By Amanda K. Triplett, Georgios Artavanis, William M. Hasling, Reed M. Maxwell, Amy Defnet, Amy M. Johnson, William Lytle, Andrew Bennett, Elena Leonarduzzi, Lisa K. Gallagher, Laura E. Condon
Hydrologic models are an integral part of understanding and managing water supply. There are countless hydrologic models available that differ in their complexity, scale and focus on different parts of the hydrologic cycle. ParFlow is a fully integrated, physics-based model that simulates surface and subsurface flow simultaneously (Ashby & Falgout, 1996; Jones & Woodward, 2001; Kollet & Maxwell, 2006; Maxwell, 2013). ParFlow is also coupled with a land surface model which allows it to simulate the full terrestrial hydrologic cycle from bedrock to treetops (Kollet & Maxwell, 2008; Maxwell & Miller, 2005). It has been applied to a myriad of watersheds across the US and around the world to answer questions of water supply and groundwater–surface water interactions.
ParFlow is a scientifically rigorous hydrologic model; however, its application by the broader community has been limited to a degree by its technical complexity which creates a high barrier to entry for new users. Intensive training and hydrologic expertise is required to appropriately build a ParFlow model from scratch.
SubsetTools is a Python package that seeks to lower the barrier to entry by allowing a user to subset published and verified ParFlow inputs and model configurations to build their own watershed models. These tools allow a user to set up and run a model in a matter of minutes, rather than weeks or months. SubsetTools is designed to interface with two domains covering the contiguous United States (CONUS), CONUS1 (Maxwell et al., 2015, 2015; O’Neill et al., 2021) and CONUS2 (Yang et al., 2023). These domains determine the structure and attributes of the hydrogeologic inputs used to build the ParFlow model. SubsetTools is the first package of its kind to fetch and process all necessary inputs and create a functional ParFlow model, all in a single workflow.
Read the paper: https://doi.org/10.21105/joss.06752