Topographic factor of water erosion – analysis of watershed morphometry and RUSLE LS factor in GIS environment

Nikolova, Valentina; Mitova, Milena; Dimitrov, Emil

doi:10.52215/rev.bgs.2022.83.1.3

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Here, As represents the contributing area to the cell, and β represents the slope gradient (Nikolova, 2022).…”

Section: Topographic Wetness Index (Twi)mentioning

confidence: 99%

GIS-Based Erosion Susceptibility Analysis in Bingöl (Türkiye): A Comparasion of Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon’s Entropy models

İNİK,

UTLU

2024

Preprint

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Soil erosion is one of the most important and critical processes occurring in Turkey, as in all parts of the world. It is of great importance to understand the processes that occur as soil erosion continues. The aim of this study is to determine the erosion susceptibility occurring in the Çapakçur Stream basin, one of the important erosion areas of Turkey. In the study, erosion susceptibility analysis was used using 19 conditioning factors based on 4 different methods (Shannon Entropy (SE), Logistic Regression (LR), Frequency Ratio (FR) and Weight of Evidence (WoE)), which are actively used today in erosion susceptibility analysis and determination of critical areas in terms of erosion. has been made. Model performances of the Analysis Results were evaluated based on a data set of 840 training (70%) points and 360 validation (30%) points using ROC and AUC values.. According to result of the ROC and AUC values show that Logistic regression seems to perform well on both training (AUC = 94.7%) and validating datasets (AUC = 93.5%). On the other hand, Weight of Evidence training (AUC = 93.5%) and testing datasets (AUC = 91.4%), Frequency Ratio training (AUC = 93.5%) and testing datasets (AUC = 92.4%). of the Weight of Evidence resault show that AUC and ROC values smilar to Logistic Regression result, but slightly lower than Logistic Regression. Additionally, Shannon Entropy shows that it performs lower than other methods on both training (AUC = 55.7%) and testing datasets (AUC = 56.3%). Conducting analyzes based on these methods, especially in erosion susceptibility studies, will facilitate both planning and the accuracy of the results obtained.

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“…Here, As represents the contributing area to the cell, and β represents the slope gradient (Nikolova, 2022).…”

Section: Topographic Wetness Index (Twi)mentioning

confidence: 99%

GIS-Based Erosion Susceptibility Analysis in Bingöl (Türkiye): A Comparasion of Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon’s Entropy models

İNİK,

UTLU

2024

Preprint

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“…In terms of geospatial raster data, TRI represents the difference in the elevation amid the neighbouring cells (Nikolova et al, 2022). It is calculated by finding the elevation difference between the focal cell and its 8 immediate cells (Equation 8).…”

Section: β = Slope Gradient In Degreesmentioning

confidence: 99%

Water induced surface erosion risk assessment in Jumar agricultural watershed, Jharkhand, India using multivariate statistics and GIS

Pandey¹,

Kumari²

2023

Preprint

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The state of Jharkhand suffers immensely from water-induced surface erosion causing loss of socio-economic Jumar watershed of Ranchi district, Jharkhand was selected for a study period of 2020 and 2021 to analyse climate change, seasonal change in quality of water and soil resources, hydro-geomorphometry and land cover land use (LULC) using GIS and multivariate statistics. The water quality analysis revealed the presence of higher content of presence of metals such as Ca, Mg, K, and Na, higher alkalinity, TDS, and turbidity in monsoon and post-monsoon months of 2021. The soil quality analysis showed that the region has excessive carbonate salts with dominant presence of metal such as calcium, magnesium and sodium, disturbing the soil structure and lowering its infiltration capacity, promoting nutrient deficiencies, and leading to possible metal contamination in few years. Lack of vegetation covers adds to surface erosion under high intensity rainfall (1685-2083mm). The correlation analysis was done seasonally for both soil and water quality revealing the strong influence of metal ions with pH, alkalinity, salinity, and TDS, showing a prominent silicate weathering and ion exchange process indicating prominent surface erosion in watershed. The PCA on water and soil quality data showed that salinity, TDS, Alkalinity, Hardness and EC are the principal components. The morphometric study showed that the Jumar watershed has rolling and slightly undulating terrain, with mild slope and dendritic stream pattern of 4th order stream. Low basin relief and drainage density, compactness constant (0.00192), Ruggedness number (0.0609), and basin relief ratio (0.00552) indicated the dominance of low stream orders (1st ) in the area pointing to low potential energy in surface flow. The hydrological indices indicated that the watershed is low risk of gully formation in lowlands. Through the application of other GIS based rainfall, LULC and spectral indices (NDMI, MSI, SAVI and BSI) it was found that there has been a huge increase in urbanization, barren land, sparse vegetation cover, and immense water stress especially in summer and winter season of 2020 and 2021 in the watershed. The Lack of water conservation and traditional agriculture using agrochemicals is very prominent in the watershed, hence an immediate need for water and soil quality management is required.

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Characterising landslide susceptibility of an environmentally fragile region of the Western Ghats in Idukki district, Kerala, India, through statistical modelling and hotspot analysis

2022

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Topographic factor of water erosion – analysis of watershed morphometry and RUSLE LS factor in GIS environment

Cited by 6 publications

References 32 publications

GIS-Based Erosion Susceptibility Analysis in Bingöl (Türkiye): A Comparasion of Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon’s Entropy models

GIS-Based Erosion Susceptibility Analysis in Bingöl (Türkiye): A Comparasion of Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon’s Entropy models

Water induced surface erosion risk assessment in Jumar agricultural watershed, Jharkhand, India using multivariate statistics and GIS

Characterising landslide susceptibility of an environmentally fragile region of the Western Ghats in Idukki district, Kerala, India, through statistical modelling and hotspot analysis

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