El sistema operacional MINERVE para la previsión de crecidas en el Cantón de Valais, Suiza

In recent decades, the watershed of the Rhône River, upstream of Lake Geneva in Switzerland, has suffered three major floods that have caused damages for over 500 million dollars. This led to the third correction of the Rhône River, which aims to improve flood protection in the basin. In this context, the MINERVE system for forecasting and flood management aims to improve the hydrometeorological information in the basin taking into account the existing network of reservoirs and hydropower plants. The first phase of the project began in 2002 with various applied research projects aiming to develop a hydrological and hydraulic model capable of quickly and easily modeling complex basins. Then, these investigations were put into operational phase in 2011 to provide a real-time operating system for flood forecasting and management in the Rhone River

Los desafíos de la modelización hidrológica y la previsión de crecidas en tiempo real en alta montaña

Flood forecasting systems are today recognized as a key element in natural hazard mitigation. The objective is to exploit the available observed and forecasted meteorological information to foresee river discharges up to several days in advance, using a hydrological model. The quality of the hydrological forecast is directly dependent on the quality of the meteorological input feeding the model. Optimal usage of the meteorological data is therefore essential. Depending on the sources of data considered and the method used to spatialize and combine the data, the interpolated precipitation intensity and spatial distribution may vary considerably. Calibration of the model is an important step in the preparation of the system. The parametrization of the model is adapted to obtain simulated discharges as similar as possible to the observed ones. Finally, by integrating in real time discharge and other observations in the computation scheme with data assimilation methods, the quality of the hydrological forecast can be further enhanced

Radar-rain gauge merging and discharge data assimilation for flood forecasting in Alpine catchments

Floods are responsible for one third of the economic losses induced by natural hazards throughout the world. To better protect the population and infrastructures, flood forecasting systems make us of weather forecasts to foresee floods several days in advance, providing more lead time for preventive measures. In the canton of Valais (Switzerland), an operational flood forecasting and management system is operational since 2013, as a result of the MINERVE project initiated in 1999. The present thesis aims at answering some of the challenges faced by this system. First, a new methodology for spatial interpolation of precipitation is implemented based on regression co-kriging using rain gauge and weather radar data. Two rain gauge networks equipped with instruments of different quality are considered. Compared to other precipitation interpolation methods, the quantitative precipitation estimates (QPE) obtained from the regression co-kriging provides the best performance over the studied area using cross-validation. The analysis highlights the need for further pre-processing of radar data, in particular to account for beam shielding by the complex topography. Integration of the above-mentioned QPE product in a snow model revealed a clear precipitation underestimation. A methodology to account for solid precipitation undercatch in QPE computation is therefore proposed. Four different QPE products are compared: the operational QPE product CombiPrecip of MeteoSwiss, the regression co-kriging QPE and two variants of it considering a correction factor for solid precipitation undercatch of 1.2 and 1.3, applied before the interpolation. The snow model is calibrated using satellite-based data from the MODIS spectroradiometer and validated using snow water equivalent measurements 11 snow monitoring sites. The best performance is obtained using the QPE product including a correction factor of 1.2. To evaluate the performance of the developed QPE products from a hydrological perspective, three sub-catchments of the MINERVE system were calibrated considering 5 different input. The GSM and SOCONT hydrological models are used to model respectively the glacial and non-glacial parts. A two-phase calibration of the model is explored, applying the MODIS-based calibration of snow-melt degree-day factors, before calibrating the other parameters using discharge data. Results suggest that the developed QPE product accounting for solid precipitation undercatch (factor 1.2) leads to the best performance over the catchment with a good radar visibility. In case of lower radar visibility, using station data provides equal or better performances. With the current implementation, the two-phase calibration did not allow to outperform the conventional calibration. Finally, an ensemble Kalman filter (EnKF) is implemented to improve initial conditions used for hydrological forecasts. Results are compared, for two high flow events, to the scenario without assimilation and to the simple assimilation scheme currently implemented in the MINERVE system, updating the soil saturation based on a discharge volume comparison over the preceding 24 hours. The Ensemble Kalman filter (EnKF) shows good performance during these events but also highlights difficulties over base flow, strengthened in presence of hydropower perturbations

Set-up and configuraion of an ensemble Kalman filter for an operational flood forecasting system

