scholarly journals A new vector-based global river network dataset accounting for variable drainage density

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peirong Lin ◽  
Ming Pan ◽  
Eric F. Wood ◽  
Dai Yamazaki ◽  
George H. Allen
Keyword(s):  
Spatial Variability ◽  
Flow Direction ◽  
River System ◽  
Drainage Density ◽  
River Network ◽  
River Networks ◽  
Digital Elevation ◽  
Machine Learning Approach ◽  
Elevation Model ◽  
National Hydrography Dataset

AbstractSpatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.

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 Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

2020 ◽  
Vol 12 (7) ◽  
pp. 1222 ◽  
Author(s):  
Luca Demarchi ◽  
Wouter van de Bund ◽  
Alberto Pistocchi
Keyword(s):  
Ensemble Learning ◽  
Large Scale ◽  
Accuracy Assessment ◽  
River System ◽  
River Network ◽  
Second Phase ◽  
Object Based ◽  
Digital Elevation ◽  
Recent Developments ◽  
Elevation Model

Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe.

scholarly journals A data set of global river networks and corresponding water resources zones divisions

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Denghua Yan ◽  
Kun Wang ◽  
Tianling Qin ◽  
Baisha Weng ◽  
Hao Wang ◽  
...  
Keyword(s):  
Water Resources ◽  
River Network ◽  
River Networks ◽  
Data Set ◽  
Plain Area ◽  
Digital Elevation ◽  
Level 1 ◽  
Elevation Model ◽  
Digital Elevation Model Data ◽  
Hydrology And Water Resources

Abstract As basic data, the river networks and water resources zones (WRZ) are critical for planning, utilization, development, conservation and management of water resources. Currently, the river network and WRZ of world are most obtained based on digital elevation model data automatically, which are not accuracy enough, especially in plains. In addition, the WRZ code is inconsistent with the river network, hindering the efficiency of data in hydrology and water resources research. Based on the global 90-meter DEM data combined with a large number of auxiliary data, this paper proposed a series of methods for generating river network and water resources zones, and then obtained high-precision global river network and corresponding WRZs at level 1 to 4. The dataset provides generated rivers with high prevision and more accurate position, reasonable basin boundaries especially in inland and plain area, also the first set of global WRZ at level 1 to 4 with unified code. It can provide an important basis and support for reasonable use of water resources and sustainable social development in the world.

scholarly journals Flood vulnerability assessment in the Accra Metropolis, southeastern Ghana

2021 ◽  
Vol 11 (7) ◽  
Author(s):  
Benjamin Wullobayi Dekongmen ◽  
Amos Tiereyangn Kabo-bah ◽  
Martin Kyereh Domfeh ◽  
Emmanuel Daanoba Sunkari ◽  
Yihun Taddele Dile ◽  
...  
Keyword(s):  
Drainage Density ◽  
Geological Structures ◽  
Flood Vulnerability ◽  
Rock Types ◽  
Digital Elevation ◽  
Annual Population ◽  
Density Values ◽  
High Runoff ◽  
Elevation Model ◽  
Very High

AbstractFloods in Ghana have become a perennial challenge in the major cities and communities located in low-lying areas. Therefore, cities and communities located in these areas have been classified as potential or natural flood-prone zones. In this study, the Digital Elevation Model (DEM) of the Accra Metropolis was used to assess the drainage density and elevation patterns of the area. The annual population estimation data and flood damages were assessed to understand the damages and population trend. This research focused primarily on the elevation patterns, slope patterns, and drainage density of the Accra Metropolis. Very high drainage density values, which range between 149 and 1117 m/m2, showed very high runoff converging areas. High drainage density was also found to be in the range of 1117–1702 m/m2, which defined the area as a high runoff converging point. The medium and low converging points of runoff were also found to be ranging between 1702–2563 m/m2 and 2563–4070 m/m2, respectively. About 32% of the study area is covered by natural flood-prone zones, whereas flood-prone zones also covered 33% and frequent flood zones represent 25%. Areas in the Accra Metropolis that fall in the Accraian and Togo series rock types experience high floods. However, the lineament networks (geological structures) that dominate the Dahomeyan series imply that the geological structures in the Dahomeyan series also channel the runoffs into the low-lying areas, thereby contributing to the perennial flooding in the Accra Metropolis.

scholarly journals Electrical resistivity and spatial variation in agriculture terraces: statistical correlation between ert and flow direction algorithms

