Performance analysis of real Time flood forecasting models for different forecast horizon using optimization algorithms

Fil: Alonso, Facundo. Universidad Nacional de Córdoba; Argentina.Fil: Le Moine, Nicolas. Université Pierre et Marie Curie; Francia.Fil: Oudin, Ludovic. Université Pierre et Marie Curie; Francia.Fil: Ribstein, Pierre. Université Pierre et Marie Curie; Francia.Fil: Bertoni, Juan Carlos. Universidad Nacional de Córdoba; Argentina.Fil: Patalano, Antoine. Universidad Nacional de Córdoba; Argentina.Fil: Ramos, Maria-Helena. IRSTEA; Francia.Fil: Perrin, Charles. IRSTEA; Francia.Fil: Nascimento, Nilo. Universidade Federal de Minas Gerais; Brasil.Hydrological models are often used as real-time flood forecasting tools. When used in this forecasting mode, these models rely on real-time observed rainfall and runoff data. Their parameters can be calibrated in simulation mode, but also in forecasting mode, i.e. for a particular forecast horizon, linked to the studied watershed response time. This second option is expected to produce better forecasting results at the calibration horizon than using model parameters calibrated in simulation. However, in operational conditions, forecasters often need to produce forectasts for various lead times (typically ranging from a few hours to a few days). Thus the use of model parameters calibrated for a specific horizon may not produce optimal results over the whole forecasting time window.Fil: Alonso, Facundo. Universidad Nacional de Córdoba; Argentina.Fil: Le Moine, Nicolas. Université Pierre et Marie Curie; Francia.Fil: Oudin, Ludovic. Université Pierre et Marie Curie; Francia.Fil: Ribstein, Pierre. Université Pierre et Marie Curie; Francia.Fil: Bertoni, Juan Carlos. Universidad Nacional de Córdoba; Argentina.Fil: Patalano, Antoine. Universidad Nacional de Córdoba; Argentina.Fil: Ramos, Maria-Helena. IRSTEA; Francia.Fil: Perrin, Charles. IRSTEA; Francia.Fil: Nascimento, Nilo. Universidade Federal de Minas Gerais; Brasil.Otras Ingeniería Civi

How to build reach-averaged rating curves for remote sensing discharge estimation? The potential of periodic geometry hypotheses

International audienceRiver discharge is an essential component in the hydrological cycle. It is used to monitor rivers, the atmosphere, and the ocean through in-situ measurements, acquired on the surface, or from remote sensing to characterize natural disasters such as floods.Estimating discharge in ungauged rivers with remote sensing data such as the Surface Water and Ocean Topography (SWOT) mission but without any prior in-situ information is difficult to solve, especially in the case of unknown bathymetry, friction, and lateral river flows. However, the current literature suggests that a better knowledge of bathymetry could considerably facilitate roughness and discharge inferring. SWOT observes water surface elevations, slopes, river widths for several overpasses. We propose an inverse method to estimate discharge in a non-uniform steady-state, maintaining longitudinal (alternating pool-riffle) and lateral (meanders) morphological variability of the river. The idea is to build a rating curve (water level - discharge relationship) at the reach scale using hydraulic signatures (quantities not related to a particular section of the reach, which characterize an aspect of the overall hydraulic behavior: e.g., flooded area as a function of Q, mean water level as a function of Q). The inverse approach requires building a model that produces rating curves that optimally correspond to the hydraulic signatures. It requires a direct hydraulic model and a geometric simplification to facilitate the resolution of the inverse problem.The approach is based on the geomorphology of rivers. Indeed, the geometry of natural rivers presents high-frequency variability, characterized by alternating flow units: fast-flowing flow units in rectilinear and shallow areas (riffles), slow-flowing flow units in deeper areas (pools at alternating banks or inner side of meandering bends). This variability generates a variability of the hydraulic variables that covary at the reach scale. However, a simplification into a uniform geometry without spatial variability reappears as a bias in the frictional parameters, thus reducing the inversion's accuracy. For this, we propose a periodic approach that consists of representing the reach equivalent geometry by sinusoidal functions.This direct periodic model is used to create a whole periodic geometry (curved based asymmetry sections, Kinoshita curves to model the meander planform) and then solve the Saint-Venant equations in the 2D Basilisk hydraulic model (http://basilisk.fr), which is based on finite volume methods with adaptive grid refinement.This model does not require boundary conditions (use of periodic boundary conditions) and provides the ability to model floodplains and thus flood mapping. In the end, there are few parameters to adjust in the model (use of parameters covariances)

Introduction d'une hystérésis SWE-SCA dans un modèle de neige degré-jour pour la modélisation pluie-débit

