Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-75-2022
© 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-15-75-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
07 Jan 2022
Model description paper |
| 07 Jan 2022
| 07 Jan 2022
Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123 Trento, Italy
Riccardo Rigon
Center Agriculture Food Environment, University of Trento, Via Mesiano 77, 38123 Trento, Italy
Related authors
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
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The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
Niccolò Tubini, Stephan Gruber, and Riccardo Rigon
The Cryosphere, 15, 2541–2568, https://doi.org/10.5194/tc-15-2541-2021, https://doi.org/10.5194/tc-15-2541-2021, 2021
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We present a new method to compute temperature changes with melting and freezing – a fundamental challenge in cryosphere research – extremely efficiently and with guaranteed correctness of the energy balance for any time step size. This is a key feature since the integration time step can then be chosen according to the timescale of the processes to be studied, from seconds to days.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
Short summary
Short summary
The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
Niccolò Tubini, Stephan Gruber, and Riccardo Rigon
The Cryosphere, 15, 2541–2568, https://doi.org/10.5194/tc-15-2541-2021, https://doi.org/10.5194/tc-15-2541-2021, 2021
Short summary
Short summary
We present a new method to compute temperature changes with melting and freezing – a fundamental challenge in cryosphere research – extremely efficiently and with guaranteed correctness of the energy balance for any time step size. This is a key feature since the integration time step can then be chosen according to the timescale of the processes to be studied, from seconds to days.
Wuletawu Abera, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 21, 3145–3165, https://doi.org/10.5194/hess-21-3145-2017, https://doi.org/10.5194/hess-21-3145-2017, 2017
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This study documents a state-of-the-art estimation of the water budget (rainfall, evapotranspiration, discharge, and soil and groundwater storage) components for the Upper Blue Nile river. The budget uses various JGrass-NewAGE components, satellite data and all ground measurements available. The analysis shows that precipitation of the basin is 1360 ± 230 mm per year. Evapotranspiration accounts for 56 %, runoff is 33 %, and storage varies from minus 10 % to plus 17 % of the annual water budget.
Riccardo Rigon, Marialaura Bancheri, and Timothy R. Green
Hydrol. Earth Syst. Sci., 20, 4929–4947, https://doi.org/10.5194/hess-20-4929-2016, https://doi.org/10.5194/hess-20-4929-2016, 2016
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The goal of the paper is to analyze the theory of water age inside a catchment while accounting for multiple outflows. It tries to propose the material under a new perspective where it lines up concepts, cleans the notation, discusses some classical results, and offers some examples that help to relate the modern achievements to the theory of the IUH, clarifying assets of both of them. In doing all of this, it also produces various new results, and some regarding solute transport.
Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, https://doi.org/10.5194/hess-20-4641-2016, 2016
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Ten algorithms for estimating DL and one for UL are integrated in a new model (LWRB) and connected to hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. We evaluated the performances of simplified models (SMs) of DL, as presented in literature formulations, and determined by automatic calibration the site-specific parameter sets for SMs of DL to improve model predictions.
S. Endrizzi, S. Gruber, M. Dall'Amico, and R. Rigon
Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, https://doi.org/10.5194/gmd-7-2831-2014, 2014
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GEOtop is a fine scale grid-based simulator that represents the heat and water budgets at and below the soil surface, reproduces the highly non-linear interactions between the water and energy balance during soil freezing and thawing and simulates snow cover. The core components of GEOtop 2.0. are described. Based on a synthetic simulation, it is shown that the interaction of processes represented in GEOtop 2.0. can result in phenomena that are relevant for applications involving frozen soils.
