Technical Review and Perspective: GLOBAL FLOOD AWARENESS SYSTEM (GLOFAS): FROM GLOBAL HYDROLOGICAL FORECASTING TO OPERATIONAL FLOOD-RISK EARLY WARNING

B. CHAPLOT

Abstract


Flood forecasting and early-warning systems have become essential components of disaster-risk reduction, particularly in regions where hydrometeorological monitoring networks remain sparse and where rapid urbanization, land-use change, and climate variability are increasing exposure to extreme hydrological events. The Global Flood Awareness System (GloFAS) represents a major advance in operational large-scale hydrology by providing spatially consistent and probabilistic river-flow forecasts across national and transboundary river basins. Nevertheless, the effective translation of global hydrological forecasts into locally actionable flood warnings remains challenging because of uncertainties in meteorological forcing, hydrological model structure, observational constraints, spatial resolution, flood-threshold estimation, and the representation of local hydraulic processes. This paper provides a technical and geographical assessment of GloFAS within the broader framework of contemporary flood forecasting and early warning. Particular emphasis is placed on the integration of hydrological modelling, Geographic Information Systems (GIS), remote sensing and Earth observation, probabilistic forecasting, flood-frequency analysis, and high-resolution hydraulic modelling. The specific challenges associated with ungauged and poorly gauged basins, urban flooding, and hydroclimatic non-stationarity are critically examined. Recent developments in artificial intelligence and machine learning are further discussed as complementary tools for bias correction, parameter regionalization, model post-processing, multisource data fusion, and uncertainty reduction. On this basis, a global-to-local flood-warning framework is proposed in which global hydrological forecasting serves as an initial layer of risk detection and is progressively complemented by regional observations, catchment-scale hydrological models, local hydraulic simulations, Earth-observation products, and exposure and vulnerability information. The analysis highlights that the greatest operational value of GloFAS does not lie in replacing national or local forecasting systems, but in providing an open, coherent, transboundary, and anticipatory hydrological information layer capable of strengthening them. Such an integrated approach is particularly relevant to data-scarce and developing regions, where global forecasting systems may substantially improve flood preparedness, provided that their outputs are locally validated, uncertainty-aware, and effectively incorporated into impact-based early-warning and decision-support frameworks.


Keywords


Global Flood Awareness System (GloFAS); flood forecasting; early-warning systems; flood risk; hydrological modelling; GIS; remote sensing; Earth observation; artificial intelligence; climate change; ungauged basins; global-to-local forecasting.

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