海河典型流域气候变化和植被恢复对水文的影响——以张家口清水河流域为例

doi:10.11707/j.1001-7488.LYKX20240259

摘要/Abstract

摘要：

目的: 量化气候变化及大规模植被恢复对流域水文的影响，以期实现考虑流域水资源和生态效益权衡的林草优化配置。方法: 以海河流域的植被恢复典型子流域清水河为对象，基于半分布式水文模型（soil and water assessment tool，SWAT）和情景模拟，定量评估气候变化和植被恢复对水文的影响。结果: 1）1969—2015年，清水河流域年均降水量呈不显著减少趋势（P > 0.05），每10年减少1.70 mm；年均气温呈显著上升趋势（P < 0.01），每10年增加0.30 ℃；年潜在蒸散呈不显著增加趋势（P > 0.05），每10年增加7.30 mm；年径流深呈显著下降趋势（P < 0.01），每10年减少5.80 mm。2）1985年与2015年两期土地利用相比发现，草地恢复为林地和耕地恢复为草地是主要植被恢复形式。3）率定后的SWAT模型可以较好地模拟清水河流域的水文变化，率定期和验证期的纳什效率系数NSE分别为0.69和0.77，决定系数R²分别为0.69和0.77，百分比偏差PBIAS分别为–0.40%和–6.00%，均方根差与标准差的比值RSR分别为0.56和0.48。4）与基准期（1984—1993年）相比，植被恢复期（2006—2015年）的流域地表径流和壤中流因气候变化分别增加0.31 和0.08 mm，因植被恢复分别减少1.43和0.46 mm，气候变化和植被恢复使流域土壤含水量减少且贡献分别为42.72%和57.28%，使流域实际蒸散增加且贡献分别为87.47%和12.53%。5）草地造林后实际年蒸散增加12.62 mm，年地表径流、壤中流和土壤含水量分别减少6.47、3.38和10.06 mm；退耕还草后年地表径流减少8.11 mm，实际年蒸散量、壤中流和土壤含水量分别增加7.27、1.21和4.37 mm。结论: 清水河流域不同水文指标对气候变化和植被恢复的响应不同，植被恢复是地表径流、壤中流、土壤含水量减少的主要驱动因素，实际蒸散增加的主要驱动因素是气候变化；不同植被恢复类型对水文的影响有显著差异，草地恢复为林地后耗水增加，退耕还草后可以保留更多降水。在水资源紧缺地区开展植被恢复，需要充分权衡不同植被类型的水文调节功能，从而制定可持续的适应性流域管理措施。

关键词: 气候变化, 植被恢复, SWAT模型, 水文指标, 定量归因

Abstract:

