1 / 18

Uncertainty in Lake Erie Residual Net Basin Supplies

Uncertainty in Lake Erie Residual Net Basin Supplies. Jacob Bruxer, M.A.Sc ., P.Eng . Environment Canada/International Upper Great Lakes Study Dr. Syed Moin, Ph.D., P.Eng . International Upper Great Lakes Study Dr. Yiping Guo , Ph.D., P.Eng . McMaster University. Presentation Overview.

latona
Download Presentation

Uncertainty in Lake Erie Residual Net Basin Supplies

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. Uncertainty in Lake Erie Residual Net Basin Supplies Jacob Bruxer, M.A.Sc., P.Eng. Environment Canada/International Upper Great Lakes Study Dr. Syed Moin, Ph.D., P.Eng. International Upper Great Lakes Study Dr. YipingGuo, Ph.D., P.Eng. McMaster University

  2. Presentation Overview • Water balance and the definition of Net Basin Supplies (NBS) and two methods (component and residual) for computing NBS • Uncertainty analysis of Lake Erie residual NBS • Sources and estimates of uncertainty in each of the various inputs (inflow, outflow, change in storage, etc.) • Combined uncertainty estimates (FOSM and Monte Carlo) • Methods proposed or underway for improving input estimates • Conclusions on Lake Erie residual NBS uncertainty • IUGLS Adaptive Management and FIRM

  3. Net Basin Supplies (NBS) • Net Basin Supplies (NBS) • Net volume of water entering (or exiting) a lake from its own basin over a specified time period • Water Balance • Component Method • Residual Method

  4. Motivation for Study • Accurate NBS estimates are required in the Great Lakes basin for: • Operational regulation of Lake Superior and Lake Ontario • Formulation and evaluation of regulation plans • Water level forecasting • Time series analyses and provide an indicator of climate change • To reduce uncertainty in NBS, first necessary to identify and quantify sources of error • Allows comparison of each of the different inputs to alternative methods for computing them • Allows for comparisons of residual NBS to other methods of estimating NBS (i.e. component) 4

  5. + ??? uncertainty ΔS

  6. ON@BUF = NMOM + PSAB1&2 + PRM + DNYSBC - RN - DWR Lake ErieOutflow • OErie = ON@BUF + OWC • ON@BUF = sum of various flow estimates • NMOM (Maid-of-Mist pool) • Stage-discharge curve • Uncertainty from flow measurements, model error, predictor variables • u95 = 6.7% ~= 120 - 180 m3/s • PSAB1&2 +PRM (Power Plants) • u95 = 4.0% ~= 140 - 160 m3/s • RN (Local Runoff) • u95 = 60 - 600% ~= 20 – 60 m3/s • Errors of up to 100 m3/s possible • ON@BUF : • u95 = 4% ~= 200 – 250 m3/s • OWC : • u95 = 8% ~= 20 m3/s OWC

  7. Detroit River Inflow • Mildly sloped channel • Stage-fall-discharge equations: • Uncertainty (95% CL) • Gauged discharge measurements = 5% • Standard error of estimates = 6.6% • Error in the mean fitted relation = 1% • Predictor variables (i.e. water levels) = 2% • Overall uncertainty ≈ 8.6% at 95% confidence level • Systematic effects can increase error and uncertainty significantly on a short term basis • e.g., Ice impacts and channel changes due to erosion, obstruction, etc. • Larger, but easier to identify

  8. Improving Flow Estimates • Newly installed International Gauging Stations on connecting channels • Horiziontal ADCP and Index-velocity ratings on St. Clair and Detroit Rivers (also on St. Marys River) • Water level gauge and stage-discharge relationship on Niagara River near Peace Bridge (outlet of Lake Erie) • Frequent flow measurements for calibration and validation • Improvements to Welland Canal index velocity rating • Bathymetry data collection in St. Clair (and soon Detroit) to monitor changes in conveyance • Other methods also being investigated • Hydrodynamic models

