A novel multiple flow direction algorithm for computing the topographic wetness index

2012 ◽  
Vol 43 (1-2) ◽  
pp. 135-145 ◽  
Author(s):  
Bin Yong ◽  
Li-Liang Ren ◽  
Yang Hong ◽  
Jonathan J. Gourley ◽  
Xi Chen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Flow Direction ◽  
Contour Length ◽  
Topographic Wetness Index ◽  
Wetness Index ◽  
Digital Elevation ◽  
Surface Saturation ◽  
Mathematical Equations ◽  
Downslope Flow ◽  
Elevation Model

The topographic wetness index (TWI), frequently used in approximately characterizing the spatial distribution of soil moisture and surface saturation within a watershed, has been widely applied in topography-related geographical processes and hydrological models. However, it is still questionable whether the current algorithms of TWI can adequately model the spatial distribution of topographic characteristics. Based upon the widely-used multiple flow direction approach (MFD), a novel MFD algorithm (NMFD) is proposed for improving the TWI derivation using a Digital Elevation Model (DEM) in this study. Compared with MFD, NMFD improves the mathematical equations of the contributing area and more precisely calculates the effective contour length. Additionally, a varying exponent strategy is adopted to dynamically determine the downslope flow-partition exponent. Finally, a flow-direction tracking method is employed to address grid cells in flat terrain. The NMFD algorithm is first applied to a catchment located upstream of the Hanjiang River in China to demonstrate its accuracy and improvements. Then NMFD is quantitatively evaluated by using four types of artificial mathematical surfaces. The results indicate that the error generated by NMFD is generally lower than that computed by MFD, and NMFD is able to more accurately represent the hydrological similarity of watersheds.

scholarly journals Deteksi Kerawanan Banjir Genangan Menggunakan Topographic Wetness Index (TWI)

2018 ◽  
Vol 18 (2) ◽  
pp. 107
Author(s):  
Fitria Nucifera ◽  
Sutanto Trijuni Putro
Keyword(s):  
Digital Terrain Model ◽  
Topographic Wetness Index ◽  
Wetness Index ◽  
Terrain Model ◽  
Control Factors ◽  
Digital Terrain ◽  
Digital Elevation ◽  
High Flood ◽  
Elevation Model ◽  
Digital Elevation Model Data

Flood is the most frequent disaster occured in Indonesia. Flood events result in loss and damage to communities and the environment. Floods are triggered by several factors including hydrometeorological factors, topography, geology, soil and human activities. Topographic factor is one of the flood trigger control factors. Topographic calculation for flood inundation detection can be done by Topographic Wetness Index (TWI) method. The TWI method focuses on topographic conditions of the region, especially the upper slopes and lower slopes to assess the trend of water accumulation in a region. TWI calculations are based on the topography of an area represented by DEM (Digital Elevation Model) data in the form of DTM (Digital Terrain Model). The high value of TWI is associated with high flood vulnerability. Based on the calculation of TWI value, flood-prone areas in Kebumen District include Adimulyo Subdistrict, Puring Subdistrict, Ambal Subdistrict, Rowokele Subdistrict and Buayan Subdistrict.

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 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 Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil

2006 ◽  
Vol 63 (3) ◽  
pp. 262-268 ◽  
Author(s):  
Elvio Giasson ◽  
Robin Thomas Clarke ◽  
Alberto Vasconcellos Inda Junior ◽  
Gustavo Henrique Merten ◽  
Carlos Gustavo Tornquist
Keyword(s):  
Land Use Planning ◽  
Soil Types ◽  
Sources Of Information ◽  
Topographic Wetness Index ◽  
Digital Elevation ◽  
Terrain Parameters ◽  
Logistic Regressions ◽  
Elevation Model ◽  
Soil Surveys ◽  
Soil Distribution

Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.

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 Off-Road Trafficability for Military Operations Using Multi-Criteria Decision Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 3425-3437
Author(s):  
M. Nazish Khan ◽  
◽  
M. Kashif ◽  
A. Shah ◽  
◽  
...  
Keyword(s):  
Decision Analysis ◽  
Satellite Images ◽  
Military Operations ◽  
Topographic Wetness Index ◽  
Multi Criteria Decision Analysis ◽  
Digital Elevation ◽  
Roughness Index ◽  
Ground Conditions ◽  
Elevation Model ◽  
North Western

