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Interpretation of Flow at Un-gaged Stations May 1, 2007 CE 394K.2 Hydrology Semester Project Spring 2007 - Dr. Maidment. Submitted by: Stephanie L. Johnson. Outline. “The Big Picture” What do we have? What do we want? Methods to get what we want Assessment of one method Conclusion
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Interpretation of Flow at Un-gaged StationsMay 1, 2007CE 394K.2 HydrologySemester ProjectSpring 2007 - Dr. Maidment Submitted by: Stephanie L. Johnson
Outline • “The Big Picture” • What do we have? • What do we want? • Methods to get what we want • Assessment of one method • Conclusion • “Side project”
Data Collection in the Watersheds • Have USGS flow data at 15-minute intervals for continuous time periods • Have random water quality information at SWQM stations • Sometimes this includes flow (not very often) • Copano Bay watershed: • 7 SWQM stations with 32 years of data • 1900 total sampling events • Flow recorded at 182 (<10%) of those events
Load Duration Curve • Derived from a flow duration curve • Basically a cumulative frequency distribution • Load = flow x concentration x conversion factor • Calculate regulatory curve (flow x max concentration) • Add monitoring data as points • Position tells if it’s in compliance
Challenge • Need flow and water quality information at the same site • In some cases this is already done • In others we need to estimate flows • How to estimate it? • Model with rainfall-runoff model or regression equations from historic data • Estimate from gaged stations
Previous Studies • Wurbs and Sisson, 1999 (TA&MU) • Performed to analyze options for use in Water Rights Analysis Package (WRAP) model • Explored: rainfall-runoff modeling, regression equations, and estimating from gaged stations from historic data • Recommended estimating from gaged stations • NRCS Curve Number Adaptation Method • Drainage Area Ratio Method • Recommend using N values equal to 1.0 Now Used Widely
Previous Studies (Cont.) • Asquith et. al, 2006 • Follow-up on Wurbs study • Explores N1 (Φ) value in the Drainage Area Ratio Method Summarized Findings Then, K = 1.0
Methodology • Wurbs Recommendation • Run Drainage Area Ratio Method with N1 (Φ) = 1.0 to calculate a flow • Calculate percent error based on actual flow • Asquith Recommendation • Calculate new Φ value • For complete record and for quantiles • Run Drainage Area Ratio Method with new N1 (Φ) value • Calculate percent error Asquith Methodology
Results Results of Drainage Area Ratio Method for the Complete Record Performed for period from 2000 to present and found similar results.
Results (cont.) Calculated Φ Values of Quantiles Using Complete Record
Discussion • Computed Φ values result in lower errors Comparison of Average Percent Errors • Expected since Φ values are calculated based on flow data • Performing on shorter time periods not necessarily better • Using quantiles is more accurate • Particularly important when reference and calculated stations have such different flow regimes
Discussion (cont.) • Calculated Φ values muchhigher than expected Comparison of Calculated Φ Values • Most values are significantly over 1.0 • Large Φ value drives areal ratio toward zero (A1<A2) • Non-reference stations all have median flows of 0 cfs • Stations 08189200 and 08189300 have 60% and 45% zero flows
Conclusions • Important to admit your failures … • Method not accurate for stations with ephemeral flows • Potential that highly dynamic weather conditions on the coast make the Drainage Area Ratio Method less accurate But…… • Asquith Φ values more accurate than Wurbs’ suggestion of N1=1.0 • Use Asquith flow regime specific Φ values to calculate flows at un-gaged stations
References Asquith, W. H., Roussel, M.C., and Vrabel, J. 2006. Statewide Analysis of the Drainage-Area Method for 34-Streamflow Percentile Ranges in Texas. US Geological Survey Scientific Investigations Report 2006-____. Wurbs, R., and Sisson, E. 1999. Comparative Evaluation of Methods for Distributing Naturalized Streamflows from Gaged to Ungaged Sites. Texas Water Resources Institute Technical Report No. 179.
Weather Stations National Climatic Data Center (www.ncdc.noaa.gov)
Linear Regression Model Where: Q0 = Average daily flow at the USGS gage station on the day to be forecast (cfs) Q-n = Average daily flow at the USGS gage station n days before the day modeled (cfs) P0 = Total precipitation at the weather station on the day to be forecast (inches) P-n = Total precipitation at the weather station n days before the day modeled (cfs) an = Calculated constant
Beeville 5 NE vs. USGS Station 08189700 Summary of Data for Analysis Variables’ Correlation to Current Day Flow: Beeville 5N versus USGS Station 08189700
Beeville 5 NE vs. USGS Station 08189700 (cont.) Multiple Regression Output: Beeville 5N versus USGS Station 08189700 Governing Equation:
Beeville 5 NE vs. USGS Station 08189300 Summary of Data for Analysis Variables’ Correlation to Current Day Flow: Beeville 5N versus USGS Station 08189300
Beeville 5 NE vs. USGS Station 08189300 (cont.) Multiple Regression Output: Beeville 5N versus USGS Station 08189300 Governing Equation:
Non-linear Regression Modeling Log transformed: Natural log (ln) transformed: Not possible to do these models since precipitation is equal to zero for the majority of the time.