近30年来，黄土高原在持续推进生态修复工程的背景下，土地利用格局发生了显著变化，导致区域“绿-水-碳”服务功能失衡。因此，揭示生态系统服务的多尺度耦合机制，对研究黄土高原可持续发展至关重要。本文以黄土高原为研究区域，结合遥感数据与社会经济数据，基于GMOP-PLUS-InVEST耦合框架，构建“需求预测-空间模拟-服务评估”研究路径，综合运用InVEST、RUSLE等模型，定量评估1990-2020年间及2050年预测情景下碳固存、粮食生产、生境质量、土壤保持和产水量5大关键生态系统服务。本研究系统解析了各服务在不同尺度下的时空演变规律及其相互关系，并探究了生态系统服务空间分异的驱动因素。本文主要结论如下：

（1）研究期内，黄土高原土地利用格局发生显著变化，整体呈现建设用地与林地扩张、耕地缩减的趋势。空间异质性分析显示，中部及沙漠地带是生态工程实施的典型响应区。各类土地利用类型间动态转换特征显著，耕地、林地与草地之间的相互转换尤为突出，且耕地向建设用地的转换持续活跃。

（2）研究期内，黄土高原生态系统服务空间格局总体保持稳定，服务功能高值区域主要分布在低海拔地区。其中，碳固存与生境质量高值区集中于西北沙地；产水量、土壤保持及粮食生产高值区则位于东南河谷。数值变化趋势显示，碳固存功能呈现出稳定的态势；粮食生产功能则表现出不断增加的趋势；生境质量呈现出先上升后下降的趋势；土壤保持和产水量功能呈现出先下降后上升的趋势。不同情景模拟表明，2050年在可持续发展情景下，生态系统服务综合表现较好。

（3）研究期内，黄土高原生态系统服务影响因素分析显示，不同生态系统服务功能的主导影响因素存在明显差异。具体而言，NDVI与坡度对碳固存、粮食生产及生境质量服务的影响较大，可视为其主导影响因素；而对于土壤保持功能，坡度与降水则起着主要的驱动作用；就产水量而言，降水与DEM被确立为主导影响因素。这些影响因素并非独立作用，而是与地貌特征、气候条件及人类活动等多重因素相互作用，共同塑造了生态系统服务格局。

（4）研究期内，黄土高原生态系统服务之间以协同关系为主，主要的权衡关系集中在粮食生产-生境质量、粮食生产-碳固存、产水量-生境质量及产水量-碳固存之间。且随着分析尺度的增大，权衡关系逐渐向协同关系转变。生态系统服务簇空间分布形成“西北生态调节-东南供给主导”的双核结构，即西北部以生境质量-碳固存生态簇、生态过渡簇为主，东南部以产水量生态簇、粮食生产-产水量生态簇及综合生态簇为主。并且随着尺度的增大，生态系统服务之间的关系越趋于一致，空间分布趋于集中。

关 键 词：黄土高原；多模型耦合；生态系统服务；多情景模拟；权衡与协同

研究类型：应用研究

In the past 30 years, under the continuous implementation of ecological restoration projects on the Loess Plateau, significant changes have occurred in the land use pattern, leading to an imbalance in the regional “green-water-carbon” service functions. Therefore, revealing the multi-scale coupling mechanisms of ecosystem services is crucial for studying the sustainable development of the Loess Plateau. This study took the Loess Plateau as the research area and combines remote sensing data with socio-economic data. Based on the GMOP-PLUS-InVEST coupling framework, a research pathway of “demand prediction - spatial simulation - service assessment” is constructed. Models such as InVEST and RUSLE were comprehensively used to quantitatively assess five key ecosystem services: carbon sequestration, food production, habitat quality, soil conservation, and water yield, during the period from 1990 to 2020 and under predicted scenarios for 2050. This study systematically analyzed the spatiotemporal evolution patterns and interrelationships of each service at different scales and explored the driving factors of spatial differentiation in ecosystem services. The main conclusions of this study are as follows:

(1) During the study period, significant changes occurred in the land use pattern of the Loess Plateau, generally showing a trend of expansion in construction land and forest land, coupled with a reduction in cultivated land. Spatial heterogeneity analysis reveals that the central and desert regions are typical response areas to the implementation of ecological projects. The dynamic conversion characteristics among various land use types are pronounced, with particularly notable conversions between cultivated land, forest land, and grassland. Additionally, the conversion of cultivated land to construction land remains continuously active.

