Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4213-2023
© Author(s) 2023. 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-16-4213-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
26 Jul 2023
Model description paper |
| 26 Jul 2023
| 26 Jul 2023
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Hugo Delottier
CORRESPONDING AUTHOR
Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchâtel, Switzerland
John Doherty
Watermark Numerical Computing, Corinda, QLD, Australia
Philip Brunner
Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchâtel, Switzerland
Related authors
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Judith Eeckman, Brian De Grenus, Floreana Miesen, James Thornton, Philip Brunner, and Nadav Peleg
EGUsphere, https://doi.org/10.5194/egusphere-2024-1832, https://doi.org/10.5194/egusphere-2024-1832, 2024
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The fate of liquid water from melting snow in winter and spring is difficult to understand in the mountains. This work uses uncommon methods to accurately track the dynamics of snowmelt and infiltration at different depths in the ground and at different altitudes. The results show that melting snow quickly infiltrates into the upper layers of the soil but is also quickly transferred into the surface layer of the soil along the slopes towards the river.
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.
Thomas Hermans, Pascal Goderniaux, Damien Jougnot, Jan H. Fleckenstein, Philip Brunner, Frédéric Nguyen, Niklas Linde, Johan Alexander Huisman, Olivier Bour, Jorge Lopez Alvis, Richard Hoffmann, Andrea Palacios, Anne-Karin Cooke, Álvaro Pardo-Álvarez, Lara Blazevic, Behzad Pouladi, Peleg Haruzi, Alejandro Fernandez Visentini, Guilherme E. H. Nogueira, Joel Tirado-Conde, Majken C. Looms, Meruyert Kenshilikova, Philippe Davy, and Tanguy Le Borgne
Hydrol. Earth Syst. Sci., 27, 255–287, https://doi.org/10.5194/hess-27-255-2023, https://doi.org/10.5194/hess-27-255-2023, 2023
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Although invisible, groundwater plays an essential role for society as a source of drinking water or for ecosystems but is also facing important challenges in terms of contamination. Characterizing groundwater reservoirs with their spatial heterogeneity and their temporal evolution is therefore crucial for their sustainable management. In this paper, we review some important challenges and recent innovations in imaging and modeling the 4D nature of the hydrogeological systems.
Guilherme E. H. Nogueira, Christian Schmidt, Daniel Partington, Philip Brunner, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 26, 1883–1905, https://doi.org/10.5194/hess-26-1883-2022, https://doi.org/10.5194/hess-26-1883-2022, 2022
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In near-stream aquifers, mixing between stream water and ambient groundwater can lead to dilution and the removal of substances that can be harmful to the water ecosystem at high concentrations. We used a numerical model to track the spatiotemporal evolution of different water sources and their mixing around a stream, which are rather difficult in the field. Results show that mixing mainly develops as narrow spots, varying In time and space, and is affected by magnitudes of discharge events.
K. Koutantou, G. Mazzotti, and P. Brunner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 477–484, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021, 2021
Fabien Cochand, Daniel Käser, Philippe Grosvernier, Daniel Hunkeler, and Philip Brunner
Hydrol. Earth Syst. Sci., 24, 213–226, https://doi.org/10.5194/hess-24-213-2020, https://doi.org/10.5194/hess-24-213-2020, 2020
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Roads in sloping fens constitute a hydraulic barrier for surface and subsurface flow. This can lead to the drying out of downslope areas of the fen as well as gully erosion. By combining fieldwork and numerical models, this study presents an assessment of the hydrogeological impact of three road structures especially designed to limit their impact. The study shows that the impact of roads on the hydrological regime in fens can be significantly reduced by using appropriate engineering measures.
James M. Thornton, Gregoire Mariethoz, Tristan J. Brauchli, and Philip Brunner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-181, https://doi.org/10.5194/tc-2019-181, 2019
Manuscript not accepted for further review
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Meltwater runoff from steep mountainous terrain holds great societal and ecological importance. Predicting snow dynamics in unmonitored areas and/or under changed climate requires computer simulations. Yet variability in alpine snow patterns poses a considerable challenge. Here we combine existing tools with high-resolution observations to both constrain and quantify the uncertainty in historical simulations. Snowpack evolution was satisfactorily reproduced and uncertainty substantially reduced.
