Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8785-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8785-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
08 Dec 2022
Model evaluation paper |
| 08 Dec 2022
| 08 Dec 2022
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
Seyed Mahmood Hamze-Ziabari
CORRESPONDING AUTHOR
Ecological Engineering Laboratory (ECOL), Environmental Engineering
Institute (IIE), Faculty of Architecture, Civil and Environmental
Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL),
1015 Lausanne, Switzerland
Ulrich Lemmin
Ecological Engineering Laboratory (ECOL), Environmental Engineering
Institute (IIE), Faculty of Architecture, Civil and Environmental
Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL),
1015 Lausanne, Switzerland
Frédéric Soulignac
Ecological Engineering Laboratory (ECOL), Environmental Engineering
Institute (IIE), Faculty of Architecture, Civil and Environmental
Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL),
1015 Lausanne, Switzerland
Commission Internationale pour la Protection des Eaux du Léman
(CIPEL), Nyon, Switzerland
Mehrshad Foroughan
Ecological Engineering Laboratory (ECOL), Environmental Engineering
Institute (IIE), Faculty of Architecture, Civil and Environmental
Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL),
1015 Lausanne, Switzerland
David Andrew Barry
Ecological Engineering Laboratory (ECOL), Environmental Engineering
Institute (IIE), Faculty of Architecture, Civil and Environmental
Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL),
1015 Lausanne, Switzerland
Related authors
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Olivia Desgué-Itier, Laura Melo Vieira Soares, Orlane Anneville, Damien Bouffard, Vincent Chanudet, Pierre Alain Danis, Isabelle Domaizon, Jean Guillard, Théo Mazure, Najwa Sharaf, Frédéric Soulignac, Viet Tran-Khac, Brigitte Vinçon-Leite, and Jean-Philippe Jenny
Hydrol. Earth Syst. Sci., 27, 837–859, https://doi.org/10.5194/hess-27-837-2023, https://doi.org/10.5194/hess-27-837-2023, 2023
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The long-term effects of climate change will include an increase in lake surface and deep water temperatures. Incorporating up to 6 decades of limnological monitoring into an improved 1D lake model approach allows us to predict the thermal regime and oxygen solubility in four peri-alpine lakes over the period 1850–2100. Our modeling approach includes a revised selection of forcing variables and provides a way to investigate the impacts of climate variations on lakes for centennial timescales.
Zhaoyang Luo, Jun Kong, Lili Yao, Chunhui Lu, Ling Li, and David Andrew Barry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-634, https://doi.org/10.5194/hess-2021-634, 2022
Manuscript not accepted for further review
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Watertable fluctuations and seawater intrusion are characteristic features of coastal unconfined aquifers. A modified expression is first proposed for the dynamic effective porosity due to watertable fluctuations. Then, the new expression is implemented in existing Boussinesq equations and a numerical model, allowing for examination of the effects of the dynamic effective porosity on watertable fluctuations and seawater intrusion in coastal unconfined aquifers, respectively.
Zhaoyang Luo, Jun Kong, Chengji Shen, Pei Xin, Chunhui Lu, Ling Li, and David Andrew Barry
Hydrol. Earth Syst. Sci., 25, 6591–6602, https://doi.org/10.5194/hess-25-6591-2021, https://doi.org/10.5194/hess-25-6591-2021, 2021
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Analytical solutions are derived for steady-state seawater intrusion in annulus segment aquifers. These analytical solutions are validated by comparing their predictions with experimental data. We find seawater intrusion is the most extensive in divergent aquifers, and the opposite is the case for convergent aquifers. The analytical solutions facilitate engineers and hydrologists in evaluating seawater intrusion more efficiently in annulus segment aquifers with a complex geometry.
Lucheng Zhan, Jiansheng Chen, Ling Li, and David A. Barry
Hydrol. Earth Syst. Sci., 22, 4449–4454, https://doi.org/10.5194/hess-22-4449-2018, https://doi.org/10.5194/hess-22-4449-2018, 2018
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Using the arithmetic averages of precipitation isotope values, Wu et al. (2017) concluded that the Badain Jaran Desert (BJD) groundwater is recharged by modern local meteoric water. However, based on weighted mean precipitation isotope values, our further analysis shows that modern precipitation on the Qilian Mountains is more likely to be the main source of the groundwater and lake water in the BJD, as found. We believe this comment provides an important improvement for their study.
