Rill erosion processes on a constantly saturated slope

2020 ◽  
Vol 34 (20) ◽  
pp. 3955-3965
Author(s):  
Yuhan Huang ◽  
Fahu Li ◽  
Wei Wang ◽  
Juan Li
Keyword(s):  
Rill Erosion ◽  
Erosion Processes

Rill erosion, processes and significance

1989 ◽  
Vol 15 (1-2) ◽  
pp. 190-192
Author(s):  
A. Hadas
Keyword(s):  
Rill Erosion ◽  
Erosion Processes

scholarly journals A field investigation on rill development and flow hydrodynamics under different upslope inflow and slope gradient conditions

2020 ◽  
Vol 51 (5) ◽  
pp. 1201-1220
Author(s):  
Pei Tian ◽  
Chengzhong Pan ◽  
Xinyi Xu ◽  
Tieniu Wu ◽  
Tiantian Yang ◽  
...  
Keyword(s):  
Soil Loss ◽  
Field Investigation ◽  
Rill Erosion ◽  
Slope Gradient ◽  
Power Functions ◽  
Stream Power ◽  
Erosion Processes ◽  
Initial Stage ◽  
Quantitative Impact ◽  
Inflow Discharge

Abstract Few studies focus on the quantitative impact of upslope inflow rate and slope gradient on rill development and erosion processes. Field plot experiments under varying inflow rates (6–36 L min−1m−1) and slope gradients (26, 42 and 57%) were conducted to address this issue. The results showed soil loss rates significantly demonstrated temporal variability in relevance to the rill developing process. Rill erosion and its contribution to soil loss increased with increasing inflow rates and slope gradients by power functions. There was a threshold inflow discharge (12–24 L min−1m−1), under which, rill erosion became the dominant erosion pattern. At the initial stage, downcutting of rill bottom and headward erosion were obvious, whereas rill broadening was significant at the actively rill developing period. Rill density increased with slope gradient increasing from 26% to 42%, and then decreased. For the 57% slope under high inflow rates (24–36 L min−1m−1), gravity caused an increase in the collapse of rills. Mean rill width increased with increasing inflow rates but decreased as slope gradients increased, while mean rill depth increased with increasing inflow rates and slope gradients. Stream power and rill flow velocity were the best hydrodynamic parameter to simulate rill erosion and rill morphology, respectively.

scholarly journals Enrichment of Organic Carbon in Sediment Transport by Interrill and Rill Erosion Processes

2008 ◽  
Vol 72 (1) ◽  
pp. 50-55 ◽  
Author(s):  
W. Schiettecatte ◽  
D. Gabriels ◽  
W. M. Cornelis ◽  
G. Hofman
Keyword(s):  
Sediment Transport ◽  
Organic Carbon ◽  
Rill Erosion ◽  
Erosion Processes

Erosion Characteristics Based on GIS and Fractal Dimensions

2011 ◽  
Vol 271-273 ◽  
pp. 1142-1145
Author(s):  
Chun Xia Yang ◽  
Bin Zhen ◽  
Li Li ◽  
Jing Huang ◽  
Peng Jiao
Keyword(s):  
Fractal Dimension ◽  
Soil Erosion ◽  
Rainfall Intensity ◽  
Fractal Theory ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Fractal Dimensions ◽  
Rill Erosion ◽  
Pattern Complexity ◽  
Erosion Processes

Soil erosion processes and erosion distribution was research focus to establish distributed mathematical equation in the soil erosion areas, GIS techniques and fractal theory provide a means to advance these studies.Slope erosion patterns of bare slope was studied under rainfall intensities of 45、90 and 130mm/h with 20°slope gradient using simulated rainfall experiment. The results showed that the time of rill appeared of lower rainfall intensity was later than that of high rainfall intensity；Within the rainfall time，the rill scale expanded increased with the increasing of rainfall intensity; The erosion distribution was studied by the three-dimensional laser scanner，The trend of rill erosion deep kept roughly consistent with that of sediment; The characteristics was analyzed of slope erosion by GIS, the fractal dimension and sediment were both increased with rainfall intensity, The fractal dimension was increasing with erosion pattern complexity. So the fractal dimension is the representative of erosion complexity.

