scholarly journals Determining the susceptibility of soils materials to erosion by rain-impacted flows

2016 ◽  
Author(s):  
P. I. A. Kinnell
Keyword(s):  
Drop Size ◽  
Rainfall Intensity ◽  
Rainfall Erosivity ◽  
Soil Erodibility ◽  
Flow Depth ◽  
Empirical Coefficient ◽  
Artificial Rainfall ◽  
Spatial Uniformity ◽  
Absolute Measure ◽  
The Relationship

Abstract. Conceptually, rain has a capacity to cause erosion (rainfall erosivity) and soils have a susceptibility to erosion by rainfall (soil erodibility) but no absolute measure of rainfall erosivity exists. Consequently, soil erodibility is nothing more than an empirical coefficient in the relationship between an index of rainfall erosivity and soil loss. Erosion by rain-impacted flow is influenced by the size, velocity and impact frequency of the raindrops but also flow depth and velocity. Experiments with artificial rainfall falling on sloping surfaces in the field usually do not enable flow depth and velocity to be well measured or controlled. Also, sprays produce artificial rainfall where the spatial uniformity in rainfall intensity, drop size and frequency is often less than desirable. Artificial rainfall produced by pendant drop formers can produce rainfall that has better spatial uniformity. Equipment for controlling flow depth and velocity over eroding surfaces has been developed and used to calibrate the effect of flow depth on the discharge of sediment by rain-impacted flow using artificial rainfall having a uniform drop-size distribution under laboratory conditions. Once calibrated, laboratory experiments can be conducted to rank soils according to their susceptibility to erosion under the flows impacted by the artificial rainfall under conditions where the erosive stress applied to the eroding surface is well controlled.

scholarly journals Identifying erosive periods by using RUSLE factors in mountain fields of the Central Spanish Pyrenees

2008 ◽  
Vol 12 (2) ◽  
pp. 523-535 ◽  
Author(s):  
M. López-Vicente ◽  
A. Navas ◽  
J. Machín
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Soil Moisture Content ◽  
Rainfall Erosivity ◽  
Soil Erodibility ◽  
Maximum Rainfall ◽  
Freeze Thaw ◽  
Soil Losses

Abstract. The Mediterranean environment is characterized by strong temporal variations in rainfall volume and intensity, soil moisture and vegetation cover along the year. These factors play a key role on soil erosion. The aim of this work is to identify different erosive periods in function of the temporal changes in rainfall and runoff characteristics (erosivity, maximum intensity and number of erosive events), soil properties (soil erodibility in relation to freeze-thaw processes and soil moisture content) and current tillage practices in a set of agricultural fields in a mountainous area of the Central Pyrenees in NE Spain. To this purpose the rainfall and runoff erosivity (R), the soil erodibility (K) and the cover-management (C) factors of the empirical RUSLE soil loss model were used. The R, K and C factors were calculated at monthly scale. The first erosive period extends from July to October and presents the highest values of erosivity (87.8 MJ mm ha−1 h−1), maximum rainfall intensity (22.3 mm h−1) and monthly soil erosion (0.25 Mg ha−1 month−1) with the minimum values of duration of erosive storms, freeze-thaw cycles, soil moisture content and soil erodibility (0.007 Mg h MJ−1 mm−1). This period includes the harvesting and the plowing tillage practices. The second erosive period has a duration of two months, from May to June, and presents the lowest total and monthly soil losses (0.10 Mg ha−1 month−1) that correspond to the maximum protection of the soil by the crop-cover ($C$ factor = 0.05) due to the maximum stage of the growing season and intermediate values of rainfall and runoff erosivity, maximum rainfall intensity and soil erodibility. The third erosive period extends from November to April and has the minimum values of rainfall erosivity (17.5 MJ mm ha−1 h−1) and maximum rainfall intensity (6.0 mm h−1) with the highest number of freeze-thaw cycles, soil moisture content and soil erodibility (0.021 Mg h MJ−1 mm−1) that explain the high value of monthly soil loss (0.24 Mg ha−1 month−1). The interactions between the rainfall erosivity, soil erodibility, and cover-management factors explain the similar predicted soil losses for the first and the third erosive periods in spite of the strong temporal differences in the values of the three RUSLE factors. The estimated value of annual soil loss with the RUSLE model (3.34 Mg ha−1 yr−1) was lower than the measured value with 137Cs (5.38 Mg ha−1 yr−1) due to the low values of precipitation recorded during the studied period. To optimize agricultural practices and to promote sustainable strategies for the preservation of fragile Mediterranean agrosystems it is necessary to delay plowing till October, especially in dryland agriculture regions. Thus, the protective role of the crop residues will extend until September when the greatest rainfall occurs together with the highest runoff erosivity and soil losses.