Objective: Quantify the impacts of climate change and large-scale vegetation restoration on watershed hydrology, with the aim of achieving optimized allocation of forest and grass that balances water resource availability and ecological benefits in the watershed. Methods: Based on the semi-distributed hydrological model ( soil and water assessment tool, SWAT ) and scenario simulation, the impacts of climate change and vegetation restoration on hydrology were quantitatively evaluated in Qingshuihe watershed, a typical watershed of vegetation restoration in Haihe basin. Result: 1) From 1969 to 2015, the average annual precipitation in Qingshuihe watershed showed an insignificant decreasing trend ( P > 0.05 ), with a decrease of 1.70 mm every 10 years. The average annual air temperature showed a significantly increasing trend (P < 0.01 ), with an increase of 0.30 ℃ every 10 years. The annual potential evapotranspiration showed an insignificant increasing trend ( P > 0.05), with an increase of 7.30 mm every 10 years. The annual runoff depth showed a significant decreasing trend ( P < 0.01 ), with a decrease of 5.80 mm every 10 years. 2) Comparing landuse between 1985 and 2015, grassland to forest and cropland to grassland are the main types of vegetation restoration. 3) The calibrated SWAT model can better simulate the hydrological changes in Qingshuihe watershed. The Nash-Sutcliffe efficiency coefficients (NSE) are 0.69 and 0.77, coefficients of determination (R²) are 0.69 and 0.77, percentage bias (PBIAS) are –0.40% and –6.00%, and ratios of the root mean square error to the standard deviation (RSR) are 0.56 and 0.48 for the calibration period and validation period, respectively. 4) Compared with the base period (1984–1993), the surface runoff and lateral flow increased by 0.31 and 0.08 mm due to climate change, and decreased by 1.43 and 0.46 mm due to vegetation restoration in the scenario period (2006–2015). Climate change and vegetation restoration respectively decreased soil water content by 42.72% and 57.28%, while increasing actual evapotranspiration by 87.47% and 12.53%, respectively. 5) After grassland restoration, the actual annual evapotranspiration increased by 12.62 mm, the annual surface runoff, lateral flow and soil water content decreased by 6.47, 3.38 and 10.06 mm, respectively. After restoring cropland to grassland, the annual surface runoff decreased by 8.11 mm, and the actual annual evapotranspiration, lateral flow and soil water content increased by 7.27, 1.21 and 4.37 mm, respectively. Conclusion: Different hydrological parameters responded differently to climate change and vegetation restoration in Qingshuihe watershed. Vegetation restoration is the main driving factor for the reduction of surface runoff, lateral flow, and soil water content, and the main driving factor for the increase of actual evapotranspiration is climate change. The impacts of different vegetation restoration types on hydrology are significantly different. After the grassland was restored to forest, the water consumption increased, and more precipitation can be retained after restoring cropland to grassland. In regions with water scarcity, it is necessary to essential to carefully balance the hydrological regulation functions of different vegetation types to develop sustainable and adaptive watershed management strategies.

Key words: climate change, vegetation restoration, SWAT model, hydrological parameters, quantitative attribution

中图分类号:

S157

崔妞妞,庞建壮,张祎帆,许行,张勤,张志强. 海河典型流域气候变化和植被恢复对水文的影响——以张家口清水河流域为例[J]. 林业科学, 2025, 61(3): 38-49.

Niuniu Cui,Jianzhuang Pang,Yifan Zhang,Hang Xu,Qin Zhang,Zhiqiang Zhang. Impacts of Climate Change and Vegetation Restoration on Hydrology in a Typical Watershed of Haihe Basin: A Case Study of Qingshuihe Watershed in Zhangjiakou[J]. Scientia Silvae Sinicae, 2025, 61(3): 38-49.

