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148 results on '"Garambois, Pierre‐André"'

1. Learning Regionalization using Accurate Spatial Cost Gradients within a Differentiable High-Resolution Hydrological Model: Application to the French Mediterranean Region

Author

Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Renard, Benjamin, Roux, Hélène, Demargne, Julie, Jay-Allemand, Maxime, and Javelle, Pierre

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach incorporating learnable regionalization mappings, based on either multi-linear regressions or artificial neural networks (ANNs), into a differentiable hydrological model. This approach demonstrates how two differentiable codes can be linked and their gradients chained, enabling the exploitation of heterogeneous datasets across extensive spatio-temporal computational domains within a high-dimensional regionalization context, using accurate adjoint-based gradients. The inverse problem is tackled with a multi-gauge calibration cost function accounting for information from multiple observation sites. HDA-PR was tested on high-resolution, hourly and kilometric regional modeling of 126 flash-flood-prone catchments in the French Mediterranean region. The results highlight a strong regionalization performance of HDA-PR especially in the most challenging upstream-to-downstream extrapolation scenario with ANN, achieving median Nash-Sutcliffe efficiency (NSE) scores from 0.6 to 0.71 for spatial, temporal, spatio-temporal validations, and improving NSE by up to 30% on average compared to the baseline model calibrated with lumped parameters. ANN enables to learn a non-linear descriptors-to-parameters mapping which provides better model controllability than a linear mapping for complex calibration cases.

Published
2023
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2. Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression

Author

Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Renard, Benjamin, and Roux, Hélène

Subjects
Computer Science - Machine Learning
Abstract

Tackling the difficult problem of estimating spatially distributed hydrological parameters, especially for floods on ungauged watercourses, this contribution presents a novel seamless regionalization technique for learning complex regional transfer functions designed for high-resolution hydrological models. The transfer functions rely on: (i) a multilayer perceptron enabling a seamless flow of gradient computation to employ machine learning optimization algorithms, or (ii) a multivariate regression mapping optimized by variational data assimilation algorithms and guided by Bayesian estimation, addressing the equifinality issue of feasible solutions. The approach involves incorporating the inferable regionalization mappings into a differentiable hydrological model and optimizing a cost function computed on multi-gauge data with accurate adjoint-based spatially distributed gradients.

Published
2023

3. Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods

Author

Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, and Javelle, Pierre

Subjects
Mathematics - Optimization and Control
Abstract

Classical calibration methods in hydrology typically rely on a single cost function computed on long-term streamflow series. Even when hydrological models achieve acceptable scores in NSE and KGE, imbalances can still arise between overall model performance and its ability to simulate flood events, particularly flash floods. In this study, the potential of using multi-scale signatures is explored to enhance multi-criteria calibration methods for spatially distributed flood modeling, which remains considerable challenges. We present a novel signatures and sensitivity-based calibration approach implemented into a variational data assimilation algorithm capable to deal with high dimensional spatially distributed hydrological optimization problems. It is tested on 141 flash flood prone catchments mostly located in the French Mediterranean region. Our approach involves computing several signatures, including flood event signatures, using an automated flood segmentation algorithm. We select suitable signatures for constraining the model based on their global sensitivity with the input parameters through global signature-based sensitivity analysis (GSSA). We then perform two multi-criteria calibration strategies using the selected signatures, including a single-objective optimization approach, which transforms the multi-criteria problem into a single-objective function, and a multi-objective optimization approach, which uses a simple additive weighting method to select an optimal solution from the Pareto set. Our results show significant improvements in both calibration and temporal validation metrics, especially for flood signatures, demonstrating the robustness and delicacy of our signatures-based calibration framework for enhancing flash flood forecasting systems.

Published
2023
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6. Benchmark dataset for hydraulic simulations of flash floods in the French Mediterranean region.

