Articles | Volume 12, issue 1
https://doi.org/10.5194/gmd-12-473-2019
© Author(s) 2019. 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-12-473-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
29 Jan 2019
Model description paper |
| 29 Jan 2019
| 29 Jan 2019
A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)
UWA School of Agriculture & Environment, The University of Western
Australia, Crawley WA, 6009, Australia
Louise C. Bruce
UWA School of Agriculture & Environment, The University of Western
Australia, Crawley WA, 6009, Australia
Casper Boon
UWA School of Agriculture & Environment, The University of Western
Australia, Crawley WA, 6009, Australia
Brendan Busch
UWA School of Agriculture & Environment, The University of Western
Australia, Crawley WA, 6009, Australia
Cayelan C. Carey
Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA
David P. Hamilton
Australian Rivers Institute, Griffith University, Brisbane QLD,
Australia
Paul C. Hanson
Center for Limnology, University of Wisconsin – Madison, Madison, WI,
USA
Jordan S. Read
U.S. Geological Survey, Water Mission Area, Middleton, WI, USA
Eduardo de Sousa
UWA School of Agriculture & Environment, The University of Western
Australia, Crawley WA, 6009, Australia
Michael Weber
Department of Lake Research, Helmholtz Centre for Environmental
Research – UFZ, Magdeburg, Germany
Luke A. Winslow
Department of Biological Sciences, Rensselaer Polytechnic Institute,
Troy, NY, USA
Related authors
Peisheng Huang, Karl Hennig, Jatin Kala, Julia Andrys, and Matthew R. Hipsey
Hydrol. Earth Syst. Sci., 24, 5673–5697, https://doi.org/10.5194/hess-24-5673-2020, https://doi.org/10.5194/hess-24-5673-2020, 2020
Short summary
Short summary
Our results conclude that the climate change in the past decades has a remarkable effect on the hydrology of a large shallow lagoon with the same magnitude as that caused by the opening of an artificial channel, and it also highlighted the complexity of their interactions. We suggested that the consideration of the projected drying trend is essential in designing management plans associated with planning for environmental water provision and setting water quality loading targets.
Benya Wang, Matthew R. Hipsey, and Carolyn Oldham
Geosci. Model Dev., 13, 4253–4270, https://doi.org/10.5194/gmd-13-4253-2020, https://doi.org/10.5194/gmd-13-4253-2020, 2020
Short summary
Short summary
Surface water nutrients are essential to manage water quality, but it is hard to analyse trends. We developed a hybrid model and compared with other models for the prediction of six different nutrients. Our results showed that the hybrid model had significantly higher accuracy and lower prediction uncertainty for almost all nutrient species. The hybrid model provides a flexible method to combine data of varied resolution and quality and is accurate for the prediction of nutrient concentrations.
J. Nikolaus Callow, Matthew R. Hipsey, and Ryan I. J. Vogwill
Hydrol. Earth Syst. Sci., 24, 717–734, https://doi.org/10.5194/hess-24-717-2020, https://doi.org/10.5194/hess-24-717-2020, 2020
Short summary
Short summary
Secondary dryland salinity is a global land degradation issue. Our understanding of causal processes is adapted from wet and hydrologically connected landscapes and concludes that low end-of-catchment runoff indicates land clearing alters water balance in favour of increased infiltration and rising groundwater that bring salts to the surface causing salinity. This study shows surface flows play an important role in causing valley floor recharge and dryland salinity in low-gradient landscapes.
Amar V. V. Nanda, Leah Beesley, Luca Locatelli, Berry Gersonius, Matthew R. Hipsey, and Anas Ghadouani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-307, https://doi.org/10.5194/hess-2017-307, 2017
Revised manuscript not accepted
Short summary
Short summary
When anthropological effects result in changes to wetland hydrology; this often leads to a decline in their ecological integrity. We present a policy oriented approach that assesses the suitability of management when rigorous ecological data are lacking. We link ecological objectives from management authorities to threshold values for water depth defined in policy. Results show insufficient water levels for key ecological objectives and we conclude that current policy is ineffective.
Y. Elshafei, M. Sivapalan, M. Tonts, and M. R. Hipsey
Hydrol. Earth Syst. Sci., 18, 2141–2166, https://doi.org/10.5194/hess-18-2141-2014, https://doi.org/10.5194/hess-18-2141-2014, 2014
Y. Li, G. Gal, V. Makler-Pick, A. M. Waite, L. C. Bruce, and M. R. Hipsey
Biogeosciences, 11, 2939–2960, https://doi.org/10.5194/bg-11-2939-2014, https://doi.org/10.5194/bg-11-2939-2014, 2014
L. C. Bruce, P. L. M. Cook, I. Teakle, and M. R. Hipsey
Hydrol. Earth Syst. Sci., 18, 1397–1411, https://doi.org/10.5194/hess-18-1397-2014, https://doi.org/10.5194/hess-18-1397-2014, 2014
S. E. Thompson, M. Sivapalan, C. J. Harman, V. Srinivasan, M. R. Hipsey, P. Reed, A. Montanari, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 5013–5039, https://doi.org/10.5194/hess-17-5013-2013, https://doi.org/10.5194/hess-17-5013-2013, 2013
A. L. Ruibal-Conti, R. Summers, D. Weaver, and M. R. Hipsey
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-11035-2013, https://doi.org/10.5194/hessd-10-11035-2013, 2013
Revised manuscript not accepted
Austin Delany, Robert Ladwig, Cal Buelo, Ellen Albright, and Paul C. Hanson
Biogeosciences, 20, 5211–5228, https://doi.org/10.5194/bg-20-5211-2023, https://doi.org/10.5194/bg-20-5211-2023, 2023
Short summary
Short summary
Internal and external sources of organic carbon (OC) in lakes can contribute to oxygen depletion, but their relative contributions remain in question. To study this, we built a two-layer model to recreate processes relevant to carbon for six Wisconsin lakes. We found that internal OC was more important than external OC in depleting oxygen. This shows that it is important to consider both the fast-paced cycling of internally produced OC and the slower cycling of external OC when studying lakes.