M. Schirmer, J. Luster, N. Linde, P. Perona, E. A. D. Mitchell, D. A. Barry, J. Hollender, O. A. Cirpka, P. Schneider, T. Vogt, D. Radny, and E. Durisch-Kaiser
Hydrol. Earth Syst. Sci., 18, 2449–2462, https://doi.org/10.5194/hess-18-2449-2014, https://doi.org/10.5194/hess-18-2449-2014, 2014
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Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments
An open-source refactoring of the Canadian Small Lakes Model for estimates of evaporation from medium-sized reservoirs
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PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
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Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
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STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
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River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
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The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
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This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
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This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
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The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
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Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
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The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
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Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Matevž Vremec, Raoul Collenteur, and Steffen Birk
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-63, https://doi.org/10.5194/gmd-2024-63, 2024
Revised manuscript accepted for GMD
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Geoscientists commonly use various Potential EvapoTranpiration (PET) formulas for environmental studies, which can be prone to errors and sensitive to climate change. PyEt, a tested and open-source Python package, simplifies the application of 20 PET methods for both time series and gridded data, ensuring accurate and consistent PET estimations suitable for a wide range of environmental applications.
Jenny Kupzig, Nina Kupzig, and Martina Floerke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-47, https://doi.org/10.5194/gmd-2024-47, 2024
Revised manuscript accepted for GMD
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Valid simulation results from global hydrological models (GHM) are essential, e.g., to studying climate change impacts. Regionalization is a necessary step, to adapt GHM to ungauged basins to enable such valid simulations. In this study, we highlight the impact of regionalization on global simulations by using different regionalization methods. Applying two valid regionalization strategies globally we’ve found that the “outflow to the ocean” changed in the range of inter-model differences.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
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We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-407, https://doi.org/10.5194/egusphere-2024-407, 2024
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The soil water potential (SWP) determines various soil water processes. Because it cannot be measured directly by remote sensing techniques, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of SWP.
João Careto, Rita Cardoso, Ana Russo, Daniela Lima, and Pedro Soares
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-9, https://doi.org/10.5194/gmd-2024-9, 2024
Revised manuscript accepted for GMD
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In this study, a new drought index is proposed, which not only is able to identify the same events but also can improve the results obtained from other established drought indices. The index is empirically based and is extremely straightforward to compute. It is as well, a daily drought index with the ability to not only assess flash droughts but also events at longer aggregation scales, such as the traditional monthly indices.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-151, https://doi.org/10.5194/gmd-2023-151, 2023
Revised manuscript accepted for GMD
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We develop an operational forecast system, COATLINES-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model requires a relatively small computational demand and results compare well with near real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and waves predictions can improve in accuracy.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-190, https://doi.org/10.5194/gmd-2023-190, 2023
Revised manuscript accepted for GMD
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Accurate hydrological modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall-runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
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.
Cited articles
Akitomo, K., Kurogi, M., and Kumagai, M.: Numerical study of a thermally
induced gyre system in Lake Biwa, Limnology, 5, 103–114,
https://doi.org/10.1007/s10201-004-0122-9, 2004.
Bai, X., Wang, J., Schwab, D. J., Yang, Y., Luo, L., Leshkevich, G. A., and
Liu, S.: Modeling 1993–2008 climatology of seasonal general circulation and
thermal structure in the Great Lakes using FVCOM, Ocean Model., 65, 40–63,
https://doi.org/10.1016/j.ocemod.2013.02.003, 2013.
Baracchini, T., Wüest, A., and Bouffard, D.: Meteolakes: An operational
online three-dimensional forecasting platform for lake hydrodynamics, Water
Res., 172, 115529,
https://doi.org/10.1016/j.watres.2020.115529, 2020a.
Baracchini, T., Chu, P. Y., Šukys, J., Lieberherr, G., Wunderle, S., Wüest, A., and Bouffard, D.: Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4, Geosci. Model Dev., 13, 1267–1284, https://doi.org/10.5194/gmd-13-1267-2020, 2020b.
Beletsky, D. and Schwab, D.: Climatological circulation in Lake Michigan,
Geophys. Res. Lett., 35, L21604,
https://doi.org/10.1029/2008GL035773, 2008.
Beletsky, D., Saylor, J. H., and Schwab, D. J.: Mean circulation in the
Great Lakes, J. Great Lakes Res., 25, 78–93, https://doi.org/10.1016/S0380-1330(99)70718-5, 1999.