scholarly journals Impact of Disdrometer Types on Rainfall Erosivity Estimation

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 963
Author(s):  
Lisbeth Lolk Johannsen ◽  
Nives Zambon ◽  
Peter Strauss ◽  
Tomas Dostal ◽  
Martin Neumann ◽  
...  
Keyword(s):  
Drop Size ◽  
First Generation ◽  
Rainfall Intensity ◽  
Measurement Instrument ◽  
Rainfall Erosivity ◽  
Data Handling ◽  
Rainfall Characteristics ◽  
Different Types ◽  
Spatial Differences ◽  
Soil Erosion By Water

Soil erosion by water is affected by the rainfall erosivity, which controls the initial detachment and mobilization of soil particles. Rainfall erosivity is expressed through the rainfall intensity (I) and the rainfall kinetic energy (KE). KE–I relationships are an important tool for rainfall erosivity estimation, when direct measurement of KE is not possible. However, the rainfall erosivity estimation varies depending on the chosen KE–I relationship, as the development of KE–I relationships is affected by the measurement method, geographical rainfall patterns and data handling. This study investigated how the development of KE–I relationships and rainfall erosivity estimation is affected by the use of different disdrometer types. Rainfall data were collected in 1-min intervals from six optical disdrometers at three measurement sites in Austria, one site in Czech Republic and one site in New Zealand. The disdrometers included two disdrometers of each of the following types: the PWS100 Present Weather Sensor from Campbell Scientific, the Laser Precipitation Monitor from Thies Clima and the first generation Parsivel from OTT Hydromet. The fit of KE–I relationships from the literature varied among disdrometers and sites. Drop size and velocity distributions and developed KE–I relationships were device-specific and showed similarities for disdrometers of the same type across measurement sites. This hindered direct comparison of results from different types of disdrometers, even when placed at the same site. Thus, to discern spatial differences in rainfall characteristics the same type of measurement instrument should be used.

Rainfall Threshold of Rainfall-Induced Landslides Based on Laboratory Test

2013 ◽  
Vol 353-356 ◽  
pp. 1011-1014
Author(s):  
Yan Su ◽  
Jun Bing Qiu ◽  
Yue Ting Du
Keyword(s):  
Rainfall Intensity ◽  
Orthogonal Experiment ◽  
Slope Angle ◽  
Rainfall Threshold ◽  
Vegetation Coverage ◽  
Mountainous Area ◽  
Slope Surface ◽  
Side Slope ◽  
Artificial Rainfall ◽  
The Relationship

A landslide model test under the artificial rainfall was built according to the rainfll-induced landslide in Fujian mountainous area. The rainfall intensity, the slope types (mainly on vegetation coverage) and the grade of side slope were the main factors in the test. The rainfall threshold of rainfall-induced landslide on shallow bedrock was obtained from the test. The relationship between the cumulative precipitation and slope angle and slope surface types was analyzed from the orthogonal experiment by multiple regression analysis. Results show that most slope failures are caused by the infiltration of rainwater. Conclusion show that when the slope angle and vegetation cover are given, critical hazard threshold can be predicted, and the corresponding landslide sliding time can be gained by combining with the rainfall intensity.

scholarly journals Identifying erosive periods by using RUSLE factors in mountain fields of the Central Spanish Pyrenees

2007 ◽  
Vol 4 (4) ◽  
pp. 2111-2142 ◽  
Author(s):  
M. López-Vicente ◽  
A. Navas ◽  
J. Machín
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Rainfall Intensity ◽  
Soil Moisture Content ◽  
Rainfall Erosivity ◽  
Soil Erodibility ◽  
Maximum Rainfall ◽  
Freeze Thaw ◽  
Tillage Practices ◽  
Soil Losses

