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Description
Wildfires have significant impacts on hydrologic and geomorphic processes. Post-fire runoff and sediment flow variations can greatly impact downstream communities, homes, lives, and cause huge economic losses. Predicting post-fire runoff and sediment transport in steep, ungauged terrains remains a challenge and there is a need to understand and improve the parameterization of post-fire hydro-geomorphic models. This research used high-resolution Terrestrial Laser Scanning (TLS) Light Detection and Ranging (LiDAR) to estimate post-fire changes in channel volume and associated uncertainties. The TLS LiDAR techniques provided better understanding of geomorphologic processes. Using TLS LiDAR images acquired after the 2012 Waldo Canyon Fire (Colorado, USA) this research reviewed TLS LiDAR methods and estimation of the uncertainty in point clouds and Digital Elevation Models (DEMs). Different analysis methods were compared, including DEM of difference (DOD), Cloud to Cloud (C2C), Cloud to Mesh (C2M), and Multiple Model to Model Cloud Comparison (M3C2) to estimate topographic and volumetric changes in rugged terrains. This research also modeled post-fire sediment yield in stream channels after the 2012 Waldo Canyon Fire. KINematic Runoff and EROSion (KINEROS) and Geo-spatial interface for Water Erosion Prediction Project (GeoWEPP) were developed with Geographical Information System (GIS)-based information, including a Digital Elevation Model, land cover, soil classification, climate data, and soil burn severity. Three post-fire TLS LiDAR images (Apr 2013, Sep 2013, and Sep 2014) were used to estimate total erosion and deposition at the reach scale. The TLS-based information was applied to calibrate the post- fire models. The uncalibrated simulations from KINEROS ranged from 125-3870 kg/ha of sediment in Williams Downstream reach, which overestimated (410% in the first year) and underestimated (87% in the second year) the erosion. Model calibration reduced the Root Mean Square Error (RMSE) of sediment to 0.016% for the first year and 0.09% for the second year. Comparing TLS LiDAR results with GeoWEPP outputs revealed 28-1151% overestimation or underestimation for various study reaches. GeoWEPP calibration reduced RMSE to 0.06%-50% for sediment yield. Results of this research will enhance understanding of wildfire disturbance on hydro-geomorphic processes. Findings will also improve model parameterization that can be used to guide post-fire management and predictions.
