Data assimilation method for real-time flash flood forecasting using a physically based distributed model

The MARINE model (Roux et al, 2011) is a physically based distributed model dedicated to real time flash flood forecasting on small to medium catchments. The infiltration capacity is evaluated by the Green and Ampt equation and the surface runoff calculation is divided into two parts: the land surface flow and the flow in the drainage network both based on kinematic wave hypothesis. In order to take into account rainfall spatial-temporal variability as well as the various behaviours of soil types among the catchment, the model is spatially distributed, which can also help to understand the flood driving processes. The model integrates remote sensing data such as the land coverage map with spatial resolution adapted to hydrological scales. Minimal data requirements for the model are: the Digital Elevation Model describing catchment topography and the location and description of the drainage network. Moreover some parameters are not directly measurable and need to be calibrated. Most of the sources of uncertainties can be propagated thanks to variational method (Castaings et al, 2009) and finally help to determine time dependent uncertainty intervals. This study also investigates the methodology developed for real-time flash flood forecasting using the MARINE model and data assimilation techniques. According to prior sensitivity analyses and calibrations, parameters values were determined as constants or initial guess. Then a data assimilation method called the adjoint state method is used to update some of the most sensitive parameters to improve accuracy of discharges predictions. The forecast errors are evaluated as a function of lead time and discussed from an operational point of view. Multiple strategies in term of updatable parameters set, length of time window, parameters bounds and observation threshold used to trigger the assimilation method are discussed regarding accuracy, robustness and real-time feasibility

Étude régionale des crues éclair de l'arc méditerranéen français ; élaboration de méthodologies de transfert à des bassins versants non jaugés

D'un point de vue climatique la région méditerranéenne est propice aux évènements pluvio-orageux intenses, particulièrement en automne. Ces pluies s'abattent sur des bassins versants escarpés. La promptitude des crues ne laisse qu’un temps très court pour la prévision. L'amplitude de ces crues dépend de la grande variabilité des pluies et des caractéristiques des bassins versants. Les réseaux d'observations ne sont habituellement pas adaptés à ces petites échelles spatiales et l'intensité des événements affecte souvent la fiabilité des données quand elles existent d'où l'existence de bassin non jaugés. La régionalisation en hydrologie s'attache à la détermination de variables hydrologiques aux endroits où ces données manquent. L'objectif de cette thèse est de contribuer à poser les bases d’une méthodologie adaptée à la transposition des paramètres d'un modèle hydrologique distribué dédié aux crues rapides de bassins versants bien instrumentés à des bassins versants non jaugés, et ce sur une large zone d’étude. L'outil utilisé est le modèle hydrologique distribué MARINE [Roux et al., 2011] dont l'une des originalités est de disposer d'un modèle adjoint permettant de mener à bien des calibrations et des analyses de sensibilité spatio-temporelles qui servent à améliorer la compréhension des mécanismes de crue et à l'assimilation de données en temps réel pour la prévision. L'étude des sensibilités du modèle MARINE aborde la compréhension des processus physiques. Une large gamme de comportements hydrologiques est explorée. On met en avant quelques types de comportements des bassins versants pour la région d'étude [Garambois et al., 2012a]. Une sélection des évènements de calibration et une technique de calibration multi évènements aident à l'extraction d'un jeu de paramètres par bassin versant. Ces paramétrisations sont testées sur des évènements de validation. Une méthode de décomposition de la variance des résultats conduit aux sensibilités temporelles du modèle à ses paramètres. Cela permet de mieux appréhender la dynamique des processus physiques rapides en jeu lors de ces crues [Garambois et al., 2012c]. Les paramétrisations retenues sont transférées à l'aide de similarités hydrologiques sur des bassins versants non jaugés, à des fins de prévision opérationnelle

Inferring river properties with SWOT like data

Inverse problems in hydraulics are still open questions such as the estimation of river discharges. Remotely sensed measurements of hydrosystems can provide valuable information but adequate methods are still required to exploit it. The future Surface Water and Ocean Topography (SWOT) mission would provide new cartographic measurements of inland water surfaces. The highlight of SWOT will be its almost global coverage and temporal revisits on the order of 1 to 4 times per 22 days repeat cycle [1]. Lots of studies have shown the possibility of retrieving discharge given the river bathymetry or roughness and/or in situ time series. The new challenge is to use SWOT type data to inverse the triplet formed by the roughness, the bathymetry and the discharge. The method presented here is composed of two steps: following an inverse formulation from [2], the first step consists in retrieving an equivalent bathymetry profile of a river given one in situ depth measurement and SWOT like data of the water surface, that is to say water elevation, free surface slope and width. From this equivalent bathymetry, the second step consists in solving mass and Manning equation in the least square sense [3]. Nevertheless, for cases where no in situ measurement of water depth is available, it is still possible to solve a system formed by mass and Manning equations in the least square sense (or with other methods such as Bayesian ones, see e.g. [4]). We show that a good a priori knowledge of bathymetry and roughness is compulsory for such methods. Depending on this a priori knowledge, the inversion of the triplet (roughness, bathymetry, discharge) in SWOT context was evaluated on the Garonne River [5, 6]. The results are presented on 80 km of the Garonne River downstream of Toulouse in France [7]. An equivalent bathymetry is retrieved with less than 10% relative error with SWOT like observations. After that, encouraging results are obtained with less than 10% relative error on the identified discharge