(2) During the study period, the spatial pattern of ecosystem services on the Loess Plateau remained generally stable, with high-value service areas primarily distributed in low-elevation regions. Specifically, high-value areas for carbon sequestration and habitat quality are concentrated in the northwestern sandy lands, while high-value areas for water yield, soil conservation, and food production are located in the southeastern river valleys. Numerical trend analysis indicates that carbon sequestration function remains stable; food production function shows an increasing trend; habitat quality initially rises and then declines; and soil conservation and water yield functions exhibit a trend of initial decline followed by an increase. Simulations under different scenarios suggest that under a sustainable development scenario in 2050, the overall performance of ecosystem services will be relatively good.

(3) During the study period, the analysis of the influencing factors of ecosystem services in the Loess Plateau revealed significant differences in the dominant influencing factors among different ecosystem service functions. Specifically, NDVI and slope have a relatively large impact on carbon sequestration, food production, and habitat quality services, and can be regarded as their dominant influencing factors. For soil conservation function, slope and precipitation play a major driving role. In terms of water yield, precipitation and DEM have been identified as the dominant influencing factors. These influencing factors do not act independently; instead, they interact with multiple factors such as geomorphic features, climatic conditions, and human activities, jointly shaping the pattern of ecosystem services.

(4) During the study period, the relationships among ecosystem services on the Loess Plateau are predominantly synergistic, with the main trade-offs concentrated between food production and habitat quality, food production and carbon sequestration, water yield and habitat quality, as well as water yield and carbon sequestration. As the analysis scale increases, trade-off relationships gradually transition to synergistic relationships. The spatial distribution of ecosystem service clusters forms a dual-core structure of “northwest ecological regulation - southeast supply dominance”, with the northwest primarily dominated by habitat quality - carbon sequestration ecological clusters and ecological transition clusters, and the southeast primarily dominated by water yield ecological clusters, food production - water yield ecological clusters, and comprehensive ecological clusters. Furthermore, as the scale increases, the relationships between ecosystem services become more consistent, and their spatial distribution becomes more concentrated.

Key words: Loess Plateau; Multi-model coupling; Ecosystem services; Multi-scenario simulation; Tradeoff and coordination

Thesis: Applied Research

参考文献

[1] Li P F, Mu X M, Holden J, et al. Comparison of soil erosion models used to study the Chinese Loess Plateau[J]. Earth-Science Reviews, 2017, 170: 17-30.

[2] Wang C, Wang S, Fu B J, et al. Precipitation gradient determines the tradeoff between soil moisture and soil organic carbon, total nitrogen, and species richness in the Loess Plateau, China[J]. Science of the Total Environment, 2017, 575: 1538-1545.

[3] Chen B M, Jing X, Liu S S, et al. Intermediate human activities maximize dryland ecosystem services in the long-term land-use change: Evidence from the Sangong River watershed, northwest China[J]. Journal of Environmental Management, 2022, 319: 115708.

[4] Gomes E, Inácio M, Bogdzevič K, et al. Future land-use changes and its impacts on terrestrial ecosystem services: A review[J]. Science of The Total Environment, 2021, 781: 146716.

[5] Mach M E, Martone R G, Chan K M. Human impacts and ecosystem services: Insufficient research for trade-off evaluation[J]. Ecosystem services, 2015, 16: 112-120.

[6] 林柳璇, 尤添革, 刘金福, 等. 1985—2015年厦门市土地利用变化及驱动力[J]. 福建农林大学学报(自然科学版), 2019, 48(1): 103-110.

[7] 李聪慧, 马彩虹, 滑雨琪, 等. 黄河上游荒漠绿洲生态系统服务对三生用地变化的响应——以银川市为例[J]. 资源科学, 2023, 45(1): 190-203.

[8] 彭焕智, 周国华, 崔树强, 等. 湘江流域土地利用多功能性评价及障碍因子识别[J]. 水土保持研究, 2022, 29(4): 308-315.

[9] 刘春艳, 朱康文, 刘吉平. 三峡库区重庆段土地覆盖和生物多样性功能演化及预测[J]. 农业工程学报, 2017, 33(19): 258-267.

[10] 雷军成, 刘纪新, 雍凡, 等. 基于CLUE-S和InVEST模型的五马河流域生态系统服务多情景评估[J]. 生态与农村环境学报, 2017, 33(12): 1084-1093.

