1 / 52

Water Quality Data Analysis

Water Quality Data Analysis. You don’t need to be afraid of numbers. Goals For Today. Create a frame of reference for your data Tell a complete story from your data Ask the right questions based upon your goals Reduce feelings of intimidation – you can dig in and interpret real data.

rosine
Download Presentation

Water Quality Data Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Water Quality Data Analysis You don’t need to be afraid of numbers

  2. Goals For Today • Create a frame of reference for your data • Tell a complete story from your data • Ask the right questions based upon your goals • Reduce feelings of intimidation – you can dig in and interpret real data

  3. Getting Perspective • Where are you in the larger watershed? • What does this mean about your expectations for water quality? • There are 90,000 miles of flowing water in Indiana, countless miles of intermittent stream segments and headwater wetlands. • As much as 80% of a stream’s water quality is inherited in the headwaters.

  4. Water Threatened / Total Miles in Watershed Impaired Waters of the US (1998) US EPA 2001

  5. Headwaters and Intermittent Streams

  6. All Sites Are Not Created Equal… • Headwater, tributary and main sections function differently and require different interpretation of their data • Stream functionality steadily changes along length • wetlands and adjacent floodplains vary • insect and animals vary • volumes and flow vary

  7. River Continuum Concept • Headwaters Streams • heavily shaded, leaf litter is important • shredders / collectors are abundant • Mid-order Streams • less shaded, algae more important • grazers abundant • Large Rivers • not shaded, phytoplankton present • collectors important

  8. Water Quality Regulated through Clean Water Act (1972) Waters of U.S. must be “fishable and swimmable” by 1983 Eliminate all pollution discharge to waters by 1985 Cuyahoga River, June 22 1969 http://blog.cleveland.com/metro/2009/06/cuyahoga_river_fire_40_years_a.html

  9. There is no single definition of clean water. The Clean Water Act defines clean water according to how we use it.

  10. How Do We Use Water? • Beneficial uses: • Drinking water • Agriculture • Industry • Recreation • Fisheries and other aquatic life • Aesthetics

  11. Critical Inter-relationships Who directly influences whom?

  12. Interrelationships

  13. What Are The Most Important Parameters? • What are your goals? • What do you monitor? • E. coli • Sediment • Nutrients • Oxygen • If one of your critical parameters is high, where do you begin to look for answers?

  14. Let’s talk about the parameters…

  15. Total Bacteria FamilyEnterobacteriaceae Total Coliforms Fecal Coliforms E. coli Pathogenic E.coli

  16. is the #1 source of water pollution to IN waterways! Sediment

  17. TSS and Turbidity • Both are indicators of the amount of solids suspended in the water • Mineral (e.g., soil particles) • Organic (e.g., algae, detritus) • TSS measures the actual weight of material per volume of water (mg/L) • Turbidity measures the amount of light scattered (NTU) • TSS allows the determination of an actual concentration or quantity of material while turbidity does not

  18. Is There Such a Thing as ‘Clean’ Sediment? • The greater the clay and organic content of the soil, the greater the storage bank to hold nutrients • The more positive charges associated with the nutrient, the tighter adsorption to the soil • Phosphorus has valence charge of -3, +3, or +5 NO!! Soils are electrical systems that have remarkable potential to attract and hold NUTRIENTS!

  19. Let’s Talk N and P Organic Inorganic • R-NH2, O=P(OR)3 • Amino acids, proteins, nucleic acids ,DNA, RNA, ATP, membranes, etc. • Accumulate in the sediment, are in plants & living things • NO3-, NO2- , NH4+, PO4-3 • Inorganic forms mostly occur in the water column (high solubility) unless recently released in sediments due to surrounding conditions • Are often low when algae are blooming –are being “used up” • Inorganic forms have short life spans in the big picture – they are quickly “taken up

  20. Meet Phosphorus photo credit: Raven, Peter, Linda Berg. Environment third edition. Harcourt:2001

  21. Meet Nitrogen

  22. The Role of Oxygen • Presence of oxygen has different desirability between N & P • Denitrification – occurs without oxygen - GOOD • Phosphorus release occurs without oxygen - BAD • Different longevity of impacts

  23. One More Important “Inter-relationship”… • Is the site with the highest concentration our biggest concern or highest priority? • A quick reminder about concentration vs. load!

