地球科学进展 ›› 2023, Vol. 38 ›› Issue (1): 57 -69. doi: 10.11867/j.issn.1001-8166.2022.055

综述与评述 上一篇    下一篇

内陆水体初级生产力评估方法研究进展
陆瑶 1 , 2( ), 黄良波 1 , 3, 贾珺杰 1 , 2, 高扬 1 , 2( )   
  1. 1.中国科学院地理科学与资源研究所,生态网络观测与模拟重点实验室,北京 100101
    2.中国科学院大学资源与环境学院,北京 100049
    3.中国市政工程 中南设计研究总院有限公司,湖北 武汉 430001
  • 收稿日期:2021-11-09 修回日期:2022-05-16 出版日期:2023-01-10
  • 通讯作者: 高扬 E-mail:luy.18s@igsnrr.ac.cn;gaoyang@igsnrr.ac.cn
  • 基金资助:
    国家自然科学基金杰出青年科学基金项目“流域碳氮耦合循环及其生态效应”(42225103)

Estimation of Primary Productivity of Inland Water

Yao LU 1 , 2( ), Liangbo HUANG 1 , 3, Junjie JIA 1 , 2, Yang GAO 1 , 2( )   

  1. 1.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3.Central China Municipal Engineering Design Research Institute Co. , Ltd. , Wuhan 430001, China
  • Received:2021-11-09 Revised:2022-05-16 Online:2023-01-10 Published:2023-02-02
  • Contact: Yang GAO E-mail:luy.18s@igsnrr.ac.cn;gaoyang@igsnrr.ac.cn
  • About author:LU Yao (1995-), female, Yangquan City, Shanxi Province, Ph. D student. Research area includes inland water productivity and carbon cycle. E-mail: luy.18s@igsnrr.ac.cn
  • Supported by:
    the National Science Foundation for Distinguished Young Scholars of China “Coupling cycle of carbon and nitrogen in watershed and its ecological effects”(42225103)

内陆水体是全球碳循环的重要参与者,在调节气候变化方面发挥着关键作用。内陆水体初级生产力指内陆水体(包括湖泊、水库、河流和湿地)中初级生产者(包括浮游藻类植物和挺水、浮水、潜水大型植物)单位时间、单位面积上由光合作用产生的有机物质总量,其大小反映了系统有机碳库和无机碳库之间的定量联系。评估内陆水体初级生产力不仅能帮助解析初级生产者光合作用碳固存机理,也有助于量化内陆水体碳吸收量,进而探知不同区域生态环境差异,揭示内陆水体在全球生态系统碳循环中的重要性。内陆水体初级生产力估算方法较多,包括黑白瓶法、垂向归纳模型法和13C法等,各方法都有其适用范围和局限性,对这些方法的不合理使用制约着对内陆水体初级生产力的变异性及其驱动机制的揭示。通过归纳整理近年来国内外内陆水体初级生产力估算方法,对各个方法的机理、优缺点和适用性进行对比和总结,并着重介绍2种新兴的基于溶解氧浓度或氧同位素的方法(即diel O2技术和18/16O技术),进而为深入开展内陆水体新陈代谢、生产力及养分循环方面研究提供重要技术支撑。

Inland water is an important component of the global carbon (C) cycle and plays a key role in regulating climate change. The Primary Productivity (PP) of inland water is defined as the amount of organic matter produced by primary producers in inland water bodies through photosynthesis per unit time and unit area, which reflects the quantitative relationship between the organic and inorganic C pools. The assessment of inland water PP can help analyze the C cycle mechanism of photosynthesis and quantify the C absorption of aquatic ecosystems to examine the differences in the ecological environment in different regions and evaluate the importance of inland water bodies in the global ecosystem C cycle. There are many methods for estimating PP in inland water, including the light-dark bottle incubation method, the vertically generalized production model method, and the 13C method. Each of these have application scopes and limitations. The unreasonable use of PP restricts the understanding of its variability and driving mechanism in inland water bodies. The mechanism, advantages, disadvantages, and applicability of each method are compared by summarizing domestic and international research on PP estimation methods in recent years. Two new methods based on dissolved oxygen concentration or oxygen isotopes, namely, diel O2 technology and 18/16O technology, are introduced. This study serves as a reference for research on inland water metabolism, productivity, and nutrient cycles.

