Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6085-2022
© 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-15-6085-2022
© 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
| 
03 Aug 2022
Model description paper |
| 03 Aug 2022
| 03 Aug 2022
Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains
Léo Pujol
CORRESPONDING AUTHOR
Laboratoire des sciences de l'ingenieur, de l'informatique
et de l'imagerie (ICUBE), Fluid Mechanics Team, CNRS, Université de
Strasbourg, France
Pierre-André Garambois
INRAE (Irstea), Aix Marseille Université, RECOVER, Aix-en-Provence, France
Jérôme Monnier
Institut de Mathematiques de Toulouse (IMT), Toulouse, France
INSA Toulouse, Toulouse, France
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Ngo Nghi Truyen Huynh, Pierre-André Garambois, Benjamin Renard, François Colleoni, Jérôme Monnier, and Hélène Roux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3665, https://doi.org/10.5194/egusphere-2024-3665, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Understanding and modeling flash flood-prone areas remains challenging due to limited data and scale-relevant hydrological theory. While machine learning shows promise, its integration with process-based models is difficult. We present an approach incorporating machine learning into a high-resolution hydrological model to correct internal fluxes and transfer parameters between watersheds. Results show improved accuracy, advancing development of learnable and interpretable process-based models.
Juliette Godet, Pierre Nicolle, Nabil Hocini, Eric Gaume, Philippe Davy, Frederic Pons, Pierre Javelle, Pierre-André Garambois, Dimitri Lague, and Olivier Payrastre
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-472, https://doi.org/10.5194/essd-2024-472, 2024
Preprint under review for ESSD
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This paper describes a dataset that includes input, output, and validation data for the simulation of flash flood hazards and three specific flash flood events in the French Mediterranean region. This dataset is particularly valuable as flood mapping methods often lack sufficient benchmark data. Additionally, we demonstrate how the hydraulic method we used, named Floodos, produces highly satisfactory results.
Maxime Jay-Allemand, Julie Demargne, Pierre-André Garambois, Pierre Javelle, Igor Gejadze, François Colleoni, Didier Organde, Patrick Arnaud, and Catherine Fouchier
Proc. IAHS, 385, 281–290, https://doi.org/10.5194/piahs-385-281-2024, https://doi.org/10.5194/piahs-385-281-2024, 2024
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This work targets the improvement of a hydrologic model used for flash flood warnings. A gridded model is used to spatially describe the hydrological processes. We develop a method to estimate the best model setup based on scarce river flow observations. It uses a complex algorithm combined with geographical descriptors to generate gridded parameters that better capture catchment characteristics. Results are promising, improving the discharge estimations where no observations are available.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, and Pierre Javelle
Hydrol. Earth Syst. Sci., 26, 5793–5816, https://doi.org/10.5194/hess-26-5793-2022, https://doi.org/10.5194/hess-26-5793-2022, 2022
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Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.
François Colleoni, Pierre-André Garambois, Pierre Javelle, Maxime Jay-Allemand, and Patrick Arnaud
EGUsphere, https://doi.org/10.5194/egusphere-2022-506, https://doi.org/10.5194/egusphere-2022-506, 2022
Preprint archived
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This contribution presents the first evaluation of Variational Data Assimilation successfully applied over a large sample to the spatially distributed calibration of a newly taylored grid-based parsimonious model structure and corresponding adjoint. High performances are obtained in spatio-temporal validation and at flood time scales, especially for mediterranenan and oceanic catchments. Regional sensitivity analysis revealed the importance of the non conservative and production components.
Abubakar Haruna, Pierre-Andre Garambois, Helene Roux, Pierre Javelle, and Maxime Jay-Allemand
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-414, https://doi.org/10.5194/hess-2021-414, 2021
Manuscript not accepted for further review
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We compared three hydrological models in a flash flood modelling framework. We first identified the sensitive parameters of each model, then compared their performances in terms of outlet discharge and soil moisture simulation. We found out that resulting from the differences in their complexities/process representation, performance depends on the aspect/measure used. The study then highlights and proposed some future investigations/modifications to improve the models.
