RMC-BestFit is a Bayesian estimation and fitting software developed collaboratively by the U.S. Army Corps of Engineers’ Risk Management Center (RMC) and the Engineer Research and Development Center’s Coastal and Hydraulics Laboratory (CHL).

Designed to support the Flood Risk Management, Planning, and Dam and Levee Safety communities, RMC-BestFit provides tools for incorporating multiple sources of information into hydrologic frequency analyses. These sources may include systematic records, historical information, paleoflood evidence, regional data, rainfall-runoff model results, measurement uncertainty, and expert judgment.

The application combines a modern, menu-driven interface with tools for data management, statistical modeling, Bayesian estimation, uncertainty analysis, model comparison, visualization, and reporting.

Version 2.0.0 Official Release

RMC-BestFit Version 2.0.0 significantly expands the capabilities introduced in Version 1.0. In addition to input data management, distribution fitting, and univariate analysis, Version 2.0.0 provides a broader platform for Bayesian flood-frequency, time-series, multivariate, and hydrologic risk analysis.
Key capabilities include:

• Time-Series Data Management: Import, manage, plot, transform, and analyze temporal hydrologic data.
• Public Data Downloads: Bring time-series data from public sources, including USGS, GHCN, ABOM, and CHMN directly into RMC-BestFit projects.
• Extreme-Event Extraction: Create block-maxima and peaks-over-threshold datasets from hydrologic time series.
• Time-Series Modeling: Develop AR, MA, ARIMA, and ARIMAX models for temporal data.
• Trend and Hypothesis Testing: Evaluate trends, nonstationarity, and other statistical characteristics of hydrologic records.
• Measurement-Error Modeling: Incorporate measurement uncertainty directly into statistical analyses.
• Expanded Probability Distributions: Select from a broader range of distributions for diverse datasets and hydrologic conditions.
• Nonstationary Flood-Frequency Analysis: Model distribution parameters and flood-frequency relationships that vary over time or with covariates.
• Flexible Prior Distributions: Define parameter and quantile priors using multiple distribution options, including Jeffreys’ Rule for scale parameters.
• Mixture Models: Analyze flood records influenced by multiple populations or generating processes.
• Composite Analyses: Perform competing-risks, mixture, and model-averaging analyses.
• Point-Process Analysis: Model the frequency and magnitude of threshold exceedances.
• Bivariate Analysis: Use copulas to characterize dependence between hydrologic variables.
• Coincident-Frequency Analysis: Evaluate joint, conditional, and coincident hydrologic risks.
• Rating-Curve Analysis: Estimate stage-discharge relationships while accounting for uncertainty.
• Bayesian Diagnostics and Model Comparison: Evaluate model performance, propagate posterior uncertainty, and conduct information-criterion-based model averaging.
• Reusable .NET Model Library: Access the RMC.BestFit model library as a NuGet package for programmatic workflows and integration with other applications.