Modeling DEM Data Uncertainties for Monte Carlo Simulations of Ice Sheet Models

Hebeler, Felix; Purves, Ross S.

doi:10.1201/9781420069273.ch14

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…This decreasing impact of DEM uncertainty on ISM results with increasing total ice-sheet size, as measured by the relative standard deviation of ice extent and volume across MCS runs (Fig. 10), is consistent with previous findings (Hebeler and Purves, 2008), as is the larger impact of DEM uncertainty on modelled ice volume than on ice extent. These results reflect the shrinking influence of bedrock topography in models where ice-sheet configurations result in larger ice masses.…”

Section: Dem Uncertainty Testssupporting

confidence: 80%

“…However, the data used in the generation of these bed topographies is often associated with large uncertainties, which vary as a function of locale (Hebeler and Purves, 2008). Furthermore, the interpolation methods used to resample these data to resolutions appropriate for typical ISMs introduce further uncertainties (Hebeler and Purves, 2004).…”

Section: Topography In Ice-sheet Modelsmentioning

confidence: 99%

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The impact of parametric uncertainty and topographic error in ice-sheet modelling

Hebeler¹,

Purves²,

Jamieson

2008

J. Glaciol.

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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.

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Section: Dem Uncertainty Testssupporting

confidence: 80%

Section: Topography In Ice-sheet Modelsmentioning

confidence: 99%

The impact of parametric uncertainty and topographic error in ice-sheet modelling

Hebeler¹,

Purves²,

Jamieson

2008

J. Glaciol.

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“…It is therefore important to explore how sensitive sub-grid approaches are to DEM uncertainty. To assess this robustness, a DEM uncertainty model (Hebeler and Purves, 2008b) is used to simulate GLOBE DEM error. The GLOBE DEM became available at 30-arcsec resolution in 1998, and was derived from a number of different data sources.…”

Section: Uncertainty Analysismentioning

confidence: 99%

The influence of resolution and topographic uncertainty on melt modelling using hypsometric sub‐grid parameterization

Hebeler

Purves

2008

Hydrological Processes

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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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“…They recommend that error surfaces are related to a range of topographic variables; roughness, minimum and mean extremity and aspect. They suggested that global statistics for a range of topographic indices are robust to the introduction of uncertainty and topographic indices such as elevation roughness defined as the standard deviation of elevations in a 3 × 3 neighborhood in addition to the slope roughness expressed as the standard deviation of slopes in a 3 × 3 neighborhood are sufficient to study the influence of elevation uncertainty on derivation of such topographic indices [12].…”

Section: Introductionmentioning

confidence: 99%

Creation and Analysis of Earth’s Surface Roughness Maps from Airborne LiDAR Measurements in Downtown Urban Landscape

Asal¹

2019

JGIS

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The Earth's surface roughness constitutes an important parameter in terrain analysis for studying different environmental and engineering problems. Authors gave different definitions and measures for the earth's surface roughness that usually depend on exploitation of digital elevation data for its reliable determination. This research aimed at exploring the different approaches for defining and extraction of the Earth's surface roughness from Airborne LiDAR Measurements. It also aimed at evaluating the effects of the window size of the standard deviation filter on the created roughness maps in downtown landscapes using three known approaches namely; standard deviation filtering of the Digital Elevation Model (DEM), standard deviation filtering of the slope gradient model and standard deviation filtering of the profile curvature model. In this context, different roughness maps have been created from Airborne LiDAR measurements of the City of Toronto, Canada using the three filtering approaches with varying window sizes. Visual analysis has shown color tones of small roughness values with smooth textures dominate the roughness maps from small window sizes of the standard deviation filter, however, increasing the window sizes has produced wider variations of the color tones and rougher texture roughness maps. The standard deviations and ranges of the roughness maps from LiDAR DEM have increased due to increasing the filter window size while the skewness and kurtosis have decreased due to increasing the window size, indicating that the roughness maps from larger window sizes are statistically more symmetrical and more consistent. Thus, kurtosis has decreased by 53% and 82% due to increasing the window size to 7 × 7 and 15 × 15 respectively. The standard deviations of the roughness maps from the slope gradient model have increased due to increasing the window size till 15 × 15 while they have decreased with more increases. However, skewness has decreased due to increasing the window size How to cite this paper: Asal, F.F.F.

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Modeling DEM Data Uncertainties for Monte Carlo Simulations of Ice Sheet Models

Cited by 5 publications

References 12 publications

The impact of parametric uncertainty and topographic error in ice-sheet modelling

The impact of parametric uncertainty and topographic error in ice-sheet modelling

The influence of resolution and topographic uncertainty on melt modelling using hypsometric sub‐grid parameterization

Creation and Analysis of Earth’s Surface Roughness Maps from Airborne LiDAR Measurements in Downtown Urban Landscape

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