Beletsky, D., Hawley, N., and Rao, Y. R.: Modeling summer circulation and
thermal structure of Lake Erie, J. Geophys. Res.-Oceans, 118,
6238–6252, https://doi.org/10.1002/2013JC008854, 2013.
Bennett, J. R.: On the dynamics of wind-driven lake currents, J. Phys.
Oceanogr., 4, 400–414, https://doi.org/10.1175/1520-0485(1974)004<0400:OTDOWD>2.0.CO;2, 1974.
Bennington, V., McKinley, G. A., Kimura, N., and Wu, C. H.: General
circulation of Lake Superior: Mean, variability, and trends from 1979 to
2006, J. Geophys. Res.-Oceans, 115, C12015,
https://doi.org/10.1029/2010JC006261, 2010.
Birchfield, G. E.: Horizontal transport in a rotating basin of parabolic
depth profile, J. Geophys. Res., 72, 6155–6163, https://doi.org/10.1029/JZ072i024p06155, 1967.
Campin, J. M., Heimbach P., Losch, M., Forget, G., edhill3, Adcroft, A., amolod, Menemenlis, D., dfer22, Hill, C., Jahn, O., Scott, J., stephdut, Mazloff, M., Fox-Kemper, B., antnguyen13, Doddridge, E., Fenty, I., Bates, M., Eichmann, A., Smith, T., Martin, T., Lauderdale, J., Abernathey, R., samarkhatiwala, hongandyan, Deremble, B., dngoldberg, Bourgault, P., and Dussin, R.: MITgcm/MITgcm: checkpoint67z (Version checkpoint67z), Zenodo [software], https://doi.org/10.5281/zenodo.4968496, 2021.
Chang, Y. L. and Oey, L. Y.: Analysis of STCC eddies using the Okubo–Weiss
parameter on model and satellite data, Ocean Dynam., 64, 259–271,
https://doi.org/10.1007/s10236-013-0680-7, 2014.
Cimatoribus, A. A., Lemmin, U., Bouffard, D., and Barry, D. A.: Nonlinear
dynamics of the nearshore boundary layer of a large lake (Lake Geneva), J. Geophys. Res.-Oceans, 123, 1016–1031, https://doi.org/10.1002/2017JC013531, 2018.
Cimatoribus, A. A., Lemmin, U., and Barry, D. A.: Tracking Lagrangian
transport in Lake Geneva: A 3D numerical modeling investigation, Limnol.
Oceanogr., 64, 1252–1269, https://doi.org/10.1002/lno.11111,
2019.
CIPEL: Rapports sur les études et recherches entreprises dans le bassin
lémanique, Campagne 2018, Commission internationale pour la protection
des eaux du Léman (CIPEL), Nyon, Switzerland, https://www.cipel.org/wp-content/uploads/2018/04/RapportScientifique_camp_2016_VF.pdf (last access: 28 October 2022), 2019.
Corman, J. R., McIntyre, P. B., Kuboja, B., Mbemba, W., Fink, D., Wheeler,
C. W., Gans, C., Michel, E., and Flecker, A. S.: Upwelling couples chemical
and biological dynamics across the littoral and pelagic zones of Lake
Tanganyika, East Africa, Limnol. Oceanogr., 55, 214–224, https://doi.org/10.4319/lo.2010.55.1.0214, 2010.
Cotte, G. and Vennemann, T. W.: Mixing of Rhône River water in Lake
Geneva: Seasonal tracing using stable isotope composition of water, J. Great
Lakes Res., 46, 839–849,
https://doi.org/10.1016/j.jglr.2020.05.015, 2020.
Csanady, G. T.: Wind-induced barotropic motions in long lakes, J. Phys.
Oceanogr., 3, 429–438,
https://doi.org/10.1175/1520-0485(1973)003<0429:WIBMIL>2.0.CO;2, 1973.
Csanady, G. T.: Hydrodynamics of large lakes, Annu. Rev. Fluid Mech., 7,
357–386, https://doi.org/10.1146/annurev.fl.07.010175.002041,
1975.
DiGiacomo, P. M. and Holt, B.: Satellite observations of small coastal
ocean eddies in the Southern California Bight, J. Geophys. Res.-Oceans,
106, 22521–22543, https://doi.org/10.1029/2000JC000728,
2001.