图/表 9

图1

图2

表1

表2

图3

图4

图5

表3

图6

参考文献 0

	阿里木江·卡斯木, 张雪玲, 梁洪武. 天山北坡城市群季节性地表温度与景观格局空间关系分析. 地理研究, 2024, 43 (5): 1267- 1287. doi: 10.11821/dlyj020230795
	Kasimu A L M J, Zhang X L, Liang H W. The spatial relationship between seasonal surface temperature and landscape pattern of the urban agglomeration on the northern slope of the Tianshan mountains. Geographical Research, 2024, 43 (5): 1267- 1287. doi: 10.11821/dlyj020230795
	曹文旭. 2022. 潮河流域水循环演变规律及其对气候变化的响应预估. 北京: 北京林业大学.
	Cao W X. 2022. Evolution of the water cycle and its response to predicted climate change in the Chaohe watershed. Beijing: Beijing Forestry University. ［in Chinese］
	曹文旭, 张志强, 查同刚, 等. 基于Budyko假设的潮河流域气候和植被变化对实际蒸散发的影响研究. 生态学报, 2018, 38 (16): 5750- 5758.
	Cao W X, Zhang Z Q, Zha T G, et al. Exploring the effects of vegetation dynamics and climate changes on the Chaohe watershed actual evapotranspiration—Budyko hypothesis approach. Acta Ecologica Sinica, 2018, 38 (16): 5750- 5758.
	丁相毅, 贾仰文, 王　浩, 等. 气候变化对海河流域水资源的影响及其对策. 自然资源学报, 2010, 25 (4): 604- 613.
	Ding X Y, Jia Y W, Wang H, et al. Impacts of climate change on water resources in the Haihe river basin and corresponding countermeasures. Journal of Natural Resources, 2010, 25 (4): 604- 613.
	冯　棋, 杨　磊, 王　晶, 等. 黄土丘陵区植被恢复的土壤碳水效应. 生态学报, 2019, 39 (18): 6598- 6609.
	Feng Q, Yang L, Wang J, et al. Response of soil moisture and soil organic carbon to vegetation restoration in deep soil profiles in Loess Hilly region. Acta Ecologica Sinica, 2019, 39 (18): 6598- 6609.
	郭军庭, 张志强, 王盛萍, 等. 应用SWAT模型研究潮河流域土地利用和气候变化对径流的影响. 生态学报, 2014, 34 (6): 1559- 1567.
	Guo J T, Zhang Z Q, Wang S P, et al. Appling SWAT model to explore the impact of changes in land use and climate on the streamflow in a watershed of Northern China. Acta Ecologica Sinica, 2014, 34 (6): 1559- 1567.
	郭伟伟. 2012. 张家口地区植被覆盖度动态分析研究. 北京: 北京林业大学.
	Guo W W. 2012. Vegetable coverage dynamic research and analysis in Zhangjiakou. Beijing: Beijing Foresty University. ［in Chinese］
	侯金龙, 马志强, 杨　澄, 等. 京津冀地区植被碳源/汇的时空变化特征及影响因素分析. 生态环境学报, 2024, 33 (9): 1329- 1338.
	Hou J L, Ma Z Q, Yang C, et al. Analysis of spatio-temporal variation of vegetation carbon sources and sinks in the Beijing-Tianjin-Hebei region and influencing factors. Ecology and Environmental Sciences, 2024, 33 (9): 1329- 1338.
	李发文, 陶仁杰. 变化环境下子牙河流域水文过程影响要素分析. 水资源保护, 2023, 39 (5): 9- 17. doi: 10.3880/j.issn.1004-6933.2023.05.002
	Li F W, Tao R J. Analysis of factors influencing hydrological processes in the Ziya River basin under changing environment. Water Resources Protection, 2023, 39 (5): 9- 17. doi: 10.3880/j.issn.1004-6933.2023.05.002
	李　赟, 徐　利, 李雅丽, 等. 2023. 首都水源涵养区水环境因子空间变化特征研究——以清水河和白河流域为例. 河北建筑工程学院学报, 41(4): 156−165.
	Li Y, Xu L, Li Y L, et al. 2023. Study on the spatial variation characteristics of waterenvironment factors in the capital water conservation areas : a case study in the Qingshui River and the Bai River basins. Journal of Hebei Institute of Architecture and Civil Engineering, 41(4): 156−165. ［in Chinese］
	李　卓, 孙然好, 张继超, 等. 京津冀城市群地区植被覆盖动态变化时空分析. 生态学报, 2017, 37 (22): 7418- 7426.
	Li Z, Sun R H, Zhang J C, et al. Temporal-spatial analysis of vegetation coverage dynamics in Beijing-Tianjin-Hebei metropolitan regions. Acta Ecologica Sinica, 2017, 37 (22): 7418- 7426.
	蔺星娜, 牛健植, 贾京伟, 等. 张家口清水河上游流域植被指数时空变化特征. 中国水土保持科学, 2018, 16 (1): 123- 130.
	Lin X N, Niu J Z, Jia J W, et al. Spatial-temporal variation of vegetation in Qingshui River basin, Zhangjiakou. Science of Soil and Water Conservation, 2018, 16 (1): 123- 130.
	娄淑兰, 刘目兴, 易　军, 等. 三峡山地不同类型植被和坡位对土壤水文功能的影响. 生态学报, 2019, 39 (13): 4844- 4854.
	Lou S L, Liu M X, Yi J, et al. Influence of vegetation coverage and topographic position on soil hydrological function in the hillslope of the three gorges area. Acta Ecologica Sinica, 2019, 39 (13): 4844- 4854.
	裴宏伟, 杨　佳, 张红娟, 等. 变化环境下清水河流域径流演变特征及驱动力. 南水北调与水利科技(中英文), 2020, 18 (2): 1- 13.
	Pei H W, Yang J, Zhang H J, et al. Characteristics and driving factors of runoff evolution for the Qingshui River watershed under changing environment. South-to-North Water Transfers and Water Science& Technology (Chinese and English), 2020, 18 (2): 1- 13.
	史晓亮, 李　颖, 赵　凯, 等. 诺敏河流域植被覆盖时空演变及其与径流的关系研究. 干旱区资源与环境, 2013, 27 (6): 54- 60.
	Shi X L, Li Y, Zhao K, et al. Temporal and spatial changes of vegetation cover and the relationship with runoff in Nuomin River basin. Journal of Arid Land Resources and Environment, 2013, 27 (6): 54- 60.
	石彦琴, 陈源泉, 隋　鹏, 等. 农田土壤紧实的发生、影响及其改良. 生态学杂志, 2010, 29 (10): 2057- 2064.
	Shi Y Q, Chen Y Q, Sui P, et al. Cropland soil compaction: its casuses influences and improvement. Chinese Journal of Ecology, 2010, 29 (10): 2057- 2064.
	石智宇, 赵　清, 王雅婷, 等. 沂河流域植被覆盖时空演变及其与径流的关系研究. 水土保持研究, 2023, 30 (1): 54- 61.
	Shi Z Y, Zhao Q, Wang Y T, et al. Temporal and spatial evolution of vegetation cover and its relationship with runoff in Yihe river basin. Research of Soil and Water Conservation, 2023, 30 (1): 54- 61.