[11] 刘耀彬, 戴璐, 董玥莹. 环鄱阳湖区分区土地利用景观格局变化模拟研究[J]. 长江流域资源与环境, 2015, 24(10): 1762-1770.

[12] 张宇硕, 吴殿廷, 吕晓. 土地利用/覆盖变化对生态系统服务的影响：空间尺度视角的研究综述[J]. 自然资源学报, 2020, 35(5): 1172-1189.

[13] 吴传钧. 中国土地利用[M]. 北京: 科学出版社, 1994: 422.

[14] Zhao Q Q, Li J, Cuan Y D, et al. The evolution response of ecosystem cultural services under different scenarios based on system dynamics[J]. Remote Sensing, 2020, 12(3): 418.

[15] 华文剑, 陈海山, 李兴. 中国土地利用/覆盖变化及其气候效应的研究综述[J]. 地球科学进展, 2014, 29(9): 1025-1036.

[16] Hua T, Zhao W W, Cherubini F, et al. Sensitivity and future exposure of ecosystem services to climate change on the Tibetan Plateau of China[J]. Landscape Ecology, 2021, 36(12): 3451-3471.

[17] Liu X P, Liang X, Li X, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects[J]. Landscape and urban planning, 2017, 168: 94-116.

[18] 凯吾沙·塔依尔, 黎华, 古丽米热·艾尔肯, 等. FLUS与灰色预测模型支持下的乌鲁木齐地区碳排放时空演变与预测[J]. 水土保持学报, 2023, 37(4): 214-226.

[19] 王旭东, 姚尧, 任书良, 等. 耦合FLUS和Markov的快速发展城市土地利用空间格局模拟方法[J]. 地球信息科学学报, 2022, 24(1): 100-113.

[20] 严金明. 土地利用结构的系统分析与优化设计——以南京市为例[J]. 南京农业大学学报, 1996, (2): 88-95.

[21] Liu H J, Yan F Y, Tian H. Towards low-carbon cities: Patch-based multi-objective optimization of land use allocation using an improved non-dominated sorting genetic algorithm-II[J]. Ecological Indicators, 2022, 134: 108455.

[22] Zhang H H, Zeng Y N, Jin X B, et al. Simulating multi-objective land use optimization allocation using Multi-agent system—A case study in Changsha, China[J]. Ecological modelling, 2016, 320: 334-347.

[23] 裴亮, 陈晨, 戴激光, 等. 基于马尔科夫模型的大凌河流域土地利用/覆被变化趋势研究[J]. 土壤通报, 2017, 48(3): 525-531.

[24] 蔚芳, 蒋雨薇. 基于SD-PLUS耦合模型的杭州市土地利用多情景模拟[J]. 农业资源与环境学报, 2025, 42(1): 44-56.

[25] 张晓荣, 李爱农, 南希, 等. 基于FLUS模型和SD模型耦合的中巴经济走廊土地利用变化多情景模拟[J]. 地球信息科学学报, 2020, 22(12): 2393-2409.

[26] 胡波洋, 张蓬涛, 白宁, 等. 基于CLUE-S和GMOP模型的青龙满族自治县土地利用情景模拟[J]. 中国农业资源与区划, 2020, 41(7): 173-182.

[27] 许静, 廖星凯, 甘崎旭, 等. 耦合GMOP与FLUS模型的黄河流域甘肃段生态风险评估与预测[J]. 生态学杂志, 2024, 43(5): 1498-1508.

[28] 黎夏, 叶嘉安. 知识发现及地理元胞自动机[J]. 中国科学(D辑:地球科学), 2004, (9): 865-872.

[29] 余洲, 李明玉, 钱雨扬, 等. 基于CA＿Markov模型多情景模拟的三峡库区土地利用变化及其生态环境效应[J]. 水土保持研究, 2024, 31(3): 363-372.

[30] Verburg P H, Soepboer W, Veldkamp A, et al. Modeling the spatial dynamics of regional land use: the CLUE-S model[J]. Environmental management, 2002, 30: 391-405.

[31] 王萍, 林乐乐, 彭杨靖, 等. 基于CLUE-S模型的浙江省土地利用结构变化趋势分析[J]. 生态科学, 2024, 43(3): 196-206.

[32] 王超越, 郭先华, 陆晨东, 等. 基于FLUS的成渝城市群三生空间演变及模拟预测[J]. 生态科学, 2024, 43(5): 157-168.

[33] Liang X, Guan Q F, Clarke K C, et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China[J]. Computers, Environment and Urban Systems, 2021, 85: 101569.

