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University of Waterloo Hydrology Lab 1/35 Modelling the Entire Great Lakes and the Ottawa River Watershed Nick Kouwen Department of Civil Engineering University of Waterloo, Waterloo, ON,Canada http://www.watflood.ca
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University of Waterloo Hydrology Lab 2/35 With a large amount of help from: Environment Canada Alain Pietroniro (Watershed setup) Pierre Pellerin (Synoptic & NWM data) Champa Neal (Flow data)
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University of Waterloo Hydrology Lab 3/35 Geography Lesson St. Mary’s R. St. Clair R. Detroit R. Niagara R. St. Lawrence R. Superior Michigan Huron GB Erie Ontario Ottawa R.
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University of Waterloo Hydrology Lab 4/35 WATFLOOD Features: Primary application is flood forecasting and flood studies Long time series for climate studies and frequency analysis Ability to model regions from a few km 2 to Millions of km 2 Automated watershed setup (ENSIM, MAPMAKER, TOPAZ) Optimal use of gridded data eg. Land cover, DEM’s, NWP model output, Radar data Universally applicable parameter set Fast Very easy to use interface for routine work Pick-up truck version
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University of Waterloo Hydrology Lab 5/35 Highlights: ENSIM – pre and post processor Grouped Response Units GRU’s Wetland model – coupled river-wetland hydraulics (also bank storage) Tracer model – flow sourcing (glaciers, groundwater, wetlands, etc.) There are many other useful features
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University of Waterloo Hydrology Lab 6/35 EnsimHydrologic Developed by the Canadian Hydraulics Centre CHC Funded by Environment Canada
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University of Waterloo Hydrology Lab 7/35 Start with a DEM S. Ontario in this case EnsimHydrologic work space L. Huron L. Ontario Waterloo Toronto
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University of Waterloo Hydrology Lab 8/35 Delineate drainage & Watersheds automatically Specify WATFLOOD grid.
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University of Waterloo Hydrology Lab 9/35 Zoom & edit data Extract WATFLOOD data
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University of Waterloo Hydrology Lab 10/35 Watflood Theory GRU’s : Grouped Response Units
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University of Waterloo Hydrology Lab 11/35 Elmira LANDSAT 10 km grid (or whatever) 100 km^2 area receives equal meteorological input group all areas with similar hydrological characteristics within a grid for 6 hydrological computations/grid some people model each pixel or each field separately - ok for science, not operations (10^4 computations/grid)
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University of Waterloo Hydrology Lab 12/35 Group Response Unit - to deal with basin heterogeneity Physically Based Streamflow Routing
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University of Waterloo Hydrology Lab 13/35 Percent Coniferous Forest Source: USGS GLOBAL LAND COVER CHARACTERISTICS DATA BASE
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University of Waterloo Hydrology Lab 14/35 Percent Crops Source: USGS GLOBAL LAND COVER CHARACTERISTICS DATA BASE
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University of Waterloo Hydrology Lab 15/35 Hydrological Modelling Precipitation Interception Surface Runoff Unsaturated Zone Saturated Zone Depression Storage Infiltration Wetting Front Interflow Base Flow Evapotranspiration Channel Flow Wetlands Model executed for each land cover GRU on each Grid each Hour
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University of Waterloo Hydrology Lab 16/35 Previous experience: Original model setup & calibration for the Grand River watershed in S. Ontario Applications include: Columbia River N. of US Border – 50,000 km 2 Mackenzie River ~ 1,7000,000 km 2 Rhone, Rhine, Po and Danube rivers as part of MAP (Mesoscale Alpine Project)
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University of Waterloo Hydrology Lab 17/35 MAP (Fall 1999) Computed flows compared to observed flows for the Danube River in Germany & Austria Met data from the high resolution MC2 Numerical Weather Model MC2 & WATFLOOD ~3 km grid
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University of Waterloo Hydrology Lab 18/35 Tracer Module Components Tracer 1 Sub-basin separation Tracer 2 Land-cover separation Tracer 3 Rain-on-stream tracer Tracer 4 Flow-type separation - surface - interflow - baseflow Tracer 5 Snow-melt as a f n (flow-type) - surface + surface melt - interflow + melt drainage - baseflow + interflow melt drainage Tracer 6 Glacial Melt - surface - interflow - baseflow Tracer 0 Baseflow separation
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University of Waterloo Hydrology Lab 19/35 Model verification E.G. Baseflow has been compared to isotope analysis of streamflow sources All other model components have been similarly verified
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University of Waterloo Hydrology Lab 20/35 Great Lakes & Ottawa River Model Meteorological Data: 6 hour Synoptic data for initial setup for October 2000 – August 2003 3 hour GEM (Global Environmental Model) data for July & August 2003
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University of Waterloo Hydrology Lab 21/35
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University of Waterloo Hydrology Lab 22/35 Movie clip is an example of distributed Synoptic Data (Note the moving Bull’s eyes)
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University of Waterloo Hydrology Lab 23/35 Synoptic data
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University of Waterloo Hydrology Lab 24/35 Next movie clip is for July 2003 using GEM data (GEM is Canada’s operational weather forcasting model)
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University of Waterloo Hydrology Lab 25/35
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University of Waterloo Hydrology Lab 26/35 Animation of Snow Cover (SWE in mm)
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University of Waterloo Hydrology Lab 27/35
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University of Waterloo Hydrology Lab 28/35 Animation of Grid Outflow
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University of Waterloo Hydrology Lab 29/35
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University of Waterloo Hydrology Lab 30/35 Flow stations: Canada only (to date)
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University of Waterloo Hydrology Lab 31/35 Computed hydrographs for 50 Sub-Watersheds 400-13500 km 2
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University of Waterloo Hydrology Lab 32/35
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University of Waterloo Hydrology Lab 33/35 Lake Routing St. Mary’s R. St. Clair R. Detroit R. Niagara R. St. Lawrence R. Superior Michigan Huron GB Erie Ontario Ottawa R.
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University of Waterloo Hydrology Lab 34/35 Lake Routing Rules (natural state): St. Marys River Q = 824.7*(SUP-181.43)^1.5 St. Clair River Q = 82.2*((MHU+STC)/2-166.98)^1.87*(MHU-STC)^0.36 Detroit River Q = 28.8*(STC-164.91)^2.28*(STC-ERI)^0.305 Niagara River Q = 558.3*(ERI-169.86)^1.60 St. Lawrence River Q =555.823*(Oswego-0.0014(Year-1985)-69.474)^1.5
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University of Waterloo Hydrology Lab 35/35 Needs work. Ave. lake levels are ok. Variation is inadequate. Effect of weeds, ice & operations not yet incorporated.
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University of Waterloo Hydrology Lab 36/35 Summary Great tools are required to model large areas such as the Great Lakes & Ottawa River basin. Pre-processor – set up watershed files Post-processor – debugging & visualization GRU’s ensure vastly different hydrological units are represented appropriately at the large scale Gridded model Efficient ingestion of gridded data: DEM, Land cover, meteorological data (radar, numerical weather models) Much tweaking to be done!
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