Articles | Volume 24, issue 2
https://doi.org/10.5194/hess-24-697-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-697-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Feb 2020
Research article |
| 17 Feb 2020
| 17 Feb 2020
Multimodel simulation of vertical gas transfer in a temperate lake
Sofya Guseva
CORRESPONDING AUTHOR
Institute for Environmental Sciences, University of Koblenz-Landau, Landau, Germany
previous at: Department of Geography, Lomonosov Moscow State University, Moscow, Russia
Tobias Bleninger
Graduate Program on Water Resources and Environmental Engineering, Federal University of Paraná, Curitiba, Brazil
Klaus Jöhnk
CSIRO Land and Water, Black Mountain, Canberra ACT 2601, Australia
Bruna Arcie Polli
Graduate Program on Water Resources and Environmental Engineering, Federal University of Paraná, Curitiba, Brazil
Pacific Northwest National Laboratory, Richland, Washington, USA.
Wim Thiery
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium
Qianlai Zhuang
Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana, USA
James Anthony Rusak
Dorset Environmental Science Centre, Ontario Ministry of Environment, Conservation and Parks, Dorset, Ontario, P0A 1E0, Canada
Huaxia Yao
Dorset Environmental Science Centre, Ontario Ministry of Environment, Conservation and Parks, Dorset, Ontario, P0A 1E0, Canada
Andreas Lorke
Institute for Environmental Sciences, University of Koblenz-Landau, Landau, Germany
Victor Stepanenko
Laboratory for Supercomputer Modeling of Climate System Processes, Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
Department of Meteorology and Climatology, Faculty of Geography, Lomonosov Moscow State University, Moscow, Russia
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Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 3833–3852, https://doi.org/10.5194/hess-29-3833-2025, https://doi.org/10.5194/hess-29-3833-2025, 2025
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Flow depth and velocity determine various river functions, but their high-resolution simulations are expensive. Here, we developed a downscaling approach that can provide fast and accurate estimation of high-resolution river hydrodynamics. The 84-fold acceleration achieved by the method makes reliable flood risk analysis that needs hundreds or thousands of model runs feasible. More importantly, it provides an opportunity to couple large-scale hydrodynamics with local processes in river models.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data, 17, 2713–2733, https://doi.org/10.5194/essd-17-2713-2025, https://doi.org/10.5194/essd-17-2713-2025, 2025
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We have developed new maps that reveal how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2500 gauges and a wealth of climate and environmental information. The maps are a critical step in understanding and predicting how carbon moves through our environment, hence making them a useful tool for tackling climate challenges.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
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Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella
Biogeosciences, 22, 1651–1671, https://doi.org/10.5194/bg-22-1651-2025, https://doi.org/10.5194/bg-22-1651-2025, 2025
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Boreal rivers are significant sources of carbon dioxide (CO2) and methane (CH4) for the atmosphere, but the controls of these emissions are uncertain. We measured 4 months of CO2 and CH4 exchanges between a regulated boreal river and the atmosphere with eddy covariance. We found statistical relationships between the gas exchange and several environmental variables, the most important of which were dissolved CO2 partial pressure in water, wind speed and water temperature.