Author

Godet, Juliette, Nicolle, Pierre, Hocini, Nabil, Gaume, Eric, Davy, Philippe, Pons, Frederic, Javelle, Pierre, Garambois, Pierre-André, Lague, Dimitri, and Payrastre, Olivier

Subjects
DIGITAL elevation models, WATERMARKS, FLOODS, FLOOD warning systems
Abstract

The absence of validation or comparison data for verifying flood mapping methods poses a significant challenge in developing operational hydraulic approaches. This article aims to address this gap by presenting a benchmark dataset for flash flood mapping in the French Mediterranean region. The dataset described in this paper (Nicolle et al., 2024) includes flood hazard maps and simulation results of three actual flash flood events, all computed in steady regime at a 5-meter resolution using a 2D SWE model (neglecting inertia) named Floodos (Davy et al., 2017). Additionally, it includes the input data necessary (Digital Terrain Models, inflow discharges, hydrographic network) for conducting similar simulations with other hydrodynamic modeling approaches, in both steady and unsteady regimes. A comprehensive validation dataset, comprising observed flood extents, high water marks, and rating curves, is also provided, enabling a detailed evaluation of 2D hydraulic simulation results. The simulation results from Floodos, compared against stage-discharge rating curves available at gauging stations, yielded highly encouraging outcomes. The median error (sim. - obs.) was -0.04 m for the 2-year return period and -0.14 m across all simulated return periods, ranging from 2 to 1000 years. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region.

Author

Huynh, Ngo Nghi Truyen, Garambois, Pierre‐André, Colleoni, François, Renard, Benjamin, Roux, Hélène, Demargne, Julie, Jay‐Allemand, Maxime, and Javelle, Pierre

Subjects
ARTIFICIAL neural networks, COST functions, FLOOD forecasting, HYDROLOGIC models, INVERSE problems
Abstract

Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA‐PR) approach incorporating learnable regionalization mappings, based on either multi‐linear regression or artificial neural networks (ANNs), into a differentiable hydrological model. This approach demonstrates how two differentiable codes can be linked and their gradients chained, enabling the exploitation of heterogeneous data sets across extensive spatio‐temporal computational domains within a high‐dimensional regionalization context, using accurate adjoint‐based gradients. The inverse problem is tackled with a multi‐gauge calibration cost function accounting for information from multiple observation sites. HDA‐PR was tested on high‐resolution, hourly and kilometric regional modeling of 126 flash‐flood‐prone catchments in the French Mediterranean region. The results highlight a strong regionalization performance of HDA‐PR especially in the most challenging upstream‐to‐downstream extrapolation scenario with ANN, achieving median Nash‐Sutcliffe efficiency (NSE) scores from 0.6 to 0.71 for spatial, temporal, spatio‐temporal validations, and improving NSE by up to 30% on average compared to the baseline model calibrated with lumped parameters. Multiple evaluation metrics based on flood‐oriented hydrological signatures also indicate that the use of an ANN leads to better performances than a multi‐linear regression in a validation context. ANN enables to learn a non‐linear descriptors‐to‐parameters mapping which provides better model controllability than a linear mapping for complex calibration cases. Key Points: Novel approach for regional calibration of a distributed hydrologic model using learnable and non‐linear descriptors‐to‐parameters mappingsOriginal combination of numerical adjoint model and neural network Jacobian: accurate gradients enable high‐dimensional optimizationExtensive case study in flash‐flood‐prone Mediterranean region shows effective regionalization of high‐resolution model with neural network [ABSTRACT FROM AUTHOR]

Published
2024
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8. A first look at river discharge from SWOT satellite observations

Author

Andreadis, Konstantinos M., primary, Coss, Stephen Paul, additional, Durand, Michael, additional, Gleason, Colin Joseph, additional, Simmons, Travis Thomas, additional, Tebaldi, Nikki, additional, Bjerklie, David Michael, additional, Brinkerhoff, Craig, additional, Dudley, Robert W., additional, Gejadze, Igor, additional, Larnier, Kevin, additional, Malaterre, Pierre-Olivier, additional, Oubanas, Hind, additional, Allen, George Henry, additional, Bates, Paul D, additional, David, Cédric Hervé, additional, Domeneghetti, Alessio, additional, Marc, Luciana Fenoglio, additional, Frasson, Renato Prata de Moraes, additional, Garambois, Pierre-André, additional, Gehring, Jaclyn, additional, Getirana, Augusto, additional, Hughes, Marissa, additional, Lee, Jonghyun, additional, Matte, Pascal, additional, Minear, J. Toby, additional, Monnier, Jérôme, additional, Muhebwa, Aggrey, additional, Tourian, Mohammad Javad, additional, Pavelsky, Tamlin M, additional, Riggs, Ryan M, additional, Rodriguez, Ernesto, additional, Sikder, Md Safat, additional, Taneja, Jay, additional, Tarpanelli, Angelica, additional, Wang, Jida, additional, and Yadav, Bidhyananda, additional

