Estimating storm erosion with a rainfall simulator

1997 ◽  
Vol 77 (4) ◽  
pp. 669-676 ◽  
Author(s):  
S. C. Nolan ◽  
L. J. P. van Vliet ◽  
T. W. Goddard ◽  
T. K. Flesch
Keyword(s):  
Soil Loss ◽  
Conventional Tillage ◽  
Reduced Tillage ◽  
Natural Soil ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Rainfall Simulator ◽  
Rainfall Events ◽  
Plot Size ◽  
Natural Rainfall

Interpreting soil loss from rainfall simulators is complicated by the uncertain relationship between simulated and natural rainstorms. Our objective was to develop and test a method for estimating soil loss from natural rainfall using a portable rainfall simulator (1 m2 plot size). Soil loss from 12 rainstorms was measured on 144-m2 plots with barley residue in conventional tillage (CT), reduced tillage (RT) and zero tillage (ZT) conditions. A corresponding "simulated" soil loss was calculated by matching the simulator erosivity to each storm's erosivity. High (140 mm h−1) and low (60 mm h−1) simulation intensities were examined. The best agreement between simulated and natural soil loss occurred using the low intensity, after making three adjustments. The first was to compensate for the 38% lower kinetic energy of the simulator compared with natural rain. The second was for the smaller slope length of the simulator plot. The third was to begin calculating simulator erosivity only after runoff began. After these adjustments, the simulated soil loss over all storms was 99% of the natural soil loss for CT, 112% for RT and 95% for ZT. Our results show that rainfall simulators can successfully estimate soil loss from natural rainfall events. Key words: Natural rainfall events, simulated rainfall, erosivity, tillage

scholarly journals Estimation of annual soil erosion dynamics (2005 - 2015) in Pakistan using Revised Universal Soil Loss Equation (RUSLE)

2021 ◽  
Author(s):  
Hammad Gilani ◽  
Adeel Ahmad ◽  
Isma Younes ◽  
Sawaid Abbas
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Management Practices ◽  
Climatic Factors ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Conservation Practice ◽  
Administrative Units ◽  
Soil Loss Equation

Abrupt changes in climatic factors, exploitation of natural resources, and land degradation contribute to soil erosion. This study provides the first comprehensive analysis of annual soil erosion dynamics in Pakistan for 2005 and 2015 using publically available climatic, topographic, soil type, and land cover geospatial datasets at 1 km spatial resolution. A well-accepted and widely applied Revised Universal Soil Loss Equation (RUSLE) was implemented for the annual soil erosion estimations and mapping by incorporating six factors; rainfall erosivity (R), soil erodibility (K), slope-length (L), slope-steepness (S), cover management (C) and conservation practice (P). We used a cross tabular or change matrix method to assess the annual soil erosion (ton/ha/year) changes (2005-2015) in terms of areas and spatial distriburtions in four soil erosion classes; i.e. Low (<1), Medium (1–5], High (5-20], and Very high (>20). Major findings of this paper indicated that, at the national scale, an estimated annual soil erosion of 1.79 ± 11.52 ton/ha/year (mean ± standard deviation) was observed in 2005, which increased to 2.47 ±18.14 ton/ha/year in 2015. Among seven administrative units of Pakistan, in Azad Jammu & Kashmir, the average soil erosion doubled from 14.44 ± 35.70 ton/ha/year in 2005 to 28.03 ± 68.24 ton/ha/year in 2015. Spatially explicit and temporal annual analysis of soil erosion provided in this study is essential for various purposes, including the soil conservation and management practices, environmental impact assessment studies, among others.

GIS estimation of annual average soil loss rate from Hangar River watershed using RUSLE

2019 ◽  
Vol 11 (2) ◽  
pp. 529-539 ◽  
Author(s):  
Mahmud Mustefa ◽  
Fekadu Fufa ◽  
Wakjira Takala
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Loss Rate ◽  
Study Finding ◽  
Mitigation Measures ◽  
Rainfall Erosivity ◽  
Vegetative Cover ◽  
Annual Average ◽  
Slope Length ◽  
Soil Loss Rate