International audienceDegree-day snow models have the advantage of requiring few data for running and calibration, which is of the utmost importance for real-time hydrological forecasting or assessment of the impact of climate change on snow-driven catchments hydrological regimes. The CemaNeige model is a daily 2-parameter degree-day model that proved to be very efficient for discharge simulation when run together with a daily rainfall-runoff model (usually the GR4J model). In this work, we tested several ways of representing in a more realistic way the snowpack, based on the integration of SWE-SCA hysteresis. These SWE-SCA relationships aim at describing the heterogeneity of snow patterns both in space and time in the catchments. With this improved model, we showed that it is possible to make use of spatial satellite MODIS SCA data to improve the snow representation without deteriorating the discharge. The sensitivity of the relative weights between snow-based and discharge-based numerical criteria was assessed. Robustness of the model (i.e. its ability to be applied on independent periods and catchments) was improved

Introduction d'une hystérésis SWE-SCA dans un modèle de neige degré-jour pour la modélisation pluie-débit

International audienceDegree-day snow models have the advantage of requiring limited data for running and for calibration, which is of the utmost importance for real-time hydrological forecasting or assessment of the impact of climate change on the hydrological regimes of snow-driven catchments. The CemaNeige model is a daily 2-parameter degree-day model that proved to be very efficient for discharge simulation when run together with a daily rainfall-runoff model (usually the GR4J model). In this work, we tried to represent in a more realistic way the snowpack, based on the integration of a SWE-SCA hysteresis. These SWE-SCA relationships aim at describing the heterogeneity of snow patterns both in space and time in the catchments. With this improved model, we showed that it is possible to make use of spatial satellite MODIS SCA data to improve the snow representation without deteriorating the discharge. The sensitivity of the relative weights between snow-based and discharge-based numerical criteria was assessed. Robustness of the model (i.e. its ability to be applied on independent periods and catchments) was improved

Un modèle de neige conceptuel avec une résolution analytique des équations de chaleur et de changement de phase

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUInternational audienceCompared to degree-day snow models, physically-based snow models resolve more processes in an attempt to achieve a better representation of reality. Often these physically-based models resolve the heat transport equations in snow using a vertical discretization of the snowpack. The snowpack is decomposed into several layers in which the mechanical and thermal states of the snow are calculated. A higher number of layers in the snowpack allow for better accuracy but it also tends to increase the computational costs. In order to develop a snow model that estimates the temperature profile of snow with a lower computational cost, we used an analytical decomposition of the vertical profile using eigenfunctions (i.e. trigonometric functions adapted to the specific boundary conditions). The mass transfer of snow melt has also been estimated using an analytical conceptualization of runoff fingering and matrix flow. As external meteorological forcing, the model uses solar and atmospheric radiation, air temperature, atmospheric humidity and precipitations. It has been tested and calibrated at point scale at two different stations in the Alps: Col de Porte (France, 1325 m) and Weissfluhjoch (Switzerland, 2540 m). A sensitivity analysis of model parameters and model inputs will be presented together with a comparison with measured snow surface temperature, SWE, snow depth, temperature profile and snow melt data. The snow model is created in order to be ultimately coupled with hydrological models for rainfall-runoff modeling in mountainous areas. We hope to create a model faster than physically-based models but capable to estimate more physical processes than degree-day snow models. This should help to build a more reliable snow model capable of being easily calibrated by remote sensing and in situ observation or to assimilate these data for operational forecasting purposes

Introduction d'une hystérésis dans CemaNeige pour améliorer l'évolution de la fraction de surface enneigée

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUNational audienc

Revisiting a Simple Degree-Day Model for Integrating Satellite Data: Implementation of Swe-Sca Hystereses

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU [ADD1_IRSTEA]Hydrosystèmes et risques naturelsInternational audienceConceptual degree-day snow models are often calibrated using runoff observations. This makes the snow models dependent on the rainfall-runoff model they are coupled with. Numerous studies have shown that using Snow Cover Area (SCA) remote sensing observation from MODIS satellites helps to better constrain parameters. The objective of this study was to calibrate the CemaNeige degree-day snow model with SCA and runoff observations. In order to calibrate the snow model with SCA observations, the original CemaNeige SCA formulation was revisited to take into account the hysteresis that exists between SCA and the snow water equivalent (SWE) during the accumulation and melt phases. Several parametrizations of the hysteresis between SWE and SCA were taken from land surface model literature. We showed that they improve the performances of SCA simulation without degrading the river runoff simulation. With this improvement, a new calibration method of the snow model was developed using jointly SCA and runoff observations. Further analysis showed that the CemaNeige calibrated parameter sets are more robust for simulating independent periods than parameter sets obtained from discharge calibration only. Calibrating the snow model using only SCA data gave mixed results, with similar performances as using median parameters from all watersheds calibration

