Soil Erosion Analysis in a Small Forested Catchment Supported by ArcGIS Model Builder

Csáfordi, Péter; Pődör, Andrea; Bug, Jan; Gribovsyki, Zoltán

doi:10.2478/v10303-012-0004-5

Cited by 30 publications

(9 citation statements)

References 26 publications

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“…4K), as an index indicating vegetation conditions, was extracted for flooding period (March 2019) using Landsat 8 satellite images through Google Earth Engine (GEE, 2019b). The soil is an effective factor in the generation or infiltration of runoff (Csáfordi et al, 2012). The most dominant soil types of the study area are Mollisol, Alfisol, Inceptisol, Entisol, Aridisol, Rock Outcrop, and Salt flat (Fig.…”

Section: Flash-flood Influencing Factor (Ffif)mentioning

confidence: 99%

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

Hosseini

Choubin

Mosavi

et al. 2020

Science of The Total Environment

261

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Section: Flash-flood Influencing Factor (Ffif)mentioning

confidence: 99%

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

Hosseini

Choubin

Mosavi

et al. 2020

Science of The Total Environment

261

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“…Soil loss leads to a decrease of the water holding capacity, nutrient availability and organic matter content and to a reduction in the overall fertility of arable lands [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Post Fire Soil Erosion with ESA Sentinel-2 Data and RUSLE Method in Apulia Region (Southern Italy)

Santarsiero

Nolè²,

Lanorte³

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Fires are one of the main causes of environmental degradation as they have an impact on flora and fauna, can also strongly influence ecological and geomorphological processes and permanently compromise the functionality of the ecosystems and soils on which they impact. The severity of the fire event influences the superficial hydrological response and the consequent loss of soil. Precipitation on the basins recently affected by fires produces an increase in the outflow which commonly transports and deposits large volumes of sediment, both inside and downstream of the burned area. In the years following the fire, the loss of soil is very high and the degradation processes of the soils are much greater than in the pre-event. The aim of this study is to evaluate the potential annual loss due to post-fire erosion using remote sensing techniques, RUSLE (Revised Universal Soil Loss Equation) methodology and GIS tecniques in nine different event occurred in 2019 in the northern part of the Apulia Region (Southern Italy). Geographic Information System techniques and remote sensing data have been adopted to study the post-fire soil erosion risk. Satellite images are the most appropriate for environmental monitoring as they provide high resolution multispectral optical images, infact are able to monitor the development of vegetation by assessing the water content an changes in chlorophyll levels. This study can be useful to spatial planning authorities as a tool for assessing and monitoring eroded soil in areas affected by fires, representing a useful tool for land management.

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“…the upper part of the Svacenický creek catchment). The significance of the slope conditions has also been confirmed by several authors [28], [29]. The difference between the fallow land, silage corn and winter wheat is based on variable input data from the parameter catalogue.…”

Section: Fig 4 the Intensity Of The Net Erosion In The Svacenický Cmentioning

confidence: 53%

An assessment of soil water erosion in the Myjava hill land: The application of a physically-based erosion model

et al. 2018

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The scope of this study is an evaluation of potential soil water erosion using the physically-based erosion model, EROSION-3D. This model is event-based and calculates soil water erosion during an actual measured rainfall event. The calculations of the erosion model take advantage of a digital elevation model, precipitation and soil parameters, which are established in a specific parameter catalogue. The soil water erosion was modeled in two small catchments in the Myjava Hill Land (Slovakia), using 9 soil moisture scenarios and two different crops on arable areas. When considering the last 35 years of rainfall records at the Myjava meteorological station, three storm rainfall events were applied in the modeling. The results were statistically analyzed to figure out the differences between the model's functional possibilities; the modeling under the various scenarios proved a strong interaction between the values of the input factors and the results of the soil erosion.

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Soil Erosion Analysis in a Small Forested Catchment Supported by ArcGIS Model Builder

Cited by 30 publications

References 26 publications

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

Assessment of Post Fire Soil Erosion with ESA Sentinel-2 Data and RUSLE Method in Apulia Region (Southern Italy)

An assessment of soil water erosion in the Myjava hill land: The application of a physically-based erosion model

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