THE EROSION AND SEDIMENTATION HAZARD MAP OF THE CAÑON LARGO WATERSHED ON THE JICARILLA APACHE NATION, NEW MEXICO: USING LIDAR DEM DATA AND FUZZY MEMBERSHIP FUNCTIONS TO CREATE ACCURATE RISK ASSESSMENTS

The southern Jicarilla Apache Nation, also within New Mexico, faces threats to infrastructure through erosion and sedimentation. Particularly at risk are roads, bridges, pipelines, and infrastructure serving the oil and gas industry which provides significant revenue for the Jicarilla Apache Nation. We created the “Erosion and Sedimentation Hazard Map of the Cañon Largo Watershed on the Jicarilla Apache Nation, Rio Arriba and Sandoval Counties, New Mexico” under contract for the Jicarilla Apache Nation in an effort to quantify risks in a high-resolution GIS-digestible product so that users can make informed management, maintenance, and construction plans in the ~2,100 km² study area. We used fuzzy membership functions derived from GIS analysis of 1m LiDAR DEMs in a multi-criteria evaluation of risk of sedimentation and erosion. Our analysis makes use of eight causal alluvial sedimentation/erosion hazard factors identified during earlier and concurrent field-based geologic mapping. Fuzzy membership functions were integrated with analytical and empirical hierarchy processes in order to facilitate comparison and summation of individual factors. Finally, all hazard factors were combined and normalized to create an integrated assessment of risk of sedimentation and erosion. To validate the hazard map’s applicability, we performed large-scale UAV-based surveys within three small watersheds in the map area over a 14-month interval of study. These surveys show that the hazards map accurately assesses erosion and sedimentation risks in natural settings; however, anthropogenic modifications to channels (e.g., culverts, gabions, dams) present challenges to our model. In spite of this, LiDAR DEM-based GIS assessment of sedimentation and erosion risk in alluvial settings is at least as powerfully predictive as traditional geohazards risk assessment yet can be performed more rapidly in aerially-extensive or inaccessible regions where high-resolution LiDAR data exist.