Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–Č Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-2041 Peer reviewed articles 2023 Marie Dumont; Simon Gascoin; Marion RĂ©veillet; Didier Voisin; François Tuzet; Laurent Arnaud; MylĂšne Bonnefoy; Montse Bacardit Peñarroya; Carlo Carmagnola; Alexandre Deguine; AurĂ©lie Diacre; Lukas DĂŒrr; Olivier Evrard; Firmin Fontaine; Amaury Frankl; Mathieu Fructus; Laure Gandois; Isabelle Gouttevin; Abdelfateh Gherab; Pascal Hagenmuller; Sophia Hansson; HervĂ© Herbin; BĂ©atrice Josse; Brun Spatial variability of Saharan dust deposition revealed through a citizen science campaign Earth System Science Data 10.5194/essd-2023-16 Data Management & Analytics Precipitation & Ecological Systems Abstract. Saharan dust outbreaks have profound effects on ecosystems, climate, human health and the cryosphere in Europe. However, the spatial deposition pattern of Saharan dust is poorly known due to a sparse network of ground measurements. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This somewhat improvised campaign triggered wide interest since 152 samples were collected in the snow in the Pyrenees, the French Alps and the Swiss Alps in less than four weeks. An analysis of the samples showed a large variability in the dust properties and amount. We found a decrease in the deposited mass and particle sizes with distance from the source along the transport path. This spatial trend was also evident in the elemental composition of the dust as the iron mass fraction decreased from 11 % in the Pyrenees to 2 % in the Swiss Alps. At the local scale, we found a higher dust mass on south facing slopes, in agreement with estimates from high-resolution remote sensing data. This unique dataset, which resulted from the collaboration of several research laboratories and citizens, is provided as an open dataset to benefit a large community and enable further scientific investigations. 949516
publications-2042 Peer reviewed articles 2022 Foteini Baladima, Jennie L. Thomas, Didier Voisin, Marie Dumont, Clementine Junquas, Rajesh Kumar, Christophe Lavaysse, Louis Marelle, Mark Parrington, Johannes Flemming Modeling an extreme dust deposition event to the French alpine seasonal snowpack in April 2018 : Meteorological context and predictions of dust deposition Journal of Geophysical Research : Atmospheres 10.1029/2021jd035745 Data Management & Analytics Precipitation & Ecological Systems AbstractMineral dust is an important aerosol in the atmosphere and is known to reduce snow albedo upon deposition. Model predictions of dust deposition events in snow covered mountain regions are challenging due to the complexity of aerosol‐cloud interactions and the specifics of mountain meteorological systems. We use a case study of dust deposition between 30 March and 5 April 2018 to the French alpine snowpack to study the processes that control dust deposition to the seasonal snowpack. To understand processes controlling dust transport and deposition to snow, we use a combination of in situ observations at Col du Lautaret in the French Alps, satellite remote sensing, the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis global atmospheric composition, and the regional model WRF‐Chem. Specifically, we investigate the role of increased model spatial resolution within WRF‐Chem in capturing mountain meteorology, precipitation, and predicted dust deposition. Regional model results are also compared to the reanalysis global CAMS products including aerosols in the atmosphere and predicted dust deposition fluxes. We conclude that predicted mountain meteorology (e.g., precipitation) is better with increased model resolution (3 × 3 km resolution WRF‐Chem domain). This improved meteorology has significant impacts on predicted dry and wet dust deposition to the alpine snowpack. Dry deposition is important in the western part of the French Alps at low altitudes, while wet deposition dominates over the complex higher altitude mountain terrain. 949516
publications-2043 Peer reviewed articles 2023 Julien Brondex; Kévin Fourteau, Marie Dumont; Pascal Hagenmuller; Neige Calonne; François Tuzet; Henning Löwe A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion and settlement in dry snow (IvoriFEM v0.1.0) Geoscientific Model Development 10.5194/gmd-2023-97 Data Management & Analytics Precipitation & Ecological Systems Abstract. The poor treatment, or complete omission, of water vapor transport has been identified as a major limitation suffered by currently available snowpack models. Vapor and heat fluxes being closely intertwined, their mathematical representation amounts to a system of non-linear and tightly-coupled partial differential equations, which is particularly challenging to solve numerically. The choice of the numerical scheme and the representation of couplings between processes is crucial to ensure an accurate and robust solution that guarantees mass and energy conservation, while allowing time steps in the order of 15 minutes. To explore the numerical treatments fulfilling these requirements, we have developed a highly-modular finite-element program. The code is written in python. Every step of the numerical formulation and solution is coded internally, except for the inversion of the linearized system of equations. We illustrate the capabilities of our approach to tackle the coupled problem of heat conduction, vapor diffusion and settlement within a dry snowpack by running our model on several test cases proposed in recently published literature. We underline specific improvements regarding energy and mass conservation, as well as time step requirements. In particular, we show that a fully-coupled and fully-implicit time stepping approach enables to get accurate and stable solutions with little restriction on the time step. 949516