Elhmaïdi, D., Provenzale, A., and Babiano, A.: Elementary topology of
two-dimensional turbulence from a Lagrangian viewpoint and single-particle
dispersion, J. Fluid Mech., 257, 533–558,
https://doi.org/10.1017/S0022112093003192, 1993.
European Space Agency (ESA): Copernicus Open Access Hub,
https://scihub.copernicus.eu/, last access: 6 December 2022.
Gao, Y., Guan, C., Sun, J., and Xie, L.: A wind speed retrieval model for
Sentinel-1A EW mode cross-polarization images, Remote Sens., 11, 153,
https://doi.org/10.3390/rs11020153, 2019.
Hamze-Ziabari, S. M., Razmi, A. M., Lemmin, U., and Barry, D. A.: Detecting
Submesoscale cold filaments in a basin-scale gyre in large, deep Lake Geneva
(Switzerland/France), Geophys. Res. Lett., 49, e2021GL096185,
https://doi.org/10.1029/2021GL096185, 2022a.
Hamze-Ziabari, S. M., Lemmin, U., Soulignac, F., Foroughan, M., and Barry, D. A.: Data for: Basin-scale gyres and mesoscale eddies in large lakes: A novel procedure for their detection and characterization, assessed in Lake Geneva, Zenodo [data set], https://doi.org/10.5281/zenodo.7018567, 2022b.
Henson, S. A. and Thomas, A. C.: A census of oceanic anticyclonic eddies in
the Gulf of Alaska, Deep-Sea Res. Pt. I., 55, 163–176, https://doi.org/10.1016/j.dsr.2007.11.005, 2008.
Hogg, A. M. and Gayen, B.: Ocean gyres driven by surface buoyancy forcing,
Geophys. Res. Lett., 47, e2020GL088539,
https://doi.org/10.1029/2020GL088539, 2020.
Hui, Y., Farnham, D. J., Atkinson, J. F., Zhu, Z., and Feng, Y.: Circulation
in Lake Ontario: Numerical and physical model analysis, J. Hydraul. Eng.,
147, 05021004,
https://doi.org/10.1061/(ASCE)HY.1943-7900.0001908, 2021.
Ishikawa, K., Kumagai, M., Vincent, W. F., Tsujimura, S., and Nakahara, H.:
Transport and accumulation of bloom-forming cyanobacteria in a large,
mid-latitude lake: The gyre-Microcystis hypothesis, Limnology, 3, 87–96,
https://doi.org/10.1007/s102010200010, 2002.
Isern-Fontanet, J., Font, J., García-Ladona, E., Emelianov, M., Millot,
C., and Taupier-Letage, I.: Spatial structure of anticyclonic eddies in the
Algerian basin (Mediterranean Sea) analyzed using the Okubo–Weiss
parameter, Deep-Sea Res. Pt. II, 51, 3009–3028,
https://doi.org/10.1016/j.dsr2.2004.09.013, 2004.
Isern-Fontanet, J., García-Ladona, E., and Font, J.: Vortices of the
Mediterranean Sea: An altimetric perspective, J. Phys. Oceanogr., 36,
87–103, https://doi.org/10.1175/JPO2826.1, 2006.
Ji, Z. G. and Jin, K. R.: Gyres and seiches in a large and shallow lake, J.
Great Lakes Res., 32, 764–775,
https://doi.org/10.3394/0380-1330(2006)32[764:GASIAL]2.0.CO;2, 2006.
Johannessen, J. A., Shuchman, R. A., Digranes, G., Lyzenga, D. R.,
Wackerman, C., Johannessen, O. M., and Vachon, P. W.: Coastal ocean fronts
and eddies imaged with ERS 1 synthetic aperture radar, J. Geophys. Res.-Oceans, 101, 6651–6667, https://doi.org/10.1029/95JC02962,
1996.
Johannessen, J. A., Kudryavtsev, V., Akimov, D., Eldevik, T., Winther, N.,
and Chapron, B.: On radar imaging of current features: 2. Mesoscale eddy and
current front detection, J. Geophys. Res.-Oceans, 110, C07017,
https://doi.org/10.1029/2004JC002802, 2005.
Karimova, S.: Spiral eddies in the Baltic, Black and Caspian seas as seen by
satellite radar data, Adv. Space Res., 50, 1107–1124,
https://doi.org/10.1016/j.asr.2011.10.027, 2012.
Laval, B., Imberger, J., Hodges, B. R., and Stocker, R.: Modeling circulation
in lakes: Spatial and temporal variations, Limnol. Oceanogr.-Methods, 48,
983–994, https://doi.org/10.4319/lo.2003.48.3.0983, 2003.
