scholarly journals Assessment of Rainfall Kinetic-Energy–Intensity Relationships

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1994 ◽  
Author(s):  
Claudio Mineo ◽  
Elena Ridolfi ◽  
Benedetta Moccia ◽  
Fabio Russo ◽  
Francesco Napolitano
Keyword(s):  
Kinetic Energy ◽  
Soil Loss ◽  
Energy Intensity ◽  
Rainfall Intensity ◽  
Central Italy ◽  
Direct Evaluation ◽  
Hydrological Variable ◽  
The Mean ◽  
Raindrop Energy ◽  
Soil Loss Equation

Raindrop-impact-induced erosion starts when detachment of soil particles from the surface results from an expenditure of raindrop energy. Hence, rain kinetic energy is a widely used indicator of the potential ability of rain to detach soil. Although it is widely recognized that knowledge of rain kinetic energy plays a fundamental role in soil erosion studies, its direct evaluation is not straightforward. Commonly, this issue is overcome through indirect estimation using another widely measured hydrological variable, namely, rainfall intensity. However, it has been challenging to establish the best expression to relate kinetic energy to rainfall intensity. In this study, first, kinetic energy values were determined from measurements of an optical disdrometer. Measured kinetic energy values were then used to assess the applicability of the rainfall intensity relationship proposed for central Italy and those used in the major equations employed to estimate the mean annual soil loss, that is, the Universal Soil Loss Equation (USLE) and its two revised versions (RUSLE and RUSLE2). Then, a new theoretical relationship was developed and its performance was compared with equations found in the literature.

scholarly journals Modeling of water erosion by the erosion potential method in a pilot subbasin in southern Minas Gerais

2019 ◽  
Vol 40 (2) ◽  
pp. 555 ◽  
Author(s):  
André Silva Tavares ◽  
Velibor Spalevic ◽  
Junior Cesar Avanzi ◽  
Denismar Alves Nogueira ◽  
Marx Leandro Naves Silva ◽  
...  
Keyword(s):  
Soil Loss ◽  
Water Erosion ◽  
Minas Gerais ◽  
Universal Soil Loss Equation ◽  
Southeastern Brazil ◽  
Erosion Processes ◽  
Total Soil ◽  
The Mean ◽  
Soil Losses ◽  
Soil Loss Equation

Soil losses due to water erosion threaten the sustainability of agriculture and the food security of current and future generations. This study estimated potential soil losses and sediment production under different types of land uses in a subbasin in the Municipality of Alfenas, southern Minas Gerais, southeastern Brazil. The objective of this research was to evaluate the application of the Potential Erosion Method by the Intensity of Erosion and Drainage program and correlate the findings with the results obtained by the Revised Universal Soil Loss Equation as well as geoprocessing techniques and statistical analyses. In the Potential Erosion Method, the coefficient indicating the mean erosion intensity was 0.37, which corresponded to erosion category IV and indicated weak laminar erosion processes, and the total soil loss was 649.31 Mg year-1 and the mean was 1.46 Mg ha-1 year-1. These results were consistent in magnitude with those obtained in the Revised Universal Soil Loss Equation, which estimated a mean soil loss of 1.52 Mg ha-1 year-1 and a total soil loss of 668.26 Mg year-1. The Potential Erosion Method suggests that 1.5% of the area presents potential soil losses above the soil loss tolerance limit, which ranged from 5.19 to 5.90 Mg ha-1 year-1, while the Revised Universal Soil Loss Equation indicated that 7.3% of the area has potential soil losses above the limit. The maximum sediment discharge was 60 Mg year-1, meaning that 9.3% of the total soil loss reached the depositional areas of the river plains or watercourses. The Potential Erosion Method was efficient in the evaluation of water erosion in tropical soils, and the results were consistent with models widely employed in the estimation of soil losses. Thus, the model can support the evaluation of soil losses in Brazil and is a robust tool for evaluating the sustainability of agricultural activities.

The effect of kinetic energy of excess rainfall on soil loss from non-vegetated plots

Soil Research ◽  
1983 ◽  
Vol 21 (4) ◽  
pp. 445 ◽  
Author(s):  
PIA Kinnell
Keyword(s):  
Kinetic Energy ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Acceptance Rate ◽  
Slope Gradient ◽  
The Difference ◽  
Bare Fallow ◽  
Runoff And Soil Loss ◽  
Sheet Erosion

Data obtained from three 0.01 ha runoff and soil-loss plots, established with a bare fallow treatment on a yellow podzolic (Albaqualf) soil and slope gradient of 4.2%, were analysed in terms of the kinetic energy of raindrops and the efficiency of the use of that energy in generating soil loss. The results indicate that the difference between rainfall intensity and the average infiltration (acceptance) rate of the soil during an event can be used to estimate variations in the efficiency of use of rainfall energy in generating sheet erosion.