G. Formetta, S. K. Kampf, O. David, and R. Rigon
Geosci. Model Dev., 7, 725–736, https://doi.org/10.5194/gmd-7-725-2014, https://doi.org/10.5194/gmd-7-725-2014, 2014
G. Formetta, R. Rigon, J. L. Chávez, and O. David
Geosci. Model Dev., 6, 915–928, https://doi.org/10.5194/gmd-6-915-2013, https://doi.org/10.5194/gmd-6-915-2013, 2013
Related subject area
Hydrology
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
GPEP v1.0: a Geospatial Probabilistic Estimation Package to support Earth Science applications
DynQual v1.0: a high-resolution global surface water quality model
GEMS v1.0: Generalizable empirical model of snow accumulation and melt based on daily snow mass changes in response to climate and topographic drivers
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
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)
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SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
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SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
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Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-172, https://doi.org/10.5194/gmd-2023-172, 2023
Revised manuscript accepted for GMD
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed a 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.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-103, https://doi.org/10.5194/gmd-2023-103, 2023
Revised manuscript accepted for GMD
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We present a new 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 is limited.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
EGUsphere, https://doi.org/10.5194/egusphere-2023-1323, https://doi.org/10.5194/egusphere-2023-1323, 2023
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In this study, we show a new irrigation scheme included in ORCHIDEE land surface model. The new irrigation scheme restraint irrigation due to water shortage, includes water adduction, and represents environmental limits and facility to access water due to infrastructure in a simple way. Our results show that the new irrigation scheme helps simulating acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
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
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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
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
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A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
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MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
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Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
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Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
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With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
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Land surface models allow us to understand and investigate the cause and effect of environmental process changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
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Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
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The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
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Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
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This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Cited articles
