FloPy Workflows for Creating Structured and Unstructured MODFLOW Models

Abstract

FloPy is a Python package for creating, running, and post‐processing MODFLOW‐based groundwater flow and transport models. FloPy functionality has expanded to support the latest version of MODFLOW (MODFLOW 6) including support for unstructured grids. FloPy can simplify the process required to download MODFLOW‐based and other executables for Linux, MacOS, and Windows operating systems. Expanded FloPy capabilities include (1) full support for structured and unstructured spatial discretizations; (2) geoprocessing of spatial features and raster data to develop model input for supported discretization types; (3) the addition of functionality to provide direct access to simulated output data; (4) extension of plotting capabilities to unstructured MODFLOW 6 discretization types; and (5) the ability to export model data to shapefiles, NetCDF, and VTK formats for processing, analysis, and visualization by other software products. Examples of using expanded FloPy capabilities are presented for a hypothetical watershed. An unstructured groundwater flow and transport model, with several advanced stress packages, is presented to demonstrate how FloPy can be used to develop complicated unstructured model datasets from original source data (shapefiles and rasters), post‐process model results, and plot simulated results.

Streamflow Depletion Caused by Groundwater Pumping: Fundamental Research Priorities for Management‐Relevant Science

“…Therefore, there is a critical need to develop benchmarking data sets, reproducible workflows, and tools to facilitate standardized model intercomparison. These resources will provide improved opportunities to evaluate and intercompare the performance of existing tools and help rapidly develop and test new tools (Hughes et al, 2023). For instance, the recent proliferation of artificial intelligence/machine learning (AI/ML) shows promise for hydrologic simulation (Kratzert et al, 2019), but causal AI/ML methods (e.g., Althoff et al, 2021;Tsai et al, 2021) that link changes in drivers, like pumping, to outputs, like streamflow, have not yet been explored for streamflow depletion applications.…”

Opportunities and limitations of the ChatGPT Advanced Data Analysis plugin for hydrological analyses

“…Our first example (Figure 1a) shows an application of the Lyne and Hollick (1979) baseflow filter, following the approach outlined by Ladson et al (2013). With Python, R and MATLAB being widely used across the hydrological sciences (e.g., Bakker et al, 2016; Hughes et al, 2023; Irvine et al, 2015; Kratzert et al, 2022), a common issue can arise from the need to utilize functions from multiple languages. In this example, R functions from Ladson (2023) were converted to Python using the Advanced Data Analysis plugin.…”