增强回归树模型在青藏高原高寒灌丛通量数据插补中的应用

doi:10.17521/cjpe.2021.0259

植物生态学报 ›› 2022, Vol. 46 ›› Issue (12): 1437-1447.DOI: 10.17521/cjpe.2021.0259

所属专题：生态学研究的方法和技术；青藏高原植物生态学：生态系统生态学；生态系统碳水能量通量

• 中国典型生态脆弱区碳水通量过程研究专题论文 • 上一篇下一篇

增强回归树模型在青藏高原高寒灌丛通量数据插补中的应用

李红琴¹, 张亚茹¹, 张法伟²^,³^,^*(), 马文婧⁴^,⁵, 罗方林³, 王春雨³, 杨永胜²^,³, 张雷明⁶, 李英年²^,³

¹洛阳师范学院生命科学学院, 河南洛阳 471934
²中国科学院三江源国家公园研究院, 西宁 810008
³中国科学院西北高原生物研究所高原生物适应与进化重点实验室, 西宁 810008
⁴成都信息工程大学大气科学学院, 成都 610225
⁵海北藏族自治州气象局, 青海海北 810200
⁶中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室, 北京 100101

收稿日期:2021-07-13 接受日期:2021-12-03 出版日期:2022-12-20 发布日期:2023-01-13
通讯作者: *张法伟(mywing963@126.com)
基金资助:
国家重点研发计划(2017YFA0604801);国家重点研发计划(2017YFA0604802);中国科学院-青海省人民政府三江源国家公园联合研究专项(LHZX-2020-07);科技部基础资源调查专项(2019FY101300);国家自然科学基金(41730752);国家自然科学基金(41877547)

Application of boosted regression trees for the gap-filling to flux dataset in an alpine scrubland of Qingzang Plateau

LI Hong-Qin¹, ZHANG Ya-Ru¹, ZHANG Fa-Wei²^,³^,^*(), MA Wen-Jing⁴^,⁵, LUO Fang-Lin³, WANG Chun-Yu³, YANG Yong-Sheng²^,³, ZHANG Lei-Ming⁶, LI Ying-Nian²^,³

¹College of Life Sciences, Luoyang Normal University, Luoyang, Henan 471934, China
²Institute of Sanjiangyuan National Park, Chinese Academy of Sciences, Xining 810008, China
³Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
⁴School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
⁵Meteorological Bureau of Haibei Zangzu Autonomous Prefecture, Haibei, Qinghai 810200, China
⁶Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received:2021-07-13 Accepted:2021-12-03 Online:2022-12-20 Published:2023-01-13
Contact: *ZHANG Fa-Wei(mywing963@126.com)
Supported by:
National Key R&D Program of China(2017YFA0604801);National Key R&D Program of China(2017YFA0604802);Chinese Academy of Sciences-People’s Government of Qinghai Province Joint Grant on Sanjiangyuan National Park Research(LHZX-2020-07);Special Foundation for National Science and Technology Basic Research Program of China(2019FY101300);National Natural Science Foundation of China(41730752);National Natural Science Foundation of China(41877547)

摘要/Abstract

摘要：

涡度相关技术连续观测的碳水通量是准确评估生态系统固碳持水等生态功能的重要基础数据, 由于通量观测数据的缺失十分常见且比例较高, 引入现代机器学习算法以发展缺失数据的插补方法对降低研究结果不确定性具有重要意义。该研究利用青藏高原东北隅高寒金露梅(Potentilla fruticosa)灌丛已发布的2003-2005年水、热、CO₂通量数据集, 结合气温、大气水汽压、风速、太阳短波辐射、表层土壤温度和表层土壤含水量等主要环境因子构建了增强回归树模型(BRT)以插补缺失通量数据, 并与中国通量观测研究联盟(ChinaFLUX)的数据序列进行了比对, 以评估BRT在通量数据集成分析中的应用。BRT对大样本(N > 10 000)通量数据具有较好的模拟效果, 观测值与模拟值的回归斜率为1.01-1.05 (R²> 0.80)。BRT表明植被生长季(5-10月)白天30 min净CO₂交换量(NEE)主要受控于太阳短波辐射和大气水汽压, 二者对NEE变异的相对贡献之和为74.7%。表层土壤温度是生长季夜间及非生长季全天30 min NEE的主要驱动因子, 其相对贡献为68.5%。30 min显热通量(H)和潜热通量(LE)均主要受控于太阳短波辐射(相对贡献大于58.6%)。BRT插补的30 min缺失通量数据均显著小于ChinaFLUX的插补结果。除逐日NEE无显著差异外(p = 0.14), BRT的逐日生态系统总交换(GEE)、生态系统呼吸(RES)、H和LE极显著小于ChinaFLUX的数据序列分别约17.5%、21.0%、2.7%和2.2%, 但由于量级差异较小, 二者具有较高的一致性(数据序列的回归斜率在0.95-1.17)。除逐月GEE和RES外, BRT的逐月NEE、H和LE与ChinaFLUX的数据序列无显著(p > 0.09)差异。相对于ChinaFLUX数据插补方法, BRT不需要复杂的数学表达就可模拟主要环境因子的非线性作用特征, 从而进行缺失通量数据的插补, 是通量数据集成分析的一种可行方法。