Pia Ebeling, Rohini Kumar, Stefanie R. Lutz, Tam Nguyen, Fanny Sarrazin, Michael Weber, Olaf Büttner, Sabine Attinger, and Andreas Musolff
Earth Syst. Sci. Data, 14, 3715–3741, https://doi.org/10.5194/essd-14-3715-2022, https://doi.org/10.5194/essd-14-3715-2022, 2022
Short summary
Short summary
Environmental data are critical for understanding and managing ecosystems, including the mitigation of water quality degradation. To increase data availability, we present the first large-sample water quality data set (QUADICA) of riverine macronutrient concentrations combined with water quantity, meteorological, and nutrient forcing data as well as catchment attributes. QUADICA covers 1386 German catchments to facilitate large-sample data-driven and modeling water quality assessments.
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
Short summary
Short summary
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.
Robert Ladwig, Paul C. Hanson, Hilary A. Dugan, Cayelan C. Carey, Yu Zhang, Lele Shu, Christopher J. Duffy, and Kelly M. Cobourn
Hydrol. Earth Syst. Sci., 25, 1009–1032, https://doi.org/10.5194/hess-25-1009-2021, https://doi.org/10.5194/hess-25-1009-2021, 2021
Short summary
Short summary
Using a modeling framework applied to 37 years of dissolved oxygen time series data from Lake Mendota, we identified the timing and intensity of thermal energy stored in the lake water column, the lake's resilience to mixing, and surface primary production as the most important drivers of interannual dynamics of low oxygen concentrations at the lake bottom. Due to climate change, we expect an increase in the spatial and temporal extent of low oxygen concentrations in Lake Mendota.
Peisheng Huang, Karl Hennig, Jatin Kala, Julia Andrys, and Matthew R. Hipsey
Hydrol. Earth Syst. Sci., 24, 5673–5697, https://doi.org/10.5194/hess-24-5673-2020, https://doi.org/10.5194/hess-24-5673-2020, 2020
Short summary
Short summary
Our results conclude that the climate change in the past decades has a remarkable effect on the hydrology of a large shallow lagoon with the same magnitude as that caused by the opening of an artificial channel, and it also highlighted the complexity of their interactions. We suggested that the consideration of the projected drying trend is essential in designing management plans associated with planning for environmental water provision and setting water quality loading targets.
Benya Wang, Matthew R. Hipsey, and Carolyn Oldham
Geosci. Model Dev., 13, 4253–4270, https://doi.org/10.5194/gmd-13-4253-2020, https://doi.org/10.5194/gmd-13-4253-2020, 2020
Short summary
Short summary
Surface water nutrients are essential to manage water quality, but it is hard to analyse trends. We developed a hybrid model and compared with other models for the prediction of six different nutrients. Our results showed that the hybrid model had significantly higher accuracy and lower prediction uncertainty for almost all nutrient species. The hybrid model provides a flexible method to combine data of varied resolution and quality and is accurate for the prediction of nutrient concentrations.
J. Nikolaus Callow, Matthew R. Hipsey, and Ryan I. J. Vogwill
Hydrol. Earth Syst. Sci., 24, 717–734, https://doi.org/10.5194/hess-24-717-2020, https://doi.org/10.5194/hess-24-717-2020, 2020
Short summary
Short summary
Secondary dryland salinity is a global land degradation issue. Our understanding of causal processes is adapted from wet and hydrologically connected landscapes and concludes that low end-of-catchment runoff indicates land clearing alters water balance in favour of increased infiltration and rising groundwater that bring salts to the surface causing salinity. This study shows surface flows play an important role in causing valley floor recharge and dryland salinity in low-gradient landscapes.
Liancong Luo, David Hamilton, Jia Lan, Chris McBride, and Dennis Trolle
Geosci. Model Dev., 11, 903–913, https://doi.org/10.5194/gmd-11-903-2018, https://doi.org/10.5194/gmd-11-903-2018, 2018
Short summary
Short summary
We developed an autocalibration software for the hydrodynamic-ecological lake model DYRESM-CAEDYM with a massive number of water quality parameters, using a Monte Carlo sampling method, in order to reduce time-consuming iterative simulations with empirical judgements and find optimal model parameter set. The successful applications to Lake Rotorua suggest this software is much more efficient than traditional methods and of wide applicability to other water quality models.
Amar V. V. Nanda, Leah Beesley, Luca Locatelli, Berry Gersonius, Matthew R. Hipsey, and Anas Ghadouani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-307, https://doi.org/10.5194/hess-2017-307, 2017
Revised manuscript not accepted
Short summary
Short summary
When anthropological effects result in changes to wetland hydrology; this often leads to a decline in their ecological integrity. We present a policy oriented approach that assesses the suitability of management when rigorous ecological data are lacking. We link ecological objectives from management authorities to threshold values for water depth defined in policy. Results show insufficient water levels for key ecological objectives and we conclude that current policy is ineffective.
