A quick algorithm of counting flow accumulation matrix for deriving drainage networks from a DEM

2011 ◽  
Author(s):  
Yanping Wang ◽  
Yonghe Liu ◽  
Hongbo Xie ◽  
ZhongLin Xiang
Keyword(s):  
Flow Accumulation ◽  
Drainage Networks

scholarly journals Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs

2021 ◽  
Vol 13 (11) ◽  
pp. 2024
Author(s):  
Wei Zhang ◽  
Wenkai Li ◽  
Hugo A. Loaiciga ◽  
Xiuguo Liu ◽  
Shuya Liu ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Storage ◽  
Multiple Regression Model ◽  
Coefficient Of Determination ◽  
Power Functions ◽  
Flow Accumulation ◽  
Drainage Networks ◽  
Storage Change

Selecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents the MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when extracting drainage from DEMs. This work employs 36 sample sub-basins in Hubei (China) province. Firstly, topography, the normalized difference vegetation index (NDVI), and water storage change are used in building multiple regression models to calculate the drainage length. Power functions are fit to calculate the FAT of each sub-basin. Nine randomly chosen regions served as test sub-basins. The results show that: (1) water storage change and NDVI have high correlation with the drainage length, and the coefficient of determination (R2) ranges between 0.85 and 0.87; (2) the drainage length obtained from the Multiple Regression model using water storage change, NDVI, and topography as influence factors is similar to the actual drainage length, featuring a coefficient of determination (R2) equal to 0.714; (3) the MR-AP method calculates suitable FATs for each sub-basin in Hubei province, with a drainage length error equal to 5.13%. Moreover, drainage network extraction by the MR-AP method mainly depends on the water storage change and the NDVI, thus being consistent with the regional water-resources change.

scholarly journals AUTOMATED ROAD BREACHING TO ENHANCE EXTRACTION OF NATURAL DRAINAGE NETWORKS FROM ELEVATION MODELS THROUGH DEEP LEARNING

2018 ◽  
Vol XLII-4 ◽  
pp. 597-601
Author(s):  
L. Stanislawski ◽  
T. Brockmeyer ◽  
E. Shavers
Keyword(s):  
Deep Learning ◽  
Surface Water ◽  
Flow Direction ◽  
Drainage Network ◽  
Water Drainage ◽  
Cloud Data ◽  
Flow Accumulation ◽  
Drainage Networks ◽  
Detailed Surface ◽  
Digital Elevation

<p><strong>Abstract.</strong> High-resolution (HR) digital elevation models (DEMs), such as those at resolutions of 1 and 3 meters, have increasingly become more widely available, along with lidar point cloud data. In a natural environment, a detailed surface water drainage network can be extracted from a HR DEM using flow-direction and flow-accumulation modeling. However, elevation details captured in HR DEMs, such as roads and overpasses, can form barriers that incorrectly alter flow accumulation models, and hinder the extraction of accurate surface water drainage networks. This study tests a deep learning approach to identify the intersections of roads and stream valleys, whereby valley channels can be burned through road embankments in a HR DEM for subsequent flow accumulation modeling, and proper natural drainage network extraction.</p>

A storage model for the simulation of the hydraulic behaviour of drainage networks

1997 ◽  
Vol 36 (8-9) ◽  
pp. 57-63 ◽  
Author(s):  
Homayoun Motiee ◽  
Bernard Chocat ◽  
Olivier Blanpain
Keyword(s):  
Wave Model ◽  
Kinematic Wave ◽  
Hydraulic Simulation ◽  
Diffusion Wave ◽  
Storage Model ◽  
Hydraulic Behaviour ◽  
Drainage Networks ◽  
Saint Venant Equations ◽  
Kinematic Wave Model ◽  
And Diffusion

This paper presents a model for the hydraulic simulation of a drainage network using the storage concept. This model is easier to use than the complete Barre de Saint Venant equations and gives better results than the usual conceptual models, i.e. the Muskingum model, or than models obtained by the simplification of the Saint Venant equations (kinematic wave model and diffusion wave model).