中图分类号: 

图1 内陆水体输入有机碳的归趋(据参考文献[ 3 ]修改)
总生态系统呼吸( Re )是自养呼吸( Ra )和异养呼吸( Rh )的总和。NPP为净初级生产力;NEP为净生态系统生产力;GPP为总初级生产力;UV为紫外线;C 输入代表该水体支流或上游注入的碳量,C 输出代表该水体向下游排放的碳量;虚线表示碳的矿化,实线表示碳的同化;与净异养水体( Pn <0)相反,净自养型水体( Pn >0)具有净积累或净输出有机物的特点
Fig. 1 Fates of organic carbon fixed in or imported into an aquatic ecosystemmodified after reference 3 ])
Total ecosystem respiration ( Re ) is the sum of autotrophic respiration ( Ra ) and heterotrophic respiration ( Rh ). NPP: Net Primary Production; NEP: Net Ecosystem Production; GPP: Gross Primary Production; UV: Ultraviolet. Dashed lines represent degradation of C, solid lines production and/or transfer of C. Net autotrophic ecosystems ( Pn >0) have a net accumulation and/or net export organic matter, contrary to net heterotrophic ecosystems ( Pn <0)
图2 黑白瓶培养装置 20
Fig. 2 Light-dark bottle incubation device 20
图3 影响湖泊中溶解氧变化的生物和物理因子 45
地表水中溶解性有机碳的光化学UV氧化可能会显著影响测定的溶解氧浓度;氧的大气交换是由浓度梯度和风力作用共同驱动的;夏季热分层和与浅滩的水平氧交换可能引起显著的噪声
Fig. 3 Conceptual model of the biological and physical components contributing to variability in Dissolved OxygenDOin a lake 45
Photochemical UV oxidation and photoinhibition in surface waters by Dissolved Organic Carbon (DOC) may significantly influence the measured DO concentration. The oxygen exchange between water and air is driven by concentration gradient and wind. Advective mixing across a thermal stratification layer (metalimnion) during summer and horizontal exchange of oxygen with shallow littoral zones may cause significant noise
表1 diel O2 技术估算内陆水体生产力所需方程 45
Table 1 Equation for estimating primary production of inland water using diel O 2 Technology 45
序号 方程式 参数
1 O 2 t = G P P - R - F - A O 2 :氧气变化量[g O2/(m3⋅h)], t :单位时间(h),F:物理气体通量[g O2/(m2⋅h)],A:累积排水速率(mg/h)
2

O 2 s a t = e c × 1.423

C = - 173.4292 + 249.6339 × 100 T + 143.3483 × l n T 100 - 21.8492 × T 100 + S × - 0.033096 + 0.014259 × T 100 - 0.0017 × T 100 2

T:水温(K),S:盐度(‰), O 2 s a t :饱和氧浓度(mg/L),e:自然对数, C :转换系数(mL O2/L)
3

O 2 s a t ' = O 2 s a t × c o r r e c t i o n   f a c t o r

c o r r e c t i o n   f a c t o r = B P × 0.0987 - 0.0112 100

c o r r e c t i o n   f a c t o r = 0.0000005 × a l t i t u d e 2 - 0.0118 × a l t i t u d e + 99.979 100

O 2 s a t ' :气压调整后的饱和氧浓度(mg/L),BP: 大气压(mmHg),altitude:海拔(m), c o r r e c t i o n   f a c t o r :相关系数
4 O 2 m e a s = % D O   100 × O 2 s a t O 2 m e a s :氧浓度(mg/L)
5 S c = 0.0476 T 3 + 3.7818 T 2 - 120.1 T + 1800.6 S c :施密特系数
6