Published
2024
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15. Spatially distributed calibration of a hydrological model with variational optimization constrained by physiographic maps for flash flood forecasting in France.

Author

Jay-Allemand, Maxime, Demargne, Julie, Garambois, Pierre-André, Javelle, Pierre, Gejadze, Igor, Colleoni, François, Organde, Didier, Arnaud, Patrick, and Fouchier, Catherine

Subjects
FLOOD forecasting, HYDROLOGIC models, LAND cover, CALIBRATION, GLOBAL optimization, WATERSHEDS, INVERSE problems
Abstract

This contribution presents a regionalization approach to estimate spatially distributed hydrologic parameters based on: (i) the SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) hydrological modeling and assimilation platform underlying the French national flash flood forecasting system Vigicrues Flash ; (ii) the variational assimilation algorithm from , adapted to high dimensional inverse problems; (iii) spatial constraints added to the optimization problem, based on masks derived from physiographic maps (e.g., land cover, terrain slope); (iv) multi-site global optimization, which targets multiple independent watersheds. This method gives a regional estimation of the spatially distributed parameters over the whole modeled area. This study uses a distributed rainfall-runoff model with 4 parameters to calibrate, with a spatial resolution of 1×1 km 2 and a 15 min time step. Performances of the calibrated hydrological model and the parameters robustness are evaluated on two French study areas with 20 catchments in each, in spatio-temporal extrapolation based on cross-validation experiments over a 12-year period. Several spatial regularization strategies are tested to better constrain the high dimensional optimization problem. The model parameters are calibrated based on the Nash-Sutcliffe Efficiency (NSE) computed for multiple calibration basins in the study area. Results are discussed based on the Nash-Sutcliffe Efficiency and the Kling-Gupta Efficiency criteria obtained on calibration and validation catchments for two subperiods of 6 years. Further work aims to improve the global search of prior parameter sets and to better balance the adjoint sensitivity with respect to the spatial constraints resolution and catchment characteristics. This will ensure a better consistency of simulated fluxes variabilities and enhance the applicability of the regionalization method at higher spatial scales and over larger domains. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Learning Regionalization within a Differentiable High-Resolution Hydrological Model using Accurate Spatial Cost Gradients

Author

Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Renard, Benjamin, Roux, Hélène, Demargne, Julie, Javelle, Pierre, Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Renard, Benjamin, Roux, Hélène, Demargne, Julie, and Javelle, Pierre

Abstract

Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach incorporating learnable regionalization mappings, based on either multivariate regressions or neural networks, into a differentiable hydrological model. It enables the exploitation of heterogeneous datasets across extensive spatio-temporal computational domains within a high-dimensional regionalization context, using accurate adjoint-based gradients. The inverse problem is tackled with a multi-gauge calibration cost function accounting for information from multiple observation sites. HDA-PR was tested on high-resolution, hourly and kilometric regional modeling of two flash-flood-prone areas located in the South of France. In both study areas, the median Nash-Sutcliffe efficiency (NSE) scores ranged from 0.52 to 0.78 at pseudo-ungauged sites over calibration and validation periods. These results highlight a strong regionalization performance of HDA-PR, improving NSE by up to 0.57 compared to the baseline model calibrated with lumped parameters, and achieving a performance comparable to the reference solution obtained with local uniform calibration (median NSE from 0.59 to 0.79). Multiple evaluation metrics based on flood-oriented hydrological signatures are also employed to assess the accuracy and robustness of the approach. The regionalization method is amenable to state-parameter correction from multi-source data over a range of time scales needed for operational data assimilation, and it is adaptable to other differentiable geophysical models.

