Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7421-2022
© 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-15-7421-2022
© 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
| 
06 Oct 2022
Model evaluation paper |
| 06 Oct 2022
| 06 Oct 2022
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
Elchin E. Jafarov
Earth and Environmental Sciences Division, Los Alamos National
Laboratory, Los Alamos, NM, USA
Woodwell Climate Research Center, Falmouth, MA, USA
Ken D. Tape
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
Benjamin M. Jones
Institute of Northern Engineering, University of Alaska Fairbanks, Fairbanks, AK, USA
Victor Stepanenko
Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
Moscow Center for Fundamental and Applied Mathematics, Moscow, Russia
Related authors
No articles found.
Valeria Briones, Hélène Genet, Elchin E. Jafarov, Brendan M. Rogers, Jennifer D. Watts, Anna-Maria Virkkala, Annett Bartsch, Benjamin C. Maglio, Joshua Rady, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-226, https://doi.org/10.5194/essd-2025-226, 2025
Preprint under review for ESSD
Short summary
Short summary
Arctic warming is causing permafrost to thaw, affecting ecosystems and climate. Since land cover, especially vegetation, shapes how permafrost responds, accurate maps are crucial. Using machine learning, we combined existing global and regional datasets to create a hybrid detailed 1-km map of Arctic-Boreal land cover, improving the representation of forests, shrubs, and wetlands across the circumpolar.
Frieda P. Giest, Maren Jenrich, Guido Grosse, Benjamin M. Jones, Kai Mangelsdorf, Torben Windirsch, and Jens Strauss
EGUsphere, https://doi.org/10.5194/egusphere-2024-3683, https://doi.org/10.5194/egusphere-2024-3683, 2024
Short summary
Short summary
Climate warming causes permafrost to thaw, releasing greenhouse gases and affecting ecosystems. We studied sediments from Arctic coastal landscapes, including land, lakes, lagoons, and the ocean, finding that organic carbon storage and quality vary with landscape features and saltwater influence. Freshwater and land areas store more carbon, while saltwater reduces its quality. These findings improve predictions of Arctic responses to climate change and their impact on global carbon cycling.
Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão
The Cryosphere, 18, 5139–5152, https://doi.org/10.5194/tc-18-5139-2024, https://doi.org/10.5194/tc-18-5139-2024, 2024
Short summary
Short summary
Snow distribution characterization is essential for accurate snow water estimation for water resource prediction from existing in situ observations and remote-sensing data at a finite spatial resolution. Four different observed snow distribution datasets were analyzed for Gaussianity. We found that non-Gaussianity of snow distribution is a signature of the wind redistribution effect. Generally, seasonal snowpack can be approximated well by a Gaussian distribution for a fully snow-covered area.
Lydia Stolpmann, Ingmar Nitze, Ingeborg Bussmann, Benjamin M. Jones, Josefine Lenz, Hanno Meyer, Juliane Wolter, and Guido Grosse
EGUsphere, https://doi.org/10.5194/egusphere-2024-2822, https://doi.org/10.5194/egusphere-2024-2822, 2024
Preprint archived
Short summary
Short summary
We combine hydrochemical and lake change data to show consequences of permafrost thaw induced lake changes on hydrochemistry, which are relevant for the global carbon cycle. We found higher methane concentrations in lakes that do not freeze to the ground and show that lagoons have lower methane concentrations than lakes. Our detailed lake sampling approach show higher concentrations in Dissolved Organic Carbon in areas of higher erosion rates, that might increase under the climate warming.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Mauricio Arboleda-Zapata, Michael Angelopoulos, Pier Paul Overduin, Guido Grosse, Benjamin M. Jones, and Jens Tronicke
The Cryosphere, 16, 4423–4445, https://doi.org/10.5194/tc-16-4423-2022, https://doi.org/10.5194/tc-16-4423-2022, 2022
Short summary
Short summary
We demonstrate how we can reliably estimate the thawed–frozen permafrost interface with its associated uncertainties in subsea permafrost environments using 2D electrical resistivity tomography (ERT) data. In addition, we show how further analyses considering 1D inversion and sensitivity assessments can help quantify and better understand 2D ERT inversion results. Our results illustrate the capabilities of the ERT method to get insights into the development of the subsea permafrost.
M. R. Udawalpola, C. Witharana, A. Hasan, A. Liljedahl, M. Ward Jones, and B. Jones
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-M-2-2022, 203–208, https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-203-2022, https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-203-2022, 2022
Victor Lomov, Victor Stepanenko, Maria Grechushnikova, and Irina Repina
EGUsphere, https://doi.org/10.5194/egusphere-2022-329, https://doi.org/10.5194/egusphere-2022-329, 2022
Preprint withdrawn
Short summary
Short summary
We present the first mechanistic model LAKE2.3 for prediction of methane emissions from artificial reservoirs. Estimates of CH4 emissions from the Mozhaysk reservoir (Moscow region) provided by the model are demonstrated. Methane annual emissions through diffusion, ebullition and downstream degassing according to in situ measurements and model simulations are presented. The experiments with the model allowed to determine the most sensitive model parameters for calibration of methane fluxes.
