Abstract Accurately representing daily reservoir operations in large-scale hydrological and water resource modeling remains challenging due to both the complex and unclear nature of real-world operations and very limited availability of operation records for many reservoirs worldwide. To address this gap, this study introduces MODROM (MOdular Data-driven Reservoir Operation Model), a parsimonious reservoir parameterization scheme that conceptualizes reservoir operations through simple operation modules and their seasonal transitions. These operation modules are designed to be simple and parsimonious for easier generalizing from data-rich to data-scarce reservoirs. MODROM is calibrated using high-quality long-term operation records from 411 data-rich reservoirs across the contiguous United States (CONUS), and a Random Forest model is developed to provide calibrated parameters for data-scarce reservoirs based on a suite of static reservoir characteristics. Results demonstrate MODROM s strong and robust performance when calibrating using all available data for each reservoir, though the performance generally declines for reservoirs with larger regulation capacity. The generalization performance is strong under favorable sampling conditions but varies with different train-test splits due to the limited reservoir data set. Benchmarking against existing models shows that MODROM achieves enhanced performance, with a median Kling-Gupta Efficiency of approximately 0.5, compared to 0.4 for the best storage-based model and 0.2 for data-inferred model; this demonstrates distinct advantages in generalizing parameters to data-scarce reservoirs using readily available static reservoir characteristics, though the performance can be affected by train-test split due to limited reservoirs in the sample.