Estimation and Prediction of Fresh Water Runoff Based on Atmospheric Data: Preliminary results

1. Estimation and Prediction of Fresh Water Runoff Based on Atmospheric Data:Preliminary results Cody D. S. Sipkema 4th Year Environmental Engineering Co-op Student Dalhousie University and J. Chassé, BIO.

2. Project Goals • Develop an algorithm to estimate local river discharge rates by routing fresh water through watershed systems • Estimate historical freshwater discharge rates where no data is available • Predict the effect of climate change on local river discharge rates by connecting the routing model to GCMs • Incorporate this algorithm into the CRCM to provide data for regional atmosphere-ocean downscalling systems

3. Salinity

4. Context: Freshwater is important in coastal water • Freshwater maintains estuarine circulation • Activate exchange rate with the ocean • Affect stratification • Affect Ice formation

5. Tied to one implementation of CRCM • Require soil type

6. Methods: Data Used • USGS HydroSHEDS datasets • Domain: 145W-52W, 25N-60N (Spatial) • DEM (Digital Elevation Model) at 30 arc seconds • Flow Accumulation Raster at 30 arc seconds • Watershed Polygons • NCEP Reanalysis I datasets • Domain: January 1, 1948- June 27, 2007 (Temporal) Same spatial domain, data every 6 hrs. • Surface Temperature at 2m • Precipitation Rate • Latent Heat Flux

7. Methods: Data Used

8. Methods: Watersheds • Watershed Polygons extracted from shape file • Only those which drain into the Atlantic basin and are of or above 200 sqkm area are deemed “significant watersheds” • Significant watersheds are given an ID number • Solved problem of small (often single cell) coastal watersheds

9. Methods: Watersheds • Large number of small and single celled coastal watersheds • These smaller watersheds are insignificant and should be ignored • The area covered by these smaller watersheds however cannot

10. Methods: Watersheds Original Watershed Shape File Isolated Significant Watersheds

11. Methods: Watersheds • Outlets were found for each significant watershed using the flow accumulation raster • The cell within a polygon with the highest flow accumulation value was deemed the outlet • Outlet coordinates and ID numbers were stored and carried into the “New Mesh”

13. Methods: New Mesh • New mesh constructed for calculations and modeling • 0.25 Decimal Degree mesh used • 30 times coarser than the original DEM data

14. Methods: New Mesh • Cell values were assigned using significant watershed polygon list and the DEM • Cell values were assigned based on whether their center was found to be… • Within a significant watershed polygon (Cell given ID value of basin) • Within the ocean or water body (Cell given no data flag -9999) • If the cell was not found to be within either of these possibilities, they were given the ID value of their closest significant watershed neighbour (aggregation and assimilation of insignificant watershed areas) • This resulted in a “Routing Raster” that indicates where received precipitation in each cell would travel as runoff

16. Methods: Fresh Water Routing • NCEP data provided in coarser T62 Gaussian grid • For each time step (6hrs) across the spatial domain values of precipitation (P), temperature (T) and latent heat flux (LHF) were calculated (in cells containing IDs) by the bilinear interpolation method

17. Methods: Fresh Water Routing

18. Methods: Fresh Water Routing • Evaporative losses (E) were approximated using interpolated LHF values and the Latent Heat of Vaporization (LHV) at the interpolated temperature. • LHV of fresh water at a given temperature based on a cubic regression relationship • LHV(T) = −0.0000614342 (T^3) + 0.00158927(T^2) − 2.36418(T) + 2500.79 • Density of water was assumed constant • Ρwater =1000kg/(m^3)

19. Methods: Fresh Water Routing • Snow pack and melt was incorporated using a temperature indexing approach on a cell by cell basis • Snow is accumulated whenever the temperature falls below the threshold value (0 degrees Celcius) • When temperatures rise above this threshold the snow melts at a given rate constant proportional to the difference in temperature Melt= Cmelt * (Tcell-TThreshold) Where:Cmelt: Empirical coefficient ( m^3/(s*(degC)) Tcell: Interpolated cell value for temperature TThreshold: The temperature at which snow begins to melt.

20. Methods: Fresh Water Routing • Flow Delay is incorporated to approximate the travel time of water • This is done assuming a constant flow velocity for all rivers and assuming a straight path from the cell to the outlet • Thus distant cells runoff will not contribute to the flow at the outlet until a set future time dictated by the delay.

21. Methods: Fresh Water Routing

22. Preliminary Results Parameters Ef = 0.65 (Evaporation Coefficient) Cmelt =1E-8 m^3/s*C V= 1.3 m/s (River velocity)

23. Saint John River Basin Watershed ID: 65675 Significant Watershed ID: 323 Outlet Coordinates: 71.1958, 46.8125 Area:54,927.2sqkm

24. Saint John River Basin • Saint John River (Annual Means) • Correlation Coefficient = 0.736–0.81(Missing Data) • Note: Historical Data from Pokiok and Mactaquac gauging stations.

25. Saint John River Basin • Saint John River (Monthly Means) • Correlation Coefficient = 0.8097

26. Churchill River Basin Watershed ID: 30370 Significant Watershed ID: 107 Outlet Coordinates: -60.1875, 53.3208 Area: 92,698sqkm

27. Churchill River Basin • Saint John River (Annual Means) • Correlation = 0.57 • Note: Churchill Hydro Began operations in December 1971 Project started in July 1967 Controlled flow and diverted waterways (Affects results)

28. Next Step • Switch to higher resolution NARR dataset. • Precipitation Rate, Surface Temperature and Evaporation. • Further Investigate coefficients • Cmelt, and flow velocity (Ef should become rudimentary)