Basic Hydrology: Gauge Analysis

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.

Gage Analysis The Beargrass Creek example of the prior lesson is representative of the mechanics of gage analysis. The data requirements include: Sufficiently long record of systemic data (annual peak discharges computed same way each year) Historical “significant” events (extreme observations)

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

USGS Website Start here and build a QUERY

USGS Website Search Result for “Harris County + Buffalo Bayou”

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

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

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.

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

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

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

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

Plotting Position Formulas Weibull compared to Cunnane on Beargrass Creek Same magnitude, different AEP

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

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

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

Bulletin 17B The general method (as per HDM) is: 1.Base-10 logarithm of the discharge data 2.Compute mean, standard deviation, and skew of these log-transformed values (as per HDM/17B) 3.Compute weighted skew from station and regional skew 4.Identify outliers, remove and repeat steps 2 and 3 (HDM 4-27 to 4-29) 5.Compute flow values for desired AEP AEP determines K value(s)

Bulletin 17B Beargrass Creek Log transform Compute statistics Outliers

Bulletin 17B Beargrass Creek Find frequency factors (FREQFAC or 17B Tables)

Bulletin 17B Beargrass Creek Map and plot LP3 model

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

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)