Regional ecosystem simulation: A general model for simulating snow accumulation and melt in mountainous terrain
A general snow accumulation and melt model was developed to (1)determine how accurately snow accumulation and ablation can bemodeled over heterogeneous landscapes with routinely available climatologic, topographic, and vegetation data, and (2) improveestimates of annual forest snow hydrology for point and regionalcalculations of annual forest productivity. The snow model wasdesigned to operate within the Regional HydroecologicalSimulation System (RHESSys), a GIS based modeling system tomanage spatial data for distributed computer simulations onwatershed scales. One feature of the RHESSys Snow Model (RSM) isit can use satellite derived forest leaf area index (LAI) torepresent catchment forest cover; difficult to obtain in adequatecover and resolution by any other means. The model was testedover 3 water years (October to September) with data recorded by10 snow telemetry stations (SNOTEL) in 5 states ranging inmeso-climate and elevations from a coastal Oregon site (1067 m)to a continental Colorado site (3261 m).
Predictions for the 10 sites were made with identicalparameter values and only site climate varied for all sites. Theaverage difference between observed and predicted snow depletiondates for all sites and water years was 6.2 days and 8 of the 30simulations were within ± 2 days (R2 = 0.91). Radiation melt wasthe dominate snow ablation component at the Colorado site wheresublimation was 10% (LAI = 0) to 20% (LAI = 6) ofsnow loss while air temperature was the dominate component at theOregon site with sublimation reduced to 1% (LAI = 0) to6% (LAI = 6) of snow loss. LAI had a greater effectdetermining snow depletion than site aspect. Aspect increased inimportance if the snow depletion occurred during early springwhen solar insolation differences between hillslopes is greaterthan in the late spring.
An accurate prediction of daily snowpack water equivalent(SWE) was not a strong determinant for making an accurateprediction of snowpack depletion date. Predicted snowpackdepletion dates were more sensitive to timing when the snowpackreached an isothermal condition. Daily estimates of SWE were mostsensitive to correctly estimating snowfall from SNOTEL data. Thismeans that for purposes of determining the snow depletion dateswhich are useful for forest ecosystem modeling, tracking SWE isless important then triggering snowmelt. Comparisons ofsimulations to published snow depletion dates show that RSMpredicted the relative ranking and magnitude of depletion fordifferent combinations of forest cover, elevation, and aspect.
snow, model, regional simulation, forest, remote sensing
© 1997 Springer Verlag
Coughlan, J.C. & Running, S.W. Landscape Ecology (1997) 12: 119. doi:10.1023/A:1007933813251