A Review of Spatially Distributed Modeling of Snow

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species for biological studies at fine spatial scales. Research and operational efforts to implement

spatially distributed models of snow have some common interrelated issues. These include 1) the

tradeoff between model complexity and computational expense; 2) the estimation of error due to

forcing variables (i.e., surface meteorology) and due to model performance; 3) the approach to seg-

ment landscape and terrain data at suitable scales in relation to surface heterogeneity; and 4) the

challenge of validating and/or updating model predictions over large areas.

Model complexity controls the type of measurements that can be used to validate model predictions.

For example, simple bucket-type approaches, once common among land surface calculations in

general circulation models, may only allow testing the presence or absence of snow and water

equivalents within a cell, polygon, or patch. As models become increasingly complex in process

detail, one has the opportunity to use a variety of measurements for model testing. The separation

between error due to incorrect meteorological variables and due to model performance has usually

been addressed by testing at snow plots. Validation efforts at the study-plot, or local-area scale using

manual measurements, have become the standard approach to building model credibility. However,

this type of validation becomes increasingly cost prohibitive when testing snow model predictions

over larger and larger areas. Many tests of snow model predictions distributed over large areas have

suffered from the difficulty in quantitative evaluation due to variability in snow extent patterns and

snow physical properties. Recent modeling approaches parameterize the effects of land cover and

terrain heterogeneity on surface energy and mass fluxes. The effort is important whether or not the

data are segmented into regular or irregular areas and may help address what can be accomplished

with point measurements of snow properties through geostatistical stratification of sampling proce-

dures. Recent distributed model implementations have also parameterized the relationship between

snow extent and mean snow water equivalents. The albedo of areas that have incomplete snow cover

is thus estimated from predictions of water equivalents, coupling the snow model back to the atmo-

sphere model. This approach assumes that depth information is contained in snow extent, which has

not been tested over a wide variety of land cover, terrain types, or spatial scales. Finally, recent

advances in remote sensing have demonstrated construction of images of grain size, wetness, and

water equivalents. These methods offer the potential to test distributed models at compatible resolu-

tions, comparing spatial data with spatial predictions.

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