Predicting the Energy Costs of Dismounted Movements over Snow

Research Paper Title

Terrain coefficients for predicting energy costs of walking over snow.


Predicting the energy costs of human travel over snow can be of significant value to the military and other agencies planning work efforts when snow is present.

The ability to quantify, and predict, those costs can help planners determine if snow will be a factor in the execution of dismounted tasks and operations.

To adjust predictive models for the effect of terrain, and more specifically for surface conditions, on energy costs, terrain coefficients (ƞ) have been developed.

The physiological demands of foot travel over snow have been studied previously, and there are well established methods of predicting metabolic costs of locomotion.

By applying knowledge gained from prior studies of the effects of terrain and snow, and by leveraging those existing dismounted locomotion models, this paper seeks to outline the steps in developing an improved terrain coefficient (ƞ) for snow to be used in predictive modelling.


Using published data, methods, and a well-informed understanding of the physical elements of terrain, e.g., characterisation of snow sinkage (z), this study made adjustments to ƞ-values specific to snow.


This review of published metabolic cost methods suggest that an improved ƞ-value could be developed for use with the Pandolf equation, where z = depth (h)*(1 – (snow density (ρ0)/1.186)) and ƞ = 0.0005z3 + 0.0001z2 + 0.1072z + 1.2604.


While the complexity of variables related to characteristics of snow, speed of movement, and individuals confound efforts to develop a simple, predictive model, this paper provides data-driven improvements to models that are used to predict the energy costs of dismounted movements over snow.


Richmond, P.W., Potter, A.W., Looney, D.P. & Santee, W.R. (2019) Terrain coefficients for predicting energy costs of walking over snow. Applied Ergonomics. 74, pp.48-54. doi: 10.1016/j.apergo.2018.08.017. Epub 2018 Aug 15.


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