[34] 杨朔, 苏昊, 赵国平. 基于PLUS模型的城市生态系统服务价值多情景模拟——以汉中市为例[J]. 干旱区资源与环境, 2022, 36(10): 86-95.

[35] 杨伶, 王金龙, 周文强. 基于多情景模拟的洞庭湖流域LUCC与生境质量耦合演变分析[J]. 中国环境科学, 2023, 43(2): 863-873.

[36] 黄颖, 李鑫. 1995—2015年淮河生态经济区生态系统服务评估与时空变化分析[J]. 生态与农村环境学报, 2021, 37(1): 49-56.

[37] Ehrlich P, Ehrlich A. Extinction: the causes and consequences of the disappearance of species[J]. 1982: 231-233.

[38] Daily G. Nature's services: societal dependence on natural ecosystems[M]. 1998: 392.

[39] Costanza R, D'arge R, De Groot R, et al. The value of the world's ecosystem services and natural capital[J]. nature, 1997, 387(6630): 253-260.

[40] 孙刚, 盛连喜, 周道玮. 生态系统服务及其保护策略[J]. 应用生态学报, 1999, (3): 110-113.

[41] 欧阳志云, 王如松, 赵景柱. 生态系统服务功能及其生态经济价值评价[J]. 应用生态学报, 1999, (5): 635-640.

[42] 傅伯杰, 周国逸, 白永飞, 等. 中国主要陆地生态系统服务功能与生态安全[J]. 地球科学进展, 2009, 24(6): 571-576.

[43] 赵世洞, 张永民, 赖鹏飞. 千年生态系统评估报告集(一)[M]. 北京: 中国环境科学出版社, 2007: 115.

[44] 许丁雪, 吴芳, 何立环, 等. 三江源国家公园生态系统服务价值的时空演变[J]. 生态学杂志, 2024, 43(6): 1881-1890.

[45] Zhang Y K, Wang Y S. How does urbanization evolve heterogeneously in urbanized, urbanizing, and rural areas of China? Insights from ecosystem service value[J]. Geography and Sustainability, 2024: 100254.

[46] Wang Q, Bai X P. Spatiotemporal characteristics of human activity and land use on ecosystem service functions in mountainous areas of Northeast Guizhou, Southwest China[J]. Ecological Engineering, 2025, 212: 107473.

[47] 闫语, 秦耀伟, 东嘉琪, 等. 京津冀地区生态系统服务权衡与协同关系及驱动因素分析[J]. 环境科学学报, 2025, 45(3): 493-506.

[48] De Knegt B, Lof M E, Le Clec'h S, et al. Growing mismatches of supply and demand of ecosystem services in the Netherlands[J]. Journal of Environmental Management, 2025, 373: 123442.

[49] Costanza R, De Groot R, Sutton P, et al. Changes in the global value of ecosystem services[J]. Global environmental change, 2014, 26: 152-158.

[50] 谢高地, 甄霖, 鲁春霞, 等. 一个基于专家知识的生态系统服务价值化方法[J]. 自然资源学报, 2008, (5): 911-919.

[51] 王诗绮, 刘焱序, 李琰, 等. 近20年黄土高原生态系统服务研究进展[J]. 生态学报, 2023, 43(1): 26-37.

[52] 李思源, 倪欢, 牛晓楠, 等. 闽三角城市群土地利用与生态系统服务价值时空演变及未来多情景模拟[J]. 地理与地理信息科学, 2024, 40(5): 28-34+41.

[53] 张宇, 陈宇轩, 高志鹏, 等. 基于土地利用变化的鄂尔多斯市生态系统服务价值时空演化特征分析[J]. 干旱区资源与环境, 2025, 39(2): 131-140.

[54] 袁周炎妍, 万荣荣. 生态系统服务评估方法研究进展[J]. 生态科学, 2019, 38(5): 210-219.

[55] 李井浩, 柳书俊, 王志杰. 基于FLUS和InVEST模型的云贵高原土地利用与生态系统服务时空变化多情景模拟研究[J]. 水土保持研究, 2024, 31(3): 287-298.

[56] 任胤铭, 刘小平, 许晓聪, 等. 基于FLUS-InVEST模型的京津冀多情景土地利用变化模拟及其对生态系统服务功能的影响[J]. 生态学报, 2023, 43(11): 4473-4487.

[57] Bennett E M, Peterson G D, Gordon L J. Understanding relationships among multiple ecosystem services[J]. Ecology letters, 2009, 12(12): 1394-1404.

