地球科学进展 ›› 2024, Vol. 39 ›› Issue (2): 111 -123. doi: 10.11867/j.issn.1001-8166.2024.013

综述与评述 上一篇    下一篇

河流碳循环模型研究进展
刘娇娇 1 , 2( ), 刘军志 1( ), 宋超 3, 张蔚珍 1, 刘勇勤 1 , 4 , 5   
  1. 1.兰州大学 泛第三极环境中心,甘肃 兰州 730000
    2.兰州大学 大气科学学院,甘肃 兰州 730000
    3.兰州大学 生态学院,甘肃 兰州 730000
    4.中国科学院青藏高原环境变化与地表过程 重点实验室,北京 100101
    5.中国科学院大学,北京 100049
  • 收稿日期:2023-10-03 修回日期:2024-01-22 出版日期:2024-02-10
  • 通讯作者: 刘军志 E-mail:liujiaojiao21@lzu.edu.cn;liujunzhi@lzu.edu.cn
  • 基金资助:
    国家自然科学基金面上项目(42171132);甘肃省自然科学基金重点项目(23JRRA1033)

Review of the Models for Riverine Carbon Cycling

Jiaojiao LIU 1 , 2( ), Junzhi LIU 1( ), Chao SONG 3, Weizhen ZHANG 1, Yongqin LIU 1 , 4 , 5   

  1. 1.Center for the Pan-third Pole Environment, Lanzhou University, Lanzhou 730000, China
    2.College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
    3.Institute of Innovation Ecology, Lanzhou University, Lanzhou 730000, China
    4.State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Chinese Academy of Sciences, Beijing 100101, China
    5.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2023-10-03 Revised:2024-01-22 Online:2024-02-10 Published:2024-03-05
  • Contact: Junzhi LIU E-mail:liujiaojiao21@lzu.edu.cn;liujunzhi@lzu.edu.cn
  • About author:LIU Jiaojiao, Ph. D student, research area includes watershed carbon cycling model. E-mail: liujiaojiao21@lzu.edu.cn
  • Supported by:
    the National Natural Science Foundation of China(42171132);The Natural Science Foundation of Gansu Province, China(23JRRA1033)

河流是连接陆地和海洋两大碳库的“重要管道”和“生物反应器”,深入理解河流碳循环过程并建立河流碳循环模型是估算区域尺度河流碳通量的重要手段。当前,对河流碳循环模型构建与应用的探讨还较为缺乏。通过文献调研对河流碳循环机理和已有模型研究进行回顾和总结,首先归纳了河流中颗粒有机碳、溶解有机碳和溶解无机碳的主要来源及相关迁移转化过程,然后对经验统计和机理过程两大类河流碳循环模型的模拟方法、应用现状和优缺点进行了综述。经验统计模型采用统计或机器学习方法建立河流碳通量与环境因子的关系,建模较为简单,但普适性和外推性较差;机理过程模型在陆面模式或水文模型中耦合河流碳循环相关过程,物理性和可靠性较强,但较为复杂。不同模型的侧重点和对河流碳循环过程的表达各不相同,适用场景也不相同。河流碳循环模拟研究目前尚处于起步阶段,现有模型对陆地和水体碳循环过程以及人类活动影响的表达普遍不足,无法准确模拟和预测河流碳循环过程的长期变化。今后应加强对河流碳循环过程的观测,提高对陆地和水体碳循环机理的认识,进而完善其在模型中的表达,提高河流碳循环模拟的精度,为中国实现“双碳”目标提供科学支撑。

Rivers connect the terrestrial landscape and oceans and are considered “bioreactors” of carbon. Understanding the carbon cycling processes in rivers and constructing numerical models for riverine carbon cycling is imperative to estimate regional and global carbon budgets. The summary and discussion of the development and application of riverine carbon cycling models remains inadequate. This study reviewed the mechanisms and models of riverine carbon cycling based on a comprehensive literature review. First, we briefly overview the critical processes in migrating and transforming various carbon components, including particulate organic carbon, dissolved inorganic carbon, and dissolved organic carbon. Riverine carbon cycling models are classified into two types: statistical and process-based. The representative models’ simulation methods, applications, advantages, and disadvantages were compared. Based on statistical or machine learning methods, empirical statistical models establish the relationship between the riverine carbon flux and environmental factors. This type of model is simple but has poor extrapolation and universality. Process-based models are based on land surface or hydrological models coupled with river carbon cycling-related biogeochemical processes. This model simulates and predicts variations in different riverine carbon fluxes and is more reliable but complicated. Such models typically focus on different scientific problems, and the representations of riverine carbon cycling-related processes differ among these models. Simulation research on riverine carbon cycling is still in its early stages; however, many shortcomings remain. For example, the representations of terrestrial and aquatic carbon cycling and human activities in existing riverine carbon cycling models are insufficient; thus, they cannot accurately simulate and predict long-term changes in riverine carbon cycling. In the future, it will be necessary to strengthen observations of river carbon cycling processes and improve our understanding of terrestrial and aquatic carbon cycling to represent the mechanisms and processes in the model. This will improve the accuracy of riverine carbon cycling simulations and provide a scientific basis for China to achieve its double-carbon goals.

