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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>RES2020-23: Peak Flow Estimation in Urban Areas - PART 1</title>
      <link>https://rip.trb.org/View/2487329</link>
      <description><![CDATA[16. Abstract

This project addressed the need for updating the existing peak flow equations for urban basins in Tennessee. After reviewing the current state of the art, the work reported herein focuses on unraveling the complex, interacting effects that non-stationary precipitation, evolving urbanization levels, and spatial patterns in land development, rainfall, as well as antecedent conditions, all have on the hydrologic response or urbanizing basins. Potential uncertainties and biases in the estimation of extreme rainfall quantiles (IDF-DDF values), due to the low density of weather stations and the use of totalized rainfall data, and in the frequency analysis of frequent floods, due to using annual maxima instead of partial duration (peaks over threshold) series, are also investigated.

All urban basins in Tennessee have experienced growth in the amounts of developed areas in the past 20 years, and there is a significant increase in the frequency of extreme rainfall events in the region. Using rainfall data with the 15-minute resolution typically available in the U.S. introduces a negative bias that is highly variable across stations, while the low density of rain gauges increases the uncertainty in IDF-DDF values.

We derive a novel urbanization index based on hydrologic connectivity that, in contrast with the traditional approach of using percentage of impervious area (IA), is able to reflect the hydrologic effects of different spatial distributions of urbanized patches within a watershed. This index outperforms IA when used as an explanatory variable in regression equations for predicting urban peak flows.

A methodology to perform continuous hydrologic simulation with artificial neural networks is also proposed to investigate the effects of changing land cover, excluding concurrent effects of trends in precipitation.

]]></description>
      <pubDate>Tue, 07 Jan 2025 10:19:33 GMT</pubDate>
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      <title>Updating Techniques for Estimating Magnitude and Frequency of Floods for Rural Basins in the Southeastern United States</title>
      <link>https://rip.trb.org/View/1503942</link>
      <description><![CDATA[The objectives of this research project are to: (1) update magnitude and frequency of peak flows for rural, unregulated USGS stations in South Carolina where adequate data are available; (2) when appropriate and based on reviews of the data, update magnitude and frequency of peak flows at regulated USGS gages in South Carolina; (3) in coordination with the USGS South Atlantic Water Science Center NC and GA offices and the USGS Office of Surface Water, update the regional generalized skew coefficient for NC, SC, and GA; 4) in coordination with the USGS South Atlantic Water Science Center NC and GA offices, update the regional rural flood-frequency equations for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance flows; (5) update the StreamStats application to include the new gage flood-frequency estimates and the new regional regression equations; and (6) develop procedures for updating the flood-frequency estimates for stations on an annual basis with the results being provided through the StreamStats application.]]></description>
      <pubDate>Tue, 06 Mar 2018 11:04:27 GMT</pubDate>
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