Published
2023

20. Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings.

Author

Evin, Guillaume, Le Lay, Matthieu, Fouchier, Catherine, Penot, David, Colleoni, Francois, Mas, Alexandre, Garambois, Pierre-André, and Laurantin, Olivier

Subjects
HYDROLOGIC models, SPATIAL resolution, STREAMFLOW
Abstract

Hydrological modelling of small mountainous catchments is particularly challenging because of the high spatio-temporal resolution required for the meteorological forcings. In situ measurements of precipitation are typically scarce in these remote areas, particularly at high elevations. Precipitation reanalyses propose different alternative forcings for the simulation of streamflow using hydrological models. In this paper, we evaluate the performances of two hydrological models representing some of the key processes for small mountainous catchments (< 300 km 2), using different meteorological products with a fine spatial and temporal resolution. The evaluation is performed on 55 small catchments of the northern French Alps. While the simulated streamflows are adequately reproduced for most of the configurations, these evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events. However, these better performances are only obtained because the hydrological models correct the underestimations of accumulated amounts (e.g. annual) from the radar data in high-elevation areas. [ABSTRACT FROM AUTHOR]

Published
2024
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23. A Framework for Estimating Global River Discharge From the Surface Water and Ocean Topography Satellite Mission

Author

Durand, Michael, primary, Gleason, Colin J., additional, Pavelsky, Tamlin M., additional, Prata de Moraes Frasson, Renato, additional, Turmon, Michael, additional, David, Cédric H., additional, Altenau, Elizabeth H., additional, Tebaldi, Nikki, additional, Larnier, Kevin, additional, Monnier, Jerome, additional, Malaterre, Pierre Olivier, additional, Oubanas, Hind, additional, Allen, George H., additional, Astifan, Brian, additional, Brinkerhoff, Craig, additional, Bates, Paul D., additional, Bjerklie, David, additional, Coss, Stephen, additional, Dudley, Robert, additional, Fenoglio, Luciana, additional, Garambois, Pierre‐André, additional, Getirana, Augusto, additional, Lin, Peirong, additional, Margulis, Steven A., additional, Matte, Pascal, additional, Minear, J. Toby, additional, Muhebwa, Aggrey, additional, Pan, Ming, additional, Peters, Daniel, additional, Riggs, Ryan, additional, Sikder, Md Safat, additional, Simmons, Travis, additional, Stuurman, Cassie, additional, Taneja, Jay, additional, Tarpanelli, Angelica, additional, Schulze, Kerstin, additional, Tourian, Mohammad J., additional, and Wang, Jida, additional

Published
2023
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25. SMASH -SPATIALLY DISTRIBUTED MODELLING AND ASSIMILATION FOR HYDROLOGY: PYTHON WRAPPING TOWARDS ENHANCED RESEARCH-TO-OPERATIONS TRANSFER

Author

Jay-Allemand, Maxime, François, Colleoni, Garambois, Pierre-André, Javelle, Pierre, Demargne, Julie, Garambois, Pierre-André, HYDRIS hydrologie (HYDRIS), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER), Aix Marseille Université (AMU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Aix Marseille Université (AMU)

Subjects
[SDE] Environmental Sciences, [SPI]Engineering Sciences [physics], [SPI] Engineering Sciences [physics], [SDE]Environmental Sciences, [SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology
Abstract

