USGS ScienceSearch

USGS · 70227751

The MODFLOW Application Programming Interface for simulationcontrol and software interoperability

Abstract

The MODFLOW API allows other programs to control MODFLOW and interactively change variables without having to modify the source code. The MODFLOW API is based on the Basic Model Interface (BMI), which is a set of conventions that define how to initialize a simulation, update the model state by advancing in time, and finalize the run. For many existing MODFLOW coupling applications, the information provided to MODFLOW must be updated multiple times in a time step. As this capability to modify variables within a time step is not defined by the BMI, an extension to BMI was developed. This eXtended Model Interface is part of the MODFLOW API and allows such a tight coupling to other models. Examples are included for a variety of use cases, including new flexibility for users to develop custom packages without modifying the MODFLOW source code and coupling MODFLOW with other models and optimization libraries.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Joseph D. Hughes, Martijn J. Russcher, Christian D. Langevin, Eric D. Morway, Richard R. McDonald. 2022. The MODFLOW Application Programming Interface for simulationcontrol and software interoperability. https://doi.org/10.1016/j.envsoft.2021.105257

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Multivariate SWAT streamflow and surface water storage calibration enables upper Mississippi River Basin wetland change scenarios

Surface water storage (e.g., wetlands, lakes) is not typically considered in hydrological model calibrations. We tested a multivariate calibration process, incorporating Sentinel-1 and -2 surface water storage, for a Soil and Water Assessment Tool model across the 0.5 million km 2 Upper Mississippi River Basin. While 19% of the 2000 parameter sets adequately simulated discharge (Kling-Gupta efficiency >0.5), only 5% also adequately simulated surface water storage (mean absolute error <2 m), reducing model output uncertainty. Using the best calibrated model, we found that changes in surface water storage capacity most strongly affected discharge during the first annual peak flow (i.e., floods), when storage was filling. Increases in upstream surface water storage capacity resulted in projected decreases in peak flow and flashiness, with changes persisting downstream to the watershed outlet. Our findings demonstrate the importance of surface water storage in multivariate model calibration processes to inform river discharge and flood impact predictions.

Illinois, Indiana, Iowa, Minnesota, Missouri, Wisc

METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed

Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis of monitoring and modeling data, named “Monitored and Expected Total Reduction Indicator for the Chesapeake (METRIC),” was developed to provide spatially explicit trends for the subwatersheds of the Chesapeake Bay. METRIC contains up-to-date information on nitrogen, phosphorus, and sediment at 83, 66, and 66 stations, respectively, which can help watershed managers gauge expectations on the trajectory and pace of progress at localized scales. These results were further synthesized to better understand the spatial patterns of the response classes ( i.e. , agreement between the expected and monitored trends) across the Chesapeake Bay watershed.

Chesapeake Bay watershed

Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm

Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R 2 for annual 7-day maximum = 0.76; R 2 for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.

contiguous United States