Noriaki Ohara, Benjamin M. Jones, Andrew D. Parsekian, Kenneth M. Hinkel, Katsu Yamatani, Mikhail Kanevskiy, Rodrigo C. Rangel, Amy L. Breen, and Helena Bergstedt
The Cryosphere, 16, 1247–1264, https://doi.org/10.5194/tc-16-1247-2022, https://doi.org/10.5194/tc-16-1247-2022, 2022
Short summary
Short summary
New variational principle suggests that a semi-ellipsoid talik shape (3D Stefan equation) is optimum for incoming energy. However, the lake bathymetry tends to be less ellipsoidal due to the ice-rich layers near the surface. Wind wave erosion is likely responsible for the elongation of lakes, while thaw subsidence slows the wave effect and stabilizes the thermokarst lakes. The derived 3D Stefan equation was compared to the field-observed talik thickness data using geophysical methods.
Elchin E. Jafarov, Daniil Svyatsky, Brent Newman, Dylan Harp, David Moulton, and Cathy Wilson
The Cryosphere, 16, 851–862, https://doi.org/10.5194/tc-16-851-2022, https://doi.org/10.5194/tc-16-851-2022, 2022
Short summary
Short summary
Recent research indicates the importance of lateral transport of dissolved carbon in the polygonal tundra, suggesting that the freeze-up period could further promote lateral carbon transport. We conducted subsurface tracer simulations on high-, flat-, and low-centered polygons to test the importance of the freeze–thaw cycle and freeze-up time for tracer mobility. Our findings illustrate the impact of hydraulic and thermal gradients on tracer mobility, as well as of the freeze-up time.
Dylan R. Harp, Vitaly Zlotnik, Charles J. Abolt, Bob Busey, Sofia T. Avendaño, Brent D. Newman, Adam L. Atchley, Elchin Jafarov, Cathy J. Wilson, and Katrina E. Bennett
The Cryosphere, 15, 4005–4029, https://doi.org/10.5194/tc-15-4005-2021, https://doi.org/10.5194/tc-15-4005-2021, 2021
Short summary
Short summary
Polygon-shaped landforms present in relatively flat Arctic tundra result in complex landscape-scale water drainage. The drainage pathways and the time to transition from inundated conditions to drained have important implications for heat and carbon transport. Using fundamental hydrologic principles, we investigate the drainage pathways and timing of individual polygons, providing insights into the effects of polygon geometry and preferential flow direction on drainage pathways and timing.
Lydia Stolpmann, Caroline Coch, Anne Morgenstern, Julia Boike, Michael Fritz, Ulrike Herzschuh, Kathleen Stoof-Leichsenring, Yury Dvornikov, Birgit Heim, Josefine Lenz, Amy Larsen, Katey Walter Anthony, Benjamin Jones, Karen Frey, and Guido Grosse
Biogeosciences, 18, 3917–3936, https://doi.org/10.5194/bg-18-3917-2021, https://doi.org/10.5194/bg-18-3917-2021, 2021
Short summary
Short summary
Our new database summarizes DOC concentrations of 2167 water samples from 1833 lakes in permafrost regions across the Arctic to provide insights into linkages between DOC and environment. We found increasing lake DOC concentration with decreasing permafrost extent and higher DOC concentrations in boreal permafrost sites compared to tundra sites. Our study shows that DOC concentration depends on the environmental properties of a lake, especially permafrost extent, ecoregion, and vegetation.
Claire E. Simpson, Christopher D. Arp, Yongwei Sheng, Mark L. Carroll, Benjamin M. Jones, and Laurence C. Smith
Earth Syst. Sci. Data, 13, 1135–1150, https://doi.org/10.5194/essd-13-1135-2021, https://doi.org/10.5194/essd-13-1135-2021, 2021
Short summary
Short summary
Sonar depth point measurements collected at 17 lakes on the Arctic Coastal Plain of Alaska are used to train and validate models to map lake bathymetry. These models predict depth from remotely sensed lake color and are able to explain 58.5–97.6 % of depth variability. To calculate water volumes, we integrate this modeled bathymetry with lake surface area. Knowledge of Alaskan lake bathymetries and volumes is crucial to better understanding water storage, energy balance, and ecological habitat.
Ingmar Nitze, Sarah W. Cooley, Claude R. Duguay, Benjamin M. Jones, and Guido Grosse
The Cryosphere, 14, 4279–4297, https://doi.org/10.5194/tc-14-4279-2020, https://doi.org/10.5194/tc-14-4279-2020, 2020
Short summary
Short summary
In summer 2018, northwestern Alaska was affected by widespread lake drainage which strongly exceeded previous observations. We analyzed the spatial and temporal patterns with remote sensing observations, weather data and lake-ice simulations. The preceding fall and winter season was the second warmest and wettest on record, causing the destabilization of permafrost and elevated water levels which likely led to widespread and rapid lake drainage during or right after ice breakup.
Maksim Iakunin, Victor Stepanenko, Rui Salgado, Miguel Potes, Alexandra Penha, Maria Helena Novais, and Gonçalo Rodrigues
Geosci. Model Dev., 13, 3475–3488, https://doi.org/10.5194/gmd-13-3475-2020, https://doi.org/10.5194/gmd-13-3475-2020, 2020
Short summary
Short summary
The Alqueva reservoir, located in the southeast of Portugal, is the largest artificial reservoir in western Europe. It was established in 2002 to provide water and electrical resources to meet regional needs. Complex research of this reservoir is an essential scientific task in the scope of meteorology, hydrology, biology, and ecology. Two numerical models (namely, LAKE 2.0 and FLake) were used to assess the thermodynamic and biogeochemical regimes of the reservoir over 2 years of observations.