[58] Zhang L, Lei J R, Chen Z Z, et al. Spatiotemporal Differentiation and Trade-offs and Synergies of Ecosystem Services in Tropical Island Basins: A Case Study of Three Major Basins of Hainan Island[J]. Journal of Cleaner Production, 2025: 144798.

[59] Qiu J X, Carpenter S R, Booth E G, et al. Understanding relationships among ecosystem services across spatial scales and over time[J]. Environmental Research Letters, 2018, 13(5): 054020.

[60] Zhou J, Yang J, Tang Z S, et al. Driving mechanisms of ecosystem services and their trade-offs and synergies in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau[J]. Ecological Indicators, 2025, 171: 113148.

[61] 黄贤凤, 勾容, 苏维词. 贵州省生态系统服务权衡/协同与簇情景模拟[J]. 中国环境科学, 2025, 45(2): 966-980.

[62] 戴尔阜, 王晓莉, 朱建佳, 等. 生态系统服务权衡:方法、模型与研究框架[J]. 地理研究, 2016, 35(6): 1005-1016.

[63] Li H, Xu Q, Qiu H Y, et al. Study on Ecosystem Service Trade-Offs and Synergies in the Guangdong–Hong Kong–Macao Greater Bay Area Based on Ecosystem Service Bundles[J]. Land, 2024, 13(12): 2086.

[64] Qi Y, Lian X H, Wang H W, et al. Dynamic mechanism between human activities and ecosystem services: A case study of Qinghai lake watershed, China[J]. Ecological Indicators, 2020, 117: 106528.

[65] Wang H, Sun Q. Urban Sprawl and Imbalance between Supply and Demand of Ecosystem Services: Evidence from China’s Yangtze River Delta Urban Agglomerations[J]. Sustainability, 2024, 16(18): 8269.

[66] 孙才志, 于涵, 郝帅. 辽河流域生态系统服务权衡-协同效应及驱动因素[J]. 生态学报, 2025, (6): 1-16.

[67] Li S Y, Zhou Y Y, Yue D X, et al. Scenario Simulation of Ecosystem Services Based on Land Use/Land Cover Change in the Bailong River Basin, in China[J]. Land, 2024, 14(1): 25.

[68] Fang L L, Wang L C, Chen W X, et al. Identifying the impacts of natural and human factors on ecosystem service in the Yangtze and Yellow River Basins[J]. Journal of Cleaner Production, 2021, 314: 127995.

[69] 杨君, 唐兴隆, 袁淑君, 等. 湖南省多尺度生态系统服务供需关系及影响因素[J]. 水土保持通报, 2023, 43(6): 272-281.

[70] 景海超, 刘颖慧, 贺佩, 等. 青藏高原典型区生态系统服务空间异质性及其影响因素——以那曲市为例[J]. 生态学报, 2022, 42(7): 2657-2673.

[71] 汪仕美, 靳甜甜, 燕玲玲, 等. 子午岭区生态系统服务权衡与协同变化及其影响因素[J]. 应用生态学报, 2022, 33(11): 3087-3096.

[72] Gao C L, Hu B Q, Wang Z C, et al. Study on spatiotemporal changes of ecosystem service trade-offs/synergies and driving mechanisms in the key zone of mountain-river-sea coupling: A case study of the southwest Guangxi Karst-Beibu Gulf[J]. Ecological Indicators, 2024, 169: 112892.

[73] 超宝, 赵媛媛, 武海岩, 等. 2000—2020年蒙古高原生态系统服务及其对气候因子的响应[J]. 干旱区地理, 2024, 47(9): 1577-1586.

[74] 安健吉, 员学锋, 杨悦, 等. 退耕还林还草工程对陕北地区生态系统服务的影响[J/OL]. 环境科学, 2025-01-15.

[75] 王玉纯, 赵军, 付杰文. 退耕还林还草工程对干旱区内陆河流域生态系统服务的影响[J]. 生态科学, 2021, 40(6): 56-66.

[76] 石晶, 石培基, 李雪红, 等. 石羊河流域生态系统服务时空变化及其多尺度影响因素研究[J]. 地理科学进展, 2024, 43(2): 276-289.

[77] 刘康, 张寒, 张道军, 等. 基于PLUS-InVEST-Geodector模型的黄土高原生态系统碳储量情景模拟与驱动因素[J/OL]. 中国环境科学, 2025-01-06.