Characterization of process-oriented hydrologic model behavior with temporal sensitivity analysis for flash floods in Mediterranean catchments

This paper presents a detailed analysis of 10 flash flood events in the Mediterranean region using the distributed hydrological model MARINE. Characterizing catchment response during flash flood events may provide new and valuable insight into the dynamics involved for extreme catchment response and their dependency on physiographic properties and flood severity. The main objective of this study is to analyze flash-flood-dedicated hydrologic model sensitivity with a new approach in hydrology, allowing model outputs variance decomposition for temporal patterns of parameter sensitivity analysis. Such approaches enable ranking of uncertainty sources for nonlinear and nonmonotonic mappings with a low computational cost. Hydrologic model and sensitivity analysis are used as learning tools on a large flash flood dataset. With Nash performances above 0.73 on average for this extended set of 10 validation events, the five sensitive parameters of MARINE process-oriented distributed model are analyzed. This contribution shows that soil depth explains more than 80% of model output variance when most hydrographs are peaking. Moreover, the lateral subsurface transfer is responsible for 80% of model variance for some catchment-flood events’ hydrographs during slow-declining limbs. The unexplained variance of model output representing interactions between parameters reveals to be very low during modeled flood peaks and informs that model parsimonious parameterization is appropriate to tackle the problem of flash floods. Interactions observed after model initialization or rainfall intensity peaks incite to improve water partition representation between flow components and initialization itself. This paper gives a practical framework for application of this method to other models, landscapes and climatic conditions, potentially helping to improve processes understanding and representation

On the assimilation of altimetric data in 1D Saint-Venant river flow models

Given altimetry measurements, the identification capability of time varying inflow discharge Qin(t) and the Strickler coefficient K (defined as a power-law in h the water depth) of the 1D river Saint-Venant model is investigated. Various altimetry satellite missions provide water level elevation measurements of wide rivers, in particular the 21 future Surface Water and Ocean Topography (SWOT) mission. An original and synthetic reading of all the available information (data, wave propagation and the Manning-Strickler’s law residual) are represented on the so-called identifiability map. The latter provides in the space-time plane a comprehensive overview of the inverse problem features. Inferences based on Variational Data Assimilation (VDA) are investigated at the limit of the data-model inversion capability : relatively short river portions, relatively infrequent observations, that is inverse problems presenting a low identifiability index . The inflow discharge Qin(t) is infered simultaneously with the varying coefficient K(h). The bed level is either given or infered from a lower complexity model. The experiments and analysis are conducted for different scenarios (SWOT-like or multi-sensors-like). The scenarios differ by the observation frequency and by the identifiability index. Sensitivity analyses with respect to the observation errors and to the first guess values demonstrate the robustness of the VDA inferences. Finally this study aiming at fusing relatively sparse altimetric data and the 1D Saint-Venant river flow model highlights the spatiotemporal resolution lower limit, also the great potential in terms of discharge inference including for a single river reach

A Semi-Analytical Model for the Hydraulic Resistance Due to Macro-Roughnesses of Varying Shapes and Densities

friction model resulting from investigations into macro-roughness elements in fishways has been compared with a broad range of studies in the literature under very different bed configurations. In the context of flood modelling or aquatic habitats, the aim of the study is to show that the formulation is applicable to both emergent or submerged obstacles with either low or high obstacle concentrations. In the emergent case, the model takes into account free surface variations at large Froude numbers. In the submerged case, a vegetation model based on the double-averaging concept is used with a specific turbulence closure model. Calculation of the flow in the roughness elements gives the total hydraulic resistance uniquely as a function of the obstacles’ drag coefficient. The results show that the model is highly robust for all the rough beds tested. The averaged accuracy of the model is about 20% for the discharge calculation. In particular, we obtain the known values for the limiting cases of low confinement, as in the case of sandy beds

