, Walker G R. Response of mean annual evapotranspiration to vegetation changes at catchment scale[J]. Water Resources Research, 2001, 37(3): 701-708. https://doi.org/10.1029/2000WR900325

[15]

周文佐. 基于 GIS 的我国主要土壤类型土壤有效含水量研究[D]. 南京: 南京农业大学, 2003.

[16]

杨振奇, 秦富仓, 李旻宇, 等. 砒砂岩区不同土地利用类型土壤入渗性能及其影响因素研究[J]. 生态环境学报, 2020, 29(4): 733-739. https://doi.org/10.16258/j.cnki.1674-5906.2020.04.012

[17]

Niu P T, Zhang E C, Feng Y, et al. Spatial-temporal pattern analysis of land use and water yield in water source region of middle route of south-to-north water transfer project based on google earth engine[J]. Water, 2022, 14(16): 2535. https://doi.org/10.3390/w14162535

[18]

Chen J H, Wang D C, Li G D, et al. Spatial and temporal heterogeneity analysis of water conservation in Beijing-Tianjin-Hebei urban agglomeration based on the geodetector and spatial elastic coefficient trajectory models[J]. GeoHealth, 2020, 4(8): e2020GH000248. https://doi.org/10.1029/2020GH000248

[19]

Li T H, Wang X J, Jia H. Evaluate water yield and soil conservation and their environmental gradient effects in Fujian province in South China based on InVEST and geodetector models[J]. Water, 2025, 17(2): 230. https://doi.org/10.3390/w17020230

[20]

Shao Q F, Han L B, Lv L F, et al. Spatiotemporal variation and factors influencing water yield services in the Hengduan mountains, China[J]. Remote Sensing, 2023, 15(16): 4087. https://doi.org/10.3390/rs15164087

[21]

王懋源, 齐实, 郭衍瑞, 等. 藏东-川西生态维护水源涵养区产水量驱动机制[J]. 生态学报, 2024, 44(21): 9520-9534. https://doi.org/10.20103/j.stxb.202310082163

[22]

汤秋鸿, 陈德亮. 全球变化加剧水循环失衡与水资源危机[J]. 中国科学: 地球科学, 2025, 55(9): 3209-3212. https://doi.org/10.1360/SSTe-2025-0078

[23]

贾雨欣. 基于自然的解决方案的发展历程[J]. 环境保护前沿, 2022, 12(6): 1318-1324. https://doi.org/10.12677/AEP.2022.126164

基金资助

海南省重点研发项目(ZDYF2025SHFZ037)

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