U 10 = U z × α

α = 1.4125   z - 0.15

U 10 :10 m风速(m/s),z:高度(m),α:转换系数
7 k 600 = 2.07 + 0.215 U 10 1.7 100 k:活塞速度1(m/h),k600:活塞速度2(m/h)
8 k = k 600 × S c 600 - 0.5
9 F = k O 2 m e a n s - O 2 s a t F:物理气体通量[g O2/(m2⋅h)]
10 d a y f r a c t i o n = l i g h t h o u r s 24 dayfraction:日间占比,DOY:年积日,radsxdec:转换系数,lat:纬度,SR:太阳辐射(m/h), l i g h t h o u r s :光照时长(h)
11

r a d s = 2 × π × D O Y 365

d e c = 0.006918 - 0.399912 × c o s r a d s + 0.070257 × s i n r a d s - 0.006758 × c o s 2 × r a d s + 0.000907 × s i n 2 × r a d s - 0.00297 × c o s 3 × r a d s + 0.00148 × s i n 3 × r a d s

x = - 1 × s i n l a t × s i n d e c / c o s l a t × c o s d e c

S R = 3.14154 2 - a t a n x 1 - x 2

l i g h t h o u r s = S R 2 0.262

12 N E P h r = O 2 - F Z m i x N E P h r :小时净生态系统生产力[g O2/(m3⋅h)], N E P d a y t i m e :日间净生态系统生产力[g O2/(m3⋅daylight period)], Z m i x :混合层深度(m)
13 N E P d a y t i m e = m e a n   N E P h r   d u r i n g   d a y l i g h t × d a y f r a c t i o n × 24
14 R h r = O 2 - F Z m i x R h r :小时呼吸速率[g O2/(m3⋅h)], R d a y t i m e :日间呼吸速率[g O2/(m3⋅daylight period)], R d a y : 日呼吸速率[g O2/(m3⋅d)]
15 R d a y t i m e = R h r × 24 × d a y f r a c t i o n
16 R d a y = R h r × 24
17 G P P = N E P d a y t i m e + R d a y t i m e GPP:总初级生产力[g O2/(m3⋅d)]
18 N E P = G P P - R d a y NEP:净生态系统生产力[g O2/(m3⋅d)]
图4 diel O2 技术估算湖泊生产力过程流程图 45
方框中的数字指表1中的方程序号
Fig. 4 Flow chart of lake PP estimation by diel O2 technology 45
Numbers refer to equations explained in Table 1, which also explains the abbreviations
图5 氧同位素技术估算内陆水体生产力的动力学原理 11
Fig. 5 The kinetic principle of estimating primary production of inland water using 18/16O technology 11
表2 氧同位素技术估算内陆水体生产力所需方程
Table 2 Equation for estimating primary production of inland water using 18/16O technology
序号 方程式 参数
1

D O s a t = e c × 1.423

C = - 173.4292 + 249.6339 × 100 T + 143.3483 × l n T 100 - 21.8492 × T 100 + S × - 0.033096 + 0.014259 × T 100 - 0.0017 × T 100 2

T:水温(K),S:盐度(‰), D O s a t :饱和氧浓度(mg/L),e:自然对数, C :转换系数(mL O2/L)
2

D O s a t ' = D O s a t × c o r r e c t i o n   f a c t o r

c o r r e c t i o n   f a c t o r = B P × 0.0987 - 0.0112 100

c o r r e c t i o n   f a c t o r = 0.0000005 × a l t i t u d e 2 - 0.0118 × a l t i t u d e + 99.979 100

