This study analyzed streamflow data from 21 watershed basins in the Willamette Valley, Oregon to determine the relationship between land attributes and mean streamflow runoff. Regression analysis was used to build a model relating the dependent variable of mean streamflow runoff to independent land attribute variables. The analysis found the model was more effective at predicting streamflow in winter months compared to summer months. While some relationships between variables were observed, the data did not fully meet assumptions for linear regression and other significant variables may be influencing streamflow levels. More research is needed to fully understand these correlations.

1. Streamflow Variability of 21 Watershed Basins within the Wilamette Valley, Oregon By Donnych Diaz and Tracy Ryan

3. Build model that is predictor of streamflow runoff via regression analysis

4. Assess Model: Reject /Accept

5. Null hypothesis: Land attributes do not affect mean streamflow runoff

7. Stream Flow Analysis DEM Analysis (USGS) Land Cover Data (USGS) Stream Flow Data (USGS) Monthly 1958 – 2008 Roughness Factor Aspect (GIS) Slope (GIS) Elevation (GIS) Creation of Database and Shapefile with all data attributes Regression Analysis

9. Combined monthly mean and covariance

10. Grouped into Summer and Winter seasons

11. Slope

12. In percent - rise over run

13. Aspect

14. Elevation

16. Manning’s Roughness Coefficient1/nx % of Landcover n = roughness factor Where: 1/n is part of velocity formula, higher the value greater velocity.

21. Transformed all variables by square root

22. Summer and Winter

23. Summer: June – September

25. The distribution of the residual error is normal.

26. The variance of the residual error is the same for each value of the independent variable.

28. Linearity: Winter

29. Normalcy: Summer

30. Constant Variance: Winter

31. Regression Analysis: Summer Variables srsum = Summer mean runoff srslp = Slope srasp = Aspect srelev = Elevation srlcf = Landcover factor

32. Regression Analysis: Winter Variables srwin = Winter mean runoff srslp = Slope srasp = Aspect srelev = Elevation srlcf = Landcover factor

34. Significance levels:

35. Summer : .400

36. Not statistically significant, we can not reject the null hypothesis

37. Winter : .000

39. There are problems with the data meeting the assumptions for linear regression.

40. There are other significant variables that are not being taken into account.

42. Thank you Questions/Comments?