Post-fire erosion response and recovery, High Park Fire, Colorado - Schmeer, Sarah R., author; Kampf, Stephanie, advisor; MacDonald, Lee, committee member; Rathburn, Sara, committee member (Colorado State University. Libraries, 2014)
Wildfires along the Colorado Front Range are increasing in extent, severity and frequency, and a better understanding of post-fire erosion processes is needed to manage burned lands. The objectives of this study were to: 1) document post-fire sediment production after the 2012 High Park Fire burn area, Colorado, 2) determine how sediment production relates to fire, rainfall, surface cover, soil and topographic characteristics, 3) model sediment yield at the study swales using the RUSLE and ERMiT erosion models and a site-specific multivariate regression (SSMR) model developed from the field measurements, and 4) assess how well the RUSLE and SSRM models performed when using remotely-sensed data in place of field-measured data. Sediment production, rainfall, surface cover, soil and topographic characteristics were measured for 29 swales in the High Park Fire burn area from August 2012 through September 2013. Eight of the swales were mulched with either wood shreds in October 2012 or straw in June 2013. Mean sediment yield from the unmulched swales in 2012 was 0.5 Mg ha-1 yr-1, increasing to 14.3 Mg ha-1 yr-1 in 2013. The increase in 2013 was largely due to above-average rainfall amounts. Mulched swales yielded 3.1 Mg ha-1 yr-1 in 2013. Precipitation thresholds for sediment production were best identified by rainfall erosivity. The erosivity threshold in 2012 was 3 MJ mm ha-1 hr-1 increasing to 22 MJ mm ha-1 hr-1 in 2013. Annual total sediment yield in 2013 was most closely correlated with rainfall erosivity whereas 2013 event sediment yield was more closely related by the thirty-minute maximum rainfall intensity. Independent variables with the strongest significant correlations to sediment yield were surface cover and topographic characteristics. Sediment yield was positively correlated with exposed bare soil in 2012 (Pearson's correlation coefficient [r] = 0.56) and negatively correlated with vegetation cover in 2013 (r = -0.46). Sediment yield was negatively correlated with percent cover by mulch (r = -0.97), but the type of mulch material did not affect sediment yield. Slope length was negatively correlated with sediment yield (r = -0.19), and narrower swales produced more sediment per unit area than wide swales. The best 2013 annual SSMR model used average percent bare soil in spring 2013, swale width-length ratio, summer erosivity, slope length and burn severity to predict sediment yield (R2 = 0.63). The two erosion models, ERMiT and RUSLE, did not accurately predict 2013 annual sediment yields. ERMiT under-predicted sediment yields for storms with maximum thirty-minute intensity recurrence intervals of 1.5-5 years, and over-predicted sediment yield for storms with precipitation depth recurrence intervals of 30-100 years. The RUSLE model run with field-measured independent variables similarly did not accurately predict sediment yield from the hillslopes (R2 = 0.05), and when the RUSLE variables were calculated with remotely sensed or GIS-derived data the correlation with measured values was even weaker (R2 = 0.02). The SSMR model developed from field-measured variables predicted sediment yield relatively well (R2 = 0.63), but declined when using remotely-derived data (R2 = 0.46). The results of this study show that rainfall erosivity and intensity, surface cover and topography are the dominant controls on post-fire sediment yield. The interactions of these controls is not captured in the existing erosion models ERMiT and RUSLE. Furthermore, the use of remote sensing and GIS to derive model inputs reduces the accuracy of these models.