Spatial and temporal variation of metabolism in inland water transects under changing water regime conditions

Yao Lu; Yang Gao; Dag Olav Hessen; Junjie Jia; Shuoyue Wang; Xianrui Ha; Huamao Yuan; Jinming Song

doi:10.59717/j.tiw.2026.100011

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Spatial and temporal variation of metabolism in inland water transects under changing water regime conditions

1.
Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266404, China
2.
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing100101, China
3.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
4.
Department of Biosciences, University of Oslo, Oslo N-0316, Norway

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Corresponding author: gaoyang@igsnrr.ac.cn
Received Date: March 25, 2026

Accepted Date: September 04, 2026

Published Online: September 07, 2026

The Innovation Water https://doi.org/10.59717/j.tiw.2026.100011 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Gross primary productivity (GPP) and ecosystem respiration (ER) were measured using two complementary methods.
  
  Water regime indirectly regulated metabolism via water-quality variables, with 55-day seasonal hysteresis.
  
  Depletion of organic carbon supplied by the watershed and CO₂ emissions increased at high GPP.

ABSTRACT

ABSTRACT

Investigating the regulatory mechanisms of inland water metabolism can elucidate its role in biogeochemical cycling and CO₂ emissions. However, the underlying processes remain poorly understood, especially for systems with severe water regime fluctuations or large spatial heterogeneity in water regime, such as rivers and water conveyance-type lakes. The diel O₂ method and the ^18/16O technology are combined in this study to estimate the metabolism of such inland water systems along transects, covering temporal and spatial resolutions. The Yangtze River and Poyang Lake (a large water conveyance-type lake connected to the Yangtze River) were used to examine how hydrological dynamics and water quality parameters regulate aquatic metabolism. The lake metabolic rates estimated by ^18/16O technology were as follows: gross primary productivity (GPP) was 16.0±16.0 g O₂ m^-2 d^-1 (mean ± SD), ecosystem respiration (ER) was 18.0±17.8 g O₂ m^-2 d^-1, and net ecosystem productivity (NEP) was -2.0±2.8 g O₂ m^-2 d^-1, suggesting net heterotrophy. Water regime (including water level, flow, and water temperature) was strongly associated with metabolic rates, with water quality parameters (especially F^-, Fe³⁺, DOC, POC) potentially mediating these relationships. On an annual timescale, metabolism exhibited seasonal hysteresis relative to water regime, with metabolic processes lagging hydrological changes by 55 days. Peaks in GPP and ER occurred during flow recession phases rather than at peak flows. This study demonstrates that water regime fluctuations indirectly regulate aquatic metabolism by modulating water quality parameters, with regulatory mechanisms differing between temporal and spatial scales. These findings deepen our mechanistic understanding of hydrological regulation on aquatic metabolism and provide critical information for predicting CO₂ emissions from hydrologically dynamic inland waters under future environmental change.
- net ecosystem productivity /
- ^18/16O and diel O₂ method /
- heterotrophic /
- water conveyance-type lakes /
- the Poyang Lake -Yangtze River system