Abera, W., Formetta, G., Borga, M., and Rigon, R.: Estimating the water budget components and their variability in a pre-alpine basin with JGrass-NewAGE, Adv. Water Resour., 104, 37–54, https://doi.org/10.1016/j.advwatres.2017.03.010, 2017a. a
Abera, W., Formetta, G., Brocca, L., and Rigon, R.: Modeling the water budget of the Upper Blue Nile basin using the JGrass-NewAge model system and satellite data, Hydrol. Earth Syst. Sci., 21, 3145–3165, https://doi.org/10.5194/hess-21-3145-2017, 2017b. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements, Fao, Rome, FAO Irrigation and drainage paper 56, 300(9), D05109, 1998. a
Ashby, S. F. and Falgout, R. D.: A parallel multigrid preconditioned conjugate gradient algorithm for groundwater flow simulations, Nucl. Sci. Eng., 124, 145–159, https://doi.org/10.13182/NSE96-A24230, 1996. a
Bancheri, M., Serafin, F., Bottazzi, M., Abera, W., Formetta, G., and Rigon, R.: The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8, Geosci. Model Dev., 11, 2189–2207, https://doi.org/10.5194/gmd-11-2189-2018, 2018. a
Benard, P., Zarebanadkouki, M., and Carminati, A.: Physics and hydraulics of the rhizosphere network, J. Plant Nutr. Soil Sc., 182, 5–8, https://doi.org/10.1002/jpln.201800042, 2019. a
Bisht, G. and Riley, W. J.: Development and verification of a numerical library for solving global terrestrial multiphysics problems, J. Adv. Model. Earth Sy., 11, 1516–1542, https://doi.org/10.1029/2018MS001560, 2019. a, b, c, d
Bloch, J.: Effective Java: Programming Language Guide, Addison-Wesley
Professional, ISBN: 9780201310054, 2001. a
Bottazzi, M., Bancheri, M., Mobilia, M., Bertoldi, G., Longobardi, A., and Rigon, R.: Comparing evapotranspiration estimates from the GEOframe-Prospero model and three empirical models under different climate conditions, Water, 13, 1221–1243, https://doi.org/10.3390/w13091221, 2021. a, b
Brooks, R. H. and Corey, T. A.: Hydraulic properties of porous media
Hydrology Paper No. 3, Civil Engineering Department, Colorado State University, Fort Collins, CO, 1964. a
Brugnano, L. and Casulli, V.: Iterative solution of piecewise linear systems, SIAM J. Sci. Comput., 30, 463–472, https://doi.org/10.1137/070681867, 2008. a, b
Brugnano, L. and Casulli, V.: Iterative solution of piecewise linear systems and applications to flows in porous media, SIAM J. Sci. Comput., 31, 1858–1873, https://doi.org/10.1137/08072749X, 2009. a
Casulli, V.: A high-resolution wetting and drying algorithm for free-surface hydrodynamics, Int. J. Numer. Meth. Fl., 60, 391–408, https://doi.org/10.1002/fld.1896, 2009. a
Casulli, V.: A coupled surface-subsurface model for hydrostatic flows under saturated and variably saturated conditions, Int. J. Numer. Meth. Fl., 85, 449–464, https://doi.org/10.1002/fld.4389, 2017. a
Casulli, V. and Zanolli, P.: High resolution methods for multidimensional advection–diffusion problems in free-surface hydrodynamics, Ocean Model., 10, 137–151, https://doi.org/10.1016/j.ocemod.2004.06.007, 2005. a, b, c, d
Casulli, V. and Zanolli, P.: Iterative solutions of mildly nonlinear systems, J. Comput. Appl. Math., 236, 3937–3947, https://doi.org/10.1016/j.cam.2012.02.042, 2012. a, b, c
Celia, M. A., Bouloutas, E. T., and Zarba, R. L.: A general mass-conservative numerical solution for the unsaturated flow equation, Water Resour. Res., 26, 1483–1496, https://doi.org/10.1029/WR026i007p01483, 1990. a
Chistyakov, V.: On mappings of bounded variation, J. Dyn. Control Syst., 3, 261, https://doi.org/10.1007/BF02465896, 1997. a
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D. J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M., Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of hydrologic processes in Earth System Models, Water Resour. Res., 51, 5929–5956, https://doi.org/10.1002/2015WR017096, 2015a. a
