Contribution of physical and anthropogenic factors to gully erosion initiation

Rahmati, Omid; Kalantari, Zahra; Ferreira, Carla Sofia Santos; Chen, Wei; Soleimanpour, Seyed Masoud; Kapović-Solomun, Marijana; Seifollahi-Aghmiuni, Samaneh; Ghajarnia, Navid; Kazemabady, Nader Kazemi

doi:10.1016/j.catena.2021.105925

Cited by 40 publications

(13 citation statements)

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“…low susceptibility (0.2–0.4), medium susceptibility (0.4–0.6), high susceptibility (0.6–0.8), and very high susceptibility (0.8–1) (Choubin et al, 2019; Hitouri et al, 2022; Raj et al, 2022). The equal interval procedure makes it possible to compare the output of different models (Rahmati et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Scrutinizing gully erosion hotspots using hybridized deep‐learning analysis to avoid land degradation

et al. 2023

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Despite the importance of the prediction of land susceptibility to gully erosion, there is a lack of research studies adopting the deep‐learning approach. This study aimed to predict gully susceptibility hotspots using hybridized deep‐learning models and evaluate their efficiency. Field records of gully occurrences in a gully‐prone region, the Talwar watershed (6468 km2), eastern Kurdistan province, Iran, were used to generate a gully inventory dataset. A total of 14 geomorphometric, environmental, and topo‐hydrological gully drivers were selected as predictor variables. The hybridized models were developed using convolutional neural network (NNC) and metaheuristic procedures, including the gray wolf optimizer (GWO) and the imperialist competitive algorithm (ICA). The validity of the resulting outputs was investigated based on the area under the receiver operating characteristic (ROC) curve. Results revealed that the NNC‐GWO had the highest efficiency in the validation step (AUC = 97.2%), whereas the NNC‐ICA was the second‐best model (AUC = 95.1%). The standalone NNC model showed the lowest accuracy (AUC = 91.2%) in predicting gully susceptibility hotspots compared to NNC‐GWO and NNC‐ICA. Thus, both hybridized models had better predictive performance for identifying gully susceptibility in comparison with the standalone NNC model. Furthermore, according to the NNC‐GWO model, about 0.2% (1294.8 ha) and 0.05% (235.2 ha) of the study area were identified as high and very high gully susceptibility classes. In addition, the application of the standalone NNC led to an overestimation of the susceptibility degree for gully initiation. This study supports researchers efforts to increase the model's performance when working in the land degradation domain.

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Section: Methodsmentioning

confidence: 99%

Scrutinizing gully erosion hotspots using hybridized deep‐learning analysis to avoid land degradation

et al. 2023

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“…This is amplified by human and natural calamities that reduce vegetation cover as well as increasing amount and velocity of surface runoff such as deforestation, overgrazing, drought, rainstorms, steep slopes and El nino events [21,49]. Diverting surface runoff collecting along roads into natural drainage channels has also been attributed to gully formation in Kenya and Ethiopia [3,23,25,54].…”

Section: Natural Drivers Of Gully Erosionmentioning

confidence: 99%

Drivers of Gully Erosion and its Socio-economic and Environmental Effects in a Tropical Semi-arid Environment

Churu,

Kamau,

Ng’etich

et al. 2024

AJSSPN

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Gully erosion is a form of severe land degradation, which is more pronounced in semi-arid and arid environments due to their vulnerable ecosystems. Establishing the causes and effects of gully erosion is therefore fundamental in policy formulation and resource allocation for up-scaling context-specific gully mitigation and rehabilitation measures. Thus, this study aimed at assessing the causes and effects of gully erosion in semi-arid region, in the North-West part of Kenya. A cross-sectional survey, field measurements, laboratory analysis, focus group discussions and key informants’ interviews were used to collect data on drivers and effects of gully erosion. Descriptive statistics and content analysis were used to analyze the data. From the findings, 60 % of the respondents reported deforestation as the main driver of gully erosion. Further, 37 and 34 % of the respondents reported surface runoff and steep slopes, respectively, as major drivers of gully erosion. Soils in the region had a high dispersion ratio, with values of between 0.3 and 0.9, making them highly erodible. About 66 and 55 % of the respondents reported that the major effects of gully erosion were reduction in arable land size and death of livestock due to fatal falls, respectively. Approximately 14 ha of arable land and 1,483,600 Mg of sediment have been lost to gully erosion at the rate of about 2,410 Mg ha-1 over a period of 45 years. The average growth rate and density of gullies in the study site stood at 154 Mg ha-1 yr-1 and 0.7 km km-2, respectively. Four people and about 100 cattle had died due to fatal falls into the deep gullies. Thus, there is an urgent need to rehabilitate existing gullies while mitigating occurrence of new gullies in the study area. This would convert existing badlands into hotspots of biodiversity.

