Monitoring, Modeling and Decision-Making

Transcription

1 Monitoring, Modeling and Decision-Making Daren Harmel USDA-ARS, Temple, TX Grassland, Soil and Water Laboratory, Temple, TX

2 Objectives for Today Small watershed monitoring Provide How To overview for managers Present How To details for technical staff Discuss balance between project resources, monitoring goals, and data uncertainty H/WQ measurement uncertainty Present method to determine uncertainty Discuss uncertainty sources and QA/QC Compare data types Discuss monitoring, modeling, and decisionmaking Grassland, Soil and Water Laboratory, Temple, TX

3 Published Information Harmel, R.D. and P.K. Smith Consideration of measurement uncertainty in the evaluation of goodness-of-fit in hydrologic and water quality modeling. J. Hydrol. 337: Harmel, R.D., R.J. Cooper, R.M. Slade, R.L. Haney, and J.G. Arnold Cumulative Uncertainty in Measured Streamflow and Water Quality Data for Small Watersheds. Trans. ASABE 49(3): Harmel, R.D., K.W. King., B.E. Haggard, D.G. Wren, and J.M. Sheridan Practical Guidance for Discharge and Water Quality Data Collection on Small Watersheds. Trans. ASABE 49(4): Harmel, R.D., K.W. King, and R.M. Slade Automated Storm Water Sampling on Small Watersheds. Appl. Eng. Agric. 19(6): Grassland, Soil and Water Laboratory, Temple, TX

4 Small Watershed Monitoring Grassland, Soil and Water Laboratory, Temple, TX

5 Discharge and Water Quality Data Collection on Small Watersheds Prior to this research, little published guidance was available to support design and operation of small watershed monitoring projects Costs and difficulties associated with such projects were often under-estimated Projects were typically characterized by inconsistent methods, missing data values, and short-term data sets Grassland, Soil and Water Laboratory, Temple, TX

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7 Discharge and Water Quality Data Collection on Small Watersheds Developed and published practical how to guidance Small watersheds Automated storm sampling Q measurement - USGS Manual grab sampling - USGS Grassland, Soil and Water Laboratory, Temple, TX

8 Summary of My Experience Scale - Field-scale, edge-of-field, small watershed Constituents - Nutrients, sediment, Q, bacteria (?) Sampling techniques - Automated storm sampling (ISCO), low flow grab sampling (manual) Land uses -, urban residential and turfgrass, native forest and grassland Grassland, Soil and Water Laboratory, Temple, TX

9 Difficulties Involved in Storm Monitoring Requires substantial resource commitment equipment purchase and maintenance personnel (travel, work hours) lab analysis Constrained by QA/QC Automated samplers needed Storm sampling safety, timing Problems will occur Grassland, Soil and Water Laboratory, Temple, TX

10 Difficulties Involved in Storm Monitoring Grassland, Soil and Water Laboratory, Temple, TX

11 Difficulties Involved in Storm Monitoring Grassland, Soil and Water Laboratory, Temple, TX

12 Designing Sampling Projects - Difficult Balance Project Objective: Achieve sampling goal(s) within financial, personnel, time, and watershed constraints Products: 1) Water quality data 2) Measurement uncertainty Sampling Components: 1) Automated vs Manual sampling 2) Enable level 3) Sampling interval 4) Discrete vs Composite samples 5) Q measurement Grassland, Soil and Water Laboratory, Temple, TX

13 Sampling Components: Manual vs. Automated Automated sampling necessary in most projects Most samplers have: stage recorder/flow meter sample collection pump & bottle(s) programmable operation/memory Must commit to regular maintenance limit malfunctions prevent data loss Grassland, Soil and Water Laboratory, Temple, TX

14 stage/flow recorder intake bottle(s) Grassland, Soil and Water Laboratory, Temple, TX

15 Sampling Components: Enable Level High Minimum Flow Threshold Enable Level increases error, even if consider Q and estimate concentration outside sampling period Grassland, Soil and Water Laboratory, Temple, TX

16 Sampling Components: Enable Level Low Minimum Flow Threshold Enable Level reduces error, especially if consider Q and estimate concentration outside sampling period Grassland, Soil and Water Laboratory, Temple, TX

17 Sampling Components: Sampling Interval Time-Interval Sampling Advantages simple, reliable Disadvantages difficult to choose interval Q needed to calculate load Low interval recommended flow (m 3 /s) 3 flow time-interval sample (10 min) time (min) time interval composite samples per bottle (min) discrete no no no no 10 no no no no 15 no no no yes 30 no no yes no 60 no yes no no 120 yes no no no 180 no no no no Grassland, Soil and Water Laboratory, Temple, TX