Abstract. The Mediterranean environment is characterized by strong temporal variations in rainfall volume and intensity, soil moisture and vegetation cover along the year. These factors play a key role on soil erosion. The aim of this work is to identify different erosive periods in function of the temporal changes in rainfall and runoff characteristics (erosivity, maximum intensity and number of erosive events), soil properties (soil erodibility in relation to freeze-thaw processes and soil moisture content) and current tillage practices in a set of agricultural fields in a mountainous area of the Central Pyrenees in NE Spain. To this purpose the rainfall and runoff erosivity (R), the soil erodibility (K) and the cover-management (C) factors of the empirical RUSLE soil loss model were used. The R, K and C factors were calculated at monthly scale. The first erosive period extends from July to October and presents the highest values of erosivity (87.8 MJ mm ha−1 h−1), maximum rainfall intensity (22.3 mm h−1) and monthly soil erosion (0.10 Mg ha−1 month−1) with the minimum values of duration of erosive storms, freeze-thaw cycles, soil moisture content and soil erodibility (0.007 Mg h MJ−1 mm−1). This period includes the harvesting and the plowing tillage practices. The second erosive period has a duration of two months, from May to June, and presents the lowest total and monthly soil losses (0.04 Mg ha−1 month−1) that correspond to the maximum protection of the soil by the crop-cover (C factor = 0.05) due to the maximum stage of the growing season and intermediate values of rainfall and runoff erosivity, maximum rainfall intensity and soil erodibility. The third erosive period extends from November to April and has the minimum values of rainfall erosivity (17.5 MJ mm ha−1 h−1) and maximum rainfall intensity (6.0 mm h−1) with the highest number of freeze-thaw cycles, soil moisture content and soil erodibility (0.021 Mg h MJ−1 mm−1) that explain the high value of monthly soil loss (0.09 Mg ha−1 month−1). The interactions between the rainfall erosivity, soil erodibility, and cover-management factors explain the similar predicted soil losses for the first and the third erosive periods in spite of the strong temporal differences in the values of the three RUSLE factors. To optimize agricultural practices and to promote sustainable strategies for the preservation of fragile Mediterranean agrosystems it is necessary to delay plowing till October, especially in dryland agriculture regions. Thus, the protective role of the crop residues will extend until September when the greatest rainfall occurs together with the highest runoff erosivity and soil losses.

scholarly journals Influence of Gully Land Consolidation on Phreatic Water Transformation in the Loess Hilly and Gully Region

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 538
Author(s):  
Zihao Guo ◽  
Jianen Gao ◽  
Pengcheng Sun ◽  
Shaohui Dou ◽  
Juan Li ◽  
...  
Keyword(s):  
Crop Yields ◽  
Field Application ◽  
Land Consolidation ◽  
Transformation Rate ◽  
Soil Thickness ◽  
Phreatic Water ◽  
Artificial Rainfall ◽  
Outflow Rate ◽  
Water Outflow ◽  
The Relationship

Gully Land Consolidation (GLC) is a proven method to create farmlands and increase crop yields in the Loess Hilly and Gully Region, China. However, GLC influences phreatic water transformation and might cause the farmlands water disasters, such as salinization and swamping. For exploring the influence of GLC on phreatic water transformation and mitigating disasters, a series of indoor experiments were conducted in the artificial rainfall hall. Then, we simulated the phreatic water transformation patterns under more conditions with HYDRUS-3D. Finally, an engineering demonstration in the field was performed to validate our research. The indoor experiments indicated that GLC could increase phreatic water outflow rate 4.39 times and phreatic water coefficient (PWC) 2.86 times with a considerable delay. After calibration and validation with experimental data, the HYDRUS-3D was used to simulate phreatic water transformation under more soil thickness and rainfall intensities. Accordingly, we summarized the relationship among PWC, rainfall intensities, and soil thickness, and therefore suggested a blind ditch system to alleviate farmlands disasters. Field application showed that a blind ditch system could avoid disasters with 3.2 times the phreatic water transformation rate compared to loess. Our research provides implications for sustainable land uses and management in the region with thick soil covers.