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REFERENCES

[1]	Palmer M. and Ruhi A. (2019). Linkages between flow regime, biota, and ecosystem processes: Implications for river restoration. Science 365:1264. DOI:10.1126/science.aaw2087 View in Article CrossRef Google Scholar
[2]	Karpatne A., Khandelwal A., Chen X., et al. (2016). Global monitoring of inland water dynamics: State-of-the-art, challenges, and opportunities. Springer International Publishing. DOI: 10.1007/978-3-319-31858-5_7 View in Article Google Scholar
[3]	Palmer S. C. J., Kutser T. and Hunter P. D. (2015). Remote sensing of inland waters: Challenges, progress and future directions. Remote Sens. Environ. 157:1−8. DOI:10.1016/j.rse.2014.09.021 View in Article CrossRef Google Scholar
[4]	Tranvik L. J., Cole J. J. and Prairie Y. T. (2018). The study of carbon in inland waters-from isolated ecosystems to players in the global carbon cycle. Limnol. Oceanogr. Lett. 3:41−48. DOI:10.1002/lol2.10068 View in Article CrossRef Google Scholar
[5]	Battin T. J., Luyssaert S., Kaplan L. A., et al. (2009). The boundless carbon cycle. Nat. Geosci. 2:598−600. DOI:10.1038/ngeo618 View in Article CrossRef Google Scholar
[6]	Tranvik L. J., Downing J. A., Cotner J. B., et al. (2009). Lakes and reservoirs as regulators of carbon cycling and climate. Limnol. Oceanogr. 54:2298−2314. DOI:10.4319/lo.2009.54.6_part_2.2298 View in Article CrossRef Google Scholar
[7]	Gao Y., Jia J., Lu Y., Yang T., et al. (2021). Determining dominating control mechanisms of inland water carbon cycling processes and associated gross primary productivity on regional and global scales. Earth-Sci. Rev. 213:103497. DOI:10.1016/j.earscirev.2020.103497 View in Article CrossRef Google Scholar
[8]	Bogard M. J., Kuhn C. D., Johnston S. E., et al. (2019). Negligible cycling of terrestrial carbon in many lakes of the arid circumpolar landscape. Nat. Geosci. 12:180. DOI:10.1038/s41561-019-0299-5 View in Article CrossRef Google Scholar
[9]	Staehr P. A., Testa J. M., Kemp W. M., et al. (2012). The metabolism of aquatic ecosystems: history, applications, and future challenges. Aquat. Sci. 74:15−29. DOI:10.1007/s00027-011-0199-2 View in Article CrossRef Google Scholar
[10]	Bender M., Grande K., Johnson K., et al. (1987). A comparison of 4 methods for determining planktonic community production. Limnol. Oceanogr. 32:1085−1098. DOI:10.4319/lo.1987.32.5.1085 View in Article CrossRef Google Scholar
[11]	Odum H. (1956). Primary production in flowing waters. Limnol. Oceanogr. 1:102–117(1956). DOI: 10.4319/lo.1956.1.2.0102 View in Article Google Scholar
[12]	Staehr P. A., Bade D., Van de Bogert M. C., et al. (2010). Lake metabolism and the diel oxygen technique: State of the science. Limnology and Oceanography: Methods 8:628−644. DOI:10.4319/lom.2010.8.0628 View in Article CrossRef Google Scholar
[13]	Peeters F., Atamanchuk D., Tengberg A., et al. (2016). Lake metabolism: Comparison of lake metabolic rates estimated from a Diel CO₂ and the common Diel O₂ technique. PLoS One 11:e0168393. DOI:10.1371/journal.pone.0168393 View in Article CrossRef Google Scholar
[14]	Bogard M. J., Vachon D., St-Gelais N. F., et al. (2017). Using oxygen stable isotopes to quantify ecosystem metabolism in northern lakes. Biogeochemistry 133:347−364. DOI:10.1007/s10533-017-0338-5 View in Article CrossRef Google Scholar
[15]	Peeters F., Hofmann H. and Fernandez J. E. (2019). On the calculation of lake metabolic rates: Diel O² and ^18/16O technique. Water Res. 165:114990. DOI:10.1016/j.watres.2019.114990 View in Article CrossRef Google Scholar
[16]	Hotchkiss E. R., Sadro S. and Hanson P. C. (2018). Toward a more integrative perspective on carbon metabolism across lentic and lotic inland waters. Limnol. Oceanogr. Lett. 3. DOI: 10.1002/lol2.10081 View in Article Google Scholar
[17]	Bogard M. J., St-Gelais N. F., Vachon D., et al. (2020). Patterns of spring/summer open-water metabolism across boreal lakes. Ecosystems 23:1581−1597. DOI:10.1007/s10021-020-00487-7 View in Article CrossRef Google Scholar
[18]	Jeppesen E., Audet J., Davidson T. A., et al. (2021). Nutrient Loading, temperature and heat wave effects on nutrients, oxygen and metabolism in shallow lake mesocosms pre-adapted for 11 years. Water 13:127. DOI:10.3390/w13020127 View in Article CrossRef Google Scholar
