Log Pearson Type III Flood Frequency Analysis of Simulated Streamflow

Posted by Jason Lillywhite

Engineers frequently need to estimate design floods that exceed the range of observed data, like the 100-year or 500-year flood that may not have occurred in the historical record. While GoldSim excels at empirical analysis using historical resampling (as shown in our previous post on annual peak flow statistics), parametric flood frequency analysis using the Log Pearson Type III distribution enables extrapolation beyond observed data.

This tutorial demonstrates a useful approach for those incorporating hydrology in your water balance models. The example model used in this tutorial performs Log Pearson Type III analysis on simulated annual peak flows generated from rainfall-runoff modeling within GoldSim. Rather than being limited by short historical streamflow records, this method uses rainfall-runoff simulation to generate extended flow datasets from much longer, available climate data.

The integration shows how to implement Log Pearson Type III flood frequency analysis for engineering applications using the methodology from USGS Bulletin 17C. Included in this tutorial are the following functions:

• Simulation of streamflow using a rainfall runoff model
• Design flood estimates for standard return periods (2, 5, 10, 25, 50, 100, 200, 500 years)
• Quality assurance metrics

The integration uses GSPy (GoldSim-Python Bridge) to connect GoldSim's dynamic simulation capabilities with Python's advanced statistical analysis libraries, enabling flood frequency analysis within your existing GoldSim workflow.

Figure 1. GoldSim model results - Log Pearson Type III flood frequency analysis results with time series of daily simulated streamflow.

GoldSim Tutorial: Calculating and Displaying Annual Peak Flow Statistics

Posted by Jason Lillywhite

Reliable peak river flow statistics are necessary for effective design, planning, and risk communication. GoldSim can calculate a range of statistics for flood frequency analysis, such as the flow associated with a specific return period (e.g., the 99th percentile flow) or the timing distribution of the annual peak flow. This tutorial walks you through building a model to calculate these types of statistics in GoldSim, focusing on displaying results on a dashboard: 

• Median Peak Flow
• Median Time of Peak (Day of the Year)

The GoldSim Extrema element captures these annual extreme values. To display specific statistics (like a percentile or value at a percentile), these statistics need to be shown on a Dashboard, which requires a specific setup. I will show the process of screening out a certain group of results, so the result statistics reflect that screening.

Figure 1. GoldSim Model Dashboard: Annual Peak Flow Statistics

Coupling GoldSim and MODFLOW using a Python Interface

This post describes a method for coupling GoldSim with MODFLOW using GSPy (GoldSim's Python interface) and FloPy (a Python package for creating MODFLOW models). The approach builds on existing work like the Dynamic Coupling of GoldSim and MODFLOW/MT3D project but focuses on a simplified implementation suitable for educational purposes.

Implementation Approach

Traditional model coupling typically requires:

• Writing temporary input files
• Managing external executable calls
• Parsing output files for results
• Handling timing and synchronization
• Managing file system operations

The GSPy + FloPy approach uses:

1. GSPy to handle communication between GoldSim and Python
2. FloPy to create and manage MODFLOW models in memory
3. Python functions that receive GoldSim inputs and return results
4. FloPy's built-in file management for MODFLOW input/output

This implementation uses a Tutorial 2 style MODFLOW setup for educational purposes.