	索立柱, 黄明斌, 段良霞, 等. 黄土高原不同土地利用类型土壤含水量的地带性与影响因素. 生态学报, 2017, 37 (6): 2045- 2053.
	Suo L Z, Huang M B, Duan L X, et al. Zonal pattern of soil moisture and its influencing factors under different land use types on the Loess Plateau. Acta Ecologica Sinica, 2017, 37 (6): 2045- 2053.
	杨　佳. 2020. 变化环境下清水河流域水文过程演变及驱动机制研究. 河北: 河北建筑工程学院.
	Yang J. 2020. Characteristics and driving factors of runoff evolution for the Qingshui River watershed under changing environment. Hebei: Hebei University of Architecture. ［in Chinese］
	姚美初. 2023. SWAT 模型参数的不确定性分析与优化方法及其在石头口门流域应用. 吉林: 吉林大学.
	Yao M C. 2023. Uncertainty analysis and optimization method for SWAT model parameters and its application to the Shitoukoumen basin. Jilin: Jilin University. ［in Chinese］
	张步峰, 张　良, 原　彪. 张家口市清水河流域综合治理研究. 河北建筑工程学院学报, 2010, 28 (2): 67- 69. doi: 10.3969/j.issn.1008-4185.2010.02.022
	Zhang B F, Zhang L, Yuan B. A study on comprehensive harnessing of Qingshui River basin in Zhangjiakou City. Journal of Hebei Institute of Architecture and Civil Engineering, 2010, 28 (2): 67- 69. doi: 10.3969/j.issn.1008-4185.2010.02.022
	张树磊. 2018. 中国典型流域植被水文相互作用机理及变化规律研究. 北京: 清华大学.
	Zhang S L. 2018. Explore the interactins between vegetation and hydroligy under a changing environment in Chinese typical basins. Beijing: Tsinghua University. ［in Chinese］
	张永强, 孔冬冬, 张选泽, 等. 2003—2017年植被变化对全球陆面蒸散发的影响. 地理学报, 2021, 76 (3): 584- 594. doi: 10.11821/dlxb202103007
	Zhang Y Q, Sun D D, Zhang X Z, et al. Impacts of vegetation changes on global evapotranspiration in the period 2003−2017. Acta Geographica Sinice, 2021, 76 (3): 584- 594. doi: 10.11821/dlxb202103007
	张占贵, 郭　勇, 郭丽峰. 2008. 张家口市水生态环境现状与保护对策. 海河水利, (1): 18−22.
	Zhang Z G, Guo Y, Guo L F. 2008. Status and countermeasures of water entironment in Zhangjiakou City. Haihe Water Resources, (1): 18−22. ［in Chinese］
	赵　阳, 余新晓, 郑江坤, 等. 气候和土地利用变化对潮白河流域径流变化的定量影响. 农业工程学报, 2012, 28 (22): 252- 260. doi: 10.3969/j.issn.1002-6819.2012.22.035
	Zhao Y, Yu X X, Zheng J K, et al. Quantitative effects of climate variations and land-use changes on annual streamflow in Chaobai River basin. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28 (22): 252- 260. doi: 10.3969/j.issn.1002-6819.2012.22.035
	赵宇寒, 曹建生, 朱春雨, 等. 壤中流形成机制及其生态水文效应研究进展. 中国生态农业学报(中英文), 2022, 30 (1): 38- 46. doi: 10.12357/cjea.20210277
	Zhao Y H, Cao J S, Zhu C Y, et al. Research progress on the formation mechanism of subsurface flow and its eco-hydrological effects. Chinese Journal of Eco-Agriculture (Chinese and English), 2022, 30 (1): 38- 46. doi: 10.12357/cjea.20210277
	Arnold J G, Allen P M, Volk M, et al. Assessment of different representations of spatial variability on SWAT model performance. Transactions of the ASABE, 2010, 53 (5): 1433- 1443. doi: 10.13031/2013.34913
	Arnold J G, Moriasi D N, Gassman P W, et al. SWAT: model use, calibration, and validation. Transactions of the ASABE, 2012, 55 (4): 1491- 1508. doi: 10.13031/2013.42256
	Athira P, Nanda C, Sudheer K P. A computationally efficient method for uncertainty analysis of SWAT model simulations. Stochastic Environmental Research and Risk Assessment, 2018, 32 (6): 1479- 1492. doi: 10.1007/s00477-018-1538-9
	Chen L D, Huang Z L, Gong J, et al. The effect of land cover/vegetation on soil water dynamic in the hilly area of the Loess Plateau, China. Catena, 2007, 70 (2): 200- 208. doi: 10.1016/j.catena.2006.08.007
	Katul G G, Oren R, Manzoni S, et al. 2012, Evapotranspiration: a process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system. Reviews of Geophysics, 50(3): 2011RG000366.
	Levis S, Foley J A, Pollard D. Large-scale vegetation feedbacks on a doubled CO₂ climate. Journal of Climate, 2000, 13 (7): 1313- 1325. doi: 10.1175/1520-0442(2000)013<1313:LSVFOA>2.0.CO;2
	Li D H, Liu K, Wang S D, et al. Four decades of hydrological response to vegetation dynamics and anthropogenic factors in the Three-North Region of China and Mongolia. Science of the Total Environment, 2023, 857, 159546. doi: 10.1016/j.scitotenv.2022.159546
	Lin W P. Monitoring and protection of forest ecological tourism resources by dynamic monitoring system. Ecological Chemistry and Engineering S-chemia I Inzynieria Ekologiczna S, 2019, 26 (1): 189- 197.
	Lu L H, Xu Y Q, Huang A, et al. Influences of topographic factors on outcomes of forest programs and policies in a mountain region of China: a case study. Mountain Research and Development, 2020, 40 (1): R48- R60.
	Mango L M, Melesse A M, McClain M E, et al. Land use and climate change impacts on the hydrology of the upper Mara River basin, Kenya: results of a modeling study to support better resource management. Hydrology and Earth System Sciences, 2011, 15 (7): 2245- 2258. doi: 10.5194/hess-15-2245-2011
	Meng F H, Liu T, Wang H, et al. An alternative approach to overcome the limitation of HRUs in analyzing hydrological processes based on land use/cover change. Water, 2018, 10 (4): 434. doi: 10.3390/w10040434