International audience; The distributed SMASH platform is based on a gridded mesh and on a modular design. On each cell, the model features different hydrological components. Each component offers different modeling options such as snow modules, surface interception, production, transfer and percolation functions. At the grid scale, different routing models are implemented via a cell-to-cell numerical routing scheme. S MASH comes with its numerical adjoint model which is obtained by automatic differentiation with Tapenade. A variational data assimilation algorithm is implemented and helps to calibrate the distributed parameters or evaluate the model states. This algorithm uses the quasi-Newton lbfgs-b descent algorithm and the gradient of the cost function relative to the model parameters and states. This gradient is computed by a run of the adjoint model. T he numerical SMASH platform is a Fortran code. To gain in modularity and facilitate the use of SMASH in the research and engineering communities, a Python interface has been created with the new F90Wrap software. The original Fortran code has been revamped. The new structure enables to 1) control any inputs and outputs with Python, 2) keep an automatically differentiable and computationally efficient numerical Fortran model, 3) call a binary from the shell to preserve a backward compatibility with old practices. T he key to achieve this Python interface is to use Fortran modules and derived types to store all inputs and outputs variables. These Fortran structures are stored in different modules. F 90Wrap automatically generates the fortran functions and wrappers to give access to every component of each derived type. A Python class is generated to facilitate the use of these wrappers inside a Python code. T he Python object "model" aggregates all inputs/outputs variables required by SMASH. The "model" object comes with built-in methods to allow end users to perform simulations, calibrations, plotting and hdf5 export. The Python binding facilitates all post-processing since it does not requires I/O into text files anymore.

Published
2022

30. Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images.

Author

Ettalbi, Mouad, Baghdadi, Nicolas, Garambois, Pierre-André, Bazzi, Hassan, Ferreira, Emmanuel, and Zribi, Mehrez

Subjects
SOIL moisture, SYNTHETIC aperture radar, AGRICULTURE, SOIL mapping, WEATHER forecasting, AGRICULTURAL mapping
Abstract

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution, which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather-forecasting framework. This paper presents an improved and fully autonomous solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. The main findings showed that the use of a radar signal averaged over grids of a few km 2 in addition to radar signal at plot scale instead of a priori weather information provides good soil moisture estimations. The accuracy is even slightly better compared to the accuracy obtained using a priori weather information. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods

Author

Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Javelle, Pierre, Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER), and Aix Marseille Université (AMU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Multi-objective optimization, Hydrological signatures, Optimization and Control (math.OC), Variance-based sensitivity analysis, FOS: Mathematics, Pareto-optimal solution, Variational data assimilation, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Sensitivity analysis, Mathematics - Optimization and Control, Hydrological modeling, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract

Classical calibration methods in hydrology are commonly performed with a single cost function computed on long time series. Even though the hydrological model has acceptable scores in NSE and KGE, unbalancing problems can still arise between overall score and the model performance for flood events, and particularly flash floods. Enhancing multi-criteria calibration methods with multi-scale signatures to improve distributed flood modeling remains a challenge. In this study, the potential of hydrological signatures computed continuously and at the scale of flood events on long time series, is employed within various multi-criteria calibration approaches to attain a more efficient hydrological model. This work presents an improved and original signature-based calibration approach, implemented in the variational data assimilation algorithm of SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, applied over 141 catchments mostly located in the French Mediterranean region. Several signatures, especially flood event signatures are firstly computed, relying on a proposed automatic hydrograph segmentation algorithm. Suitable signatures for constraining the model are selected based on their global sensitivity analysis to model parameters. Several multi-criteria calibration strategies with the selected signatures are eventually performed, including a multi-objective optimization approach, and a single-objective optimization approach, that transforms the multi-criteria problem into a single-objective function. Note that in the first approach, the proposed technique based on a simple additive weighting method is used to select an optimal solution obtained from a set of non-inferior solutions. The suggested methods show that, for a global calibration, the average relative error in simulating the peak flow has been dropped from about 0.27 to 0.01-0.08 and from about 0.30 to 0.18-0.21 with various multi-criteria optimization strategies, respectively in calibration and temporal validation. For a distributed calibration, while the average NSE (resp. KGE) still slightly decreases from 0.78 (resp. 0.86) to 0.75 (resp. 0.81) in calibration, the quality of simulated peak flow has been enhanced about 1.5 times in average. In particular, the NSE (resp. KGE) calculated solely on 111 flood events which are picked from 23 downstream gauges has been improved from 0.80 (resp. 0.71) up to 0.83 (resp. 0.78) in median. These results have demonstrated the robustness and delicacy of the model constrained by the signatures for enhancing flash flood forecasting systems.

Published
2022
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36. Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings.