[19]	Laas A., Noges P., Koiv T., et al. (2012). High-frequency metabolism study in a large and shallow temperate lake reveals seasonal switching between net autotrophy and net heterotrophy. Hydrobiologia 694:57−74. DOI:10.1007/s10750-012-1131-z View in Article CrossRef Google Scholar
[20]	Song, C., Dodds, W. K., Rüegg, J., et al. (2018). Continental-scale decrease in net primary productivity in streams due to climate warming. Nat. Geosci. 11:415. DOI:10.1038/s41561-018-0125-5 View in Article CrossRef Google Scholar
[21]	Scharfenberger U., Jeppesen E., Beklioğlu M., et al. (2019). Effects of trophic status, water level, and temperature on shallow lake metabolism and metabolic balance: A standardized pan-European mesocosm experiment. Limnol. Oceanogr. 64:616−631. DOI:10.1002/lno.11064 View in Article CrossRef Google Scholar
[22]	Hoellein T. J., Bruesewitz D. A. and Richardson D. C. (2013). Revisiting Odum (1956): A synthesis of aquatic ecosystem metabolism. Limnol. Oceanogr. 58:2089−2100. DOI:10.4319/lo.2013.58.6.2089 View in Article CrossRef Google Scholar
[23]	Solomon C. T., Bruesewitz D. A., Richardson D. C., et al. (2013). Ecosystem respiration: Drivers of daily variability and background respiration in lakes around the globe. Limnol. Oceanogr. 58:849−866. DOI:10.4319/lo.2013.58.3.0849 View in Article CrossRef Google Scholar
[24]	Reynolds C. S. (2006). The ecology of phytoplankton. Cambridge University Press. DOI: 10.1017/CBO9780511542145 View in Article Google Scholar
[25]	Bernhardt E., Heffernan J., Grimm N., et al. (2017). The metabolic regimes of flowing waters. Limnol. Oceanogr. 63:S99-S118. DOI: 10.1002/lno.10726 View in Article Google Scholar
[26]	Yvon-Durocher G., Caffrey J. M., Cescatti A., et al. (2012). Reconciling the temperature dependence of respiration across timescales and ecosystem types. Nature 487:472−476. DOI:10.1038/nature11205 View in Article CrossRef Google Scholar
[27]	Needoba J. A., Peterson T. D. and Johnson K. S. (2012). Method for the quantification of aquatic primary production and net ecosystem metabolism using in situ dissolved oxygen sensors. Humana Press. DOI: 10.1007/978-1-61779-915-0_4 View in Article Google Scholar
[28]	Lu Y., Gao Y., Jia J., et al. (2022). Water conveyance-type lake systems shift toward carbon sources under regulatory balanced water level metabolic processes. ACS Earth Space Chem. 6:2400−2411. DOI:10.1021/acsearthspacechem.2c00141 View in Article CrossRef Google Scholar
[29]	Arndt S., Vanderborght J. P. and Regnier P. (2007). Diatom growth response to physical forcing in a macrotidal estuary: Coupling hydrodynamics, sediment transport, and biogeochemistry. J. Geophys. Res.-Oceans 112:C05045. DOI:10.1029/2006JC003581 View in Article CrossRef Google Scholar
[30]	Wang Z., Chai F., Xue H., et al. (2021). Light regulation of phytoplankton growth in San Francisco Bay studied using a 3D sediment transport model. Front. Mar. Sci. 8:633707. DOI:10.3389/fmars.2021.633707 View in Article CrossRef Google Scholar
[31]	Molinari B., Stewart‐Koster B. and Malthus T. J., (2022). Impact of water resource development on connectivity and primary productivity across a tropical river floodplain. J. Appl. Ecol. 59:1013–1025. DOI: 10.1111/1365-2664.14111. View in Article Google Scholar
[32]	Marzolf N. S. and Ardon M. (2021). Ecosystem metabolism in tropical streams and rivers: a review and synthesis. Limnol. Oceanogr. 66:1627−1638. DOI:10.1002/lno.11707 View in Article CrossRef Google Scholar
[33]	Battin T. J., Lauerwald R., Bernhardt E. S., et al. (2023). River ecosystem metabolism and carbon biogeochemistry in a changing world. Nature 613:449−459. DOI:10.1038/s41586-022-05500-8 View in Article CrossRef Google Scholar
[34]	Lu Y., Gao Y., Jia J., et al. (2021). Revealing carbon balance characteristics in a water conveyance-type lake and differences in carbon sources through its connective hydrological channels. J. Hydrol. 592:125820. DOI:10.1016/j.jhydrol.2020.125820 View in Article CrossRef Google Scholar
[35]	Loken L. C., Van Nieuwenhuyse E. E., Dahlgren R. A., et al. (2021). Assessment of multiple ecosystem metabolism methods in an estuary. Limnol. Oceanogr. Meth. 19:741−757. DOI:10.1002/lom3.10458 View in Article CrossRef Google Scholar