scholarly journals UAS photogrammetry and object-based image analysis (GEOBIA): erosion monitoring at the Kazár badland, Hungary

2016 ◽  
Vol 10 (3-4) ◽  
pp. 169-178 ◽  
Author(s):  
László Bertalan ◽  
Zoltán Túri ◽  
Gergely Szabó
Keyword(s):  
Image Analysis ◽  
Manual Control ◽  
Surface Model ◽  
Digital Surface Model ◽  
Rill Erosion ◽  
Segmentation Method ◽  
Object Based Image Analysis ◽  
Erosion Processes ◽  
Object Based ◽  
Multiresolution Segmentation

A remarkable badland valley is situated near Kazár, NE-Hungary, where rhyolite tuff outcrops as greyishwhite cliffs and white barren patches. The landform is shaped by gully and rill erosion processes. Weperformed a preliminary state UAS survey and created a digital surface model and ortophotograph. Theflight was operated with manual control in order to perform a more optimal coverage of the aerial images.The overhanging forests induced overexposed photographs due to the higher contrast with the baretuff surface. The multiresolution segmentation method allowed us to classify the ortophotograph andseparate the tuff surface and the vegetation. The applied methods and final datasets in combination withthe subsequent surveys will be used for detecting the recent erosional processes of the Kazár badland

Photogrammetricaly measured sheet and rill erosion on steep slopes

2020 ◽  
Author(s):  
Tomas Laburda ◽  
Petr Kavka ◽  
Romana Kubínová ◽  
Martin Neumann ◽  
Ondřej Marek ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Surface Runoff ◽  
Digital Elevation Models ◽  
Climatic Conditions ◽  
Rill Erosion ◽  
Rainfall Simulator ◽  
Erosion Processes ◽  
Digital Elevation ◽  
Before And After ◽  
Steep Slopes

<p>Soil erosion is a long-term problem that causes the degradation of the earth's surface depending on geomorphological and climatic conditions. Adverse combinations of these conditions can create situations where not only sheet erosion occurs, but also rill processes begin to occur due to the concentration of surface runoff. Erosion processes become undesirable and dangerous when they occur on construction sites. The presented project is basically focused on the effectiveness of protective geotextiles against soil erosion, but processes related to sheer and rill erosion were also investigated. The research was carried out on experimental plots of 4x1 meters, which were placed in the outdoor laboratory in Jirkov. These three plots were set at slopes from 22° to 34° and artificial rain was simulated on them using a rainfall simulator. A second experimental area of ​​the same size was available at the laboratory rainfall simulator at the CTU in Prague, where a modern facility was created for the purpose of soil erosion testing on steep slopes. This device can create slopes up to 40°.</p><p>The photogrammetric method „Structure from Motion“ was used for monitoring soil surface before and after each simulation. Orthophotos and digital elevation models were compared with each other to get digital elevation models of difference. Calculation of the ratio between sheet and rill erosion was done by manually creating rill polygons and by calculating the volume changes above the polygons of these rills and over the whole surface. According to preliminary results on these 4 m long slopes, the rill volume represented approximately 30 % compared to the overall volume change.</p><p>Shifts of stabilizing natural geotextiles by surface runoff and eroded material were also monitored using photogrammetric methods. Deformations and displacements were measured from differences in the detailed images before and after the simulation. Transversal veins and their shift along the slope were evaluated.</p><p>This research is funded by the TA CR  - TH02030428.</p>

Interrill and Rill Erosion on Hillslope

2012 ◽  
Vol 170-173 ◽  
pp. 1344-1347
Author(s):  
Gang Liu ◽  
Wen Nian Xu ◽  
Qiong Zhang ◽  
Zhen Yao Xia
Keyword(s):  
Future Development ◽  
Prediction Models ◽  
Research Progress ◽  
Rill Erosion ◽  
Erosion Process ◽  
Erosion Processes ◽  
Erosion Prediction ◽  
Physically Based ◽  
The Relationship

Interrill and rill erosion are commonly observed erosion processes to coexist on hillslope. Understanding of the interrill and rill erosion process is the key for the development of physically-based erosion prediction models. This paper reviewed the research progress of interrill and rill erosion, and the relationship between them. The shortages were also put forward. Finally, the trends for future development and questions are also discussed.