Y. Elshafei, M. Sivapalan, M. Tonts, and M. R. Hipsey
Hydrol. Earth Syst. Sci., 18, 2141–2166, https://doi.org/10.5194/hess-18-2141-2014, https://doi.org/10.5194/hess-18-2141-2014, 2014
Y. Li, G. Gal, V. Makler-Pick, A. M. Waite, L. C. Bruce, and M. R. Hipsey
Biogeosciences, 11, 2939–2960, https://doi.org/10.5194/bg-11-2939-2014, https://doi.org/10.5194/bg-11-2939-2014, 2014
L. C. Bruce, P. L. M. Cook, I. Teakle, and M. R. Hipsey
Hydrol. Earth Syst. Sci., 18, 1397–1411, https://doi.org/10.5194/hess-18-1397-2014, https://doi.org/10.5194/hess-18-1397-2014, 2014
S. E. Thompson, M. Sivapalan, C. J. Harman, V. Srinivasan, M. R. Hipsey, P. Reed, A. Montanari, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 5013–5039, https://doi.org/10.5194/hess-17-5013-2013, https://doi.org/10.5194/hess-17-5013-2013, 2013
A. L. Ruibal-Conti, R. Summers, D. Weaver, and M. R. Hipsey
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-11035-2013, https://doi.org/10.5194/hessd-10-11035-2013, 2013
Revised manuscript not accepted
Related subject area
Hydrology
STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds
Fluvial flood inundation and socio-economic impact model based on open data
RoGeR v3.0.5 – a process-based hydrological toolbox model in Python
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
EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions
Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications
Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community
HydroFATE (v1): a high-resolution contaminant fate model for the global river system
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Regionalization and its impact on global runoff simulations: A case study using the global hydrological model WaterGAP3 (v 1.0.0)
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks
Generalized drought index: A novel multi-scale daily approach for drought assessment
GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications
GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers
mesas.py v1.0: a flexible Python package for modeling solute transport and transit times using StorAge Selection functions
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Development and performance of a high-resolution surface wave and storm surge forecast model (COASTLINES-LO): Application to a large lake
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Deep Dive into Global Hydrologic Simulations: Harnessing the Power of Deep Learning and Physics-informed Differentiable Models (δHBV-globe1.0-hydroDL)
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
Short summary
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.
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
Short summary
Short summary
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.
Cited articles
Ashton, G. D. (Ed.): River and lake ice engineering. Water Resources
Publications, Littleton, Colorado, USA, 1986.
Antenucci, J. P., Brookes, J. D., and Hipsey, M. R.: A simple model for
quantifying Cryptosporidium transport, dilution, and potential risk
in reservoirs, J. Am. Water Works Ass., 97, 86–93, 2005.
Ayala, A. I., Cortés, A., Fleenor, W. E., and Rueda, F. J.: Seasonal
scale modeling of river inflows in stratified reservoirs: Structural vs.
parametric uncertainty in inflow mixing, Environ. Modell. Softw., 60, 84–98,
2014.
Babanin, A. V. and Makin, V. K.: Effects of wind trend and gustiness on the
sea drag: Lake George study, J. Geophys. Res.-Oceans, 113, C02015,
https://doi.org/10.1029/2007JC004233, 2008.
Bird, R. E.: A simple, solar spectral model for direct-normal and diffuse
horizontal irradiance, Sol. Energy, 32, 461–471, 1984.
Briegleb, B. P., Minnis, P., Ramanathan, V., and Harrison, E.: Comparison of
regional clear-sky albedos inferred from satellite observations and model
computations. J. Clim. Appl. Meteorol., 25, 214–226, 1986.
Bruce, L. C., Frassl, M. A., Arhonditsis, G. B., Gal, G., Hamilton, D. P.,
Hanson, P. C., Hetherington, A. L., Melack, J. M., Read, J. S., Rinke, K. and
Rigosi, A., Trolle, D., Winslow, L., Adrian, R., Ayala, A. I, Bocaniov, S.
A., Boehrer, B., Boon, C., Brookes, J. D., Bueche, T., Busch, B. D., Copetti,
D., Cortés, A., de Eyto, E., Elliott, J.A., Gallina, N., Gilboa, Y.,
Guyennon, N., Huang, L., Kerimoglu, O., Lenters, J.D., MacIntyre, S.,
Makler-Pick, V., McBride, C. G., Moreira, S., Özkundakci, D., Pilotti,
M., Rueda, F. J., Rusak, J. A., Samal, N. R., Schmid, M., Shatwell, T.,
Snorthheim, C., Soulignac, F., Valerio, G., van der Linden, L., Vetter, M.,
Vinçon-Leite, B., Wang, J., Weber, M., Wickramaratne, C., Woolway, R. I.,
Yao, H., and Hipsey, M. R.: A multi-lake comparative analysis of the General
Lake Model (GLM): Stress-testing across a global observatory network,
Environ. Modell. Softw., 102, 274–291, 2018.
Bruggeman, J. and Bolding, K.: A general framework for aquatic
biogeochemical models, Environ. Modell. Softw., 61, 249–265, 2014.
Brutsaert, W.: On a derivable formula for long-wave radiation from clear
skies, Water Resour. Res., 11, 742–744, 1975.
Bucak, T., Trolle, D., Tavşanoğlu, Ü. N., Çakıroğlu,
A. İ., Özen, A., Jeppesen, E., and Beklioğlu, M.: Modeling the
effects of climatic and land use changes on phytoplankton and water quality
of the largest Turkish freshwater lake: Lake Beyşehir, Sci. Total
Environ., 621, 802–816, 2018.
Bueche, T., Hamilton, D. P., and Vetter, M.: Using the General Lake Model
(GLM) to simulate water temperatures and ice cover of a medium-sized lake: a
case study of Lake Ammersee, Germany, Environ. Earth Sci., 76, 461,
https://doi.org/10.1007/s12665-017-6790-7, 2017.
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.: Flux
profile relationships in the atmospheric surface layer, J. Atmos. Sci., 28,
181–189, 1971.
Carey, C. C. and Gougis, R. D.: Simulation modeling of lakes in undergraduate
and graduate classrooms increases comprehension of climate change concepts
and experience with computational tools, J. Sci. Educ. Technol., 26, 1–11,
2017.
Cengel, Y. A. and Ozisk, M. N.: Solar radiation absorption in solar ponds,
Sol. Energy, 33, 581–591, 1984.
Chung, E. G., Schladow, S. G., Perez-Losada, J., and Robertson, D. M.: A
linked hydrodynamic and water quality model for the Salton Sea,
Hydrobiologia, 604, 57–75, 2008.
