The G2 erosion model: An algorithm for month-time step assessments

Karydas, Christos; Panagos, Panos

doi:10.1016/j.envres.2017.11.010

Cited by 38 publications

(24 citation statements)

References 55 publications

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“…Among the most common types of erosional processes, we can distinguish erosion by water [2], wind erosion [3], and harvest erosion [4]. At a European level, significant research is conducted to assess [5] and reduce [6] the negative effects of soil erosion. One of the most erosive processes acknowledged for water erosion is gullying [7].…”

Section: Introductionmentioning

confidence: 99%

Is Overgrazing Really Influencing Soil Erosion?

Nicu

2018

Water

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Soil erosion is a serious problem spread over a variety of climatic areas around the world. The main purpose of this paper is to produce gully erosion susceptibility maps using different statistical models, such as frequency ratio (FR) and information value (IV), in a catchment from the northeastern part of Romania, covering a surface of 550 km 2 . In order to do so, a total number of 677 gullies were identified and randomly divided into training (80%) and validation (20%) datasets. In total, 10 conditioning factors were used to assess the gully susceptibility index (GSI); namely, elevation, precipitations, slope angle, curvature, lithology, drainage density, topographic wetness index, landforms, aspect, and distance from rivers. As a novelty, overgrazing was added as a conditioning factor. The final GSI maps were classified into four susceptibility classes: low, medium, high, and very high. In order to evaluate the two models prediction rate, the AUC (area under the curve) method was used. It has been observed that adding overgrazing as a contributing factor in calculating GSI does not considerably change the final output. Better predictability (0.87) and success rate (0.89) curves were obtained with the IV method, which proved to be more robust, unlike FR method, with 0.79 value for both predictability and success rate curves. When using sheepfolds, the value decreases by 0.01 in the case of the FR method, and by 0.02 in the case of the success rate curve for the IV method. However, this does not prove the fact that overgrazing is not influencing or accelerating soil erosion. A multi-temporal analysis of soil erosion is needed; this represents a future working hypothesis.

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Section: Introductionmentioning

confidence: 99%

Is Overgrazing Really Influencing Soil Erosion?

Nicu

2018

Water

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“…G2 is an empirical model for soil erosion on month-time intervals, with two distinct modules: one for soil loss and one for sediment yield [25,26]. The module for soil loss (denoted by G2los) uses the main empirical formulas of the Universal Soil Loss Equation (USLE) [27,28], which according to Ferro (2010) [29], is a robust empirical model with a logical structure regarding the variables used to simulate the physical erosion process.…”

Section: The G2 Modelmentioning

confidence: 99%

“…With G2los, five input erosion factors are combined in a multiplicative equation, to estimate a quantitative soil loss output from sheet and interill erosion processes [26]:…”

Section: The G2 Modelmentioning

confidence: 99%

“…For the estimation of vegetation retention (V-factor) per month, the G2 model has introduced an algorithm, based on vegetation indices and quantification of land cover from experimental data available by USLE and the Erosion Potential Model (EPM or Gavrilovic model). The equation for the computation of V-factor in G2, is as follows [26]:…”

Section: V-factormentioning

confidence: 99%

“…where V mj : V-factor for month m and land cover j, in range [1,+∞) (dimensionless); imp: imperviousness degree corresponding to a range of 0-100% of soil sealing, in range [0,1) (dimensionless); LU j : empirical parameter for land cover j, in range [1,10], with lower values corresponding to intensive management or unprotected land covers and higher values corresponding to better management conditions [26]; and FC m : fractional vegetation cover for month m, in range [0,1] (dimensionless). The exponential shape of Equation 6is imposed by the relation formed when C-factor values are plotted against plant cover experimental data of USLE [27].…”

Section: V-factormentioning

confidence: 99%

See 2 more Smart Citations

Mapping Spatio-Temporal Soil Erosion Patterns in the Candelaro River Basin, Italy, Using the G2 Model with Sentinel2 Imagery

Karydas¹,

Bouarour

Zdruli

2020

Geosciences

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This study aims at mapping soil erosion caused by water in the Candelaro river basin, Apulia region, Italy, using the G2 erosion model. The G2 model can provide erosion maps and statistical figures at month-time intervals, by applying non data-demanding alternatives for the estimation of all the erosion factors. In the current research, G2 is taking a step further with the introduction of Sentinel2 satellite images for mapping vegetation retention factor on a fine scale; Sentinel2 is a ready-to-use, image product of high quality, freely available by the European Space Agency. Although only three recent cloud-free Sentinel2 images covering Candelaro were found in the archive, new solutions were elaborated to overcome time-gaps. The study in Candelaro resulted in a mean annual erosion rate of 0.87 t ha−1 y−1, while the autumn months were indicated to be the most erosive ones, with average erosion rates reaching a maximum of 0.12 t ha−1 in September. The mixed agricultural-natural patterns revealed to be the riskiest surfaces for most months of the year, while arable land was the most extensive erosive land cover category. The erosion maps will allow competent authorities to support relevant mitigation measures. Furthermore, the study in Candelaro can play the role of a pilot study for the whole Apulia region, where erosion studies are rather limited.

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Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Filchev

Kolev

2021

Earth and Environmental Sciences Library

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During past decades a marked manifestation of widespread erosion phenomena was studied worldwide. Global conservation community has launched campaigns at local, regional and continental level in developing countries for preservation of soil resources in order not only to stop or mitigate human impact on nature but also to improve life in rural areas introducing new approaches for soil cultivation. After the adoption of Sustainable Development Goals of UNs and launching several world initiatives such as the Land Degradation Neutrality (LDN) the world came to realise the very importance of the soil resources on which the biosphere relies on for its existence. The main goal of the chapter is to review of different types and structures erosion models as well as its some applications. Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment, such as: Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) in operation worldwide and in USA and Modèle d'Evaluation Spatiale de l'ALéa Erosion des Sols (MESALES) model. These and more models are being discussed in present work alongside more experimental models and methods for assessing soil erosion risk such as Artificial Intelligence (AI), Machine and Deep Learning etc. At the end of this work a prospectus for the future development of soil erosion risk assessment is drawn.

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The G2 erosion model: An algorithm for month-time step assessments

Cited by 38 publications

References 55 publications

Is Overgrazing Really Influencing Soil Erosion?

Is Overgrazing Really Influencing Soil Erosion?

Mapping Spatio-Temporal Soil Erosion Patterns in the Candelaro River Basin, Italy, Using the G2 Model with Sentinel2 Imagery

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

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