D O s a t ' :气压调整后的饱和氧浓度(mg/L),BP:大气压(mmHg),altitude:海拔(m), c o r r e c t i o n   f a c t o r :相关系数
3 D O m e a s = % D O   100 × D O s a t D O m e a s :氧浓度(mg/L)
4 S c = 0.0476 T 3 + 3.7818 T 2 - 120.1 T + 1800.6 S c :施密特系数
5 U 10 = U 3 × 1 + 0.0878 × l n 10 3 U 10 :10 m风速(m/s), U 3 :3 m风速(m/s)
6 k 600 = 2.51 + 1.58 × U 10 + 0.39 × U 10 × l o g 10 ( L A ) k O 2 :活塞速度1(m/h),k600:活塞速度2(m/h)
7 k O 2 = k 600 × S c O 2 600 - 2 3
8

A F = R s a m p l e 1 + R s a m p l e

R s a m p l e = δ 18 O 1000 × 0.0020052 + 0.0020052

AF:原子分数,Rsample:重同位素与轻同位素的比值
9

G P P = k O 2 Z m i x × D O m e a s × b - c - D O s a t × a - c d - c

a = A F a t m × α s × α g

b = A F D O × α g

c = A F D O × α c

d = A F H 2 O × α p

α s = 1.0007 , α g = 0.9972 , α c = 0.9900 , α p = 1.0000

GPP:总初级生产力[g O2/(m3⋅h)], Z m i x :混合层 深度(m), a b c d α s α g α c α p :转换系数
10 R = k O 2 Z m i x × D O m e a s × b - d - D O s a t × a - c d - c R:呼吸速率[g O2/(m3⋅h)]
11 N E P = G P P - R NEP:净生态系统生产力[g O2/(m3⋅h)]
图6 18O技术估算湖泊生产力过程流程图
方框中的数字指表2中的方程序号
Fig. 6 Flow chart of lake primary productivity estimation by 18O technology
Numbers refer to equations explained in Table 2, which also explains the abbreviations
图7 不同初级生产力估算方法的切入点及在内陆水体碳循环中的位置
Fig. 7 Basis of different primary productivity estimation methods and their location in carbon cycle of inland water
表3 内陆水体初级生产力评估方法优缺点对比
Table 3 Comparison of advantages and disadvantages of inland water primary productivity estimation methods
1 GAO Yang, JIA Junjie, LU Yao, et al. Determining dominating control mechanisms of inland water carbon cycling processes and associated gross primary productivity on regional and global scales [J]. Earth-Science Reviews, 2020, 213. DOI:10.1016/j.earscirev.2020.103497 .
2 FALKOWSKI P G, BARBER R T, SMETACEK V. Biogeochemical controls and feedbacks on ocean primary production[J]. Science, 1998, 281(5 374): 200-206.
3 STAEHR P A, TESTA J M, KEMP W M, et al. The metabolism of aquatic ecosystems: history, applications, and future challenges[J]. Aquatic Sciences, 2012, 74(1): 15-29.
4 HERNÁNDEZ-LEÓN S, KOPPELMANN R, FRAILE-NUEZ E, et al. Large deep-sea zooplankton biomass mirrors primary production in the global ocean[J]. Nature Communications, 2020, 11(1). DOI:10.1038/s41467-020-19875-710.1.038/s41467-020-19875-7 .
5 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 .
6 CHEN Liwen, ZHANG Guangxin, XU J Y, et al. Human activities and climate variability affecting inland water surface area in a high latitude river basin [J]. Water, 2020, 12(2). DOI:DOI:10.3390/w12020382 .
7 RAYMOND P A, HARTMANN J, LAUERWALD R, et al. Global carbon dioxide emissions from inland waters[J]. Nature, 2013, 503(7 476): 355-359.
8 SUTTLE C A. Viruses in the sea [J]. Nature, 2005, 437(7 057): 356-361.
9 KAUER T, KUTSER T, ARST H, et al. Modelling primary production in shallow well mixed lakes based on MERIS satellite data[J]. Remote Sensing of Environment, 2015, 163: 253-261.
