Description

1) NOAA Atlas 15: An Update to the National Precipitation Frequency Standard
Sandra Pavlovic, PE, NOAA, sandra.pavlovic@noaa.gov
Co-presenters: None

Abstract: The National Weather Service’s Office of Water Prediction (OWP), of the National Oceanic and Atmospheric Administration (NOAA), has produced an authoritative atlas of precipitation frequency estimates as volumes of the NOAA Atlas 14 "Precipitation-Frequency Atlas of the United States". These estimates are published on a Precipitation Frequency Data Server with an interactive map interface and are the de-facto standard for a wide variety of design and planning activities under federal, state, and local regulations. For example, engineers use the Atlas 14 products to design stormwater management and transportation infrastructure, develop design considerations for floodplain and watershed management, and perform hydrologic studies for reservoir and flood protection projects. . With support from the Bipartisan Infrastructure Law, OWP has received funding to update the precipitation frequency standard. This product will be referred to as NOAA Atlas 15 and will be presented in two volumes. The first volume will apply a consistent methodology that accounts for trends in observations. The second volume will apply future climate projections to generate adjustment factors for the first volume. This new update is anticipated to (1) develop a seamless spatial national analysis, (2) replace current Atlas 14 estimates based on historical data (historical estimates), (3) add new product features to account for future precipitation information (future estimates), (4) model non-stationary trends in the observational record as well as climate model ensemble outputs for the future, and (5) enhance service delivery via new Web visualizations and data services. This presentation will review the planning, and development efforts on the proposed NOAA Atlas 15 update. Methodologies that are planned will be discussed as well as additional research that is anticipated to complete product development with the help of the academic community. Progress on this collaborative effort in addition to the development timeline will be presented. Also limitations and the impact to engineering design applications of future nonstationary precipitation frequency estimates will be covered. These new estimates will provide critical information for the design of national infrastructure under a changing climate.

2) Bulletin 17B: Updated but not Outdated
Megan O'Donnell, PE, CDM Smith, odonnellm@cdmsmith.com
Co-presenters: None

Abstract: Since national flood flow frequency guidelines have been introduced 1967, many updates and revisions have been published. Bulletin 17B was published in 1981 (editorial corrections in 1982) and remained the lead guidance material on flood flow frequency analysis until its successor Bulletin 17C was published in 2018. However, as Bulletin 17C sought to update key shortcomings and approaches, specifically related gage analysis and to historical and systematic records, there are features of Bulletin 17B that were not updated nor covered within Bulletin 17C and remain useful and viable tools. For instance, techniques for confidence limits and synthetic statistics are provided in Bulletin 17B and are still applicable to this day. Such techniques serve to estimate the 1-percent-plus discharge for rainfall-runoff models, as required by FEMA. Often deriving the 1-percent-plus discharge is approached by multiplying the 1-percent event by a percent error factor, though there is more nuance to the 1-percent-plus value, what it represents, and how it can be derived for certain settings. This presentation will discuss pertinent features and techniques from Bulletin 17B that are often overlooked, provide examples of applicable uses, and identify common misconceptions and outdated information from the bulletin that were updated in publications outside of Bulletin 17C. This discussion will focus on the 1-percent-plus, differences in PeakFQ software features between the Bulletins 17B and 17C, and generalized skew.

3) High Resolution Land Cover Classification for Improved Hydraulics and Hydrology Modeling
Daniel Gwartney, WSP, daniel.gwartney@wsp.com
Co-presenters: Rehal Kharel, rehal.kharel@woodplc.com

Abstract: Land cover is used in hydrology and hydraulic (H&H) analysis to help define the characteristics of the terrain for runoff, routing, and infiltration. The National Land Cover Dataset (NLCD) is an open-source, free land cover raster dataset available for use across the United States and classifies land cover into multiple categories. This data is classified from 30m resolution imagery and is good for regional land cover characteristics, but H&H models at a local scale benefit much more from higher resolution imagery. As 2D hydraulic models are becoming ubiquitous, and with the ability of HEC-RAS to spatially vary manning's coefficient; high resolution datasets allow the models to retain the finer changes in land cover classification, enhancing model resolution. The USDA National Agricultural Inventory Program (NAIP) provides 1m resolution, four band (multispectral) imagery that across the country and is typically collected in 3-year cycles, sometimes bi-annually. The multispectral imagery consists of visible wavelengths (Blue, Green, and Red) and a band capturing near infrared (NIR) wavelengths of electromagnetic radiation. The NIR wavelengths enables high resolution differentiation between vegetated and non-vegetated features, as well as basic separation of some similar vegetated features (coniferous vs. broadleaf). H&H analysis does not require the feature separation detail available with more spectral bands, but the 4 bands coupled with higher spatial resolution yield more meaningful results by more accurately describing the surface with finer mapping units. Machine learning approaches can be used to classify NAIP imagery similarly to the NLCD layer. This presentation will focus on recent projects performed in the States of Missouri, Kansas, and Louisiana that used a supervised machine learning approach, along with their challenges and successes. The presentation will cover indices and algorithms used to prepare imagery, the process used for defining training samples, classifying the imagery, imagery post processing, and review criteria.