Kevin Larnier and Jerome Monnier
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2020-32, https://doi.org/10.5194/npg-2020-32, 2020
Revised manuscript not accepted
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A hybrid A.I. algorithm (data-driven and physically-informed) to estimate river discharges observed by the altimetry satellite SWOT (launch 2021).
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Stefania Renna, Francesco Granella, Lara Aleluia Reis, and Paulina Estela Schulz-Antipa
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Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024, https://doi.org/10.5194/gmd-17-3467-2024, 2024
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Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
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Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
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Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
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Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
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This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
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Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
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It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
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Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
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Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
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Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
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Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
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Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
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The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
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The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
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This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
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In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
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This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
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Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Ane Lopez de Gamiz-Zearra, Cecilie Hansen, Xavier Corrales, Iñaki Quincoces, Izaskun Preciado, and Eider Andonegi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1368, https://doi.org/10.5194/egusphere-2023-1368, 2023
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This paper describes the development of the first calibrated end-to-end Atlantis model for the Bay of Biscay that will help us improve the comprehension of the spatial functioning of the Bay of Biscay ecosystem and help establishing management measures of human activities. Our results highlighted the importance of lower trophic levels to the pelagic system and demonstrate the importance of having accurate and precise data for biological processes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
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We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
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Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
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Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
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Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
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To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
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A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
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The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Cited articles
Allen, M., Antwi-Agyei, P., Aragon-Durand, F., Babiker, M., Bertoldi, P., Bind,
M., Brown, S., Buckeridge, M., Camilloni, I., Cartwright, A., Masson-Delmotte, V., Zhai, P., Pörtner, H.-O.,
Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W.,
Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X.,
Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., and Waterterfield, T.
(Eds.):
Technical Summary: Global warming of 1.5 ∘C, An IPCC Special Report on the
impacts of global warming of 1.5 ∘C above pre-industrial levels and related
global greenhouse gas emission pathways, in the context of strengthening the
global response to the threat of climate change, sustainable development, and
efforts to eradicate poverty, http://pure.iiasa.ac.at/15716 (last access: 27 June 2022),
2019. a
Amara, M., Capatina-Papaghiuc, D., and Trujillo, D.: Hydrodynamical modelling
and multidimensional approximation of estuarian river flows, Comput. Vis. Sci., 6, 39–46,
https://doi.org/10.1007/s00791-003-0106-z, 2004. a
Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms,
and applications, Fundamentals of Algorithms, SIAM,
https://hal.inria.fr/hal-01402885 (last access: 27 June 2022), 2016. a
Audusse, E. and Bristeau, M.-O.: A well-balanced positivity preserving
“second-order” scheme for shallow water flows on unstructured meshes, J.