Dylan R. Harp, Vitaly Zlotnik, Charles J. Abolt, Brent D. Newman, Adam L. Atchley, Elchin Jafarov, and Cathy J. Wilson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-100, https://doi.org/10.5194/tc-2020-100, 2020
Manuscript not accepted for further review
Short summary
Short summary
Polygon shaped land forms present in relatively flat Arctic tundra result in complex landscape scale water drainage. The drainage pathways and the time to transition from inundated conditions to drained have important implications for heat and carbon transport. Using fundamental hydrologic principles, we investigate the drainage pathways and timing of individual polygons providing insights into the effects of polygon geometry and preferential flow direction on drainage pathways and timing.
Sofya Guseva, Tobias Bleninger, Klaus Jöhnk, Bruna Arcie Polli, Zeli Tan, Wim Thiery, Qianlai Zhuang, James Anthony Rusak, Huaxia Yao, Andreas Lorke, and Victor Stepanenko
Hydrol. Earth Syst. Sci., 24, 697–715, https://doi.org/10.5194/hess-24-697-2020, https://doi.org/10.5194/hess-24-697-2020, 2020
Short summary
Short summary
We compare lake models with different complexity focusing on the key factors (e.g., eddy diffusivity) which can have an influence on the distribution of the dissolved gases in water. For the first time, we compare the biogeochemical modules in the ALBM and LAKE models. The result showed a good agreement with observed data (O2), but not for CO2. It indicates the need to improve the representation of physical and biogeochemical processes in lake models.
Christian G. Andresen, David M. Lawrence, Cathy J. Wilson, A. David McGuire, Charles Koven, Kevin Schaefer, Elchin Jafarov, Shushi Peng, Xiaodong Chen, Isabelle Gouttevin, Eleanor Burke, Sarah Chadburn, Duoying Ji, Guangsheng Chen, Daniel Hayes, and Wenxin Zhang
The Cryosphere, 14, 445–459, https://doi.org/10.5194/tc-14-445-2020, https://doi.org/10.5194/tc-14-445-2020, 2020
Short summary
Short summary
Widely-used land models project near-surface drying of the terrestrial Arctic despite increases in the net water balance driven by climate change. Drying was generally associated with increases of active-layer depth and permafrost thaw in a warming climate. However, models lack important mechanisms such as thermokarst and soil subsidence that will change the hydrological regime and add to the large uncertainty in the future Arctic hydrological state and the associated permafrost carbon feedback.
Elchin E. Jafarov, Dylan R. Harp, Ethan T. Coon, Baptiste Dafflon, Anh Phuong Tran, Adam L. Atchley, Youzuo Lin, and Cathy J. Wilson
The Cryosphere, 14, 77–91, https://doi.org/10.5194/tc-14-77-2020, https://doi.org/10.5194/tc-14-77-2020, 2020
Short summary
Short summary
Improved subsurface parameterization and benchmarking data are needed to reduce current uncertainty in predicting permafrost response to a warming climate. We developed a subsurface parameter estimation framework that can be used to estimate soil properties where subsurface data are available. We utilize diverse geophysical datasets such as electrical resistance data, soil moisture data, and soil temperature data to recover soil porosity and soil thermal conductivity.
Kang Wang, Elchin Jafarov, Irina Overeem, Vladimir Romanovsky, Kevin Schaefer, Gary Clow, Frank Urban, William Cable, Mark Piper, Christopher Schwalm, Tingjun Zhang, Alexander Kholodov, Pamela Sousanes, Michael Loso, and Kenneth Hill
Earth Syst. Sci. Data, 10, 2311–2328, https://doi.org/10.5194/essd-10-2311-2018, https://doi.org/10.5194/essd-10-2311-2018, 2018
Short summary
Short summary
Ground thermal and moisture data are important indicators of the rapid permafrost changes in the Arctic. To better understand the changes, we need a comprehensive dataset across various sites. We synthesize permafrost-related data in the state of Alaska. It should be a valuable permafrost dataset that is worth maintaining in the future. On a wider level, it also provides a prototype of basic data collection and management for permafrost regions in general.
Kevin Schaefer and Elchin Jafarov
Biogeosciences, 13, 1991–2001, https://doi.org/10.5194/bg-13-1991-2016, https://doi.org/10.5194/bg-13-1991-2016, 2016
Short summary
Short summary
Respiration in frozen soils is limited to within the thin water films surrounding soil particles. We parameterize volumetric water content (VWC) in frozen soil to represent the fraction of thawed carbon to simulate substrate availability. Simulated VWC and respiration match in situ and soil incubation data. The parameterization is most applicable when simulating carbon dynamics in permafrost for time scales of 100 years or greater.
Elchin Jafarov and Kevin Schaefer
The Cryosphere, 10, 465–475, https://doi.org/10.5194/tc-10-465-2016, https://doi.org/10.5194/tc-10-465-2016, 2016
Short summary
Short summary
To improve the uncertainty in modeling of the permafrost carbon emission associated with the predicted climate warming, it is important to improve the simulation of the current permafrost carbon stock. This work shows how simulation of the frozen carbon in land system models can be improved by better addressing the coupling between plant photosynthesis, soil biogeochemistry, and soil thermodynamics.