Revisiting a Simple Degree-Day Model for Integrating Satellite Data: Implementation of Swe-Sca Hystereses

Conceptual degree-day snow models are often calibrated using runoff observations. This makes the snow models dependent on the rainfall-runoff model they are coupled with. Numerous studies have shown that using Snow Cover Area (SCA) remote sensing observation from MODIS satellites helps to better constrain parameters. The objective of this study was to calibrate the CemaNeige degree-day snow model with SCA and runoff observations. In order to calibrate the snow model with SCA observations, the original CemaNeige SCA formulation was revisited to take into account the hysteresis that exists between SCA and the snow water equivalent (SWE) during the accumulation and melt phases. Several parametrizations of the hysteresis between SWE and SCA were taken from land surface model literature. We showed that they improve the performances of SCA simulation without degrading the river runoff simulation. With this improvement, a new calibration method of the snow model was developed using jointly SCA and runoff observations. Further analysis showed that the CemaNeige calibrated parameter sets are more robust for simulating independent periods than parameter sets obtained from discharge calibration only. Calibrating the snow model using only SCA data gave mixed results, with similar performances as using median parameters from all watersheds calibration

A conceptual snow model with an analytic resolution of the heat and phase change equations

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUInternational audienceCompared to degree-day snow models, physically-based snow models resolve more processes in an attempt toachieve a better representation of reality. Often these physically-based models resolve the heat transport equationsin snow using a vertical discretization of the snowpack. The snowpack is decomposed into several layers in whichthe mechanical and thermal states of the snow are calculated. A higher number of layers in the snowpack allow forbetter accuracy but it also tends to increase the computational costs.In order to develop a snow model that estimates the temperature profile of snow with a lower computational cost, we used an analytical decomposition of the vertical profile using eigenfunctions (i.e. trigonometricfunctions adapted to the specific boundary conditions). The mass transfer of snow melt has also been estimatedusing an analytical conceptualization of runoff fingering and matrix flow.As external meteorological forcing, the model uses solar and atmospheric radiation, air temperature, atmospheric humidity and precipitations. It has been tested and calibrated at point scale at two different stations inthe Alps: Col de Porte (France, 1325 m) and Weissfluhjoch (Switzerland, 2540 m). A sensitivity analysis ofmodel parameters and model inputs will be presented together with a comparison with measured snow surfacetemperature, SWE, snow depth, temperature profile and snow melt data.The snow model is created in order to be ultimately coupled with hydrological models for rainfall-runoffmodeling in mountainous areas. We hope to create a model faster than physically-based models but capable toestimate more physical processes than degree-day snow models. This should help to build a more reliable snowmodel capable of being easily calibrated by remote sensing and in situ observation or to assimilate these data forforecasting purposes

Automatic identification of alternating morphological units in river channels using wavelet analysis and ridge extraction

The accuracy of hydraulic models depends on the quality of the bathymetric data they are based on, whatever the scale at which they are applied. The along-stream (longitudinal) and cross-sectional geometry of natural rivers is known to vary at the scale of the hydrographic network (e.g., generally decreasing slope, increasing width in the downstream direction), allowing parameterizations of main cross-sectional parameters with large-scale proxies such as drainage area or bankfull discharge (an approach coined downstream hydraulic geometry, DHG). However, higher-frequency morphological variability (i.e., at river reach scale) is known to occur for many stream types, associated with varying flow conditions along a given reach, such as the alternate bars or the pool-riffle sequences and meanders. To consider this high-frequency variability of the geometry in the hydraulic models, a first step is to design robust methods to characterize the scales at which it occurs. In this paper , we introduce new wavelet analysis tools in the field of geomorphic analysis (namely, wavelet ridge extraction) to identify the pseudo-periodicity of alternating morphological units from a general point of view (focusing on pool-riffle sequences) for six small French rivers. This analysis can be performed on a single variable (univariate case) but also on multiple variables (multivariate case). In this study, we choose a set of four variables describing the flow degrees of freedom: velocity, hydraulic radius, bed shear stress, and a planform descriptor that quantifies the local deviation of the channel from its mean direction. Finally, this method is compared with the bedform differencing technique (BDT), by computing the mean, median, and standard deviation of their longitudinal spacings. The two methods show agreement in the estimation of the wavelength in all reaches except one. The method aims to extract a pseudo-periodicity of the alternating bedforms that allow objective identification of morphological units in a continuous approach with the maintenance of correlations between variables (i.e., at many station hydraulic geometry, AMHG) without the need to define a prior threshold for each variable to characterize the transition from one unit to another