Chung, S. W., Imberger, J., Hipsey, M. R., and Lee, H. S.: The influence of
physical and physiological processes on the spatial heterogeneity of a
Microcystis bloom in a stratified reservoir, Ecol. Model., 289,
133–149, 2014.
Cogley, J. G.: The albedo of water as a function of latitude, Mon.
Weather Rev., 107, 775–781, 1979.
Cole, J. J., Prairie, Y. T., Caraco, N. F., McDowell, W. H., Tranvik, L. J.,
Striegl, R. G., Duarte, C. M., Kortelainen, P., Downing, J. A., Middelburg,
J. J., and Melack, J.: Plumbing the global carbon cycle: Integrating inland
waters into the terrestrial carbon budget, Ecosystems, 10, 172–185, 2007.
Doherty, J.: Calibration and Uncertainty Analysis for Complex Environmental
Models, Watermark Numerical Computing, Brisbane, Australia, 2015.
Dyer, A. J.: A review of flux-profile relationships, Bound-Lay. Meteorol.,
7, 363–372, 1974.
Fenocchi, A., Rogora, M., Sibilla, S., and Dresti, C.: Relevance of inflows
on the thermodynamic structure and on the modeling of a deep subalpine lake
(Lake Maggiore, Northern Italy/Southern Switzerland), Limnologica, 63,
42–56, 2017.
Fischer, H. B., List, E. G., Koh, R. C. Y., Imberger, J., and Brooks, N. H.
(Eds.): Mixing in Inland and Coastal Waters, Academic Press, New York, USA,
1979.
Flerchinger, G. N., Xaio, W., Marks, D., Sauer, T. J., and Yu,
Q.: Comparison of algorithms for incoming atmospheric long-wave
radiation, Water Resour. Res., 45, W03423, https://doi.org/10.1029/2008WR007394, 2009.
Francey, R. J. and Garratt, J. R.: Eddy flux measurements over the ocean
and related transfer coefficients, Bound-Lay. Meteorol., 14, 153–166, 1978.
Gal, G., Imberger, J., Zohary, T., Antenucci, J., Anis, A., and Rosenberg,
T.: Simulating the thermal dynamics of Lake Kinneret, Ecol. Model., 162,
69–86, 2003.
Gal, G., Hipsey, M. R., Parparov, A., Wagner, U., Makler, V., and Zohary,
T.: Implementation of ecological modeling as an effective management and
investigation tool: Lake Kinneret as a case study, Ecol. Model., 220,
1697–1718, 2009.
Ganguly, A., Agrawal, A., Boykin, P. O., and Figueiredo, R.: IP over P2P:
Enabling self-configuring virtual IP networks for grid computing, in:
International Parallel and Distributed Processing Symposium, Rhodes Island,
Greece, 5–29 April 2006.
Gu, R. and Stefan, H. G.: Validation of cold climate lake temperature
simulation, Cold Reg. Sci. Technol., 22, 99–104, 1993.
Haario, H., Laine, M., Mira, A., and Saksman, E.: DRAM: Efficient adaptive
MCMC, Stat. Comput., 16, 339–354, 2006.
Hamilton, D. P. and Schladow, S. G.: Water quality in lakes and reservoirs.
Part I Model description, Ecol. Model., 96, 91–110, 1997.
Hamilton, D. P., Carey, C. C., Arvola, L., Arzberger, P., Brewer, C., Cole,
J. J., Gaiser, E., Hanson, P. C., Ibelings, B. W., Jennings, E., and Kratz,
T. K.: A Global Lake Ecological Observatory Network (GLEON) for synthesising
high-frequency sensor data for validation of deterministic ecological models,
Inland Waters, 5, 49–56, 2015.
Hansen, G. J., Read, J. S., Hansen, J. F., and Winslow, L. A.: Projected
shifts in fish species dominance in Wisconsin lakes under climate
change, Glob. Change Biol., 23, 1463–1476, 2017.
Hanson, P. C., Weathers, K. C., and Kratz, T. K.: Networked lake science:
how the Global Lake Ecological Observatory Network (GLEON) works to
understand, predict, and communicate lake ecosystem response to global
change, Inland Waters, 6, 543–554, 2016.
Harvey, L. D. D.: Testing alternative parameterizations of lateral melting
and upward basal heat flux in a thermodynamic sea ice model, J. Geophys.
Res., 95, 7359–7365, 1990.
Henderson-Sellers, B.: Calculating the surface energy balance for lake and
reservoir modeling: A review, Rev. Geophys., 24, 625–649, 1986.
Hicks, B. B.: Some evaluations of drag and bulk transfer coefficients over
water, Bound-Lay. Meteorol., 3, 201–213, 1972.
Hicks, B. B.: A procedure for the formulation of bulk transfer coefficients
over water, Bound-Lay. Meteorol., 8, 515–524, 1975.
Hicks, B. B.: Wind Profile Relationships from the “Wangara” Experiment, Q.
J. Roy. Meteor. Soc., 102, 535–551, 1976.
Hipsey, M. R. and Sivapalan, M.: Parameterizing the effect of a
wind-shelter on evaporation from small waterbodies, Water Resour. Res., 39,
1339, https://doi.org/10.1029/2002WR001784, 2003.
Hipsey, M. R., Hamilton, D. P., Hanson, P. C., Carey, C. C., Coletti, J. Z.,
Read, J. S., Ibelings, B. W., Valesini, F. J., and, Brookes, J. D.:
Predicting the resilience and recovery of aquatic systems: A framework for
model evolution within environmental observatories, Water Resour. Res., 51,
7023–7043, 2015.
Hipsey, M. R., Boon, C., Bruce, L. C., Weber, M., Winslow, L., Read, J. S., and Hamilton, D. P.: AquaticEcoDynamics/GLM: v3.0.0_rc2, https://doi.org/10.5281/zenodo.2538486, 2019a.