Related subject area
Hydrology
Graphical representation of global water models
LM4-SHARC v1.0: resolving the catchment-scale soil–hillslope aquifer–river continuum for the GFDL Earth system modeling framework
Selecting a conceptual hydrological model using Bayes' factors computed with replica-exchange Hamiltonian Monte Carlo and thermodynamic integration
The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling
Modelling rainfall with a Bartlett–Lewis process: pyBL (v1.0.0), a Python software package and an application with short records
Virtual Joint Field Campaign: a framework of synthetic landscapes to assess multiscale measurement methods of water storage
SERGHEI v2.0: introducing a performance-portable, high-performance, three-dimensional variably saturated subsurface flow solver (SERGHEI-RE)
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
DECIPHeR-GW v1: A coupled hydrological model with improved representation of surface-groundwater interactions
Generalised drought index: a novel multi-scale daily approach for drought assessment
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
A reach-integrated hydraulic modelling approach for large-scale and real-time inundation mapping
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks
Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0)
Wastewater matters: Incorporating wastewater reclamation into a process-based hydrological model (CWatM v1.08)
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
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
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
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
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
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
Short summary
Short summary
Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Minki Hong, Nathaniel Chaney, Sergey Malyshev, Enrico Zorzetto, Anthony Preucil, and Elena Shevliakova
Geosci. Model Dev., 18, 2275–2301, https://doi.org/10.5194/gmd-18-2275-2025, https://doi.org/10.5194/gmd-18-2275-2025, 2025
Short summary
Short summary
This study shows the significance of groundwater in resolving the coupled terrestrial water–energy cycle. LM4-SHARC (soil–hillslope aquifer–river continuum) describes the hillslope groundwater using its emergent properties, yielding noticeable improvements in soil moisture/temperature and groundwater discharge predictions. The implications of groundwater-mediated hydrologic interactions between hillslopes and streams need further exploration in the Earth system modeling community.
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
Geosci. Model Dev., 18, 1709–1736, https://doi.org/10.5194/gmd-18-1709-2025, https://doi.org/10.5194/gmd-18-1709-2025, 2025
Short summary
Short summary
Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
Kerry L. Callaghan, Andrew D. Wickert, Richard Barnes, and Jacqueline Austermann
Geosci. Model Dev., 18, 1463–1486, https://doi.org/10.5194/gmd-18-1463-2025, https://doi.org/10.5194/gmd-18-1463-2025, 2025
Short summary
Short summary
We present the Water Table Model (WTM), a new model for simulating groundwater and lake levels at continental scales over millennia. The WTM enables long-term evaluations of water-table changes. As a proof of concept, we simulate the North American water table for the present and the Last Glacial Maximum (LGM), showing that North America held more groundwater and lake water during the LGM than it does today – enough to lower sea levels by 14.98 cm. The open-source code is available on GitHub.
Chi-Ling Wei, Pei-Chun Chen, Chien-Yu Tseng, Ting-Yu Dai, Yun-Ting Ho, Ching-Chun Chou, Christian Onof, and Li-Pen Wang
Geosci. Model Dev., 18, 1357–1373, https://doi.org/10.5194/gmd-18-1357-2025, https://doi.org/10.5194/gmd-18-1357-2025, 2025
Short summary
Short summary
pyBL is an open-source package for generating realistic rainfall time series based on the Bartlett–Lewis (BL) model. It can preserve not only standard but also extreme rainfall statistics across various timescales. Notably, compared to traditional frequency analysis methods, the BL model requires only half the record length (or even shorter) to achieve similar consistency in estimating sub-hourly rainfall extremes. This makes it a valuable tool for modelling rainfall extremes with short records.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Zhi Li, Gregor Rickert, Na Zheng, Zhibo Zhang, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
Geosci. Model Dev., 18, 547–562, https://doi.org/10.5194/gmd-18-547-2025, https://doi.org/10.5194/gmd-18-547-2025, 2025
Short summary
Short summary
We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel computing capabilities. SERGHEI-RE performs effectively on various computational devices: from personal computers to advanced clusters. It allows users to solve flow equations with multiple numerical schemes, making it adaptable to various hydrological scenarios. Testing results show its accuracy and performance, confirming that SERGHEI-RE is a powerful tool for hydrological research.
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev., 17, 8817–8852, https://doi.org/10.5194/gmd-17-8817-2024, https://doi.org/10.5194/gmd-17-8817-2024, 2024
Short summary
Short summary
Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP, which has been used for numerous water resource assessments since 1996. We show the effects of new model features, as well as model evaluations, against water abstraction statistics and observed streamflow and water storage anomalies. The publicly available model output for several variants is described.
Yanchen Zheng, Gemma Coxon, Mostaquimur Rahman, Ross Woods, Saskia Salwey, Youtong Rong, and Doris Wendt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-211, https://doi.org/10.5194/gmd-2024-211, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Groundwater is vital for people and ecosystems, but most physical models lack surface-groundwater interactions representation, leading to inaccurate streamflow predictions in groundwater-rich areas. This study presents DECIPHeR-GW v1, which links surface and groundwater systems to improve predictions of streamflow and groundwater levels. Tested across England and Wales, DECIPHeR-GW shows high accuracy, especially in south east England, making it a valuable tool for large-scale water management.
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024, https://doi.org/10.5194/gmd-17-8115-2024, 2024
Short summary
Short summary
This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not only identifies the same events as established indices but is also capable of improving their results. The index is empirically based and easy to compute, not requiring fitting the data to a probability distribution. The GDI can detect flash droughts and longer-term events, making it a versatile tool for drought monitoring.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024, https://doi.org/10.5194/gmd-17-7751-2024, 2024
Short summary
Short summary
We develop an operational forecast system, Coastlines-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 has relatively low computational requirements, 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 wave predictions can improve in accuracy.
