Masters Thesis

Quantifying and mapping changes in hydrologic ecosystem services from a large magnitude wildfire in Shasta and Tehama counties, California

Ecosystem services are considered goods and services that sustain and enhance human life. Humans have degraded ecosystems due to land use or land management practices favoring more profitable land use scenarios at the expense of other valuable ecosystem services. Tools and models have emerged to quantify and value ecosystem services. InVEST is an open-source, spatially explicit, ecosystem service appraisal tool which has been widely-used. A review determined that only 18% of 153 publications on ecosystem services validated their results against observed data, and only one third of the studies had any sound basis for their results. There are few studies which have conducted a sensitivity analyses on InVEST model outputs. This study assessed predicted changes in the InVEST Hydropower Water Yield due to an episodic disturbance (e.g., fire), validated model results against observed data, and determined how variation in spatial resolution (30, 100, 120, 250, 500, 1000, 4000 m) of input data affected model results. Results show that the model did not detect the fire event on water yield. The location of the fire is a more important element for producing a significant change in water yield. After model results were calibrated against observed stream gauge data, the model under-predicted by less than 1%. Annual or 30 year normal precipitation inputs do not significantly affect model results. Prior to calibration, normal precipitation resulted in a larger percentage of error 64% compared to annual data 44%. Calibration is a crucial and understated step. Resolution of land cover and soil data does not always create significant variation in model results. InVEST may be driven more by parameter values (e.g., seasonality constant, root depth, and evapotranspiration coefficients) than the resolution of soil and land cover data. These findings should assist InVEST users to more accurately parameterize their models and think critically about the appropriate study area, data inputs, and accuracy in results allowing for a widespread assessment of ecosystem services so that they may sustainably managed.

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