Hipsey, M. R., Boon, C., Busch, B., Bruce, L. C., Weber, M., Winslow, L., Read, J. S., and Hamilton, D. P.: AquaticEcoDynamics/GLM_Examples: v3.0.0_rc2, https://doi.org/10.5281/zenodo.2538489, 2019b.
Hipsey, M. R., Boon, C., Paraska, D., Bruce, L. C., and Huang, P.: AquaticEcoDynamics/libaed2: v1.3.0-rc2, Aquatic EcoDynamics (AED) Model Library & Science Manual, 1–34, https://doi.org/10.5281/zenodo.2538495, 2019c.
Hocking, G. C. and Patterson, J. C.: Quasi-two-dimensional reservoir
simulation model, J. Environ. Eng., 117, 595–613, 1991.
Hu, F., Bolding, K., Bruggeman, J., Jeppesen, E., Flindt, M. R., van Gerven,
L., Janse, J. H., Janssen, A. B. G., Kuiper, J. J., Mooij, W. M., and Trolle,
D.: FABM-PCLake – linking aquatic ecology with hydrodynamics, Geosci. Model
Dev., 9, 2271–2278, https://doi.org/10.5194/gmd-9-2271-2016, 2016.
Huang, L., Wang, J., Zhu, L., Ju, J., and Daut, G.: The warming of large
lakes on the Tibetan Plateau: Evidence from a lake model simulation of Nam
Co, China, during 1979–2012, J. Geophys. Res.-Atmos., 122, 13095–13107,
2017.
Idso, S. B. and Jackson, R. D.: Thermal radiation from the atmosphere, J.
Geophys. Res., 74, 5397–5403, 1969.
Imberger, J. and Patterson, J. C.: A dynamic reservoir simulation
model-DYRESM:5, in: Transport Models for Inland and Coastal Waters, edited
by: Fischer, H. B., Academic Press, New York, 310–361, 1981.
Imberger, J. and Patterson, J. C.: Physical Limnology, in:
Advances in Applied Mechanics, edited by: Wu, T., 27, Academic Press, Boston,
USA, 1990.
Imberger, J., Patterson, J., Hebbert, B., and Loh, I.: Dynamics of reservoir
of medium size, J. Hydraul. Eng.-ASCE, 104, 725–743, 1978.
Imboden, D. M. and Wüest, A.: Mixing Mechanisms in Lakes, in: Physics and Chemistry of
Lakes, edited by: Lerman, A., Imboden, D. M., and Gat, J. R.,
Springer-Verlag, 83–138, 1995.
Janssen, A. B. G., Arhonditsis, G. B., Beusen, A., Bolding, K., Bruce, L.,
Bruggeman, J., Couture, R. M., Downing, A. S., Elliott, J. A., Frassl, M. A.,
Gal, G., Gerla, D. J., Hipsey, M. R., Hu, F., Ives, S. C., Janse, J.,
Jeppesen, E., Jöhnk, K. D., Kneis, D., Kong, X., Kuiper, J. K., Lehmann,
M., Lemmen, C., Ozkundakci, D., Petzoldt, T., Rinke, K., Robson, B. J.,
Sachse, R., Schep, S., Schmid, M., Scholten, H., Teurlincx, S., Trolle, D.,
Troost, T. A., Van Dam, A., Van Gerven, L. A., Weijerman, M., Wells S. A.,
and Mooij, W. M.: Exploring, exploiting and evolving diversity of aquatic
ecosystem models: a community perspective, Aquat. Ecol., 49, 513–548, 2015.
Jellison, R. and Melack, J. M.: Meromixis and vertical diffusivities in
hypersaline Mono Lake, California, Limnol. Oceanogr., 38, 1008–1019, 1993.
Jeong, S.: Understanding snow process uncertainties and their impacts, PhD
thesis, University of California, Berkeley, 2009.
Ji, Z. G.: Hydrodynamics and water quality: modeling rivers, lakes, and
estuaries, John Wiley & Sons, 2008.
Kara, E. L., Hanson, P., Hamilton, D., Hipsey, M. R., McMahon, K. D., Read,
J. S., Winslow, L., Dedrick, J., Rose, K., Carey, C. C., and Bertilsson, S.:
Time-scale dependence in numerical simulations: assessment of physical,
chemical, and biological predictions in a stratified lake at temporal scales
of hours to months, Environ. Modell. Softw., 35, 104–121, 2012.
Kim, J.-W.: A generalized bulk model of the oceanic mixed layer, J. Phys.
Oceanogr., 6, 686–695, 1976.
Kirillin, G., Hochschild, J., Mironov, D., Terzhevik, A., Golosov, S., and
Nützmann, G.: FLake-Global: Online lake model with worldwide coverage,
Environ. Modell. Softw., 26, 683–684, 2011.
Kirk, J. T. O.: Light and photosynthesis in aquatic ecosystems, Cambridge
University Press, 1994.
Kleinhans, M. G. and Grasmeijer, B. T.: Bed load transport on the shoreface
by currents and waves, Coast. Eng., 53, 983–996, 2006.
Klug, J. L., Richardson, D. C., Ewing, H. A., Hargreaves, B. R., Samal, N.
R., Vachon, D., Pierson, D. C., Lindsey, A. M., O'Donnell, D. M., Effler, S.
W., and Weathers, K. C.: Ecosystem effects of a tropical cyclone on a network
of lakes in northeastern North America, Environ. Sci. Technol., 46,
11693–11701, 2012.
Kraus, E. B. and Turner, J. S.: A one-dimensional model of the seasonal
thermocline: II The general theory and its consequences, Tellus, 19, 98–106,
1967.