Robert Chlumsky, James R. Craig, and Bryan A. Tolson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-184, https://doi.org/10.5194/gmd-2024-184, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We aim to improve mapping of floods, and present a new method for hydraulic modelling that uses a combination of novel geospatial analysis and existing hydraulic modelling approaches. This method is wrapped into a modelling software called Blackbird. We compared Blackbird to two other existing options for flood mapping and found that the Blackbird model outperformed both. The Blackbird model has the potential to support real-time and large-scale flood mapping applications in the future.
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., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
Short summary
Accurate hydrologic 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 modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024, https://doi.org/10.5194/gmd-17-7083-2024, 2024
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.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
Short summary
Short summary
The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, 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 the dynamic SWP.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
Geosci. Model Dev., 17, 6819–6846, https://doi.org/10.5194/gmd-17-6819-2024, https://doi.org/10.5194/gmd-17-6819-2024, 2024
Short summary
Short summary
Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Dor Fridman, Mikhail Smilovic, Peter Burek, Sylvia Tramberend, and Taher Kahil
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-143, https://doi.org/10.5194/gmd-2024-143, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Global hydrological models are applied at high spatial resolutions to quantify water availability and evaluate water scarcity mitigation options. Yet they mostly oversee important local processes. This paper presents and demonstrates the inclusion of wastewater treatment and reclamation into a global hydrological model. As a result model performance is improved, and models are capable to utilize treated wastewater as an alternative water source.
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.
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.
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.
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.
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.
Cited articles
Abnizova, A., Siemens, J., Langer, M., and Boike, J.: Small ponds with major
impact: The relevance of ponds and lakes in permafrost landscapes to carbon
dioxide emissions, Global Biogeochem. Cy., 26, GB2041,
https://doi.org/10.1029/2011GB004237, 2012.
Alexeev, V. A., Arp, C. D., Jones, B. M., and Cai, L.: Arctic sea ice
decline contributes to thinning lake ice trend in northern Alaska, Environ.
Res. Lett., 11, 074022, https://doi.org/10.1088/1748-9326/11/7/074022, 2016.
Arp, C. D., Jones, B. M., Urban, F. E., and Grosse, G.: Hydrogeomorphic
processes of thermokarst lakes with grounded-ice and floating-ice regimes on
the Arctic coastal plain, Alaska, 25, 2422–2438,
https://doi.org/10.1002/hyp.8019, 2011.
Arp, C. D., Jones, B. M., Grosse, G., Bondurant, A. C., Romanovsky, V. E.,
Hinkel, K. M., and Parsekian, A. D.: Threshold sensitivity of shallow Arctic
lakes and sublake permafrost to changing winter climate, Geophys.
Res. Lett., 43, 6358–6365, https://doi.org/10.1002/2016GL068506,
2016.
Boike, J., Georgi, C., Kirilin, G., Muster, S., Abramova, K., Fedorova, I., Chetverova, A., Grigoriev, M., Bornemann, N., and Langer, M.: Thermal processes of thermokarst lakes in the continuous permafrost zone of northern Siberia – observations and modeling (Lena River Delta, Siberia), Biogeosciences, 12, 5941–5965, https://doi.org/10.5194/bg-12-5941-2015, 2015.
Clark, J. A. and Jafarov, E. E.: LAKE 2.0_processing-scripts
and data, Zenodo [code and data set], https://doi.org/10.5281/zenodo.5593754, 2021.
Côté, J. and Konrad, J.-M.: A generalized thermal conductivity model
for soils and construction materials, Can. Geotech. J., 42, 443–458,
https://doi.org/10.1139/t04-106, 2005.
Creighton, A. L., Parsekian, A. D., Angelopoulos, M., Jones, B. M.,
Bondurant, A., Engram, M., Lenz, J., Overduin, P. P., Grosse, G., Babcock,
E., and Arp, C. D.: Transient Electromagnetic Surveys for the Determination
of Talik Depth and Geometry Beneath Thermokarst Lakes, J. Geophys. Res.-Sol. Ea., 123, 9310–9323,
https://doi.org/10.1029/2018JB016121, 2018.
Edgar, C., Cherry, J., Cohen, L., Haupert, C., Kade, A., Laundre, J., Dam,
B. V., and McPherson, R.: Hourly meteorological data from Toolik Field
Station, Alaska (1988–2017), Arctic Data Center [data set], https://doi.org/10.18739/A2FJ29C5J, 2018.
Grant, L., Vanderkelen, I., Gudmundsson, L., Tan, Z., Perroud, M.,
Stepanenko, V. M., Debolskiy, A. V., Droppers, B., Janssen, A. B. G.,
Woolway, R. I., Choulga, M., Balsamo, G., Kirillin, G., Schewe, J., Zhao,
F., del Valle, I. V., Golub, M., Pierson, D., Marcé, R., Seneviratne, S.
I., and Thiery, W.: Attribution of global lake systems change to
anthropogenic forcing, Nat. Geosci., 14, 849–854,
https://doi.org/10.1038/s41561-021-00833-x, 2021.