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
J. Fernandes ◽  
C. Bateira ◽  
A. Costa ◽  
B. Fonseca ◽  
R. Moura
Keyword(s):  
Electrical Resistivity ◽  
Standard Deviation ◽  
Spatial Variation ◽  
Soil Depth ◽  
Flow Direction ◽  
Statistical Correlation ◽  
Average Value ◽  
Digital Elevation ◽  
Elevation Model ◽  
Soil Electrical Resistivity

AbstractThe construction of earthen embankment terraces in the Douro Region raises a set of problems related to hydrological processes. The main objective of this study is the evaluation of the spatial variation of electrical resistivity in agriculture terraces at Douro valley (Portugal). To achieve this objective, two variables are analysed, the soil electrical resistivity and the flow direction algorithm. In a field survey we recorded 13 electrical resistivity profiles. The contributing area was calculated with the algorithms D∞ (Deterministic Infinity Flow) and MFD (Multiple Flow Direction) and the results are the base of the internal runoff modelling, both supported by the digital elevation model with a spatial resolution of 1m2. A correlation between the spatial variation of the soil electrical resistivity represented by the standard deviation of the electrical resistivity for each profile and the average value of the contributing area coincident with each profile was established. The electrical resistivity standard deviation seems to be moderately well correlated according to the D∞ algorithm at about 1m of depth, and it has a good correlation at 1,5m to 2m of depth with the MFD algorithm. Taken together, the results show a significant positive statistical correlation between the electrical resistivity standard deviation and the contributing areas (MFD and D∞) depending on the soil depth.

scholarly journals Inundation Model Using UAV-derived Digital Elevation Data and PCRaster Dynamic Model in An Excessive Rainfall Event

2018 ◽  
Vol 2 (2) ◽  
pp. 152-159
Author(s):  
Dwi Setyo Aji ◽  
Warsini Handayani ◽  
Retnadi Heru Jatmiko ◽  
Agung Kurniawan
Keyword(s):  
Flow Direction ◽  
Extreme Weather ◽  
Rainfall Data ◽  
Digital Elevation ◽  
Hourly Data ◽  
Elevation Data ◽  
Additional Water ◽  
Flowing Material ◽  
Elevation Model ◽  
Uav Image

Extreme weather reportedly occurred on 28th November 2017 caused by a cyclone called Cempaka. Categorized as extreme weather since this event triggered an excessive rainfall reaching 246.8 mm in a 24-hour. Consequently, some areas in Yogyakarta Special Region are inundated. This research attempts to model the inundation of excessive rainfall using GIS software, PCRaster. The study area is concentrated in Selopamioro and Sriharjo, where Opak and Oyo rivers meet. Elevation model and rainfall data are used as the principal data to model the inundation. Elevation model is derived from the Unmanned Aerial Vehicle (UAV)  image, while, the rainfall data of a-24-hour hourly data from the Meteorological Agency is also used as an input. The elevation model works as a flow direction model and the rainfall amount plays as the flowing material. The original states of water of the river are not considered, thus the study merely describes how the certain amount of rainfall adds to the level height of terrain and modeled for 24 hours. The result maps are the area that experience of a-24-hour high intensity of rainfall. The study depicts the additional water level caused by rainfall and the concentration of excessive rainfall in the study area. This information is beneficial in order to alarm a similar future event.  

scholarly journals Floods Risk Mapping and Assessing Vulnerability of Morang, Nepal

2021 ◽  
Author(s):  
Pawan Thapa ◽  
Narayan Thapa
Keyword(s):  
Drainage Density ◽  
Google Earth ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Digital Elevation ◽  
Two Factors ◽  
Flooding Risk ◽  
Elevation Model ◽  
The One ◽  
The Impact

Abstract Background: The impact of flooding rises due to unplanned settlements, especially in developing and underdeveloped countries. This study tries to address these issues by mapping flood risk places and assessing their impact on population and household.Methods: This study used the dataset available in Google Earth Engine (GEE), Food and Agriculture Organization (FAO), Central Bureau Statistics (CBS), Earth Data for preparing slope, drainage density, digital elevation model, rainfall, land use map, and soil map. These maps create using GEE and QGIS through overlay analysis that has two factors. The one is influence and other slopes, and it has provided high and low value according to its role on flooding.Results: The risk assessment shows around twenty-four percent population is at higher risk, whereas more than three thousand settlements are prone to flooding. It depicts a significant increasing trend of floods in the Morang district.Conclusion: This settlement risk map can help determine the flood safe and very high-risk areas in the Morang district. It will support residential places' planning by the local government, urban planners, and community people to reduce flooding risk.