Manon Maisonnier, Maoyuan Feng, David Bastviken, Sandra Arndt, Ronny Lauerwald, Aidin Jabbari, Goulven Gildas Laruelle, Murray D. MacKay, Zeli Tan, Wim Thiery, and Pierre Regnier
EGUsphere, https://doi.org/10.5194/egusphere-2025-1306, https://doi.org/10.5194/egusphere-2025-1306, 2025
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A new process-based modelling framework, FLaMe v1.0 (Fluxes of Lake Methane version 1.0), is developed to simulate methane (CH4) emissions from lakes at large scales. FLaMe couples the dynamics of organic carbon, oxygen and methane in lakes and rests on an innovative, computationally efficient lake clustering approach for the simulation of CH4 emissions across a large number of lakes. The model evaluation suggests that FLaMe captures the sub-annual and spatial variability of CH4 emissions well.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
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This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-2785, https://doi.org/10.5194/egusphere-2024-2785, 2024
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Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
Yiming Xu, Qianlai Zhuang, Bailu Zhao, Michael Billmire, Christopher Cook, Jeremy Graham, Nancy French, and Ronald Prinn
EGUsphere, https://doi.org/10.5194/egusphere-2024-1324, https://doi.org/10.5194/egusphere-2024-1324, 2024
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We use a process-based model to simulate the fire impacts on soil thermal and hydrological dynamics and carbon budget of forest ecosystems in Northern Eurasia based on satellite-derived burn severity data. We find that fire severity generally increases in this region during the study period. Simulations indicate that fires increase soil temperature and water runoff. Fires lead the forest ecosystems to lose 2.3 Pg C, shifting the forests from a carbon sink to a source in this period.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Dongyu Feng, Zeli Tan, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 27, 3911–3934, https://doi.org/10.5194/hess-27-3911-2023, https://doi.org/10.5194/hess-27-3911-2023, 2023
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This study assesses the flood risks concurrently induced by river flooding and coastal storm surge along the coast of the contiguous United States using statistical and numerical models. We reveal a few hotspots of such risks, the critical spatial variabilities within a river basin and over the whole US coast, and the uncertainties of the risk assessment. We highlight the importance of weighing different risk measures to avoid underestimating or exaggerating the compound flood impacts.
Ye Yuan, Qianlai Zhuang, Bailu Zhao, and Narasinha Shurpali
EGUsphere, https://doi.org/10.5194/egusphere-2023-1047, https://doi.org/10.5194/egusphere-2023-1047, 2023
Preprint archived
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We use a biogeochemistry model to calculate the regional N2O emissions considering the effects of N2O uptake, thawing permafrost, and N deposition. Our simulations show there is an increasing trend in regional net N2O emissions from 1969 to 2019. Annual N2O emissions exhibited big spatial variabilities. Nitrogen deposition leads to a significant increase in emission. Our results suggest that in the future, the pan-Arctic terrestrial ecosystem might act as an even larger N2O.
Xiangyu Liu and Qianlai Zhuang
Biogeosciences, 20, 1181–1193, https://doi.org/10.5194/bg-20-1181-2023, https://doi.org/10.5194/bg-20-1181-2023, 2023
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We are among the first to quantify methane emissions from inland water system in the pan-Arctic. The total CH4 emissions are 36.46 Tg CH4 yr−1 during 2000–2015, of which wetlands and lakes were 21.69 Tg yr−1 and 14.76 Tg yr−1, respectively. By using two non-overlap area change datasets with land and lake models, our simulation avoids small lakes being counted twice as both lake and wetland, and it narrows the gap between two different methods used to quantify regional CH4 emissions.
Bailu Zhao and Qianlai Zhuang
Biogeosciences, 20, 251–270, https://doi.org/10.5194/bg-20-251-2023, https://doi.org/10.5194/bg-20-251-2023, 2023
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In this study, we use a process-based model to simulate the northern peatland's C dynamics in response to future climate change during 1990–2300. Northern peatlands are projected to be a C source under all climate scenarios except for the mildest one before 2100 and C sources under all scenarios afterwards.
We find northern peatlands are a C sink until pan-Arctic annual temperature reaches −2.09 to −2.89 °C. This study emphasizes the vulnerability of northern peatlands to climate change.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
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Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Victor Lomov, Victor Stepanenko, Maria Grechushnikova, and Irina Repina
EGUsphere, https://doi.org/10.5194/egusphere-2022-329, https://doi.org/10.5194/egusphere-2022-329, 2022
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We present the first mechanistic model LAKE2.3 for prediction of methane emissions from artificial reservoirs. Estimates of CH4 emissions from the Mozhaysk reservoir (Moscow region) provided by the model are demonstrated. Methane annual emissions through diffusion, ebullition and downstream degassing according to in situ measurements and model simulations are presented. The experiments with the model allowed to determine the most sensitive model parameters for calibration of methane fluxes.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Mayra Ishikawa, Wendy Gonzalez, Orides Golyjeswski, Gabriela Sales, J. Andreza Rigotti, Tobias Bleninger, Michael Mannich, and Andreas Lorke
Geosci. Model Dev., 15, 2197–2220, https://doi.org/10.5194/gmd-15-2197-2022, https://doi.org/10.5194/gmd-15-2197-2022, 2022
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Reservoir hydrodynamics is often described in numerical models differing in dimensionality. 1D and 2D models assume homogeneity along the unresolved dimension. We compare the performance of models with 1 to 3 dimensions. All models presented reasonable results for seasonal temperature dynamics. Neglecting longitudinal transport resulted in the largest deviations in temperature. Flow velocity could only be reproduced by the 3D model. Results support the selection of models and their assessment.
