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?