scholarly journals HydroSHEDS v2.0 – Refined global river network and catchment delineations from TanDEM-X elevation data

2021 ◽  
Author(s):  
Bernhard Lehner ◽  
Achim Roth ◽  
Martin Huber ◽  
Mira Anand ◽  
Günther Grill ◽  
...  
Keyword(s):  
High Resolution ◽  
Digital Elevation Model ◽  
Multiple Scales ◽  
Environmental Conservation ◽  
The Arctic ◽  
River Networks ◽  
Northern Latitude ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model

<p>Since its introduction in 2008, the HydroSHEDS database (www.hydrosheds.org) has transformed large-scale hydro-ecological research and applications worldwide by offering standardized spatial units for hydrological assessments. At its core, HydroSHEDS provides digital hydrographic information that can be applied in Geographic Information Software (GIS) or hydrological models to delineate river networks and catchment boundaries at multiple scales, from local to global. Its various data layers form the basis for applications in a wide range of disciplines including environmental, conservation, socioeconomic, human health, and sustainability studies.</p><p>Version 1 of HydroSHEDS was derived from the digital elevation model of the Shuttle Radar Topography Mission (SRTM) at a pixel resolution of 3 arc-seconds (~90 meters at the equator). It was created using customized processing and optimization algorithms and a high degree of manual quality control. Results are available at varying resolutions, ranging from 3 arc-seconds (~90 m) to 5 minutes (~10 km), and in nested sub-basin structures, making the data uniquely suitable for applications at multiple scales. A suite of related data collections and value-added information, foremost the HydroATLAS compilation of over 50 hydro-environmental attributes for every river reach and sub-basin, continuously enhance the versatility of the HydroSHEDS family of products. Yet version 1 of HydroSHEDS shows some important limitations. In particular, coverage above 60° northern latitude (i.e., largely the Arctic) is missing for the 3 arc-second product and is of low quality for coarser products because no SRTM elevation data are available for this region. Also, some areas are affected by inherent data gaps or other errors that could not be fully resolved at the time of creating version 1 of HydroSHEDS.</p><p>Today, the TanDEM-X dataset (TerraSAR-X add-on for Digital Elevation Measurement), created in partnership between the German Aerospace Agency (DLR) and Airbus, offers a new digital elevation model covering the entire global land surface including northern latitudes. In a collaborative project, this dataset is used to extract HydroSHEDS v2.0, following the same basic specifications as version 1. DLR is processing the original 12 m resolution TanDEM-X data to create a hydrologically pre-conditioned version at 3 arc-second resolution. In this step, corrections with high-resolution vegetation and settlement maps are applied to reduce distortions caused by vegetation cover and in built-up areas. Following this preprocessing, refined hydrological optimization and correction algorithms are used to derive the drainage pathways, including improved ‘stream-burning’ techniques that incorporate recent data products such as high-resolution terrestrial open water masks and improved tracing of drainage pathways as center lines in global lake and river maps. The resulting HydroSHEDS v2.0 database will provide river networks and catchment boundaries at full global coverage. Release of the data under a free license is scheduled for 2022, with regions above 60° northern latitude being completed first in 2021.</p>

scholarly journals Floods Risk Mapping and Assessing Vulnerability of Morang, Nepal

2021 ◽  
Author(s):  
Pawan Thapa ◽  
Narayan Thapa
Keyword(s):  
Drainage Density ◽  
Google Earth ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Digital Elevation ◽  
Two Factors ◽  
Flooding Risk ◽  
Elevation Model ◽  
The One ◽  
The Impact

Abstract Background: The impact of flooding rises due to unplanned settlements, especially in developing countries. This study tries to address these issues by mapping flood risk places and assessing their impact on population and household.Methods: This study used the dataset available in Google Earth Engine (GEE), Food and Agriculture Organization (FAO), Central Bureau Statistics (CBS), Earth Data for preparing slope, drainage density, digital elevation model, rainfall, land use map, and soil map. These maps create using GEE and QGIS through overlay analysis that has two factors. The one is influence and other slopes, and it has provided high and low value according to its role on flooding.Results: The risk assessment shows around twenty-four percent population is at higher risk, whereas more than three thousand settlements are prone to flooding. It depicts a significant increasing trend of floods in the Morang district.Conclusion: This settlement risk map can help determine the flood safe and very high-risk areas in the Morang district. It will support residential places' planning by the local government, urban planners, and community people to reduce flooding risk.

Sign in / Sign up

Export Citation Format

Share Document