Laenen, A. and LeTourneau, A. P.: Upper Klamath Lake nutrient loading study
– Estimate of wind-induced resuspension of bed sediment during periods of
low lake elevation, U.S. Geological Survey Open-File Report, 95-414, 11 pp.,
1996.
Launiainen, J.: Derivation of the relationship between the Obukhov stability
parameter and the bulk Richardson number for flux-profile studies, Bound-Lay.
Meteorol., 76, 165–179, 1995.
Launiainen, J. and Cheng, B.: Modelling of ice thermodynamics in natural
water bodies, Cold Reg. Sci. Technol., 27, 153–178, 1998.
Launiainen, J. and Vihma, T.: Derivation of turbulent surface fluxes – An
iterative flux-profile method allowing arbitrary observing heights, Environ.
Softw., 5, 113–124, 1990.
Magee, M. R., Wu, C. H., Robertson, D. M., Lathrop, R. C., and Hamilton, D.
P.: Trends and abrupt changes in 104 years of ice cover and water temperature
in a dimictic lake in response to air temperature, wind speed, and water
clarity drivers, Hydrol. Earth Syst. Sci., 20, 1681–1702,
https://doi.org/10.5194/hess-20-1681-2016, 2016.
Makler-Pick, V., Gal, G., Shapiro, J., and Hipsey, M. R.: Exploring the role
of fish in a lake ecosystem (Lake Kinneret, Israel) by coupling an
individual-based fish population model to a dynamic ecosystem model, Can. J.
Fish. Aquat. Sci., 68, 1265–1284, 2011.
Markfort, C. D., Perez, A. L. S., Thill, J. W., Jaster, D. A.,
Porté-Agel, F., and Stefan, H. G.: Wind sheltering of a lake by a tree
canopy or bluff topography, Water Resour. Res., 46, 1–13, 2010.
Martynov, A., Sushama, L., Laprise, R., Winger, K., and Dugas, B.:
Interactive lakes in the Canadian Regional Climate Model, version 5: The role
of lakes in the regional climate of North America, Tellus A, 64, 1–22, 2012.
Matzinger, A., Schmid, M., Veljanoska-Sarafiloska, E., Patceva, S., Guseska,
D., Wagner, B., Müller, B., Sturm, M., and Wüest, A.: Eutrophication
of ancient Lake Ohrid: global warming amplifies detrimental effects of
increased nutrient inputs, Limnol. Oceanogr., 52, 338–353, 2007.
McCord, S. A. and Schladow, S. G.: Numerical simulations of degassing
scenarios for CO2-rich Lake Nyos, Cameroon, J. Geophys. Res., 103,
12355–12364, 1998.
McKay, G. A.: Problems of measuring and evaluating snow cover, in:
Proceedings of Workshop Seminar of Snow Hydrology, Secretariat Canadian
National Committee for the IHD, Ottawa, 49–62, 1968.
Menció, A., Casamitjana, X., Mas-Pla, J., Coll, N., Compte, J.,
Martinoy, M., Pascual, J., and Quintana, X. D.: Groundwater dependence of
coastal lagoons: The case of La Pletera salt marshes (NE Catalonia), J.
Hydrol., 552, 793–806, 2017.
Mooij, W. M., Trolle, D., Jeppesen, E., Arhonditsis, G., Belolipetsky, P.
V., Chitamwebwa, D. B. R., Degermendzhy, A. G., DeAngelis, D. L., De
Senerpont Domis, L. N., Downing, A. S., Elliott, A. E., Fragoso Jr., C.R.,
Gaedke, U., Genova, S.N., Gulati, R. D., Håkanson, L., Hamilton, D. P.,
Hipsey, M. R., Hoen, J., Hülsmann, S., Los, F. J., Makler-Pick, V.,
Petzoldt, T., Prokopkin, I. G., Rinke, K., Schep, S. A., Tominaga, K., Van
Dam, A. A., Van Nes, E. H., Wells, S. A., and Janse, J. H.: Challenges and
opportunities for integrating lake ecosystem modelling approaches, Aquat.
Ecol., 44, 633–667, 2010.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the
atmosphere near the ground, Jr. Akad. Nauk SSSR Geofiz. Inst., 24, 163–187,
1954.
Mueller, H., Hamilton, D. P., and Doole, G. J.: Evaluating services and
damage costs of degradation of a major lake ecosystem, Ecosyst. Serv., 22,
370–380, 2016.
NRC (National Research Council): Next generation science standards: For
states, by states, The National Academies Press, Washington DC, USA, 2013.
O'Reilly, C. M., Sharma, S., Gray, D. K., Hampton, S. E., Read, J. S.,
Rowley, R. J., Schneider, P., Lenters, J. D., McIntyre, P. B., Kraemer, B.
M., Weyhenmeyer, G. A.,
Straile, D.,
Dong, B.,
Adrian, R.,
Allan, M. G.,
Anneville, O.,
Arvola, L.,
Austin, J.,
Bailey, J. L.,
Baron, J. S.,
Brookes, J. D.,
de Eyto, E.,
Dokulil, M. T.,
Hamilton, D. P.,
Havens, K.,
Hetherington, A. L.,
Higgins, S. N.,
Hook, S.,
Izmest'eva, L. R.,
Joehnk, K. D.,
Kangur, K.,
Kasprzak, P.,
Kumagai, M.,
Kuusisto, E.,
Leshkevich, G.,
Livingstone, D. M.,
MacIntyre, S.,
May, L.,
Melack, J. M.,
Mueller-Navarra, D. C.,
Naumenko, M.,
Noges, P.,
Noges, T.,
North, R. P.,
Plisnier, P.-D.,
Rigosi, A.,
Rimmer, A.,
Rogora, M.,
Rudstam, L. G.,
Rusak, J. A.,
Salmaso, N.,
Samal, N. R.,
Schindler, D. E.,
Schladow, S. G.,
Schmid, M.,
Schmidt, S. R.,
Silow, E.,
Soylu, M. E.,
Teubner, K.,
Verburg, P.,
Voutilainen, A.,
Watkinson, A.,
Williamson, C. E.,
and
Zhang, G.,: Rapid and highly variable
warming of lake surface waters around the globe, Geophys. Res. Lett., 42,
10773–10781, 2015.
