POST-FIRE DEBRIS FLOW OBSERVATIONS FOLLOWING THE GRIZZLY CREEK FIRE, GLENWOOD CANYON, COLORADO, USA: LESSONS LEARNED

We evaluated the performance of the United States Geological Survey (USGS) postfire debris-flow (PFDF) likelihood and volume models over a two-year period within the 2020 Grizzly Creek Fire burn perimeter in Glenwood Canyon, Colorado, USA. For this study we developed 2 observational datasets: 1) paired rainfall intensity-debris flow response measurements using a network of 11 rain gages for evaluating the likelihood model, and 2) repeat lidar data for evaluating the volume model. The likelihood model predicts rainfall-intensity thresholds that are expected to trigger a debris flow at a specific stream segment or basin, and the volume model predicts the volume of material mobilized by debris flows. Our results show that 89% of first year postfire debris flows were triggered by rainfall intensities exceeding the USGS PFDF likelihood model thresholds. In contrast, no debris flows were observed the second year postfire, despite eight storms with rainfall intensities exceeding modeled thresholds. Consequently, we found the operational USGS PFDF likelihood model may be too conservative after the first year due to vegetation recovery. Additionally, the USGS PFDF volume model overpredicted debris flow volumes during the first year postfire, with observed volumes between one-fifth to one-quarter of the predicted volume. This suggests the operational USGS PFDF volume model could be adjusted with a correction factor for this region to account for local differences in sediment production. These findings reveal that overall vegetation recovery and sediment exhaustion affects both first-year volume predictions and second-year rainfall thresholds, and therefore should be taken into account with the development of future models.