[36]	Barbosa P. M., Bodmer P., Stadler M., et al. (2023). Ecosystem metabolism Is the dominant source of carbon dioxide in three young boreal cascade-reservoirs (La Romaine Complex, Quebec). J. Geophys. Res.-Biogeosci. 128:e2022JG007253. DOI:10.1029/2022JG007253 View in Article CrossRef Google Scholar
[37]	Dai Z., Liu J. T., Wei W., et al. Detection of the three gorges dam influence on the changjiang (Yangtze River) submerged delta. Sci. Rep. 4:6600(2014). DOI: 10.1038/srep06600 View in Article Google Scholar
[38]	Chai Y., Li Y., Yang Y., et al. (2019). Influence of climate variability and reservoir operation on streamflow in the Yangtze River. Sci. Rep. 9:5060(2019). DOI: 10.1038/s41598-019-41583-6 View in Article Google Scholar
[39]	Milliman J. D. and Farnsworth K. L. (2013). River discharge to the coastal ocean: a global synthesis. Cambridge University Press. DOI: 10.1017/CBO9780511781247 View in Article Google Scholar
[40]	Guo R., Zhu Y. and Liu Y. (2020). A Comparison study of precipitation in the Poyang and the Dongting lake basins from 1960-2015. Sci Rep 10:3381. DOI:10.1038/s41598-020-60243-8 View in Article CrossRef Google Scholar
[41]	Gao J. H., Xu X., Jia J., et al. (2015). A numerical investigation of freshwater and sediment discharge variations of Poyang Lake catchment, China over the last 1000years. Holocene 25:1470−1482. DOI:10.1177/0959683615585843 View in Article CrossRef Google Scholar
[42]	Wu Z., He H., Cai Y., et al. (2014). Spatial distribution of chlorophyll a and its relationship with the environment during summer in Lake Poyang: A Yangtze-connected lake. Hydrobiologia 732:61−70. DOI:10.1007/s10750-014-1844-2 View in Article CrossRef Google Scholar
[43]	Jia J., Gao Y., Song X., et al. (2019). Characteristics of phytoplankton community and water net primary productivity response to the nutrient status of the Poyang Lake and Gan River, China. Ecohydrology 12:e2136. DOI:10.1002/eco.2136 View in Article CrossRef Google Scholar
[44]	Wang X., Xu J., Wu Z., et al. (2019). Effect of annual prescribed burning of wetlands on soil organic carbon fractions: A 5-year study in Poyang, China. Ecol. Eng. 138:219−226. DOI:10.1016/j.ecoleng.2019.07.028 View in Article CrossRef Google Scholar
[45]	Wang S., Gao Y., Jia J., et al. (2022). Determining whether hydrological processes drive carbon source and sink conversion shifts in a large floodplain-lake system in China. Water Res. 224:119105. DOI:10.1016/j.watres.2022.119105 View in Article CrossRef Google Scholar
[46]	Liu X., Teubner K. and Chen Y. (2016). Water quality characteristics of Poyang Lake, China, in response to changes in the water level. Hydrol. Res. 47:238−248. DOI:10.2166/nh.2016.209 View in Article CrossRef Google Scholar
[47]	Qin Y., Gou Y., Yu Z., et al. (2021). Effects of environmental factors on the methane and carbon dioxide fluxes at the middle of Three Gorges Reservoir. J. Water Clim. Chang. 12:4007−4020. DOI:10.2166/wcc.2021.081 View in Article CrossRef Google Scholar
[48]	Johnson K. S. (2010). Simultaneous measurements of nitrate, oxygen, and carbon dioxide on oceanographic moorings: Observing the Redfield ratio in real time. Limnol. Oceanogr. 55:615−627. DOI:10.4319/lo.2010.55.2.0615 View in Article CrossRef Google Scholar
[49]	Bas-Silvestre M., Quintana X. D., Compte J., et al. (2020). Ecosystem metabolism dynamics and environmental drivers in mediterranean confined coastal lagoons. Estuar. Coast. Shelf Sci. 245:106989. DOI:10.1016/j.ecss.2020.106989 View in Article CrossRef Google Scholar
[50]	Yu G., Chen Z., Piao S., et al. (2014). High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region. Proc. Natl. Acad. Sci. USA 111:4910−4915. DOI:10.1073/pnas.1317065111 View in Article CrossRef Google Scholar
[51]	Davide V., Pardos M., Diserens J., et al. (2003). Characterisation of bed sediments and suspension of the river Po (Italy) during normal and high flow conditions. Water Res. 37:2847−2864. DOI:10.1016/S0043-1354(03)00133-7 View in Article CrossRef Google Scholar
[52]	Tan Z., Yao H. and Zhuang Q. (2018). A small temperate lake in the 21st century: Dynamics of water temperature, ice phenology, dissolved oxygen, and chlorophyll a. Water Resour. Res. 54:4681–4699 DOI:10.1029/2017WR022334 View in Article CrossRef Google Scholar
[53]	Markou G., Vandamme D. and Muylaert K. (2014). Microalgal and cyanobacterial cultivation: The supply of nutrients. Water Res. 65:186−202. DOI:10.1016/j.watres.2014.07.025 View in Article CrossRef Google Scholar