中图分类号: 

图1 河流碳循环基本过程示意图
Fig. 1 Conceptual diagram of riverine carbon cycling
图2 河流碳循环模型分类
Fig. 2 Classification of riverine carbon cycling models
表1 代表性河流碳侧向输出经验统计预测模型比较
Table 1 Representative empirical models to predict the lateral export of riverine carbon
表2 代表性河流碳气体交换速率模型
Table 2 Representative models to predict gas transfer velocity of riverine carbon
图3 河流碳循环过程子模块
斜体表示不同子模块,正体表示需要模拟的过程
Fig. 3 The subroutine for the riverine carbon cycling model
Italics indicate different subroutines, roman indicates the specific process
表3 基于陆面模式的河流碳循环模型
Table 3 The riverine carbon cycling models based on LSM
模型名称 分辨率 模块组成 模拟河流碳分量 应用区域 模型评述
ORCHILEAK (Organising Carbon and Hydrology in Dynamic Ecosystems LEAK branch) 0.5°~1° ①④⑥⑧⑩⑪ DOC/CO2 热带 65 - 66 及欧洲地区 67 可以重现不同气候区陆地和水体碳通量的季节和年际变化。忽略了河流内部生物化学过程对 CO2排放 的影响
DLEM (Dynamic Land Ecosystem Model) 0.083 3° ①④⑥⑦⑧⑨⑩⑪ DOC/DIC/POC/CO2 美国 68 - 69 可以量化人类活动影响下不同形态河流碳的长期变化。忽略了河流内部生物化学过程对河流CO 2排放的贡献;缺少对CO 2排放模拟结果的验证
TRIPLEX‐HYDRA (TRIPLEX-Hydrological Routing Algorithm) 0.5° ①④⑥⑦⑨⑩⑪ DOC/POC 全球 70 可用于模拟全球河流有机碳输出的长期变化特征。难以捕捉径流中出现的峰/谷值,验证时间尺度较粗(月或年平均)
NICE-BGC (National Integrated Catchment-based Eco-hydrology and Biogeochemical Cycle) ①④⑦⑩⑪ DOC/DIC/POC/CO2 全球 71 耦合了大量成熟模型,能够模拟不同河流碳组分。没有直接模拟陆源碳进入河流的过程;验证时间尺度较粗(季节或年平均)
LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) 50 m ①④⑥ DOC 亚北极地区 72 实现了植被—土壤—河流碳动力学耦合,能够用于详细描述微观尺度的过程。模型结构复杂,参数确定困难,难以应用于大区域的长期模拟
表4 基于水文模型框架的河流碳循环模型
Table 4 The riverine carbon cycling models based on hydrological model
模型名称 离散化方式 模块组成 模拟河流碳分量 应用区域 模型评述
RIM (Riparian Flow-Concentration Integration Model) 集总式 ④⑥ DOC 瑞典 75 - 76 考虑了土壤DOC浓度随深度的非线性变化;但应用范围较局限
MIKE-SHE-MTT (MIKE System Hydrological European-Mean Travel Time) 分布式 ④⑥⑪ DOC 瑞典 77 无需单独校准就能用于相似流域;能够同时成功模拟地下水和河流内DOC浓度年均值和空间差异。模型在日尺度上表现较差
FLEX1 集总式 ③⑤ DOC 法国 78 输入参数要求低,模型可移植性强。仅适用于短期模拟;自由度过高参数不确定性较大
Birkel 2 集总式 ③⑥ DOC 英国 79 、德国 80 澳大利亚 81 能够同时率定径流、同位素及DOC浓度。干旱期模拟效果较差;还需检验模型长期模拟效果
Yurova2 集总式 ④⑥ DOC 瑞典 82 结合室内实验验证,能够很好地重现观测到的宏观及微物理过程。仅适用于湿地/泥炭地流域;参数获取困难
BioRT-Flux-PIHM (Biogeochemical Reactive Transport-Flux-Penn State Integrated Hydrologic Model) 集总式/分布式 ④⑥ DOC 美国 83 针对一个过程提供了多种参数化方案。简化了土壤碳循环过程的模拟,可能高估了DOC在夏季的积累
ECO3D (ECOsystem 3D) 分布式 ④⑥⑪ DOC 美国 84 详细考虑了空间异质性,计算公式物理意义明确;同时考虑了冰川动力学。计算量和不确定性都较大
RHESSys1 (Regional Hydro-Ecological Simulation System) 分布式 ①④⑥⑪ DOC 美国 85 可以根据研究需要对植被、地下水模拟方法进行灵活配置。没有考虑土壤DOC分布的空间差异,不确定性较大
