1. Basic Hydrology & Hydraulics: DES 601
Module 4
Gauge Analysis

2. Gage Analysis
Gage analysis is use of historical records to construct a frequency curve for a gauging station.
This frequency curve is then applied in one of two ways:
If the location of interest is near the gage, on the same stream, draining the same watershed, then the discharge can be directly inferred from the frequency curve for the design AEP
If the location of interest is on the same stream, but not particularly nearby, gage transposition may be possible.
Module 4

3. Gage Analysis
The mechanics of gage analysis was presented in the prior module
The data requirements include:
Sufficiently long record of systemic data (annual peak discharges computed same way each year)
Historical “significant” events (extreme observations)
Module 4

4. Gage Analysis Record length (systemic)
HDM Table 4-3 lists minimum record lengths for various AEP values.
1% AEP should use at least 20 years of record
Data sources include:
USGS NWIS (Website)
USGS Texas
IBWC
Older “paper-based” records
Module 4

5. USGS Website
Start here and build a QUERY
Module 4

6. USGS Website Search Result for “Harris County + Buffalo Bayou”
Module 4

7. USGS Website Select the Station ID can find Peak Discharges Module 4
40-yr. record, adequate for
1% AEP analysis
as per HDM
Module 4

8. USGS Website Typical annual peak file from USGS Module 4
Annual peaks are by “water year”
Like a fiscal year, multiple water
years span calendar years
Module 4

9. Gauges and Data Different kinds of gages
Continuous record (usually stage, then rated to produce discharge)
Located at control section if possible
Crest-Stage (captures peak stage)
Uses slope-area to estimate discharge
Post-event site visit recommended to survey debris-line as independent check of estimate
Both kinds in operation in Texas. Operated by USGS (and other local entities) on TxDOT ROW.
Module 4

10. Continuous Gage (DCP)
Continuous gage use some kind of stilling well, and transducers to measure stage and send to satellite
During visits, a nearby staff gage is read to independently validate the transducer readings
Module 4

11. Crest-Stage Gage
Vertical pipe has holes in bottom – becomes a stilling well.
Inside a staff gage and small amount of cork “flour” records water surface elevation.
Analyst visits site routinely (or after event) and records cork elevation and re-sets gage.
The elevations are marked on a staff inside the pipe with pencil (and dated)
Slope area between several nearby pipes is used to estimate discharge
Module 4

12. Graphical Estimation: Plotting position formulas
We have already seen an application of plotting positions.
A plotting position formula gives an estimate of the probability value associated with specific observations of a stochastic sample set, based solely on their respective positions within the ranked (ordered) sample set.
i is the rank number of an observation in the ordered set, n is the number of observations in the sample set
Bulletin 17B
Module 4

13. Plotting Position Formulas
Values assigned by a plotting position formula are solely based on set size and observation position
The magnitude of the observation itself has no bearing on the position assigned it other than to generate its position in the sorted series (i.e. its rank)
Weibull - In common use; Bulletin 17B
Cunnane – General use
Blom - Normal Distribution Optimal
Gringorten - Gumbel Distribution Optimal
Module 4

14. Plotting Position Formulas
Weibull compared to Cunnane on Beargrass Creek
Same magnitude, different AEP
Module 4

15. Plotting Position Formulas
Choice is a matter of judgment and preference
Analyst should be aware of impact of the choice
HDM encourages Bulletin 17B (hence Weibull) but does not mandate as only option
Modern literature encourages Cunnane
Module 4

16. Bulletin 17B
Flood Flow Frequency method (HDM) follows the Bulletin #17B Procedures
Texas specific refinements
Discussed in the HDM
Module 4

17. Bulletin 17B Texas Specific
HDM provides guidance for minimum record lengths
Uses log-Pearson Type III distribution model (and associated fitting procedure)
Uses a weighted skew value
Outliers
Transposition as indicated
Module 4

18. Bulletin 17B COULDN’T FIND IN SECTION OF PARTICIPANT GUIDE
The general method (as per HDM) is:
Base-10 logarithm of the discharge data
Compute mean, standard deviation, and skew of these log-transformed values (as per HDM/17B)
Compute weighted skew from station and regional skew
Identify outliers, remove and repeat steps 2 and 3 (HDM 4-27 to 4-29)
Compute flow values for desired AEP
AEP determines K value(s)
Module 4
COULDN’T FIND IN SECTION OF PARTICIPANT GUIDE

19. Outliers
A series of observed flows may include annual peak discharge rates that do not seem to belong to the population of the series.
The values may be extremely large or extremely small with respect to the rest of the series of observations.
Such values may be outliers that should be excluded from the set of data to be analyzed or treated as historical data.
Module 4

20. Bulletin 17B Beargrass Creek Log transform Compute statistics Outliers
Module 4

21. Bulletin 17B Beargrass Creek
Find frequency factors (FREQFAC or 17B Tables)
Module 4

22. Bulletin 17B
Beargrass Creek
Map and plot LP3 model
Module 4

23. Bulletin 17B – Texas Specific Refinement
Skew Adjustment
Adjust station skew for location in Texas
HDM describes the procedure for the weighted skew
Module 4

24. Summary
Gage analysis uses data from gages at or near location of interest
Different kinds of gages, both produce estimates of peak discharge – data from various sources (USGS, etc.)
Plotting positions for graphical estimates
Bulletin 17B for Log-Pearson Type 3 (and other) distribution models
Skew adjustments for Texas (in the HDM)
Outlier identification (in the HDM)
Module 4