Grenier, C., Anbergen, H., Bense, V., Chanzy, Q., Coon, E., Collier, N.,
Costard, F., Ferry, M., Frampton, A., Frederick, J., Gonçalvès, J.,
Holmén, J., Jost, A., Kokh, S., Kurylyk, B., McKenzie, J., Molson, J.,
Mouche, E., Orgogozo, L., Pannetier, R., Rivière, A., Roux, N.,
Rühaak, W., Scheidegger, J., Selroos, J.-O., Therrien, R., Vidstrand,
P., and Voss, C.: Groundwater flow and heat transport for systems undergoing
freeze-thaw: Intercomparison of numerical simulators for 2D test cases,
Adv. Water Resour., 114, 196–218,
https://doi.org/10.1016/j.advwatres.2018.02.001, 2018.
Grosse, G., Jones, B., and Arp, C.: 8.21 Thermokarst Lakes, Drainage, and
Drained Basins, in: Treatise on Geomorphology, edited by: Shroder, J. F.,
Academic Press, San Diego, 325–353,
https://doi.org/10.1016/B978-0-12-374739-6.00216-5, 2013.
Guo, M., Zhuang, Q., Yao, H., Golub, M., Leung, L. R., Pierson, D., and Tan,
Z.: Validation and Sensitivity Analysis of a 1-D Lake Model Across Global
Lakes, J. Geophys. Res.-Atmos., 126, e2020JD033417,
https://doi.org/10.1029/2020JD033417, 2021.
Hinkel, K., Lenters, J., Arp, C., and Frey, K.: Collaborative Research:
Toward a Circumarctic Lakes Observation Network (CALON) – Multiscale
observations of lacustrine systems, Arctic Data Center [data set], https://doi.org/10.18739/A2VM42Z1H, 2012.
Iakunin, M., Stepanenko, V., Salgado, R., Potes, M., Penha, A., Novais, M. H., and Rodrigues, G.: Numerical study of the seasonal thermal and gas regimes of the largest artificial reservoir in western Europe using the LAKE 2.0 model, Geosci. Model Dev., 13, 3475–3488, https://doi.org/10.5194/gmd-13-3475-2020, 2020.
Jafarov, E., Clark, J., Piliouras, A., Tape, K., Jones, B., and Rowland, J.:
The LAKE model input dataset for three Arctic lakes, ESS-DIVE: Deep Insight
for Earth Science Data, ESS-Dive [data set], https://doi.org/10.15485/1808368, 2021.
Jeffries, M. O. and Morris, K.: Instantaneous daytime conductive heat flow
through snow on lake ice in Alaska, Hydrol. Process., 20, 803–815,
https://doi.org/10.1002/hyp.6116, 2006.
Jeffries, M. O., Zhang, T., Frey, K., and Kozlenko, N.: Estimating
late-winter heat flow to the atmosphere from the lake-dominated Alaskan
North Slope, J. Glaciol., 45, 315–324, https://doi.org/10.3189/S0022143000001817, 1999.
Jones, B., Grosse, G., and Clark, J.: Fox Den Lake bed water temperature,
Northwest Seward Peninsula, Alaska, 2009–2013, Arctic Data Center [data set], https://doi.org/10.18739/A2W37KX02, 2021.
Jorgenson, M. T., Yoshikawa, K., Kanevskiy, M., Shur, Y., Romanovsky, V.,
Marchenko, S., Grosse, G., Brown, J., and Jones, B.: Permafrost Characteristics of Alaska, in:
Proceedings of the ninth international conference on permafrost, Fairbanks, AK, University of Alaska, 29 June 2008, 3, 121–122, 2008.
Jorgenson, M. T. and Shur, Y.: Evolution of lakes and basins in northern
Alaska and discussion of the thaw lake cycle, J. Geophys.
Res.-Earth, 112, F02S17, https://doi.org/10.1029/2006JF000531, 2007.
Jorgenson, M. T., Shur, Y. L., and Pullman, E. R.: Abrupt increase in
permafrost degradation in Arctic Alaska, Geophys. Res. Lett., 33, L02503,
https://doi.org/10.1029/2005GL024960, 2006.
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X., Smith, W. L., Su, W., and Ham, S.-H.: Surface Irradiances
of Edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy
Balanced and Filled (EBAF) Data Product, J. Climate, 31, 4501–4527,
https://doi.org/10.1175/JCLI-D-17-0523.1, 2018.
Kirillin, G., Hochschild, J., Mironov, D., Terzhevik, A., Golosov, S., and
Nützmann, G.: FLake-Global: Online lake model with worldwide coverage,
Environ. Model. Softw., 26, 683–684,
https://doi.org/10.1016/j.envsoft.2010.12.004, 2011.
Kling, G.: Toolik Lake Inlet discharge data collected during summers of 2010
to 2018, Arctic LTER, Toolik Research Station, Alaska, ver 2, Environmental Data Initiative [data set], https://doi.org/10.6073/pasta/169d1bae55373c44a368727573ef70eb, 2019.
Langer, M., Westermann, S., Boike, J., Kirillin, G., Grosse, G., Peng, S.,
and Krinner, G.: Rapid degradation of permafrost underneath waterbodies in
tundra landscapes – Toward a representation of thermokarst in land surface
models, J. Geophys. Res.-Earth, 121, 2446–2470, https://doi.org/10.1002/2016JF003956, 2016.
Leppäranta, M.: Freezing of Lakes and the Evolution of their Ice Cover, 1st edn.,
Springer Berlin, Heidelberg, 301 pp., https://doi.org/10.1007/978-3-642-29081-7, 2015.
Lin, Z., Niu, F., Xu, Z., Xu, J., and Wang, P.: Thermal regime of a
thermokarst lake and its influence on permafrost, Beiluhe Basin,
Qinghai-Tibet Plateau, Permafrost Periglac., 21, 315–324, https://doi.org/10.1002/ppp.692, 2010.