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015b. a, b
Clark, M. P., Zolfaghari, R., Green, K. R., Trim, S., Knoben, W. J. M.,
Bennet, A., and Nijssen, B.: The numerical implementation of land models:
problem formulation and laugh tests, J. Hydrometeorol., 22, 1627–1648, https://doi.org/10.1175/JHM-D-20-0175.1, 2021. a, b, c
Constantz, J. and Murphy, F.: The temperature dependence of ponded infiltration under isothermal conditions, J. Hydrol., 122, 119–128, https://doi.org/10.1016/0022-1694(91)90175-H, 1991. a, b
Dai, Y., Wei, N., Yuan, H., Zhang, S., Shangguan, W., Liu, S., Lu, X., and Xin, Y.: Evaluation of soil thermal conductivity schemes for use in land surface modeling, J. Adv. Model. Earth Sy., 11, 3454–3473, https://doi.org/10.1029/2019MS001723, 2019. a, b, c
D'Amato, C.: BrokerGEO, GitHub [code], available at: https://github.com/geoframecomponents/BrokerGEO, last
access: 20 April 2021. a
D'Amato, C., Tubini, N., Benettin, P., Rinaldo, A., Noto, V., and Rigon, R.:
Investigation of a critical zone column with the GEOframe Plant Atmosphere
Continuum Estimator (GEO-SPACE), Hydrol. Earth Syst. Sci., in
preparation, 2022. a
David, O.: CSV Data Files, codeBeamer [data set], available at: https://alm.engr.colostate.edu/cb/wiki/16970, last access: 1 April 2021. a
David, O., Ascough II, J. C., Lloyd, W., Green, T. R., Rojas, K., Leavesley, G. H., and Ahuja, L. R.: A software engineering perspective on environmental modeling framework design: The Object Modeling System, Environ. Modell. Softw., 39, 201–213, https://doi.org/10.1016/j.envsoft.2012.03.006, 2013. a, b, c, d, e, f, g, h, i, j, k
Di Nucci, C.: Theoretical derivation of the conservation equations for single phase flow in porous media: a continuum approach, Meccanica, 49, 2829–2838, https://doi.org/10.1007/s11012-014-0022-y, 2014. a
Dong, Y., McCartney, J. S., and Lu, N.: Critical review of thermal conductivity models for unsaturated soils, Geotech. Geol. Eng., 33, 207–221, https://doi.org/10.1007/s10706-015-9843-2, 2015. a
Dunne, T. and Black, R. D.: An experimental investigation of runoff production in permeable soils, Water Resour. Res., 6, 478–490, https://doi.org/10.1029/WR006i002p00478, 1970. a
Eckel, B.: Thinking in JAVA, Prentice Hall Professional, 2003. a
Endrizzi, S., Gruber, S., Dall'Amico, M., and Rigon, R.: GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects, Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, 2014. a, b
Engeler, I., Franssen, H. H., Müller, R., and Stauffer, F.: The importance of coupled modelling of variably saturated groundwater flow-heat transport for assessing river–aquifer interactions, J. Hydrol., 397, 295–305, https://doi.org/10.1016/j.jhydrol.2010.12.007, 2011. a, b
Formetta, G., Rigon, R., Chávez, J. L., and David, O.: Modeling shortwave solar radiation using the JGrass-NewAge system, Geosci. Model Dev., 6, 915–928, https://doi.org/10.5194/gmd-6-915-2013, 2013. a, b, c, d
Formetta, G., Antonello, A., Franceschi, S., David, O., and Rigon, R.: Hydrological modelling with components: A GIS-based open-source framework, Environ. Modell. Softw., 55, 190–200, https://doi.org/10.1016/j.envsoft.2014.01.019, 2014a. a, b, c, d
Formetta, G., Kampf, S. K., David, O., and Rigon, R.: Snow water equivalent modeling components in NewAge-JGrass, Geosci. Model Dev., 7, 725–736, https://doi.org/10.5194/gmd-7-725-2014, 2014b. a
Formetta, G., Bancheri, M., David, O., and Rigon, R.: Performance of site-specific parameterizations of longwave radiation, Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, 2016. a, b
