石羊河流域生态系统服务相互作用的时空变化及驱动机制研究

doi:10.12118/j.issn.1000-6060.2023.708

摘要/Abstract

摘要：

了解生态系统服务的时空变化及其内部复杂关系对管理生态系统服务是至关重要的。以石羊河流域为研究区，评估流域2010、2015年和2020年6种生态系统服务，分析栅格和乡镇尺度上生态系统服务权衡协同及生态系统服务簇的时空变化，利用增强回归树模型探讨研究区生态系统服务簇的驱动机制。结果表明：（1）各类服务空间分异明显，产水量、碳储量、土壤保持和生境质量呈“西南高-东北低”的空间格局，粮食供应主要集中于流域中部和北部的耕地区，休闲娱乐高值区分布于流域南部及中部和北部人口密集地区；研究期间内各类服务均有不同程度提高，其中土壤保持的增幅最大，碳储量和生境质量增幅较小。（2）2个尺度上生态系统服务权衡协同作用呈相似性，但权衡协同作用强度不同，总体上表现为12对协同关系，3对权衡关系。（3）2个尺度上生态系统服务簇的空间格局相似，流域南部分布除粮食供应外其他5种服务为主的服务簇，流域中部和北部民勤绿洲地区分布与粮食供应和休闲娱乐为主的服务簇，其他地区的生态环境相对恶劣，分布其中的服务簇内各项服务供应较低；研究期间内服务簇存在明显的空间和数量转移变化。（4）多种因素在研究区生态系统服务簇的变化中起着重要作用，影响因子在不同年份的影响程度略不相同，其中土地利用类型、归一化植被指数、年降水量和海拔是生态系统服务簇变化的重要驱动因子。

关键词: 生态系统服务簇, 权衡协同, 增强回归树模型, 石羊河流域

Abstract:

Understanding the spatiotemporal distribution and the internal complex relationships of ecosystem services is essential for their management. With the Shiyang River Basin, Gansu Province, China as the research area, six ecosystem services were evaluated in 2010, 2015, and 2020. The trade-off/synergy of ecosystem services and the spatial changes of service bundles on the grid and township scale were analyzed, and a boosted regression tree model was used to analyze the driving mechanism of ecosystem service bundles in the research area. The results showed the following: (1) The spatial differences of various services were obvious. The spatial pattern of water yield, carbon storage, soil conservation, and habitat quality was “southwest high-northeast low”. Food production was mainly distributed in the farming areas in the north-central part of the basin, and the high-value areas of recreational service were distributed in the southern areas and the central parts of the basin and in densely populated areas in the north. During the research period, all kinds of services were improved to varying degrees, with the increase in soil conservation having the largest improvement and the increase in carbon storage and habitat quality having the smallest. (2) The trade-offs and synergies of ecosystem services on two scales showed similarities, but their intensities were different. Overall, there were twelve pairs of synergy and three pairs of trade-off relationships. (3) The spatial patterns of ecosystem service bundles on two scales were similar. In addition to food production, there were five service-related service bundles in the southern part of the river basin. In the central and northern Minqin oasis areas of the basin, there were service bundles related to food production and recreational service. The ecological environment in other areas was relatively harsh. There was no outstanding service supply in the service bundles, but there were obvious changes in the number and space transfer of service bundles during the study period. (4) Many factors played an important role in the changes in ecosystem service bundles in the research area, and the impact factors were slightly different in different years. Among them, land use type, normalized difference vegetation index, annual precipitation, and altitude were the main drivers of the changes in ecosystem service bundles.

Key words: ecosystem service bundle, trade-offs and synergies, boosted regression tree model, Shiyang River Basin

胡飞鹏, 赵军, 孙紫云, 刘坚, 托瑞. 石羊河流域生态系统服务相互作用的时空变化及驱动机制研究[J]. 干旱区地理, 2024, 47(10): 1755-1766.

HU Feipeng, ZHAO Jun, SUN Ziyun, LIU Jian, TUO Rui. Spatiotemporal changes and driving mechanism of ecosystem service interactions in the Shiyang River Basin[J]. Arid Land Geography, 2024, 47(10): 1755-1766.