10 CHEN Shuang, YIN Gaofang, ZHAO Nanjing, et al. Measurement of primary productivity of phytoplankton based on photosynthetic electron transport rate [J]. Acta Optica Sinica, 2018, 38(11): 334-341.
陈双, 殷高方, 赵南京, 等. 基于光合电子传递速率的浮游植物初级生产力测量[J]. 光学学报, 2018, 38(11): 334-341.
11 BOGARD M J, VACHON D, ST-GELAIS N F, et al. Using oxygen stable isotopes to quantify ecosystem metabolism in northern lakes[J]. Biogeochemistry, 2017, 133(3): 347-364.
12 TOBIAS C R, BÖHLKE J K, HARVEY J W. The oxygen-18 isotope approach for measuring aquatic metabolism in high productivity waters[J]. Limnology and Oceanography, 2007, 52(4): 1 439-1 453.
13 HOTCHKISS E R, HALL R O. High rates of daytime respiration in three streams: use of δ18O-O2 and O2 to model diel ecosystem metabolism [J]. Limnology and Oceanography, 2014, 59(3): 798-810.
14 HOLTGRIEVE G W, SCHINDLER D E, BRANCH T A, et al. Simultaneous quantification of aquatic ecosystem metabolism and reaeration using a Bayesian statistical model of oxygen dynamics[J]. Limnology and Oceanography, 2010, 55(3): 1 047-1 063.
15 DENG Yubing, ZHANG Yunlin, LI Deping, et al. Temporal and spatial dynamics of phytoplankton primary production in Lake Taihu derived from MODIS data [J]. Remote Sensing, 2017, 9(3). DOI:10.3390/rs9030195 .
16 YAN Xizhu. The different methods for determing primary production [J]. Chinese Journal of Fisheries, 2000, 13(1): 81-86.
阎希柱. 初级生产力的不同测定方法[J]. 水产学杂志, 2000, 13(1):81-86.
17 LOKEN L C, van NIEUWENHUYSE E E, DAHLGREN R A, et al. Assessment of multiple ecosystem metabolism methods in an estuary[J]. Limnology and Oceanography: Methods, 2021, 19(11): 741-757.
18 PRAVEEN Joshi HS, RAMACHANDRA Naik AT, NARSHIVUDU Daggula, et al. Primary productivity and phytoplankton diversity in Pilikula Lake, Dakshina Kannada dist, Karnataka, India [J]. Journal of Entomology and Zoology Studies, 2019, 7(2): 133-139.
19 MOLINARI B, STEWART-KOSTER B, ADAME M F, et al. Relationships between algal primary productivity and environmental variables in tropical floodplain wetlands[J]. Inland Waters, 2021, 11(2): 180-190.
20 TAO Hongbo. Comparison of two estimation methods of vertical primary productivity in Baihua Lake[J]. Modern Agricultural Science and Technology, 2015(19): 224-225, 228.
陶红波. 百花湖初级生产力的2种估算方法比较[J]. 现代农业科技, 2015(19): 224-225, 228.
21 ZENG Taiheng, LIU Guoxiang, HU Zhengyu. Estimation of phytoplankton primary production of lakes in the middle and lower reaches of the Yangtze River[J]. Resources and Environment in the Yangtze Basin, 2011, 20(6): 717-722.
曾台衡, 刘国祥, 胡征宇. 长江中下游湖区浮游植物初级生产力估算[J]. 长江流域资源与环境, 2011, 20(6): 717-722.
22 ZHANG Yunlin, FENG Sheng, MA Ronghua, et al. Spatial pattern of euphotic depth and estimation of phytoplankton primary production in Lake Taihu in autumn 2004[J]. Journal of Lake Sciences, 2008, 20(3): 380-388.