Frampton, A., Painter, S. L., and Destouni, G.: Permafrost degradation and subsurface-flow changes caused by surface warming trends, Hydrogeol. J., 21, 271–280, https://doi.org/10.1007/s10040-012-0938-z, 2013. a
Freeze, R. A. and Harlan, R. L.: Blueprint for a physically-based, digitally-simulated hydrologic response model, J. Hydrol., 9, 237–258, 1969. a
GEOframe: FairUse&Publication Policy_v1.pdf, OSF, available at: https://osf.io/wgdyq/, last access: 21 December 2021a. a
GEOframe: WHETGEO-1D, available at: https://geoframe.blogspot.com/2021/05/whetgeo-1d.html, last access: 18 May 2021b. a
GEOframe Summer School: http://geoframe.blogspot.com/2021/06/geoframe-summer-school-2021-gss2021.html, last access: 21 December 2021. a
Germann, P. and Beven, K.: Water flow in soil macropores I. An experimental approach, J. Soil Sci., 32, 1–13, https://doi.org/10.1111/j.1365-2389.1981.tb01681.x, 1981. a
Greenspan, D. and Casulli, V.: Numerical Analysis for Applied Mathematics, Science, and Engineering, Addison Wesley, https://doi.org/10.1201/9780429493393, 1988. a
Gugole, F., Dumbser, M., and Stelling, G.: An efficient three-dimensional semi-implicit finite volume scheme for the solution of coupled free-surface and variably saturated sub-surface flow, in: EGU General Assembly Conference Abstracts, 600, 2018. a
Hall, C. A., Saia, S. M., Popp, A. L., Dogulu, N., Schymanski, S. J., Drost, N., van Emmerik, T., and Hut, R.: A Hydrologist's Guide to Open Science, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-392, in review, 2021. a
Hay, L. E., Leavesley, G. H., Clark, M. P., Markstrom, S. L., Viger, R. J., and Umemoto, M.: Step wise, multiple objective calibration of a hydrologic model for a snowmelt dominated basin 1, J. Am. Water Resour. As., 42, 877–890, https://doi.org/10.1111/j.1752-1688.2006.tb04501.x, 2006. a, b
Horton, R. E.: The role of infiltration in the hydrologic cycle, EOS T. Am. Geophys. Un., 14, 446–460, https://doi.org/10.1029/TR014i001p00446, 1933. a
Hrachowitz, M., Benettin, P., Van Breukelen, B. M., Fovet, O., Howden, N. J., Ruiz, L., Van Der Velde, Y., and Wade, A. J.: Transit times–The link between hydrology and water quality at the catchment scale, WIREs Water, 3, 629–657, https://doi.org/10.1002/wat2.1155, 2016. a
IDEAS: Methodologies and “How To”, available at:
https://ideas-productivity.org/ideas-classic/how-to/, last access:
20 October 2021. a
Jones, J. E. and Woodward, C. S.: Newton–Krylov-multigrid solvers for large-scale, highly heterogeneous, variably saturated flow problems, Adv. Water Resour., 24, 763–774, https://doi.org/10.1016/S0309-1708(00)00075-0, 2001. a
Kelley, C. T.: Solving nonlinear equations with Newton's method, SIAM, https://doi.org/10.1137/1.9780898718898, 2003. a
Kennedy, J. and Eberhart, R.: Particle swarm optimization, in: Proceedings of
ICNN'95-international conference on neural networks, Perth, WA, Australia, 27 November–1 December 1995, IEEE, vol. 4, 1942–1948, 1995. a
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Adv. Water Resour., 29, 945–958, https://doi.org/10.1016/j.advwatres.2005.08.006, 2006. a
Kosugi, K.: General model for unsaturated hydraulic conductivity for soils with lognormal pore-size distribution, Soil Sci. Soc. Am. J., 63, 270–277, https://doi.org/10.2136/sssaj1999.03615995006300020003x, 1999. a
Kurylyk, B. L. and Watanabe, K.: The mathematical representation of freezing and thawing processes in variably-saturated, non-deformable soils, Adv. Water Resour., 60, 160–177, https://doi.org/10.1016/j.advwatres.2013.07.016, 2013. a
Liu, S., Lu, L., Mao, D., and Jia, L.: Evaluating parameterizations of aerodynamic resistance to heat transfer using field measurements, Hydrol. Earth Syst. Sci., 11, 769–783, https://doi.org/10.5194/hess-11-769-2007, 2007. a
