22 research outputs found
Water temperature modeling in the Garonne River (France)
No full textStream water temperature is one of the most important parameters for water quality and ecosystem studies. Temperature can influence many chemical and biological processes and therefore impacts on the living conditions and distribution of aquatic ecosystems. Simplified models such as statistical models can be very useful for practitioners and water resource management. The present study assessed two statistical models – an equilibrium-based model and stochastic autoregressive model with exogenous inputs – in modeling daily mean water temperatures in the Garonne River from 1988 to 2005. The equilibrium temperature-based model is an approach where net heat flux at the water surface is expressed as a simpler form than in traditional deterministic models. The stochastic autoregressive model with exogenous inputs consists of decomposing the water temperature time series into a seasonal component and a short-term component (residual component). The seasonal component was modeled by Fourier series and residuals by a second-order autoregressive process (Markov chain) with use of short-term air temperatures as exogenous input. The models were calibrated using data of the first half of the period 1988–2005 and validated on the second half. Calibration of the models was done using temperatures above 20 ◦C only to ensure better prediction of high temperatures that are currently at stake for the aquatic conditions of the Garonne River, and particularly for freshwater migrating fishes such as Atlantic Salmon (Salmo salar L.). The results obtained for both approaches indicated that both models performed well with an average root mean square error for observed temperatures above 20 ◦C that varied on an annual basis from 0.55 ◦C to 1.72 ◦C on validation, and good predictions of temporal occurrences and durations of three temperature threshold crossings linked to the conditions of migration and survival of Atlantic Salmon
Characterization of process-oriented hydrologic model behavior with temporal sensitivity analysis for flash floods in Mediterranean catchments
No full textThis 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
Hybrid Neural Network -Variational Data Assimilation algorithm to infer river discharges from SWOT-like data
No full textInternational audienceEstimating discharges from altimetric measurements only, for ungauged rivers (in particular, those with unknown bathymetry b(x)), is an ill-posed inverse problem. We develop here an algorithm to estimate without prior flow information other than global open datasets. Additionally, the ill-posedness feature of this inverse problem is re-investigated. Inversions based on a Variational Data Assimilation (VDA) approach enable accurate estimation of spatio-temporal variations of the discharge, but with a bias scaling the overall estimate. This key issue, which was already highlighted in our previous studies, is partly solved by considering additional hydrological information (the drainage area, ) combined with a Machine Learning (ML) technique. Purely data-driven estimations obtained from an Artificial Neural Network (ANN) provide a reasonably good estimation at a large scale ( m). This first estimation is then employed to define the first guess of an iterative VDA algorithm. The latter relies on the Saint-Venant flow model and aims to compute the complete unknowns (discharge , bathymetry , friction coefficient ) at a fine scale (approximately m). The resulting complete inversion algorithm is called the H2iVDI algorithm for "Hybrid Hierarchical Variational Discharge Inference". Numerical experiments have been analyzed for 29 heterogeneous worldwide river portions.The obtained estimations present an overall bias (less than 30\% for rivers with similar characteristics than those used for calibration) smaller than previous results, with accurate spatio-temporal variations of the flow. After a learning period of the observed rivers (e.g. one year), the algorithm provides two complementary estimators: a dynamic flow model enabling estimations at a fine scale and spatio-temporal extrapolations, and a low complexity estimator (based on a dedicated algebraic low Froude flow model).This last estimator provides reasonably accurate estimations (less than 30\% for considered rivers) at a large scale from newly acquired WS measurements in real-time, therefore making it a potentially operational algorithm
River discharge and bathymetry estimations from SWOT altimetry measurements
No full textInternational audienceAn inversion algorithm to estimate the discharge of rivers observed by the forthcoming SWOT mission (wide swath altimetry) is developed and assessed in detail. The algorithm relies on an advanced variational data assimilation formulation applied to the Saint-Venant equations (1D shallow-water) combined with a lower complexity model (low Froude assumption and locally stationary). This modeling approach enables to estimate from the altimetry measurements the three flow features: the discharge Q(t) associated with an effective bathymetry b(x) and a (non constant) roughness coefficient K. The river geometry is built up with effective cross sections defined from the altimetry measurements. The numerical results are analyzed for three rivers (\sim100 km long each) presenting rapid flow variations compared to the observation frequency. Two scenarios of observation are considered: frequent satellite overpasses corresponding to the SWOT Cal-Val orbit (\sim1 day period) and SWOT like data corresponding to a \sim21 days period with 1 to 4 passes at mid-latitudes. Given prior mean values (of Q or/and b), the numerical experiments demonstrate that the inference of the river discharge Q(t) and the bathymetry profile b(x) may be accurate. Various prior values sources are investigated in view of worldwide applications. Once the assimilation of a long period of measurements is done (e.g. one year long time series), the low complexity flow model is correctly calibrated and provides accurate discharge estimations in real-time from newly acquired measurements