张运林, 冯胜, 马荣华, 等. 太湖秋季真光层深度空间分布及浮游植物初级生产力的估算[J]. 湖泊科学, 2008, 20(3): 380-388.
23 MA Mingzhen. Spatial-temporal pattern of chlorophyll a concentration and its response to changes of nitrogen and phosphorus in Poyang Lake (China) in recent 30 years [D]. Beijing:Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 2020.
马明真. 近30年鄱阳湖叶绿素a时空格局变化及其对氮磷变化的响应特征[D]. 北京:中国科学院地理科学与资源研究所, 2020.
24 YIN Yan, ZHANG Yunlin, SHI Zhiqiang, et al. Estimation of spatial and seasonal changes in phytoplankton primary production in Meiliang Bay, Lake Taihu, based on the Vertically Generalized Production Model and MODIS data[J]. Acta Ecologica Sinica, 2012, 32(11): 3 528-3 537.
殷燕, 张运林, 时志强, 等. 基于VGPM模型和MODIS数据估算梅梁湾浮游植物初级生产力[J]. 生态学报, 2012, 32(11): 3 528-3 537.
25 LI Yunliang, ZHANG Yunlin, LIU Mingliang. Calculation and retrieval of euphotic depth of Lake Taihu by remote sensing[J]. Journal of Lake Sciences, 2009, 21(2): 165-172.
李云亮, 张运林, 刘明亮. 太湖真光层深度的计算及遥感反演[J]. 湖泊科学, 2009, 21(2): 165-172.
26 CUI Lijuan, QIU Yue, FEI Teng, et al. Using remotely sensed suspended sediment concentration variation to improve management of Poyang Lake, China [J]. Lake and Reservoir Management, 2013, 29(1): 47-60.
27 CHEN Yuwei, CHEN Kaining, HU Yaohui. Discussion on possible error for phytoplankton chlorophyll-a concentration analysis using hot-ethanol extraction method[J]. Journal of Lake Sciences, 2006, 18(5): 550-552.
陈宇炜, 陈开宁, 胡耀辉. 浮游植物叶绿素a测定的“热乙醇法”及其测定误差的探讨[J]. 湖泊科学, 2006, 18(5): 550-552.
28 JIA Junjie, GAO Yang, LU Yao, et al. Trace metal effects on gross primary productivity and its associative environmental risk assessment in a subtropical lake, China [J]. Environmental Pollution, 2020, 259. DOI:10.1016/j.envpol.2019.113848 .
29 JIA Junjie, GAO Yang, SONG Xianwei, et al. Characteristics of phytoplankton community and water net primary productivity response to the nutrient status of the Poyang Lake and Gan River, China [J]. Ecohydrology, 2019, 12(7). DOI:10.1002/eco.2136 .
30 WANG Shuoyue, GAO Yang, JIA Junjie, et al. Water level as the key controlling regulator associated with nutrient and gross primary productivity changes in a large floodplain-lake system (Lake Poyang), China [J]. Journal of Hydrology, 2021, 599. DOI:10.1016/j.jhydrol.2021.126414 .
31 JIA Junjie, WANG Yafeng, LU Yao, et al. Driving mechanisms of gross primary productivity geographical patterns for Qinghai-Tibet Plateau lake systems [J]. The Science of the Total Environment, 2021, 791. DOI:10.1016/j.scitotenv.2021.148286 .
32 NIELSEN E S. Measurement of the production of organic matter in the sea by means of carbon-14[J]. Nature, 1951, 167(4 252): 684-685.
33 NIELSEN E S. The use of radio-active carbon (C14) for measuring organic production in the sea[J]. ICES Journal of Marine Science, 1952, 18(2): 117-140.