INCA-C (Integrated Catchments-C) 集总式/半分布式 ④⑥⑧⑩ DOC、DIC、CO2 加拿大 86 、英国 87 、瑞典 76 88 、挪威 88 应用广泛,已经在不同类型流域验证测试。DIC的生产及CO2排放较简略,长期模拟存在较大不确定性
SWAT-Carbon (The Soil & Water Assessment Tool-Carbon) 分布式 ①④⑥⑦⑧⑨⑩⑪ DOC、DIC、POC 美国 89 - 90 、加拿大 91 能够模拟 不同形态河流碳 的输出,能够考虑部分人类活动影响,计算量适中。DIC计算未包含风化过程
10 LI M X, PENG C H, ZHOU X L, et al. Modeling global riverine DOC flux dynamics from 1951 to 2015[J]. Journal of Advances in Modeling Earth Systems, 2019, 11(2): 514-530.
11 LI M X, PENG C H, HE N P. Global patterns of particulate organic carbon export from land to the ocean[J]. Ecohydrology, 2022, 15(2). DOI:10.1002/eco.2373 .
12 AN Zhihong, SUN Ziyong, HU Yalu, et al. Export of dissolved organic carbon in streams draining permafrost-dominated areas: a review[J]. Geological Science and Technology Information, 2018, 37(1): 204-211.
安志宏, 孙自永, 胡雅璐, 等. 多年冻土区河流溶解性有机碳输出的研究进展[J]. 地质科技情报, 2018, 37(1): 204-211.
13 DUAN Weiyan, HUANG Chang. Research progress on the carbon cycle of rivers and lakes[J]. China Environmental Science, 2021, 41(8): 3 792-3 807.
段巍岩, 黄昌. 河流湖泊碳循环研究进展[J]. 中国环境科学, 2021, 41(8): 3 792-3 807.
14 ZHANG Yongling. The review of the research of the riverine organic carbon cycle[J]. Journal of Henan Polytechnic University (Natural Science), 2012, 31(3): 344-351.
张永领. 河流有机碳循环研究综述[J]. 河南理工大学学报(自然科学版), 2012, 31(3): 344-351.
15 WANG Yuchao, XU Xuan, CAO Penghe, et al. A review of carbon dioxide emissions from streams[J]. Chinese Journal of Ecology, 2022, 41(1): 182-189.
王玉超, 徐璇, 曹鹏鹤, 等. 溪流二氧化碳排放研究进展[J]. 生态学杂志, 2022, 41(1): 182-189.
16 THURMAN E M. Organic geochemistry of natural waters[M]. Dordrech: Springer, 1985.
17 KALBITZ K, SOLINGER S, PARK J H, et al. Controls on the dynamics of dissolved organic matter in soils: a review[J]. Soil Science, 2000, 165(4): 277-304.
18 GALY V, PEUCKER-EHRENBRINK B, EGLINTON T. Global carbon export from the terrestrial biosphere controlled by erosion[J]. Nature, 2015, 521(7 551): 204-207.
19 WEN Z D, SONG K S, SHANG Y X, et al. Natural and anthropogenic impacts on the DOC characteristics in the Yellow River continuum[J]. Environmental Pollution, 2021, 287. DOI:10.1016/j.envpol.2021.117231 .
20 HOTCHKISS E R, JrHA LL R O, SPONSELLER R A, et al. Sources of and processes controlling CO2 emissions change with the size of streams and rivers[J]. Nature Geoscience, 2015, 8(9): 696-699.
