RivRetrieve-Python: A Python package for facilitating and unifying access to global streamflow data

Large-sample hydrology datasets (e.g. CAMELS) provide structured hydro-meteorological time series data together with time-varying and static catchment attributes. They are fundamental to modern hydrological analysis, supporting hypothesis testing, model development, and the synthesis of generalisable hydrological insights across large and heterogeneous sets of river basins. However, the present generation of large-sample datasets have several shortcomings. First, they are difficult to update as new information becomes available. In addition, they often provide only a small subset of the variables collected at hydrometric gauging sites and usually exclude sub-daily data, while inconsistent naming conventions across the various datasets make data integration challenging. Finally, they may not include the quality flags that are often assigned to individual measurements by the measuring authority. To address these issues, we present RivRetrieve-Python (https://github.com/kratzert/RivRetrieve-Python), a new open source library that provides access to streamflow, stage and river temperature from more than 18 hydrometric APIs with more than 60 000 gauge stations in total at the time of writing (January 2026). An object-oriented design abstracts the implementation details of hydrometric APIs to provide users with a consistent interface irrespective of the data source or variable. We provide helper functions to simplify data retrieval from multiple catchments at once. We suggest that RivRetrieve-Python will streamline global to continental hydrological analysis and enable future research on real-time river monitoring and digital twins, hydrological prediction, and sub-daily hydrological variability and extremes.