Comput. Phys., 206, 311–333, https://doi.org/10.1016/j.jcp.2004.12.016, 2005. a
Audusse, E., Bouchut, F., Bristeau, M.-O., Klein, R., and Perthame, B.: A Fast
and Stable Well-Balanced Scheme with Hydrostatic Reconstruction for Shallow
Water Flows, SIAM J. Sci. Comput., 25, 2050–2065,
https://doi.org/10.1137/S1064827503431090, 2004. a
Barth, T.: Numerical Methods for conservative Laws on Structured and
Unstructured Meshes, Tech. rep., VKI Lecture Series, 2003. a
Barthélémy, S., Ricci, S., Morel, T., Goutal, N., Le Pape, E., and
Zaoui, F.: On operational flood forecasting system involving 1D
Bates, P., Trigg, M., Neal, J., and Dabrowa, A.: LISFLOOD-FP, User manual,
School of Geographical Sciences, University of Bristol, Bristol, UK, https://vdocument.in/lisflood-fp-user-manual-university-of-bristol (last access: 1 August 2022), 2013. a
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.: A simple inertial
formulation of the shallow water equations for efficient two-dimensional
flood inundation modelling, J. Hydrol., 387, 33–45,
https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010. a
Bertalanffy, L. v.: General Systems Theory, 1968. a
Beven, K.: Prophecy, reality and uncertainty in distributed hydrological
modelling, Adv. Water Resour., 16, 41–51, 1993. a
Beven, K. J.: Uniqueness of place and process representations in hydrological modelling, Hydrol. Earth Syst. Sci., 4, 203–213, https://doi.org/10.5194/hess-4-203-2000, 2000. a
Biancamaria, S., Lettenmaier, D., and Pavelsky, T.: The SWOT Mission and Its
Capabilities for Land Hydrology, Surv. Geophys., 37, 307–337,
https://doi.org/10.1007/s10712-015-9346-y, 2016. a
Biancamaria, S., Frappart, F., Leleu, A.-S., Marieu, V., Blumstein, D.,
Desjonquères, J.-D., Boy, F., Sottolichio, A., and Valle-Levinson, A.:
Satellite radar altimetry water elevations performance over a 200 m wide
river: Evaluation over the Garonne River, Adv. Space Res., 59,
128–146, https://doi.org/10.1016/j.asr.2016.10.008, 2017. a
Brêda, J., Paiva, R., Bravo, J., Passaia, O., and Moreira, D.: Assimilation
of satellite altimetry data for effective river bathymetry, Water Resour.
Res., 55, 7441–7463,
https://doi.org/10.1029/2018WR024010, 2019. a
Brunner, G. W.: HEC-RAS River Analysis System, Hydraulic Reference Manual,
Version 1.0., Tech. rep., Hydrologic Engineering Center Davis, CA, https://apps.dtic.mil/sti/pdfs/ADA311952.pdf (last access: 27 June 2022) 1995. a
Buffard, T. and Clain, S.: Monoslope and Multislope MUSCL Methods for
unstructured meshes, J. Comput. Phys., 229, 3745–3776,
https://doi.org/10.1016/j.jcp.2010.01.026, 2010. a
Chévrier, P. and Galley, H.: A Van Leer finite volume scheme for the Euler
equations on unstructured meshes, ESAIM-Math. Model. Num., 27, 183–201, 1993. a
Chow, V.: Open-channel Hydraulics, McGraw-Hill civil engineering series, McGraw-Hill, New-York, USA, ISBN 9780070859067, 1959. a
Collischonn, W., Allasia, D., Da Silva, B. C., and Tucci, C. E.: The MGB-IPH
model for large-scale rainfall-runoff modelling, Hydrolog. Sci.
J., 52, 878–895, https://doi.org/10.1623/hysj.52.5.878,
2007. a
Coron, L., Thirel, G., Delaigue, O., Perrin, C., and Andréassian, V.: The
suite of lumped GR hydrological models in an R package, Environ.
Modell. Softw., 94, 166–171,
https://doi.org/10.1016/j.envsoft.2017.05.002, 2017. a, b
Couderc, F., Madec, R., Monnier, J., and Vila, J.-P.: DassFow-Shallow,
Variational Data Assimilation for Shallow-Water Models: Numerical Schemes,
User and Developer Guides, University of Toulouse, CNRS,
IMT, INSA, ANR, Research report,
https://hal.archives-ouvertes.fr/hal-01120285 (last access: 27 June 2022), 2013. a, b, c, d, e
Cunge, J. A., Holly, F., M., and Verwey, A.: Practical Aspects of Computational
River Hydraulics, Pitam Publishing, ISBN 978-0273084426, 1980. a
Davy, P., Croissant, T., and Lague, D.: A precipiton method to calculate river
hydrodynamics, with applications to flood prediction, landscape evolution
models, and braiding instabilities, J. Geophys. Res.-Earth, 122, 1491–1512,
https://doi.org/10.1002/2016JF004156, 2017. a, b
Delestre, O., Darboux, F., James, F., Lucas, C., Laguerre, C., and Cordier, S.:
FullSWOF: Full Shallow-Water equations for Overland Flow, J. Open Source Softw., 2, 448, https://doi.org/10.21105/joss.00448, 2017. a
Finaud-Guyot, P., Garambois, P.-A., Chen, S., Dellinger, G., Ghenaim, A., and
Terfous, A.: 1D
Fleischmann, A. S., Paiva, R. C. D.,
Collischonn, W., Siqueira, V. A., Paris, A., Moreira, D.