21 HOSEN J D, AHO K S, FAIR J H, et al. Source switching maintains dissolved organic matter chemostasis across discharge levels in a large temperate river network[J]. Ecosystems, 2021, 24(2): 227-247.
22 AUFDENKAMPE A K, MAYORGA E, RAYMOND P A, et al. Riverine coupling of biogeochemical cycles between land, oceans, and atmosphere[J]. Frontiers in Ecology and the Environment, 2011, 9(1): 53-60.
23 LASAREVA E V, PARFENOVA A M, ROMANKEVICH E A, et al. Organic matter and mineral interactions modulate flocculation across arctic river mixing zones[J]. Journal of Geophysical Research: Biogeosciences, 2019, 124(6): 1 651-1 664.
24 TANK S E, RAYMOND P A, STRIEGL R G, et al. A land‐to-ocean perspective on the magnitude, source and implication of DIC flux from major arctic rivers to the arctic ocean[J]. Global Biogeochemical Cycles, 2012, 26(4). DOI:10.1029/2011GB004192 .
25 LIU J K, HAN G L. Effects of chemical weathering and CO2 outgassing on δ13C DIC signals in a Karst watershed[J]. Journal of Hydrology, 2020, 589. DOI:10.1016/j.jhydrol.2020.125192 .
26 AARNOS H, GÉLINAS Y, KASURINEN V, et al. Photochemical mineralization of terrigenous DOC to dissolved inorganic carbon in ocean[J]. Global Biogeochemical Cycles, 2018, 32(2): 250-266.
27 MAYORGA E, AUFDENKAMPE A K, MASIELLO C A, et al. Young organic matter as a source of carbon dioxide outgassing from amazonian rivers[J]. Nature, 2005, 436(7 050): 538-541.
28 ENGEL F, ATTERMEYER K, AYALA A I, et al. Phytoplankton gross primary production increases along cascading impoundments in a temperate, low-discharge river: insights from high frequency water quality monitoring[J]. Scientific Reports, 2019, 9(1). DOI:10.1038/s41598-019-43008-w .
29 HUANG T H, FU Y H, PAN P Y, et al. Fluvial carbon fluxes in tropical rivers[J]. Current Opinion in Environmental Sustainability, 2012, 4(2): 162-169.
30 CHRIST M J, DAVID M B. Temperature and moisture effects on the production of dissolved organic carbon in a spodosol[J]. Soil Biology and Biochemistry, 1996, 28(9): 1 191-1 199.
31 KEMMITT S J, WRIGHT D, GOULDING K W T, et al. pH regulation of carbon and nitrogen dynamics in two agricultural soils[J]. Soil Biology and Biochemistry, 2006, 38(5): 898-911.
32 CASSON N J, EIMERS M C, WATMOUGH S A, et al. The role of wetland coverage within the near‐stream zone in predicting of seasonal stream export chemistry from forested headwater catchments[J]. Hydrological Processes, 2019, 33(10): 1 465-1 475.
33 LIU D, TIAN L Q, JIANG X T, et al. Human activities changed organic carbon transport in Chinese Rivers during 2004-2018[J]. Water Research, 2022, 222. DOI:10.1016/j.watres.2022.118872 .
34 MEYBECK M. Carbon, nitrogen, and phosphorus transport by world rivers[J]. American Journal of Science, 1982, 282(4): 401-450.
35 LAUDON H, BERGGREN M, ÅGREN A, et al. Patterns and dynamics of Dissolved Organic Carbon (DOC) in boreal streams: the role of processes, connectivity, and scaling[J]. Ecosystems, 2011, 14(6): 880-893.
36 CREED I F, MCKNIGHT D M, PELLERIN B A, et al. The river as a chemostat: fresh perspectives on dissolved organic matter flowing down the river continuum[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2015, 72(8): 1 272-1 285.
37 KOULOURI M, GIOURGA C. Land abandonment and slope gradient as key factors of soil erosion in mediterranean terraced lands[J]. Catena, 2007, 69(3): 274-281.
38 YANG Weidong, ZENG Lianbo, LI Xiang. Advances in research of carbon sinks and their influencing factors evaluation[J]. Advances in Earth Science, 2023, 38(2): 151-167.
杨卫东,曾联波,李想. 碳汇效应及其影响因素研究进展[J]. 地球科学进展,2023, 38(2): 151-167.