Jinshi Jian, Xuan Du, Juying Jiao, Xiaohua Ren, Karl Auerswald, Ryan Stewart, Zeli Tan, Jianlin Zhao, Daniel L. Evans, Guangju Zhao, Nufang Fang, Wenyi Sun, Chao Yue, and Ben Bond-Lamberty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-87, https://doi.org/10.5194/essd-2022-87, 2022
Manuscript not accepted for further review
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Field soil loss and sediment yield due to surface runoff observations were compiled into a database named AWESOME: Archive for Water Erosion and Sediment Outflow MEasurements. Annual soil erosion data from 1985 geographic sites and 75 countries have been compiled into AWESOME. This database aims to be an open framework for the scientific community to share field-based annual soil erosion measurements, enabling better understanding of the spatial and temporal variability of annual soil erosion.
Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
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Existing riverbed sediment particle size data are sparsely available at individual sites. We develop a continuous map of median riverbed sediment particle size over the contiguous US corresponding to millions of river segments based on the existing observations and machine learning methods. This map is useful for research in large-scale river sediment using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
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We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Junrong Zha and Qianlai Zhuang
Biogeosciences, 18, 6245–6269, https://doi.org/10.5194/bg-18-6245-2021, https://doi.org/10.5194/bg-18-6245-2021, 2021
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This study incorporated moss into an extant biogeochemistry model to simulate the role of moss in carbon dynamics in the Arctic. The interactions between higher plants and mosses and their competition for energy, water, and nutrients are considered in our study. We found that, compared with the previous model without moss, the new model estimated a much higher carbon accumulation in the region during the last century and this century.
Jinshi Jian, Xuan Du, Ryan D. Stewart, Zeli Tan, and Ben Bond-Lamberty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-283, https://doi.org/10.5194/essd-2020-283, 2020
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Field soil loss due to surface runoff observations were compiled into a global database (SoilErosionDB). The database focuses on three erosion-related metrics – surface runoff, soil erosion, and nutrient leaching – and also records background information. Data from 99 geographic sites and 22 countries around the world have been compiled into SoilErosionDB. SoilErosionDB aims to be a data framework for the scientific community to share field-based soil erosion measurements.
Junrong Zha and Qianla Zhuang
Biogeosciences, 17, 4591–4610, https://doi.org/10.5194/bg-17-4591-2020, https://doi.org/10.5194/bg-17-4591-2020, 2020
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This study incorporated microbial dormancy into a detailed microbe-based biogeochemistry model to examine the fate of Arctic carbon budgets under changing climate conditions. Compared with the model without microbial dormancy, the new model estimated a much higher carbon accumulation in the region during the last and current century. This study highlights the importance of the representation of microbial dormancy in earth system models to adequately quantify the carbon dynamics in the Arctic.