Laval, B. E., Imberger, J., and Findikakis, A. N.: Dynamics of a large
tropical lake: Lake Maracaibo, Aquat. Sci., 67, 337–349, https://doi.org/10.1007/s00027-005-0778-1, 2005.
Le Thi, A. D., De Pascalis, F., Umgiesser, G., and Wildi, W.: Structure
thermique et courantologie du Léman (Thermal structure and circulation
patterns of Lake Geneva), Arch. Sci., 65, 65–80, https://archive-ouverte.unige.ch/unige:27717 (last access: 29 October 2022), 2012.
Lemmin, U. (Ed.): Mouvements des masses d'eau (Water mass movement), in: Dans les abysses du Léman (Descent into the abyss of Lake
Geneva), Presses Polytechniques et Universitaires Romandes (PPUR), Lausanne,
Switzerland, ISBN 978-2-88915-105-9, 2016.
Lemmin, U.: Insights into the dynamics of the deep hypolimnion of Lake
Geneva as revealed by long-term temperature, oxygen, and current
measurements, Limnol. Oceanogr., 65, 2092–2107,
https://doi.org/10.1002/lno.11441, 2020.
Lemmin, U. and D'Adamo, N.: Summertime winds and direct cyclonic
circulation: Observations from Lake Geneva, Ann. Geophys, 14, 1207–1220,
https://doi.org/10.1007/s00585-996-1207-z, 1996.
Lemmin, U., Mortimer, C. H., and Bäuerle, E.: Internal seiche dynamics
in Lake Geneva, Limnol. Oceanogr., 50, 207–216,
https://doi.org/10.4319/lo.2005.50.1.0207, 2005.
Lin, S., Boegman, L., Shan, S., and Mulligan, R.: An automatic lake-model application using near-real-time data forcing: development of an operational forecast workflow (COASTLINES) for Lake Erie, Geosci. Model Dev., 15, 1331–1353, https://doi.org/10.5194/gmd-15-1331-2022, 2022.
Liu, F., Zhou, H., and Wen, B.: DEDNet: Offshore eddy detection and location
with HF radar by deep learning, Sensors, 21, 126,
https://doi.org/10.3390/s21010126, 2021.
McKinney, P., Holt, B., and Matsumoto, K.: Small eddies observed in Lake
Superior using SAR and sea surface temperature imagery, J. Great Lakes Res.,
38, 786–797, https://doi.org/10.1016/j.jglr.2012.09.023,
2012.
McWilliams, J. C.: The emergence of isolated coherent vortices in turbulent
flow, J. Fluid Mech., 146, 21–43, https://doi.org/10.1017/S0022112084001750, 1984.
Mahadevan, A.: The impact of submesoscale physics on primary productivity of
plankton, Annu. Rev. Mar. Sci., 8, 161–184, https://doi.org/10.1146/annurev-marine-010814-015912, 2016.
Mao, M. and Xia, M.: Monthly and episodic dynamics of summer circulation in
Lake Michigan, J. Geophys. Res.-Oceans, 125, e2019JC015932,
https://doi.org/10.1029/2019JC015932, 2020.
Marmorino, G. O., Holt, B., Molemaker, M. J., DiGiacomo, P. M., and Sletten,
M. A.: Airborne synthetic aperture radar observations of “spiral eddy”
slick patterns in the Southern California Bight, J. Geophys. Res.-Oceans,
115, C05010, https://doi.org/10.1029/2009JC005863, 2010.
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A
finite-volume, incompressible Navier Stokes model for studies of the ocean
on parallel computers, J. Geophys. Res.-Oceans, 102, 5753–5766,
https://doi.org/10.1029/96JC02775, 1997.
Nakayama, K., Shintani, T., Shimizu, K., Okada, T., Hinata, H., and Komai,
K.: Horizontal and residual circulations driven by wind stress curl in Tokyo
Bay, J. Geophys. Res.-Oceans, 119, 1977–1992,
https://doi.org/10.1002/2013JC009396, 2014.
Nõges, T. and Kangro, K.: Primary production of phytoplankton in a
strongly stratified temperate lake, Hydrobiologia, 547, 105–122,
https://doi.org/10.1007/1-4020-4363-5_10, 2005.