EXAMINING THE EFFECTS OF RESOLUTION AND ROADS ON DEM-DERIVED DRAINAGE NETWORKS

2019 ◽  
Author(s):  
J.P. Gannon ◽  
◽  
Diane Styers ◽  
David Kinner ◽  
Mark Lord
Keyword(s):  
Drainage Networks

scholarly journals Inclusion of Hydraulic Controls in Rehabilitation Models of Drainage Networks to Control Floods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 514
Author(s):  
Leonardo Bayas-Jiménez ◽  
F. Javier Martínez-Solano ◽  
Pedro L. Iglesias-Rey ◽  
Daniel Mora-Melia ◽  
Vicente S. Fuertes-Miquel
Keyword(s):  
Genetic Algorithm ◽  
Drainage System ◽  
Optimal Solution ◽  
Hydraulic Control ◽  
Drainage Systems ◽  
Storm Water Management Model ◽  
Drainage Networks ◽  
Extreme Rain ◽  
Rain Events ◽  
The Cost

A problem for drainage systems managers is the increase in extreme rain events that are increasing in various parts of the world. Their occurrence produces hydraulic overload in the drainage system and consequently floods. Adapting the existing infrastructure to be able to receive extreme rains without generating consequences for cities’ inhabitants has become a necessity. This research shows a new way to improve drainage systems with minimal investment costs, using for this purpose a novel methodology that considers the inclusion of hydraulic control elements in the network, the installation of storm tanks and the replacement of pipes. The presented methodology uses the Storm Water Management Model for the hydraulic analysis of the network and a modified Genetic Algorithm to optimize the network. In this algorithm, called the Pseudo-Genetic Algorithm, the coding of the chromosomes is integral and has been used in previous studies of hydraulic optimization. This work evaluates the cost of the required infrastructure and the damage caused by floods to find the optimal solution. The main conclusion of this study is that the inclusion of hydraulic controls can reduce the cost of network rehabilitation and decrease flood levels.

An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions

2017 ◽  
Vol 45 (4) ◽  
pp. 319-328 ◽  
Author(s):  
Lawrence V. Stanislawski ◽  
Kornelijus Survila ◽  
Jeffrey Wendel ◽  
Yan Liu ◽  
Barbara P. Buttenfield
Keyword(s):  
Surface Water ◽  
Open Source ◽  
High Performance ◽  
Water Drainage ◽  
Drainage Networks

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

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.

scholarly journals Hydraulic transmissivity inferred from ice-sheet relaxation following Greenland supraglacial lake drainages

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ching-Yao Lai ◽  
Laura A. Stevens ◽  
Danielle L. Chase ◽  
Timothy T. Creyts ◽  
Mark D. Behn ◽  
...  
Keyword(s):  
Laboratory Experiments ◽  
Ice Sheet ◽  
Drainage System ◽  
Greenland Ice Sheet ◽  
Field Observations ◽  
Surface Uplift ◽  
Drainage Networks ◽  
Order Of Magnitude ◽  
Lake Drainage ◽  
Magnitude Increase

AbstractSurface meltwater reaching the base of the Greenland Ice Sheet transits through drainage networks, modulating the flow of the ice sheet. Dye and gas-tracing studies conducted in the western margin sector of the ice sheet have directly observed drainage efficiency to evolve seasonally along the drainage pathway. However, the local evolution of drainage systems further inland, where ice thicknesses exceed 1000 m, remains largely unknown. Here, we infer drainage system transmissivity based on surface uplift relaxation following rapid lake drainage events. Combining field observations of five lake drainage events with a mathematical model and laboratory experiments, we show that the surface uplift decreases exponentially with time, as the water in the blister formed beneath the drained lake permeates through the subglacial drainage system. This deflation obeys a universal relaxation law with a timescale that reveals hydraulic transmissivity and indicates a two-order-of-magnitude increase in subglacial transmissivity (from 0.8 ± 0.3 $${\rm{m}}{{\rm{m}}}^{3}$$ m m 3 to 215 ± 90.2 $${\rm{m}}{{\rm{m}}}^{3}$$ m m 3 ) as the melt season progresses, suggesting significant changes in basal hydrology beneath the lakes driven by seasonal meltwater input.

A flooding algorithm for extracting drainage networks from unprocessed digital elevation models

2013 ◽  
Vol 59 ◽  
pp. 116-123 ◽  
Author(s):  
Antonio Rueda ◽  
José M. Noguera ◽  
Carmen Martínez-Cruz
Keyword(s):  
Digital Elevation Models ◽  
Drainage Networks ◽  
Digital Elevation
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