Cited articles
Ashton, G. D.: River and lake ice thickening, thinning and snow ice formation, Cold Reg. Sci. Technol., 68, 3–19, https://doi.org/10.1016/j.coldregions.2011.05.004, 2011. a, b, c
Balsamo, G., Dutra, E., Stepanenko, V. M., Viterbo, P., Miranda, P., and Mironov, D.: Deriving an effective lake depth from satellite lake surface temperature data: a feasibility study with MODIS data, Boreal Environ. Res., 15, 178–190, https://doi.org/10.21957/be525ccu, 2010. a
Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M., and Enrich-Prast, A.: Freshwater methane emissions offset the continental carbon sink, Science, 331, 50, https://doi.org/10.1126/science.1196808, 2011. a
Camacho, A.: On the occurrence and ecological features of deep chlorophyll maxima (DCM) in Spanish stratified lakes, Limnetica, 25, 453–478, 2006. a
Cantin, A., Beisner, B. E., Gunn, J. M., Prairie, Y. T., Winter, J. G.: Effects of thermocline deepening on lake plankton communities, Can. J. Fish. Aquat., 68, 260–276, 2011. a
Choulga, M., Kourzeneva, E., Zakharova, E., and Doganovsky, A.: Estimation of the mean depth of boreal lakes for use in numerical weather prediction and climate modeling, Tellus A, 66, 21295, https://doi.org/10.3402/tellusa.v66.21295, 2014. a
Cox, E. T.: Counts and Measurements of Ontario Lakes: Watershed Unit Summaries Based on Maps of Various Scales by Wathershed Unit, Ontario Ministry of Natural Resourses, Toronto, Ontario, Canada, 1978. a
Docquier, D., Thiery, W., Lhermitte, S., and Van Lipzig, N.: Multi-year wind dynamics around Lake Tanganyika, Clim. Dynam., 47, 3191–3202, https://doi.org/10.1007/s00382-016-3020-z, 2016. a
Duffy, C. J., Dugan, H. A., and Hanson, P. C.: The age of water and carbon in lake-catchments: A simple dynamical model, Limnol. Oceanogr. Lett., 3, 236–245, https://doi.org/10.1002/lol2.10070, 2018. a
Fang, X. and Stefan, H. G.: Long-term lake water temperature and ice cover simulations/measurements, Cold Reg. Sci. Technol., 24, 289–304, 1996. a
Fer, I., Lemmin, U., and Thorpe, S. A.: Cascading of water down the sloping sides of a deep lake in winter, Geophys. Res. Lett., 28, 2093–2096, https://doi.org/10.1029/2000GL012599, 2001. a
Fer, I., Lemmin, U., and Thorpe, S. A.: Winter cascading of cold water in Lake Geneva, J. Geophys. Res.-Oceans, 107, 3060, https://doi.org/10.1029/2001JC000828, 2002. a
Forbes, G. S. and Meritt, J. H.: Mesoscale vortices over the Great Lakes in wintertime, Mon. Weather Rev., 112, 377–381, 1984. a
Futter, M. N., Starr, M., Forsius, M., and Holmberg, M.: Modelling the effects of climate on long-term patterns of dissolved organic carbon concentrations in the surface waters of a boreal catchment, Hydrol. Earth Syst. Sci., 12, 437–447, https://doi.org/10.5194/hess-12-437-2008, 2008. a
Goudsmit, G. H., Burchard, H., Peeters, F., and Wüest, A.: Application of k−ε turbulence models to enclosed basins: The role of internal seiches, J. Geophys. Res.-Oceans, 107, 3230, https://doi.org/10.1029/2001JC000954, 2002. a
Heiskanen, J. J., Mammarella, I., Ojala, A., Stepanenko, V., Erkkilá, K. M., Miettinen, H., and Vesala, T.: Effects of water clarity on lake stratification and lake‐atmosphere heat exchange, J. Geophys. Res.-Atmos., 120, 7412–7428, https://doi.org/10.1002/2014JD022938, 2015. a
Hondzo, M. and Stefan, H. G.: Lake water temperature simulation model, J. Hydraul. Eng., 119, 1251–1273, 1993. a
Hostetler, S. W., Bates, G. T., and Giorgi, F.: Interactive coupling of a lake thermal model with a regional climate model, J. Geophys. Res.-Atmos., 98, 5045–5057, 1993. a
