A simulation of rill bed incision processes in upland concentrated flows

Qin, Chao; Zheng, Fenli; Zhang, X. C.; Xu, Ximeng; Liu, Gang

doi:10.1016/j.catena.2018.02.013

Cited by 28 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Figure 10, the situations from 2002 to 2021 show the progressive development of an erosion ripple to a gully. This is a genetic relationship often described in many parts of the world [69,70]. When erosion channels appear on the soil surface, runoff concentrates, scours, carries soil particles, and rill erosion develops.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The processes of rill formation and integration into networks were initially studied in the context of agricultural fields, badlands, and experimental plots where gradients and flow discharges are relatively small (Brunton & Bryan, 2000; Bryan & Poesen, 1989; Foster et al., 1984; Mancilla et al., 2005). These rill‐forming events generally involve relatively dilute runoff with maximum solids concentrations of <20% or less, on gradients less than 0.4 (22°) (Polyakov & Nearing, 2003; Qin, Zheng, Zhang, et al., 2018; Shen et al., 2016; Wirtz et al., 2012). Rill network studies have been expanded onto steep slopes with gradients >0.4 in disturbed mountain environments (Collins & Dunne, 1986) and artificial hillslopes (Govindaraju & Kavvas, 1992) but the runoff in these cases was dilute, and the studies were primarily descriptive.…”

Section: Previous Studiesmentioning

confidence: 99%

Post‐Wildfire Generation of Debris‐Flow Slurry by Rill Erosion on Colluvial Hillslopes

2021

View full text Add to dashboard Cite

Landscapes after wildfire commonly experience accelerated hillslope erosion, which often contributes to the mobilization and volume of debris flows. However, quantitative studies of the erosion and its relationship to rainfall, runoff, and landscape characteristics have been limited to a narrow range of physiographic conditions. We estimated the volume and delivery rate of slurry (a water‐sediment mixture) supplied to stream channels during a post‐wildfire rainstorm that generated large debris flows in six catchments above Montecito, CA, in 2018. We mapped the distribution of rills and measured their cross‐sectional geometries to quantify the influences of runoff, lithology, and hillslope characteristics on the sediment volumes released by rill erosion, and we scaled the results up to the 19.5 km2 of burned hillslopes in the source catchments. We computed the likely rate of surface runoff during the rainstorm and developed an empirical model for the evolution of a representative hillslope‐spanning rill to illustrate the magnitude and speed of the erosion process. Rilling was the dominant form of erosion across the hillslopes of the source catchments, and the rapid evacuation and mixing of water and sediment during rill formation supplied a slurry with high solids concentrations to stream channels. Colluvium on shale formations was more continuous, finer‐grained, and probably less permeable than colluvium on sandstones, and these differences affected the extent and dimensions of rills. As a result, shale hillslopes were the dominant source of slurry to the debris flows and supplied over twice as much slurry per unit burn area as sandstones.

show abstract

Sediment Transport Dynamics Modeling of Overland Flow on Gentle Slopes Based on Flume Experiments

Zhang,

Wang

et al. 2024

Land Degrad Dev

View full text Add to dashboard Cite

Soil erosion is a global environmental issue, and sediment transport capacity (Tc) is critical for developing soil erosion models. This study conducted flume drainage experiments at six flow discharges (0.15, 0.25, 0.35, 0.45, 0.55, and 0.65 L s−1) and eight slope gradients (1.5, 3.0, 4.5, 6.0, 7.5, 9.0, 10.5, and 12.0°) to investigate how the Tc of gentle slopes in the northeastern hilly region of China and water erosion factors are related and to establish a Tc model of overland flow. The study demonstrated a power–law relationship between Tc, flow discharge, and slope gradient, as evidenced by a high coefficient of determination (R2; 0.94) and Nash–Sutcliffe efficiency (NSE; 0.92) values. Additionally, there was a positive correlation between Tc and average flow velocity, with R2 and NSE values of 0.86 and 0.84, respectively. Among the hydrodynamic parameters tested, the average flow velocity was determined to be the most effective Tc predictor, surpassing stream power (R2 > 0.7, NSE > 0.7), shear stress (R2 > 0.6, NSE > 0.6), and unit stream power (R2 < 0.5, NSE < 0.5). These findings lay the foundation for developing process‐based sediment transport models for gentle slopes in the northeastern hilly regions of China.

show abstract

A simulation of rill bed incision processes in upland concentrated flows

Cited by 28 publications

References 44 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

Post‐Wildfire Generation of Debris‐Flow Slurry by Rill Erosion on Colluvial Hillslopes

Sediment Transport Dynamics Modeling of Overland Flow on Gentle Slopes Based on Flume Experiments

Contact Info

Product

Resources

About