1. SWAN Continuous Water Quality Monitoring Cuyler Smith 1 1 1 11 1 1 1 11 11 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0

2. Overview  Brief summary of project  How we handle project data  Workarounds  Remaining issues or problems  Questions

3. Project Summary  Water Quality at SWAN Core Parameters Core Parameters Temperature, pH, DO, Conductivity, TurbidityTemperature, pH, DO, Conductivity, Turbidity

4.  Primary Data Streams 1.Temperature Array (1-2 / lake) (Annual): Temperature and light; data cannon! (Annual): Temperature and light; data cannon! 2.Lake Profiles (many sites / lake) (Summer): Underwater weather stations; 5 WQ parameters (Summer): Underwater weather stations; 5 WQ parameters 3.Continuous Water Quality Monitoring (Select tributaries) (90 days): 7 WQ parameters (90 days): 7 WQ parameters 4.Water Level Recording (Select inlets/outlets) (90 days): Water level and temperature (90 days): Water level and temperature Project Summary

5. Example: Lake Clark 3 rd largest lake in the NPS, behind Naknek Lake (KATM) and Yellowstone Lake.

6. Scale of Data: Lake Clark in 2010  Middle Basin Temperature Array: 11 temperature thermistors logging hourly year round x 24 hrs / day x 365 days = 96,360 temperature records 11 temperature thermistors logging hourly year round x 24 hrs / day x 365 days = 96,360 temperature records 3 light sensors logging hourly year round x 24 hrs / day x 365 days = 26,280 light records 3 light sensors logging hourly year round x 24 hrs / day x 365 days = 26,280 light records  Lake Profiles: 5 WQ parameters logged x 15 depth intervals on descent x 2 (repeat log on ascent) x 30 sites lake-wide = 4,500 WQ records 5 WQ parameters logged x 15 depth intervals on descent x 2 (repeat log on ascent) x 30 sites lake-wide = 4,500 WQ records

7. Scale of Data: Lake Clark in 2010  Continuous Water Quality Monitoring: 7 WQ parameters logged hourly x 24 hrs / day x approx. 90 days x 2 sites = 15,120 WQ records 7 WQ parameters logged hourly x 24 hrs / day x approx. 90 days x 2 sites = 15,120 WQ records  Water Level Recording: Level transducer + temperature sensor logging hourly x 24 hrs / day x approx. 90 days x 4 sites = 17,280 records Level transducer + temperature sensor logging hourly x 24 hrs / day x approx. 90 days x 4 sites = 17,280 records  Date / Time Stamp associated with all records = 136,500 Total Records for Lake Clark in 2010: 296,040 (Does NOT include metadata!)

8. How we handle project data.xls.csv.txt metadata.dat Current metadata.csv

9. How we handle project data.xls.csv.txt metadata.dat Proposed metadata Database

10. In the Age of Aquarius

11. Workarounds  Organization and storage relies on file naming and folder structure.  Pull from numerous Excel and Aquarius files to aggregate for analysis and reporting.  Manual consolidation of metadata (calibration, deployment info, header info, etc.)

12. Remaining Issues and Problems  Too much data for our current file-based method! Data spread over too many files Data spread over too many files No central repository, no searching No central repository, no searching  Tracking metadata for each record What instrument recorded what value, when, instrument condition and calibration history, etc. What instrument recorded what value, when, instrument condition and calibration history, etc. Accountability, History Accountability, History  Analysis and Reporting Summary and aggregation Summary and aggregation Accessibility to others, sharing Accessibility to others, sharing

13. A water quality repository gives us insight…

15. Parallels  Water Quality – Underwater weather!  Soil Temperature Loggers – Vegetation Vital Signs  Temperature and Salinity – Marine Nearshore Vital Signs  Weather and Climate

16. Questions