Operational data assimilation of Earth observation hydrological data across contrasted river basins: insights from the SEED-FD project

Operational hydrological forecasting systems still suffer from uneven performance across regions, particularly in data-scarce environments, where errors in model states and parameters propagate rapidly and limit short- to medium-range forecast skill. Within the SEED-FD (Strengthening Extreme Events Detection for Floods and Droughts) project, we investigate how multi-source data assimilation strategies can be configured to improve the propagation of observational information into short- to medium-range hydrological forecasts under operational constraints.

We implement and evaluate data assimilation workflows based on an Ensemble Kalman Filter (EnKF) within the GLOFAS system, as used in the Copernicus Emergency Management Service (CEMS) Hydrological Forecast Modelling Chain. Multiple observation types are considered, including in-situ river discharge, satellite-derived discharge and water levels, and altimetric water level observations from Earth Observation (EO) missions. Assimilation experiments are conducted across several contrasted river basins representative of diverse hydro-climatic and socio-environmental conditions, including the Niger, Paraná, and Juba–Shebelle basins.

The analysis focuses on short- to medium-range streamflow forecasts and examines how different assimilation configurations influence the persistence and propagation of corrections beyond the assimilation window. In particular, we compare state-only approaches, including filtering and smoothing strategies, with exploratory joint state-parameter estimation experiments, with the aim of identifying configurations that maximize the temporal impact of observational information while remaining compatible with operational requirements. Ensemble-based methods are employed throughout the study to ensure consistency with probabilistic forecasting frameworks.

This work presents the results of these experiments and discusses key scientific aspects relevant to the design of data assimilation strategies for improving the propagation of corrections in large-scale operational flood and drought forecasting systems.