This study has been carried out in the Pathankot region, having strategic importance in terms of military operations. It explores the ability of remote sensing and GIS in assessing off-road trafficability which is integral part of terrain intelligence. Number of thematic layers has been prepared using Sentinal -2 satellite images and PALSAR Digital Elevation Model (DEM) viz. LULC, Slope, Topographic Wetness Index (TWI), Terrain Roughness Index (TRI) and ground conditions to assess the potential of off-road trafficability in the study area for military operations. Results clearly depict that most of the region is suitable for off-road movement. However, north western part is showing less suitability. Keywords PALSAR; Multi-criteria Decision Analysis; AHP; Trafficability

Geoinformation mapping of landscapes on the example of the Primorskiy Ridge (Baikal region)

2020 ◽  
Vol 954 (12) ◽  
pp. 20-30
Author(s):  
Yu.V. Vanteeva ◽  
Е.А. Rasputina ◽  
S.V. Solodyankina
Keyword(s):  
Digital Elevation Model ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Topographic Wetness Index ◽  
Space Images ◽  
Topographic Position Index ◽  
Digital Elevation ◽  
Different Seasons ◽  
Position Index ◽  
Elevation Model

The authors present the results of geoinformation mapping the Primorskiy Ridge landscapes using Landsat 8 satellite images, the digital elevation model SRTM and the factor-dynamic classification of geosystems. At the first stage, the remote sensing data for different seasons were classified using the ISODATA method. Then, using the digital elevation model, the landforms were classified basing upon the topographic position index. According to combining the classification parameters of one of the space images and digital elevation model, each polygon is automatically assigned to a certain preliminary type of landscapes using boolean expressions. Legend adjustments were made basing upon the fieldwork materials. As a result, a digital landscape map of the southern part of the Primorsky Ridge was created; it reflects the landscape structure at the level of facies groups and contains attributive information about the landform, altitude, slope and aspect, topographic wetness index. The analysis of the landscape pattern showed a high fragmentation of landscape polygons, formed due to overlay operations, which indicates the need for generalization of landscape contours.

scholarly journals Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

2014 ◽  
Vol 18 (9) ◽  
pp. 3623-3634 ◽  
Author(s):  
A. M. Ågren ◽  
W. Lidberg ◽  
M. Strömgren ◽  
J. Ogilvie ◽  
P. A. Arp
Keyword(s):  
Surface Flow ◽  
Topographic Wetness Index ◽  
Water Index ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Soil Disturbances ◽  
Soil Wetness ◽  
Elevation Model ◽  
Terrain Indices

Abstract. Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This – in turn – can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (TWI) and the newly developed cartographic depth-to-water index (DTW) were the best soil wetness predictors. While the TWI derivations were sensitive to scale, the DTW derivations were not and were therefore numerically robust. Since the DTW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1–2 ha for till-derived landforms versus 8–16 ha for a coarse-textured alluvial floodplain.

scholarly journals Reducing scale dependence in TOPMODEL using a dimensionless topographic index

2009 ◽  
Vol 13 (12) ◽  
pp. 2399-2412 ◽  
Author(s):  
A. Ducharne
Keyword(s):  
Cell Size ◽  
Scale Dependence ◽  
Contour Length ◽  
Topographic Index ◽  
Dem Resolution ◽  
Unit System ◽  
Topographic Slope ◽  
Digital Elevation ◽  
Numerical Effect ◽  
Elevation Model

Abstract. This paper stems from the fact that the topographic index used in TOPMODEL is not dimensionless. In each pixel i in a catchment, it is defined as xi=ln(ai/Si), where ai is the specific contributing area per unit contour length and Si is the topographic slope. In the SI unit system, ai/Si is in meters, and the unit of xi is problematic. We propose a simple solution in the widespread cases where the topographic index is computed from a regular raster digital elevation model (DEM). The pixel length C being constant, we can define a dimensionless topographic index yi=xi-lnC. Reformulating TOPMODEL equations to use yi instead of xi helps giving the units of all their terms and emphasizes the scale dependence of these equations via the explicit use of C outside from the topographic index, in what can be defined as the transmissivity at saturation per unit contour length T0/C. The term lnC defines the numerical effect of DEM resolution, which contributes to shift the spatial mean x of the classical topographic index when the DEM cell size C varies. A key result is that both the spatial mean y of the dimensionless index and T0/C are much more stable with respect to DEM resolution than their counterparts x and T0 in the classical framework. This shows the importance of the numerical effect in the dependence of the classical topographic index to DEM resolution, and reduces the need to recalibrate TOPMODEL when changing DEM resolution.

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