Streamlining Integrated Hydrologic Modeling at Continental Scale: A Workflow for Quasi–Real-Time ParFlow-CLM Simulations over CONUS

Recent advances in integrated hydrologic modeling have advanced our ability to simulate coupled surface–subsurface processes, offering critical insights into water dynamics at large scales. Specifically, simulations of the Contiguous United States (CONUS) have been recently conducted with ParFlow-CLM, a highly parallelizable integrated hydrologic modeling platform. These large-scale simulations have supported investigations of stream–aquifer connectivity and hydrologic sensitivity to climate forcing, yet their implementation remains technically demanding.

Assembling such simulations involves numerous challenges, including the configuration of complex HPC environments, management of evolving and voluminous climate forcings, large-scale input and output data handling, and heavy postprocessing workflows. These barriers limit the broader adoption and operational use of integrated models.

Here we present a semi-automated workflow for running ParFlow-CLM simulations over the CONUS domain in quasi–real-time. The workflow, designed to operate in weekly cycles, integrates Python and Shell scripting with the hf_hydrodata Python package to automate data preparation, model execution, and output management. We demonstrate its application on three widely used HPC platforms, highlighting its scalability and adaptability.

This contribution directly supports the community’s effort to make integrated modeling more accessible, reproducible, and operationally feasible at continental scales. By reducing technical overhead, the workflow promotes broader participation in high-resolution hydrologic modeling and facilitates timely water resources decision-making.