18 Sampling Components: Sampling Interval Flow-Interval Sampling Advantages produces EMC easy to choose interval uniform sampling for various watershed sizes Disadvantages must accurately measure Q mm interval recommended flow (m 3 /s) 3 flow flow-interval sample (2.5 mm) time (min) Grassland, Soil and Water Laboratory, Temple, TX

19 Sampling Components: Discrete vs Composite Discrete Sampling collection of 1 sample per bottle difficult to sample complete storms of various duration complete information on within-storm concentrations (if can sample complete event) Grassland, Soil and Water Laboratory, Temple, TX

20 Sampling Components: Discrete vs Composite Composite Sampling collection of 2+ samples per bottle little or no increase in error reduced information on within-storm concentrations Grassland, Soil and Water Laboratory, Temple, TX

21 Sampling Components: Q Measurement To make continuous discharge measurement: establish stage-discharge relationship measure stage - preferably in stilling well determine Q with stage-discharge relationship Grassland, Soil and Water Laboratory, Temple, TX

22 Sampling Components: Q Measurement Grassland, Soil and Water Laboratory, Temple, TX

23 Sampling Components: Q Measurement Precalibrated flow control structure eliminates need to develop stage-discharge relationship (rating curve) Grassland, Soil and Water Laboratory, Temple, TX

24 Sampling Components: Q Measurement Flow control (hydraulic) structure highly recommended provides consistent, accurate Q measurement for load and EMC determination instream area-velocity sensors may be preferred in certain conditions Stilling Well Grassland, Soil and Water Laboratory, Temple, TX

25 Remember the Difficult Balance??? Project Objective: Achieve sampling goal(s) within financial, personnel, time, and watershed constraints Products: 1) Water quality data 2) Measurement uncertainty Sampling Components: 1) Automated vs Manual sampling 2) Enable level 3) Sampling interval 4) Discrete vs Composite samples 5) Q measurement Grassland, Soil and Water Laboratory, Temple, TX

26 Balancing Resources and Data Quality Alternatives to save $$ by reducing number of samples Alternative Cost Error Recommend Raise enable level save $ increase No Increase sampling interval save $ increase No Composite samples save $ little to no increase Yes Grassland, Soil and Water Laboratory, Temple, TX

27 Balancing Resources and Data Quality Discrete Flow-Interval Sampling with 24 bottles Sample Runoff NO 3 -N 1 2 mm 4.0 mg/l 2 4 mm 3.0 mg/l 3 6 mm 2.0 mg/l 4 8 mm 1.0 mg/l Analysis Cost = $100 Composite Flow-Interval Sampling with Single Bottle Samples Runoff NO 3 -N 4 8 mm 2.5 mg/l Analysis Cost = $25 Grassland, Soil and Water Laboratory, Temple, TX

28 Balancing Resources and Data Quality Gather as much watershed-specific information as possible significantly improves monitoring design and operation sampling sites, sampling intervals estimate number of samples per year Set sampling goal(s) early in project development will determine data collection needs If concentration, Q measurement unnecessary If loads or EMCs, Q measurement required If first flush or max. concentration, take discrete samples If avg. concentration, consider composite sampling Grassland, Soil and Water Laboratory, Temple, TX

29 Balancing Resources and Data Quality Alternatives to reduce overall costs Alternative Cost Error Recommend Decrease # sites save $$$ small increase Decrease # storms save $$ large increase Yes No, no, no Grassland, Soil and Water Laboratory, Temple, TX

30 H/WQ Measurement Uncertainty Grassland, Soil and Water Laboratory, Temple, TX

31 H/WQ Measurement Uncertainty All measurements introduce some level of uncertainty into the resulting value, but this uncertainty is typically ignored. Why?? Until recently Scientists had not established an adequate understanding of measurement uncertainty No complete uncertainty (error propagation) analysis had been conducted on measured H/WQ data No easy-to-use tool was available for uncertainty estimation Grassland, Soil and Water Laboratory, Temple, TX

32 H/WQ Measurement Uncertainty In 2006, we developed a framework for estimating uncertainty in measured streamflow and water quality data (TSS, N, P) from small watersheds Established data collection procedural categories discharge measurement, sample collection, sample preservation and storage, laboratory analysis, data processing and management Presented and applied a simple estimation method (RMSE) determines combined uncertainty for all steps within categories EP = ( ) E E E E E Q C PS A + DPM Grassland, Soil and Water Laboratory, Temple, TX