Jammet, M., Crill, P., Dengel, S., and Friborg, T.: Large methane emissions from a subarctic lake during spring thaw: Mechanisms and landscape significance, J. Geophys. Res.-Biogeo., 120, 2289–2305, https://doi.org/10.1002/2015JG003137, 2015. a
Jöhnk, K. D. and Umlauf, L.: Modeling the metalimnetic oxygen minimum in a medium sized alpine lake, Ecol. Model., 136, 67–80, https://doi.org/10.1016/S0304-3800(00)00381-1, 2001. a, b
Jöhnk, K. D., Huisman, J. E. F., Sharples, J., Sommeijer, B. E. N., Visser, P. M., and Stroom, J. M.: Summer heatwaves promote blooms of harmful cyanobacteria, Global Change Biol., 14, 495–512, https://doi.org/10.1111/j.1365-2486.2007.01510.x, 2008. a, b
Kaden, H., Peeters, F., Lorke, A., Kipfer, R., Tomonaga, Y., and Karabiyikoglu, M.: Impact of lake level change on deep‐water renewal and oxic conditions in deep saline Lake Van, Turkey, Water Resour. Res., 46, W11508, https://doi.org/10.1029/2009WR008555, 2010. a
Karlsson, J., Giesler, R., Persson, J., and Lundin, E.: High emission of carbon dioxide and methane during ice thaw in high latitude lakes, Geophys. Res. Lett., 40, 1123–1127, https://doi.org/10.1002/grl.50152, 2013. a
Kirillin, G. B., Forrest, A. L., Graves, K. E., Fischer, A., Engelhardt, C., and Laval, B. E.: Axisymmetric circulation driven by marginal heating in ice‐covered lakes, Hydrol. Geophys. Res. Lett., 42, 2893–2900, https://doi.org/10.1002/2014GL062180, 2015. a
Kirillin, G. B., Wen, L., and Shatwell, T.: Seasonal thermal regime and climatic trends in lakes of the Tibetan highlands, Hydrol. Earth Syst. Sci., 21, 1895–1909, https://doi.org/10.5194/hess-21-1895-2017, 2017. a
Kiuru, P., Ojala, A., Mammarella, I., Heiskanen, J., Kämäräinen, M., Vesala, T., and Huttula, T.: Effects of climate change on CO2 concentration and efflux in a humic boreal lake: A modeling study, J. Geophys. Res.-Biogeo., 123, 2212–2233, https://doi.org/10.1029/2018JG004585, 2018. a
Kourzeneva, E.: External data for lake parameterisation in Numerical Weather Prediction and climate modeling, Boreal Environ. Res., 15, 165–177, 2010. a
Lehner, B. and Döll, P.: Development and validation of a global database of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22, https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004. a
Leppäranta, M.: Freezing of lakes and the evolution of their ice cover: Springer, Heidelberg, New York, Dordrecht, London, p. 301, https://doi.org/10.1007/978-3-642-29081-7, 2015. a
Long, Z., Perrie, W., Gyakum, J., Caya, D., and Laprise, R.: Northern lake impacts on local seasonal climate, J. Hydrometeorol., 8, 881–896, https://doi.org/10.1175/JHM591.1, 2007. a
Mahrt L.: Surface heterogeneity and vertical structure of the boundary layer, Bound.-Lay. Meteorol., 96, 33–62, https://doi.org/10.1023/A:1002482332477, 2000. a
McCullough, I. M., Dugan, H. A., Farrell, K. J., Morales-Williams, A. M., Ouyang, Z., Roberts, D., Scordo, F., Bartlett, S. L., Burke, S. M., Doubek, J. P., Krivak-Tetley, F. E., Skaff, N. K., Summers, J. C., Weathers, K. C., and Hanson, P. C.: Dynamic modeling of organic carbon fates in lake ecosystems, Ecol. Model., 386, 71–82, https://doi.org/10.1016/j.ecolmodel.2018.08.009, 2018. a
Mironov, D., Terzhevik, A., Kirillin, G., and Simoncelli, S.: FLake model website, available at: http://www.flake.igb-berlin.de/, last access: 16 February 2020. a
Mitchell, P. and Prepas, E. E. (Eds.): Atlas of Alberta lakes, The University of Alberta Press, Edmonton, 1990. a