图/表 10

图1

表1

表2

生态系统服务评估方法"

生态系统服务		计算公式	描述
供应服务	粮食供应	$G x = N D V I x N D V I s u m × G s u m$	根据以往研究，粮食总产量按照耕地NDVI值与耕地总NDVI值的比值进行分配，以此确定研究区内各栅格的粮食供应能力^[18]。G_x为栅格x的粮食供应量（t）；NDVI_x为耕地栅格x的NDVI值；NDVI_sum为研究区内所有耕地的NDVI总和；G_sum为研究区内耕地内的粮食总产量（t）。
供应服务	产水量	$Y x = 1 - A E T x P x × P x$	利用InVEST模型中的Water yield模块对水源供给进行计算，模型中的参数根据有关研究成果确定^[19]。Y_x为栅格x的年产量（mm）；P_x和AET_x分别为栅格x的年降雨量和年实际蒸散量（mm）。
调节服务	碳储量	$C x = C a b o v e + C b e l o w + C s o i l + C d e a d$	利用InVEST模型中的Carbon Storage Sequestration模块计算固碳储量，碳库参数根据有关研究成果确定^[20]。C_x为总碳储量（t·hm^-2）；C_above为地上碳储量（t·hm^-2）；C_below为地下碳储量（t·hm^-2）；C_dead为死亡有机质（t·hm^-2）；C_soil为土壤碳储量（t·hm^-2）。
调节服务	土壤保持	$S C = R × K × L S × P × (1 - C)$	采用修正水土流失方程估算土壤保持^[21]。SC为土壤保持量（t·hm^-2·a^-1）；R为降雨侵蚀力因子；K为土壤可侵蚀力因子；LS为坡度坡长因子；C为植被覆盖因子；P为土壤保持因子。
支持服务	生境质量	$Q x j = H j 1 - D x j z D x j z + k z$	利用InVEST模型中Habitat Quality模块计算生境分布和退化情况^[22]。Q_xj为第j类土地利用类型栅格x的生境质量指数；H_j为土地利用类型j的生境适宜度；D_xj为土地利用类型j中的栅格单元x的生境退化程度；k为半饱和系数。其中威胁因子及其敏感度的赋值参照相关研究成果确定^[19]。
文化服务	休闲娱乐	RS=Σ（NPP_i+POP_i+ROAD_i）	文化服务指人类直接或间接从生态系统中所获得的非物质性收益。基于森林游憩服务量化模型，引入NPP取代娱乐得分，对流域的休闲娱乐进行评估^[23-24]。根据公式对得分求和后重分类为0~10，得分越高，休闲娱乐服务越强。RS为休闲娱乐的总得分；NPP_i为植被净初级生产力水平得分；POP_i为人口聚集临近度得分；ROAD_i为道路的距离得分。

表2

图2

表3

图3

图4

图5

图6

图7

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数据	数据描述	分辨率	数据来源
土地利用数据	2010、2015、2020年三期土地利用数据	30 m	中国科学院资源环境科学与数据中心（https://www.resdc.cn/）
气象数据	2010—2020年潜在蒸散发数据、年降水量和年均气温数据	1 km	中国科学院资源环境科学与数据中心（https://www.resdc.cn/）
土壤数据	基于世界土壤数据库的泛第三极土壤数据库	1 km	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）
地形数据	DEM	30 m	地理空间数据云（https://www.gscloud.cn/）
社会经济数据	GDP	1 km	中国科学院资源环境科学与数据中心（https://www.resdc.cn/）
	人口密度数据	100 m	WorldPOP（https://hub.worldpop.org/）
	2010—2020年甘肃粮食生产统计数据		甘肃省统计局（http://tjj.gansu.gov.cn/）
其他数据	2010—2020年NDVI和NPP数据	30 m	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）
	县域和乡镇边界；河流水系数据		国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）
	交通道路数据，包括公路、铁路等		OSM(https://openmapt.org/)

年份	FP/t	WY/t	CS/t	SC/t	HQ	RS
2010	3.71×10⁶	2.31×10⁹	5.60×10⁸	4.53×10⁸	0.4572	3.35
2015	4.79×10⁶	3.05×10⁹	5.64×10⁸	5.71×10⁸	0.4602	3.84
2020	4.96×10⁶	3.18×10⁹	5.62×10⁸	7.19×10⁸	0.4576	3.97