M., Papa, F., Bitar, A. A., Parrens, M., Aires, F., and
Garambois, P. A.: Trade-offs between
1-D and 2-D regional river hydrodynamic models, Water Resour. Res., 56,
e2019WR026812, https://doi.org/10.1029/2019WR026812, 2020. a, b, c
Galland, J.-C., Goutal, N., and Hervouet, J.-M.: TELEMAC: A new numerical model
for solving shallow water equations, Adv. Water Resour., 14,
138–148, 1991. a
Garambois, P., Calmant, S., Roux, H., Paris, A., Monnier, J., Finaud-Guyot, P.,
Montazem, A., and da Silva, J.: Hydraulic visibility: Using satellite
altimetry to parameterize a hydraulic model of an ungauged reach of a braided
river, Hydrol. Process., 31, 756–767,
https://doi.org/10.1002/hyp.11033, 2017. a, b
Garambois, P.-A. and Monnier, J.: Inference of effective river properties from
remotely sensed observations of water surface, Adv. Water Resour.,
79, 103–120,
https://doi.org/10.1016/j.advwatres.2015.02.007, 2015. a, b
Garambois, P.-A., Larnier, K., Monnier, J., Finaud-Guyot, P., Verley, J.,
Montazem, A., and Calmant, S.: Variational estimation of effective channel
and ungauged anabranching river discharge from multi-satellite water heights
of different spatial sparsity, J. Hydrol., 581, 124409,
https://doi.org/10.1016/j.jhydrol.2019.124409, 2020. a, b, c, d
Garandeau, L., Belleudy, A., Javelle, P., Organde, D., Janet, B., Demargne, J.,
De Saint-Aubin, C., and Fouchier, C.: Vigicrues Flash, un service
automatique d'avertissement pour les crues rapides, De la prévision
des crues à la gestion de crise, Société Hydrotechnique
de France, Avignon, France, 11,
https://hal.inrae.fr/hal-02608801 (last access: 27 June 2022), 2018. a
Gejadze, I. Y. and Monnier, J.: On a 2D zoom for the 1D shallow water
model: Coupling and data assimilation, Comput. Method. Appl. M., 196, 4628–4643,
https://doi.org/10.1016/j.cma.2007.05.026, 2007. a
Gervasio, P., Lions, J.-L., and Quarteroni, A.: Heterogeneous coupling by
virtual control methods, Numer. Math., 90, 241–264,
https://doi.org/10.1007/s002110100303, 2001. a
Goutal, N. and Maurel, F.: A finite volume solver for 1D shallow-water
equations applied to an actual river, Int. J. Numer. Meth. Fl., 38, 1–19,
https://doi.org/10.1002/fld.201, 2002. a
Grimaldi, S., Li, Y., Walker, J., and Pauwels, V.: Effective representation of
river geometry in hydraulic flood forecast models, Water Resour. Res.,
54, 1031–1057, https://doi.org/10.1002/2017WR021765, 2018. a, b
Gudmundsson, G. H.: Transmission of basal variability to a glacier surface,
J. Geophys. Res.-Sol. Ea., 108, B5,
https://doi.org/10.1029/2002JB002107, 2003. a
Guinot, V.: Wave propagation in fluids: models and numerical techniques, 2nd edn., vol. 49, edited by: ISTE Ltd., ISBN 978-9812707789, 2010. a
Guinot, V., Delenne, C., Rousseau, A., and Boutron, O.: Flux closures and
source term models for shallow water models with depth-dependent integral
porosity, Adv. Water Resour., 122, 1–26,
https://doi.org/10.1016/j.advwatres.2018.09.014, 2018. a, b, c
Hascoet, L. and Pascual, V.: The Tapenade automatic differentiation tool:
principles, model, and specification, ACM T. Math.