Ling, F. and Liao, Q.: Simple Model for Thermal Regime of Permafrost Surrounding an Expanding Thermokarst Lake, Pacific Journal of Applied Mathematics, 8, 115–122, 2016.
Ling, F. and Zhang, T.: Numerical simulation of permafrost thermal regime
and talik development under shallow thaw lakes on the Alaskan Arctic Coastal
Plain, J. Geophys. Res., 108, 4511, https://doi.org/10.1029/2002JD003014, 2003.
Ling, F. and Zhang, T.: Modeling study of talik freeze-up and permafrost
response under drained thaw lakes on the Alaskan Arctic Coastal Plain, J. Geophys. Res., 109, D01111,
https://doi.org/10.1029/2003JD003886, 2004.
Ling, F. and Zhang, T.: Simulating heat source effect of a thermokarst lake
in the first 540 years on the Alaskan Arctic using a simple lake expanding
model, Cold Reg. Sci. Technol., 160, 176–183,
https://doi.org/10.1016/j.coldregions.2019.01.009, 2019.
MacIntyre, S. and Cortes, A.: Time series of water temperature, specific
conductance, and oxygen from Toolik Lake, North Slope, Alaska, 2015–2016, Arctic Data Center [data set],
https://doi.org/10.18739/A2Z892F91, 2017.
Mironov, D. V.: Parameterization of Lakes in Numerical Weather Prediction:
Description of a Lake Model, Technical Report, 41 pp., German Weather Service, Offenbach am
Main, Germany, 2008.
Morris, K., Jeffries, M., and Duguay, C.: Model simulation of the effects of
climate variability and change on lake ice in central Alaska, USA, Ann. Glaciol., 40,
113–118, https://doi.org/10.3189/172756405781813663, 2005.
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H. H.,
Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov,
A., Khomutov, A., Kääb, A., Leibman, M. O., Lewkowicz, A. G., Panda,
S. K., Romanovsky, V., Way, R. G., Westergaard-Nielsen, A., Wu, T., Yamkhin,
J., and Zou, D.: Northern Hemisphere permafrost map based on TTOP modelling
for 2000–2016 at 1 km2 scale, Earth-Sci. Rev., 193, 299–316,
https://doi.org/10.1016/j.earscirev.2019.04.023, 2019.
Painter, S. L.: Three-phase numerical model of water migration in partially
frozen geological media: model formulation, validation, and applications,
Comput. Geosci., 15, 69–85, https://doi.org/10.1007/s10596-010-9197-z, 2011.
Painter, S. L., Coon, E. T., Atchley, A. L., Berndt, M., Garimella, R.,
Moulton, J. D., Svyatskiy, D., and Wilson, C. J.: Integrated
surface/subsurface permafrost thermal hydrology: Model formulation and
proof-of-concept simulations, Water Resour. Res., 52, 6062–6077,
https://doi.org/10.1002/2015WR018427, 2016.
Parsekian, A. D., Creighton, A. L., Jones, B. M., and Arp, C. D.: Surface
nuclear magnetic resonance observations of permafrost thaw below floating,
bedfast, and transitional ice lakes, Geophysics, 84, EN33–EN45,
https://doi.org/10.1190/geo2018-0563.1, 2019.
Patterson, J. C., Hamblin, P. F., and Imberger, J.: Classification and
dynamic simulation of the vertical density structure of lakes, Limnol.
Oceanogr., 29, 845–861, https://doi.org/10.4319/lo.1984.29.4.0845, 1984.
Peng, E.-X., Sheng, Y., Hu, X.-Y., Wu, J.-C., and Cao, W.: Thermal effect of
thermokarst lake on the permafrost under embankment, Advances in Climate
Change Research, 12, 76–82, https://doi.org/10.1016/j.accre.2020.10.002,
2021.
Provost, A. M. and Voss, C. I.: SUTRA, a model for saturated-unsaturated,
variable-density groundwater flow with solute or energy
transport – Documentation of generalized boundary conditions, a modified
implementation of specified pressures and concentrations or temperatures,
and the lake capability, SUTRA, a model for saturated-unsaturated,
variable-density groundwater flow with solute or energy
transport – Documentation of generalized boundary conditions, a modified
implementation of specified pressures and concentrations or temperatures,
and the lake capability, book 6, chap. A52, U.S. Geological Survey, Reston, VA, 62 pp.,
https://doi.org/10.3133/tm6A52, 2019.
Rouse, W. R., Douglas, M. S. V., Hecky, R. E., Hershey, A. E., Kling, G. W.,
Lesack, L., Marsh, P., Mcdonald, M., Nicholson, B. J., Roulet, N. T., and
Smol, J. P.: Effects of Climate Change on the Freshwaters of Arctic and
Subarctic North America, Hydrol. Process., 11, 873–902,
https://doi.org/10.1002/(SICI)1099-1085(19970630)11:8<873::AID-HYP510>3.0.CO;2-6, 1997.
Rowland, J. C., Travis, B. J., and Wilson, C. J.: The role of advective heat
transport in talik development beneath lakes and ponds in discontinuous
permafrost, Geophys. Res. Lett., 38, L17504,
https://doi.org/10.1029/2011GL048497, 2011.
Rueda, F. J. and MacIntyre, S.: Modelling the fate and transport of
negatively buoyant storm–river water in small multi-basin lakes,
Environ. Model. Softw., 25, 146–157,
https://doi.org/10.1016/j.envsoft.2009.07.002, 2010.
Rutan, D. A., Kato, S., Doelling, D. R., Rose, F. G., Nguyen, L. T.,
Caldwell, T. E., and Loeb, N. G.: CERES Synoptic Product: Methodology and
Validation of Surface Radiant Flux, J. Atmos. Ocean.
Tech., 32, 1121–1143, https://doi.org/10.1175/JTECH-D-14-00165.1,
2015.
Smith, A., Lott, N., and Vose, R.: The Integrated Surface Database: Recent
Developments and Partnerships, B. Am. Meteorol.
Soc., 92, 704–708, 2011.
Spanoudaki, K., Stamou, A. I., and Nanou-Giannarou, A.: Development and
verification of a 3-D integrated surface water–groundwater model, J. Hydrol., 375, 410–427, https://doi.org/10.1016/j.jhydrol.2009.06.041,
2009.
Stepanenko, V.: LAKE (Version 2.0), Zenodo [code], https://doi.org/10.5281/zenodo.6353238, 2022.
Stepanenko, V. and Lykossov, V.: Numerical modeling of the heat and moisture
transport in a lake-soil system, Russ. Meteorol. Hydrol, 3, 69–75, 2005.
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., Goyette, S., Martynov, A., Perroud, M., Fang, X., and
Mironov, D.: First steps of a Lake Model Intercomparison Project: LakeMIP,
Boreal Environ. Res, 15, 191–202, http://hdl.handle.net/10138/233085 (last access: 29 September 2022), 2010.
Stepanenko, V. M., Machul'skaya, E. E., Glagolev, M. V., and Lykossov, V.
N.: Numerical modeling of methane emissions from lakes in the permafrost
zone, Izv. Atmos. Ocean. Phys., 47, 252–264,
https://doi.org/10.1134/S0001433811020113, 2011.
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.
Stepanenko, V. M., Repina, I. A., Ganbat, G., and Davaa, G.: Numerical
Simulation of Ice Cover of Saline Lakes, Izv. Atmos. Ocean. Phys., 55,
129–138, https://doi.org/10.1134/S0001433819010092, 2019.
Stepanenko, V. M., Valerio, G., and Pilotti, M.: Horizontal Pressure
Gradient Parameterization for One-Dimensional Lake Models, J.
Adv. Model. Earth Sy., 12, e2019MS001906,
https://doi.org/10.1029/2019MS001906, 2020.
Sturm, M. and Liston, G. E.: The snow cover on lakes of the Arctic Coastal
Plain of Alaska, U.S.A., J. Glaciol., 49, 370–380,
https://doi.org/10.3189/172756503781830539, 2003.
Tan, Z., Zhuang, Q., and Anthony, K. W.: Modeling methane emissions from
arctic lakes: Model development and site-level study, J. Adv.
Model. Earth Sy., 7, 459–483, https://doi.org/10.1002/2014MS000344,
2015.
Tan, Z., Zhuang, Q., Shurpali, N. J., Marushchak, M. E., Biasi, C., Eugster,
W., and Anthony, K. W.: Modeling CO2 emissions from Arctic lakes: Model
development and site-level study, J. Adv. Model. Earth
Sy., 9, 2190–2213, https://doi.org/10.1002/2017MS001028, 2017.
Urban, F. E.: Data Release associated with Data Series – DOI/GTN-P Climate and
Active-Layer Data Acquired in the National Petroleum Reserve-Alaska and the
Arctic National Wildlife Refuge, 1998–2019 (ver. 3.0, March 2021), U.S. Geological Survey data release [data set],
https://doi.org/10.5066/F7VX0FGB, 2017.
Walker, D. A., Raynolds, M. K., Daniëls, F. J. A., Einarsson, E.,
Elvebakk, A., Gould, W. A., Katenin, A. E., Kholod, S. S., Markon, C. J.,
Melnikov, E. S., Moskalenko, N. G., Talbot, S. S., and Yurtsev, B. A.: The
Circumpolar Arctic vegetation map, J. Veg. Sci., 16,
267–282, https://doi.org/10.1111/j.1654-1103.2005.tb02365.x, 2005.
Wielicki, B. A., Barkstrom, B. R., Baum, B. A., Charlock, T. P., Green, R.
N., Kratz, D. P., Lee, R. B., Minnis, P., Smith, G. L., Wong, T., Young, D.
F., Cess, R. D., Coakley, J. A., Crommelynck, D. A. H., Donner, L., Kandel,
R., King, M. D., Miller, A. J., Ramanathan, V., Randall, D. A., Stowe, L.
L., and Welch, R. M.: Clouds and the Earth's Radiant Energy System (CERES):
algorithm overview, IEEE T. Geosci. Remote, 36,
1127–1141, https://doi.org/10.1109/36.701020, 1998.
Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S., and Bastviken, D.:
Climate-sensitive northern lakes and ponds are critical components of
methane release, Nat. Geosci., 9, 99–105,
https://doi.org/10.1038/ngeo2578, 2016.
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., 54,
2273–2282, https://doi.org/10.4319/lo.2009.54.6_part_2.2273, 2009.
Zhang, T. and Jeffries, M. O.: Modeling interdecadal variations of lake-ice
thickness and sensitivity to climatic change in northernmost Alaska, Ann. Glaciol., 31,
339–347, https://doi.org/10.3189/172756400781819905, 2000.
Short summary
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.
Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect...