[78] 马世瑛, 陈英, 裴婷婷, 等. 黄土高原生态系统服务时空变化及权衡与协同研究[J]. 甘肃农业大学学报, 2023, 58(6): 189-200.

[79] 李妙宇, 上官周平, 邓蕾. 黄土高原地区生态系统碳储量空间分布及其影响因素[J]. 生态学报, 2021, 41(17): 6786-6799.

[80] 薛曾辉. 黄土高原生态系统服务功能时空变化及生态综合分区研究[D]. 咸阳: 西北农林科技大学, 2024.

[81] 刘洋. 黄河流域生态系统服务及其权衡协同关系特征[D]. 咸阳: 西北农林科技大学, 2023.

[82] 李朋飞, 黄霖霖, 臧宇哲, 等. 黄土高原彬长矿区土壤侵蚀特征及其与环境变化的关系[J]. 农业工程学报, 2024, 40(5): 158-167.

[83] 胡雪丽, 徐凌, 张树深. 基于 CA-Markov 模型和多目标优化的大连市土地利用格局[J]. 应用生态学报, 2013, 24(6): 1652.

[84] 赵荣钦, 黄贤金, 钟太洋, 等. 区域土地利用结构的碳效应评估及低碳优化[J]. 农业工程学报, 2013, 29(17): 220-229.

[85] 周嘉, 王钰萱, 刘学荣, 等. 基于土地利用变化的中国省域碳排放时空差异及碳补偿研究[J]. 地理科学, 2019, 39(12): 1955-1961.

[86] Quarmby N, Milnes M, Hindle T, et al. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction[J]. International Journal of Remote Sensing, 1993, 14(2): 199-210.

[87] Benavidez R, Jackson B, Maxwell D, et al. A review of the (Revised) Universal Soil Loss Equation ((R) USLE): With a view to increasing its global applicability and improving soil loss estimates[J]. Hydrology and Earth System Sciences, 2018, 22(11): 6059-6086.

[88] 章文波, 谢云, 刘宝元. 利用日雨量计算降雨侵蚀力的方法研究[J]. 地理科学, 2002, 22(6): 705-711.

[89] Sharpley A N, Williams J R. EPIC-Erosion/productivity impact calculator. I: model documentation. II: user manual[J]. Technical Bulletin-United States Department of Agriculture, 1990, (1768).

[90] Mccool D K, Brown L C, Foster G, et al. Revised slope steepness factor for the Universal Soil Loss Equation[J]. Transactions of the ASAE, 1987, 30(5): 1387-1396.

[91] Liu B Y, Nearing M A, Risse L. Slope gradient effects on soil loss for steep slopes[J]. Transactions of the ASAE, 1994, 37(6): 1835-1840.

[92] 符素华, 刘宝元, 周贵云, 等. 坡长坡度因子计算工具[J]. 中国水土保持科学, 2015, 13(5): 105-110.

[93] 蔡崇法, 丁树文, 史志华, 等. 应用 USLE 模型与地理信息系统 IDRISI 预测小流域土壤侵蚀量的研究[J]. 水土保持学报, 2000, 14(2): 19-24.

[94] 耿文广, 诸云强, 陈鹏飞. 黄土高原逐年土壤侵蚀模数1-km栅格数据集(2001-2015)[J]. 全球变化数据学报 (中英文), 2022, 6(1): 85-92.

[95] 吴卿. 长三角生态系统服务多尺度供需格局、影响因素与优化管理策略[D]. 杭州: 浙江大学, 2023.

[96] Wang X J, Liu G X, Xiang A C, et al. Terrain gradient response of landscape ecological environment to land use and land cover change in the hilly watershed in South China[J]. Ecological Indicators, 2023, 146: 109797.

[97] Li Z H, Xia J, Deng X Z, et al. Multilevel modelling of impacts of human and natural factors on ecosystem services change in an oasis, Northwest China[J]. Resources, Conservation and Recycling, 2021, 169: 105474.

[98] Li R N, Zheng H, O’connor P, et al. Time and space catch up with restoration programs that ignore ecosystem service trade-offs[J]. Science Advances, 2021, 7(14): eabf8650.

[99] Fang Z, Ding T H, Chen J Y, et al. Impacts of land use/land cover changes on ecosystem services in ecologically fragile regions[J]. Science of the Total Environment, 2022, 831: 154967.

[100]Feng X M, Fu B J, Piao S L, et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits[J]. Nature Climate Change, 2016, 6(11): 1019-1022.