39 SUCHET P A, PROBST J L. Modelling of atmospheric CO2 consumption by chemical weathering of rocks: application to the garonne, congo and amazon basins[J]. Chemical Geology, 1993, 107(3/4): 205-210.
40 WU Weihua, ZHENG Hongbo, YANG Jiedong, et al. Chemical weathering of large river catchments in china and the global carbon cycle[J]. Quaternary Sciences, 2011, 31(3): 397-407.
吴卫华, 郑洪波, 杨杰东, 等. 中国河流流域化学风化和全球碳循环[J]. 第四纪研究, 2011, 31(3): 397-407.
41 SCHLESINGER W H, MELACK J M. Transport of organic carbon in the world’s rivers[J]. Tellus, 1981, 33(2): 172-187.
42 LUDWIG W, SUCHET P A, PROBST J. River discharges of carbon to the world’s oceans: determining local inputs of alkalinity and of dissolved and particulate organic carbon[J]. Sciences de la terre et des Planètes (Comptes rendus de l’ Académie des Sciences), 1996, 323: 1 007-1 014.
43 LUDWIG W, PROBST J L, KEMPE S. Predicting the oceanic input of organic carbon by continental erosion[J]. Global Biogeochemical Cycles, 1996, 10(1): 23-41.
44 AITKENHEAD J A, MCDOWELL W H. Soil C∶N ratio as a predictor of annual riverine DOC flux at local and global scales[J]. Global Biogeochemical Cycles, 2000, 14(1): 127-138.
1 LUDWIG W, PROBST J L. River sediment discharge to the oceans: present-day controls and global budgets[J]. American Journal of Science, 1998, 298(4): 265-295.
2 COLE J J, PRAIRIE Y T, CARACO N F, et al. Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget[J]. Ecosystems, 2007, 10(1): 172-185.
3 BATTIN T J, KAPLAN L A, FINDLAY S, et al. Biophysical controls on organic carbon fluxes in fluvial networks[J]. Nature Geoscience, 2008, 1(2): 95-185.
4 REGNIER P, RESPLANDY L, NAJJAR R G, et al. The land-to-ocean loops of the global carbon cycle[J]. Nature, 2022, 603(7 901): 401-410.
5 DRAKE T W, RAYMOND P A, SPENCER R G M. Terrestrial carbon inputs to inland waters: a current synthesis of estimates and uncertainty[J]. Limnology and Oceanography Letters, 2018, 3(3): 132-142.
6 DOWNING J. Global abundance and size distribution of streams and rivers[J]. Inland Waters, 2012, 2(4): 229-236.
7 PILLA R M, GRIFFITHS N A, GU L H, et al. Anthropogenically driven climate and landscape change effects on inland water carbon dynamics: what have we learned and where are we going?[J]. Global Change Biology, 2022, 28(19): 5 601-5 629.
8 YIN Jiabo, GUO Shenglian, WANG Jun, et al. Thermodynamic driving mechanisms for the formation of global precipitation extremes and ecohydrological effects [J]. Science China: Earth Sciences, 2023, 66(1): 92-110.
尹家波, 郭生练, 王俊, 等. 全球极端降水的热力学驱动机理及生态水文效应 [J]. 中国科学: 地球科学, 2023, 66(1): 92-110.
9 VONK J E, TANK S E, WALVOORD M A. Integrating hydrology and biogeochemistry across frozen landscapes[J]. Nature Communications, 2019, 10(1). DOI:10.1038/s41467-019-13361-5 .
45 LI M X, PENG C H, WANG M, et al. The carbon flux of global rivers: a re-evaluation of amount and spatial patterns[J]. Ecological Indicators, 2017, 80: 40-51.
46 HARARUK O, JONES S E, SOLOMON C T. Hydrologic export of soil organic carbon: continental variation and implications[J]. Global Biogeochemical Cycles, 2022, 36(6). DOI: 10.1029/2021GB007161 .
47 KÖHLER S J, BUFFAM I, SEIBERT J, et al. Dynamics of stream water TOC concentrations in a boreal headwater catchment: controlling factors and implications for climate scenarios[J]. Journal of Hydrology, 2009, 373(1/2): 44-56.
48 ÅGREN A, BUFFAM I, BISHOP K, et al. Modeling stream dissolved organic carbon concentrations during spring flood in the boreal forest: a simple empirical approach for regional predictions[J]. Journal of Geophysical Research: Biogeosciences, 2010, 115(G1). DOI:10.1029/2009JG001013 .