Author

Evin, Guillaume, Lay, Matthieu Le, Fouchier, Catherine, Mas, Alexandre, Colleoni, François, Penot, David, Garambois, Pierre-André, and Laurantin, Olivier

Subjects
HYDROLOGIC models, WATERSHEDS, SPATIAL resolution, STREAMFLOW
Abstract

Hydrological modelling of small mountainous catchments is particularly challenging because of the high spatio-temporal resolution required for the meteorological forcings. In-situ measurements of precipitation are typically scarce in these remote areas, particularly at high elevations. Precipitation reanalyses propose different alternative forcings for the simulation of streamflow using hydrological models. In this paper, we evaluate the performances of two hydrological models representing some of the key processes for small mountainous catchments, using different meteorological products with a fine spatial and temporal resolution. The evaluation is performed on 55 small catchments of the Northern French Alps. While the simulated streamflows are adequately reproduced for most of the configurations, these evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events. However, these better performances are only obtained because the hydrological models correct the underestimations of accumulated amounts (e.g. annual) from the radar data in high-elevation areas. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Suivi des variables hydrologiques par télédétection et modélisation dans le bassin du fleuve Congo

Author

Paris, Adrien, primary, Calmant, Stéphane, additional, Gosset, Marielle, additional, Fleischmann, Ayan S., additional, Conchy, Tainá Sampaio Xavier, additional, Garambois, Pierre‐André, additional, Bricquet, Jean‐Pierre, additional, Papa, Fabrice, additional, Tshimanga, Raphael M., additional, Guzanga, Georges Gulemvuga, additional, Siqueira, Vinícius Alencar, additional, Tondo, Blaise‐Leandre, additional, Paiva, Rodrigo, additional, Silva, Joecila Santos, additional, and Laraque, Alain, additional

Published
2022
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38. Monitoring Hydrological Variables from Remote Sensing and Modeling in the Congo River Basin

Author

Paris, Adrien, primary, Calmant, Stéphane, additional, Gosset, Marielle, additional, Fleischmann, Ayan S., additional, Conchy, Taina Sampaio Xavier, additional, Garambois, Pierre‐André, additional, Bricquet, Jean‐Pierre, additional, Papa, Fabrice, additional, Tshimanga, Raphael M., additional, Guzanga, Georges Gulemvuga, additional, Siqueira, Vinícius Alencar, additional, Tondo, Blaise‐Leandre, additional, Paiva, Rodrigo, additional, Silva, Joecila Santos, additional, and Laraque, Alain, additional

Published
2022
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39. 1D/2D porosity model for urban flood modeling: case of a dense street networks

Author

Finaud-Guyot Pascal, Garambois Pierre-André, Chen Shangzhi, Dellinger Guilhem, Ghenaim Abdellah, and Terfous Abdellali

Subjects
Environmental sciences, GE1-350
Abstract

An increasing urbanization of floodplains has led to higher vul- nerability of urban areas and building a new generation of robust, accurate and computationally affordable models dedicated to urban floods is highly re- quired for improving prediction systems and mitigation measures. A better understanding of urban floods hydrodynamics may also be required. In view to achieve computationally affordable and reliable simulations a new 1D/2D parsimonious hydraulic model Flood1D2D is introduced for flood modeling in complex branched urban networks. It takes advantage of a cut-cell technique and a new effective model able to take into account some essential sub-grid flow features such as flow vein contractions due to recirculations downstream of a crossroad. It is shown that this local phenomenon can be correctly repro- duced thanks to this 1D/2D coupled SW model parameterized with roughness and porosity. The ability of the model to reproduce realistic flood discharge distributions at the subdistrict scale is also tested on a part of the branched net- work of ICUBE experimental rig. Further studies should tackle the issues of calibration and unsteady modeling.

