EARLS
Daniel Klotz, Peter Miersch, Thiago V. M. do Nascimento, Fabrizio Fenicia, Martin Gauch, Jakob Zscheischler
Abstract
Data is central to hydrological science. It drives our understanding of hydrological processes, supports model development, and enables anticipatory water management. This contribution introduces EARLS: European Aggregated Reconstructions for Large-sample Studies. EARLS offers daily streamflow reconstructions for more than 10,000 basins in Europe including uncertainty estimates, covering the period from 1953 to 2020. All simulations in this study use a Long Short-Term Memory (LSTM) based rainfall-runoff model. LSTMs represent the state of the art in rainfall-runoff modeling and are particularly well suited for predictions in ungauged basins. We evaluate the quality of the reconstruction through quantitative evaluation on two held-out sets of basins and by conducting a qualitative assessment that compares EARLS based peak flow and flood timing analysis to previous large-scale hydrological studies. Consequently, EARLS represents a new generation of datasets that harness the capabilities of Deep Learning to obtain accurate and high-resolution data.
The Paper
Read ESSD paper
here
Contact
Email: daniel.klotz@ufz.de