	Mengistu A G, Woldesenbet T A, Dile Y T, et al. Modeling impacts of projected land use and climate changes on the water balance in the Baro basin, Ethiopia. Heliyon, 2023, 9 (3): e13965. doi: 10.1016/j.heliyon.2023.e13965
	Moriasi D N, Arnold J G, Van Liew M W, et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 2007, 50 (3): 885- 900. doi: 10.13031/2013.23153
	Nie W M, Yuan Y P, Kepner W, et al. Assessing impacts of landuse and landcover changes on hydrology for the upper San Pedro watershed. Journal of Hydrology, 2011, 407 (1-4): 105- 114. doi: 10.1016/j.jhydrol.2011.07.012
	Pan T S, Zuo L J, Zhang Z X, et al. Impact of land use change on water conservation: a case study of Zhangjiakou in Yongding River. Sustainability, 2021, 13 (1): 22.
	Pang J Z, Zhang H L, Xu Q X, et al. Hydrological evaluation of open-access precipitation data using SWAT at multiple temporal and spatial scales. Hydrology and Earth System Sciences, 2020, 24 (7): 3603- 3626. doi: 10.5194/hess-24-3603-2020
	Pereira D R, Almeida A Q, Martinez M A, et al. Impacts of deforestation on water balance components of a watershed on the Brazilian East Coast. Revista Brasileira De Ciência Do Solo, 2014, 38 (4): 1350- 1358.
	Pereira D R, Martinez M A, Pruski F F, et al. Hydrological simulation in a basin of typical tropical climate and soil using the SWAT model part I: calibration and validation tests. Journal of Hydrology: Regional Studies, 2016, 7, 14- 37. doi: 10.1016/j.ejrh.2016.05.002
	Qubaja R, Amer M, Tatarinov F, et al. Partitioning evapotranspiration and its long-term evolution in a dry pine forest using measurement-based estimates of soil evaporation. Agricultural and Forest Meteorology, 2020, 281, 107831. doi: 10.1016/j.agrformet.2019.107831
	Song X P, Hansen M C, Stehman S V, et al. Global land change from 1982 to 2016. Nature, 2018, 560, 639- 643. doi: 10.1038/s41586-018-0411-9
	Sun G, Zhou G Y, Zhang Z Q, et al. Potential water yield reduction due to forestation across China. Journal of Hydrology, 2006, 328 (3): 548- 558.
	Villarini G, Wasko C. Humans, climate and streamfow. Nature Climate Change, 2021, 11, 725- 726. doi: 10.1038/s41558-021-01137-z
	Wang S P, McVicar T R, Zhang Z Q, et al. Globally partitioning the simultaneous impacts of climate-induced and human-induced changes on catchment streamflow: a review and meta-analysis. Journal of Hydrology, 2020, 590, 125387. doi: 10.1016/j.jhydrol.2020.125387
	Wei X H, Li Q, Zhang M F, et al. Vegetation cover—another dominant factor in determining global water resources in forested regions. Global Change Biology, 2018, 24 (2): 786- 795. doi: 10.1111/gcb.13983
	Woldesenbet T A, Elagib N A, Ribbe L, et al. Hydrological responses to land use/cover changes in the source region of the upper Blue Nile basin, Ethiopia. Science of the Total Environment, 2017, 575, 724- 741. doi: 10.1016/j.scitotenv.2016.09.124
	Yan T Z, Bai J W, Lee Zhi Yi A, et al. SWAT-simulated streamflow responses to climate variability and human activities in the Miyun Reservoir basin by considering streamflow components. Sustainability, 2018, 10 (4): 941. doi: 10.3390/su10040941
	Yang L S, Feng Q, Yin Z L, et al. Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe river, Northwest China. Hydrological Processes, 2017, 31 (5): 1100- 1112. doi: 10.1002/hyp.11098
	Yang L, Zhao G J, Tian P, et al. Runoff changes in the major river basins of China and their responses to potential driving forces. Journal of Hydrology, 2022, 607, 127536. doi: 10.1016/j.jhydrol.2022.127536
	Yang W T, Long D, Bai P. Impacts of future land cover and climate changes on runoff in the mostly afforested river basin in North China. Journal of Hydrology, 2019, 570, 201- 219. doi: 10.1016/j.jhydrol.2018.12.055
	Yin Y Y, Wang L, Wang Z J, et al. 2020. Quantifying water scarcity in Northern China within the context of climatic and societal changes and South‐to‐North Water Diversion. Earth’s Future, 8(8): e2020EF001492.
	Zeng Z Z, Peng L Q, Piao S L. Response of terrestrial evapotranspiration to Earth’s greening. Current Opinion in Environmental Sustainability, 2018, 33, 9- 25. doi: 10.1016/j.cosust.2018.03.001
	Zhang L, Nan Z T, Yu W J, et al. Comparison of baseline period choices for separating climate and land use/land cover change impacts on watershed hydrology using distributed hydrological models. Science of the Total Environment, 2018, 622-623, 1016- 1028. doi: 10.1016/j.scitotenv.2017.12.055
	Zhao G J, Mu X M, Tian P, et al. Climate changes and their impacts on water resources in semiarid regions: a case study of the Wei River basin, China. Hydrological Processes, 2013, 27 (26): 3852- 3863. doi: 10.1002/hyp.9504
	Zhou Y Y, Fu D J, Lu C X, et al. Positive effects of ecological restoration policies on the vegetation dynamics in a typical ecologically vulnerable area of China. Ecological Engineering, 2021, 159, 106087. doi: 10.1016/j.ecoleng.2020.106087
	Zhu Z C, Piao S L, Myneni R B, et al. Greening of the Earth and its drivers. Nature Climate Change, 2016, 6 (8): 791- 795. doi: 10.1038/nclimate3004

