EStreams: Building an integrated dataset of streamflow, hydro-climatic variables and landscape attributes for catchments in Europe

High-quality datasets are essential to hydrological analysis1. Although many such datasets exist, their accessibility is typically time-consuming and often challenging. Recently, there has been a significant spread of large-sample hydrology (LSH) datasets. Many of these datasets are referred to asCatchment Attributes and MEteorology for Large-sample Studies (CAMELS) or derivations14, covering hydro-climatic and landscape static attributes and time series data. These data have collectively been made available5 including first extensions based on daily time series such as the Global Runoff Data Base(https://www.bafg.de/GRDC)6. Additionally, there have been collection efforts for global streamflow data indices and signatures79. However, such globally accessible dataset represent only a small fraction of what is currently available. Here we present EStreams, a new dataset and data-access catalogue of streamflow, hydro-climatic variables and landscape descriptors for over 15,000 catchments in 39 European countries, setto be released in 2024. The data spans up to 100 years of streamflow data and includes several open-source catchment aggregated landscape attributes on topography, soil, lithology, vegetation, and land cover, as well as climatic forcing and streamflow time-series, hydro-climatic signatures and a catalogue of streamflow providers (European streamflow data and where to find them). EStreams offers both an extensive and extensible data collection along with codes for data retrieval, aggregation and processing. Our goal is to extend current large-sample datasets and take a step towards integrating hydro-climatic and landscape data across Europe. References 1. Addor, N., Newman, A. J., Mizukami, N. Clark, M. P. The CAMELS data set: Catchment attributes and meteorology for large-sample studies. Hydrol Earth Syst Sci 21, 52935313 (2017). 2. Coxon, G. et al. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth Syst Sci Data 12, 24592483 (2020). 3. Hge, M. et al. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland. Earth Syst Sci Data 15, 57555784 (2023). 4. Klingler, C., Schulz, K. Herrnegger, M. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe. Earth Syst Sci Data 13, 45294565 (2021). 5. Kratzert, F. et al. Caravan - A global community dataset for large-sample hydrology. Scientific Data 2023 10:1 10, 111 (2023). 6. Frber, C. et al. GRDC-Caravan: extending the original dataset with data from the Global Runoff Data Centre (0.1) Data set. Zenodo https://zenodo.org/records/8425587 (2023) doi:10.5281/ZENODO.8425587. 7. Do, H. X., Gudmundsson, L., Leonard, M. Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM)-Part 1: The production of a daily streamflow archive and metadata. Earth Syst Sci Data 10, 765785 (2018). 8. Gudmundsson, L., Do, H. X., Leonard, M. Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM)-Part 2: Quality control, time-series indices and homogeneity assessment. Earth Syst Sci Data 10, 787804 (2018). 9. Chen, X., Jiang, L., Luo, Y. Liu, J. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022). Earth Syst Sci Data 15, 44634479 (2023).