Published
2018
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40. Coupling topographic airborne lidar and photo-sieving methods for grain-size mapping

Author

Piasny, Guillaume, Chardon, Valentin, Garambois, Pierre-André, Finaud-Guyot, Pascal, Lague, Dimitri, Schmitt, Laurent, University of Strasbourg, Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Géosciences Rennes (GR), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), and Dubigeon, Isabelle

Subjects
[SDU.STU.GM] Sciences of the Universe [physics]/Earth Sciences/Geomorphology, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

41. Modèle opérationnel de prise en compte des échanges rue-bâti en contexte d'inondation urbaine

Author

Choley, Cécile, Finaud-Guyot, Pascal, Garambois, Pierre-André, Mose, Robert, École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER), Aix Marseille Université (AMU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-18-CE01-0020, ANR-18-CE01-0020,DEUFI,Détails de l'impact des inondations urbaines(2018), Finaud-Guyot, Pascal, Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), and Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[PHYS]Physics [physics], Shallow water equations, [SDE]Environmental Sciences, [PHYS.MECA.MEFL] Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], Street-building exchanges model, [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], ComputingMilieux_MISCELLANEOUS, Flooding management, Numerical model
Abstract

International audience; Floods in cities are a worrying phenomenon as cities concentrate a growing population and activity and are expected to increase in both intensity and frequency in the context of climate change. Flood management provided by public services relies especially on 2D models that do not consider the potential street-building exchanges, considering buildings to be waterproof. A new model is proposed to represent each building as a mass conservation law and an additional source term in the classical 2D shallow water equations. A synthetic test case, with a channel and 200 buildings, shows that the buildings lead to a reduction of both the discharge (30%) and the water depth (18%) peaks in the street and generate a lag time in their time evolution. The interest of the proposed model is highlighted on the modeling of the Richelieu district (Nîmes, France): street-building exchanges impact significantly (up to 0.6 m and 1 m/s) the street hydrodynamics. The water level modifications are generalized in the synthetic case whereas they are more localized in the real-world simulation. Moreover, the model simulates the building water depth allowing for the determination of the maximal level and the submersion duration. These new results pave the way to new information for flood damage analysis and operational management.

Published
2021

42. A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission

Author

Durand, Michael, primary, Gleason, Colin Joseph, additional, Pavelsky, Tamlin M, additional, Frasson, Renato Prata de Moraes, additional, Turmon, Michael J., additional, David, Cédric Hervé, additional, Altenau, Elizabeth Humphries, additional, Tebaldi, Nikki, additional, Larnier, Kevin, additional, Monnier, Jérôme, additional, Malaterre, Pierre-Olivier, additional, Oubanas, Hind, additional, Allen, George Henry, additional, Bates, Paul D, additional, Bjerklie, David Michael, additional, Coss, Stephen Paul, additional, Dudley, Robert W., additional, Fenoglio Marc, Luciana, additional, Garambois, Pierre-André, additional, Lin, Peirong, additional, Margulis, Steven A, additional, Matte, Pascal, additional, Minear, J. Toby, additional, Muhebwa, Aggrey, additional, Pan, Ming, additional, Peters, Daniel, additional, Riggs, Ryan Matthew, additional, TARPANELLI, ANGELICA, additional, Schulze, Kerstin, additional, Tourian, Mohammad Javad, additional, and Wang, Jida, additional

Published
2021
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44. Exploring the Factors Controlling the Error Characteristics of the Surface Water and Ocean Topography Mission Discharge Estimates

Author

Frasson, Renato Prata de Moraes, primary, Durand, Michael T., additional, Larnier, Kevin, additional, Gleason, Colin, additional, Andreadis, Konstantinos M., additional, Hagemann, Mark, additional, Dudley, Robert, additional, Bjerklie, David, additional, Oubanas, Hind, additional, Garambois, Pierre‐André, additional, Malaterre, Pierre‐Olivier, additional, Lin, Peirong, additional, Pavelsky, Tamlin M., additional, Monnier, Jérôme, additional, Brinkerhoff, Craig B., additional, and David, Cédric H., additional

Published
2021
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45. Systemic approach for local energy mix assessment

Author

Dougier, Nathanael, Garambois, Pierre-André, Gomand, Julien, Roucoules, Lionel, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER), Aix Marseille Université (AMU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Garambois, Pierre

Subjects
Optimization, [SPI]Engineering Sciences [physics], Multi-energy system, [SPI] Engineering Sciences [physics], [SPI.NRJ]Engineering Sciences [physics]/Electric power, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], ZABR, Microgrids, Systems modelling, [SPI.NRJ] Engineering Sciences [physics]/Electric power, Decision support
Abstract

International audience; Whereas energy mainly comes from main national power plants, distributed energy resources and storage technologies would allow local territories to choose their energy sources and increase their autonomy. This paper presents a decision-support tool that propose to find new system architecture based compromises between economic, technical and environmental objectives. Based on a sys-temic approach, it takes into account a broad range of technologies and assess multi-scale territories thanks to a physical modelling. Numerical simulations show the influence of different parameters on the ability of a system to balance power demand.

