Objectives of the proposed work are to study the snow surface roughness during late winter and melting season, to relate these changes to weather conditions, and to assess the influence of changes in surface roughness on hydrologic response models.
Snow surface roughness is critical to surface - atmosphere exchanges, including the investigation of snowmelt at several scales; surface energy exchange; meltwater flux in the snowpack, and wind transport and erosion of the snow cover in winter. Snow surface roughness also influences Remote Sensing return signals from the snow surface. To date surface roughness has usually been estimated as the roughness length, estimated from flow conditions in the boundary layer.
In the proposed work we plan to
(1) evaluate snow surface roughness/microtopography by direct measurement with an instrument especially designed for this purpose, (2) classify snow surface types geostatistically, (3) relate snow surface roughness parameters to meteorologic and micrometerologic time series data and (4) provide realistic (= measured) roughness characterization as input for hydrologic models: (a) roughness length as input for commonly used models and (b) multidimensional parameters including correlation length, characteristic length, height and spacing of surface features and their anisotropies, which will require an adaptation of snow hydrological models.
Snow surface conditions will be measured with the Glacier Roughness Sensor (GRS), and kinematic GPS (Global Positioning System) data will be collected at Niwot Ridge, Colorado Front Range. This is the site of a Long-Term Ecological Research (LTER) Project. Hydrologic and environmental data collected as part of the LTER and associated programs will provide the empirical evaluation of the importance of measuring surface roughness directly.
Geostatistical methods developed especially for surface characterization will be applied and refined in the data analysis. Geostatistical surface characterization utilizes a feature vector where components capture high-resolution-morphologic properties and facilitate a classification of snow surface types.