Variational estimation of effective channel and ungauged anabranching river discharge from multi-satellite water heights of different spatial sparsity
No full textMulti-satellite sensing of continental water surfaces (WS) represents an unprecedented and increasing potential for studying ungauged hydrological and hydraulic processes from their signatures, especially on complex flow zones such as anabranching rivers. However the estimation of discharge from WS observations only is a very challenging, ill-posed, inverse problem due to unknown bathymetry and friction in ungauged rivers, measurements nature, quality and spatio-temporal resolutions regarding the flow (model) scales. This paper proposes an effective 1D hydraulic modeling approach of sufficient complexity to describe anabranching river flows from sparse multisatellite observations using the HiVDI inverse method presented in Laurier et al. (2019) with an augmented control vector including a spatially distributed friction law K (x, h) depending on the flow depth h. It is shown on 71 km of the Xingu River (anabranching, Amazon basin) with altimetric water height timeseries that a fairly accurate upstream discharge hydrograph and effective patterns of channel bathymetry and friction can be inferred simultaneously. The coherence between the sparse observation grid and the fine hydraulic model grid is ensured in the optimization process by imposing a piecewise linear bathymetry profile b(x), which is consistent with the hydraulic visibility of WS signatures (Garambois et al., 2017; Montazem et al., 2019). The discharge hydrograph Q(t) at observation times and effective bathymetry-friction (b(x), K(x, h)) patterns are retrieved from 8 years of satellite altimetry (ENVISAT) at 6 virtual stations (VS) along flow. Next, the potential of the forthcoming SWOT data, dense in space, is highlighted by inferring a discharge hydrograph and dense patterns of effective river bathymetry and friction; a physically consistent scaling of friction by reaches enables to consider more dense bathymetry controls. Finally a numerical analysis shows: (i) the importance of an unbiased prior information in the inference of a triplet (Q, b(x), K(x, h)) from WS observations; (ii) the clear signatures of river bottom slope break in low flows and width variations in high flows, through the analysis of the friction slope term, which is consistent with the findings of Montazem et al. (2019) from WS curvature analysis
Monitoring of the detected forces induced by spheroid growth during four days.
No full textForces detected using (A) microdevices with a pillar diameter = 28 μm and K = 8 nN/μm (n = 12, 16, 21, and 20 microdevices from day 1 to day 4), and (B) microdevices with pillar diameter = 36μm and K = 23 nN/μm (n = 8, 13, 10, and 12 microdevices from day 1 to day 4). Comparisons between days were performed using the two-tailed Student’s t test, 95% interval confidence: * = P <0.05, *** = P <0.001, **** = P <0.0001. Values are the mean ± standard deviation.</p
Inverse algorithms for 2D shallow water equations in presence of wet dry fronts. Application to flood plain dynamics
No full textInternational audienceThe 2D shallow water equations adequately model some geophysical flows with wet-dry fronts (e.g. flood plain or tidal flows); nevertheless deriving ac- curate, robust and conservative numerical schemes for dynamic wet-dry fronts over complex topographies remains a challenge. Furthermore for these flows, data are generally complex, multi-scale and uncertain. Robust variational in- verse algorithms, providing sensitivity maps and data assimilation processes may contribute to breakthrough shallow wet-dry front dynamics modelling. The present study aims at deriving an accurate, positive and stable finite vol- ume scheme in presence of dynamic wet-dry fronts, and some corresponding inverse computational algorithms (variational approach). The schemes and algorithms are assessed on classical and original benchmarks plus a real flood plain test case (Lèze river, France). Original sensitivity maps with respect to the (friction, topography) pair are performed and discussed. The iden- tification of inflow discharges (time series) or friction coefficients (spatially distributed parameters) demonstrate the algorithms efficiency
Robust finite volume schemes for 2D shallow water models. Application to flood plain dynamics
No full textThis study proposes original combinations of higher order Godunov type finite volume schemes and time discretization schemes for the 2d shallow water equations, leading to fully second-order accuracy with well-balanced property. Also accuracy, positiveness and stability properties in presence of dynamic wet/dry fronts is demonstrated. The test cases are the classical ones plus extra new ones representing the geophysical flow features and difficulties
EO -Hydrolab Global river discharge estimation -expertise framework
No full textInternational audienc
EO -Hydrolab Global river discharge estimation -expertise framework
No full textInternational audienc