49 LIU D, BAI Y, HE X, et al. Changes in riverine organic carbon input to the ocean from mainland china over the past 60 years[J]. Environment International, 2020, 134. DOI:10.1016/j.envint.2019.105258 .
50 SAMSON C C, RAJAGOPALAN B, SUMMERS R S. Modeling source water TOC using hydroclimate variables and local polynomial regression[J]. Environmental Science & Technology, 2016, 50(8): 4 413-4 421.
51 BOITHIAS L, SAUVAGE S, MERLINA G, et al. New insight into pesticide partition coefficient kd for modelling pesticide fluvial transport: application to an agricultural catchment in south-western france[J]. Chemosphere, 2014, 99: 134-142.
52 ZHANG L J, XUE M, WANG M, et al. The spatiotemporal distribution of dissolved inorganic and organic carbon in the main stem of the Changjiang (Yangtze) River and the effect of the three gorges reservoir[J]. Journal of Geophysical Research: Biogeosciences, 2014, 119(5): 741-757.
53 BUTMAN D, RAYMOND P A. Significant efflux of carbon dioxide from streams and rivers in the united states[J]. Nature Geoscience, 2011, 4(12): 839-842.
54 RAYMOND P A, HARTMANN J, LAUERWALD R, et al. Global carbon dioxide emissions from inland waters[J]. Nature, 2013, 503(7 476): 355-359.
55 ABRIL G, BOUILLON S, DARCHAMBEAU F, et al. Technical note: large overestimation of pCO2 calculated from pH and alkalinity in acidic, organic-rich freshwaters[J]. Biogeosciences, 2015, 12(1): 67-78.
56 LIU S D, KUHN C, AMATULLI G, et al. The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers[J]. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(11). DOI:10.1073/pnas.2106322119 .
57 HORGBY Å, SEGATTO P L, BERTUZZO E, et al. Unexpected large evasion fluxes of carbon dioxide from turbulent streams draining the world’s mountains[J]. Nature Communications, 2019, 10(1). DOI: 10.1038/s41467-019-12905-z .
58 ALIN S R, de FÁTIMA F L R M, SALIMON C I, et al. Physical controls on carbon dioxide transfer velocity and flux in low-gradient river systems and implications for regional carbon budgets[J]. Journal of Geophysical Research, 2011, 116(G1). DOI:10.1029/2010JG001398 .
59 RAYMOND P A, COLE J J. Gas exchange in rivers and estuaries: choosing a gas transfer velocity[J]. Estuaries, 2001, 24(2): 312-317.
60 RAYMOND P A, ZAPPA C J, BUTMAN D, et al. Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers[J]. Limnology and Oceanography: Fluids and Environments, 2012, 2(1): 41-53.
61 ULSETH A J, HALL R O, BOIX C M, et al. Distinct air-water gas exchange regimes in low-and high-energy streams[J]. Nature Geoscience, 2019, 12(4): 259-263.
62 SCHELKER J, SINGER G A, ULSETH A J, et al. CO2 evasion from a steep, high gradient stream network: importance of seasonal and diurnal variation in aquatic pCO2 and gas transfer[J]. Limnology and Oceanography, 2016, 61(5): 1 826-1 838.
63 LAUERWALD R, LARUELLE G G, HARTMANN J, et al. Spatial patterns in CO2 evasion from the global river network[J]. Global Biogeochemical Cycles, 2015, 29(5): 534-554.
64 RAN L S, BUTMAN D E, BATTIN T J, et al. Substantial decrease in CO2 emissions from chinese inland waters due to global change[J]. Nature Communications, 2021, 12(1). DOI:10.1038/s41467-021-21926-6 .
65 LAUERWALD R, REGNIER P, GUENET B, et al. How simulations of the land carbon sink are biased by ignoring fluvial carbon transfers: a case study for the amazon basin[J]. One Earth, 2020, 3(2): 226-236.
66 LAUERWALD R, REGNIER P, CAMINO-SERRANO M, et al. ORCHILEAK (revision 3875): a new model branch to simulate carbon transfers along the terrestrial-aquatic continuum of the amazon basin[J]. Geoscientific Model Development, 2017, 10(10): 3 821-3 859.
67 GOMMET C, LAUERWALD R, CIAIS P, et al. Spatiotemporal patterns and drivers of terrestrial Dissolved Organic Carbon (DOC) leaching into the european river network[J]. Earth System Dynamics, 2022, 13(1): 393-418.