W. Kurtz, H.-J. Hendricks Franssen, P. Brunner, and H. Vereecken
Hydrol. Earth Syst. Sci., 17, 3795–3813, https://doi.org/10.5194/hess-17-3795-2013, https://doi.org/10.5194/hess-17-3795-2013, 2013
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Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
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Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
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Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
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Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
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Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
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We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
João Careto, Rita Cardoso, Ana Russo, Daniela Lima, and Pedro Soares
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-9, https://doi.org/10.5194/gmd-2024-9, 2024
Revised manuscript accepted for GMD
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In this study, a new drought index is proposed, which not only is able to identify the same events but also can improve the results obtained from other established drought indices. The index is empirically based and is extremely straightforward to compute. It is as well, a daily drought index with the ability to not only assess flash droughts but also events at longer aggregation scales, such as the traditional monthly indices.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
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. Discuss., https://doi.org/10.5194/gmd-2023-213, https://doi.org/10.5194/gmd-2023-213, 2023
Revised manuscript accepted for GMD
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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 resources assessments since 1996. We show the effects of new model features and model evaluations against observed streamflow and water storage anomalies as well as water abstractions statistics. The publically available model output for several variants is described.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-151, https://doi.org/10.5194/gmd-2023-151, 2023
Revised manuscript accepted for GMD
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We develop an operational forecast system, COATLINES-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model requires a relatively small computational demand and results compare well with near real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and waves predictions can improve in accuracy.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
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.
Cited articles
Anderson, M. P., Woessner, W. W., and Hunt, R. J.:
Applied groundwater modeling: simulation of flow and advective transport, Academic, Cambridge, MA, ISBN: 978-0-12-058103-0, 2015.
Aquanty Inc.:
HydroGeoSphere Theory Manual, Waterloo, ON, p. 101, https://www.aquanty.com/hgs-download (last access: 14 July 2023), 2022.
Brunner, P. and Simmons, C. T.:
HydroGeoSphere: a fully integrated, physically based hydrological model, Groundwater 50, 170–176, https://doi.org/10.1111/j.1745-6584.2011.00882.x, 2012.
Brunner, P., Doherty, J., and Simmons, C. T.:
Uncertainty assessment and implications for data acquisition in support of integrated hydrologic models, Water Resour. Res., 48, W07513, https://doi.org/10.1029/2011WR011342, 2012.
Brunner, P., Therrien, R., Renard, P., Simmons, C. T., and Hendricks Franssen, H. J.:
Advances in understanding river-groundwater interactions, Rev. Geophys., 55, 818–854, https://doi.org/10.1002/2017RG000556, 2017.
Chen, X.:
Measurement of streambed hydraulic conductivity and its anisotropy, Environ. Geol., 39, 12, https://doi.org/10.1007/s002540000172, 2000.
Chen, Y. and Oliver, D. S.:
Levenberg–marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification, Computat. Geosci., 17, 689–703, https://doi.org/10.1007/s10596-013-9351-5, 2013.
Dann, R., Close, M., Flintoft, M., Hector, R., Barlow, H., Thomas, S., and Francis, G.:
Characterization and estimation of hydraulic properties in an alluvial gravel vadose zone, Vadoze Zone J., 8, 651—663, 2009.
Dausman, A. M., Doherty, J., Langevin, C. D., and Sukop, M. C.:
Quantifying data worth toward reducing predictive uncertainty, Groundwater, 48, 729–740, https://doi.org/10.1111/j.1745-6584.2010.00679.x, 2010.
de Groot-Hedlin, C. and Constable, S.:
Occam's inversion to generate smooth, two-dimensional models from magnetotelluric data, Geophysics, 55, 1613–1624, https://doi.org/10.1190/1.1442813, 1991.
Delottier, H., Therrien, R., Young, N. L., and Paradis, D.:
A hybrid approach for integrated surface and subsurface hydrologic simulation of baseflow with Iterative Ensemble Smoother, J. Hydrol., 606, 127406 https://doi.org/10.1016/j.jhydrol.2021.127406, 2022.
Delottier, H., Doherty, J., and Brunner, P.: PESTDSI2_dataset_GMD (v.1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7707976, 2023.
Doherty, J.:
Ground water model calibration using pilot points and regularisation, Groundwater, 41, 170–177, https://doi.org/10.1111/j.1745-6584.2003.tb02580.x, 2003.
Doherty, J.:
Manual for PEST, Watermark Numerical Computing, http://www.pesthomepage.org (last access: last access: 14 July 2023), 2022.