Molot, L. A. and Dillon, P. J.: Nitrogen/phosphorus ratios and the prediction of chlorophyll in phosphorus-limited lakes in central Ontario, Can. J. Fish. Aquat. Sci., 48, 140–145, 1991. a
Perroud, M., Goyette, S., Martynov, A., Beniston, M., and Anneville, O.: Simulation of multiannual thermal profiles in deep Lake Geneva: a comparison of one-dimensional lake models, Limnol. Oceanogr.-Meth., 54, 1574–1594, https://doi.org/10.4319/lo.2009.54.5.1574, 2009. a
Phelps, A. R., Peterson, K. M., and Jeffries, M. O.: Methane efflux from high‐latitude lakes during spring ice melt, J. Geophys. Res.-Atmos., 103, 29029–29036, https://doi.org/10.1029/98JD00044, 1998. a
Polli, B. A. and Bleninger, T.: Modelagem do transporte de calor no reservatório Vossoroca, in: 21 Simpósio Brasileiro De Recursos
Hídricos, 22–27 November 2015, Federal District, Brasilia, Brazil, 1–8, 2015. a
Polli, B. A. and Bleninger, T.: Reservoir 1D heat transport model, J. Appl. Water Eng. Res., 1, 1–16, https://doi.org/10.1080/23249676.2018.1497560, 2018. a
Poole, H. H. and Atkins, W. R. G.: Photo-electric measurements of submarine illumination throughout the year, J. Mar. Biol. Assoc. UK, 16, 297–324, 1929. a
Raymond, P. A., Hartmann, J., Lauerwald, R., Sobek, S., McDonald, C., Hoover, M., Butman, D., Striegl, R., Mayorga, E., Humborg, C., Kortelainen, P., Dürr, H., Meybeck, M., Ciais, P., and Guth, P.: Global carbon dioxide emissions from inland waters, Nature, 503, 355–359, https://doi.org/10.1038/nature12760, 2013. a
Samolyubov, B. I.: Bottom stratified currents, Nauchny Mir, Moscow, 464 pp., 1999. a
Shatwell, T., Adrian, R., and Kirillin, G.: Planktonic events may cause polymictic-dimictic regime shifts in temperate lakes, Sci. Rep.-UK, 6, 24361, https://doi.org/10.1038/srep24361, 2016. a
Stefan, H. G. and Fang, X.: Dissolved oxygen model for regional lake analysis, Ecol. Model., 71, 37–68, https://doi.org/10.1016/0304-3800(94)90075-2, 1994. a
Stepanenko, V. M. and Debolskiy, A. V.: LAKE model website, available at: http://tesla.parallel.ru/Viktor/LAKE/wikis/LAKE-model, last access: 16 February 2020. a
Stepanenko, V. M., Machulskaya, E. E., Glagolev, M. V., and Lykossov, V. N.: Numerical modeling of methane emissions from lakes in the permafrost zone, Izv. Atmos. Ocean Phys., 47, 252–264, https://doi.org/10.1134/S0001433811020113, 2011. a, b, c
Stepanenko, V. M., Jöhnk, K. D., Machulskaya, E., Perroud, M., Subin, Z., Nordbo, A., Mammarella, I., and Mironov, D.: Simulation of surface energy fluxes and stratification of a small boreal lake by a set of one-dimensional models, Tellus A, 66, 21389, https://doi.org/10.3402/tellusa.v66.21389, 2014. a, b
Stepanenko, V. M., Mammarella, I., Ojala, A., Miettinen, H., Lykosov, V., and Vesala, T.: LAKE 2.0: a model for temperature, methane, carbon dioxide and oxygen dynamics in lakes, Geosci. Model Dev., 9, 1977–2006, https://doi.org/10.5194/gmd-9-1977-2016, 2016. a, b, c, d
Stepanenko, V. M., Repina, I. A., Ganbat, G., and Davaa, G.: Numerical simulation of ice cover of saline lakes, Izv. Atmos. Ocean Phys., 55, 129–138, https://doi.org/10.1134/S0001433819010092, 2019. a
Subin, Z. M., Riley, W. J., and Mironov, D.: An improved lake model for climate simulations: model structure, evaluation, and sensitivity analyses in CESM1, J. Adv. Model Earth Syst., 4, M02001, https://doi.org/10.1029/2011MS000072, 2012. a
Tan, Z. and Zhuang, Q.: Arctic lakes are continuous methane sources to the atmosphere under warming conditions, Environ. Res. Lett., 10, 054016, https://doi.org/10.1088/1748-9326/10/5/054016, 2015. a, b
