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Quantifying Representative Sampling Using a Hydrologic Analysis Tool StormCon 2015 Austin, TX Christian Carleton, PH, CPSWQ, CPESC August 4, 2015 Research Engineer/Hydrologist Office of Water Programs California State University, Sacramento

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Quantifying Representative Sampling Using a Hydrologic Analysis Tool

Christian Carleton, PH, CPSWQ, CPESC Research Engineer/Hydrologist Office of Water Programs California State University, Sacramento

StormCon 2015

Austin, TX August 4, 2015

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Outline

Introduction Data Review Process Step 1 - Flow Data Quality Step 2 - Sample Collection Timing Step 3 - Percent Capture Conclusion Questions

StormCon 2015 Austin, TX August 2-6, 2015

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Perspective…and Disclaimer

  • OWP has over 15 year of stormwater

research experience.

  • Research based monitoring with some

regulatory compliance monitoring.

  • Primarily flow-weighted composite Event

Mean Concentration (EMC) water quality sampling.

StormCon 2015 Austin, TX August 2-6, 2015

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What is representative sampling?

Statistics

Subset from a population such that the group of samples has the same distribution of characteristics as the entire population.

Stormwater Monitoring

Flow and water quality data that has the same range and frequency of occurrences as the entire runoff event from a particular location. Locations have the same characteristics as the larger system of interest.

StormCon 2015 Austin, TX August 2-6, 2015

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Representative Sample

Defined in the study plan, Quality Assurance Project Plan (QAPP), Sampling and Analysis Plan (SAP) or similar document. Lots of resources available to plan for and incorporate representative sampling. Not many resources available to determine if a sample is still representative after it has been collected.

StormCon 2015 Austin, TX August 2-6, 2015

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Data Review Process

Step 1 - Flow Data Quality

Study plan requirements Collection errors Known Relationships

Step 2 - Sample Collection Timing

Individual samples taken at appropriate times during event.

Step 3 - Percent Capture

Quantify how much of the runoff event was sampled.

StormCon 2015 Austin, TX August 2-6, 2015

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Step 1 – Flow Data Quality

Flow-Weighted Sampling

Most accurate intra-event sampling scheme. Typically used to obtain Event Mean Concentration (EMC). Error in flow measurements = inappropriate sample timing. Not a true EMC composite sample.

Load Calculations

Load = EMC x Runoff Volume Error in runoff volume directly translates to error in load calculations.

StormCon 2015 Austin, TX August 2-6, 2015

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Study Plan Requirements

Rainfall Data

Duration Depth Volume Intensity

Average Instantaneous Max 1-hr Max

Runoff Data

Duration Time to Peak Peak Flow Rate Total Volume

StormCon 2015 Austin, TX August 2-6, 2015

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Collection Errors

View Raw Data for Inconsistencies

Physical Constraints

  • Flow begins after rainfall

begins.

  • Flow ends after rainfall

ends.

  • Samples collected during

runoff.

StormCon 2015 Austin, TX August 2-6, 2015

0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 Depth Time

Rainfall Depth v Time

04/03/2010 14:24 04/03/2010 19:12 04/04/2010 00:00 04/04/2010 04:48 04/04/2010 09:36 04/04/2010 14:24 04/04/2010 19:12 04/05/2010 00:00 04/05/2010 04:48 04/05/2010 09:36 04/05/2010 14:24 04/05/2010 19:12 1 83 165 247 329 411 493 575 657 739 821 903 985 1067 1149 1231 1313 1395 1477 1559 1641 1723 Time

Rainfall Time v Record

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Known Relationships

Volumetric Runoff Coefficient (Rv)

Measured: 𝑆𝑀 =

π‘Šπ‘ π‘ π‘ π‘ π‘ π‘  π‘Šπ‘ π‘ π‘ π‘ π‘ π‘ π‘ π‘ 

(Driscoll 1983)

Predicted: 𝑆 𝑀 = 0.858𝑗3 βˆ’ 0.78𝑗2 + 0.774𝑗 + 0.04

(Urbonas 1999; WEF and ASCE 1998)

Relative Percent Difference (RPD) 𝐽𝐽 𝑆𝑆𝑆 = 𝑆𝑄𝑄𝑄𝑗𝑄𝑄𝑄𝑄 βˆ’ 𝑁𝑄𝑁𝑁𝑁𝑄𝑄𝑄 𝑆𝑄𝑄𝑄𝑗𝑄𝑄𝑄𝑄 ≀ π‘ˆπ‘ˆπ‘„π‘„π‘π‘ˆπ‘ˆπ‘ˆπ‘„π‘ˆπ‘π‘ˆπ‘π‘„ 𝑄. 𝑕. , 0.2 π‘„π‘ˆπ‘„π‘’ 𝐡𝑄𝑄𝑄𝐡𝑄 Time of Concentration (Tc)

Measured: Time from hyetograph center-of-mass to hydrograph center-of-mass or other method. Predicted: NRCS Method from TR-55 or other method.

