Quantitative description of depression storage using a digital surface model

Ullah, Wasi; Dickinson, W. T.

doi:10.1016/0022-1694(79)90007-6

Cited by 31 publications

(13 citation statements)

References 2 publications

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“…For example, Abedini et al and colleagues [25][26][27]33] obtained parallel results in relation to the depression number. In our study, both the shallow and the single-pixel depressions also followed this negative trend in the group of ALS-DTMs.…”

Section: The Variation Of the Hydrological Depression Variablesmentioning

confidence: 88%

“…Furthermore, the potential of high-resolution DTMs to represent linear anthropogenic features, such as depressions, and the use of these for more accurate flow pattern modelling in human modified landscapes [4] has been examined. Additionally, research concerning the effects of depressions on hydrologic models [5], the effects of the terrain slope [33], the effects of surface roughness [34,35] on depressions and also the impacts of grid size on hydrologic connectivity has been performed.…”

Section: Introductionmentioning

confidence: 99%

“…These effects cause changes in the shape and size of hydrographs, runoff volume and time [5,25,40]. Thus, the aforementioned influences are brought about by changes in water detention and direct surface runoff and are mainly achieved by adding, storing and restoring detention and absorption areas for water on a watershed scale.…”

Section: Introductionmentioning

confidence: 99%

“…Depressions can be simple or complex. Simple depressions have one pour point (PP) and complex ones have more than one pour point of which one is the actual pour point (APP) and the others are shared pour points (SPP) [40].…”

Section: Introductionmentioning

confidence: 99%

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Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Vesakoski

Alho

Hyyppä

et al. 2014

Water

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Topographic depressions have an important role in hydrological processes as they affect the water balance and runoff response of a watershed. Nevertheless, research has focused in detail neither on the effects of acquisition and processing methods nor on the effects of resolution of nationwide grid digital terrain models (DTMs) on topographic depressions or the hydrological impacts of depressions. Here, we quantify the variation of hydrological depression variables between DTMs with different acquisition methods, processing methods and grid sizes based on nationwide 25 m × 25 m and 10 m × 10 m DTMs and 2 m × 2 m ALS-DTM in Finland. The variables considered are the mean depth of the depression, the number of its pixels, and its area and volume. Shallow and single-pixel depressions and the effect of mean filtering on ALS-DTM were also studied. Quantitative methods and error models were employed. In our study, the depression variables were dependent on the scale, area and acquisition method. When the depths of depression pixels were compared with the most accurate DTM, the maximum errors were OPEN ACCESSWater 2014, 6 272 found to create the largest differences between DTMs and hence dominated the amount and statistical distribution of the depth error. On the whole, the ability of a DTM to accurately represent depressions varied uniquely according to each depression, although DTMs also displayed certain typical characteristics. Thus, a DTM's higher resolution is no guarantee of a more accurate representation of topographic depressions, even though acquisition and processing methods have an important bearing on the accuracy.

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Section: The Variation Of the Hydrological Depression Variablesmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Vesakoski

Alho

Hyyppä

et al. 2014

Water

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“…Ocallaghan does this by smoothing the DEM data; however, only tiny and shallow depressions can be removed, while larger and deeper depressions remain (Ocallaghan and Mark, 1984). The depression-filling method in a real sense was first used to study the roughness of soil, such as calculating a soil's depression storage capacity using DEM data (Moore and Larson, 1979;Onstad, 1984;Planchon and Darboux, 2002;Ullah and Dickinson, 1979). The idea of filling these de-pressions by increasing the cell's elevation to the minimal elevation of the cells that are found on the boundary of the depression is recommended by Jenson and Domingue (1988) and Marks et al (1984).…”

Section: Introductionmentioning

confidence: 99%

Another Fast and Simple DEM Depression-Filling Algorithm Based on Priority Queue Structure

Liu

Zhang

2009

Atmospheric and Oceanic Science Letters

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The production of digital elevation models for experimental model landscapes

Hancock

Willgoose

2001

Earth Surf Processes Landf

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Automated digital photogrammetry was used to produce digital elevation models of experimental model landscapes under controlled laboratory conditions as part of a series of rainfall erosion experiments looking at the evolution of landforms in response to erosion. The method allowed the elevations of the experimental landscapes to be studied in great detail on a regular grid digital terrain map with relatively very little effort. Digital photogrammetry produced elevation data at a resolution of 6 mm with a standard deviation of 2Á0 mm over an experimental catchment relief of approximately 200 mm; this resolution is considerably better than that achievable by conventional manual photogrammetry. The density of grid points was sufficiently high that small-scale details such as knickpoints developing in channels were represented. The method can facilitate the study of both experimental and natural landscapes in great detail.

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Quantitative description of depression storage using a digital surface model

Cited by 31 publications

References 2 publications

Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Another Fast and Simple DEM Depression-Filling Algorithm Based on Priority Queue Structure

The production of digital elevation models for experimental model landscapes

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