关键词: 高寒灌丛, 涡度相关技术, 通量数据插补, 增强回归树模型

Abstract:

Aims The continuous observation datasets of water, heat, and carbon fluxes measured by the eddy covariance technique are important basis for accurate assessment of regional carbon sequestration and water-holding capacity. However, the rate of gaps in flux datasets is high and common due to various reasons, and different gap-filling methods increase the uncertainties of the related studies. The aim of this study is to introduce and test the applicability of boosted regression trees model (BRT), one of the up-to-date machine learning algorithms, for the gap- filling to flux datasets.

Methods Based on the published valid dataset of water, heat and CO₂ flux, and main environmental factors, including air temperature, atmospheric water vapor pressure, wind speed, solar shortwave radiation, topsoil temperature, and topsoil water content of an alpine Potentilla fruticosa scrubland on the northeastern Qingzang Plateau from 2003 to 2005, the BRT were trained to fill flux data gaps and the results were compared to those corresponding data serials provided by Chinese Flux Observation and Research Network (ChinaFLUX).

Important findings The results showed that the BRT performed well for a large amount of samples (N > 10 000) and the regression slopes of observation data against predicted value were between 1.01 and 1.05 with R² > 0.80. The BRT revealed that the daytime 30-min CO₂ flux (net ecosystem CO₂ exchange, NEE) in the growing season (i.e., May to October) was mainly controlled by solar shortwave radiation and atmospheric vapor pressure, whose relative contributions to NEE variability were up to 74.7%. The topsoil temperature was the determinant for NEE at night during the growing season and the whole day during the non-growing season, and its relative contribution was 68.5%. The 30-min sensible heat flux (H) and latent heat flux (LE) were both linearly related to solar radiation, and their relative contributions were above 58.6%. 30-min flux data gap amount filled by the BRT was significantly less than those by ChinaFLUX. Except for daily net ecosystem CO₂ exchange (p = 0.14), daily gross ecosystem CO₂ exchange (GEE), ecosystem respiration (RES), H, and LE of the BRT were significantly less than those of ChinaFLUX by 17.5%, 21.0%, 2.7%, and 2.2%, respectively. However, there was a reasonable consistency between the daily fluxes of 2003-2005 interpolated by the BRT and by ChinaFLUX due to the small magnitude difference (the regression slopes of the two data series were between 0.95 and 1.17). Except for monthly GEE and RES, monthly NEE, H, and LE of the BRT had no significant difference between the BRT and ChinaFLUX (p > 0.09). Compared with the ChinaFLUX gap-filling method, BRT can simulate the nonlinear relationships between fluxes and environmental factors without complicated mathematical expressions and quantify the relative contribution of environmental factors to the flux data gaps, which is a feasible technique for the integrated analysis of flux data.

Key words: alpine scrubland, eddy covariance techniques, flux dataset gap-filling, boosted regression trees

李红琴, 张亚茹, 张法伟, 马文婧, 罗方林, 王春雨, 杨永胜, 张雷明, 李英年. 增强回归树模型在青藏高原高寒灌丛通量数据插补中的应用. 植物生态学报, 2022, 46(12): 1437-1447. DOI: 10.17521/cjpe.2021.0259

LI Hong-Qin, ZHANG Ya-Ru, ZHANG Fa-Wei, MA Wen-Jing, LUO Fang-Lin, WANG Chun-Yu, YANG Yong-Sheng, ZHANG Lei-Ming, LI Ying-Nian. Application of boosted regression trees for the gap-filling to flux dataset in an alpine scrubland of Qingzang Plateau. Chinese Journal of Plant Ecology, 2022, 46(12): 1437-1447. DOI: 10.17521/cjpe.2021.0259

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2021.0259

https://www.plant-ecology.com/CN/Y2022/V46/I12/1437

图/表 8

图1 一个2预测因子(X1和X2)、5叶节点(Y1, Y2, Y3, Y4和Y5)的回归树。t1, t2, t3和t4是最优预测因子的分裂值。

Fig. 1 A regression tree with 5 leaf nodes (Y1, Y2, Y3, Y4 and Y5) of 2 variables (X1 and X2). t1, t2, t3 and t4 are the splitting value of an optimal predictor.

图2 高寒灌丛30 min的净CO2交换量(A)、显热(H)和潜热(LE)通量(B)、气温(C)、大气水汽压(D)、风速(E)、太阳短波辐射(F)、5 cm土壤温度(G)和10 cm土壤含水量(H)的年际动态。

Fig. 2 Interannual variations of 30-min net CO2 exchange (A), sensible heat flux (H)(B), latent heat flux (LE)(B), air temperature (Ta)(C), atmospheric water vapor (D), wind speed (Ws)(E), solar shortwave radiation (Swin)(F), topsoil temperature at 5 cm depth (Ts)(G), and topsoil water content at 10 cm depth (SWC)(H) during 2003 to 2005 in an alpine scrubland.