Published
2020

49. Adjoint-based spatially distributed calibration of a grid GR-based parsimonious hydrological model over 312 French catchments with SMASH platform.

Author

Colleoni, François, Garambois, Pierre-André, Javelle, Pierre, Jay-Allemand, Maxime, and Arnaud, Patrick

Abstract

Reducing uncertainty and improving robustness and spatio-temporal extrapolation capabilities remain key challenges in hydrological modeling especially for flood forecasting over large areas. Parsimonious model structures and effective optimization strategies are crucially needed to tackle the difficult issue of distributed hydrological model calibration from sparse integrative discharge data, that is in general high dimensional inverse problems. This contribution presents the first evaluation of Variational Data Assimilation (VDA), very well suited to this context but still rarely employed in hydrology because of high technicality, and successfully applied here to the spatially distributed calibration of a newly taylored grid-based parsimonious model structure and corresponding adjoint. It is based on the Variational Data Assimilation (VDA) framework of SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, underlying the French national flash flood forecasting system Vigicrues Flash. It proposes an upgraded distributed hourly rainfall-runoff model structure employing GR-based operators, including a non-conservative flux, and its adjoint obtained by automatic differentiation for VDA. The performances of the approach are assessed over annual, seasonal and floods timescales via standard performance metrics and in spatio-temporal validation. The gain of using the proposed non-conservative 6-parameters model structure is highlighted in terms of performance and robustness, compared to a simpler 3-parameters structure. Spatially distributed calibrations lead to a significant gain in terms of reaching high performances in calibration and temporal validation on the catchments sample, with median efficiencies respectively of NSE = 0.88 (resp. 0.85) and NSE = 0.8 (resp. 0.79) over the total time window on period p2 (resp. p1). Simulated signatures in temporal validation over 1443 (resp. 1522) flood events on period p2 (resp. p1) are quite good with median flood (NSE; KGE) of (0.63; 0.59) (resp. (0.55; 0.53)). Spatio-temporal validations, i.e. on pseudo ungauged cases, lead to encouraging performances also. Moreover, the influence of certain catchment characteristics on model performance and parametric sensitivity is analyzed. Best performances are obtained for Oceanic and Mediterranean basins whereas it performs less well over Uniform basins with significant influence of multi-frequency hydrogeological processes. Interestingly, regional sensitivity analysis revealed that the non conservative water exchange parameter and the production parameter, impacting the simulated runoff amount, are the most sensitive parameters along with the routing parameter especially for faster responding catchments. This study is a first step in the construction of a flexibe and versatile multi-model and optimization framework with hydbrid methods for regional hydrological modeling with multi-source data assimilation. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Flash flood impacts nowcasting within the PICS project (2018-2022): End-users involvement and first results

Author

Javelle, Pierre, primary, Payrastre, Olivier, additional, Boudevillain, Brice, additional, Bourgin, François, additional, Bouttier, François, additional, Caumont, Olivier, additional, Charpentier-Noyer, Maryse, additional, Ducrocq, Veronique, additional, Fleury, Axelle, additional, Garambois, Pierre-André, additional, Gaume, Eric, additional, Hocini, Nabil, additional, Janet, Bruno, additional, Jay-Allemand, Maxime, additional, Lague, Dimitri, additional, Lovat, Alexane, additional, Moncoulon, David, additional, Naulin, Jean-Philippe, additional, Nicolle, Pierre, additional, Peredo, Daniela, additional, Perrin, Charles, additional, Pons, Frédéric, additional, Ramos, Maria-Helena, additional, Ruin, Isabelle, additional, and Terti, Galateia, additional

Published
2021
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