StormCon 2015 Austin, TX August 2-6, 2015

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Step 2 - Sample Collection Timing

Check to see if samples were collected at an appropriate time so that they are representative of the runoff. Qualitative - Graphs Quantitative - Uniformity Index

StormCon 2015 Austin, TX August 2-6, 2015

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Rate Graph

StormCon 2015 Austin, TX August 2-6, 2015

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.00 0.01 0.02 0.03 0.04 0.05 0.06

Rainfall Intensity (in/hr) Runoff Flow Rate (cfs) Time

Runoff Successful Sample Rainfall

Time-Weighted Sampling

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Rate Graph

StormCon 2015 Austin, TX August 2-6, 2015

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.00 0.01 0.02 0.03 0.04 0.05 0.06

Rainfall Intensity (in/hr) Runoff Flow Rate (cfs) Time

Runoff Successful Sample Rainfall

Flow-Weighted Sampling

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Cumulative Depth Graph

StormCon 2015 Austin, TX August 2-6, 2015

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Depth (in) Time

Rainfall Runoff Successful Sample

Flow-Weighted Sampling

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Cumulative Depth Graph

StormCon 2015 Austin, TX August 2-6, 2015

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Depth (in) Time

Rainfall Runoff Successful Sample

Time-Weighted Sampling

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Autosampler Backlog

Autosampler sample routine

Purge Rinse Sample Purge

Routine can take 2+ minutes to complete Urban drainages can have very flashy runoff responses. If the trigger for the next sample is received before the previous sample routine is completed, then it is added to the sample queue.

StormCon 2015 Austin, TX August 2-6, 2015

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Autosampler Backlog

Rate Graph

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Rainfall Intensity (in/hr) Runoff Flow Rate (cfs) Time

Runoff Successful Sample Rainfall

Cumulative Depth Graph

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Depth (in) Time

Rainfall Runoff Successful Sample

StormCon 2015 Austin, TX August 2-6, 2015

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Qualitative

Uniformity Index Want to determine if the intervals are equal.

Time or volume interval between samples

StormCon 2015 Austin, TX August 2-6, 2015

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Uniformity Index

Coefficient of Variation (COV)

𝑉𝐽 = π·π·π‘ˆ = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐸𝐸𝑀𝐸𝑇𝑇𝐸𝐸𝑇 (𝜏)

𝑁𝐸𝑇𝑇 (𝜈)

COV is a normalized standard deviation Allows for comparison of the variability between different data sets.

Small COV -> Little Variation Big COV -> Large Variation

Uniformity Threshold (UT)

𝐽𝐽 𝑉𝐽 ≀ π‘‰π‘ˆ π‘„π‘ˆπ‘„π‘’ π‘‰π‘’π‘—π½π‘ˆπ‘„π‘‰ π½π‘’π‘„π‘„π‘„π½π‘π‘ˆ

(0.5 Threshold determined by trial-and error.)

StormCon 2015 Austin, TX August 2-6, 2015

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Step 3 - Percent Capture

  • For composite samples.
  • Many different methods

– Percentage of entire runoff time occurring between first and last samples. – Percentage of entire runoff volume occurring between first and last samples. – Etc.

StormCon 2015 Austin, TX August 2-6, 2015

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OWP Method

Assumes flow-weighted sampling 𝑆𝐷 = π‘ˆ

𝑇𝐸𝑠

π‘ˆ

𝑇𝑠𝑇𝐸𝑠𝑠

Γ— 100

Vrep = represented volume Vrunoff = total runoff volume

StormCon 2015 Austin, TX August 2-6, 2015

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Represented Volume (Vrep)

Volume represented in the sample. Best visualized with a cumulative runoff (mass curve) hydrograph.

StormCon 2015 Austin, TX August 2-6, 2015

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Represented Volume (Vrep)

StormCon 2015 Austin, TX August 2-6, 2015

S1 S2 S3 S4 S5 S6 Volume Time

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Represented Volume (Vrep)

StormCon 2015 Austin, TX August 2-6, 2015

S1 S2 S3 S4 S5 S6 βˆ† V1 βˆ† V2 βˆ† V3 βˆ† V4 βˆ† V5 βˆ† V6 Volume Time

π‘ˆ

𝑇𝐸𝑠 = βˆ†π‘ˆ 𝐸 𝑛 𝐸=1

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Represented Volume (Vrep)

StormCon 2015 Austin, TX August 2-6, 2015

S1 S2 S3 S4 S5 S6 Volume Time S1 S2 S3 S4 S5 S6 Ξ”V1 Ξ”V2 Ξ”V4 Ξ”V6 Volume Time

π‘ˆ

𝑇𝐸𝑠 = βˆ‘

βˆ†π‘ˆ

𝐸 𝑛 𝐸=1

= βˆ†π‘ˆ

1+βˆ†π‘ˆ 2+βˆ†π‘ˆ 4+βˆ†π‘ˆ 6

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Total Runoff Volume (Vrunoff)

Total event runoff volume. Numeric integration method.

π‘ˆ

𝑇𝑠𝑇𝐸𝑠𝑠 =

π‘Ÿ 𝑄

𝑇=𝑇 𝑇=0

= π‘Ÿ 𝑄 + π‘Ÿ 𝑄 + 1 2

π‘‡βˆ’1 𝑇=0

𝑄 + 1 βˆ’ 𝑄

StormCon 2015 Austin, TX August 2-6, 2015

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Percent Capture

𝑆𝐷 = π‘ˆ

𝑇𝐸𝑠

π‘ˆ

𝑇𝑠𝑇𝐸𝑠𝑠

Γ— 100

Vrep = represented volume Vrunoff = total runoff volume

Minimum Allowable PC

If PC β‰₯ Minimum Allowable them Representative

StormCon 2015 Austin, TX August 2-6, 2015

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Conclusion

Any monitoring project should have a post-data collection QC process. 3-point data review process

Flow Data Quality Project Requirements Data Errors Known Relationships Sample Collection Timing Rate Graph Cumulative Depth Graph Uniformity Index Percent Capture

Intent is to ensure quality monitoring data is generated, either for research or regulatory compliance purposes.

StormCon 2015 Austin, TX August 2-6, 2015

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Questions?

Christian Carleton

christian.carleton@owp.csus.edu

Office of Water Programs

http://www.owp.csus.edu

Thank You