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Precipitation Frequency and Storm Analysis in Operational Hydrology


HydroLearn

Problem Statement

To effectively communicate potential risks, operational forecasters and hydrologists require a strong understanding of the probability and severity of extreme rainfall events. Precipitation frequency-duration curves are a fundamental tool in this endeavor, quantifying the likelihood of rainfall events with different intensities and durations at particular locations. The complexities involved in constructing and interpreting these curves for weather forecasts, infrastructure design, emergency responses, and within the context of a changing climate highlight a critical need for a learning module to develop the necessary knowledge for these operational applications.

Module Overview

This module equips operational hydrologists with the fundamental context needed for real-time forecasting during extreme rainfall events and for contributing to long-term planning and response efforts, with the flexibility to incorporate considerations of changing climatic conditions.

Topics Covered

Precipitation statistics; Probability distribution; Exceedance Probability and Return Period; Depth-Duration-Frequency Curve; Stationary vs. Nonstationary; NOAA Atlas 14; NOAA Atlas 15; Access forecast rainfall; Access gauge rainfall.

Prerequisites

Before starting this module, learners should have:

  • Basic programming knowledge in Python.
  • Some background in statistics.
  • Familiarity with HydroShare and Jupyter Notebooks.

For foundational material, learners are encouraged to review the following HydroLearn modules:

Learning Objectives

At the end of this module, students will be able to:

  • Derive the depth duration frequency curve (DDF)
  • Interpret the frequency and return period of extreme events
  • Evaluate the severity of forecast and recorded storm events
  • Describe a precipitation frequency application in hydrology and engineering
  • This will be accomplished through a series of short readings on fundamental concepts, accompanied by learning activities in sections.

    Suggested Implementation

    This module is broken down into sections with small units. Each section is self-contained and can be exercised individually.

    Course Authors

    Course Staff Image #1

    Mohamed Abdelkader

    IIHR—Hydroscience & Engineering, University of Iowa

    mohamed-abdelkader@uiowa.edu

    Course Staff Image #2

    Yinphan Tsang

    University of Hawaiʻi at Mānoa

    tsangy@hawaii.edu

    Target Audience

    Operational hydrologists and forecasters

    Tools Needed

    Computer with access to Internet and basic programming knowledge in python.

    Expected Effort

    The module developers estimate that this module will take between 2 to 3 hours to complete.

    Adapted From

    This module is adapted from

    Development of Design Storms

    This module uses Hurricane Helene as a case study and use datasets from Asheville, NC. This module utilizes Python JupyterNotebook to allow a streamlined process in the learning activities.

         

    Data Disclaimer

           

    This experimental data represents the NWS’s best approximation of the maximum inundation extent that occurred on Sep. 26 (or Sep. 27), 2024, as of Jan 6, 2025, based upon modeled river discharge. These maps were not publicly available during the event (coinciding with Hurricane Helene). This information should be used for educational purposes only.        

       

    Acknowledgments

    This project received funding under award NA22NWS4320003 by National Oceanic and Atmospheric Administration (NOAA) Cooperative Institute Program to the Cooperative Institute for Research on Hydrology (CIROH) through the University of Alabama. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the opinions of NOAA.

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    2. Course Number

      OP_020
    3. Classes Start

    4. Estimated Effort

      3:00
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