Spatial modelling of gully erosion in Mazandaran Province, northern Iran

Zabihi, Mohsen; Mirchooli, Fahimeh; Motevalli, Alireza; Darvishan, Abdulvahed Khaledi; Pourghasemi, Hamid Reza; Zakeri, Mohamad Ali; Sadighi, Fatemeh

doi:10.1016/j.catena.2017.10.010

Cited by 190 publications

(116 citation statements)

References 60 publications

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“…Dominant in the area are north-facing slopes and flat areas (Figure 4h). Drainage density is another important factor in assessing gully erosion susceptibility [8,25]; high values of this parameter are associated with large surface runoff ratio. This parameter was calculated using the line density tool in ArcGIS and reclassified into four classes as follows <0.64, 0.65-1.2, 1.3-1.8, and 1.9-3.2 km/km 2 ( Figure 4i).…”

Section: And If Better Validation Results Are Obtained In This Waymentioning

confidence: 99%

“…The soil moisture affects the material on the slopes, thus diminishing soil stability. It is a common factor used in gully erosion susceptibility studies [8]. Within the study area, the topographic wetness index was calculated and classified into four classes −7-−2, −1-−0.1, 0-30, and >30 (Figure 4e).…”

Section: Gully Inventory Map and Conditioning Factorsmentioning

confidence: 99%

“…Gully erosion can be defined as an erosion process in which deep channels are generated by runoff water removing topsoil to a certain depth [8]. Once the gully channels are formed, the process of growing is very fast; this is also induced by the local environmental factors, climate, and land use changes [9].…”

Section: Introductionmentioning

confidence: 99%

“…At an international level, there is a limited number of studies that tackle this issue [31,33]. This study comes as a necessity in an area with a very high potential to soil erosion and Over the last few years, statistical modelling for gully erosion has encountered an upward trend; whether employing one or more statistical models to determine gully erosion susceptibility, many authors are evaluating gully erosion susceptibility based on some local environmental factors and the weights derived from each method: the information value (IV) method [22], logistic regression [23], multivariate adaptive regression splines (MARS) [24], weights of evidence (WOE); frequency ratio (FR) [25], and index of entropy (IOE) [8].…”

Section: Introductionmentioning

confidence: 99%

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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: And If Better Validation Results Are Obtained In This Waymentioning

confidence: 99%

Section: Gully Inventory Map and Conditioning Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Is Overgrazing Really Influencing Soil Erosion?

Nicu

2018

Water

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“…The ROC curve represents the trade-off between two rates (the false-positive and true-positive rates on the X and Y axes). The AUC values are interpreted as reflecting the following model accuracies: 0.6-0.7 poor, 0.6-0.7 medium, 0.7-0.8 good, 0.8-0.9 very good, and 0.9-1 excellent 37,38 . In the current study, different techniques and measures were applied to evaluate the robustness and uncertainty of the RF model for three different hazards, namely, floods, forest fires, and landslides.…”

Section: Construction Of Flood Forest Fire and Landslide Conditionimentioning

confidence: 99%

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

Pourghasemi

Kariminejad

Amiri

et al. 2020

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168

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the aim of the current study was to suggest a multi-hazard probability assessment in fars province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. for four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.The Sendai Framework, with its comprehensive vision, recommends more efforts to decrease disaster risk and increase sustainable development. Especially communities who are increasingly susceptible to natural hazards should adhere to these guidelines and plan accordingly. In this regard, the multi-hazard approach is often used in risk reduction projects and studies addressing risks associated with human activities or climate change on a regional and local scale 1 . It is obvious that introducing a universal set of multi-hazard assessment techniques is of fundamental importance for reducing disaster risk, and constitutes a valuable asset to share with other stakeholders, including the private sectors, local governments, and other stakeholders.The use of the term multi-hazard in the current research is related to the objective of risk reduction among natural hazards, including flood, landslides, and forest fires, in a specified spatial distribution in this study 2,3 . Recently, susceptibility modeling approaches related to single processes have advanced considerably for river floods 4 and landslides 5-7 . However, there is still neither a common terminology nor a uniform conceptual approach for analyzing multiple hazards in conjunction. This is not unexpected because multi-hazard analyses are not the sum of ...

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Predicting gully erosion susceptibility in South Africa by integrating literature directives with regional spatial data

Olivier

Wiel

Clercq

2023

Earth Surf Processes Landf

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Gully erosion has been identified as a severe land degradation process with environmental and socio‐economic consequences. Identifying areas susceptible to gully erosion will aid in developing strategies to inhibit future degradation. Various approaches have been implemented to predict and map gully erosion susceptibility but are mostly restricted to small geographical extents because of process limitations. Here, we introduce a novel method that predicts gully erosion susceptibility on a regional/national scale (1.22 million km2) by synthesising literature directives with a statistical approach. Findings from a literature review were used to extract physiographic properties associated with gully erosion that was conditioned to characterise susceptibility by using the Frequency Ratio model. The conditioned physiographic properties were aggregated by a weighted overlay procedure using an aggregation of controlling factors derived from the literature review as a weighting system. The gully susceptibility index (GSI) model was validated against a published gully inventory map (n = 163 019) and randomly generated 1‐km2 tessellation zones from which primary validation data were derived. Although uncertainties within the modelling procedure exist (e.g., gully site distribution, the spatial resolution of input data and determination of gully points), the validation shows that the GSI model is generally robust, identifying areas of contrasting susceptibilities. Furthermore, findings converge with other susceptibility metrics, which have been derived by different methodologies. Because empirical gully erosion research has been conducted worldwide, this model could be applied to regional‐scale gully susceptibility modelling assessments (as a solitary method or combined with primary data) in other parts of the world. Additionally, the GSI model can be adopted to model environmental change scenarios.

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Spatial modelling of gully erosion in Mazandaran Province, northern Iran

Cited by 190 publications

References 60 publications

Is Overgrazing Really Influencing Soil Erosion?

Is Overgrazing Really Influencing Soil Erosion?

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

Predicting gully erosion susceptibility in South Africa by integrating literature directives with regional spatial data

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