Jean Odry
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View article: Combination of Global and Regional Hydrological Forecasts
Combination of Global and Regional Hydrological Forecasts Open
<p>In recent years, the number of large-scale hydrological forecasting systems has been steadily growing. This may lead to regions having numerous models spatially overlapping each other. Some of these regions have what we will refer…
View article: Assimilation of multiple types of snow observations through a large scale spatialized particle filter
Assimilation of multiple types of snow observations through a large scale spatialized particle filter Open
<p>Particle filtering is interesting for snow data assimilation because of its minimal assumptions. However, implementing a particle filter over a large spatial domain is challenging for many reasons. For instance, the number of requ…
View article: Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles Open
Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model prior to the…
View article: Bayesian merging of large scale and local scale hydrological forecasts
Bayesian merging of large scale and local scale hydrological forecasts Open
<p>Global or large-scale hydrological forecasting systems covering entire countries, continents and even the entire planet are growing in popularity. As more large-scale hydrological forecasting systems emerge, it is likely that they…
View article: Reply on RC2
Reply on RC2 Open
Abstract. Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model pr…
View article: Reply on RC1
Reply on RC1 Open
Abstract. Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model pr…
View article: Comment on tc-2021-322
Comment on tc-2021-322 Open
Abstract. Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model pr…
View article: Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles Open
The use of particle filters for data assimilation is increasingly popular because of its minimal assumptions. Nevertheless, implementing a particle filter over domains of large spatial dimensions remains challenging, as the number of requi…
View article: Investigating ANN architectures and training to estimate snow water equivalent from snow depth
Investigating ANN architectures and training to estimate snow water equivalent from snow depth Open
Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Mea…
View article: Using an ensemble of artificial neural networks to convert snowdepth to snow water equivalent over Canada
Using an ensemble of artificial neural networks to convert snowdepth to snow water equivalent over Canada Open
Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Mea…
View article: Mapping SWE in near real time across a large territory using a particle filter
Mapping SWE in near real time across a large territory using a particle filter Open
<p>In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in si…
View article: Spatial disaggregation of a nationwide flood frequency analysis method
Spatial disaggregation of a nationwide flood frequency analysis method Open
Small catchments are largely under-represented in gauged catchments samples used to calibrate nationwide flood quantiles estimation methods. For instance, in France, catchments of less than 10 km² represent 3% of the gauged catchments but …
View article: Comparison of Flood Frequency Analysis Methods for Ungauged Catchments in France
Comparison of Flood Frequency Analysis Methods for Ungauged Catchments in France Open
The objective of flood frequency analysis (FFA) is to associate flood intensity with a probability of exceedance. Many methods are currently employed for this, ranging from statistical distribution fitting to simulation approaches. In many…
View article: Assessment of probabilistic areal reduction factors of precipitations for the entire French territory with gridded rainfall data
Assessment of probabilistic areal reduction factors of precipitations for the entire French territory with gridded rainfall data Open
The starting point of our study was the availability of maps of rainfall quantiles available for the entire French mainland territory at the spatial resolution of 1 km². These maps display the rainfall amounts estimated for different rainf…
View article: SHYREG, a national database of flood frequency estimation
SHYREG, a national database of flood frequency estimation Open
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