Setting the Course for Improved Water Quality Identifying Data Gaps; Developing a Monitoring Plan

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1 Setting the Course for Improved Water Quality Identifying Data Gaps; Developing a Monitoring Plan An impaired waters training program for local government leaders and other water resource managers Session 7 wq-iw3-57

2 Your project to date Scoped the project Conducted data inventory Began the review of existing data Now: Continue data review Identify data gaps Develop Monitoring Plan

3 Thought You cannot manage what you do not understand and you cannot understand what you do not measure Good quality and enough data allow us to identify causes and quantify sources of pollution in a waterbody

4 Considerations The data you ve compiled to date may not provide needed answers If not, you will need to collect more data

5 Don t move ahead until you are ready! Using incomplete or poor quality data sets Wrong pollutant source and delivery dynamics Poor or improper load allocations Scientifically indefensible data Money wasted

6 Focus Presentation focus Planning for the collection of water quality data, including flow/stage Later modules Other data sets: biological, land use, geomorphological, etc.

7 Reminder For a TMDL Study, it is important to develop a monitoring plan for each type of data: Flow essential for calculating load allocations Chemistry identifies pollutant concentrations Biology assists in identifying sources, impacts Geomorphology helps in understanding the watershed system

8 Planning is critical 1. Develop Monitoring Plan to guide collection of new data 2. Make decision now about the analytical tools you will use Will the cost be justified? Will tool produce accurate, defensible results? Is there technical support if you have questions? What/how much data does the tool or model you want to use need? (Modules 9 and will address tools and models in in more detail)

9 Before developing monitoring plan Read EPA/MPCA Protocols Protocols for specific impairments Dissolved oxygen Fecal coliform Excess nutrients Turbidity

10 When examining existing data: Is it the right data? Do you have?/do you need? Water data chemical biological physical (geomorphology, stream channel) flow information Windshield surveys / walking tour observations Watershed data soils land uses inventories demographics Climatological data

11 When examining existing data: Do you have the right quality of data? All data includes errors Absolute certainty seldom achieved Collected data must be of sufficient quality to enable scientifically defensible decisions

12 Do you have the right data quality? 1.What were collection conditions & methods? 2.Were collection goals met? 3.Was sampling QA/QC followed? 4.Was lab certified? 5.Were lab s detection limits the same? 6.Does the data meet acceptable measurement criteria? 6. Was there a QA/QC plan? 7. What was measurement quality of flow data? 8. Is the data too old to compare to current data? 9. Were meters/equipment calibrated properly? It It takes a good deal of of effort to to answer these questions!

13 Do you have enough data? 1.Can you identify impairment causes / sources? 2. Do you have enough data to understand ways physical / chemical / biological systems are integrated? If not, what must you do? 3. Can you calculate pollutant loads (TMDL, WLAs, LAs)? 4. Can you select and design pollution control measures? 5. Is data defensible?

14 What s your answer? Not sure? If answering these questions leaves you feeling uncomfortable or uncertain, you may have significant data gaps If you believe data is solid It may be time to complete your TMDL

15 What types of data gaps exist? Informational? Temporal? Spatial?

16 Types of data gaps Informational Photo: Larry Gunderson A certain type of data is not available Example: You do not have the flow data needed to calculate pollutant loads

17 Types of data gaps Temporal Data collected long ago; under different conditions Data collected in a different season Data collected over different hydrologic conditions

18 Types of data gaps Data not collected with needed spatial distribution 1? Spatial?? 3 2 Example: Certain land areas in watershed are underrepresented

19 Document, describe data gaps Informational Temporal Spatial Use worksheet 6-1 to document gaps and to define monitoring needs

20 Developing a monitoring plan Plan should be iterative Plan a work in progress Be open to changing sampling design as you gain knowledge Document changes

21 Developing a monitoring plan Plan should be comprehensive Must include plan for biomonitoring and geomorphology Must be able to compute pollutant loads chemical concentration data is not enough also need stream flow data Data should represent the range of flows Must have enough data to be defensible

22 Developing a monitoring plan Involve a multi-disciplinary team when developing the plan Ensure appropriate staff and resources to implement the plan Note: Much of of the written plan can be extracted from your worksheets

23 Monitoring considerations Number of years needed to collect adequate data Number of staff needed to implement Importance / size of TMDL

24 Monitoring considerations What others are doing coordinate! Scientific rigor objectives should define required level must have enough to be defensible

25 Other considerations Include in Monitoring Plan Water quality variables that may need to be addressed in a future TMDL (assuming current listing parameters) Long-term monitoring activities to evaluate effectiveness of best management practices over time

26 Monitoring plan outline Introduction Background Data collection goals Data collection objectives Field sampling plan Equipment needs, availability Parameters analyzed (ex. DO, turbidity, etc.) Laboratory analysis plan Budget (monitoring, analysis) Timeline Roles / responsibilities Data storage and management plan Quality assurance project plan

27 Monitoring plan outline Introduction Background Data collection goals Data collection objectives Field sampling plan Equipment needs, availability Parameters analyzed Laboratory analysis plan Budget Timeline Roles / responsibilities Data storage and management plan Quality assurance project plan

28 Develop data collection goals -- Examples Typical data collection goals Determine current conditions (pollutant loads, biological integrity, etc.) source contributions conditions under which pollutants are delivered Develop and/or calibrate hydrologic or water quality models