Abstract Currently, soil erosion is the major environmental problem in the Blue Nile, Hangar watershed in particular. This study aimed to estimate the spatially distributed mean annual soil erosion and map the most vulnerable areas in Hangar watershed using the revised universal soil loss equation. In this model, rainfall erosivity (R-factor), soil erodibility (K-factor), slope steepness and slope length (LS-factor), vegetative cover (C-factor), and conservation practice (P-factor) were considered as the influencing factors. Maps of these factors were generated and integrated in ArcGIS and then the annual average soil erosion rate was determined. The result of the analysis showed that the amount of soil loss from the study area ranges from 1 to 500 tha−1 yr−1 with an average annual soil loss rate of 32 tha−1 yr−1. Considering contour ploughing with terracing as a fully developed watershed management, the resulting soil loss rate was reduced from 32 to 19.2 tha−1 yr−1. Hence, applying contour ploughing with terracing effectively reduces the vulnerability of the watershed by 40%. Based on the spatial vulnerability of the watershed, most critical soil erosion areas were situated in the steepest part of the watershed. The result of the study finding is helpful for stakeholders to take appropriate mitigation measures.

scholarly journals Estimation of soil loss by the USLE model in a mountain basin in the south of Santa Catarina state, Brazil

2020 ◽  
Vol 24 ◽  
pp. e20
Author(s):  
Lucas Kister Amaral ◽  
Sabrina Baesso Cadorin ◽  
Álvaro José Back ◽  
Fernanda Dagostin Szymanski ◽  
Claudia Weber Corseuil
Keyword(s):  
River Basin ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Intense Rainfall ◽  
Slope Length ◽  
Control Practice ◽  
Runoff Potential ◽  
Shallow Soils ◽  
The Impact ◽  
Very High

Water erosion is a factor of soil degradation that is triggered by the impact of raindrops originated by intense rainfall disaggregating the soil, followed by the carrying of particles by surface runoff. In the erosion process, in addition to soil loss, nutrients, fertilizers, and pesticides are carried resulting in water courses and water pollution. Erosion can have a major impact on agricultural production, when soil use and management techniques are not used. Therefore, this study aimed to evaluate the soil loss in the Malacara river basin, which is a sub-basin of the Mampituba river basin characterized by a contrasting relief, with high altitudes in the escarps of Serra Geral and floodplain. The method used for the development of this research was the application of the Universal Soil Loss Equation (USLE). USLE soil loss estimation requires the following factors: rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), soil use and management (C), and erosion control practice factor (P). The estimated rainfall erosivity was 5,754.2 MJ mm ha-1 h-1 year-1. Erodibility was determined for the soils present in the basin, highlighting a high value for gleysoil. The topographic factor (LS) showed values from 0 to greater than 20, which corresponds to the low to very high runoff potential. The floodplain showed lower runoff rates, while for the locations close to the enclosed valleys in the Malacara canyon, the runoff potential varied from high to very high. The soil use and management factors and conservation practices (CP) obtained a maximum value of 0.404, corresponding to the exposed soil; the second most representative class was agricultural areas, with a value of 0.145. The soil loss in the Malacara river basin varied from 0 to more than 200 t ha-1 year-1. In fact, 87.38% of the area presents a degree of sheet erosion normal to slight and, only 2.94% of the area has a high or very high degree of erosion. Moreover, due to the relief characteristics with shallow soils and intense rainfall in mountainous basins, knowing and understanding soil losses due to erosion is crucial for the adequate management of water resources in river basins. 

scholarly journals ESTIMATION OF ANNUAL AVERAGE SOIL LOSS, BASED ON RUSLE MODEL IN KALLAR WATERSHED, BHAVANI BASIN, TAMIL NADU, INDIA

2015 ◽  
Vol II-2/W2 ◽  
pp. 207-214 ◽  
Author(s):  
S. Abdul Rahaman ◽  
S. Aruchamy ◽  
R. Jegankumar ◽  
S. Abdul Ajeez
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Tamil Nadu ◽  
Rainfall Erosivity ◽  
Storm Events ◽  
Rill Erosion ◽  
Slope Length ◽  
Conservation Practice ◽  
Erosion Risk ◽  
Wide Range

Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation- RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h<sup>-1</sup>/ y<sup>-1</sup>. Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.

scholarly journals The overshadow of the human evolvement process in the dynamics of soil drift of an agricultural watershed in Nilgiri Hills, India