scholarly journals Experimental Study of Freeze-Thaw/Water Compound Erosion and Hydraulic Conditions as Affected by Thawed Depth on Loessal Slope

2020 ◽  
Vol 8 ◽  
Author(s):  
Wei Wang ◽  
Zhanbin Li ◽  
Rui Yang ◽  
Tian Wang ◽  
Peng Li
Keyword(s):  
Soil Loss ◽  
Loss Rate ◽  
Rainfall Intensity ◽  
Freeze Thaw ◽  
Erosion Processes ◽  
Hydraulic Conditions ◽  
The Mean ◽  
The Relationship ◽  
Thaw Water ◽  
Soil Loss Rate

Freeze-thaw cycles have significant influences on slope erosion processes. In this study, simulated rainfall laboratory experiments were implemented to investigate erosion processes and the relationship between the soil loss rate and hydraulics conditions under different thawed depths and rainfall intensities. The results indicated that linear regression could be used to describe the relationship between the soil loss rate and runoff time. Soil loss rate, as measured by the curve slope k (represented the increase rate in the soil loss rate), generally increased with runoff time over different thawed depths across all rainfall intensities. The k values generally increased with rainfall intensity from 0.6 to 1.2 mm/min, with the exception of the 4 cm thawed slope, for which the k values initially increased before decreasing with rainfall intensity from 0.6 to 1.2 mm/min. The mean soil loss rate and range also increased with thawed depth under the same rainfall intensity. Finally, the interaction of rainfall intensity and thawed depth had the greatest effect on soil loss rate, while stream erosion power was the hydraulic parameter that exhibited the best soil loss rate prediction performance. The results presented herein improve the understanding of the response of freeze-thaw/water compound erosion to hydraulic conditions.

scholarly journals A Study Case on Estimation of Soil Loss and Sediment Yield in Curtin University, Malaysia

2021 ◽  
Vol 1 (2) ◽  
pp. 62-73
Author(s):  
Hui Yee Ngieng ◽  
Leong Kong Yong ◽  
Striprabu Strimari
Keyword(s):  
Sediment Yield ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Google Earth ◽  
Universal Soil Loss Equation ◽  
Storm Event ◽  
Sieve Analysis ◽  
Runoff Depth ◽  
Factor C ◽  
Soil Loss Equation

Because of human activities, soil erosion has been one of the most concerning issues in Malaysia in the past decades. This study aimed to estimate the amount of soil loss and sediment yield at Curtin University, Malaysia by using the Revised Universal Soil Loss Equation (RUSLE) and the Modified Universal Soil Loss Equation (MUSLE), respectively. The parameters of RUSLE include rainfall erosivity factor (R), soil erodibility factor (K), slope length factor (L), slope steepness factor (S), cover-management factor (C) and support practice factor (P). The rainfall data (10 years) from the Sarawak Meteorological Department was used to determine the R-factor. The K-factor was determined by sieve analysis, hydrometer analysis, the Standard Proctor Test (SPT), and organic content testing. The L-and S-factors were performed by measuring on site and using Google Earth. The C-and P-factors were based on the ground surface cover condition (bare soil in this study). In the MUSLE, the runoff factor comprises V and Qp, while the other parameters are the same as in the RUSLE. The runoff depth, V, is equivalent to the rainfall intensity. Rainfall intensities were recorded by using a rain gauge. The highest rainfall intensity was used for runoff depth. The Rational method has been utilized to calculate Qp. The amount of soil loss estimated was 119.97 tons/ha/year and the sediment yield amount estimated was 0.76 ton/storm event in Curtin University, Malaysia.

scholarly journals Estimation of the annual rainfall erosivity index based on hourly rainfall data in a tropical region

2020 ◽  
Author(s):  
Ming-Hsi Lee ◽  
I-Ping Hsu
Keyword(s):  
Kinetic Energy ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Tropical Region ◽  
Rainfall Erosivity ◽  
Data Set ◽  
Data Accumulation ◽  
Hourly Rainfall