Tan, Z., Zhuang, Q., and Walter Anthony, K.: Modeling methane emissions from arctic lakes: Model development and site level study, J. Adv. Model Earth Syst., 7, 459–483, https://doi.org/10.1002/2014MS000344, 2015. a, b
Tan, Z., Yao, H., and Zhuang, Q.: 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, https://doi.org/10.1029/2017WR022334, 2018. a, b
Thiery, W., Stepanenko, V. M., Fang, X., Jöhnk, K. D., Li, Z., Martynov, A., Perroud, M., Subin, Z. M., Darchambeau, F., Mironov, D., and Van Lipzig, N. P.: LakeMIP Kivu: evaluating the representation of a large, deep tropical lake by a set of one-dimensional lake models, Tellus A, 66, 21390, https://doi.org/10.3402/tellusa.v66.21390, 2014a. a, b, c
Thiery, W., Martynov, A., Darchambeau, F., Descy, J. P., Plisnier, P. D., Sushama, L., and van Lipzig, N. P.: Understanding the performance of the FLake model over two African Great Lakes, Geosci. Model Dev., 7, 317–337, https://doi.org/10.5194/gmd-7-317-2014, 2014b. a
Thiery, W., Davin, E. L., Panitz, H.-J., Demuzere, M., Lhermitte, S., and Lipzig, N.: The Impact of the African Great Lakes on the Regional Climate, J. Climat, 28, 4061–4085, https://doi.org/10.1175/JCLI-D-14-00565.1, 2015. a, b
Thiery, W., Davin, E. L., Seneviratne, S. I., Bedka, K., Lhermitte, S., and van Lipzig, N. P.: Hazardous thunderstorm intensification over Lake Victoria, Nat. Commun., 7, 12786, https://doi.org/10.1038/ncomms12786, 2016. a
Thiery, W., Gudmundsson, L., Bedka, K., Semazzi, F. H., Lhermitte, S., Willems, P., Van Lipzig, N. P., and Seneviratne, S. I.: Early warnings of hazardous thunderstorms over Lake Victoria, Environ. Res. Lett., 12, 074012, https://doi.org/10.1088/1748-9326/aa7521, 2017. a
Tranvik, L. J., Downing, J. A., Cotner, J. B., Loiselle, S. A., Striegl, R. G., Ballatore, T. J., Dillon, P., Finlay, K., Fortino, K., Knoll, L. B., Kortelainen, P. L., Kutser, T., Larsen, S., Laurion, I., Leech, D. M., McCallister, S. L., McKnight, D. M., Melack, J. M., Overholt, B. S., Porter, J. A., Prairie, Y., Renwick, W. H., Roland, F., Sherma, B. S., Schindler, D. W., Sobeck, S., Tremblay, A., Vanni, M. J., Verschoor, A. M., von Wachenfeldt, E., and Weyhenmeyer, G. A.: Lakes and reservoirs as regulators of carbon cycling and climate, Limnol. Oceanogr., 54, 2298–2314, https://doi.org/10.1029/2000JD900719, 2009. a
Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S., and Bastviken, D.: Climate-sensitive northern lakes and ponds are critical components of methane release, Nat. Geosci., 9, 99–105, https://doi.org/10.1038/ngeo2578, 2016. a
Yao, H., Samal, N. R., Joehnk, K. D., Fang, X., Bruce, L. C., Pierson, D. C., Rusak, J. A., and James, A.: Comparing ice and temperature simulations by four dynamic lake models in Harp Lake: past performance and future predictions, Hydrol. Process., 28, 4587–4601, https://doi.org/10.1002/hyp.10180, 2014. a, b
Short summary
We compare lake models with different complexity focusing on the key factors (e.g., eddy diffusivity) which can have an influence on the distribution of the dissolved gases in water. For the first time, we compare the biogeochemical modules in the ALBM and LAKE models. The result showed a good agreement with observed data (O2), but not for CO2. It indicates the need to improve the representation of physical and biogeochemical processes in lake models.
We compare lake models with different complexity focusing on the key factors (e.g., eddy...
