Sophie Ricci

Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting

The present study describes the assimilation of river water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of a one-dimensional hydraulic model which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was applied using a four- day sliding window over the flood event. The background error covariances for water level and discharge were repre- sented with anisotropic correlation functions where the cor- relation length upstream of the observation points is larger than the correlation length downstream of the observation points. This approach was motivated by the implementation of a Kalman Filter algorithm on top of a diffusive flood wave propagation model. The study was carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state during the flood event leads to a significant improve- ment in the water level and discharge in both analysis and forecast modes.

An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results.

Differential influence of instruments in nuclear core activity evaluation by data assimilation

The global neutronic activity fields of a nuclear core can be reconstructed using data assimilation. Indeed, data assimilation allows to combine both measurements from instruments and information from a model, to evaluate the best possible neutronic activity within the core. We present and apply a specific procedure which evaluates the influence of measures by adding or removing instruments in a given measurement network (possibly empty). The study of various network configurations for the instruments in the nuclear core establishes that the influence of the instruments depends both on the independent instrumentation location and on the chosen network.

Temporal Variance-Based Sensitivity Analysis of the River-Routing Component of the Large-Scale Hydrological Model ISBA–TRIP: Application on the Amazon Basin

AbstractThe continental part of the water cycle is commonly represented with hydrological models. Yet, there are limits in their capacity to accurately estimate water storage and dynamics because of their coarse spatial resolution, simplified physics, and an incomplete knowledge of atmospheric forcing and input parameters. These errors can be diminished using data assimilation techniques. The model’s most sensitive parameters should be identified beforehand. The objective of the present study is to highlight key parameters impacting the river-routing scheme Total Runoff Integrating Pathways (TRIP) while simulating river water height and discharge as a function of time focusing on the annual cycle. Thus, a sensitivity analysis based on the decomposition of model output variance (using a method called ANOVA) is utilized and applied over the Amazon basin. Tested parameters are perturbed with correcting factors. First, parameter-correcting coefficients are considered uniform over the entire basin. The results...

Evaluation of Regional-Scale River Depth Simulations Using Various Routing Schemes within a Hydrometeorological Modeling Framework for the Preparation of the SWOT Mission

The Surface Water and Ocean Topography (SWOT) mission will provide free water surface elevations, slopes, and river widths for rivers wider than 50 m. Models must be prepared to use this new finescale information by explicitly simulating the link between runoff and the river channel hydraulics. This study assesses one regional hydrometeorological model’s ability to simulate river depths. The Garonne catchment in southwestern France (56 000 km2) has been chosen for the availability of operational gauges in the river network and finescale hydraulic models over two reaches of the river. Several routing schemes, ranging from the simple Muskingum method to time-variable parameter kinematic and diffusive waves schemes, are tested. The results show that the variable flow velocity schemes are advantageous for discharge computations when compared to the original Muskingum routing method. Additionally, comparisons between river depth computations and in situ observations in the downstream Garonne River led to root-mean-square errors of 50–60 cm in the improved Muskingum method and 40–50 cm in the kinematic–diffusive wave method. The results also highlight SWOT’s potential to improve the characterization of hydrological processes for subbasins larger than 10 000 km2, the importance of an accurate digital elevation model, and the need for spatially varying hydraulic parameters.

Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows

Data assimilation is widely used to improve flood forecasting capability, especially through parameter inference requiring statistical information on the uncertain input parameters (upstream discharge, friction coefficient) as well as on the variability of the water level and its sensitivity with respect to the inputs. For particle filter or ensemble Kalman filter, stochastically estimating probability density function and covariance matrices from a Monte Carlo random sampling requires a large ensemble of model evaluations, limiting their use in real-time application. To tackle this issue, fast surrogate models based on polynomial chaos and Gaussian process can be used to represent the spatially distributed water level in place of solving the shallow water equations. This study investigates the use of these surrogates to estimate probability density functions and covariance matrices at a reduced computational cost and without the loss of accuracy, in the perspective of ensemble-based data assimilation. This study focuses on 1-D steady state flow simulated with MASCARET over the Garonne River (South-West France). Results show that both surrogates feature similar performance to the Monte-Carlo random sampling, but for a much smaller computational budget; a few MASCARET simulations (on the order of 10–100) are sufficient to accurately retrieve covariance matrices and probability density functions all along the river, even where the flow dynamic is more complex due to heterogeneous bathymetry. This paves the way for the design of surrogate strategies suitable for representing unsteady open-channel flows in data assimilation.