publications-2044 Peer reviewed articles 2023 Daniela Krampe, Frank Krauker, Marie Dumont, Andreas Herber Snow and meteorological conditions at Villum Research Station, Northeast Greenland: on the adequacy of using atmospheric reanalysis for detailed snow simulations Frontiers in Earth Science 10.3389/feart.2023.1053918/full Data Management & Analytics Precipitation & Ecological Systems No abstract available 949516
publications-2045 Peer reviewed articles 2022 Bertrand Cluzet, Matthieu Lafaysse, CĂ©sar Deschamps-Berger, Matthieu Vernay, Marie Dumont Propagating information from snow observations with CrocO ensemble data assimilation system : a 10-years case study over a snow depth observation network The Cryosphere 10.5194/tc-16-1281-2022 Simulation & Modeling Precipitation & Ecological Systems Abstract. The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterizations, and unresolved terrain features. In situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large-scale modeling errors by means of data assimilation. In this work, we assimilate HS observations from an in situ network of 295 stations covering the French Alps, Pyrenees, and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialized snow cover modeling framework, we investigate whether such observations can be used to correct neighboring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modeling, based on a particle filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localization strategies to assimilate snow observations. These approaches are evaluated in a leave-one-out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that an intermediate localization radius of 35–50 km yields a slightly lower root mean square error (RMSE), and a better spread–skill than the strategy of assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modeling errors are the largest, e.g., the Haute-AriĂšge, Andorra, and the extreme southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses and, therefore, snow simulations. In situ HS observations thus show an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas. 949516
publications-2046 Peer reviewed articles 2021 Marie Dumont, Pascal Hagenmuller, Philippe Lapalus, Bernard Lesaffre, Anne Dufour, Neige Calonne, Sabine Rolland du Roscoat, Edward Ando Experimental and model-based investigation of the links between snow bidirectional reectance and snow microstructure The Cryosphere 10.5194/tc-15-3921-2021 Data Management & Analytics Precipitation & Ecological Systems Abstract. Snow stands out from materials at the Earth’s surface owing to its unique optical properties. Snow optical properties are sensitive to the snow microstructure, triggering potent climate feedbacks. The impacts of snow microstructure on its optical properties such as reflectance are, to date, only partially understood. However, precise modelling of snow reflectance, particularly bidirectional reflectance, are required in many problems, e.g. to correctly process satellite data over snow-covered areas. This study presents a dataset that combines bidirectional reflectance measurements over 500–2500 nm and the X-ray tomography of the snow microstructure for three snow samples of two different morphological types. The dataset is used to evaluate the stereological approach from Malinka (2014) that relates snow optical properties to the chord length distribution in the snow microstructure. The mean chord length and specific surface area (SSA) retrieved with this approach from the albedo spectrum and those measured by the X-ray tomography are in excellent agreement. The analysis of the 3D images has shown that the random chords of the ice phase obey the gamma distribution with the shape parameter m taking the value approximately equal to or a little greater than 2. For weak and intermediate absorption (high and medium albedo), the simulated bidirectional reflectances reproduce the measured ones accurately but tend to slightly overestimate the anisotropy of the radiation. For such absorptions the use of the exponential law for the ice chord length distribution instead of the one measured with the X-ray tomography does not affect the simulated reflectance. In contrast, under high absorption (albedo of a few percent), snow microstructure and especially facet orientation at the surface play a significant role in the reflectance, particularly at oblique viewing and incidence. 949516
publications-2047 Peer reviewed articles 2024 Kevin Fourteau, Julien Brondex, Fanny Brun, Marie Dumont A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers Geoscientific Model Development 10.5194/gmd-17-1903-2024 Data Management & Analytics Precipitation & Ecological Systems Abstract. The surface energy budget drives the melt of the snow cover and glacier ice and its computation is thus of crucial importance in numerical models. This surface energy budget is the result of various surface energy fluxes, which depend on the input meteorological variables and surface temperature; of heat conduction towards the interior of the snow/ice; and potentially of surface melting if the melt temperature is reached. The surface temperature and melt rate of a snowpack or ice are thus driven by coupled processes. In addition, these energy fluxes are non-linear with respect to the surface temperature, making their numerical treatment challenging. To handle this complexity, some of