参数名称Parameter name	调整方法Adjustment methods	文件格式File format	参数描述Parameter description	初始范围 Initial range		最适范围 Optimum range
参数名称Parameter name	调整方法Adjustment methods	文件格式File format	参数描述Parameter description	最小值Minimum value	最大值Maximum value	最小值Minimum value	最大值Maximum value
CN2	r	.mgt	土壤保护服务的径流曲线数Soil conservation services runoff curve number	–0.5	0.5	0.13	0.22
SOL_Z()	r	.sol	土壤表层到底层深度Depth from soil surface to bottom of layer/mm	–0.5	0.5	–0.12	0.06
CANMX	v	.hru	最大冠层蓄水量Maximum canopy storage/mm	0	100	43.51	61.93
SMFMN	v	.bsn	一年中积雪的最小融化速率（冬至日）Minimum melt rate for snow during the year ( winter solstice )/(mm·℃^–1d^–1)	0	20	2.45	5.17
SOL_K()	r	.sol	土壤饱和导水率Soil saturated hydraulic conductivity/(mm·h^?1)	–0.8	0.8	–0.51	–0.07
ESCO	v	.hru	调整土壤表层蒸发速率的系数Adjusting the coefficient of soil surface evaporation rate	0	1	0.51	0.65
RCHRG_DP	v	.gw	浅层地下水渗透到深层地下水的水量比例Proportion of water percolating from shallow groundwater to deep groundwater	0	1	0.84	0.96
GW_REVAP	v	.gw	地下水返回土壤层的水量比例Proportion of groundwater returning to the soil layer	0.02	0.2	0.04	0.08
SFTMP	v	.bsn	降雪气温Snowfall temperature/℃	–20	20	–6.94	–3.97
CH_N2	v	.rte	主河道曼宁系数Main channel Manning coefficient	–0.01	0.3	0.22	0.27
OV_N	v	.hru	坡面流曼宁系数Manning coefficient of overland flow	0.01	30	3.14	10.11
HRU_SLP	r	.hru	平均坡度Mean slope steepness/(°)	0	1	0.81	0.89
TLAPS	v	.sub	气温递减率Temperature lapse rate/(℃·km^?1)	–10	10	–4.36	–1.76
EPCO	v	.hru	调整植物从土壤中吸收水分效率的系数Adjusting the coefficient of water uptake efficiency of plants from soil	0	1	0.51	0.62

