In the field of operational flood forecasting, uncertainties linked to hydrological forecast are often crucial. In this work, data assimilation techniques are employed to improve hydrological variable estimates coming from numerical simulations using all the available real-time water level measurements. The proposed assimilation scheme, a classical Kalman filter extension to non-linear systems,...

[1] Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive framework for Bayesian revision has been recently developed and applied to operational flood ...

Accurate streamflow predictions are crucial for mitigating flood damage and addressing operational flood scenarios. In recent years, sequential data assimilation methods have drawn attention due to their potential to handle explicitly the various sources of uncertainty in hydrologic models. In this study, we implement two ensemble-based sequential data assimilation methods for streamflow foreca...

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 onedimensional 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...

Appropriate precautions in the case of flood occurrence often require long lead times (several days) in hydrological forecasting. This in turn implies large uncertainties that are mainly inherited from the meteorological precipitation forecast. Here we present a case study of the extreme flood event of August 2005 in the Swiss part of the Rhine catchment (total area 34 550 km2). This event caus...

Over the last decade, neural networks-based flood forecast systems have been increasingly used in hydrological studies. Usually, input data of the network are composed by past measurements of flows and rainfalls, without providing a description of the saturation state of the basin, which in contrast plays a key role in the rainfall-runoff process. Here, we adopt a fuzzy approach in order to pro...

Rational decision making (for flood warning, navigation, or reservoir systems) requires that the total uncertainty about a hydrologic predictand (such as river stage, discharge, or runoff volume) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Hydrologic knowledge is typically embodied in a deterministic catchment model. Fundamentals...

Abstract As the severity and occurrence of flood events tend to intensify with climate change, need for forecasting capability increases. In this regard, Flood Detection, Alert rapid Mapping (FloodDAM) project, funded by Space Climate Observatory initiatives, was set out develop pre-operational tools dedicated enabling quick responses in flood-prone areas, improve reactivity decision support sy...