Parallel Computing Flow Accumulation in Large Digital Elevation Models

Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030186 ◽

2021 ◽

Vol 10 (3) ◽

pp. 186

Author(s):

HuiHui Zhang ◽

Hugo A. Loáiciga ◽

LuWei Feng ◽

Jing He ◽

QingYun Du

Keyword(s):

Spatial Scales ◽

Digital Elevation Models ◽

Regional Scale ◽

Drainage Density ◽

River Network ◽

Landscape Patterns ◽

Estimation Methods ◽

River Networks ◽

Flow Accumulation ◽

Digital Elevation

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

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Parallel computing of catchment basins of rivers in large digital elevation models

2010 International Conference on High Performance Computing & Simulation ◽

10.1109/hpcs.2010.5547157 ◽

2010 ◽

Cited By ~ 3

Author(s):

Hiep-Thuan Do ◽

Sebastien Limet ◽

Emmanuel Melin

Keyword(s):

Parallel Computing ◽

Digital Elevation Models ◽

Digital Elevation

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A new approach for dealing with depressions in digital elevation models when calculating flow accumulation values

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133310384542 ◽

2010 ◽

Vol 34 (6) ◽

pp. 781-809 ◽

Cited By ~ 44

Author(s):

Neil Arnold

Keyword(s):

Digital Elevation Models ◽

Physical Geography ◽

Main Flow ◽

High Quality ◽

Common Procedure ◽

New Approach ◽

Flow Accumulation ◽

Digital Elevation ◽

Fine Spatial Resolution ◽

Very High

Calculation of flow accumulation (also known as upstream area) matrices from digital elevation models (DEMs) is a very common procedure in hydrological studies, and also has been used in other disciplines within physical geography, such as glaciology. A problem with such calculations has always been the presence of closed depressions in DEMs; flow is directed towards such areas, but then cannot ‘escape’. In many implementations of flow accumulation algorithms such depressions have been removed from the DEM with some form of pre-processing algorithm which typically transform depressions into flat areas, across which area can then be routed. This approach effectively assumes that all depressions in a DEM are therefore artifacts, and not true features within the landscape. The proliferation of very high quality, high precision, and fine spatial resolution DEMs in recent years means that such an assumption is increasingly difficult to support. In this paper, some of the main flow accumulation algorithms and some existing techniques for dealing with closed depressions in DEMs are reviewed. A new algorithm is presented which assumes that such depressions are real features in the landscape, and which allows them to ‘fill’ and then ‘overflow’ into downstream areas within the DEM. Examples with a synthetic and two real DEMs suggest that, at least in these cases, the assumption that depressions are real is justified. These results also suggest that determining the size distribution for depressions within a DEM could form the basis for identifying whether artifact depressions are a problem in individual DEMs.

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