图3 高寒灌丛生长季白天与其他时段(生长季夜间及非生长季全天)的30 min净CO2交换量(A), 以及生长季和非生长季的显热通量(B)和潜热通量(C)的增强回归树的观测值与模拟值的关系。MAE, 平均绝对误差; RMSE, 均方根误差。

Fig. 3 Relationships between the observed data of 30-min net CO2 exchange (NEE)(A), sensible heat flux (H)(B) and latent heat flux (LE)(C) with the predicted data by the boosted regression trees for NEE during the growing season daytime and other periods (nighttime during growing season and the whole non-growing season), and for H and LE during growing and non-growing season in an alpine scrubland. MAE, mean absolute error; RMSE, root mean square error.

图4 高寒灌丛环境因子对生长季白天和其他时段(生长季夜间及非生长季全天) 30 min净CO2交换量(A)、生长季与非生长季30 min显热通量(H)及潜热通量(LE)变异的相对贡献(B)。

Fig. 4 Relative contributions of environmental factors to the variations of 30 min net CO2 exchange at daytime during growing season and other periods (nighttime during growing season and the whole non-growing season)(A), sensible heat flux (H), and latent heat flux (LE) during growing season and non-growing season in an alpine scrubland (B). SWC, topsoil water content at 10 cm depth; Swin, shortwave radiation; Ta, air temperature; Ts, topsoil temperature at 5 cm depth; Vapor, atmospheric water vapor pressure; Ws, wind speed.

图5 高寒灌丛主要环境因子与增强回归树拟合的不同时段30 min净CO2交换量(A, B, 生长季白天; C, 生长季夜间及非生长季全天)、显热通量(D, 生长季; E, 非生长季)和潜热通量(F, 生长季; G, 非生长季)的关系。

Fig. 5 Relationships between fitted 30-min net CO2 exchange (A, B, daytime during growing season; C, nighttime during growing season and the whole non-growing season), sensible heat flux (D, growing season; E, non-growing season) and latent heat flux (F, growing season; G, non-growing season) from boosted regression trees with main environmental factors during the different stages in an alpine scrubland. H, sensible heat flux; LE, latent heat flux; Swin, solar shortwave radiation; Ts, topsoil temperature at 5 cm depth; Vapor, atmospheric water vapor pressure.

图6 高寒灌丛增强回归树模型(BRT)与中国通量观测研究联盟(ChinaFLUX)对生长季白天(A)和其他时段(生长季夜间及非生长季全天)(B) 30 min净CO2交换量, 生长季(C)和非生长季(D)显热通量, 生长季(E)和非生长季(F)潜热通量的缺失值插补结果的比对。

Fig. 6 Comparisons between the boosted regression trees (BRT) and Chinese flux observation and research network (ChinaFLUX) for gap-filled 30 min net CO2 exchange and during growing-season (NEE)(A) and other periods (nighttime during growing season and the whole non-growing season)(B), 30 min sensible heat flux (H) during growing season (C) and non-growing season (D), and latent heat flux (LE) during growing season (E) and non-growing season (F) in an alpine scrubland.

图7 高寒灌丛增强回归树模型(BRT)与中国通量观测研究联盟(ChinaFLUX)的逐日CO2通量(A)、显热通量(B)与潜热通量(C)数据序列的比对。GEE, 总生态系统CO2交换量; NEE, 净生态系统CO2交换量; RES, 生态系统呼吸。

Fig. 7 Comparisons between the boosted regression trees (BRT) and Chinese flux observation and research network (ChinaFLUX) for daily CO2 fluxes (A), sensible heat flux (H)(B) and latent heat flux (LE)(C) in an alpine scrubland. GEE, gross ecosystem CO2 exchange; NEE, net ecosystem CO2 exchange; RES, ecosystem respiration.

图8 高寒灌丛增强回归树模型(BRT)与中国通量观测研究联盟(ChinaFLUX)的逐月CO2通量(A)和热量通量(B)对比。GEE, 总生态系统CO2交换量; H, 显热通量; LE, 潜热通量; NEE, 净生态系统CO2交换量; RES, 生态系统呼吸。

Fig. 8 Comparisons between the boosted regression trees (BRT) and Chinese flux observation and research network (ChinaFLUX) for the modelled monthly CO2 fluxes (A) and heat fluxes (B) in an alpine scrubland. GEE, gross ecosystem CO2 exchange; H, sensible heat flux; LE, latent heat flux; NEE, net ecosystem CO2 exchange; RES, ecosystem respiration.

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增强回归树模型在青藏高原高寒灌丛通量数据插补中的应用

Application of boosted regression trees for the gap-filling to flux dataset in an alpine scrubland of Qingzang Plateau

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