Lloyd, W., David, O., Ascough II, J. C., Rojas, K. W., Carlson, J. R., Leavesley, G. H., Krause, P., Green, T. R., and Ahuja, L. R.: Environmental modeling framework invasiveness: Analysis and implications, Environ. Modell. Softw., 26, 1240–1250, https://doi.org/10.1016/j.envsoft.2011.03.011, 2011. a
Lu, N.: Generalized soil water retention equation for adsorption and capillarity, J. Geotech. Geoenviron., 142, 04016051, https://doi.org/10.1061/(ASCE)GT.1943-5606.0001524, 2016. a
Mcdonough, J. M.: Introductory Lectures on Turbulence: Physics, Mathematics and Modeling, Mechanical Engineering Textbook Gallery, 2, available at: https://uknowledge.uky.edu/me_textbooks/2 (last access: 21 December 2021), 2007. a
Menard, C. B., Essery, R., Krinner, G., Arduini, G., Bartlett, P., Boone, A., Brutel-Vuilmet, C., Burke, E., Cuntz, M., Dai, Y., Decharme, B., Dutra, E., Fang, X., Fierz, C., Gusev, Y., Hagemann, S., Haverd, V., Kim, H., Lafaysse, M., Marke, T., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Schädler, G., Semenov, V. A., Smirnova, T., Strasser, U., Swenson, S., Turkov, D., Wever, N., and Yuan, H.: Scientific and human errors in a snow model intercomparison, B. Am. Meteorol. Soc., 102, E61–E79, https://doi.org/10.1175/BAMS-D-19-0329.1, 2020. a
Mualem, Y.: A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12, 513–522, https://doi.org/10.1029/WR012i003p00513, 1976. a
Muskat, M. and Meres, M. W.: The flow of heterogeneous fluids through porous media, Physics, 7, 346–363, https://doi.org/10.1063/1.1745403, 1936. a
National Research Council: Basic Research Opportunities in Earth Science, The National Academies Press, Washington, DC, https://doi.org/10.17226/9981, 2001. a
Newman, S.: Building Microservices: Designing Fine-Grained Systems, ISBN-10: 1491950358, 2015. a
Nicolsky, D. and Romanovsky, V. E.: Modeling long-term permafrost degradation, J. Geophys. Res.-Earth, 123, 1756–1771, https://doi.org/10.1029/2018JF004655, 2018. a
Nobel, P. S.: Physicochemical & environmental plant physiology, Academic Press, ISBN 0-12-520025-0, 1999. a
Ochsner, T. E., Horton, R., and Ren, T.: A new perspective on soil thermal properties, Soil Sci. Soc. Am. J., 65, 1641–1647, https://doi.org/10.2136/sssaj2001.1641, 2001. a
Paniconi, C. and Putti, M.: A comparison of Picard and Newton iteration in the numerical solution of multidimensional variably saturated flow problems, Water Resour. Res., 30, 3357–3374, https://doi.org/10.1029/94WR02046, 1994. a, b
Paniconi, C. and Putti, M.: Physically based modeling in catchment hydrology at 50: Survey and outlook, Water Resour. Res., 51, 7090–7129, https://doi.org/10.1002/2015WR017780, 2015. a
Paniconi, C. and Wood, E. F.: A detailed model for simulation of catchment scale subsurface hydrologic processes, Water Resour. Res., 29, 1601–1620, https://doi.org/10.1029/92WR02333, 1993. a
PC Progress Discussion Forums: https://www.pc-progress.com/forum/viewtopic.php?f=3&t=3632, last access: 20 April 2021. a
Peckham, S. D., Hutton, E. W., and Norris, B.: A component-based approach to integrated modeling in the geosciences: The design of CSDMS, Comput. Geosci., 53, 3–12, https://doi.org/10.1016/j.cageo.2012.04.002, 2013. a
Penna, D., Hopp, L., Scandellari, F., Allen, S. T., Benettin, P., Beyer, M., Geris, J., Klaus, J., Marshall, J. D., Schwendenmann, L., Volkmann, T. H. M., von Freyberg, J., Amin, A., Ceperley, N., Engel, M., Frentress, J., Giambastiani, Y., McDonnell, J. J., Zuecco, G., Llorens, P., Siegwolf, R. T. W., Dawson, T. E., and Kirchner, J. W.: Ideas and perspectives: Tracing terrestrial ecosystem water fluxes using hydrogen and oxygen stable isotopes – challenges and opportunities from an interdisciplinary perspective, Biogeosciences, 15, 6399–6415, https://doi.org/10.5194/bg-15-6399-2018, 2018. a