The annual mean rainfall erosivity (R) indicates the potential soil loss caused by the precipitation and runoff and is used to predict the soil loss from agricultural hillslopes. R is calculated from rainfall stations with continuously recording rainfall databases. However, many short-term real-time rainfall databases that also relate to the rainfall intensity are not readily available around Taiwan, with the hourly rainfall data being predominantly available. The annual mean rainfall erosivity calculated by the 10-min rainfall data accumulation converted to the 30-min rainfall data (R<sub>10_30</sub>) can be estimated using the annual mean rainfall erosivity calculated by the 10-min rainfall data accumulation convert to the hourly rainfall data (R<sub>10_60</sub>) that are calculated from the kinetic energy calculated by the 10-min rainfall data accumulation converted to the hourly rainfall data (E<sub>60j</sub>). The maximum 60-min rainfall intensity calculated by the 10-min rainfall data accumulation converted to the hourly rainfall data (I<sub>60j</sub>) has been established in rainfall stations throughout southern Taiwan. The 10-min rainfall data set consists of 15 221 storm events from 2002 to 2017 monitored by 51 rainfall stations located in the tropical regions in Taiwan. According to the results of this study, the average conversion factors of the kinetic energy (1.04), rainfall erosivity (1.47), and annual mean rainfall erosivity (1.30) could be estimated based on the 10-min rainfall data.

scholarly journals Estimation of Soil Loss Using Remote Sensing And GIS-Based Universal Soil Loss Equation In Northern Catchment of Lake Tana Sub-Basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Author(s):  
Veera Narayana Balabathina ◽  
Raju RP ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Lake Tana ◽  
Erosion Risk ◽  
Priority Areas ◽  
Erosion Rates ◽  
The Mean ◽  
Soil Loss Equation

Abstract Background: Soil erosion, one of the major environmental challenges, is influenced by topography, climate, soil characteristics, and human activities and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. The present study attempts to estimate soil erosion risk in the Northern catchment of Lake Tana basin, situated in northwest part of Ethiopia, with available data through the application of the Universal Soil Loss Equation model integrated with Geographic Information System and remote sensing technologies to identify priority areas for controlling soil erosion. In addition, it analyzes the effect of land use and land cover, topography, erodibility, and drainage density on soil erosion potential of the catchment, and the possible relationships among them. Results: The results show that the mean annual soil loss of catchment is estimated at 37.89 ± 59.2 t ha−1yr−1 with a total annual soil loss of 1,705,370 tons. The topography (LS-factor), followed by the support practice (P-factor) and the soil erodibility (K-factor) were the most sensitive factors affecting soil erosion in the catchment. To identify high priority areas for management, the study area was subdivided into five major sub-basins and further categorized into five erosion classes based on erosion severity. The mean soil erosion rates of the Derma, Megech, Gumara, Garno, and Gabi Kura River sub-basins are 46.8, 40.98, 30.95, 30.04, and 29.66 t ha−1yr−1, respectively. About 58.9% of the area was found in very low erosion risk which extends from 0-1 t ha−1yr−1 and accounted only 1.1% of total soil loss, while 12.4% of the area was found to be under high and extreme erosion risk with erosion rates of 10 t ha−1yr−1 or more that contributes about 82.1% of total soil loss warrant high priority for reducing the risk of soil erosion. Conclusions: This study permits the understanding of the soil erosion process and the various factors that lead to the spatial variability of the risk in the catchment, and thus enhances the effectiveness of proposed conservation strategies for sustainable land management.

scholarly journals ASSESSMENT AND MAPPING OF RAINFALL EROSIVITY INDEX (R) FOR MAJHA REGION, PUNJAB IS A STATE IN NORTHERN, INDIA

2021 ◽  
Vol 9 (3) ◽  
pp. 15-23
Author(s):  
Prashant Kumar
Keyword(s):  
Cover Crop ◽  
Soil Loss ◽  
Management Practices ◽  
Rainfall Intensity ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Northern India ◽  
Close Observation ◽  
Erosivity Index ◽  
Soil Loss Equation

Purpose: This study gives a critical assessment of the rainfall erosivity factor (R) for selected sites in the Majha region, representing different locations use of mean monthly rainfall data.  Methodology: By applying empirical methods, the rainfall intensity for all the locations were obtained and was further determined at three different intervals of 30-minutes, 45-minutes and 60-minutes, respectively. The rainfall erosivity factor (R) was calculated by the revised universal soil loss equation (RUSLE). Main Findings: Using RUSLE, the rainfall erosivity factor (R) for each of the locations was measured as follows; EI = 3878.49 (MJmmha-1hr-1), EI = 4013.71 (MJmmha-1hr-1), EI = 4302.24 (MJmmha-1hr-1) for Majha region of Amritsar, Tarntaran and Pathankot respectively. A close observation of the data obtained revealed that as rainfall intensity increased with the duration, the rainfall erosivity index reduced or decreased. Implications of study: Nevertheless, it is expected that if proper cover crop and management practices are applied despite the region, the study area falls within, rainfall erosivity can be cushioned, thus reducing further erosion tendencies and enhancing food production chances from productive lands within the area. The novelty of study: The rainfall erosivity factor (R) was calculated by the revised universal soil loss equation (RUSLE).

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