Variational assimilation of streamflow data in distributed flood forecasting

Discharge assimilation in a distributed flood forecasting model

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,...

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

Operational flood forecasting requires that accurate estimates of the uncertainty associated with modelgenerated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimizat...

Flood forecasting for heteroscedastic streamflow processes

The paper presents a nonlinear heteroscedastic model for flow forecasting. The model is composed of two submodels: the former provides the expected value of the flow, conditional on available information, e.g. past flow and precipitation records; the latter provides the variance of the prediction error as a function of past values of the prediction error itself and precipitation measures. The p...

Adjoint sensitivity analysis and variational data assimilation for flash flood forecasting

Sequential data assimilation for streamflow forecasting using a distributed hydrologic model: particle filtering and ensemble Kalman filtering

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...