68 TIAN H Q, YANG Q C, NAJJAR R G, et al. Anthropogenic and climatic influences on carbon fluxes from eastern North America to the Atlantic Ocean: a process-based modeling study[J]. Journal of Geophysical Research: Biogeosciences, 2015, 120(4): 757-772.
69 YAO Y Z, TIAN H Q, PAN S F, et al. Riverine carbon cycling over the past century in the mid-atlantic region of the United States[J]. Journal of Geophysical Research: Biogeosciences, 2021, 126(5). DOI:10.1029/2020JG005968 .
70 LI Mingxu. The development of TRIPLEX-hydra model and its spatio-temporal simulations of organic carbon flux exported by global rivers[D]. Xianyang:Northwest A&F University, 2019.
李明旭. TRIPLEX-hydra模型的构建及其对全球河流有机碳输送时空变化的模拟研究[D]. 咸阳:西北农林科技大学, 2019.
71 NAKAYAMA T. Impact of anthropogenic disturbances on carbon cycle changes in terrestrial‐aquatic‐estuarine continuum by using an advanced process‐based model[J]. Hydrological Processes, 2022, 36(2). DOI:10.1002/hyp.14471 .
72 TANG J, YUROVA A Y, SCHURGERS G, et al. Drivers of dissolved organic carbon export in a subarctic catchment: importance of microbial decomposition, sorption-desorption, peatland and lateral flow[J]. Science of the Total Environment, 2018, 622: 260-274.
73 COE M T. Modeling terrestrial hydrological systems at the continental scale: testing the accuracy of an atmospheric GCM[J]. Journal of Climate, 2000, 13(4): 686-704.
74 FISHER R A, KOVEN C D. Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(4). DOI:10.1029/2018MS001453 .
75 WINTERDAHL M, FUTTER M, KÖHLER S, et al. Riparian soil temperature modification of the relationship between flow and dissolved organic carbon concentration in a boreal stream[J]. Water Resources Research, 2011, 47(8). DOI:10.1029/2010WR010235 .
76 ONI S K, FUTTER M N, TEUTSCHBEIN C, et al. Cross-scale ensemble projections of dissolved organic carbon dynamics in boreal forest streams[J]. Climate Dynamics, 2014, 42(9/10): 2 305-2 321.
77 JUTEBRING S E, LIDMAN F, SJÖBERG Y, et al. Groundwater travel times predict DOC in streams and riparian soils across a heterogeneous boreal landscape[J]. Science of the Total Environment, 2022, 849. DOI:10.1016/j.scitotenv.2022.157398 .
78 STROHMENGER L, FOVET O, HRACHOWITZ M, et al. Is a simple model based on two mixing reservoirs able to reproduce the intra-annual dynamics of DOC and NO3 stream concentrations in an agricultural headwater catchment?[J]. Science of the Total Environment, 2021, 794. DOI:10.1016/j.scitotenv.2021.148715 .
79 BIRKEL C, SOULSBY C, TETZLAFF D. Integrating parsimonious models of hydrological connectivity and soil biogeochemistry to simulate stream DOC dynamics[J]. Journal of Geophysical Research: Biogeosciences, 2014, 119(5): 1 030-1 047.
80 BIRKEL C, BRODER T, BIESTER H. Nonlinear and threshold-dominated runoff generation controls DOC export in a small peat catchment[J]. Journal of Geophysical Research: Biogeosciences, 2017, 122(3): 498-513.
81 BIRKEL C, DUVERT C, CORREA A, et al. Tracer-aided modeling in the low‐relief, wet‐dry tropics suggests water ages and DOC export are driven by seasonal wetlands and deep groundwater[J]. Water Resources Research, 2020, 56(4). DOI:10.1029/2019WR026175 .
82 YUROVA A, SIRIN A, BUFFAM I, et al. Modeling the dissolved organic carbon output from a boreal mire using the convection-dispersion equation: importance of representing sorption[J]. Water Resources Research, 2008, 44(7). DOI:10.5194/hess-24-945-2020 .
83 WEN H, PERDRIAL J, ABBOTT B W, et al. Temperature controls production but hydrology regulates export of dissolved organic carbon at the catchment scale[J]. Hydrology and Earth System Sciences, 2020, 24(2): 945-966.