气象、水文指标 Meteorological and hydrological parameters	基准期 Base period（S1）	植被恢复期 Vegetation restoration period（S4）	总变化 Total change	各因素导致的变化量 Changes caused by each factor		相对贡献率 Relative contribution rate(%)
气象、水文指标 Meteorological and hydrological parameters	基准期 Base period（S1）	植被恢复期 Vegetation restoration period（S4）	总变化 Total change	气候变化 Climate change	植被恢复 Vegetation restoration	气候变化 Climate change	植被恢复 Vegetation restoration
降水量Precipitation /mm	433.73	454.37	20.64	—	—	—	—
气温 Air temperature /℃	4.50	5.10	0.60	—	—	—	—
干旱指数Drought index	1.45	1.57	0.12	—	—	—	—
地表径流 Surface runoff /mm	5.09	3.97	?1.12	0.31	?1.43	27.68	?127.68
壤中流 Lateral flow /mm	6.15	5.77	?0.38	0.08	?0.46	21.05	?121.05
0~100 cm土层土壤含水量 0–100 cm soil layer soil water content /mm	30.12	27.99	?2.13	?0.91	?1.22	?42.72	?57.28
实际蒸散发 Actual evapotranspiration /mm	418.04	436.56	18.52	16.20	2.32	87.47	12.53