29 Monitoring plan outline Introduction Background Data collection goals Data collection objectives Field sampling plan, Equipment needs, availability Parameters analyzed Laboratory analysis plan Budget Timeline Roles / responsibilities Data storage and management plan Quality assurance project plan

30 Data collection objectives -- Examples Sample Goal: Compute existing pollutant load Objectives: 1. Collect 25+ samples (including high and low flows) at appropriate time periods 2. Conduct accurate stage tracking within 7/100 of a foot 3. Develop well-defined current rating curve 4. Use certified laboratory for all sample analysis

31 Monitoring plan outline Introduction Background Data collection goals Data collection objectives Field sampling plan Equipment needs, availability Parameters analyzed Laboratory analysis plan Budget Timeline Roles / responsibilities Data storage and management plan Quality assurance project plan

32 Field Sampling Plan It is important that you determine: When to Where to How you will What to

33 When do you monitor to characterize water quality? Emphasize sampling activities during event flows (e.g. snow melt / rainfall runoff in precanopy season, March/April June) Consider size of stream/river and length of hydrograph when determining sampling timing and frequency Gather samples across spectrum of seasons and flows (base flow, event flows, in between) Collect more than one year of monitoring data when possible

34 Where do you monitor to characterize water quality? When selecting a site Depends on goals and objectives Select representative sites Well- mixed water column No direct inputs nearby To determine pollutant loads, monitor near tributary confluences and watershed mouths To characterize streams, use longitudinal study and/or network study of tributaries MORE

35 Where do you monitor to characterize water quality? When selecting a site Safe (no traffic, steep banks, etc.) Stable cross-section for flow gauging studies Fewer sites which get you better data may be a good goal Ask DOT about future plans for bridges you intend to use

36 How do we monitor? Typical methods will be presented in Module 9

37 What do you monitor? Depends on data goals, objectives, gaps Refer to worksheets and previous discussions to specify what to monitor Aim to collect more than the data you need to finish the TMDL (can be more cost-effective)

38 Monitoring plan outline Introduction Background Data collection goals Data collection objectives Field sampling plan Equipment needs, availability Parameters analyzed Laboratory analysis plan Budget Timeline Roles / responsibilities Data storage and management plan Quality assurance project plan

39 Roles and responsibilities Monitoring Plan should specify key roles & responsibilities Local sponsor Project Manager Citizens MPCA Tech staff And others: consultants, other agencies, etc.

Citizen monitoring is an important component of monitoring plans Citizen monitoring can: be an effective way to build local support increase

40 Citizen monitoring is an important component of monitoring plans Citizen monitoring can: be an effective way to build local support increase appreciation for the waterbody gather useful information (if consistent & technically accurate) Be clear in your objectives about the role of citizen monitoring!

41 Citizen monitoring Make sure plan reflects time needed to train citizens Success depends on careful training Ensure they follow strict training procedures Transparency tube monitoring is a good starting point Good data depends on good training!

42 Monitoring plan outline Introduction Background Data collection goals Data collection objectives Field sampling plan Equipment needs, availability Parameters analyzed Laboratory analysis plan Budget Timeline Roles / responsibilities Data storage and management plan Quality assurance project plan

43 Data storage and management Monitoring Plan must follow strict data submission and entry protocols Have strict protocols: HYDSTRA (Time series data) STORET* (Water quality data)

44 Monitoring plan outline Introduction Background Data collection goals Data collection objectives Field sampling plan Equipment needs, availability Parameters analyzed Laboratory analysis plan Budget Timeline Roles / responsibilities Data storage and management plan Quality assurance project plan (QAPP)

45 What is a QAPP? Follow detailed procedures to ensure quality data (quality assurance) Water sampling and lab analytical methods (ensure samples are collected, handled and analyzed appropriately) Document other procedures and methodologies for: Biological monitoring Flow measurements GIS Geomorphology

46 Quality assurance project plan (QAPP) The QAPP Vitally important Required for all TMDL studies Water sampling protocol / QAPP analysis coordinated by Roger Fisher, MPCA

47 Monitoring plan outline Introduction Background Data collection goals Data collection objectives Field sampling plan Equipment needs, availability Parameters analyzed Laboratory analysis plan Budget Timeline Roles / responsibilities Data storage and management plan Quality assurance project plan

48 Expected time commitment Remember: Collecting good quality data cannot be rushed

49 Summary Good quality data and the right data provide the foundation for a complete and accurate TMDL Study Analyze existing data that came from your data inventory Determine whether you have the data needed to answer critical questions Develop Monitoring Plan before heading to the field

50 Summary Develop clear data collection goals for the TMDL Study Plan field work after Monitoring Plan approved Follow QAPP when conducting field work Only use certified labs to analyze water quality samples Submit all data to MPCA & STORET or HYDSTRA teams following existing protocols (methods and analysis techniques covered in Module 10)

51 A river is more than an amenity It is a treasure. It offers a necessity of life that must be rationed among those who have power over it. Oliver Wendall Holmes, 193

Session 7: Identify Data Gaps Develop Monitoring Plan

Session 7: Identify Data Gaps Develop Monitoring Plan In This Training Session Acronyms Introduction Introduction Examine the data you have Read and understand MPCA/EPA protocols Determine whether you