34 ICHIMURA S E, SAIJO Y, ARUGA Y. Photosynthetic characteristics of marine phytoplankton and their ecological meaning in the chlorophyll method[J]. Shokubutsugaku Zasshi, 1962, 75(888): 212-220.
35 LÓPEZ-SANDOVAL D C, DELGADO-HUERTAS A, AGUSTÍ S. The 13C method as a robust alternative to 14C-based measurements of primary productivity in the Mediterranean Sea[J]. Journal of Plankton Research, 2018, 40(5): 544-554.
36 SUN Ruyong. General ecology [M]. Beijing: Peking University Press, 1993.
孙儒泳. 普通生态学[M]. 北京:北京大学出版社, 1993.
37 DOKULIL M T, QIAN K M. Photosynthesis, carbon acquisition and primary productivity of phytoplankton: a review dedicated to Colin Reynolds[J]. Hydrobiologia, 2021, 848(1): 77-94.
38 HAMA T, MIYAZAKI T, OGAWA Y, et al. Measurement of photosynthetic production of a marine phytoplankton population using a stable 13C isotope[J]. Marine Biology, 1983, 73(1): 31-36.
39 GLASSTONE S, LAIDLER K J, EYRING H. The theory of rate process: the kinetics of chemical reactions, viscosity, diffusion and electrochemical phenomena [R]. McGraw-Hill Book Company, 1941.
40 MOUSSEAU L, DAUCHEZ S, LEGENDRE L, et al. Photosynthetic carbon uptake by marine phytoplankton: comparison of the stable (13C) and radioactive (14C) isotope methods[J]. Journal of Plankton Research, 1995, 17(7): 1 449-1 460.
41 SLAWYK G, MINAS M, COLLOS Y, et al. Comparison of radioactive and stable isotope tracer techniques for measuring photosynthesis: 13C and 14C uptake by marine phytoplankton[J]. Journal of Plankton Research, 1984, 6(2): 249-257.
42 LÓPEZ-SANDOVAL D C, DELGADO-HUERTAS A, CARRILLO-de-ALBORNOZ P, et al. Use of cavity ring-down spectrometry to quantify 13C-primary productivity in oligotrophic waters[J]. Limnology and Oceanography: Methods, 2019, 17(2): 137-144.
43 KISHIMOTO N, YAMAMOTO C, SUZUKI K, et al. Does a decrease in chlorophyll a concentration in lake Biwa mean a decrease in primary productivity by phytoplankton? [J]. Journal of Water and Environment Technology, 2015, 13(1): 1-14.
44 MIN J, HA S Y, HUR J, et al. Primary productivity and photosynthetic pigment production rates of periphyton and phytoplankton in lake paldang using 13C tracer[J]. Korean Journal of Ecology and Environment, 2019, 52(3): 202-209.
45 STAEHR P A, BADE D, van de BOGERT M C, et al. Lake metabolism and the diel oxygen technique: state of the science[J]. Limnology and Oceanography: Methods, 2010, 8(11): 628-644.
46 SARGENT M C, AUSTIN T S. Organic productivity of an atoll[J]. Eos, Transactions American Geophysical Union, 1949, 30(2): 245-249.
47 ODUM H T, ODUM E P. Trophic structure and productivity of a windward coral reef community on Eniwetok atoll[J]. Ecological Monographs, 1955, 25(3): 291-320.
48 ODUM H T. Primary production in flowing waters1[J]. Limnology and Oceanography, 1956, 1(2): 102-117.
49 ODUM H T. Trophic structure and productivity of silver springs, Florida[J]. Ecological Monographs, 1957, 27(1): 55-112.