Doherty, J.:
Calibration and uncertainty analysis for complex environmental models, Published by Watermark Numerical Computing, Brisbane, Australia, 227 pp., ISBN 978-0-9943786-0-6, http://www.pesthomepage.org (last access: 14 July 2023), 2015.
Doherty, J.: PEST Source code (9th May 2023), Zenodo [code], https://doi.org/10.5281/zenodo.7913402, 2023.
Doherty, J. and Moore, C.:
Decision support modeling: data assimilation, uncertainty quantification, and strategic abstraction, Groundwater, 58, 327–337, https://doi.org/10.1111/gwat.12969, 2020.
Epting, J., Huggenberger, P., Radny, D., Hammes, F., Hollender, J., Page, R. M., Weber, S., Bänninger, D., and Auckenthaler, A.:
Spatiotemporal scales of river-groundwater interaction – The role of local interaction processes and regional groundwater regimes, Sci. Total Environ., 618, 1224–1243. https://doi.org/10.1016/j.scitotenv.2017.09.219, 2018.
Epting, J., Vinnaa, L. R., Piccolroaz, S., Affolter, A., and Scheidler, S.:
Impacts of climate change on Swiss alluvial aquifers–A quantitative forecast focused on natural and artificial groundwater recharge by surface water infiltration, Journal of Hydrology X, 17, 100140, https://doi.org/10.1016/j.hydroa.2022.100140, 2022.
Fienen, M. N., Doherty, J., Hunt, R. J., and Reeves, J.:
Using prediction uncertainty analysis to design hydrologic monitoring networks:Example applications from the Great Lakes water availability pilot project, Vol. 2010–5159, USGS, Reston, Virgina, USA, 2010.
Ghysels, G., Benoit, S., Awol, H., Jensen, E. P., Debele Tolche, A., Anibas, C., and Huysmans, M.:
Characterization of meter-scale spatial variability of riverbed hydraulic conductivity in a lowland river (Aa River, Belgium), J. Hydrol., 559, 1013–1027, https://doi.org/10.1016/j.jhydrol.2018.03.002, 2018.
Gianni, G., Doherty, J., and Brunner, P.:
Conceptualization and calibration of anisotropic alluvial systems: Ptifalls and biases, Groundwater, 57, 409–419, https://doi.org/10.1111/gwat.12802, 2018.
He, J., Sarma, P., Bhark, E., Tanaka, S., Chen, B., Wen, X.-H., and Kamath, J.:
Quantifying expected uncertainty reduction and value of information using ensemble-variance analysis, SPE J., 23, 428–448, https://doi.org/10.2118/182609-PA, 2018.
Hermans T.:
Prediction-focused approaches: an opportunity for hydrology, Groundwater, 55, 683–687, https://doi.org/10.1111/gwat.12548, 2017.
HydroAlgorithmics Pty Ltd.:
AlgoMesh User Guide, Melbourne, VIC, https://www.hydroalgorithmics.com/software/algomesh (last access: 14 July 2023), 2022.
James, S. C., Doherty, J., and Eddebarh, A.-A.:
Practical Postcalibration uncertainty analysis: Yucca Mountain, Nevada, USA, Groundwater, 47, 851–869, https://doi.org/10.1111/j.1745-6584.2009.00626.x, 2009.
Juda, P., Straubhaar, J., and Renard, P.:
Comparison of three recent discrete stochastic inversion methods and influence of the prior choice, C. R. Geosci., 355, 1–26, https://doi.org/10.5802/crgeos.160, 2023.
Khambhammettu, P., Renard, P., and Doherty, J.:
The Traveling Pilot Point method. A novel approach to parameterize the inverse problem for categorical fields, Adv. Water Resour., 138, 103556, https://doi.org/10.1016/j.advwatres.2020.103556, 2020.
Kikuchi, C.:
Toward increased use of data worth analyses in groundwater studies, Groundwater, 55, 670–673, https://doi.org/10.1111/gwat.12562, 2017.
Lam, D.-T., Kerrou, J., Renard, P., Benabderrahmane, H., and Perrochet, P.:
Conditioning multi-gaussian groundwater flow parameters to transient hydraulic head and flowrate data with iterative ensemble smoothers: a synthetic case study, Front. Earth Sci., 8, 202, https://doi.org/10.3389/feart.2020.00202, 2020.
