塔布河流域生态系统服务时空格局及影响因素分析

doi:10.16258/j.cnki.1674-5906.2024.07.015

生态环境学报 ›› 2024, Vol. 33 ›› Issue (7): 1142-1152.DOI: 10.16258/j.cnki.1674-5906.2024.07.015

• 研究论文【环境科学】 • 上一篇下一篇

塔布河流域生态系统服务时空格局及影响因素分析

张维琛¹^,²(), 王惺琪¹, 王博杰¹^,^*()

1.内蒙古大学生态与环境学院，草原生态安全省部共建协同创新中心，内蒙古呼和浩特 010021
2.中国水利水电科学研究院内蒙古阴山北麓草原生态水文国家野外科学观测研究站，内蒙古呼和浩特 010021

收稿日期:2024-03-04 出版日期:2024-07-18 发布日期:2024-09-04
通讯作者: *王博杰。E-mail: wbj8383@163.com
作者简介:张维琛（1999年生），女，硕士研究生，研究方向为生态系统服务与人类福祉。E-mail: zweichen0726@163.com
基金资助:
草原生态安全协同创新中心重大课题(MK0143A032021);草原生态安全协同创新中心重点项目(YSZD202301);国家自然科学基金项目(32060316)

Spatiotemporal Pattern and Influencing Factors of the Ecosystem Services in the Tabu River Basin

ZHANG Weichen¹^,²(), WANG Xingqi¹, WANG Bojie¹^,^*()

1. College of Ecology and Environment, Inner Mongolia University, Collaborative Innovation Center for Grassland and Ecological Security (Jointly Supported by the Ministry of Education of China and Inner Mongolia Autonomous Region), Hohhot 010021, P. R. China
2. Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Hohhot 010021, P. R. China

Received:2024-03-04 Online:2024-07-18 Published:2024-09-04

摘要/Abstract

摘要：

中国干旱半干旱地区的生态环境脆弱，水资源匮乏，不合理的人类活动导致生态系统退化，探究内蒙古塔布河流域生态系统服务的时空变化特征、影响因素及其交互关系，对于生态系统的可持续管理更有针对性和现实性。基于遥感和GIS技术，利用模型模拟2000、2010和2020年内蒙古塔布河流域产水、粮食供给、防风固沙、固碳等4项生态系统服务时空格局，并通过地理探测器分析生态系统服务的影响因子及其交互作用。结果表明：在空间上，塔布河流域各项生态系统服务总体呈现南高北低的趋势。2000－2020年间，各项生态系统服务总体呈上升趋势，均在2020年达到峰值。其中，产水量增加了94 mm，提高区域占流域总面积的97.4%；粮食产量逐年增加，2020年达到44 t·km⁻²，增加区域占总面积的95.9%；防风固沙服务在不断提升，截至2020年，高值区的防风固沙量达到114 t·km⁻²，增加的区域占总面积的78.3%；固碳呈现出先下降后上升的趋势，在2020年达到最高值，为644 g·m⁻²·a⁻¹，增加区域占总面积的87%。影响该区域生态系统服务的主要因素有年降水、年均温、归一化植被指数（NDVI）等，交互效应对生态系统服务的影响变得更为显著，特别是年降水与土地利用方式的组合，影响系数的增长率高达61%。受地形、气候、植被等自然因素和土地利用方式、人口密度等社会经济因素的影响，2000－2020年塔布河流域的产水、粮食供给、防风固沙及碳固服务均有大幅提升，全域生态系统服务有70%以上的区域呈增加趋势。该研究有助于进一步了解有关季节性河流各生态系统服务及其影响因素的差异，可为干旱半干旱地区的流域生态系统管理和可持续发展提供参考。

关键词: 生态系统服务, 塔布河流域, 时空格局, 影响因素, 地理探测器

Abstract:

The fragile ecological environment and scarce water resources in China's arid and semi-arid regions have been affected by unsustainable human activities, leading to ecosystem degradation. This study investigated spatiotemporal variations, influencing factors, and their interactions with ecosystem services in the Tab River Basin, Inner Mongolia, to provide targeted and practical insights for sustainable ecosystem management. Using remote sensing and GIS technology, a model was employed to simulate the spatiotemporal patterns of four ecosystem services (water provision, food supply, wind prevention, sand fixation, and carbon sequestration) in the Tabun River Basin for 2000, 2010, and 2020. Additionally, a geographic detector was used to analyze the influencing factors and their interactive effects on ecosystem services. The findings revealed a spatial trend of overall higher values in the southern part and lower values in the northern part of the Tabun River Basin. From 2000‒2020, all ecosystem services exhibited an increasing trend, reaching peak values in 2020. Specifically, water provision increased by 94 mm, covering 97.36% of the basin's total area; food supply increased annually, reaching 44 t·km⁻² in 2020, covering 95.96% of the total area; wind prevention and sand fixation services continued to improve, with the high-value area reaching 114 t·km⁻² by 2020, covering 78.31% of the total area; carbon sequestration exhibited a decreasing-increasing trend, reaching the highest value of 644 g·m⁻²·a⁻¹ in 2020, covering 87% of the total area. The major factors influencing ecosystem services in this region include annual precipitation, annual mean temperature, and the normalized difference vegetation index (NDVI), and their interactive effects become more significant. The combination of annual precipitation and land use had an impact coefficient growth rate of 61%. Influenced by natural factors, including topography, climate, vegetation, and socioeconomic factors, such as land use and population density, the period from 2000 to 2020 witnessed a substantial improvement in water provision, food supply, wind prevention, sand fixation, and carbon sequestration services in the Tabun River Basin, with over 70% of the overall ecosystem services in the region showing an increasing trend. This study contributes to a deeper understanding of the disparities in seasonal river ecosystem services and their influencing factors, providing valuable insights into the management and sustainable development of arid and semiarid basin ecosystems.

Key words: ecosystem services, Tabu River Basin, spatiotemporal patterns, influencing factors, geographic detector

中图分类号:

X171.1

张维琛, 王惺琪, 王博杰. 塔布河流域生态系统服务时空格局及影响因素分析[J]. 生态环境学报, 2024, 33(7): 1142-1152.

ZHANG Weichen, WANG Xingqi, WANG Bojie. Spatiotemporal Pattern and Influencing Factors of the Ecosystem Services in the Tabu River Basin[J]. Ecology and Environment, 2024, 33(7): 1142-1152.

图/表 8

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服务类型	计算模型及原理	所需参数
产水	InVEST模型产水模块: $Yxj=\left( 1-\frac{{{A}_{xj}}}{Px} \right)\times Px$ (1)	Y_xj ——x栅格单元上土地利用类型j的产水量; A_xj ——j类土地利用类型x栅格单元上的年均实际蒸散量; P_x——x栅格单元上年均降雨量
粮食供给	经验公式 (赵文亮等, 2012): $Gi\mathrm{=}\frac{Ni}{Nsum}\times Gsum$ (2)	G_i——第i个栅格所分配的粮食产量; G_sum——粮食总产量; N——第i个栅格的归一化植被指数; N_sum——研究区耕地或草地的归一化植被指数之和
防风固沙	RWEQ模型 (Fryrear, 1999): Q_max=109.8×(W_F×E_F×S_CF×K′×C_OG) (3) s=150.71×(W_F×E_F×S_CF×K′×C_OG)^−0.3711(4) ${{S}_{L}}=\frac{2x}{{{s}^{2}}}{{Q}_{\max }}{{e}^{-{{(\frac{x}{s})}^{2}}}}$ (5) S_Lpv=S_Lp−S_Lv(6)	S_Lpv——防风固沙量; S_Lp——潜在风蚀量; S_Lv——实际风蚀量; Q_max——潜在最大输沙能力; x——最大风蚀出现距离; W_F、E_F、S_CF、K′、C_OG——气候因子、土壤侵蚀因子、土壤结皮因子、地表粗糙度因子和植被覆盖因子
固碳	改进CASA模型 (朱文泉, 2005): P₍_x_,_t₎=A₍_x_,_t₎×ε₍_x_,_t₎ (7)	P₍_x_,_t₎——t时间内在像元x处的净初级生产力; A₍_x_,_t₎——t时间在像元x处所吸收的光合有效辐射; ε₍_x_,_t₎——实际光能利用率

