Land cover (LC) represents one of the key descriptors of landscape and ecosystems and as such also plays a critical role in hydrological modelling, since land cover type fundamentally affects water retention and runoff processes and regime. For this purpose, a highly accurate LC was derived based on times series of Sentinel-2 data for the whole catchment area as one of the main inputs for deep learning-based flood forecasting and modelling approach. Fine spatial and temporal resolution, suitability for large scale application and certainty of continuity in future along with automatized processing secure a good base for operational use.

Digital terrain models are an important source for parameterization of hydrological models. In addition, a number of other characteristics related to floods can be derived on their basis and are also suitable for floodplain management or delimitation of flood risk zones. Although very high resolution models must be used for accurate modeling, freely available elevation models such as EU-DEM represents cost effective option for large area applications.

Computer vision and machine learning techniques are capable to adopt additional spatiotemporal data to improve and recognize complex patterns such as runoff. Several variables influence rainfall-runoff and drainage conditions and among Landcover or meteorological variables, the vegetation structure is of key importance and has a strong response in spectral reflectance. Besides standard vegetation index as NDVI, the Leaf Area Index plays a critical role and can be derived from the time series of Copernicus Sentinel 2 data at high spatial and temporal resolution.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis and combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics (Copernicus). Even though not with NRT availability, ERA5-Land provides hourly estimates of variables such as temperature or precipitation inevitable for rainfall-runoff modelling and flood forecast.