BATMAN: Statistical analysis for expensive computer codes made easy

Numerical software has reached a sufficient maturity to represent physical phenomena. High fidelity simulation is possible with continuous advances in numerical methods and in High Performance Computing (HPC). Still, deterministic simulations only provide limited knowledge on a system as uncertainties in the numerical model and its inputs translate into uncertainties in the outputs. Ensemble-based methods are used to construct a numerical or experimental dataset from which statistics are inferred.

Variational Data Assimilation With Telemac. Proof Of Concept For Model State Correction On The Berre Lagoon 3D-Model

TELEMAC is a component of the open-source integrated suite of solvers TELEMAC-MASCARET for use in the field of free-surface flow that solves the Reynolds Averaged Navier-Stokes equations. Generally speaking, uncertainties in the model formulation itself due to simplified physics and also in the input fields to the model such as the boundary conditions, initial conditions and hydraulic parameters translate into errors in the simulated hydraulic variables. In spite of significant advances in numerical schemes, description of geographical data (topography, bathymetry) and environmental conditions (hydrologycal and meteorological fields), the representation of the true state of a system as well as its forecasted state remains imperfect and some of these limits can be overcome combining observations with simulation via data assimilation techniques. This paper presents the implementation of a 3D-Var FGAT variational data assimilation algorithm as a proof of concept for improving TELEMAC simulations and forecast. The demonstration is made on the Berre lagoon application with TELEMAC-3D: the salinity state is sequentially corrected assimilating in situ salinity measurements.

Uncertainty Quantification for River Flow Simulation Applied to a Real Test Case: The Garonne Valley

Sensitivity analysis techniques have been widely used in multitude of applications to quantify the impact of inputs variables imprecision on the accuracy of the model output variables. Depending on the problem at hand, an appropriate method of sensitivity analysis should be selected. Direct and adjoint sensitivity analysis are two complementary approaches known to be efficient. While the direct approach provides an assessment of the propagation of the error of a given input parameter in the studied system, the adjoint approach enables to identify the source of the uncertainty of a given output variable with respect to several input parameters. Direct methods have been extensively investigated in different geophysical applications, particularly in the context of the hydraulic modeling. In this work, several methods will be described and applied to the same benchmark during over-flooding events. The effect of uncertainties in the boundary conditions, the spatially distributed functions (bed level, river width, friction, etc.) and the numerical parameters on the model state variables (discharge, water surface elevation, etc.) is examined. This study has been carried out on the Garonne River test case, along a 50 km downstream reach, using 1D full Saint-Venant hydraulic models SIC2 (Irstea) or Mascaret (EDF), and 2D Telemac model (EDF). Results illustrate the influence of individual and combined contributions of input variables uncertainties.

Uncertainty Quantification for the Gironde Estuary Hydrodynamics with TELEMAC2D

In a context of development and implementation of data assimilation techniques in Gironde estuary for flood forecasting, a TELEMAC2D model is used to compute water depth and velocity fields at each node of an unstructured mesh. Upstream, the model boundaries are, respectively, La Reole and Pessac on the Garonne and Dordogne rivers. The maritime boundary is 32 km off the mouth of Gironde estuary, located in Le Verdon. This model, which contains 7351 nodes and 12,838 finite elements, does not take into account overflows. This paper presents a global SA study in the context of flood forecasting in the Gironde estuary. It aims at identifying which input variables should be better described for water levels to be better simulated and forecasted in the estuary. On the one hand, a propagation and quantification of uncertainties by a unidirectional analysis method was carried out. On the other hand, a variance sensitivity study (ANOVA) was carried out, by calculating the total and partial sensitivity Sobol’ indices for all numerical parameters (wind influence coefficient, Strickler friction coefficients for four zones) and time-dependent forcings of the model (rivers discharges and maritime boundary conditions). It led to the identification of parameters and forcings to which the model is most sensitive for each area of the estuary, as well as the identification of very low interdependencies, in order to choose the variables to assimilate later. The standardized variation coefficients for 1981 event as well as Sobol’ indices for 2003 event show a predominance of the influence of the maritime boundary conditions all along the estuary and of the Strickler coefficient corresponding to the zone considered for the estuarine part and the confluence, to which must be added the Garonne discharge as a predominant parameter for the latter. Unsurprisingly, the upstream part of river zones is influenced primarily by the friction coefficient and the respective river flows of Garonne and Dordogne rivers.