Rapid Model Development for GSFLOW With Python and pyGSFLOW

Abstract

Following the advancement of high-performance computing and sensor technology and the increased availability of larger climate and land-use data sets, hydrologic models have become more sophisticated. Instead of simple boundary conditions, these data sets are incorporated with the aim of providing more accurate insights into hydrologic processes. Integrated surface-water and groundwater models are developed to represent the most important processes that affect the distribution of water in hydrologic systems. GSFLOW is an integrated hydrologic modeling software that couples surface-water processes from PRMS and groundwater processes from MODFLOW and simulates feedbacks between both components of the hydrologic system. Development of GSFLOW models has previously required multiple tools to separately create surface-water and groundwater input files. The use of these multiple tools, custom workflows, and manual processing complicates reproducibility and confidence in model results. Based on a need for rapid, reproduceable, and robust methods, we present two example problems that showcase the latest updates to pyGSFLOW. The software package, pyGSFLOW, is an end-to-end data processing tool made from open-source Python libraries that enables the user to edit, write input files, run models, and postprocess model output. The first example showcases pyGSFLOW’s capabilities by developing a streamflow network in the Russian River watershed with an area of 3,850 km2 located on the coast of northern California. A second example examines the effects of model discretization on hydrologic prediction for the Sagehen Creek watershed with an area of 28 km2, near Lake Tahoe, California, in the northern Sierra Nevada.