Characterizing Forest Structure for Snow Prediction using Terrestrial Laser Scanning

Project Overview

The Natural Resources Conservation Service stated that Colorado needed a wet March this year to get any real drought relief, noting that the March 1 snowpack measurement reflected a “nominal” increase in the month of February, but statewide snowpack remained at just 73 percent of average. In a statement, the agency stated that, “Unless Colorado sees weather patterns in March that bring well above average snowfall and precipitation to the state, there will not be much relief from the current drought conditions.”

This year, several basins in Colorado, Wyoming, and New Mexico are at less than 50 percent of their normal snowpack. Drought is expected to persist or intensify in much of the current drought-stricken areas. Key to understanding these dry conditions is our ability to accurately measure snowpacks throughout the region.

Snowpack Challenges

Seasonal snowpacks are difficult to measure and model in complex forested terrain. These measurements are necessary in order to reliably predict weather, climate, and water resources in much of the western U.S. The interactions between local vegetation structure and climate play a central role in the accumulation and ablation of snow, but it remains challenging to represent snowpack processes and distributions at the scales that land surface models are typically applied. As a result, it is unclear how ongoing changes to vegetation and climate will alter the fluxes of water, carbon, and nutrients from seasonally snow-covered forests. A study led by Adrian Harpold at The Institute of Arctic and Alpine Research focuses on understanding how forest structure controls snowpack distributions and hydrologic fluxes across a gradient of elevation, topography, and climate.

UNAVCO Support

A Terrestrial Laser Scanning (TLS) system has been used in the past to characterize the forest structure at sites where intensive under-canopy snowpack monitoring is occurring and airborne laser scanning (ALS) has been employed. Combining the technologies will allow a better estimate of forest structure and snowpack energy balance. Ultimately, this type of information can be employed to increase the effectiveness of land surface models in forested and complex terrain. This method will be employed at three field sites: Boulder Creek, CO, Jemez River, NM, and Kings River, CA. These are all locations of a Critical Zone Observatory (CZO), a network of 6 U.S. environmental observatories run by the National Science Foundation. The TLS will be used to scan an area of about 200 by 200 meters at each site. These scans will be pre-selected based on the location of snow and micro-met stations.

Significance

By using existing measurement and modeling infrastructure, this project has the unique opportunity to study snowpack and hydrologic response at three Western U.S. sites: cold continental (Boulder Creek, CO), warm continental (Jemez River, NM), and maritime (Kings River, CA). Over the long term, the project will determine how vegetation structure controls stand and catchment-scale snowpack distributions across elevation, topography, and climate as well as evaluate the ability to predict hydrologic fluxes with a widely-used land surface model when stand and catchment-scale snow and vegetation distributions are known from LiDAR measurements.

Project Information

  • Principal Investigators: Adrian Harpold, NSF EAR Postdoctoral Fellowship
  • Engineers: Keith Williams
  • Date: October 2012
  • Locations: INSTAAR Mountain Research Station
  • Funding Source: NSF

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