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

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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 Presentation by Dr. William H. Asquith, USGS, Austin March 18, 2004

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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. - PowerPoint PPT Presentation

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

Page 1: 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-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

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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.

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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

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L-moment diagrams are the state-of-the-art tool

for selection distributions to model environmental data.

Distribution L-moments compared to data

L-moments. Differences between distributions are clear

and unambiguous.

L-moment diagrams

1. Kappa (4 parameters)

2. Pearson Type III (3 para.)

3. Gamma (2 parameters)

4. Exponential (2 parameter) Kappa distribution is MOST ACCURATE.

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Kappa Distribution

Dimensionless Frequency

Curves

MIT has LIMITED influence on the curve--so does geographic location

EASY TO USE

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Dimension-less Kappa Distribution Frequency

Curves

• Limited spatial differences

• Flexible

• Unambiguous

• Easy to interpret and use

"frequency factors"

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Comparison of Exponential,

Gamma, KappaDimensionless Distributions

of Storm Depth

Exponential used in analytical BMP equations.

EPA and others suggest Gamma.

Kappa most accurate (cutting-edge) and throws greater outliers.

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Mean Storm Depth for

8-hour MIT

Large east-to-west grad.

Maps used with dimensionless frequency curve to generate storm depth distribution.

21 maps provided

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Mean Storm Depth for

24-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.

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COUNTY MEAN_08hr_DEPTH 8hr-90th% El Paso 0.233 in 0.59 inLubbock .406 in 1.02 inTravis .494 in 1.24 inHays .564 in 1.42 inHarris .590 in 1.49 in

COUNTY MEAN_24hr_DEPTH 24hr-90th% El Paso 0.275 in 0.68 inLubbock .522 in 1.30 inTravis .672 in 1.67 inHays .743 in 1.85 inHarris .810 in 2.02 in

90th Percentile Storm Depths

2.52 is the8 hour - 90 percent frequency factor

2.49 is the24 hour -90 percent frequency factor

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Expected Depth (E[s]) in a BMPMIT < 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.

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THANKS

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APPENDIX

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Precipitation Related Publications by the TxDOT Research Program

1. Asquith (1998): USGS

2. Lanning-Rush, Asquith, andSlade (1999): USGS

3. Asquith (1999): USGS

4. Asquith andFamiglietti (2000): Journal

5. Al-Asaadi (2002): MS thesis

6. Asquith (2003): PhD diss.

7. Asquith, Bumgarner,Fahlquist (2003): Journal

8. Asquith and Thompson (2003): Proceedings

8. Strand (2003): MS thesis

9. Asquith and Roussel (2003): USGS

10. Asquith, Roussel, Thompson, Cleveland, and Fang (2004): USGS

11. Sether-Williams, Asquith, Cleveland, Fang, and Thompson (2004): USGS

12. Asquith, Roussel, Cleveland, Fang, and Thompson (????): USGS

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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.

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Dimensionless Exponential

Gamma

Kappa

Distribution Parameter Estimates

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Countywide Mean TablesTables 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.

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Runoff-Producing Storms in Texas

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Triangular Hyetographs

Results from NWS hourly data

Results from runoff-producing storms in Texas

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Atlas ofDepth-

Duration Frequency of Precipitation

in Texas

• 96 maps

• 8 recurrence intervals

• 12 durations

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Atlas of Mean Interoccurrence

Interval of Daily Precipitation for

Selected Rainfall

Thresholds

Interoccurrence interval of0.25 inches or more of daily

precipitation

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Synthetic Unit Hydrographs