Determining the Effects of Rainfall-Runoff on Soil Surface Roughness and Erosion Processes via a Laser Scanner

2010 ◽  
Author(s):  
D. C. Dermisis ◽  
A. N. (Thanos) Papanicolaou
Keyword(s):  
Surface Roughness ◽  
Laser Scanner ◽  
Soil Surface ◽  
Rainfall Runoff ◽  
Erosion Processes ◽  
Soil Surface Roughness

Study on the Measurement Method of Soil Surface Roughness

2011 ◽  
Vol 383-390 ◽  
pp. 5357-5362
Author(s):  
Zi Cheng Zheng ◽  
Shu Qin He
Keyword(s):  
Surface Roughness ◽  
Laser Scanner ◽  
Soil Surface ◽  
Poor Correlation ◽  
Roller Chain ◽  
Soil And Water Loss ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
Slope Farmland

Based on the determination method of the comprehensive domestic and international surface roughness, by the method of indoor artificial rainfall, the determination of soil surface roughness had been studied from the measurement accuracy, time-consuming and resolution. The results showed that the laser scanner method was the best to determine the surface roughness, followed by pin meter method, the roller chain meter method, and the ruler was the worst. The results of determination had the better correlation between the laser scanner method and the roller chain meter method before rainfall, however they had poor correlation after rainfall. They had the better correlation between the laser scanner method and pin meter method both before rainfall and after rainfall. And on this basis, the relationships were established among the different methods. The results provide theory basis for the further study on soil surface roughness. At the same time, it would serve for harnessing soil and water loss of the slope farmland in Loess Plateau.

scholarly journals Erosion Process and Temporal Variations in the Soil Surface Roughness of Spoil Heaps under Multi-Day Rainfall Simulation

2020 ◽  
Vol 12 (14) ◽  
pp. 2192
Author(s):  
Jiaorong Lv ◽  
Yongsheng Xie ◽  
Han Luo
Keyword(s):  
Surface Roughness ◽  
Erosion Rate ◽  
Soil Surface ◽  
Rainfall Event ◽  
Rainfall Simulation ◽  
Rainfall Events ◽  
Erosion Processes ◽  
Spoil Heap ◽  
Soil Surface Roughness ◽  
Spoil Heaps

The extensive artificially accelerated erosion of spoil heaps on newly engineered landforms is a key ecological management point requiring better understanding. Soil surface roughness is a crucial factor influencing erosion processes; however, study on spoil heap erosion with a view of surface roughness is lacking. This study investigated the erosion processes and the spatiotemporal variation of surface roughness on spoil heaps, and then, analyzed how the roughness affected the hydrological and sediment yield characteristics. Sequences of four artificial rainstorms with constant rainfall intensity (90 mm/h) were applied to cone-shaped spoil heaps (ground radius 3.5 m, height 2.3 m) of a loess soil containing 30 mass percent rock fragments. The surface elevation was sampled by a laser scanner. For the surface roughness indicators, the root mean square height (rmsh) and the correlation length (cl) increased sharply during the first rainfall event, and in the last three rainfall events, rmsh increased slightly and cl showed a relative decrease. The initial rmsh/cl of the whole slope surface ranged from 0.063 to 0.135, and increased with the rainfall sequence, thus, indicating that the spoil heap surface became rougher. Increasing soil roughness in the rainfall sequence delayed the initial runoff time and increased the runoff yield. The average runoff coefficient of the spoil heaps was 0.658. The average erosion rate of each rainfall event can be simulated by a regression equation of the corresponding average runoff rate and median cl (R-square of 0.816). Soil slumping with an average volume of 0.014 m3 occurred in the first two rainfall events, thus, significantly changing the roughness and peak instant erosion rate. Together, the results revealed the effects of surface roughness on the erosion of spoil heaps and would provide a useful reference for soil loss prediction and control.

scholarly journals Quantifying the changes of soil surface microroughness due to rainfall impact on a smooth surface

2017 ◽  
Vol 24 (3) ◽  
pp. 569-579 ◽  
Author(s):  
Benjamin K. B. Abban ◽  
A. N. (Thanos) Papanicolaou ◽  
Christos P. Giannopoulos ◽  
Dimitrios C. Dermisis ◽  
Kenneth M. Wacha ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Laser Scanner ◽  
Gaussian Model ◽  
Surface Profile ◽  
Soil Surface ◽  
Soil Conditions ◽  
Agricultural Field ◽  
Quantitative Evidence ◽  
Soil Surface Roughness ◽  
Surface Microroughness

Abstract. This study examines the rainfall-induced change in soil microroughness of a bare smooth soil surface in an agricultural field. The majority of soil microroughness studies have focused on surface roughness on the order of ∼ 5–50 mm and have reported a decay of soil surface roughness with rainfall. However, there is quantitative evidence from a few studies suggesting that surfaces with microroughness less than 5 mm may undergo an increase in roughness when subject to rainfall action. The focus herein is on initial microroughness length scales on the order of 2 mm, a low roughness condition observed seasonally in some landscapes under bare conditions and chosen to systematically examine the increasing roughness phenomenon. Three rainfall intensities of 30, 60, and 75 mm h−1 are applied to a smoothened bed surface in a field plot via a rainfall simulator. Soil surface microroughness is recorded via a surface-profile laser scanner. Several indices are utilized to quantify the soil surface microroughness, namely the random roughness (RR) index, the crossover length, the variance scale from the Markov–Gaussian model, and the limiting difference. Findings show a consistent increase in roughness under the action of rainfall, with an overall agreement between all indices in terms of trend and magnitude. Although this study is limited to a narrow range of rainfall and soil conditions, the results suggest that the outcome of the interaction between rainfall and a soil surface can be different for smooth and rough surfaces and thus warrant the need for a better understanding of this interaction.

scholarly journals Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion

2020 ◽  
Vol 12 (1) ◽  
pp. 232-241
Author(s):  
Na Ta ◽  
Chutian Zhang ◽  
Hongru Ding ◽  
Qingfeng Zhang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Soil Surface ◽  
Rainfall Events ◽  
Tillage Practices ◽  
Surface Microtopography ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
The Difference ◽  
Sheet Erosion

AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.

scholarly journals Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4386
Author(s):  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
Tarahom Mesri-Gundoshmian ◽  
Aitazaz A. Farooque ◽  
Hassan Afzaal
Keyword(s):  
Surface Roughness ◽  
Stereo Vision ◽  
Agricultural Soils ◽  
Depth Map ◽  
Soil Surface ◽  
Regression Equations ◽  
Height Profile ◽  
Peak Fitting ◽  
Soil Surface Roughness ◽  
Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

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