Patterson, J. C. and Hamblin, P. F.: Thermal simulation of a lake with
winter ice cover, Limnol. Oceanogr., 33, 323–338, 1988.
Patterson, J. C., Hamblin, P. F., and Imberger, J.: Classification and
dynamics simulation of the vertical density structure of lakes, Limnol.
Oceanogr., 29, 845–861, 1984.
Paulson, C. A.: The mathematical representation of wind speed and
temperature profiles in the unstable atmospheric surface layer, J. Appl.
Meteorol., 9, 857–861, 1970.
Peeters, F., Straile, D.m Loke, A., and Livingstone, D. M.: Earlier onset of
the spring phytoplankton bloom in lakes of the temperate zone in a warmer
climate, Glob, Change Biol., 13, 1898–1909, 2007.
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, 2009.
Porter, J. H., Hanson, P. C., and Lin, C. C.: Staying afloat in the sensor data
deluge, Trends Ecol. Evol., 27, 121–129, 2012.
Read, J. S., Hamilton, D. P., Jones, I. D., Muraoka, K., Winslow, L. A.,
Kroiss, R., Wu, C. H., and Gaiser, E.: Derivation of lake mixing and
stratification indices from high-resolution lake buoy data, Environ. Modell.
Softw., 26, 1325–1336, 2011.
Read, J. S., Hansen, G., Van Den Hoek, J., Hanson, P. C., Bruce, L. C., and
Markfort, C. D.: Simulating 2368 temperate lakes reveals weak coherence in
stratification phenology, Ecol. Model., 291, 142–150, 2014.
Read, J. S., Gries, C., Read, E. K., Klug, J., Hanson, P. C., Hipsey, M. R.,
Jennings, E., O'Reilly, C., Winslow, L., Pierson, D., McBride, C., and
Hamilton, D. P.: Generating community-built tools for data sharing and
analysis in environmental networks, Inland Waters, 6, 637–644, 2016.
Rigosi, A., Hanson, P. C., Hamilton, D. P., Hipsey, M. R., Rusak, J. A.,
Bois, J., Sparber, K., Chorus, I., Watkinson, A. J., Qin, B., Kim, B., and
Brookes, J. D.: Determining the probability of cyanobacterial blooms: the
application of Bayesian networks in multiple lake systems, Ecol. Appl., 25,
186–199, 2015.
Riley, M. and Stefan, H.: MINLAKE: A dynamic lake water quality simulation
model, Ecol. Model., 43, 155–182, 1988.
Rogers, C. K., Lawrence, G. A., and Hamblin, P. F.: Observations and
numerical simulation of a shallow ice-covered mid-latitude lake, Limnol.
Oceanogr., 40, 374–385, 1995.
Romarheim, A. T., Tominaga, K., Riise, G., and Andersen, T.: The importance
of year-to-year variation in meteorological and runoff forcing for water
quality of a temperate, dimictic lake, Hydrol. Earth Syst. Sci., 19,
2649–2662, https://doi.org/10.5194/hess-19-2649-2015, 2015.
Salmon, S. U., Hipsey, M. R., Wake, G. W., Ivey, G. N., and Oldham, C. E.
Quantifying lake water quality evolution: Coupled geochemistry,
hydrodynamics, and aquatic ecology in an acidic pit lake, Environ. Sci.
Technol., 51, 9864–9875, 2017.
Saloranta, T. M. and Andersen, T.: MyLake – A multi-year lake simulation
model code suitable for uncertainty and sensitivity analysis simulations,
Ecol. Model., 207, 45–60, 2007.
Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A.,
Fortus, D., Shwartz, Y., Hug, B., and Krajcik, J.: Developing a learning
progression for scientific modeling: Making scientific modeling accessible
and meaningful for learners, J. Res. Sci. Teach., 46, 632–654, 2009.
Sheng, Y. P. and Lick, W.: The transport and resuspension of sediments in a
shallow lake, J. Geophys. Res., 84, 1809–1826, 1979.
Sherman, F. S., Imberger, J., and Corcos, G. M.: Turbulence and mixing in
stably stratified waters, Annu. Rev. Fluid Mech., 10, 267–288, 1978.
Snortheim, C. A., Hanson, P. C., McMahon, K. D., Read, J. S., Carey, C. C.,
and Dugan, H. A.: Meteorological drivers of hypolimnetic anoxia in a
eutrophic, north temperate lake, Ecol. Model., 343, 39–53, 2017.
Spigel, R. H.: Wind mixing in lakes, PhD thesis, University of California,
Berkeley, USA, 1978.
Spigel, R. H. and Imberger, J.: The classification of mixed-layer dynamics
in lakes of small to medium size, J. Phys. Oceanogr., 10, 1104–1121, 1980.
Stepanenko, V., 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.
Stepanenko, V. M., Martynov, A., Jöhnk, K. D., Subin, Z. M., Perroud, M.,
Fang, X., Beyrich, F., Mironov, D., and Goyette, S.: A one-dimensional model
intercomparison study of thermal regime of a shallow, turbid midlatitude
lake, Geosci. Model Dev., 6, 1337–1352,
https://doi.org/10.5194/gmd-6-1337-2013, 2013.
Stewart, J., Cartier, J. L., and Passmore, C. M.: Developing understanding
through model-based inquiry, in: How Students Learn, edited by: Donovan, M.
S. and Bransford, J. D., National Research Council, Washington DC, USA,
515–565, 2005.
Strub, P. T. and Powell, T. M. Surface temperature and transport in Lake
Tahoe: inferences from satellite (AVHRR) imagery, Cont. Shelf Res., 7,
1001–1013, 1987.
