EARLS: a runoff reconstruction dataset for Europe

作者：Daniel Klotz, Peter Miersch, Thiago Victor Medeiros do Nascimento, Fabrizio Fenicia, Corinna Frank, Martin Gauch, Jakob Zscheischler · 发表于：Earth system science data · 年份：2026 · DOI：10.5194/essd-18-5485-2026 · 被引用次数：3 · 研究领域：Hydrology and Watershed Management Studies、Flood Risk Assessment and Management、Hydrology and Drought Analysis

Abstract. Data 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 2023. The reconstruction is derived from a single Long Short-Term Memory (LSTM) based rainfall–runoff model trained on more than 5,000 basins. LSTMs represent the state of the art in rainfall–runoff modeling and are well suited to provide 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 flows and flood timing to previous large-scale hydrological studies. EARLS represents a new generation of datasets that harness the capabilities of Deep Learning to obtain accurate and high-resolution data. EARLS is available at https://doi.org/10.5281/zenodo.13864843 (Klotz et al., 2024b)