Streamflow Variability of 21 Watershed Basins within the Wilamette Valley, Oregon<br />By Donnych Diaz and Tracy Ryan<br />
Project Scope<br /><ul><li>Analysis of 21 watersheds: Dependent variable mean streaflow runoff  to independent variables l...
Build model that is predictor of streamflow runoff via regression analysis
Assess Model: Reject /Accept
Null hypothesis: Land attributes do not affect mean streamflow runoff
Alternate hypothesis: Land attributes do affect mean streamflow runoff</li></li></ul><li>8<br />
Stream Flow Analysis<br />DEM Analysis (USGS)<br />Land Cover Data  (USGS)<br />Stream Flow Data <br />(USGS)<br />Monthly...
Data<br /><ul><li>Runoff Data
Combined monthly mean and covariance
Grouped into Summer and Winter seasons
Slope
In percent - rise over run
Aspect
Elevation
Range from 541 to 3171.5 m</li></li></ul><li>Data<br /><ul><li>Landcover Data
Manning’s Roughness Coefficient</li></ul>1/nx % of Landcover<br />n = roughness factor<br />Where: 1/n is part of velocity...
Regression Analysis<br />SPSS Linear Regression<br /><ul><li>Multivariate
Transformed all variables by square root
Upcoming SlideShare
Loading in...5
×

StreamFlow Variability of 21 Watersheds, Oregon: Analysis

691

Published on

Streamflow Variability of 21 Watershed Basins in Oregon

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
691
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

StreamFlow Variability of 21 Watersheds, Oregon: Analysis

  1. 1. Streamflow Variability of 21 Watershed Basins within the Wilamette Valley, Oregon<br />By Donnych Diaz and Tracy Ryan<br />
  2. 2. Project Scope<br /><ul><li>Analysis of 21 watersheds: Dependent variable mean streaflow runoff to independent variables land attributes
  3. 3. Build model that is predictor of streamflow runoff via regression analysis
  4. 4. Assess Model: Reject /Accept
  5. 5. Null hypothesis: Land attributes do not affect mean streamflow runoff
  6. 6. Alternate hypothesis: Land attributes do affect mean streamflow runoff</li></li></ul><li>8<br />
  7. 7. Stream Flow Analysis<br />DEM Analysis (USGS)<br />Land Cover Data (USGS)<br />Stream Flow Data <br />(USGS)<br />Monthly<br /> 1958 – 2008<br />Roughness Factor<br />Aspect (GIS)<br />Slope (GIS)<br />Elevation (GIS)<br />Creation of Database and Shapefile with all <br />data attributes<br />Regression Analysis<br />
  8. 8. Data<br /><ul><li>Runoff Data
  9. 9. Combined monthly mean and covariance
  10. 10. Grouped into Summer and Winter seasons
  11. 11. Slope
  12. 12. In percent - rise over run
  13. 13. Aspect
  14. 14. Elevation
  15. 15. Range from 541 to 3171.5 m</li></li></ul><li>Data<br /><ul><li>Landcover Data
  16. 16. Manning’s Roughness Coefficient</li></ul>1/nx % of Landcover<br />n = roughness factor<br />Where: 1/n is part of velocity formula, higher the value greater velocity.<br />
  17. 17.
  18. 18.
  19. 19.
  20. 20. Regression Analysis<br />SPSS Linear Regression<br /><ul><li>Multivariate
  21. 21. Transformed all variables by square root
  22. 22. Summer and Winter
  23. 23. Summer: June – September
  24. 24. Winter: December – February</li></li></ul><li>Four Assumptions of Linear Regression<br /><ul><li>The relationship between the dependent and independent variables is linear.
  25. 25. The distribution of the residual error is normal.
  26. 26. The variance of the residual error is the same for each value of the independent variable.
  27. 27. There is no autocorrelation between the variables.</li></li></ul><li>Linearity: Summer<br />
  28. 28. Linearity: Winter<br />
  29. 29. Normalcy: Summer<br />
  30. 30. Constant Variance: Winter<br />
  31. 31. Regression Analysis: Summer<br />Variables<br />srsum = Summer mean runoff<br />srslp = Slope<br />srasp = Aspect<br />srelev = Elevation<br />srlcf = Landcover factor<br />
  32. 32. Regression Analysis: Winter<br />Variables<br />srwin = Winter mean runoff<br />srslp = Slope<br />srasp = Aspect<br />srelev = Elevation<br />srlcf = Landcover factor<br />
  33. 33. Model Effectiveness<br /><ul><li>F-test per ANOVA
  34. 34. Significance levels:
  35. 35. Summer : .400
  36. 36. Not statistically significant, we can not reject the null hypothesis
  37. 37. Winter : .000
  38. 38. Is statistically significant, we can reject the null hypothesis</li></li></ul><li>Conclusions<br /><ul><li>This regression model is more effective in the winter when water input into streams is higher.
  39. 39. There are problems with the data meeting the assumptions for linear regression.
  40. 40. There are other significant variables that are not being taken into account.
  41. 41. More research needs to be done to fully ascertain the nature of these correlations.</li></li></ul><li>References:<br />Luce, C. H., and Z. A. Holden (2009), Declining annual streamflow distributions in the Pacific Northwest United States, 1948–2006, Geophys. Res. Lett., 36, L16401, doi:10.1029/2009GL039407. <br />Fu, G., M.E. Barber, and S. Chen (2009), Hydro-climactic variability and trends in Washington State for the last 50 years, Hydrological Process, doi: 10.1002/hyp.7527.<br />
  42. 42. Thank you<br />Questions/Comments?<br />
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×