1. SENSORS, CYBERINFRASTRUCTURE, AND EXAMINATION OF HYDROLOGIC AND HYDROCHEMICAL RESPONSE IN THE LITTLE BEAR RIVER OBSERVATORY TEST BED Jeffery S. Horsburgh David K. Stevens, David G. Tarboton, Nancy O. Mesner, and Amber Spackman Jones Support: CBET 0610075

2. Sensors and sensor networks Cyberinfrastructure development Data publication Demonstrating techniques and technologies for design and implementation of large-scale environmental observatories WATERS Network 11 Environmental Observatory Test Beds 2 National Hydrologic Information Server San Diego Supercomputer Center

3. Little Bear River Test Bed How can high frequency measurements of turbidity help us quantify suspended sediment and total phosphorus fluxes? – Special case of a general problem of using surrogates to quantify hard to measure constituents – Important for managing water quality and for environmental observatory planning How can high-frequency sensor data collected at multiple sites improve hydrologic and hydrochemical process understanding? Across Test Beds: How can cyberinfrastructure facilitate data management and community data sharing?

4. Little Bear River Sensor Network 7 water quality and streamflow monitoring sites – Temperature – Dissolved Oxygen – pH – Specific Conductance – Turbidity – Water level/discharge 2 weather stations – Temperature – Relative Humidity – Solar radiation – Precipitation – Barometric Pressure – Wind speed and direction Spread spectrum radio telemetry network

5. Estimates of TSS and TP from Turbidity Least squares regression for TSS Regression with maximum likelihood estimation for TP (censored data) Little Bear River Near Paradise, UT

6. TP and TSS Loading 2006 TSS and TP from turbidity using surrogate relationships ~50-60% of the annual load occurs during one month of the year Provides information about flow pathways Little Bear River Near Paradise, UT

7. Effects of Sampling Frequency Spring 2006

8. Upper South Fork Little Bear (2007- 2008) Two Component separation based on conductance: 43 % Baseflow 57 % Quickflow Baseflow does not extend into the peaks of the snowmelt hydrograph

9. Estimates of Biological Parameters

10. ODM Database ODM Database WaterOneFlow Web Services GetSites GetSiteInfo GetVariableInfo GetValues WaterOneFlow Web Services GetSites GetSiteInfo GetVariableInfo GetValues 1. Data Collection Stream guaging Water quality monitoring Groundwater level monitoring Climate Monitoring 2. Data Files are Loaded into ODM Using Controlled Vocabularies 3. Implementation of WaterOneFlow Web Services and Registration with Central Registry ODM Database ODM Database Excel Files Excel Files Text Files Text Files Access Files Access Files Binary Files Binary Files Central Registry

12. WATERS Test Bed Data Publication Network June 17, 2008

13. Conclusions Early snowmelt generates the vast majority of the annual TSS and TP load via surface pathways from snowmelt close to the streams that carry TP and TSS loads Water quality constituent loads estimated using weekly or monthly data: – Are not representative of the high variability in discharge and constituent concentrations – Tend to under predict the true loading Discharge from slow subsurface pathways (i.e., baseflow) is relatively constant throughout the year and does not extend to a great degree into the peaks of the spring snowmelt hydrograph More than half of the annual discharge is from fast pathways (i.e., quickflow) that dominate the spring snowmelt hydrograph and dilute the relatively constant baseflow Metrics based on high-frequency profiles of DO concentrations and saturation deficits, are useful indicators of instream biological activity and can easily be calculated from high-frequency data

14. Conclusions Organization of data using the HIS enabled data management, analysis, and publication Cyberinfrastructure demonstrates how common systems can support a larger community