Lima, M. M., Emerick, A. A., and Ortiz, C. E. P.:
Data-space inversion with ensemble smoother, Comput. Geosci., 24, 1179–1200, https://doi.org/10.1007/s10596-020-09933-w, 2020.
Linde, N., Renard, P., Mukerji, T., and Caers, J.:
Geological realism in hydrogeological and geophysical inverse modeling: A review, Adv. Water Resour., 86, 86–101, https://doi.org/10.1016/j.advwatres.2015.09.019, 2015.
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.
Pyrcz, M. J., Boisvert, J. B., and Deutsch, C. V.:
ALLUVSIM: A program for event-based stochastic modeling of fluvial depositional systems, Comput. Geosci., 35, 1671–85, https://doi.org/10.1016/j.cageo.2008.09.012, 2009.
Remy, N., Boucher, A., and Wu, J.:
Applied Geostatistics with SGeMS: A User's Guide, Cambridge University Press, ISBN 978-0-521-51414-9, 2009.
Satija, A. and Caers, J.:
Direct forecasting of subsurface flow response from non-linear dynamic data by linear least-squares in canonical functional principal component space, Adv. Water Resour., 77, 69–81, https://doi.org/10.1016/j.advwatres.2015.01.002, 2015.
Scanlon, B. R, Fakhreddine, S., Rateb, A., de Graaf, I., Famiglietti, J., Gleeson, T., Grafton, R. Q., Jobbagy, E., Kebede, S., Kolusu, S. R., Konikow, L. F., Long, D., Mekonnen, M., Mueller Schmied, H., Mukherjee, A., MacDonald, A., Reedy, R. C., Shamsudduha, M., Simmons, C. T., Sun, A., Taylor, R. G., Villholth, K. G., Vörösmarty, C. J., and Zheng, C.: Global water resources and the role of groundwater in a resilient water future, Nature Reviews Earth & Environment, 4, 1—15, https://doi.org/10.1038/s43017-022-00378-6, 2023.
Scheidt, C., Jeong, C., Mukerji, T., and Caers, J.:
Probabilistic falsification of prior geologic uncertainty with seismic amplitude data: application to a turbidite reservoir case, Geophysics, 80, M89–M12, https://doi.org/10.1190/geo2015-0084.1, 2015.
Schilling, O. S., Gerber, C., Purtschert, R., Kipfer, R., Hunkeler, D., and Brunner, P.:
Advancing physically-based flow simulations of alluvial systems through atmospheric noble gases and the novel 37Ar tracer method, Water Resour. Res., 53, 10465–10490, https://doi.org/10.1002/2017WR020754, 2017.
Schilling, O. S., Partington, D. J., Doherty, J., Kipfer, R., Hunkeler, D., and Brunner, P.:
Buried paleo-channel detection with a groundwater model, tracer-based observations and spatially varying, preferred anisotropy pilot point calibration, Geophys. Res. Lett., 49, e2022GL098944, https://doi.org/10.1029/2022GL098944, 2022.
Simmons, C. T., Brunner, P., Therrien, R., and Sudicky, E. A.:
Commemorating the 50th anniversary of the Freeze and Harlan (1969) Blueprint for a physically-based, digitally-simulated hydrologic response model, J. Hydrol., 584, 124309, https://doi.org/10.1016/j.jhydrol.2019.124309, 2020.
Sun, W. and Durlofsky, L. J.:
A new data-space inversion procedure for efficient uncertainty quantification in subsurface flow problems, Math. Geosci., 49, 679–715, https://doi.org/10.1007/s11004-016-9672-8, 2017.
Tonkin, M. J. and J. Doherty.:
Calibration-constrained Monte Carlo analysis of highly parameterized models using subspace techniques, Water Resour. Res., 45, W00B10, https://doi.org/10.1029/2007WR006678, 2009.
Vecchia, A. V. and Cooley, R. L.:
Simultaneous confidence and prediction intervals for nonlinear regression models with application to a groundwater flow model, Water Resour. Res., 23, 1237–1250, https://doi.org/10.1029/WR023i007p01237, 1987.
Vrugt, J. A.:
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB Implementation, Environ. Model. Softw., 75, 273–316, https://doi.org/10.1016/j.envsoft.2015.08.013, 2016.
White, J. T.:
A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions, Environ. Model. Softw., 109, 191–201, https://doi.org/10.1016/j.envsoft.2018.06.009, 2018.
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.
Long run times are usually a barrier to the quantification and reduction of predictive...