Priestley, C. H. B. and Taylor, R. J.: On the assessment of surface heat flux and evaporation using large-scale parameters, Mon. Weather Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972. a
Quarteroni, A., Sacco, R., and Saleri, F.: Numerical mathematics, vol. 37,
Springer Science and Business Media, ISBN-10: 3642071015, 2010. a
Raupach, M. and Thom, A. S.: Turbulence in and above plant canopies, Annu. Rev. Fluid Mech., 13, 97–129, https://doi.org/10.1146/annurev.fl.13.010181.000525, 1981. a
Regenass, D., Schlemmer, L., Jahr, E., and Schär, C.: It rains and then? Numerical challenges with the 1D Richards equation in kilometer-resolution land surface modelling, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-426, in review, 2021. a
Richards, L. A.: Capillary conduction of liquids through porous mediums, Physics, 1, 318–333, https://doi.org/10.1063/1.1745010, 1931. a
Richardson, L. F.: Weather prediction by numerical process, Cambridge University Press, https://doi.org/10.1002/qj.49704820311, 1922. a
Rigon, R., Bertoldi, G., and Over, T. M.: GEOtop: A distributed hydrological model with coupled water and energy budgets, J. Hydrometeorol., 7, 371–388, https://doi.org/10.1175/JHM497.1, 2006a. a, b, c
Rigon, R., Ghesla, E., Tiso, C., and Cozzini, A.: The HORTON machine: a system for DEM analysis The reference manual, Tech. rep., University of Trento, ISBN 9788884431479, available at: http://hdl.handle.net/11572/48576 (last access: 21 December 2021), 2006b. a
Rigon, R., Bancheri, M., Formetta, G., and de Lavenne, A.: The geomorphological unit hydrograph from a historical-critical perspective, Earth Surf. Proc. Land., 41, 27–37, https://doi.org/10.1002/esp.3855, 2016a. a
Rigon, R., Bancheri, M., and Green, T. R.: Age-ranked hydrological budgets and a travel time description of catchment hydrology, Hydrol. Earth Syst. Sci., 20, 4929–4947, https://doi.org/10.5194/hess-20-4929-2016, 2016b. a
Rizzoli, A., Svensson, M., Rowe, E., Donatelli, M., Muetzelfeldt, R., van der Wal, T., van Evert, F., and Villa, F.: Modelling framework (SeamFrame) requirements, Tech. rep., SEAMLESS, 2006. a
Roe, P. L.: Approximate Riemann solvers, parameter vectors, and difference schemes, J. Comput. Phys., 43, 357–372, https://doi.org/10.1016/0021-9991(81)90128-5, 1981. a
Romano, N., Brunone, B., and Santini, A.: Numerical analysis of one-dimensional unsaturated flow in layered soils, Adv. Water Resour., 21, 315–324, https://doi.org/10.1016/S0309-1708(96)00059-0, 1998. a, b
Ronan, A. D., Prudic, D. E., Thodal, C. E., and Constantz, J.: Field study and simulation of diurnal temperature effects on infiltration and variably saturated flow beneath an ephemeral stream, Water Resour. Res., 34, 2137–2153, https://doi.org/10.1029/98WR01572, 1998. a, b
Saito, H., Šimůnek, J., and Mohanty, B. P.: Numerical analysis of coupled water, vapor, and heat transport in the vadose zone, Vadose Zone J., 5, 784–800, https://doi.org/10.2136/vzj2006.0007, 2006. a
Shewchuk, J. R.: An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, available at: https://www.bibsonomy.org/bibtex/2175fea4e807e879ebcb76b79a9992437/timo (last access: 21 December 2021), 1994. a
Šimůnek, J., Van Genuchten, M. Th., and Šejna, M.: The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Version 3.0, HYDRUS Softw., Ser. 1, Dep. of Environ. Sci., Univ. of California, Riverside, CA, 2012. a
Sophocleous, M.: Analysis of water and heat flow in unsaturated-saturated porous media, Water Resour. Res., 15, 1195–1206, https://doi.org/10.1029/WR015I005P01195, 1979. a