Okubo, A.: Horizontal dispersion of floatable particles in the vicinity of
velocity singularities such as convergences, Deep-Sea Res. Pt. I, 17, 445–454,
https://doi.org/10.1016/0011-7471(70)90059-8, 1970.
Ostrovsky, I. and Sukenik, A.: Spatial heterogeneity of biogeochemical
parameters in a subtropical lake, in: Monitoring and Modelling Lakes and
Coastal Environments, Springer, Dordrecht, https://doi.org/10.1007/978-1-4020-6646-7_6, 2008.
Pasquero, C., Provenzale, A., and Babiano, A.: Parameterization of
dispersion in two-dimensional turbulence, J. Fluid Mech., 439, 279–303,
https://doi.org/10.1017/S0022112001004499, 2001.
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., 54, 1574–1594,
https://doi.org/10.4319/lo.2009.54.5.1574, 2009.
Pickett, R. L. and Rao, D. B.: One-and two-gyre circulations in homogeneous
lakes. International Field Year for the Great Lakes Bulletin 19, 45–49, U.S.
Dept. of Commerce, National Oceanic and Atmospheric Administration,
Rockville, Maryland. https://play.google.com/books/reader?id=AtZ918jAK10C&hl=enGB&pg=GBS.PA45 (last access: 29 October 2022), 1977.
Qazi, W. A., Emery, W. J., and Fox-Kemper, B.: Computing ocean surface
currents over the coastal California current system using 30-min-lag
sequential SAR images, IEEE T. Geosci. Elect., 52,
7559–7580, https://doi.org/10.1109/TGRS.2014.2314117, 2014.
Rahaghi, A. I., Lemmin, U., Cimatoribus, A., Bouffard, D., Riffler, M.,
Wunderle, S., and Barry, D. A.: Improving surface heat flux estimation for a
large lake through model optimization and two-point calibration: The case of
Lake Geneva, Limnol. Oceanogr.-Methods, 16, 576–593, https://doi.org/10.1002/lom3.10267, 2018.
Rahaghi, A. I., Lemmin, U., Cimatoribus, A. A., and Barry, D. A.: The
importance of systematic spatial variability in the surface heat flux of a
large lake: A multiannual analysis for Lake Geneva, Water Resour. Res.,
55, 10248–10267, https://doi.org/10.1029/2019WR024954,
2019.
Ralph, E. A.: Scales and structures of large lake eddies, Geophys. Res.
Lett., 29, 30-1–30-4, https://doi.org/10.1029/2001GL014654,
2002.
Rao, D. B. and Murty, T. S.: Calculation of the steady state wind-driven
circulations in Lake Ontario, Arch. Meteor. Geophy. A,
19, 195–210, https://doi.org/10.1007/BF02249005, 1970.
Razmi, A. M., Barry, D. A., Bakhtyar, R., Le Dantec, N., Dastgheib, A.,
Lemmin, U., and Wüest, A.: Current variability in a wide and open
lacustrine embayment in Lake Geneva (Switzerland), J. Great Lakes Res.,
39, 455–465, https://doi.org/10.1016/j.jglr.2013.06.011,
2013.
Razmi, A. M., Lemmin, U., Bouffard, D., Wüest, A., Uittenbogaard, R. E.,
and Barry, D. A.: Gyre formation in open and deep lacustrine embayments: The
example of Lake Geneva, Switzerland, Environ. Fluid Mech., 17, 415–428,
https://doi.org/10.1007/s10652-016-9494-8, 2017.
Reiss, R. S., Lemmin, U., Cimatoribus, A. A., and Barry, D. A.: Wintertime
coastal upwelling in Lake Geneva: An efficient transport process for
deepwater renewal in a large, deep lake, J. Geophys. Res.-Oceans, 125,
e2020JC016095, https://doi.org/10.1029/2020JC016095, 2020.
Rueda, F. J., Schladow, S. G., Monismith, S. G., and Stacey, M. T.: On the
effects of topography on wind and the generation of currents in a large
multi-basin lake, Hydrobiologia, 532, 139–151, https://doi.org/10.1007/s10750-004-9522-4, 2005.
Schwab, D. J. and Beletsky, D.: Relative effects of wind stress curl,
topography, and stratification on large-scale circulation in Lake Michigan,
J. Geophys. Res.-Oceans, 108, 3044,
https://doi.org/10.1029/2001JC001066, 2003.