服务类型	计算模型及原理	所需参数
产水	InVEST模型产水模块: $Yxj=\left( 1-\frac{{{A}_{xj}}}{Px} \right)\times Px$ (1)	Y_xj ——x栅格单元上土地利用类型j的产水量; A_xj ——j类土地利用类型x栅格单元上的年均实际蒸散量; P_x——x栅格单元上年均降雨量
粮食供给	经验公式 (赵文亮等, 2012): $Gi\mathrm{=}\frac{Ni}{Nsum}\times Gsum$ (2)	G_i——第i个栅格所分配的粮食产量; G_sum——粮食总产量; N——第i个栅格的归一化植被指数; N_sum——研究区耕地或草地的归一化植被指数之和
防风固沙	RWEQ模型 (Fryrear, 1999): Q_max=109.8×(W_F×E_F×S_CF×K′×C_OG) (3) s=150.71×(W_F×E_F×S_CF×K′×C_OG)^−0.3711(4) ${{S}_{L}}=\frac{2x}{{{s}^{2}}}{{Q}_{\max }}{{e}^{-{{(\frac{x}{s})}^{2}}}}$ (5) S_Lpv=S_Lp−S_Lv(6)	S_Lpv——防风固沙量; S_Lp——潜在风蚀量; S_Lv——实际风蚀量; Q_max——潜在最大输沙能力; x——最大风蚀出现距离; W_F、E_F、S_CF、K′、C_OG——气候因子、土壤侵蚀因子、土壤结皮因子、地表粗糙度因子和植被覆盖因子
固碳	改进CASA模型 (朱文泉, 2005): P₍_x_,_t₎=A₍_x_,_t₎×ε₍_x_,_t₎ (7)	P₍_x_,_t₎——t时间内在像元x处的净初级生产力; A₍_x_,_t₎——t时间在像元x处所吸收的光合有效辐射; ε₍_x_,_t₎——实际光能利用率

变量名称	离散方法	类别
海拔高度	几何间隔	10
年均温	分位数间隔	9
年降水	自然间隔	10
归一化植被指数	分位数间隔	10
人均生产总值	分位数间隔	10
人口密度	分位数间隔	9
人类足迹	分位数间隔	9
土地利用方式	手动	6

变量名称	离散方法	类别
海拔高度	几何间隔	10
年均温	分位数间隔	9
年降水	自然间隔	10
归一化植被指数	分位数间隔	10
人均生产总值	分位数间隔	10
人口密度	分位数间隔	9
人类足迹	分位数间隔	9
土地利用方式	手动	6

数据名称	空间分辨率	数据来源
降水	1 km	国家青藏高原科学数据中心 (https://data.tpdc.ac.cn/)
蒸散发	1 km	国家青藏高原科学数据中心 (https://data.tpdc.ac.cn/)
土地利用数据	30 m	中国科学院资源环境科学与数据中心 (http://www.resdc.cn/)
海拔高度	30 m	地理空间数据云 (http://www.gscloud.cn)
归一化植被指数	30 m	中国科学院资源环境科学与数据中心 (https://www.resdc.cn/)
土壤类型	1 km	国家青藏高原科学数据中心 (https://data.tpdc.ac.cn/)
温度	30 m	国家气象数据中心 (https://data.cma.cn/)
太阳辐射	30 m	国家气象数据中心 (https://data.cma.cn/)
人口密度	1 km	Open Spatial Demographic Data and Research (https://www.worldpop.org/)
人类足迹指数	1 km	中国农业大学土地科学与技术学院的城市环境监测及建模团队 (https://www.x-mol.com/groups/li_xuecao/news/48145)

塔布河流域生态系统服务时空格局及影响因素分析

Spatiotemporal Pattern and Influencing Factors of the Ecosystem Services in the Tabu River Basin

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生态系统服务	影响因子	对应q值	交互因子	对应q值	交互影响	增长率/%
产水	土地利用方式	0.4002	人口密度	0.2135	0.5771	44.2
产水	土地利用方式	0.4002	海拔高度	0.5110	0.8008	56.7
粮食供给	年均温	0.4216	年降水	0.1304	0.4901	16.3
粮食供给	年均温	0.4216	土地利用方式	0.1896	0.4667	10.7
防风固沙	年均温	0.3677	年降水	0.1568	0.5113	39.1
防风固沙	年均温	0.3677	人口密度	0.1407	0.4533	23.3
固碳	年均温	0.5041	年降水	0.2246	0.5655	12.2
固碳	年均温	0.5041	土地利用方式	0.2836	0.5747	14.0

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