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Storms Statistics for Texas "a small portion of" TxDOT RMC-3 Research Project 0-4194

Storms Statistics for Texas "a small portion of" TxDOT RMC-3 Research Project 0-4194. U.S. Geological Survey Texas Tech University Lamar University University of Houston. Presentation by Dr. William H. Asquith, USGS, Austin March 18, 2004. Research Relevance.

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Storms Statistics for Texas "a small portion of" TxDOT RMC-3 Research Project 0-4194

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  1. Storms Statistics for Texas"a small portion of"TxDOT RMC-3Research Project0-4194 U.S. Geological Survey Texas Tech University Lamar University University of Houston Presentation byDr. William H. Asquith, USGS, Austin March 18, 2004

  2. Research Relevance • TxDOT lets about $3billion/yr in construction contracts. • TxDOT rule-of-thumb is that 40% of this total or $1.2B/yr is for drainage control or other handling of water. • Some drainage control associated with BMPs • BMP design is influenced by statistics of storms • There is no comprehensive and single-source framework for estimating storm statistics in Texas for advanced BMP design • Publication of results provides TxDOT with a citable reference(s) to facilitate codification of storm statistics, armoring for litigation, and means to influence design guidance from external agencies.

  3. Statistics of Storms • eNM, OK, TX • NWS hourly data • 155 million values • 774 stations • MIT: 6, 8, 12, 18, 24, 48, and 72 hours • Storm Arrival, Depth, and Duration • percentiles • L-moments • dist. eq. form • Hyetographs • new analysis • Example Problems

  4. L-moment diagrams L-moment diagrams are the state-of-the-art tool for selection distributions to model environmental data.Distribution L-moments compared to dataL-moments. Differences between distributions are clear and unambiguous. • Kappa (4 parameters) • Pearson Type III (3 para.) • Gamma (2 parameters) • Exponential (2 parameter) Kappa distribution is MOST ACCURATE.

  5. Kappa DistributionDimensionless Frequency Curves MIT has LIMITED influence on the curve--so does geographic location EASY TO USE

  6. Dimension-less Kappa Distribution Frequency Curves "frequency factors" • Limited spatial differences • Flexible • Unambiguous • Easy to interpret and use

  7. Comparison of Exponential, Gamma, KappaDimensionless Distributionsof Storm Depth Exponential used in analytical BMP equations. EPA and others suggest Gamma. Kappa most accurate (cutting-edge) and throws greater outliers.

  8. Mean Storm Depth for8-hour MIT Large east-to-west grad. Maps used with dimensionless frequency curve to generate storm depth distribution. 21 maps provided

  9. Mean Storm Depth for24-hour MIT Maps for Arrival Rate Maps for Storm Depth Maps for Storm Duration (Tables also provided.) Easy to use, consistent, and logical with many administrative subdivisions.

  10. 90th Percentile Storm Depths COUNTY MEAN_08hr_DEPTH 8hr-90th% El Paso 0.233 in 0.59 in Lubbock .406 in 1.02 in Travis .494 in 1.24 in Hays .564 in 1.42 in Harris .590 in 1.49 in 2.52 is the8 hour - 90 percent frequency factor COUNTY MEAN_24hr_DEPTH 24hr-90th% El Paso 0.275 in 0.68 in Lubbock .522 in 1.30 in Travis .672 in 1.67 in Hays .743 in 1.85 in Harris .810 in 2.02 in 2.49 is the24 hour -90 percent frequency factor

  11. Expected Depth (E[s]) in a BMP MIT < draw-down timeE[s] = 0.0748 inches MIT = draw-down timeE[s] = 0.0511 inches In this case, having storm statistics at the draw-down time MIT yields a smaller expected depth and the calculations are tremendously easier. Exponential Distribution of storm arrival, depth, and duration.

  12. THANKS

  13. APPENDIX

  14. Precipitation Related Publications by the TxDOT Research Program • Asquith (1998): USGS • Lanning-Rush, Asquith, andSlade (1999): USGS • Asquith (1999): USGS • Asquith andFamiglietti (2000): Journal • Al-Asaadi (2002): MS thesis • Asquith (2003): PhD diss. • Asquith, Bumgarner,Fahlquist (2003): Journal • Asquith and Thompson (2003): Proceedings • Strand (2003): MS thesis • Asquith and Roussel (2003): USGS • Asquith, Roussel, Thompson, Cleveland, and Fang (2004): USGS • Sether-Williams, Asquith, Cleveland, Fang, and Thompson (2004): USGS • Asquith, Roussel, Cleveland, Fang, and Thompson (????): USGS

  15. Hydrology (Precipitation and Runoff) Influence Infra$tructure Sand filtration Best-Management Practice (BMP) behind the speaker's house in north Austin in Shoal Creek watershed.

  16. Distribution Parameter Estimates Dimensionless Exponential Gamma Kappa

  17. Countywide Mean Tables Tables listing countywide mean values for storm arrival rate, depth, and duration for eNM, OK, and TX are provided. Countywide tables are convenient as many administrative jurisdictions are coincident with county boundaries.

  18. Runoff-Producing Storms in Texas

  19. Results from NWS hourly data Triangular Hyetographs Results from runoff-producing storms in Texas

  20. Atlas ofDepth-Duration Frequency of Precipitation in Texas • 96 maps • 8 recurrence intervals • 12 durations

  21. Atlas of Mean Interoccurrence Interval of Daily Precipitation for Selected Rainfall Thresholds Interoccurrence interval of0.25 inches or more of daily precipitation

  22. Synthetic Unit Hydrographs

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