Software, 39, 1–43, https://doi.org/10.1145/2450153.2450158, 2013. a, b, c, d
Hocini, N., Payrastre, O., Bourgin, F., Gaume, E., Davy, P., Lague, D., Poinsignon, L., and Pons, F.: Performance of automated methods for flash flood inundation mapping: a comparison of a digital terrain model (DTM) filling and two hydrodynamic methods, Hydrol. Earth Syst. Sci., 25, 2979–2995, https://doi.org/10.5194/hess-25-2979-2021, 2021. a, b, c
Hostache, R., Lai, X., Monnier, J., and Puech, C.: Assimilation of
spatially distributed water levels into a shallow-water flood model. Part
II: Use of a remote sensing image of Mosel River, J. Hydrol., 390, 257–268,
https://doi.org/10.1016/j.jhydrol.2010.07.003, 2010. a
Hunter, N. M., Bates, P. D., Neelz, S.,
Pender, G., Villanueva, I., Wright, N. G., Liang, D.,
Falconer, R. A., Lin, B., Waller, S., Crossley, A. J., and
Mason, D. C.: Benchmarking 2D
hydraulic models for urban flooding, in: Proceedings of the Institution of
Civil Engineers-Water Management, Thomas Telford Ltd, 161, 13–30,
https://doi.org/10.1680/wama.2008.161.1.13, 2008. a
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., Nurhati, I. S., Pinto, I., Seneviratne, S. I., van den Hurk, B., and Vera, C. S.: An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets, Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, 2020. a
Jay-Allemand, M., Javelle, P., Gejadze, I., Arnaud, P., Malaterre, P.-O., Fine, J.-A., and Organde, D.: On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment, Hydrol. Earth Syst. Sci., 24, 5519–5538, https://doi.org/10.5194/hess-24-5519-2020, 2020. a, b
Kirstetter, G., Delestre, O., Lagrée, P.-Y., Popinet, S., and Josserand, C.: B-flood 1.0: an open-source Saint-Venant model for flash-flood simulation using adaptive refinement, Geosci. Model Dev., 14, 7117–7132, https://doi.org/10.5194/gmd-14-7117-2021, 2021. a
Lai, X. and Monnier, J.: Assimilation of spatially distributed water levels
into a shallow-water flood model. Part I: Mathematical method and test case,
J. Hydrol., 377, 1–11,
https://doi.org/10.1016/j.jhydrol.2009.07.058, 2009. a
Le Lay, M.: Modélisation hydrologique dans un contexte de variabilité
hydro-climatique. Une approche comparative pour l'étude du cycle
hydrologique à méso-échelle au Bénin, PhD thesis, Institut
National Polytechnique de Grenoble (INPG),
https://tel.archives-ouvertes.fr/tel-00116912 (last access: 27 June 2022), 2006. a
Leopold, L. B. and Maddock, T.: The hydraulic geometry of stream channels and
some physiographic implications, US Government Printing Office, 252, https://doi.org/10.3133/pp252,
1953. a
Lorenc, A. C., Ballard, S. P., Bell, R. S., Ingleby,
N. B., Andrews, P. L. F., Barker, D. M., Bray, J. R., Clayton, A. M.,
Dalby, T., Li, D., Payne, T. J., and Saunders, F. W.: The Met. Office global
three-dimensional variational data assimilation scheme, Q. J. Roy. Meteor. Soc., 126, 2991–3012, 2000. a
Malou, T. and Monnier, J.: Double-scale diffusive wave model dedicated to spatial river observation and associated covariance kernel for variational data assimilation , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10355, https://doi.org/10.5194/egusphere-egu21-10355, 2021. a
Malou, T. and Monnier, J.: Covariance kernels investigation from diffusive wave
equations for data assimilation in hydrology, Inverse Probl.,
https://doi.org/10.1088/1361-6420/ac509d, accepted, 2022. a