50 LOTTIG N R, PHILLIPS J S, BATT R D, et al. Estimating pelagic primary production in lakes: comparison of 14C incubation and free-water O2 approaches[J]. Limnology and Oceanography: Methods, 2022, 20(1): 34-45.
51 FERNÁNDEZ C B, CHMIEL H E, MINAUDO C, et al. Primary and net ecosystem production in a large lake diagnosed from high-resolution oxygen measurements[J]. Water Resources Research, 2021, 57(5). DOI:10.1029/2020WR029283 .
52 ALFONSO M B, VITALE A J, MENÉNDEZ M C, et al. Estimation of ecosystem metabolism from diel oxygen technique in a saline shallow lake: La Salada (Argentina)[J]. Hydrobiologia, 2015, 752(1): 223-237.
53 WINSLOW L A, ZWART J A, BATT R D, et al. Lake Metabolizer: an R package for estimating lake metabolism from free-water oxygen using diverse statistical models[J]. Inland Waters, 2016, 6(4): 622-636.
54 BOGARD M J, KUHN C D, JOHNSTON S E, et al. Negligible cycling of terrestrial carbon in many lakes of the arid circumpolar landscape[J]. Nature Geoscience, 2019, 12(3): 180-185.
55 BOCANIOV S A, SCHIFF S L, SMITH R E H. Plankton metabolism and physical forcing in a productive embayment of a large oligotrophic lake: insights from stable oxygen isotopes[J]. Freshwater Biology, 2012, 57(3): 481-496.
56 VOGT R J, ST-GELAIS N F, BOGARD M J, et al. Surface water CO2 concentration influences phytoplankton production but not community composition across boreal lakes[J]. Ecology Letters, 2017, 20(11): 1 395-1 404.
57 XIAO S B, LIU L, WANG W, et al. A Fast-Response Automated Gas Equilibrator\u00a0(FaRAGE) for continuous in situ measurement of CH4 and CO2 dissolved in water[J]. Hydrology and Earth System Sciences, 2020, 24: 3 871-3 880.
58 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.
[1] 郑旻, 罗敏, 潘彬彬, 陈多福. 海洋沉积物溶解氧消耗研究进展[J]. 地球科学进展, 2023, 38(3): 236-255.
[2] 何志, 田军. 中中新世气候转型期太平洋深层环流变化与碳循环[J]. 地球科学进展, 2023, 38(1): 17-31.
[3] 仲雷,葛楠,马耀明,傅云飞,马伟强,韩存博,王显,程美琳. 利用静止卫星估算青藏高原全域地表潜热通量[J]. 地球科学进展, 2021, 36(8): 773-784.
[4] 刘建安, 于雪晴, 彭彤, 杜金洲. 近海海水养殖海域海底地下水排放的研究进展[J]. 地球科学进展, 2021, 36(12): 1235-1246.
[5] 李旭明,李来峰,王浩贤,王野,陈旸. 土壤中次生与碎屑组分的差异性剥蚀[J]. 地球科学进展, 2020, 35(8): 826-838.
[6] 温学发,张心昱,魏杰,吕斯丹,王静,陈昌华,宋贤威,王晶苑,戴晓琴. 地球关键带视角理解生态系统碳生物地球化学过程与机制[J]. 地球科学进展, 2019, 34(5): 471-479.
[7] 吴泽燕,章程,蒋忠诚,罗为群,曾发明. 岩溶关键带及其碳循环研究进展[J]. 地球科学进展, 2019, 34(5): 488-498.
[8] 黄恩清,孔乐,田军. 冷水珊瑚测年与大洋中—深层水碳储库[J]. 地球科学进展, 2019, 34(12): 1243-1251.
[9] 唐子剑, 康明, 李军. 基于勘探工程位置建模方法和储量估算[J]. 地球科学进展, 2017, 32(8): 839-849.
[10] 汪品先. 巽他陆架——淹没的亚马逊河盆地?[J]. 地球科学进展, 2017, 32(11): 1119-1125.
[11] 贾国东. 冰期出露的巽他陆架:重要的陆地碳储库?[J]. 地球科学进展, 2017, 32(11): 1157-1162.
[12] 聂红涛, 王蕊, 赵伟, 罗晓凡, 祁第, 鹿有余, 张远辉, 魏皓. 北冰洋太平洋扇区碳循环变化机制研究面临的关键科学问题与挑战[J]. 地球科学进展, 2017, 32(10): 1084-1092.
[13] 焦念志, 李超, 王晓雪. 海洋碳汇对气候变化的响应与反馈[J]. 地球科学进展, 2016, 31(7): 668-681.
[14] 张洪瑞, 刘传联, 梁丹. 热带海洋生产力:现代过程与地质记录[J]. 地球科学进展, 2016, 31(3): 277-285.
[15] 赵彬, 姚鹏, 于志刚. 有机碳—氧化铁结合对海洋环境中沉积有机碳保存的影响[J]. 地球科学进展, 2016, 31(11): 1151-1158.
阅读次数
全文


摘要