Shilo, E., Ashkenazy, Y., Rimmer, A., Assouline, S., Katsafados, P., and
Mahrer, Y.: Effect of wind variability on topographic waves: Lake Kinneret
case, J. Geophys. Res.-Oceans, 112, C12024, https://doi.org/10.1029/2007JC004336, 2007.
Shimizu, K., Imberger, J., and Kumagai, M.: Horizontal structure and
excitation of primary motions in a strongly stratified lake, Limnol.
Oceanogr., 52, 2641–2655, https://doi.org/10.4319/lo.2007.52.6.2641, 2007.
Simons, T. J.: Circulation models of lakes and inland seas, Can. B. Fish.
Aquat. Sci., 203, 146, 1980.
Souza, J. M. A. C., de Boyer Montégut, C., and Le Traon, P. Y.: Comparison between three implementations of automatic identification algorithms for the quantification and characterization of mesoscale eddies in the South Atlantic Ocean, Ocean Sci., 7, 317–334, https://doi.org/10.5194/os-7-317-2011, 2011.
Steissberg, T. E., Hook, S. J., and Schladow, S. G.: Measuring surface
currents in lakes with high spatial resolution thermal infrared imagery,
Geophys. Res. Lett., 32, L11402, https://doi.org/10.1029/2005GL022912, 2005.
Umlauf, L. and Lemmin, U.: Interbasin exchange and mixing in the
hypolimnion of a large lake: The role of long internal waves, Limnol.
Oceanogr., 50, 1601–1611,
https://doi.org/10.4319/lo.2005.50.5.1601, 2005.
Vallis, G. K. (2nd Ed.): Atmospheric and oceanic fluid dynamics:
Fundamentals and large-scale circulation. Cambridge: Cambridge University
Press, https://doi.org/10.1017/9781107588417, 2017.
Voudouri, A., Avgoustoglou, E., and Kaufmann, P.: Impacts of observational
data assimilation on operational forecasts, Perspectives on atmospheric
sciences, 143–149, Springer, Cham, https://doi.org/10.1007/978-3-319-35095-0_21, 2017.
Verburg, P., Antenucci, J. P., and Hecky, R. E.: Differential cooling drives
large-scale convective circulation in Lake Tanganyika, Limnol. Oceanogr.,
56, 910–926, https://doi.org/10.4319/lo.2011.56.3.0910,
2011.
Wang, B. and An, S. I.: A method for detecting season-dependent modes of
climate variability: S-EOF analysis, Geophys. Res. Lett., 32, L15710,
https://doi.org/10.1029/2005GL022709, 2005.
Wang, C., Tandeo, P., Mouche, A., Stopa, J. E., Gressani, V., Longepe, N.,
Vandemark, D., Foster, R. C., and Chapron, B.: Classification of the global
Sentinel-1 SAR vignettes for ocean surface process studies, Remote Sens.
Environ., 234, 111457,
https://doi.org/10.1016/j.rse.2019.111457, 2019.
Wanner, H. and Furger, M.: The Bise–Climatology of a regional wind north
of the Alps, Physics Meteorol. Atmos. Phys., 43, 105–115,
https://doi.org/10.1007/BF01028113, 1990.
Weiss, J.: The dynamics of enstrophy transfer in two-dimensional
hydrodynamics, Phys. D, 48, 273–294,
https://doi.org/10.1016/0167-2789(91)90088-Q, 1991.
Wu, T., Qin, B., Huang, A., Sheng, Y., Feng, S., and Casenave, C.: Reconsideration of wind stress, wind waves, and turbulence in simulating wind-driven currents of shallow lakes in the Wave and Current Coupled Model (WCCM) version 1.0, Geosci. Model Dev., 15, 745–769, https://doi.org/10.5194/gmd-15-745-2022, 2022.
Xiu, P., Chai, F., Shi, L., Xue, H., and Chao, Y.: A census of eddy
activities in the South China Sea during 1993–2007, J. Geophys. Res.-Oceans, 115, C03012, https://doi.org/10.1029/2009JC005657,
2010.
Zhan, S., Beck, R. A., Hinkel, K. M., Liu, H., and Jones, B. M.:
Spatio-temporal analysis of gyres in oriented lakes on the Arctic Coastal
Plain of northern Alaska based on remotely sensed images, Remote Sens.,
6, 9170–9193, https://doi.org/10.3390/rs6109170, 2014.
Short summary
A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
A procedure combining numerical simulations, remote sensing, and statistical analyses is...