[54]	Jane S. F., Hansen G. J. A., Kraemer B. M., et al. (2021). Widespread deoxygenation of temperate lakes. Nature 594:66–70. DOI: 10.1038/s41586-021-03550-y View in Article Google Scholar
[55]	Azevedo I. C., Bordalo A. A. and Duarte P. (2014). Influence of freshwater inflow variability on the Douro estuary primary productivity: A modelling study. Ecol. Model. 272:1−15. DOI:10.1016/j.ecolmodel.2013.09.010 View in Article CrossRef Google Scholar
[56]	Chen Z. Y., Li J. F., Shen H. T., et al. (2001). Yangtze River of China: Historical analysis of discharge variability and sediment flux. Geomorphology 41:7−91. DOI:10.1016/S0169-555X(01)00106-4 View in Article CrossRef Google Scholar
[57]	Wu S., Hu Z., Wang Z., et al. (2021). Spatiotemporal variations in extreme precipitation on the middle and lower reaches of the Yangtze River Basin (1970-2018). Quat. Int. 592:80−96. DOI:10.1016/j.quaint.2021.04.010 View in Article CrossRef Google Scholar
[58]	Ding T., Gao J., Tian S., et al. (2014). Chemical and isotopic characteristics of the water and suspended particulate materials in the Yangtze River and their geological and environmental implications. Acta Geol. Sin.-Engl. Ed. 88:276−360. DOI:10.1111/1755-6724.12197 View in Article CrossRef Google Scholar
[59]	Zhang L., Xue M., Wang M., et al. (2014). 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. Geophys. Res.-Biogeosci. 119:741−757. DOI:10.1002/2012JG002230 View in Article CrossRef Google Scholar
[60]	Thrane J. E., Hessen, D. O. and Andersen T. (2014). The absorption of light in lakes: Negative impact of dissolved organic carbon on primary productivity. Ecosystems 17:1040−1052. DOI:10.1007/s10021-014-9776-2 View in Article CrossRef Google Scholar
[61]	Bade D. L., Carpenter S. R., et al. (2004). Controls of δ¹³C-DIC in lakes: Geochemistry, lake metabolism, and morphometry. Limnol. Oceanogr. 49:1160−1172. DOI:10.4319/lo.2004.49.4.1160 View in Article CrossRef Google Scholar
[62]	Bernhardt E. S., Savoy P., Vlah M. J., et al. (2022). Light and flow regimes regulate the metabolism of rivers. Proc. Natl. Acad. Sci. USA 119:e2121976119. DOI:10.1073/pnas.2121976119 View in Article CrossRef Google Scholar
[63]	Crichton R. (2002). Inorganic biochemistry of iron metabolism: From molecular mechanisms to clinical consequences, 2nd Edition. DOI: 10.1002/0470845791 View in Article Google Scholar
[64]	Forsberg B. R., Melack J. M., Richey J. E., et al. (2017). Regional and seasonal variability in planktonic photosynthesis and planktonic community respiration in Amazon floodplain lakes. Hydrobiologia 800:187−206. DOI:10.1007/s10750-017-3222-3 View in Article CrossRef Google Scholar
[65]	Holtgrieve G. W., Arias M. E., Irvine K. N., et al. (2013). Patterns of ecosystem metabolism in the Tonle Sap Lake, cambodia with links to capture fisheries. PLoS One 8:e71395. DOI:10.1371/journal.pone.0071395 View in Article CrossRef Google Scholar
[66]	Rudorff C. M., Melack J. M., MacIntyre S., et al. (2011). Seasonal and spatial variability of CO2 emission from a large floodplain lake in the lower Amazon. J. Geophys. Res.-Biogeosci. 116:G04007. DOI:10.1029/2011JG001919 View in Article CrossRef Google Scholar
[67]	Xu Y. J. and Xu Z. (2022). Carbon dioxide degassing and lateral dissolved carbon export during the unprecedented 2019 Mississippi river mega flood - Implications for large river carbon transport under future climate. J. Hydrol. 614:128650. DOI:10.1016/j.jhydrol.2022.128650 View in Article CrossRef Google Scholar

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Lu Y., Gao Y., Hessen D. O., et al. (2026). Spatial and temporal variation of metabolism in inland water transects under changing water regime conditions. The Innovation Water 1:100011. https://doi.org/10.59717/j.tiw.2026.100011

Lu Y., Gao Y., Hessen D. O., et al. (2026). Spatial and temporal variation of metabolism in inland water transects under changing water regime conditions. The Innovation Water 1:100011. https://doi.org/10.59717/j.tiw.2026.100011

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Spatial and temporal variation of metabolism in inland water transects under changing water regime conditions

GRAPHICAL ABSTRACT

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