Streamflow Variability of 21 Watershed Basins within the
Wilamette Valley, Oregon
By Donnych Diaz and Tracy Ryan
Analysis of 21 watersheds: Dependent variable mean streaflow runoff to independent variables land attributes
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
Project Scope
8
DEM Analysis (USGS)
Stream Flow Analysis
Land Cover Data (USGS)
Creation of Database and Shapefile with all
data attributes
Aspect (GIS) Slope (GIS)
Stream Flow Data (USGS)
Monthly 1958 – 2008Roughness Factor
Regression Analysis
Elevation (GIS)
Data
Runoff Data• Combined monthly mean and
covariance• Grouped into Summer and Winter
seasonsSlope
• In percent - rise over runAspectElevation
• Range from 541 to 3171.5 m
Data
Landcover Data• Manning’s Roughness Coefficient
1/n x % of Landcovern = roughness factor
Where: 1/n is part of
velocity formula, higher the value greater velocity.
LandcoverRoughness Factor
Barren Land 0.030
Cultivated Crops 0.035
Deciduous Forest 0.100
Developed, High Intensity 0.030
Developed, Low Intensity 0.030
Developed, Medium Intensity 0.030
Developed, Open Space 0.030
Emergent Herbaceuous Wetlands 0.050
Evergreen Forest 0.120
Hay/Pasture 0.030
Herbaceuous 0.050
Mixed Forest 0.100
Open Water 0.035
Perennial Snow/Ice 0.050
Shrub/Scrub 0.050
Woody Wetlands 0.050
Regression Analysis
• SPSS Linear Regression• Multivariate• Transformed all variables by square
root• Summer and Winter
oSummer: June – SeptemberoWinter: December – February
Four Assumptions of Linear Regression
The relationship between the dependent and independent variables is linear.
The distribution of the residual error is normal.
The variance of the residual error is the same for each value of the independent variable.
There is no autocorrelation between the variables.
Linearity: Summer
Linearity: Winter
Normalcy: Summer
Constant Variance: Winter
Regression Analysis: Summer
ANOVAb
ModelSum of
Squares df
Mean
Square F Sig.
1 Regression 90.882 4 22.721 1.077 .400a
Residual 337.467 16 21.092
Total 428.349 20
a. Predictors: (Constant), srlcf, srasp, srslp, srelev
b. Dependent Variable: srsum
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 33.782 28.609 1.181 .255
srslp -.509 .563 -.211 -.903 .380 .898 1.113
srasp .080 1.635 .011 .049 .962 .961 1.041
srelev .312 .285 .301 1.096 .290 .654 1.530
srlcf -9.005 4.677 -.538 -1.926 .072 .630 1.586
a. Dependent Variable: srsum
Variablessrsum = Summer mean runoffsrslp = Slopesrasp = Aspectsrelev = Elevationsrlcf = Landcover factor
ANOVAb
Model Sum of
Squares df Mean Square F Sig.
1 Regression 1126.636 4 281.659 11.278 .000a
Residual 399.598 16 24.975
Total 1526.234 20
a. Predictors: (Constant), srlcf, srasp, srslp, srelev
b. Dependent Variable: srwin
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 43.820 31.131 1.408 .178
srslp .183 .613 .040 .299 .769 .898 1.113
srasp 3.287 1.779 .241 1.848 .083 .961 1.041
srelev -1.156 .310 -.590 -3.728 .002 .654 1.530
srlcf -8.932 5.089 -.283 -1.755 .098 .630 1.586
a. Dependent Variable: srwin
Regression Analysis: WinterVariablessrwin = Winter mean runoffsrslp = Slopesrasp = Aspectsrelev = Elevationsrlcf = Landcover factor
Model Effectiveness
F-test per ANOVASignificance levels:
• Summer : .400oNot statistically significant, we can
not reject the null hypothesis• Winter : .000
oIs statistically significant, we can reject the null hypothesis
Conclusions
This regression model is more effective in the winter when water input into streams is higher.There are problems with the data
meeting the assumptions for linear regression.
There are other significant variables that are not being taken into account.More research needs to be done to fully
ascertain the nature of these correlations.
References:
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.
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.
Thank youQuestions/Comments?