The dynamic process of slope rill erosion analyzed with a digital close range photogrammetry observation system under laboratory conditions

Jiang, Yanmin; Shi, Haijing; Wen, Zhu; Guo, Ming; Jing, Zhao; Xiaoping, Cao; Fan, Yongming; Zheng, Chengshi

doi:10.1016/j.geomorph.2019.106893

Cited by 31 publications

(11 citation statements)

References 42 publications

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“…Water depth, flow velocity, and erosive force increase as the gully develops. If the water table is reached, the mode of erosion changes drastically; the gully profile, initially V-shaped (ravina-type gullies), turns into U-shaped (voçoroca-type gullies); and a rapid upstream progression can be observed, mobilizing considerable amounts of material [71]. The second case is characterized by negative values on the PC1 and PC2 axes.…”

Section: Processes Associated With the Diversity Of Gully Conditionsmentioning

confidence: 99%

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

et al. 2022

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In Brazil, the development of gullies constitutes widespread land degradation, especially in the state of South Mato Grosso, where fighting against this degradation has become a priority for policy makers. However, the environmental and anthropogenic factors that promote gully development are multiple, interact, and present a complexity that can vary by locality, making their prediction difficult. In this framework, a database was constructed for the Rio Ivinhema basin in the southern part of the state, including 400 georeferenced gullies and 13 geo-environmental descriptors. Multivariate statistical analysis was performed using principal component analysis (PCA) to identify the processes controlling the variability in gully development. Susceptibility maps were created through four machine learning models: multivariate discriminant analysis (MDA), logistic regression (LR), classification and regression tree (CART), and random forest (RF). The predictive performance of the models was analyzed by five evaluation indices: accuracy (ACC), sensitivity (SST), specificity (SPF), precision (PRC), and Receiver Operating Characteristic curve (ROC curve). The results show the existence of two major processes controlling gully erosion. The first is the surface runoff process, which is related to conditions of slightly higher relief and higher rainfall. The second also reflects high surface runoff conditions, but rather related to high drainage density and downslope, close to the river network. Human activity represented by peri-urban areas, construction of small earthen dams, and extensive rotational farming contribute significantly to gully formation. The four machine learning models yielded fairly similar results and validated susceptibility maps (ROC curve > 0.8). However, we noted a better performance of the random forest (RF) model (86% and 89.8% for training and test, respectively, with an ROC curve value of 0.931). The evaluation of the contribution of the parameters shows that susceptibility to gully erosion is not governed primarily by a single factor, but rather by the interconnection between different factors, mainly elevation, geology, precipitation, and land use.

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Section: Processes Associated With the Diversity Of Gully Conditionsmentioning

confidence: 99%

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

et al. 2022

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“…The concentrated runoff generated from upstream area is the main force driving gully headcut erosion. Jiao et al (1999) concluded that the more serious soil erosion is generally caused by the A-type rainstorm with the rainfall duration of 25 to 178 min compared to other types of rainstorms in the Loess Plateau. Thus, an extreme case of rainfall duration (180 min) was considered in this study, and the recurrence period of A-type rainstorms was designated as 30 years.…”

Section: Inflow Discharge Designmentioning

confidence: 99%

Spatiotemporal changes in flow hydraulic characteristics and soil loss during gully headcut erosion under controlled conditions

Guo

Chen

Wang

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. The spatiotemporal changes in flow hydraulics and energy consumption and their associated soil erosion remain unclear during gully headcut retreat. A simulated scouring experiment was conducted on five headcut plots consisting of upstream area (UA), gully headwall (GH), and gully bed (GB) to elucidate the spatiotemporal changes in flow hydraulic, energy consumption, and soil loss during headcut erosion. The flow velocity at the brink of a headcut increased as a power function of time, whereas the jet velocity entry to the plunge pool and jet shear stress either logarithmically or linearly decreased over time. The jet properties were significantly affected by upstream flow discharge. The Reynolds number, runoff shear stress, and stream power of UA and GB increased as logarithmic or power functions of time, but the Froude number decreased logarithmically over time. The Reynolds number, shear stress, and stream power decreased by 56.0 %, 63.8 %, and 55.9 %, respectively, but the Froude number increased by 7.9 % when flow dropped from UA to GB. The accumulated energy consumption of UA, GH, and GB positions linearly increased with time. In total, 91.12 %–99.90 % of total flow energy was consumed during headcut erosion, of which the gully head accounted for 77.7 % of total energy dissipation, followed by UA (18.3 %), and GB (4.0 %). The soil loss rate of the “UA-GH-GB” system initially rose and then gradually declined and levelled off. The soil loss of UA and GH decreased logarithmically over time, whereas the GB was mainly characterized by sediment deposition. The proportion of soil loss at UA and GH is 11.5 % and 88.5 %, respectively, of which the proportion of deposited sediment on GB reached 3.8 %. The change in soil loss of UA, GH, and GB was significantly affected by flow hydraulic and jet properties. The critical energy consumption initiating soil erosion of UA, GH, and GB is 1.62, 5.79, and 1.64 J s−1, respectively. These results are helpful for deepening the understanding of gully erosion process and hydrodynamic mechanisms and can also provide a scientific basis for the construction of gully erosion model and the design of gully erosion prevention measures.

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“…LiDAR, despite the higher time and cost expenditure, proves a feasible tool for change detection (Li et al, 2020a), helping to improve the understanding of soil erosion forms, as soil crusts (Hu et al, 2020a) or rill characteristics (Vinci et al, 2015). Jiang et al (2020) monitor rilling on an artificial plot via LiDAR and SfM. 290…”

Section: Remote Sensingmentioning

confidence: 99%

Can the models keep up with the data? Possibilities of soil and soil surface assessment techniques in the context of process based soil erosion models – A Review

Epple

Kaiser

Schindewolf³

et al. 2021

Preprint

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Abstract. Climate change, accompanied by intensified extreme weather events, results in changes in intensity, frequency and magnitude of soil erosion. These unclear future developments make adaption and improvement of soil erosion modelling approaches all the more important. Hypothesizing that models cannot keep up with the data, this review gives an overview of 44 process based soil erosion models, their strengths and weaknesses and discusses their potential for further development with respect to new and improved soil and soil erosion assessment techniques. We found valuable tools in areas, as remote sensing, tracing or machine learning, to gain temporal and spatial distributed high resolution parameterization and process descriptions which could lead to a more holistic modelling approach. Most process based models are so far not capable to implement cross-scale erosional processes or profit from the available resolution on a temporal and spatial scale. We conclude that models need further development regarding their process understanding, adaptability in respect to scale as well as their parameterization and calibration. The challenge is the development of models which are able to simulate soil erosion processes as close to reality as possible, as user-friendly as possible and as complex as it needs to be.

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The dynamic process of slope rill erosion analyzed with a digital close range photogrammetry observation system under laboratory conditions

Cited by 31 publications

References 42 publications

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

Spatiotemporal changes in flow hydraulic characteristics and soil loss during gully headcut erosion under controlled conditions

Can the models keep up with the data? Possibilities of soil and soil surface assessment techniques in the context of process based soil erosion models – A Review

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