2021 ◽  
Author(s):  
Sangeetha Ramakrishnan ◽  
Ambujam Neelakanda Pillai Kanniperumal
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Agricultural Watershed ◽  
Rainfall Erosivity ◽  
Landsat Images ◽  
Slope Length ◽  
Data Format ◽  
Conservation Practice ◽  
Soil Erosion Rate ◽  
Agricultural Planning

The Nilgiri Biosphere, being one of the critical catchments, a small agricultural watershed of Udhagamandalam has been analysed to show the need to improve the agriculture by reducing the soil erosion. For this study, the land use and land cover classification was undertaken using Landsat images to highlight the changes that have occurred between 1981 and 2019. The Revised Universal Soil Loss Equation (RUSLE) method and the Geographic Information System (GIS) was used in this study to determine the soil erosion vulnerability of Sillahalla watershed in the Nilgiri Hills in Tamilnadu. This study will help to promote the economic development of the watershed with proper agricultural planning and erosion management. This study focuses on the estimation of the average annual soil loss and to classify the spatial distribution of the soil loss as a map with the RUSLE method and GIS. To estimate the average annual soil loss of the study area, GIS layers of the RUSLE factors like rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C) and conservation practice (P) were computed in a raster data format. The total soil loss and average annual soil loss of the study area for 1981–1990,1991–2000, 2001–2010, 2011–2019 were found to be 0.2, 0.254, 0.3, 0.35 million t/year and 31.33, 37.78, 46.7, 51.89 t/ha/year, respectively. The soil erosion rate is classified into different classes as per the FAO guidelines and this severity classification map was prepared to identify the vulnerable areas.

Assessment of Soil Loss using Revised Universal Soil Loss Equation (RUSLE): A Remote Sensing and GIS Approach

2018 ◽  
Vol 2 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ajaykumar Kadam ◽  
B. N. Umrikar ◽  
R. N. Sankhua
Keyword(s):  
Remote Sensing ◽  
Soil Loss ◽  
Remote Sensing Data ◽  
Tropical Rainfall Measuring Mission ◽  
Remote Sensing And Gis ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Gis Environment ◽  
Soil Loss Equation

A comprehensive methodology that combines Revised Universal Soil Loss Equation (RUSLE), Remote Sensing data and Geographic Information System (GIS) techniques was used to determine the soil loss vulnerability of an agriculture mountainous watershed in Maharashtra, India. The spatial variation in rate of annual soil loss was obtained by integrating raster derived parameter in GIS environment. The thematic layers such as TRMM [Tropical Rainfall Measuring Mission] derived rainfall erosivity (R), soil erodibility (K), GDEM based slope length and steepness (LS), land cover management (C) and factors of conservation practices (P) were calculated to identify their effects on average annual soil loss. The highest potential of estimated soil loss was 688.397 t/ha/yr. The mean annual soil loss is 1.26 t/ha/yr and highest soil loss occurs on the main watercourse, since high slope length and steepness. The spatial soil loss maps prepared with RUSLE method using remote sensing and GIS can be helpful as a lead idea in arising plans for land use development and administration in the ecologically sensitive hilly areas.

scholarly journals Assessment of Soil Erosion by RUSLE Model using Remote Sensing and GIS of Indla-Ghatkhed, District-Amravati, Maharashtra

2021 ◽  
pp. 247-253
Author(s):  
N. W. Ingole ◽  
S. S. Vinchurkar
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Crop Cover ◽  
Conservation Practice ◽  
Slope Length Factor ◽  
Pixel Value ◽  
Factor C ◽  
Soil Erosion Assessment

The catchment boundary of Indla Ghatkhed watershed covers an area about 14..62 sq km. The erosion is a natural geomorphic process occurring continually over the earth’s surface and it largely depends on topography, vegetation, soil and climatic variables and, therefore, exhibits pronounced spatial variability due to catchments heterogeneity and climatic variation. This problem can be circumvented by discrediting the catchments into approximately homogeneous sub-areas using Geographic Information System (GIS). Soil erosion assessment modeling was carried out based on the Revised Universal Soil Loss Equation (RUSLE). A set of factors are involved in RUSLE equation are A = Average annual soil loss (mt/ha/year), R = Rainfall erosivity factor (mt/ha/year), k = Soil erodibility factor, LS = Slope length factor, C = Crop cover management factor, P = Supporting conservation practice factor. These factors extracted from different surface features by analysis and brought in to raster format. The output depicts the amount of sediment rate from a particular grid in spatial domain and the pixel value of the outlet grid indicates the sediment yield at the outlet of the watershed.