[1]	黄云,徐黎亮,郑博福,宋旭,胡方清,朱锦奇,万炜. 亚热带典型森林生产力及碳利用率的气候变化响应[J]. 林业科学, 2025, 61(3): 121-134.
[2]	张雨田,石军南,张怀清,吴炳伦. 洞庭湖湿地植被时空动态及其驱动力分析[J]. 林业科学, 2024, 60(8): 1-13.
[3]	朱教君,王高峰,张怀清,高添. 关于“气候智慧林业”研究的思考[J]. 林业科学, 2024, 60(7): 1-7.
[4]	秦坤蓉,秦华,林立,王海洋. 种子功能性状在道路边坡植物群落形成过程中的作用[J]. 林业科学, 2024, 60(3): 141-149.
[5]	赵杼祺, 胡振宏, 何鲜, 黄志群. 森林木质残体微生物群落构建机制研究进展[J]. 林业科学, 2024, 60(2): 106-117.
[6]	申佳艳,范泽鑫,张慧,彭新华,李金花,余潇,杨文雄,李云芳,李新宇,刘悦宁,苏建荣. 云南3种松树径向生长的气候因子响应异质性[J]. 林业科学, 2024, 60(11): 48-62.
[7]	葛婉婷,刘莹,赵智佳,张珅,李洁,杨桂娟,曲冠证,王军辉,麻文俊. 不同气候情景下黄心梓木在我国的潜在适生区预测[J]. 林业科学, 2024, 60(11): 63-74.
[8]	刘磊,赵立娟,刘佳奇,张辉盛,张志伟,黄瑞芬,高瑞贺. 基于优化的MaxEnt模型预测气候变化下松褐天牛在我国的潜在适生区[J]. 林业科学, 2024, 60(11): 139-148.
[9]	薛盼盼,缪宁,岳喜明,陶琼,张远东,冯秋红,毛康珊. 青藏高原东缘岷江冷杉径向生长对升温响应分异的坡向和海拔差异[J]. 林业科学, 2023, 59(7): 65-77.
[10]	韦雪蕾,张国钢,贾茹,姬云瑞,徐红英,杨泽玉,刘化金,刘宇霖,杨培宇. 黑龙江兴凯湖水鸟多样性变化及其影响因素[J]. 林业科学, 2023, 59(6): 118-129.
[11]	王亚,王军辉,王福德,刘逸夫,谭灿灿,袁艳超,聂稳,刘建锋,常二梅,贾子瑞. 末次间冰期以来及未来气候情景下红皮云杉适生分布区模拟[J]. 林业科学, 2023, 59(12): 1-12.
[12]	张淑宁,陈俊兴,敖敦,红梅,张雅茜,包福海,王淋,乌云塔娜,白玉娥,包文泉. 气候变化背景下我国长柄扁桃潜在适生区预测[J]. 林业科学, 2023, 59(12): 25-36.
[13]	司莉青,王明玉,陈锋,舒立福,赵凤君,李伟克. 雷电分布特征与雷击森林火预警[J]. 林业科学, 2023, 59(10): 1-8.
[14]	王爱君,路东晔,张国盛,黄海广,王颖,呼斯楞,敖民. 基于MaxEnt模拟欧亚大陆气候变化下叉子圆柏的潜在分布[J]. 林业科学, 2021, 57(8): 43-55.
[15]	白蕤,李宁,刘少军,陈小敏,邹海平,吕润. 未来气候变化背景下橡胶树白根病在中国的风险分析[J]. 林业科学, 2021, 57(6): 37-45.