A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

A spatially distributed hydrological model, dedicated to flood simulation, is developed on the basis of physical process representation (infiltration, overland flow, channel routing). Estimation of model parameters requires data concerning topography, soil properties, vegetation and land use. Four parameters are calibrated for the entire catchment using one flood event. Model sensitivity to individual parameters is assessed using Monte-Carlo simulations. Results of this sensitivity analysis with a criterion based on the Nash efficiency coefficient and the error of peak time and runoff are used to calibrate the model. This procedure is tested on the Gardon d'Anduze catchment, located in the Mediterranean zone of southern France. A first validation is conducted using three flood events with different hydrometeorological characteristics. This sensitivity analysis along with validation tests illustrates the predictive capability of the model and points out the possible improvements on the model's structure and parameterization for flash flood forecasting, especially in ungauged basins. Concerning the model structure, results show that water transfer through the subsurface zone also contributes to the hydrograph response to an extreme event, especially during the recession period. Maps of soil saturation emphasize the impact of rainfall and soil properties variability on these dynamics. Adding a subsurface flow component in the simulation also greatly impacts the spatial distribution of soil saturation and shows the importance of the drainage network. Measures of such distributed variables would help discriminating between different possible model structures

Relations between streamflow indices, rainfall characteristics and catchment physical descriptors for flash flood events

Flash flood is a very intense and quick hydrologic response of a catchment to rainfall. This phenomenon has a high spatial-temporal variability as the generating storm often hits small catchments (few km²). Given the small spatial temporal scales and high variability of flash floods, their prediction remains a hard exercise as the necessary data are often scarce. This study investigates the potential of hydrologic indices at different scales to improve understanding of flash floods dynamics and characterize catchment response in a model independent approach. These hydrologic indices gather information on hydrograph shape or catchment dynamic for instance and are useful to examine catchment signature in function of their size. Results show that for middle-size (>100 km²) catchments response shape can be correlated to storm cell position within the catchment contrarily to smaller catchments. In a multi-scale point of view, regional characteristics about catchment geomorphology or rainfall field statistics should provide useful insight to find pertinent hydrologic response indices. The combined use of these indices with a physically-based distributed modelling could facilitate calibration on ungauged catchments

Using a multi-hypothesis framework to improve the understanding of flow dynamics during flash floods

A method of multiple working hypotheses was applied to a range of catchments in the Mediterranean area to analyse different types of possible flow dynamics in soils during flash flood events. The distributed, process-oriented model, MARINE, was used to test several representations of subsurface flows, including flows at depth in fractured bedrock and flows through preferential pathways in macropores. Results showed the contrasting performances of the submitted models, revealing different hydrological behaviours among the catchment set. The benchmark study offered a characterisation of the catchments’ reactivity through the description of the hydrograph formation. The quantification of the different flow processes (surface and intra-soil flows) was consistent with the scarce in situ observations, but it remains uncertain as a result of an equifinality issue. The spatial description of the simulated flows over the catchments, made available by the model, enabled the identification of counterbalancing effects between internal flow processes, including the compensation for the water transit time in the hillslopes and in the drainage network. New insights are finally proposed in the form of setting up strategic monitoring and calibration constraints

On the assimilation of SWOT type data into 2D shallow-water models

In river hydraulics, assimilation of water level measurements at gauging stations is well controlled, while assimilation of images is still delicate. In the present talk, we address the richness of satellite mapped information to constrain a 2D shallow-water model, but also related difficulties. 2D shallow models may be necessary for small scale modelling in particular for low-water and flood plain flows. Since in both cases, the dynamics of the wet dry front is essential, one has to elaborate robust and accurate solvers. In this contribution we introduce robust second order, stable finite volume scheme [CoMaMoViDaLa]. Comparisons of real like tests cases with more classical solvers highlight the importance of an accurate flood plain modelling. A preliminary inverse study is presented in a flood plain flow case, [LaMo] [HoLaMoPu]. As a first step, a 0th order data processing model improves observation operator and produces more reliable water level derived from rough measurements [PuRa]. Then, both model and flow behaviours can be better understood thanks to variational sensitivities based on a gradient computation and adjoint equations. It can reveal several difficulties that a model designer has to tackle. Next, a 4D-Var data assimilation algorithm used with spatialized data leads to improved model calibration and potentially leads to identify river discharges. All the algorithms are implemented into DassFlow software (Fortran, MPI, adjoint) [Da]. All these results and experiments (accurate wet-dry front dynamics, sensitivities analysis, identification of discharges and calibration of model) are currently performed in view to use data from the future SWOT mission