scholarly journals Set-up and calibration of a portable small scale rainfall simulator for assessing soil erosion processes at interrill scale

2017 ◽  
Vol 43 (1) ◽  
pp. 63 ◽  
Author(s):  
J. J. Zemke
Keyword(s):  
Soil Erosion ◽  
Kinetic Energy ◽  
Rainfall Intensity ◽  
Small Scale ◽  
Rainfall Erosivity ◽  
Fall Velocity ◽  
Rainfall Simulator ◽  
Spatial Homogeneity ◽  
Erosion Processes ◽  
Natural Rainfall

A portable rainfall simulator was built for assessing runoff and soil erosion processes at interrill scale. Within this study, requirements and constraints of the rainfall simulator are identified and discussed. The focus lies on the calibration of the simulator with regard to spatial rainfall homogeneity, rainfall intensity, drop size, drop fall velocity and rainfall kinetic energy. These parameters were obtained using different methods including a Laser Precipitation Monitor. A detailed presentation of the operational characteristics is given. The presented rainfall simulator setup featured a rainfall intensity of 45.4 mm·h-1 with a spatial homogeneity of 80.4% based on a plot area of 0.64 m². Because of the comparatively low drop height (2 m), the diameter-dependent terminal fall velocity (1.87 m·s-1) was lower than benchmark values for natural rainfall. This conditioned also a reduced rainfall kinetic energy (4.6 J·m-2·mm-1) compared to natural rainfall with same intensity. These shortfalls, a common phenomenon concerning portable rainfall simulators, represented the best possible trade-off between all relevant rainfall parameters obtained with the given simulator setup. Field experiments proved that the rainfall erosivity was constant and replicable.

scholarly journals Regional soil erosion assessment based on a sample survey and geostatistics

2018 ◽  
Vol 22 (3) ◽  
pp. 1695-1712 ◽  
Author(s):  
Shuiqing Yin ◽  
Zhengyuan Zhu ◽  
Li Wang ◽  
Baoyuan Liu ◽  
Yun Xie ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Sample Survey ◽  
Shaanxi Province ◽  
Rainfall Erosivity ◽  
Soil Erodibility ◽  
Slope Length ◽  
Slope Steepness ◽  
Soil Erosion Assessment

Abstract. Soil erosion is one of the most significant environmental problems in China. From 2010 to 2012, the fourth national census for soil erosion sampled 32 364 PSUs (Primary Sampling Units, small watersheds) with the areas of 0.2–3 km2. Land use and soil erosion controlling factors including rainfall erosivity, soil erodibility, slope length, slope steepness, biological practice, engineering practice, and tillage practice for the PSUs were surveyed, and the soil loss rate for each land use in the PSUs was estimated using an empirical model, the Chinese Soil Loss Equation (CSLE). Though the information collected from the sample units can be aggregated to estimate soil erosion conditions on a large scale; the problem of estimating soil erosion condition on a regional scale has not been addressed well. The aim of this study is to introduce a new model-based regional soil erosion assessment method combining a sample survey and geostatistics. We compared seven spatial interpolation models based on the bivariate penalized spline over triangulation (BPST) method to generate a regional soil erosion assessment from the PSUs. Shaanxi Province (3116 PSUs) in China was selected for the comparison and assessment as it is one of the areas with the most serious erosion problem. Ten-fold cross-validation based on the PSU data showed the model assisted by the land use, rainfall erosivity factor (R), soil erodibility factor (K), slope steepness factor (S), and slope length factor (L) derived from a 1 : 10 000 topography map is the best one, with the model efficiency coefficient (ME) being 0.75 and the MSE being 55.8 % of that for the model assisted by the land use alone. Among four erosion factors as the covariates, the S factor contributed the most information, followed by K and L factors, and R factor made almost no contribution to the spatial estimation of soil loss. The LS factor derived from 30 or 90 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data worsened the estimation when used as the covariates for the interpolation of soil loss. Due to the unavailability of a 1 : 10 000 topography map for the entire area in this study, the model assisted by the land use, R, and K factors, with a resolution of 250 m, was used to generate the regional assessment of the soil erosion for Shaanxi Province. It demonstrated that 54.3 % of total land in Shaanxi Province had annual soil loss equal to or greater than 5 t ha−1 yr−1. High (20–40 t ha−1 yr−1), severe (40–80 t ha−1 yr−1), and extreme (> 80 t ha−1 yr−1) erosion occupied 14.0 % of the total land. The dry land and irrigated land, forest, shrubland, and grassland in Shaanxi Province had mean soil loss rates of 21.77, 3.51, 10.00, and 7.27 t ha−1 yr−1, respectively. Annual soil loss was about 207.3 Mt in Shaanxi Province, with 68.9 % of soil loss originating from the farmlands and grasslands in Yan'an and Yulin districts in the northern Loess Plateau region and Ankang and Hanzhong districts in the southern Qingba mountainous region. This methodology provides a more accurate regional soil erosion assessment and can help policymakers to take effective measures to mediate soil erosion risks.

