Digital elevation models at a variety of resolutions are increasingly being used in geomorphology, for example in comparing the hypsometric properties of multiple catchments. A considerable body of research has investigated the sensitivity of topographic indices to resolution and algorithms, but little work has been done to address the impact of DEM uncertainty and elevation value error on derived products. By using higher resolution data from the Shuttle Radar Topography Mission -of supposed higher accuracy -for comparison with the widely used GLOBE 1 km data set, error surfaces for three mountainous regions were calculated. Correlation analysis showed that error surfaces are related to a range of topographic variables for all three regions, namely roughness, minimum and mean extremity and aspect. This correlation of error with local topography was used to develop a model of uncertainty including a stochastic component, permitting Monte Carlo Simulations. These suggest that global statistics for a range of topographic indices are robust to the introduction of uncertainty. However, the derivation of watersheds and related statistics per watershed (e.g. hypsometry) is shown to vary significantly as a result of the introduced uncertainty.

Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACT. Ice-sheet models (ISMs) developed to simulate the behaviour of continental-scale ice sheets under past, present or future climate scenarios are subject to a number of uncertainties from various sources. These sources include the conceptualization of the ISM and the degree of abstraction and parameterizations of processes such as ice dynamics and mass balance. The assumption of spatially or temporally constant parameters (such as degree-day factor, atmospheric lapse rate or geothermal heat flux) is one example. Additionally, uncertainties in ISM input data such as topography or precipitation propagate to the model results. In order to assess and compare the impact of uncertainties from model parameters and climate on the GLIMMER ice-sheet model, a parametric uncertainty analysis (PUA) was conducted. Parameter variation was deduced from a suite of sensitivity tests, and accuracy information was deduced from input data and the literature. Recorded variation of modelled ice extent across the PUA runs was 65% for equilibrium ice sheets. Additionally, the susceptibility of ISM results to modelled uncertainty in input topography was assessed. Resulting variations in modelled ice extent in the range of 0.8-6.6% are comparable to that of ISM parameters such as flow enhancement, basal traction and geothermal heat flux.

Modelling of physical processes such as ablation or runoff at continental or global scales provides a key challenge: a high degree of abstraction is required in order to minimize computational demands, while spatial and temporal variability of key processes, often at the sub‐scale level, need to be adequately captured and reproduced within a lower resolution model. For some approaches, such as temperature index models, downscaling to lower resolutions is straightforward. However a key issue when using these downscaled models is to assess the impact of scaling on model behaviour and results, including the associated uncertainties. We assess the impact of scaling on both a simple and an enhanced temperature index melt model from 100 m to 1, 5 and 10 km resolutions. Different sub‐grid parameterization approaches are applied to both models across all resolutions and tested for their suitability against high‐resolution reference data, with the aim of developing a robust, scalable and computationally undemanding parameterization. Results show patterns of over‐ and underestimation of potential melt rates for both models, with clear dependencies on scale, terrain roughness and variations of temperature thresholds, among other quantities. The sub‐grid parameterizations tested in this article are found to effectively compensate these effects at little additional computational cost. Copyright © 2008 John Wiley & Sons, Ltd.

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