Application of a flow-measurement structure for sediment-laden streams

2009 ◽  
Vol 36 (9) ◽  
pp. 1539-1543
Author(s):  
Mustafa Göğüş ◽  
A. Cüneyt Gerek ◽  
A. Burcu Altan-Sakarya
Keyword(s):  
Water Level ◽  
Flow Measurement ◽  
Sediment Load ◽  
Real Data ◽  
Flow Depth ◽  
Rating Curve ◽  
Natural Streams ◽  
The Relationship ◽  
High Sediment ◽  
Single Relation

Generally, measurement of flow in natural streams is accomplished by measuring the flow depth. Hence, the relationship between the water level and discharge should be obtained in advance. However, in streams with high sediment load, the bottom level may change due to sediment deposition, preventing the single relation between water level and discharge. This paper summarizes the application of a flow-measurement structure for sediment-laden streams. The proposed structure is designed and built in Turkey and has been under operation since 1998 without any sedimentation problem. The agreement between the real data obtained from the structure and the theoretical rating curve is quite reasonable.

scholarly journals Improved Curve Number Estimation in SWAT by Reflecting the Effect of Rainfall Intensity on Runoff Generation

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 163 ◽  
Author(s):  
Dejian Zhang ◽  
Qiaoyin Lin ◽  
Xingwei Chen ◽  
Tian Chai
Keyword(s):  
Rainfall Intensity ◽  
Assessment Tool ◽  
Flow Simulation ◽  
Daily Rainfall ◽  
Curve Number ◽  
Base Flow ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
The Relationship

Determining the amount of rainfall that will eventually become runoff and its pathway is a crucial process in hydrological modelling. We proposed a method to better estimate curve number by adding an additional component (AC) to better account for the effects of daily rainfall intensity on rainfall-runoff generation. This AC is determined by a regression equation developed from the relationship between the AC series derived from fine-tuned calibration processes and observed rainfall series. When incorporated into the Soil and Water Assessment Tool and tested in the Anxi Watershed, it is found, overall, the modified SWAT (SWAT-ICN) outperformed the original SWAT (SWAT-CN) in terms of stream flow, base flow, and annual extreme flow simulation. These models were further evaluated with the data sets of two adjacent watersheds. Similar results were achieved, indicating the ability of the proposed method to better estimate curve number.

Quantitative Evaluation of the Relationship Between Slope Gradient and Infiltration Capacity Based on a Rainfall Experiment Using Pit Sand

2020 ◽  
Vol 15 (6) ◽  
pp. 745-753
Author(s):  
Toru Danjo ◽  
◽  
Tomohiro Ishizawa
Keyword(s):  
Surface Layer ◽  
Slope Stability ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Horizontal Distance ◽  
Slope Gradient ◽  
Slope Surface ◽  
Infiltration Capacity ◽  
Tank Model ◽  
The Relationship

The infiltration of rainfall into a slope surface may affect slope stability; thus, it is important to understand the amount of rainfall infiltration (hereafter referred to as the “infiltration capacity”) for a slope surface layer when evaluating slope stability. This research focuses on slope gradient, a factor affecting the infiltration capacity, and performs two types of water-spraying experiments using pit sand under the same conditions but with different slope gradients. In the first experiment, the surface flow rate and soil loss were measured using an earth-tank model with a horizontal distance of 0.5 m, depth of 0.1 m, and width of 0.2 m to form slope gradients of 2°, 20°, and 40° to clarify the effect of slope gradient on the infiltration capacity. In the second experiment, a water-spraying experiment that closely simulated natural rainfall was performed at a large-scale rainfall facility owned by the National Research Institute for Earth Science and Disaster Resilience (NIED), Japan. This experiment used an earth-tank model with a horizontal distance of 1.21 m, depth of 0.5 m, and width of 0.5 m to form slope gradients of 2°, 10°, 20°, 30°, and 40° with the aim of proposing a quantitative evaluation method for the relationship between the slope gradient and infiltration capacity. The results showed that the soil loss and infiltration capacity increased as the slope gradient increased in the case of the pit sand used in the experiments. This was confirmed to be due to the fact that an increased gradient allowed grains with diameters of <50 μm in the slope surface layer to flow out easily, thereby increasing the infiltration capacity. In addition, the relationship between the rainfall intensity and infiltration capacity revealed that the infiltration capacity varied depending on the rainfall intensity and slope gradient, which is unlike the relationship for constant values such as the permeability coefficient. Moreover, the research findings indicated a strong, positive linear relationship (R2 = 0.98) between the slope gradient and fitting factor Ic. Therefore, the relationship between rainfall intensity and the infiltration capacity could be expressed using the fitting factor Ic. This suggests the possibility of quantitatively evaluating the relationships between rainfall intensity, the infiltration slope gradient, and the infiltration capacity.

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