scholarly journals POTENCIAL EROSIVO DAS CHUVAS EM LAVRAS, MG: DISTRIBUIÇÃO, PROBABILIDADE DE OCORRÊNCIA E PERÍODO DE RETORNO

Irriga ◽  
2006 ◽  
Vol 11 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Adão Wagner Pêgo Evangelista ◽  
Luiz Gonsaga de Carvalho ◽  
Luiz Gonsaga de Carvalho ◽  
Antonio Augusto Aguilar Dantas ◽  
Antonio Augusto Aguilar Dantas ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Soil Loss ◽  
Conventional Tillage ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Water Losses ◽  
Period 2 ◽  
Seedbed Preparation ◽  
Index Value ◽  
Soil Loss Equation

POTENCIAL EROSIVO DAS CHUVAS EM LAVRAS, MG:DISTRIBUIÇÃO, PROBABILIDADE DE OCORRÊNCIA E PERÍODO DE RETORNO  Adão Wagner Pêgo Evangelista; Luiz Gonsaga de Carvalho; Antonio Augusto Aguilar Dantas; Daniel Teixeira BernardinoDepartamento de Engenharia, Universidade Federal de Lavras, Lavras, MG, [email protected]  1 RESUMO Foi estudado o potencial erosivo das chuvas em Lavras (MG) visando à implementação do planejamento do uso da terra local. Para tanto, determinou-se o fator erosividade (R) da equação universal de perda de solo por erosão, para uma série pluviográfica de 15 anos. Seu valor calculado foi de 5403 MJ mm (ha h ano)-1, o qual é esperado ocorrer no local pelo menos uma vez a cada 2,9 anos, com uma probabilidade de 34%. Os valores individuais máximos estimados para os períodos de retorno de 2, 5, 20, 50 e 100 anos foram 524, 789, 1465, 2206 e 3007 MJ mm (ha h)-1, respectivamente. Nos meses de setembro a dezembro, são esperadas altas perdas de solo e água, posto que 40 % do valor anual do índice de erosividade ocorre nesse período, quando é utilizado o preparo convencional do solo e a cobertura vegetal é incipiente. UNITERMOS: fator R, equação universal de perda de solo, conservação do solo.  EVANGELISTA , A. W. P.,; CARVALHO, L. G. de; DANTAS, A. A. A.; BERNARDINO, D. T.; RAINFALL EROSIVE POTENTIAL IN LAVRAS, MINAS GERAIS STATE, BRAZIL:DISTRIBUITION, OCCURRENCE PROBABLILITY AND RETURN PERIOD  2 ABSTRACT Rainfall erosive potential in Lavras, MinasGeraisState, has been studied to improve its land use planning. Rainfall erosivity factor (R), used in the universal soil loss equation, was calculated for a rain gauges series of 15 years. 5403 MJ mm (ha h year)-1 was the calculated value and it is expected to occur at least once every 2.9 years, with a 34% occurrence probability. Maximum individual EI30 values estimated for return periods of 2, 5, 20, 50 and 100 years were 524, 789, 1465, 2206 and 3007 MJ mm (ha h)-1, respectively. Highest soil and water losses are expected from September to March, since 40 % of the annual erosivity index value occurs within that period, when conventional tillage is used for seedbed preparation and the canopy is incipient. KEYWORDS: R factor, universal soil loss equation, conservation soil.

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