Multivariate data assimilation in snow modelling at Alpine sites

The knowledge of snowpack dynamics is of critical importance to several real-time applications such as agricultural production, water resource management, flood prevention, hydropower generation, especially in mountain basins. Snowpack state can be estimated by models or from observations, even though both these sources of information are affected by several errors

An Enkf-Based Scheme for Snow Multivariable Data Assimilation at an Alpine Site

Abstract The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 - December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations

Sequential data assimilation for streamflow forecasting: assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model at basin scale

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New airGR developments: semi-distribution and data assimilation

International audienceairGR (Coron et al., 2017, 2020) is an R package that offers the possibility to use the GR rainfall-runoff models developed in the Hydrology Research Group at INRAE (formerly at Irstea). It allows running seven hydrological models (including GR4J) dedicated to different time steps (hourly to annual) that can be combined to a snow accumulation and melt model (CemaNeige).</p><p>Thanks to the success of the airGR package, that was downloaded 45,000 times so far among 50 countries in the world and was used in dozen of publications since its release[1], its development team carries on its efforts to offer new features and improve the computer codes. This is how after offering a first add-on, the airGRteaching package, expressly developed for educational purposes, the team now offers tools dedicated to semi-distribution and data assimilation.</p><p>Using (semi-)distributed models is often necessary to explicitly represent spatial climatic and physiographic heterogeneities and to allow an analysis of their impact on the watershed response. Consequently, in the latest version of the airGR package, we introduced the semi-distribution of GR models, which are traditionally lumped, on a sub-basin basis. This development will also ultimately enable possibilities of implementing on a modular way different transfer functions as well as integrated water resource management (see package airGRiwrm in Abstract EGU21-2190).</p><p>In addition, a new package, called airGRdatassim, was recently proposed (Piazzi et al., 2021a, b) as an add-on to the airGR package. airGRdatassim enables the user to assimilate discharge observations via both Ensemble Kalman filter (EnKF) and particle filter (PF) schemes. Besides improving the simulations of GR models, this new package extends the potential applications of airGR to forecasting purposes by allowing for a reliable assessment of the initial conditions of streamflow forecasts

Modélisation et prévision en montagne avec les modèles GR et CemaNeige

International audienceÀ la suite de l’essor de R en tant que langage de programmation scientifique, de l’exigence croissante d’une recherche plus transférable et de la croissance de la disponibilité des données en hydrologie, les paquets R contenant des modèles hydrologiques deviennent de plus en plus disponibles en tant que ressource open source pour les hydrologues. Correspondant au cœur du flux de travail des études hydrologiques, leur valeur est de plus en plus significative en ce qui concerne la fiabilité des méthodes et des résultats. Malgré le caractère distinctif des ensembles et des modèles, aucune étude n’a jamais fourni de comparaison des ensembles R pour la modélisation conceptuelle des précipitations et du ruissellement du point de vue de l’utilisateur en comparant leur philosophie, les caractéristiques du modèle et la facilité d’utilisation. Nous avons sélectionné huit ensembles en fonction de notre capacité à exécuter leurs modèles de manière cohérente sur des exemples simples de modélisation hydrologique. Nous avons uniformément analysé la structure exacte de sept des modèles hydrologiques intégrés dans ces paquets R en termes de stockages et de flux conceptuels, de discrétisation spatiale, d’exigences en matière de données et de production fournie. L’analyse a montré que des choix de modélisation très différents sont associés à ces paquets, ce qui met l’accent sur divers concepts hydrologiques. Ces spécificités ne sont pas toujours suffisamment bien expliquées par la documentation du package. Par conséquent, une synthèse des fonctionnalités du package a été effectuée du point de vue de l’utilisateur. Cette synthèse permet d’éclairer le choix des colis qui pourraient/devraient être utilisés en fonction du problème en question. À cet égard, les caractéristiques techniques, la documentation, les implémentations R et les temps de calcul ont été étudiés. De plus, en fournissant un cadre pour la comparaison des ensembles, cette étude est un pas en avant vers le soutien de méthodes et de résultats plus transférables et réutilisables pour la modélisation hydrologique en R

An Enkf-Based Scheme for Snow Multivariable Data Assimilation at an Alpine Site

The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 - December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations

Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography

Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow products, namely H10—Snow detection (SN-OBS-1) and H12—Effective snow cover (SN-OBS-3) supplied by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is evaluated against both in-situ snow measurements and webcam digital imagery. The study area encompasses three different regions, located in Finland, the Italian Alps and Turkey, to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over the winter seasons 2016/17 and 2017/18 show a satisfying agreement between Sentinel-2 data and ground-based observations, both in terms of snow extent and fractional snow cover. H-SAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances