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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. [ABSTRACT FROM AUTHOR]

MODFLOW 6 is the latest in a line of six "core" versions of MODFLOW released by the U.S. Geological Survey. The MODFLOW 6 architecture supports incorporation of additional hydrologic processes, in addition to groundwater flow, and allows interaction between processes. The architecture supports multiple model instances and multiple types of models within a single simulation, a flexible approach to formulating and solving the equations that represent hydrologic processes, and recent advances in interoperability, which allow MODFLOW to be accessed and controlled by external programs. The present version of MODFLOW 6 consolidates popular capabilities available in MODFLOW variants, such as the unstructured grid support in MODFLOW‐USG, the Newton‐Raphson formulation in MODFLOW‐NWT, and the support for partitioned stress boundaries in MODFLOW‐CDSS. The flexible multi‐model capability allows users to configure MODFLOW 6 simulations to represent the local‐grid refinement (LGR) capabilities available in MODFLOW‐LGR, the multi‐species transport capabilities in MT3DMS, and the coupled variable‐density capabilities available in SEAWAT. This paper provides a new, holistic and integrated overview of simulation capabilities made possible by the MODFLOW 6 architecture, and describes how ongoing and future development can take advantage of the program architecture to integrate new capabilities in a way that is minimally invasive and automatically compatible with the existing MODFLOW 6 code. [ABSTRACT FROM AUTHOR]

An agricultural water use package has been developed for MODFLOW 6 using the MODFLOW Application Programming Interface (API). The MODFLOW API Agricultural Water Use Package (API‐Ag) was based on the approach to simulate irrigation demand in the MODFLOW‐NWT and GSFLOW Agricultural Water Use (AG) Package. The API‐Ag Package differs from the previous approach by implementing new features and support for additional irrigation providers. New features include representation of deficit and over‐irrigation, Multi‐Aquifer Well and Lake Package irrigation providers, and support for structured, vertex, and unstructured grid models. Three example problems are presented that demonstrate how the API‐Ag Package improves representation of highly managed systems and are further used to validate the irrigation demand and delivery formulations. Irrigation volumes simulated in the three example problems show excellent agreement with the MODFLOW‐NWT AG Package. [ABSTRACT FROM AUTHOR]

The latest release of MODFLOW 6, the current core version of the MODFLOW groundwater modeling software, debuted a new package dubbed the "mover" (MVR). Using a generalized approach, MVR facilitates the transfer of water among any arbitrary combination of simulated features (i.e., pumping wells, stream, drains, lakes, etc.) within a MODFLOW 6 simulation. Four "rules" controlling the amount of water transferred from a providing feature to a receiving feature are currently available. In this way, MVR can represent natural connections between features, for example, streams entering or exiting lakes, and perhaps more interestingly, it also can transfer water among simulated features to more accurately simulate water management. An example model representative of an agricultural setting demonstrates some of the available MVR connections. For example, an irrigation event that transfers surface water from an irrigation delivery ditch to multiple cropped areas demonstrates a "one-to-many" connection that is possible within MVR. Conversely, irrigation or precipitation runoff from multiple fields may be routed to a particular stream segment using "many-to-one" MVR connections. MVR supports many additional connection types, several of which are demonstrated by the included example problem. [ABSTRACT FROM AUTHOR]

C. Nelson Neale [a] Joseph B. Hughes [a,b] C.H. Ward [a] Abstract This study investigated the unsaturated zone properties that affect ground water reaeration (i.e., the diffusive flux of oxygen [...]

To evaluate the use of general-purpose graphics processing units (GPGPUs) to improve the performance of MODFLOW, an unstructured preconditioned conjugate gradient (UPCG) solver has been developed. The UPCG solver uses a compressed sparse row storage scheme and includes Jacobi, zero fill-in incomplete, and modified-incomplete lower-upper (LU) factorization, and generalized least-squares polynomial preconditioners. The UPCG solver also includes options for sequential and parallel solution on the central processing unit (CPU) using OpenMP. For simulations utilizing the GPGPU, all basic linear algebra operations are performed on the GPGPU; memory copies between the central processing unit CPU and GPCPU occur prior to the first iteration of the UPCG solver and after satisfying head and flow criteria or exceeding a maximum number of iterations. The efficiency of the UPCG solver for GPGPU and CPU solutions is benchmarked using simulations of a synthetic, heterogeneous unconfined aquifer with tens of thousands to millions of active grid cells. Testing indicates GPGPU speedups on the order of 2 to 8, relative to the standard MODFLOW preconditioned conjugate gradient (PCG) solver, can be achieved when (1) memory copies between the CPU and GPGPU are optimized, (2) the percentage of time performing memory copies between the CPU and GPGPU is small relative to the calculation time, (3) high-performance GPGPU cards are utilized, and (4) CPU-GPGPU combinations are used to execute sequential operations that are difficult to parallelize. Furthermore, UPCG solver testing indicates GPGPU speedups exceed parallel CPU speedups achieved using OpenMP on multicore CPUs for preconditioners that can be easily parallelized. [ABSTRACT FROM AUTHOR]