33 Procedural Categories discharge measurement - individual measurements, d vs Q relation sample collection - EWI, grab, automated, sampling interval sample preservation/storage - processing, preservation, storage laboratory analysis - reagent and standard, method, instrument data processing and management - mistakes, missing data Grassland, Soil and Water Laboratory, Temple, TX

34 Measurement Uncertainty - METHODS In 2007, we completed software tool called Data Uncertainty Estimation Tool or DUET-H/WQ Based on the uncertainty estimation framework Applied to data sets from Hamilton, TX Riesel, TX Austin, TX Waterloo, IN Centerburg, OH Grassland, Soil and Water Laboratory, Temple, TX

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36 H/WQ Measurement Uncertainty Grassland, Soil and Water Laboratory, Temple, TX

37 H/WQ Measurement Uncertainty Grassland, Soil and Water Laboratory, Temple, TX

38 H/WQ Measurement Uncertainty Grassland, Soil and Water Laboratory, Temple, TX

39 H/WQ Measurement Uncertainty Grassland, Soil and Water Laboratory, Temple, TX

40 H/WQ Measurement Uncertainty Grassland, Soil and Water Laboratory, Temple, TX

41 H/WQ Measurement Uncertainty Each procedural category can contribute substantial uncertainty; therefore, QA/QC protocols should address all discharge measurement typically adequately addressed sample collection typically poorly addressed sample preservation/storage typically emphasized laboratory analysis typically emphasized Grassland, Soil and Water Laboratory, Temple, TX

42 H/WQ Measurement Uncertainty DUET-H/WQ uses uncertainty estimates for each step in each procedural category to determine the cumulative or combined uncertainty for each measured value. Grassland, Soil and Water Laboratory, Temple, TX

43 H/WQ Measurement Uncertainty Q TSS NO3-N PO4-P Tot. N Tot. P Grassland, Soil and Water Laboratory, Temple, TX

44 Measurement Uncertainty - Conclusions Measured data (and model predictions) are uncertain Estimating and understanding uncertainty enhances monitoring design better informs decision-making leads to more reasonable regulation improves model application and evaluation What s next...??? attempt to make uncertainty estimation a routine task in H/WQ data collection and reporting finalize method to consider both measurement and model uncertainty in model goodness-of-fit evaluation Grassland, Soil and Water Laboratory, Temple, TX

45 Monitoring, Modeling, and Decision-Making Grassland, Soil and Water Laboratory, Temple, TX

46 We have to use sound science!! I don t trust models!! How can we get the biggest bang for our cleanup buck?? Grassland, Soil and Water Laboratory, Temple, TX

47 Monitoring, Modeling, and Decision-Making Facts about measured data stakeholders place more trust in never have enough because it s expensive, time-consuming, wrong scale, different conditions, etc. Facts about models need actual data to increase confidence in predictions extremely valuable for simulating alternative practices, spatial relationships, various conditions, and future scenarios Both are necessary, since neither provides all the information needed for water quality decision-making. Grassland, Soil and Water Laboratory, Temple, TX

48 Monitoring, Modeling, and Decision-Making So should we rely on modeling or monitoring in water quality decision-making?? WRONG QUESTION Right question is How do we appropriately use modeling and monitoring in water quality decision-making?? Grassland, Soil and Water Laboratory, Temple, TX

49 Monitoring, Modeling, and Decision-Making Major (typical) decision to make is What is the best way to solve this water quality problem?? This requires answering What are the important contributors to this problem?? What are the best practices to implement?? Where are the best locations to install these practices?? How can effectiveness be evaluated (post-implementation)?? Grassland, Soil and Water Laboratory, Temple, TX

50 Monitoring, Modeling, and Decision-Making Science-based options to answer these questions Science-based options cost stakeholder trust reliability monitor high high moderate model moderate low moderate professional judgement low low low Grassland, Soil and Water Laboratory, Temple, TX

51 Monitoring, Modeling, and Decision-Making Most decisions can be made with similar approach to adaptive mgmt 1. Determine sources - data, model, BPJ, and/or stakeholder input 2. Estimate contributions by various sources - model 3. Make conservative reductions (implement BMPs) for significant, willing sources use models to optimize practice type and location 4. Determine if reductions lead to desired improvement data 5. If necessary - conduct research to better understand processes - improve model to better represent processes - make further reductions with additional BMPs based on monitoring data, improved science, cost-benefit considerations Grassland, Soil and Water Laboratory, Temple, TX

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