Malou, T., Garambois, P.-A., Paris, A., Monnier, J., and Larnier, K.:
Generation and analysis of stage-fall-discharge laws from coupled
hydrological-hydraulic river network model integrating sparse multi-satellite
data, J. Hydrol., 603, 126993,
https://doi.org/10.1016/j.jhydrol.2021.126993, 2021. a, b, c
Marin, J. and Monnier, J.: Superposition of local zoom models and simultaneous
calibration for 1D–2D shallow water flows, Math. Comput. Simulat., 80, 547–560,
https://doi.org/10.1016/j.matcom.2009.09.001, 2009. a
Martin, N. and Monnier, J.: Inverse rheometry and basal properties inference
for pseudoplastic geophysical flows, Eur. J. Mech. B-Fluid.,
50, 110–126,
https://doi.org/10.1016/j.euromechflu.2014.11.011, 2015. a
Miglio, E., Perotto, S., and Saleri, F.: Model coupling techniques for
free-surface flow problems: Part I, Nonlinear Anal.-Theor., 63, e1885–e1896,
https://doi.org/10.1016/j.na.2005.03.083, 2005a. a
Miglio, E., Perotto, S., and Saleri, F.: Model coupling techniques for
free-surface flow problems: Part II, Nonlinear Anal.-Theor., 63, e1897–e1908,
https://doi.org/10.1016/j.na.2005.03.085, 2005b. a
Milly, P.: Climate, soil water storage, and the average annual water balance,
Water Resour. Res., 30, 2143–2156,
https://doi.org/10.1029/94WR00586, 1994. a
Monnier, J.: Variational data assimilation: from optimal control to large scale
data assimilation, Open Online Course, INSA Toulouse,
https://www.math.univ-toulouse.fr/~jmonnie/Enseignement/CourseVDA.pdf (last access: 27 June 2022),
2014. a
Monnier, J.: Variational Data Assimilation and Model Learning, https://hal.archives-ouvertes.fr/hal-03040047 (last access: 1 August 2022), 2021. a
Monnier, J., Couderc, F., Dartus, D., Larnier, K., Madec, R., and Vila, J.-P.:
Inverse algorithms for 2D shallow water equations in presence of wet dry
fronts: Application to flood plain dynamics, Adv. Water Resour., 97,
11–24, https://doi.org/10.1016/j.advwatres.2016.07.005,
2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Montazem, A., Garambois, P.-A., Calmant, S., Finaud-Guyot, P., Monnier, J.,
Moreira, D., Minear, J., and Biancamaria, S.: Wavelet-Based River
Segmentation Using Hydraulic Control-Preserving Water Surface Elevation
Profile Properties, Geophys. Res. Lett., 46, 6534–6543,
https://doi.org/10.1029/2019GL082986, 2019. a, b, c
Nguyen, P., Thorstensen, A., Sorooshian, S., Hsu, K., AghaKouchak, A., Sanders,
B., Koren, V., Cui, Z., and Smith, M.: A high resolution coupled
hydrologic-hydraulic model (HiResFlood-UCI) for flash flood modeling,
J. Hydrol., 541, 401–420,
https://doi.org/10.1016/j.jhydrol.2015.10.047, 2016. a, b, c
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil,
F., and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall-runoff model?: Part 2 Towards a simple and efficient potential
evapotranspiration model for rainfall-runoff modelling, J. Hydrol.,
303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026,
2005. a
Özgen, I., Zhao, J.-h., Liang, D.-f., and Hinkelmann, R.: Wave propagation
speeds and source term influences in single and integral porosity shallow
water equations, Water Sci. Eng., 10, 275–286,
https://doi.org/10.1016/j.wse.2017.12.003, 2017. a
Pontes, P., Fan, F., Fleischmann, A., Paiva, R., Buarque, D., Siqueira, V.,
Jardim, P., Sorribas, M., and Collischonn, W.: MGB-IPH model for