  9. Change in Storage (ΔS) • Change in the lake-wide mean water level from the beginning-of-month (BOM) to the end-of-month (EOM) • Sources of Uncertainty: • Gauge accuracy (+/- 0.3 cm) • Rounding error (+/- 0.5 cm) • Temporal variability (+/- 0.3 cm) • Spatial variability • Lake area (negligible) • Glacial Isostatic Adjustment (GIA) (Negligible on a monthly basis) • Thermal expansion and contraction

  10. Spatial Variability • Caused primarily by meteorological effects (i.e., winds, barometric pressure, seiche) • Differences in water levels measured at opposite ends of the lake can be upwards of a few metres • Gauge measurements at different locations around the lake are averaged to try to balance and reduce these errors • Spatial variability errors result from slope of lake surface and imbalance in the weighting given to different gauges

  11. Spatial Variability • Compared BOM water levels from four-gauge average to 9-gauge Thiessen weighted network average (Quinn and Derecki, 1976) for period 1980-2009 • Logistic distribution fit differences well • BOM standard error ~= 0.6 to 1.6 cm, depending on the month • Largest errors in the fall/winter

  12. Thermal Expansion and Contraction (ΔSTh) • Normally considered negligible, but can be significant source of error • Measured water column temperature data is not available • Adapted method proposed by Meredith (1975) • Related dimensionless vertical temperature profiles for each month to measured surface temperatures to estimate vertical temperature dist. • Computed volume at BOM and EOM and determined difference • Conclusions based on results of both surface temp. datasets and all three sets of temp. profiles

  13. Improving Change in Storage • Review and revision of gauge network and/or averaging scheme used to compute BOM water levels • Additional gauges • Thiessen or other weighting scheme or interpolation method • Hydrodynamic/thermodynamic lake models • Model lake surface and meteorological impacts • Model volume temperature distribution to estimate ΔSTh • Measured temperature data (e.g., buoys, research vessels/lake carriers) • Satellite altimetry • e.g., NASA Surface Water Ocean Topography (SWOT) mission

  14. Combined Uncertainty in NBS • Determining combined estimate of uncertainty in NBS quite simple due to mathematical simplicity of the model • Used both First-order second moment (FOSM) and Monte Carlo methods • Results almost identical • Linear model • Variance of model inputs described consistently • Uncertainty varies by month • Absolute uncertainty is fairly similar • Relative uncertainty greatest in the summer and November (> than 100% in some cases)

  15. Erie Residual NBS Conclusions • Evaluating uncertainty in each input the most difficult part of overall NBS uncertainty analysis • FOSM and Monte Carlo methods gave nearly identical results • Uncertainty in BOM water levels as currently computed and change in storage is large • Same magnitude as Detroit River inflow and in some months greater than Niagara River flow uncertainty • Uncertainty due to change in storage due to thermal expansion and contraction is in addition to this • Uncertainty in change in storage possibly easiest to reduce • To reduce uncertainty in Erie NBS must reduce uncertainty in each of the different major inputs (i.e. inflow, outflow and change in storage) • Reduction of uncertainty in one input will not significantly reduce uncertainty in residual NBS

  16. IUGLS Adaptive Management (AM) and FIRM • In past 50 years there have only been a handful of years when there was not a water level related IJC study underway • A lot of good work is done during these studies, but there is limited continuity between them • AM allows for a structured process for the continued use, updating and improvement of the hydroclimate knowledge acquired during the IJC Study processes • FIRM: Framework for Integrated Research and Modelling • Workshop and subsequent follow-up • Outline key data and research needs/priorities to improve understanding and estimation of the water budget components, including those described in this report and others • IUGLS recommendations in final report to come

  17. Acknowledgements • Supervisors: Dr. S. Moin and Dr. Y. Guo • Colleagues at Environment Canada, US Army Corps of Engineers, Great Lakes Environmental Research Laboratory, Ontario Power Generation Thank-you!

More Related