To forecast riverine floods, short-range forecasts are normally provided. In such cases the initial hydrological conditions highly influence the predictability of a flood event. The study evaluates the potential of an ensemble Kalman filter (EnKF) for the operational flood forecasting system in the Upper Rhone River basin. Observed discharge date is used to update the initial conditions of the hydrological model. Past flood events in the Reckingen subbasin are modelled to assess the robustness of the methodology and the quality of flood predictions

Spatial interpolation of precipitation from multiple rain gauge networks and weather radar data for operational applications in Alpine catchments

Increasing meteorological data availability and quality implies an adaptation of the interpolation methods for data combination. In this paper, we propose a new method to efficiently combine weather radar data with data from two heated rain gauge networks of different quality. The two networks being non-collocated (no common location between the two networks), pseudo cross-variograms are used to compute the linear model of coregionalization for kriging computation. This allows considering the two networks independently in a co-kriging approach. The methodology is applied to the Upper Rhône River basin, an Alpine catchment in Switzerland with a complex topography and an area of about 5300 km2. The analysis explores the newly proposed Regression cokriging approach, in which two independent rain gauge networks are considered as primary and secondary kriging variables. Regression co-kriging is compared to four other methods, including the commonly applied Inverse distance weighting method used as baseline scenario. Incorporation of additional networks located within and around the target region in the interpolation computation is also explored. The results firstly demonstrate the added value of the radar information as compared to using only ground stations. As compared to Regression kriging using only the network of highest quality, the Regression co-kriging method using both networks slightly increases the performance. A key outcome of the study is that Regression co-kriging performs better than Inverse distance weighting even for the data availability scenario when the radar network was providing lower quality radar data over the studied basin. The results and discussion underline that combining meteorological information from different rain gauge networks with different equipments remains challenging for operational purposes. Future research in this field should in particular focus on additional pre-processing of the radar data to account for example for areas of low visibility of the weather radars due to the topography

Application of an Ensemble Kalman filter to a semi-distributed hydrological flood forecasting system in alpine catchments

One of the key success factor for hydrological forecasts is the establishment of initial conditions that represent well the conditions of the simulated basin at the beginning of the forecast. Real-time Data Assimilation (DA) has been shown to allow improving these initial conditions. In this article, two DA approaches are compared with the reference scenario working without DA (Control). In both approaches, discharge data at gauging station are assimilated. In the first approach, a volume-based update (VBU) compares the simulated and observed volumes over the past 24 hours to compute a correction factor used to update the soil water saturation in the upstream part of the semi-distributed hydrological model. In the second approach, an Ensemble Kalman Filter (EnKF) is implemented to account for the uncertainty in precipitation, temperature and discharge data. The comparison is carried out over 2 sub-basins of the Upper Rhône River basin upstream of Lake Geneva, where the MINERVE flood forecasting and management system is implemented. Results differ over the two studied basins. In one basin, the two data assimilation perform better than the Control simulation with the lowest error given by the VBU up to a forecast horizon of 35 hour and by the EnKF for higher forecast horizons. In the second basin, EnKF gives the lowest error over the few first hours of forecast, but then provides the weakest performance. The lowest error is given by the Control simulation, because the model already performs very well on the event without data assimilation

Estimation spatiale des précipitations et assimilation de données de débit pour la prévision hydrologique en milieu alpin

L’étude présentée propose des solutions à certains défis rencontrés dans le cadre du système de prévision et de gestion des crues MINERVE du Canton du Valais, opérationnel depuis 2013. Le premier axe de recherche est dédié à l’interpolation spatiale des précipitations en combinant les données radar et celles des pluviomètres à l’aide d’un co-krigeage avec régression. Une correction de la sous-capture des précipitations solides aux pluviomètres est explorée avec des facteurs correcteurs appliqués au niveau des stations avant interpolation. Les estimations quantitatives de précipitations obtenues sont ensuite utilisées dans un modèle de simulation de l’enneigement, en utilisant des données satellitaires pour le calage. La comparaison des équivalents en eau de la neige simulés avec des mesures au sol suggère que la méthodologie explorée de correction des précipitations permet de réduire fortement la sous-estimation de la quantité de neige. Le calage du modèle hydrologique de trois sous-bassins a montré qu’une amélioration des performances était ainsi possible en intégrant les estimations quantitatives de précipitations par rapport à l’utilisation uniquement des données de pluviomètres, mais que cela nécessitait une bonne visibilité du radar. Finalement, l’implémentation d’un filtre de Kalman d’ensemble pour assimiler des données de débit dans le système de prévision est explorée