Srivastava, R. and Yeh, T.-C. J.: Analytical solutions for one-dimensional, transient infiltration toward the water table in homogeneous and layered soils, Water Resour. Res., 27, 753–762, https://doi.org/10.1029/90WR02772, 1991. a, b, c, d
Tomin, P. and Lunati, I.: Investigating Darcy-scale assumptions by means of a
multiphysics algorithm, Adv. Water Resour., 95, 80–91,
https://doi.org/10.1016/j.advwatres.2015.12.013, 2016. a
Tubini, N. and Rigon, R.: GEOframePy, available at: https://pypi.org/project/geoframepy/, last access: 22 October 2021a. a
Tubini, N. and Rigon, R.: OMS project for WHETGEO-1D, v-1.0-beta, Zenodo [data set], https://doi.org/10.5281/zenodo.4749319, 2021b (data available at: https://github.com/GEOframeOMSProjects/OMS_Project_WHETGEO1D, last access: 21 December 2021). a, b
Tubini, N. and Rigon, R.: WHETGEO-1D v1.0-beta, Zenodo [code], https://doi.org/10.5281/zenodo.5112727, 2021c (data available at: https://github.com/geoframecomponents/WHETGEO-1D, last access: 21 December 2021). a
Tubini, N., Formetta, G., Rigon, R., Bancheri, M., Serafin, F., Bottazzi, M., Tasin, S., Dalla Torre, D., and D'Amato, C.: Day 1-2 WHETGEO-1D, OSF [code], https://doi.org/10.17605/OSF.IO/Z53C6, 2021a. a
Unidata: Chunking Data with NetCDF-4, available at:
https://www.unidata.ucar.edu/software/netcdf/workshops/2008/nc4chunking/index.html,
last access: 1 April 2021a. a
Unidata: Formats and Performance, available at: https://www.unidata.ucar.edu/software/netcdf/workshops/2008/performance/index.html, last access: 1 April 2021b. a
Unidata: NetCDF version 4.3.22, UCAR/Unidata Program Center, Boulder, CO, https://doi.org/10.5065/D6H70CW6, 2021. a
Van Genuchten, M. T.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980. a
Vanderborght, J., Kasteel, R., Herbst, M., Javaux, M., Thiéry, D., Vanclooster, M., Mouvet, C., and Vereecken, H.: A set of analytical benchmarks to test numerical models of flow and transport in soils, Vadose Zone J., 4, 206–221, https://doi.org/10.2113/4.1.206, 2005. a, b, c
Walvoord, M. A. and Kurylyk, B. L.: Hydrologic impacts of thawing permafrost–A review, Vadose Zone J., 15, 1–20, https://doi.org/10.2136/vzj2016.01.0010, 2016.
a
Walvoord, M. A., Voss, C. I., and Wellman, T. P.: Influence of permafrost distribution on groundwater flow in the context of climate-driven permafrost thaw: Example from Yukon Flats Basin, Alaska, United States, Water Resour. Res., 48, W07524, https://doi.org/10.1029/2011WR011595, 2012. a
Whitaker, S.: Flow in porous media I: A theoretical derivation of Darcy's law, Transport Porous Med., 1, 3–25, https://doi.org/10.1007/BF01036523, 1986. a
Wierenga, P., Hagan, R. M., and Nielsen, D.: Soil temperature profiles during infiltration and redistribution of cool and warm irrigation water, Water Resour. Res., 6, 230–238, https://doi.org/10.1029/WR006i001p00230, 1970. a
Yang, Y., Wu, J., Zhao, S., Han, Q., Pan, X., He, F., and Chen, C.: Assessment of the responses of soil pore properties to combined soil structure amendments using X-ray computed tomography, Sci. Rep.-UK, 8, 695–705, https://doi.org/10.1038/s41598-017-18997-1, 2018. a
Zhang, S., Meurey, C., and Calvet, J.-C.: Identification of soil-cooling rains in southern France from soil temperature and soil moisture observations, Atmos. Chem. Phys., 19, 5005–5020, https://doi.org/10.5194/acp-19-5005-2019, 2019. a
Short summary
This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the water and energy budgets in a soil column. WHETGEO-1D is intended to be the first building block of a new customisable land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code and is fully integrated into the GEOframe/OMS3 system, allowing the use of the many ancillary tools it provides.
This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the...