  24. Results →Findings → Conclusions Making sense of it all.

  25. Turning Data to Information • Data entry and validation • Summarizing data to help with interpretation • Summary tables • Graphs • Simple stats • Data Interpretation • Allows you to learn from the water quality data • Helps improve your monitoring program • REAL reason you collected data • Conclusion • Explanation of why or how conditions occur

  26. Summary Table Table for Sampling Location Table for Water Quality Parameter • Useful to look at trends in data • How does a parameter change over time at a location? • How does a parameter change as you move downstream on given date? • Useful to see how different parameter are related to each other.

  27. Graphs and Charts • Presenting numerical data is difficult using tables filled with numbers • Graphs and charts summarize findings and show bottom line • Types of graphs • Line graph • Time-history • Spatial-trend • Bar graph • Pie chart

  28. Time-history

  29. Spatial-trend

  30. Bar Graph • Useful for comparing values from different time periods or locations. • No suitable for trend analysis.

  31. Pie Chart • Used to compare categories within the data set as a whole.

  32. Why Use Simple Statistics? • To reduce the volume of data you need to look at • To describe your data • To represent values that are “typical of the data set • To reveal the variability of the data • To reveal patterns and trends

  33. Looking at Real Data

  34. SR 67 12 10 SR 28 11 3 13 2 9 1 6 4 5 8 7

  35. Let’s look at some data

  36. Graphs/Graphing Exercise Get to know your site!

  37. Q&A on parameters Get out your worksheet!

  38. Is the data “reflective” of what you know to be true?

  39. Data Synergy – telling the whole story

  40. + 10 % - 10 %

  41. Do Two Points Make a Trend? • Trend or Coincidence? • How big of conclusions do we draw? • At what scale? • Across subwatersheds? • Across stream segments? • Site data relative to one another? • Relative to State standards? • Relative to a reference site?

  42. Determining Relative Level of Degradation • For all parameters, all subwatersheds were ranked against each other • All parameters equally weighted? • 303d List • Water quality thresholds • Loads (TSS, Total N, Total P, and TOC) • Macroinvertebrate Bioassessment

  43. Land Use Influences • Ok, so we have a certain concentration in the stream and a certain load of pollutants being feed downstream… • But where are the pollutants coming from and what can we expect based on land use?

  44. Land Use TotPhos (lb/acre/yr) TotSusSolids (lb/acre/yr) Rural Cropland 18-4550 0.18-4.1 Pasture 27-71 0.1-0.4 Forest 1-730 0.02-0.6 Idle 6-730 0.02-0.6 Urban Residential 550-2050 0.4-1.2 Commercial 45-740 0.1-0.8 Industrial 400-1517 0.8-3.7 Devl Urban 24,500 20 Models Help Us Make Predictions Source: Sonzogni et al., 1980 Source: Sonzogni et al., 1980 Source: Sonzogni et al., 1980

  45. Common Sources

  46. Who Monitors (Other data sources) • Federal Agencies • EPA • USGS • Forest Service • NOAA • Fish and Wildlife • States • IDEM • DNR • Health Department • Drinking Water Agencies • Universities • Counties • Municipalities • Tribes • Regulated Communities • Advocacy Organization • Nature Conservancy • Sporting Organizations • Watershed Organizations • Schools, 4-H

  47. How Does Data Compare: • How does it compare: • Consider detection limits • Correlated by different measures • TSS vs. Turbidity E. coli vs. total coliform • Varying biological protocol • Varying matrices (IBIs)

More Related