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“…However, forest road networks can have signi cant impacts on the environment, especially on soil erosion and sedimentation (Parsakhoo et al 2014; Sekot et al 2021). Soil erosion and sedimentation are major environmental problems in many parts of the world, including Iran (Pardini et al 2003;Rahmati et al 2022), where extensive forest road networks have been developed over the years. Forest road networks can increase soil erosion and sedimentation by increasing the amount and velocity of water runoff (Cao, Elliot, et al 2021), reducing the vegetative cover (Wang et al 2022), and destabilizing the soil (Youse et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Forest road networks can increase soil erosion and sedimentation by increasing the amount and velocity of water runoff (Cao, Elliot, et al 2021), reducing the vegetative cover (Wang et al 2022), and destabilizing the soil (Youse et al 2016). One of the biggest problems caused by road construction is the creation and increase of various forms of erosion, such as landslide (Haigh et al 1995), surface erosion (Elliot et al 2009), gully erosion (Rahmati et al 2022), and debris-ow around the roads (Emami et al 2020). During road construction in forest lands, a large area of forests and range lands along the road network is destroyed, and a signi cant amount of soil and stone is moved from its place or dumped on adjacent lands, causing double the intensity of environmental destruction (Akay et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A hot-spot analysis of forest roads based on soil erosion and sediment in Zagros forest lands, Iran

Yousefi

Emami

Nekoeimehr

et al. 2023

Preprint

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Forest roads have been recognized as one of the significant contributors to soil erosion processes in forested areas. The construction and maintenance of forest roads can cause severe environmental impacts, including soil erosion, sedimentation, and degradation of aquatic ecosystems. In summary, this study contributes to our understanding of the impacts of forest road networks on soil erosion and sedimentation in the context of Zagros forest land, Iran. This study aims to assess soil erosion and sedimentation on forest roads in four case studies in Zagros forest lands, Iran. The study collected data using field surveys and SEDMODL equations to determine input factors and sedimentation and erosion rates. The study found that road erosion is strongly correlated with geological factor, road width, and precipitation factors. The height changes of 144 benchmarks were recorded during one study year (2021–2022) in four study roads, and the measurement results of erosion benchmarks indicated an average soil erosion of 3, 2.6, 4.7, and 3.5 mm per year around Bidleh, Kohian, Nazi, and Tabrak roads, respectively. The study measured soil erosion and sedimentation at three distances (5, 15, and 25 meters) from the road and found a significant difference in the height changes of the benchmarks at varying distances from the study roads. Hot-spot analysis was conducted using GIS 10.8, and the results indicated that a significant portion of the studied forest roads had very high erosion production and hot-spots. The results of Hot-spot analysis indicated that 30.8%, 22.6%, 39.8%, and 14.5% of the study forest roads in Nazi, Tabrak, Bidleh, and Kohian roads, respectively, are identified as areas with very high erosion production and hot-spots. These results highlight the need for effective management strategies to minimize the impact of erosion on road infrastructure and the surrounding environment. Overall, this study provides important insights into soil erosion and sedimentation on forest roads, and the findings presented here can be used to inform future road construction and maintenance.

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Contribution of physical and anthropogenic factors to gully erosion initiation

Cited by 40 publications

References 58 publications

Scrutinizing gully erosion hotspots using hybridized deep‐learning analysis to avoid land degradation

Scrutinizing gully erosion hotspots using hybridized deep‐learning analysis to avoid land degradation

Drivers of Gully Erosion and its Socio-economic and Environmental Effects in a Tropical Semi-arid Environment

A hot-spot analysis of forest roads based on soil erosion and sediment in Zagros forest lands, Iran

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