Uncertainties associated with digital elevation models for hydrologic applications: a review

Wechsler, Suzanne P.

doi:10.5194/hess-11-1481-2007

Cited by 292 publications

(211 citation statements)

References 118 publications

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“…These experiments suggest that the TOPKAPI hypothesis and the calibrated hydraulic conductivity are quite realistic on the Liebenbergsvlei catchment. It is also worth noting that a part of this increase of the hydraulic conductivity could be explained by the precision of the DEM: (i) in terms of resolution, since the 1 km resolution used here has been identified by Martina (2004) as the upper limit of physical scale above which the TOPKAPI model parameters no longer match the physics and also (ii) in terms of precision of the cell heights and corresponding slopes, that can have a strong influence on the parameter values (the reader is referred to Wechsler et al, 2007 for an interesting review of the hydrological model uncertainties associated with a DEM).…”

Section: Uncertainties Associated With Hydrological Modellingmentioning

confidence: 99%

Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Vischel

Pegram²,

Sinclair³

et al. 2008

Hydrol. Earth Syst. Sci.

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Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km 2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R 2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

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Section: Uncertainties Associated With Hydrological Modellingmentioning

confidence: 99%

Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Vischel

Pegram²,

Sinclair³

et al. 2008

Hydrol. Earth Syst. Sci.

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“…However, scaling and parameterisation can have significant impacts on model behaviour and results. In order to improve the parameterisation or assess the uncertainty associated with the model results, it is important to qualify and quantify these impacts (Wechsler, 2007;Hebeler and Purves, in press,a;Endreny and Wood, 2001). …”

Section: Resolution Effects and Uncertaintymentioning

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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“…In this regard, the accuracy of DEMs the Root Mean Square Error of Elevation (RMSE) is a most widely used technique which indicates the difference between the estimated and true value of DEMs products (Wise, 2000). However, according to Wechsler (2007) the RMSE method sometimes does not calculate an accurate assessment of how precisely each grid in a DEM represents topographical features. To solve this issue, number of researchers proposed spatial simulation methods for assessing the uncertainty of elevation estimates in each DEMs grid (Holmes et al, 2000;Carlisle, 2005;Wechsler and Kroll, 2006;Abd Aziz et al, 2012).…”

Section: Introdctionmentioning

confidence: 99%

“…The spatial simulation process analyses the spatial correlation in data to produce equiprobable estimates (realizations) of each particular grid in the DEMs. These realizations provide a range within which the true estimate lies and can be used to quantify the uncertainty at each DEM grid (Wechsler, 2007). Within this research we aimed to assess the inherent uncertainty associated with DEMs when applied to object based landslide mapping.…”

Section: Introdctionmentioning

confidence: 99%

“…"DEMs represent the topography that drives surface flow and are arguably one of the more important data sources for deriving variables" (Wechsler, 2007(Wechsler, , 1481. Due to the decrease in data and computer costs, increase in computing power as well as developing of geoscience technologies, the DEMs data has become increasingly available to spatial data users (Wechsler, 2007). The term of DEMs are often assumed to be accurate representations of the real-world terrain surfaces.…”

Section: Introdctionmentioning

confidence: 99%

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Assessing uncertainties associated with digital elevation models for object based landslide delination

Feizizadeh¹,

Blaschke²

2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:Digital elevation models (DEMs) are representations of topography with inherent errors that constitute uncertainty. DEMs data are often used in object based analyses without quantifying the effects of these errors. The main objective of this research is to establish a semi-automated object-based image analysis (OBIA) methodology for modelling uncertainty associated with DEMs when applied for locating landslides. In order to assess the uncertainty of DEMs, the Monte Carlo Simulation methodology was employed for evaluation of the effects of uncertainty on elevation and derived topographic parameters. The effect of DEM error is investigated, using stochastic conditional simulation to generate multiple equally likely representations of an actual terrain surface. Accordingly, distributional measures including accuracy surfaces, spatial autocorrelation indices, and variograms were also employed to quantify the magnitude and spatial pattern of the uncertainty. The semi-automated object based rule-sets for landslide delineation were developed based on two approaches a): without uncertainty assessing of DEMs and b): under applying uncertainty assessing of DEMs to examine the probable and possible uncertainties in delineating the landslides and measuring the improved accuracy after minimizing these associated uncertainties. The results of two approaches were validated using a landslide inventory database and very accurate GPS dataset. Results indicated very significant improvement in accuracy of results (> 28 %) when employing DEMs under uncertainty assessment. This research demonstrates how application of this methodology can address DEMs uncertainty, contributing to more responsible use of elevation and derived topographic parameters, and ultimately results obtained from their use.

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Uncertainties associated with digital elevation models for hydrologic applications: a review

Cited by 292 publications

References 118 publications

Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

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

Assessing uncertainties associated with digital elevation models for object based landslide delination

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