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Stream 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.

The development of vegetation in the river bed and in the banks can affect the hydrodynamic conditions and the flow behavior of a watercourse. This can increase the risk of flooding and sediment transport. Therefore, it is important to develop analytical approaches to predict the resistance caused by vegetation and model its effect on the flow. This is the objective of this work which investigates the ability of different analytical models to predict the vertical velocity profile as well as the resistance induced by flexible submerged vegetation in open channels. Then it is possible to select the appropriate model that will be applied in the real case of rivers. The model validation is determined after a comparison between the data measured in the different experiments carried out and those from literature. For dense vegetation, the role of the Reynolds number is emphasized in particular with a model using the Darcy-Brinkman equation in the canopy. With a simple permeability, this model is relevant to estimate friction. However, for larger Reynolds number, models based on the fully turbulent flow assumption provide better results. [ABSTRACT FROM AUTHOR]

When a buoyant inflow of higher density enters a reservoir, it sinks below the ambient water and forms an underflow. Downstream of the plunge point, the flow becomes progressively diluted due to the fluid entrainment. The entrainment rate is strongly dependent on the Richardson number and reaches a constant value well downstream of the plunge point. This study is concerned with the analysis of the plunging phenomenon and the determination of the entrainment. A k-[Epsilon] model including buoyancy effects, both in a sloping and a diverging channel, is used to reproduce the main flow characteristics. A relation between the depth at the plunge point in a channel of constant width and in a diverging channel is established, and theoretical results for the calculation of the dense layer thickness are provided. The latter indicates that the spreading rate of the dense layer in a diverging channel is a function of both the entrainment rate and the channel width. The predictions of the plunge line location are in agreement with most semiempirical equations.

This paper presents a detailed analysis of 10 flash flood events in the Mediterranean region using the distributed hydrological model MARINE. Characterizing catchment's response during flash flood events may provide a new and valuable insight into the processes involved for extreme flood response and their dependency on catchment properties and flood severity. The main objective of this study is to analyze hydrologic model sensitivity in the case of flash floods 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 non-linear and non-monotonic mappings with a low computational cost. This study uses hydrologic model and sensitivity analysis as learning tools to derive temporal sensitivity analysis with a variance based method in the case of 10 flash floods that occurred in the French Pyrenees and Cévennes foothills. This constitutes a huge dataset given the scarcity of data about flash flood events. With Nash performances above 0.73 on average for this extended set of validation events, the five sensitive parameters of MARINE distributed physically based 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. [ABSTRACT FROM AUTHOR]

The variational methods widely used for other environmental systems are applied to a spatially distributed flash flood model coupling kinematic wave overland flow and Green Ampt infiltration. Using an idealized configuration where only parametric uncertainty is addressed, the potential of this approach is illustrated for sensitivity analysis and parameter estimation. Adjoint sensitivity analysis provides an extensive insight into the relation between model parameters and the hydrological response and enables the use of efficient gradient based optimization techniques. [ABSTRACT FROM AUTHOR]

Flash floods are characterized by their violence and the rapidity of their occurrence. Because these events are rare and unpredictable, but also fast and intense, their anticipation with sufficient lead time for warning and broadcasting is a primary subject of research. Because of the heterogeneities of the rain and of the behavior of the surface, spatially distributed hydrological models can lead to a better understanding of the processes and so on they can contribute to a better forecasting of flash flood. Our main goal here is to develop an operational and robust methodology for flash flood forecasting. This methodology should provide relevant data (information) about flood evolution on short time scales, and should be applicable even in locations where direct observations are sparse (e.g. absence of historical and modern rainfalls and streamflows in small mountainous watersheds). The flash flood forecast is obtained by the physically based, space-time distributed hydrological model "MARINE" (Model of Anticipation of Runoff and INondations for Extreme events). This model is presented and tested in this paper for a real flash flood event. The model consists in two steps, or two components: the first component is a "basin" flood module which generates flood runoff in the upstream part of the watershed, and the second component is the "stream network" module, which propagates the flood in the main river and its subsidiaries. The basin flash flood generation model is a rainfall-runoff model that can integrate remotely sensed data. Surface hydraulics equations are solved with enough simplifying hypotheses to allow real time exploitation. The minimum data required by the model are: (i) the Digital Elevation Model, used to calculate slopes that generate runoff, it can be issued from satellite imagery (SPOT) or from French Geographical Institute (IGN); (ii) the rainfall data from meteorological radar, observed or anticipated by the French Meteorological Service (Météo France); and (iii) the spatially distributed soil and other surface properties viewed from space (land cover map from SPOT or LANDSAT, main rivers, …). The stream flood propagation model simulates flood propagation in main rivers by solving 1-D Saint Venant equations. The data required for this part of the model are the river morphology, topography and roughness. The MARINE model has already been used previously for real time flash floods forecasting in the frame of the PACTES project on "forecasting and anticipation of floods with spatial techniques" (funded by the CNES and the French Ministry of Research) concerning the catastrophic 1999 flash flood that occurred in the South of France. The main advantages of MARINE are its ability to run on insufficiently gauged basins (with the help of satellite information) and to run in an operational mode for real-time flood forecasting. [ABSTRACT FROM AUTHOR]