Lab-0021

Flood Frequency Analyses Python

Model
pandas

Duration
2.0 hrs

Updated
2020-09-24

Download
download

Run online using:

Jupyter

Contributor(s)

Alejandra Ortiz at Colby College.

Introduction

This exercise will provide some experience with methods used for predicting flood frequency and magnitude. We will be using the US Geological Survey (USGS) website to retrieve historical stream gauge data of the sort used to predict the likelihood of flood events of particular magnitudes during a given time interval. Such predictions are the basis for numerous engineering, restoration and development projects in and around rivers. 1. Practice using Python Pandas DataFrames to import, manipulate, and visualize data, 2. Learn some powerful tools for subsetting your dataframes, and 3. Practice visualizing data

Classroom organization
Here is a

Learning objectives
Skills

Pandas Dataframes
Data Visualization
Subsetting dataframes

Key concepts

Flood Frequency Analyses
Rating Curves
Time-series gaps

Lab notes

Here is a helpful link - https://www.usgs.gov/centers/sa-water/science/flood-frequency-information?qt-science_center_objects=0#qt-science_center_objects

You could include requirements that students check their results for the given rivers (Fishing Creek, Tar River, Ellerbe Creek, and Roanoke River) to the USGS calculations for an extended assignment.

I have adapted a lab similar to this one by SERC (https://serc.carleton.edu/hydromodules/steps/168500.html) from T. Perron using excel to analyze flood frequency of a river into python. It would be easy enough to include more background on Wollman & Miller, geomorphic work, the characterstic flood, extreme value analyses, etc dependent on your course interest and focus.

Requirements
Need to download peak annual stream data and daily discharge data for a given location.

Acknowledgements
CSDMS and Mark Piper provided support for the creation and hosting of my jupyter hub notebooks

References