Flash flood modeling with the MARINE hydrological distributed model

International audienceFlash 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

Toward a relational concept of uncertainty: about knowing too little, knowing too differently, and accepting not to know

Uncertainty of late has become an increasingly important and controversial topic in water resource management, and natural resources management in general. Diverse managing goals, changing environmental conditions, conflicting interests, and lack of predictability are some of the characteristics that decision makers have to face. This has resulted in the application and development of strategies such as adaptive management, which proposes flexibility and capability to adapt to unknown conditions as a way of dealing with uncertainties. However, this shift in ideas about managing has not always been accompanied by a general shift in the way uncertainties are understood and handled. To improve this situation, we believe it is necessary to recontextualize uncertainty in a broader way¿relative to its role, meaning, and relationship with participants in decision making¿because it is from this understanding that problems and solutions emerge. Under this view, solutions do not exclusively consist of eliminating or reducing uncertainty, but of reframing the problems as such so that they convey a different meaning. To this end, we propose a relational approach to uncertainty analysis. Here, we elaborate on this new conceptualization of uncertainty, and indicate some implications of this view for strategies for dealing with uncertainty in water management. We present an example as an illustration of these concepts. Key words: adaptive management; ambiguity; frames; framing; knowledge relationship; multiple knowledge frames; natural resource management; negotiation; participation; social learning; uncertainty; water managemen

Real-Time Operation of River-Reservoir Systems During Flood Conditions Using Optimization-Simulation Model with One- and Two-Dimensional Modeling

abstract: Flooding is a critical issue around the world, and the absence of comprehension of watershed hydrologic reaction results in lack of lead-time for flood forecasting and expensive harm to property and life. It happens when water flows due to extreme rainfall storm, dam breach or snowmelt exceeds the capacity of river system reservoirs and channels. The objective of this research was to develop a methodology for determining a time series operation for releases through control gates of river-reservoir systems during flooding events in a real-time using one- and/or two-dimensional modeling of flows through river-reservoir systems. The optimization-simulation methodology interfaces several simulation-software coupled together with an optimization model solved by genetic algorithm coded in MATLAB. These software include the U.S. Army Corps of Engineers HEC-RAS linked the genetic algorithm in MATLAB to come up with an optimization-simulation model for time series gate openings to control downstream elevations. The model involves using the one- and two-dimensional ability in HEC-RAS to perform hydrodynamic routing with high-resolution raster Digital Elevation Models. Also, the model uses both real-time gridded- and gaged-rainfall data in addition to a model for forecasting future rainfall-data. This new model has been developed to manage reservoir release schedules before, during, and after an extraordinary rainfall event that could cause extreme flooding. Further to observe and control downstream water surface elevations to avoid exceedance of threshold of flood levels in target cells in the downstream area of study, and to minimize the damage and direct effects in both the up and downstream. The application of the complete optimization-simulation model was applied to a portion of the Cumberland River System in Nashville, Tennessee for the flooding event of May 2010. The objective of this application is to demonstrate the applicability of the model for minimizing flood damages for an actual flood event in real-time on an actual river basin. The purpose of the application in a real-time framework would be to minimize the flood damages at Nashville, Tennessee by keeping the flood stages under the 100-year flood stage. This application also compared the three unsteady flow simulation scenarios: one-dimensional, two-dimensional and combined one- and two-dimensional unsteady flow.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201