Presentation Title

Hydrological Patterns and the Effects of Land Use on TSS Concentrations and Yields in the McCarthy Creek Watershed, Portland, Oregon

Presenter(s) Information

David FarmerFollow

Start Date

5-2-2018 4:00 PM

End Date

5-2-2018 6:00 PM

Abstract

The spread of impervious surface areas and the reduction of tree canopy via urbanization has numerous water quality impacts on Pacific Northwest watersheds. One such impact is elevated levels of Total Suspended Solids (TSS) in run-off during storm events, particularly in steep, forested watersheds undergoing urbanization. This project was developed in collaboration with the West Multnomah Soil and Water Conservation District and focused on the McCarthy Creek Watershed, as a case-study watershed located in Portland, OR. The study aimed to identify elevated TSS concentrations and yields during storm events on both mainstem and tributary sampling locations, establish the relationship between TSS and rainfall variables (rainfall depth, rainfall intensity, and rainfall duration), and determine the most significant land cover variables as predictors of TSS based on delineated sub-watersheds in McCarthy Creek. These data were collected through a combination of grab sampling, hourly autosampling, and GIS analysis during the course of the later winter and early spring. All of the sampling locations exceeded informal TSS thresholds during storm events, but no statistically significant differences were found in TSS concentrations or yields for any of the sampling locations. Rainfall depth was the most significant predictor of TSS concentrations in the mainstem locations and autosampler results, while rainfall intensity was the most significant predictor of TSS in tributary sampling locations. Land cover regression models did not uncover any significant relationships between TSS and land cover variables. McCarthy Creek Watershed may so spatially homogenous in terms of land cover that TSS concentrations are similar throughout the watershed, resulting in low variance explained by land cover variables.

Subjects

GIS / modeling, Land/watershed management, Water quality

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Feb 5th, 4:00 PM Feb 5th, 6:00 PM

Hydrological Patterns and the Effects of Land Use on TSS Concentrations and Yields in the McCarthy Creek Watershed, Portland, Oregon

The spread of impervious surface areas and the reduction of tree canopy via urbanization has numerous water quality impacts on Pacific Northwest watersheds. One such impact is elevated levels of Total Suspended Solids (TSS) in run-off during storm events, particularly in steep, forested watersheds undergoing urbanization. This project was developed in collaboration with the West Multnomah Soil and Water Conservation District and focused on the McCarthy Creek Watershed, as a case-study watershed located in Portland, OR. The study aimed to identify elevated TSS concentrations and yields during storm events on both mainstem and tributary sampling locations, establish the relationship between TSS and rainfall variables (rainfall depth, rainfall intensity, and rainfall duration), and determine the most significant land cover variables as predictors of TSS based on delineated sub-watersheds in McCarthy Creek. These data were collected through a combination of grab sampling, hourly autosampling, and GIS analysis during the course of the later winter and early spring. All of the sampling locations exceeded informal TSS thresholds during storm events, but no statistically significant differences were found in TSS concentrations or yields for any of the sampling locations. Rainfall depth was the most significant predictor of TSS concentrations in the mainstem locations and autosampler results, while rainfall intensity was the most significant predictor of TSS in tributary sampling locations. Land cover regression models did not uncover any significant relationships between TSS and land cover variables. McCarthy Creek Watershed may so spatially homogenous in terms of land cover that TSS concentrations are similar throughout the watershed, resulting in low variance explained by land cover variables.