The Bulletin 17B framework assumes that the annual peak flow data included in a flood frequency analysis are from a homogeneous population. However, flood frequency analysis over the western United States is complicated by annual peak flow records that frequently contain annual flows generated from distinctly different flood generating mechanisms. These flood series contain multiple zero flows and/or potentially influential low floods (PILFs) that substantially deviate from the overall pattern in the data. Moreover, they often also contain extreme flood events representing different hydrometeorologic agents. Among the different flood generating mechanisms, atmospheric rivers (ARs) are responsible for large, regional‐scale floods. The spatial and fractional contribution of ARs in annual peak flow data is examined based on 1375 long‐term U.S. Geological Survey (USGS) streamgage sites with at least 30 years of data. Six main areas in which flooding is impacted by ARs at varying degrees were found throughout the western United States. The Pacific Northwest and the northern California coast have the highest fraction of AR‐generated peaks (∼80–100%), while eastern Montana, Wyoming, Utah, Colorado, and New Mexico have nearly no impacts from ARs. The individual regions of the central Columbia River Basin in the Pacific Northwest, the Sierra Nevada, the central and southern California coast, and central Arizona all show a mixture of 30–70% AR‐generated flood peaks. Analyses related to the largest flood peaks on record and to the estimated annual exceedance probabilities highlight the strong impact of ARs on flood hydrology in this region, together with marked regional differences.
Methods for estimating the magnitude and frequency of floods in California that are not substantially affected by regulation or diversions have been updated. Annual peak-flow data through water year 2006 were analyzed for 771 streamflow-gaging stations (streamgages) in California having 10 or more years of data. Flood-frequency estimates were computed for the streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to logarithms of annual peak flows for each streamgage. Lowoutlier and historic information were incorporated into the flood-frequency analysis, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low outliers. Special methods for fitting the distribution were developed for streamgages in the desert region in southeastern California. Additionally, basin characteristics for the streamgages were computed by using a geographical information system. Regional regression analysis, using generalized least squares regression, was used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged basins in California that are outside of the southeastern desert region. Flood-frequency estimates and basin characteristics for 630 streamgages were combined to form the final database used in the regional regression analysis. Five hydrologic regions were developed for the area of California outside of the desert region. The final regional regression equations are functions of drainage area and mean annual precipitation for four of the five regions. In one region, the Sierra Nevada region, the final equations are functions of drainage area, mean basin elevation, and mean annual precipitation. Average
For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1-888-ASK-USGS.For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprodTo order this and other USGS information products, visit http://store.usgs.gov Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this report is in the public domain, permission must be secured from the individual copyright owners to reproduce any copyrighted materials contained within this report. Tables Table 1. Streamflow-gaging stations and statistical data used to analyze regional skew in California and to determine flood frequency for the Sacramento-San Joaquin River Basin, California …………………………………………………… 23 Table 2. Basin characteristics for analyzing regional skew in California ………………… 23 Table 3. Basin characteristics for sites used in the regional skew analysis, California …… 24 Table 4. Results of trend tests for annual peak discharge at selected sites in California … 24 Table 5. Key dam sites and drainage areas, periods of estimated unregulated annual-maximum-daily discharge, and periods of concurrent unregulated annual-maximum-daily and peak-discharge data ………………………………… 25 Table 6. Unregulated, annual-maximum-daily discharge for key dam sites, California …… 26 Table 7. Regional skew models for California ……………………………………………… 38 AbstractImproved flood-frequency information is important throughout California in general and in the Sacramento-San Joaquin River Basin in particular, because of an extensive network of flood-control levees and the risk of catastrophic flooding. A key first step in updating flood-frequency information is determining regional skew. A Bayesian generalized least squares (GLS) regression method was used to derive a regional-skew model based on annual peakdischarge data for 158 long-term (30 or more years of record) stations throughout most of California. The desert areas in southeastern California had too few long-term stations to reliably determine regional skew for that hydrologically distinct region; therefore, the desert areas were excluded from the regional skew analysis for California. Of the 158 long-term stations used to determine regional skew, 145 have minimally regulated annual-peak discharges, and 13 stations are dam sites for which unregulated peak discharges were estimated from unregulated daily maximum discharge data furnished by the U.S. Army Corp of Engineers. Station skew was determined by using an expected moments algorithm (EMA) program for fitting the Pearson Type 3 flood-frequency distribution to the logarithms of annual peak-discharge data.The Bayesian GLS regression method previously developed was modified because of the large cross correlations among concurrent recorded peak discharges in California and the use of censored data and historical flood...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.