Item: Laser mapping of mountain snowpacks: enabling resilient management of water resources and avalanche hazard in a changing world
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Title: Laser mapping of mountain snowpacks: enabling resilient management of water resources and avalanche hazard in a changing world
Proceedings: International Snow Science Workshop Proceedings 2018, Innsbruck, Austria
Authors:
- Jeffrey Deems [ National Snow and Ice Data Center, University of Colorado, Boulder, CO USA ]
Date: 2018-10-07
Abstract: Many resource and hazard management operations - from water supply to avalanche hazard mitigation - support decision-making by comparing present to past conditions. Index-based snowmelt runoff forecasting and nearest-neighbor avalanche models work this way, leveraging an observational period of record to predict the likely outcome of a specific storm or water year. Though this kind of methodology can be effective, current conditions are increasingly deviating from the historic record posing challenges to these approaches. Part of adapting to this changing environment is to reduce reliance on our historically-based, index methods and improve our ability to observe the actual state of the snowpack. Two examples of high-resolution snow depth and SWE mapping exemplify efforts to add resilience to water management and avalanche control operations: 1) The NASA Airborne Snow Observatory operationally maps snow depth, SWE, and snow albedo across full mountain watersheds, supporting operational water management in California, and research efforts in California, Colorado, Switzerland, and elsewhere. 2) Repeat TLS snow depth mapping has supported planning and evaluation of active avalanche control measures, helping refine explosives tramway design and assess Gazex exploder placement. These case studies and other efforts comprise a "spatial revolution" enabling distributed, physical monitoring and simulation of snow dynamics and hydrology in complex terrain.
Object ID: ISSW2018_O04.3.pdf
Language of Article: English
Presenter(s):
Keywords: remote sensing, spatial variability, snow depth mapping, forecast modeling.
Page Number(s): 308-312
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