hydrological and hydraulic simulation of large floodplain river systems
coupled with open source GIS, Environ. Modell. Softw., 94,
1–20, https://doi.org/10.1016/j.envsoft.2017.03.029, 2017. a
Pujol, L., Garambois, P.-A., Finaud-Guyot, P., Monnier, J., Larnier, K.,
Mosé, R., Biancamaria, S., Yesou, H., Moreira, D., Paris, A., and
Calmant, S.: Estimation of multiple inflows and effective channel by
assimilation of multi-satellite hydraulic signatures: The ungauged
anabranching Negro river, J. Hydrol., 591, 125331,
https://doi.org/10.1016/j.jhydrol.2020.125331, 2020. a, b, c, d, e, f, g, h, i
Pujol, L., Garambois, P.-A., and Monnier, J.: DassFlow2D-V3 code and cases, Zenodo [code], https://doi.org/10.5281/zenodo.6342723, 2022. a, b
Rodríguez, E., Durand, M., and Frasson, R. P. d. M.: Observing rivers with
varying spatial scales, Water Resour. Res., 56, 9,
https://doi.org/10.1029/2019WR026476, 2020. a
Sanders, B. F., Schubert, J. E., and Detwiler, R. L.: ParBreZo: A parallel,
unstructured grid, Godunov-type, shallow-water code for high-resolution flood
inundation modeling at the regional scale, Adv. Water Resour., 33,
1456–1467, https://doi.org/10.1016/j.advwatres.2010.07.007,
2010.
a, b
Schuite, J., Flipo, N., Massei, N., Rivière, A., and Baratelli, F.:
Improving the Spectral Analysis of Hydrological Signals to Efficiently
Constrain Watershed Properties, Water Resour. Res., 55, 4043–4065,
https://doi.org/10.1029/2018WR024579, 2019. a
Schumann, G. J.-P. and Domeneghetti, A.: Exploiting the proliferation of
current and future satellite observations of rivers, Hydrol. Process.,
30, 2891–2896, https://doi.org/10.1002/hyp.10825, 2016. a
Steinstraesser, J. G. C., Delenne, C., Finaud-Guyot, P., Guinot, V., Casapia,
J. K., and Rousseau, A.: SW2D-LEMON: a new software for upscaled shallow
water modeling, in: Simhydro 2021 – 6th International Conference Models for
complex and global water issues-Practices and expectations, Sophia Antipolis, 16–18 June 2021,
https://hal.inria.fr/hal-03224050/ (last access: 27 June 2022), 2021. a
Uhe, P., Mitchell, D., Bates, P. D., Addor, N., Neal, J., and Beck, H. E.: Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0, Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, 2021. a, b
Vila, J.-P.: Simplified Godunov schemes for 2×2 systems of conservation laws,
SIAM J. Numer. Anal., 23, 1173–1192, https://doi.org/10.1137/0723079, 1986. a
Vila, J.-P. and Villedieu, P.: Convergence of an explicit finite volume scheme
for first order symmetric systems, Numer. Math., 94, 573–602,
https://doi.org/10.1007/s00211-002-0396-y,
2003. a
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B:
Fortran subroutines for large-scale bound-constrained optimization, ACM
T. Math. Software, 23, 550–560,
https://doi.org/10.1145/279232.279236, 1997. a, b, c
Short summary
This contribution presents a new numerical model for representing hydraulic–hydrological quantities at the basin scale. It allows modeling large areas at a low computational cost, with fine zooms where needed. It allows the integration of local and satellite measurements, via data assimilation methods, to improve the model's match to observations. Using this capability, good matches to in situ observations are obtained on a model of the complex Adour river network with fine zooms on floodplains.
This contribution presents a new numerical model for representing hydraulic–hydrological...