the current numerical models tend to rely on a sequential treatment of the involved physical processes, in which surface fluxes, heat conduction, and melting are treated with some degree of decoupling. Similarly, some models do not explicitly define a surface temperature and rather use the temperature of the internal point closest to the surface instead. While these kinds of approaches simplify the implementation and increase the modularity of models, they can also introduce several problems, such as instabilities and mesh sensitivity. Here, we present a numerical methodology to treat the surface and internal energy budgets of snowpacks and glaciers in a tightly coupled manner, including potential surface melting when the melt temperature is reached. Specific care is provided to ensure that the proposed numerical scheme is as fast and robust as classical numerical treatment of the surface energy budget. Comparisons based on simple test cases show that the proposed methodology yields smaller errors for almost all time steps and mesh sizes considered and does not suffer from numerical instabilities, contrary to some classical treatments. 949516
publications-2048 Peer reviewed articles 2021 Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, MichaĂ«l Ablain, RĂ©mi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, Samuel Morin Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service The Cryosphere 10.5194/tc-15-4975-2021 Data Management & Analytics Precipitation & Ecological Systems Abstract. The High Resolution Snow & Ice Monitoring Service was launched in 2020 to provide near-real-time, pan-European snow and ice information at 20 m resolution from Sentinel-2 observations. Here we present an evaluation of the snow detection using a database of snow depth observations from 1764 stations across Europe over the hydrological year 2016–2017. We find a good agreement between both datasets with an accuracy (proportion of correct classifications) of 94 % and kappa of 0.81. More accurate (+6 % kappa) retrievals are obtained by excluding low-quality pixels at the cost of a reduced coverage (−13 % data). 949516
publications-2049 Peer reviewed articles 2021 Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0 Geophysics Model Development 10.5194/gmd-14-7329-2021 Simulation & Modeling Precipitation & Ecological Systems Abstract. In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo, which leads to significant uncertainties. Here, we present the Versatile ALbedo calculation metHod based on spectrALLy fixed radiative vAriables (VALHALLA version 1.0) to optimize spectral snow albedo calculation. For this optimization, the energy absorbed by the snowpack is calculated by the spectral albedo model Two-streAm Radiative TransfEr in Snow (TARTES) and the spectral irradiance model Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART). This calculation takes into account the spectral characteristics of the incident radiation and the optical properties of the snow based on an analytical approximation of the radiative transfer of snow. For this method, 30 wavelengths, called tie points (TPs), and 16 reference irradiance profiles are calculated to incorporate the absorbed energy and the reference irradiance. The absorbed energy is then interpolated for each wavelength between two TPs with adequate kernel functions derived from radiative transfer theory for snow and the atmosphere. We show that the accuracy of the absorbed energy calculation primarily depends on the adaptation of the irradiance of the reference profile to that of the simulation (absolute difference <1 W m−2 for broadband absorbed energy and absolute difference <0.005 for broadband albedo). In addition to the performance in terms of accuracy and calculation time, the method is adaptable to any atmospheric input (broadband, narrowband) and is easily adaptable for integration into a radiative scheme of a global or regional climate model. 949516
publications-2050 Peer reviewed articles 2021 César Deschamps-Berger, Bertrand Cluzet, Marie Dumont, Matthieu Lafaysse, Etienne Berthier, Pascal Fanise, Simon Gascoin Improving the spatial distribution of snow cover simulations by assimilation of satellite stereoscopic imagery. Water Ressources Research 10.1029/2021wr030271 Data Management & Analytics Precipitation & Ecological Systems AbstractMoutain snow cover is highly variable both spatially and temporally and has a tremendous impact on ecosystems and human activities. Numerical models provide continuous estimates of the variability of snow cover properties in time and space. However, they suffer from large uncertainties, for instance originating from errors in the meteorological inputs. Here, we show that the snow depth variability at 250 m spatial resolution can be well simulated by assimilating snow depth maps from satellite photogrammetry in a detailed snowpack model. The assimilation of a single snow depth map per snow season using a particle filter is sufficient to improve the simulated snow depth and its spatial variability, originally poorly represented due to missing physical processes and errors in the precipitation inputs. Assimilation of snow depth only is nevertheless not sufficient for both compensating for strong bias in precipitation and for selecting the most appropriate representation of the physical processes in the snow model. Regarding this limitation, combined assimilation of snow depths maps and other snow observations, such as snow cover area, surface temperature or reflectance, is a promising avenue for accurate simulations of mountain snow cover. 949516