Subratie, K., Aditya, S., Figueiredo, R., Carey, C. C., and Hanson, P. C.:
GRAPLEr: A distributed collaborative environment for lake ecosystem modeling
that integrates overlay networks, high-throughput computing, and web
services, Concurr. Comp.-Pract. E, 29, e4139, https://doi.org/10.1002/cpe.4139, 2017.
Swinbank, W. C.: Longwave radiation from clear skies, Q. J. Roy.
Meteor.
Soc., 89, 339–348, 1963.
Tabata, S.: A simple but accurate formula for the saturation vapour pressure
over liquid water, J. Appl. Meteorol., 12, 1410–1411, 1973.
Thain, D., Tannenbaum, T., and Livny, M.: Distributed computing in practice:
The Condor experience, Concurr. Comp.-Pract. E, 17, 323–356, 2005.
Ticehurst J. L., Newham, L. T. H., Rissik, D., Letcher, R. A., and Jakeman,
A. J.: A Bayesian network approach for assessing the sustainability of
coastal lakes in New South Wales, Australia, Environ. Modell. Softw., 22,
1129–1139, 2007.
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., and
Kortelainen, P. L.: Lakes and reservoirs as regulators of carbon cycling and
climate, Limnol. Oceanogr., 54, 2298–2314, 2009.
Trolle, D., Hamilton, D. P., Hipsey, M. R., Bolding, K., Bruggeman, J.,
Mooij, W. M., Janse, J. H., Nielsen, A., Jeppesen, E., Elliott, J. E.,
Makler-Pick, V., Petzoldt, T., Rinke, K., Flindt, M. R., Arhonditsis, G. B.,
Gal, G., Bjerring, R., Tominaga, K., Hoen, J., Downing, A. S., Marques, D.
M., Fragoso Jr., C. R., Søndergaard, M., and Hanson, P. C.: A
community-based framework for aquatic ecosystem models, Hydrobiologia, 683,
25–34, 2012.
TVA (Tennessee Valley Authority): Heat and mass transfer between a water
surface and the atmosphere, Water Resources Research Laboratory Report 14,
Report No. 0-6803, 1972.
UNESCO: The Practical Salinity Scale 1978 and the International Equation of
State of Sea water 1980, UNESCO Technical Paper Marine Science, 36, 1981.
Vavrus, S. J., Wynne, R. H., and Foley, J. A.: Measuring the sensitivity of
southern Wisconsin lake ice to climate variations and lake depth using a
numerical model, Limnol. Oceanogr., 41, 822–831, 1996.
Vickers, D., Mahrt, L., and Andreas, E. L.: Estimates of the 10-m neutral
sea surface drag coefficient from aircraft eddy-covariance measurements, J.
Phys. Oceanogr., 43, 301–310, 2013.
Weathers, K. C., Groffman, P. M., Van Dolah, E., Bernhardt, E. S., Grimm, N.
B., McMahon, K. D., Schimel, J., Paolisso, M., Maranger, R. J., Baer, S.,
Brauman, K. A., and Hinckley, E.: Frontiers in ecosystem ecology from a
community perspective: The future is boundless and bright, Ecosystems, 19,
753–770, 2016.
Weber, M., Rinke, K., Hipsey, M. R., and Boehrer, B.: Optimizing withdrawal
from drinking water reservoirs to reduce downstream temperature pollution and
reservoir hypoxia, J. Environ. Manage., 197, 96–105, 2017.
Weinstock, J.: Vertical turbulence diffusivity for weak or strong stable
stratification, J. Geophys. Res., 86, 9925–9928, 1981.
Williamson, C. E., Saros, J. E., Vincent, W. F., and Smol, J. P.: Lakes and
reservoirs as sentinels, integrators, and regulators of climate change,
Limnol. Oceanogr., 5, 2273–2282, 2009.
Winslow, L. A., Hansen, G. J. A., Read, J. S., and Notaro, M.: Data
Descriptor: Large-scale modeled contemporary and future water temperature
estimates for 10774 Midwestern U.S. Lakes, Scientific Data, 4, 170053,
https://doi.org/10.1038/sdata.2017.53, 2017.
Woolway, R. I., Verburg, P., Merchant, C. J., Lenters, J. D., Hamilton, D.
P., Brookes, J., Kelly, S., Hook, S., Laas, A., Pierson, D., and Rimmer, A.:
Latitude and lake size are important predictors of over-lake atmospheric
stability, Geophys. Res. Lett., 44, 8875–8883, 2017.
Wu, J.: Wind induced entrainment across a stable density interface, J. Fluid
Mech., 61, 275–278, 1973.
Xenopoulos, M. A. and Schindler, D. W.: The environmental control of
near-surface thermoclines in boreal lakes, Ecosystems, 4, 699–707, 2001.
Yajima, H. and Yamamoto, S.: Improvements of radiation estimations for a
simulation of water temperature in a reservoir, Journal of Japan Society of
Civil Engineers, Ser. B1 (Hydraulic Engineering), 71, 775–780, 2015.
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, 2014.
Yeates, P. S. and Imberger, J.: Pseudo two-dimensional simulations of
internal and boundary fluxes in stratified lakes and reservoirs,
International Journal of River Basin Research, 1, 1–23, 2003.
Zhang, W. and Arhonditsis G. B.: A Bayesian hierarchical framework for
calibrating aquatic biogeochemical models, Ecol. Model., 220, 2142–2161,
2009.
Short summary
The General Lake Model (GLM) has been developed to undertake simulation of a diverse range of wetlands, lakes, and reservoirs. The model supports the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of lake sensors and researchers attempting to understand lake functioning and address questions about how lakes around the world vary in response to climate and land use change. The paper describes the science basis and application of the model.
The General Lake Model (GLM) has been developed to undertake simulation of a diverse range of...