84 LIAO C, ZHUANG Q L, LEUNG L R, et al. Quantifying dissolved organic carbon dynamics using a three-dimensional terrestrial ecosystem model at high spatial-temporal resolutions[J]. Journal of Advances in Modeling Earth Systems, 2019, 11(12): 4 489-4 512.
85 SON K, LIN L, BAND L, et al. Modelling the interaction of climate, forest ecosystem, and hydrology to estimate catchment dissolved organic carbon export[J]. Hydrological Processes, 2019, 33(10): 1 448-1 464.
86 FUTTER M N, BUTTERFIELD D, COSBY B J, et al. Modeling the mechanisms that control in-stream dissolved organic carbon dynamics in upland and forested catchments[J]. Water Resources Research, 2007, 43(2). DOI:10.1029/2006WR004960 .
87 XU J, MORRIS P J, LIU J, et al. Increased dissolved organic carbon concentrations in peat‐fed UK water supplies under future climate and sulfate deposition scenarios[J]. Water Resources Research, 2020, 56(1). DOI:10.1029/2019WR025592 .
88 LEDESMA J L J, KÖHLER S J, FUTTER M N. Long-term dynamics of dissolved organic carbon: implications for drinking water supply[J]. Science of the Total Environment, 2012, 432. DOI:10.1016/j.scitotenv.2012.05.071 .
89 DU X Z, ZHANG X S, MUKUNDAN R, et al. Integrating terrestrial and aquatic processes toward watershed scale modeling of dissolved organic carbon fluxes[J]. Environmental Pollution, 2019, 249: 125-135.
90 QI J Y, DU X Z, ZHANG X S, et al. Modeling riverine dissolved and particulate organic carbon fluxes from two small watersheds in the northeastern United States[J]. Environmental Modelling & Software, 2020, 124. DOI:10.1016/j.envsoft.2019.104601 .
91 DU X Z, LOISELLE D, ALESSI D S, et al. Hydro-climate and biogeochemical processes control watershed organic carbon inflows: development of an in-stream organic carbon module coupled with a process-based hydrologic model[J]. Science of the Total Environment, 2020, 718. DOI:10.1016/j.scitotenv.2020.137281 .
92 YIN J B, GENTINE P, SLATER L, et al. Future socio-ecosystem productivity threatened by compound drought-heatwave events[J]. Nature Sustainability, 2023, 6: 259-272.
93 HUANG Chunlin, HOU Jinliang, LI Weide, et al. Data assimilation in terrestrial hydrology based on deep learning fusing remote sensing big data: research advances and key scientific issues[J]. Advances in Earth Science, 2023, 38(5): 441-452.
黄春林,侯金亮,李维德,等. 深度学习融合遥感大数据的陆地水文数据同化:进展与关键科学问题[J].地球科学进展,2023, 38(5): 441-452.
94 LI Xin, MA Hanqing, RAN Youhua, et al. Terrestrial carbon cyclemodel-data fusion: progress and challenges[J]. Science China: Earth Sciences, 2021, 64(10): 1 645-1 657.
李新, 马瀚青, 冉有华, 等. 陆地碳循环模型—数据融合:前沿与挑战[J]. 中国科学:地球科学, 2021, 64(10):1 645-1 657.
95 LI Yimin, TAN Zhenyu, YANG Chen, et al. Extraction of algal blooms in Dianchi Lake based on multi-source satellites using machine learning algorithms[J]. Advances in Earth Science, 2022, 37(11): 1 141-1 156.
李一民,谭振宇,杨辰,等. 基于多源卫星的滇池藻华提取机器学习算法研究[J]. 地球科学进展,2022, 37(11): 1 141-1 156.
96 XU Yongsheng, GAO Le, ZHANG Yunhua. New deneration altimetry satellite SWOT and its reference to China’s swath altimetrysatellite[J]. Remote Sensing Technology and Application, 2017, 32(1): 84-94.
徐永生, 高乐, 张云华. 美国新一代测高卫星SWOT——评述我国宽刈幅干涉卫星的发展借鉴[J]. 遥感技术与应用, 2017, 32(1): 84-94.
97 REGNIER P, FRIEDLINGSTEIN P, CIAIS P, et al. Anthropogenic perturbation of the carbon fluxes from land to ocean[J]. Nature Geoscience, 2013, 6(8): 597-607.
98 HE Daming, TANG Qicheng. Chinese international rivers [M]. Beijing:Science Press, 2000.
何大名, 汤奇成. 中国国际河流[M]. 北京:科学出版社, 2000.
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