Summary Monitoring inland water levels is crucial for understanding hydrological processes. Satellite altimetry has proved to be an excellent technique to precisely measure water levels for inland water bodies. The ICESat-2/ATL13 product is solely dedicated to inland water bodies. Here, we present a robust framework for effectively employing ATL13 data to retrieve time series river water levels. ATL13 segments were utilized within the river boundary considering the dynamic nature of water extent. Cloud flags were used to discard invalid observations. Further median values were only considered to get more precise water levels. Water level profile was extracted from multiple HDF5 files of a specific region in an automated manner in the R environment. Statement of the Need There are two types of satellite altimeters, radar and laser altimeter (LiDAR). Compared to radar altimeters, laser altimeters are characterized by small footprints and high-density samplings as well as high accuracies, making them more suitable for small or narrow water bodies . The Advanced Topographic Laser Altimeter System (ATLAS) has been employed in Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) which was launched in September 15, 2018, as a follow-on of the ICESat mission. ICESat-2 has huge potential to do research on inland waters. ATL13 product contains several parameters pertaining to the water surface level of inland water bodies, the data quality flags and the sensor characteristics corresponding to data collection time. Since a large number of parameters are present in the ATL13 HDF5 file, the selection of the relevant parameters and further filtering of each parameter based on specific requirements has to be carried out on the user side. For time-series water level studies over an area, multiple HDF5 files would need to be processed. Hence, an automated approach using a coding language would be an efficient choice for speedy extraction and filtering of relevant ATL13 parameters to construct the water level time series. This paper proposed a robust framework in the R environment for easier extraction of water level time series from ATL13 data. The framework provided here can be used to conduct similar studies over another river or reservoir, which can complement other hydrological studies, e.g., discharge studies. This study also paves the way for water level studies that can utilise data from the new and upcoming sensors which are or will be placed in the lower earth orbit, such as GEDI, SWOT and NISAR missions. Acknowledgements The authors are thankful to Goddard Space Flight Center and National Snow and Ice Data Center Distributed Active Archive Center for distributing the ICESat-2 data (ver 4). Code availability Source code is normally made available on request to the researchers. Conditions apply in the case of model versions still under active development. We will try to help queries related to this study. Please do not hesitate to contact Surajit Ghosh if you have any queries related to this study. Contact: surajitghosh.ind@gmail.com