Improved slope adjustment functions for soil erosion prediction

Soil Research ◽  
2003 ◽  
Vol 41 (8) ◽  
pp. 1489 ◽  
Author(s):  
G. J. Sheridan ◽  
H. B. So ◽  
R. J. Loch
Keyword(s):  
Soil Erosion ◽  
Clay Content ◽  
Fold Increase ◽  
Transport Processes ◽  
Rill Erosion ◽  
Sediment Delivery ◽  
Erosion Processes ◽  
Erosion Prediction ◽  
Slope Adjustment ◽  
The Relationship

Numerous studies in the last 60 years have investigated the relationship between land slope and soil erosion rates. However, relatively few of these have investigated slope gradient responses: (a) for steep slopes, (b)�for specific erosion processes, and (c) as a function of soil properties. Simulated rainfall was applied in the laboratory on 16 soils and 16 overburdens at 100 mm/h to 3 replicates of unconsolidated flume plots 3 m long by 0.8 m wide and 0.15 m deep at slopes of 20, 5, 10, 15, and 30% slope in that order. Sediment delivery at each slope was measured to determine the relationship between slope steepness and erosion rate. Data from this study were evaluated alongside data and existing slope adjustment functions from more than 55 other studies from the literature. Data and the literature strongly support a logistic slope adjustment function of the form S = A + B/[1 + exp (C – D sin θ)] where S is the slope adjustment factor and A, B, C, and D are coefficients that depend on the dominant detachment and transport processes. Average coefficient values when interill-only processes are active are A –1.50, B 6.51, C 0.94, and D 5.30 (r2 = 0.99). When rill erosion is also potentially active, the average slope response is greater and coefficient values are A –1.12, B 16.05, C 2.61, and D 8.32 (r2 = 0.93). The interill-only function predicts increases in sediment delivery rates from 5 to 30% slope that are approximately double the predictions based on existing published interill functions. The rill + interill function is similar to a previously reported value. The above relationships represent a mean slope response for all soils, yet the response of individual soils varied substantially from a 2.5-fold to a 50-fold increase over the range of slopes studied. The magnitude of the slope response was found to be inversely related (log–log linear) to the dispersed silt and clay content of the soil, and 3 slope adjustment equations are proposed that provide a better estimate of slope response when this soil property is known. Evaluation of the slope adjustment equations proposed in this paper using independent datasets showed that the new equations can improve soil erosion predictions.

scholarly journals Finding Possible Weakness in the Runoff Simulation Experiments to Assess Rill Erosion Changes without Non-Intermittent Surveying Capabilities

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6254
Author(s):  
Alexander André Remke ◽  
Jesus Rodrigo-Comino ◽  
Stefan Wirtz ◽  
Johannes B. Ries
Keyword(s):  
3D Models ◽  
Rill Erosion ◽  
Runoff Simulation ◽  
Simulation Experiments ◽  
Erosion Processes ◽  
Camera Array ◽  
Point Density ◽  
Before And After ◽  
Insight Into ◽  
Terrestrial Photogrammetry

The Terrestrial Photogrammetry Scanner (TEPHOS) offers the possibility to precisely monitor linear erosion features using the Structure from Motion (SfM) technique. This is a static, multi-camera array and dynamically moves the digital videoframe camera designed to obtain 3-D models of rills before and after the runoff experiments. The main goals were to (1) obtain better insight into the rills; (2) reduce the technical gaps generated during the runoff experiments using only one camera; (3) enable the visual location of eroded, transported and accumulated material. In this study, we obtained a mean error for all pictures reaching up to 0.00433 pixels and every single one of them was under 0.15 pixel. So, we obtained an error of about 1/10th of the maximum possible resolution. A conservative value for the overall accuracy was one pixel, which means that, in our case, the accuracy was 0.0625 mm. The point density, in our example, reached 29,484,888 pts/m2. It became possible to get a glimpse of the hotspots of sidewall failure and rill-bed incision. We conclude that the combination of both approaches—rill experiment and 3D models—will make easy under laboratory conditions to describe the soil erosion processes accurately in a mathematical–physical way.

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