scholarly journals Application of Angot precipitation index in the assessment of rainfall erosivity: Vojvodina Region case study (North Serbia)

2021 ◽  
Vol 61 (2) ◽  
pp. 123-153
Author(s):  
Tin Lukić ◽  
Tanja Micić Ponjiger ◽  
Biljana Basarin ◽  
Dušan Sakulski ◽  
Milivoj Gavrilov ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Precipitation Index ◽  
Mitigation Strategies ◽  
Rainfall Erosivity ◽  
Erosivity Index ◽  
Statistical Approaches ◽  
Soil Erosion By Water ◽  
Precipitation Trends ◽  
Rainfall Erosion

The paper aims to provide an overview of the most important parameters (the occurrence, frequency and magnitude) in Vojvodina Region (North Serbia). Monthly and annual mean precipitation values in the period 1946–2014, for the 12 selected meteorological stations were used. Relevant parameters (precipitation amounts, Angot precipitation index) were used as indicators of rainfall erosivity. Rainfall erosivity index was calculated and classified throughout precipitation susceptibility classes liable of triggering soil erosion. Precipitation trends were obtained and analysed by three different statistical approaches. Results indicate that various susceptibility classes are identified within the observed period, with a higher presence of very severe rainfall erosion in June and July. This study could have implications for mitigation strategies oriented towards reduction of soil erosion by water.

scholarly journals Modelling soil erosion at European scale: towards harmonization and reproducibility

2014 ◽  
Vol 2 (4) ◽  
pp. 2639-2680 ◽  
Author(s):  
C. Bosco ◽  
D. de Rigo ◽  
O. Dewitte ◽  
J. Poesen ◽  
P. Panagos
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Computational Modelling ◽  
Mitigation Strategies ◽  
Rainfall Erosivity ◽  
Large Spatial Scale ◽  
Nutrient Contents ◽  
Continental Scale ◽  
Country Level ◽  
Soil Erosion By Water

Abstract. Soil erosion by water is one of the most widespread forms of soil degradation. The loss of soil as a result of erosion can lead to decline in organic matter and nutrient contents, breakdown of soil structure and reduction of the water holding capacity. Measuring soil loss across the whole landscape is impractical and thus research is needed to improve methods of estimating soil erosion with computational modelling, upon which integrated assessment and mitigation strategies may be based. Despite the efforts, the prediction value of existing models is still limited, especially at regional and continental scale. A new approach for modelling soil erosion at large spatial scale is here proposed. It is based on the joint use of low data demanding models and innovative techniques for better estimating model inputs. The proposed modelling architecture has at its basis the semantic array programming paradigm and a strong effort towards computational reproducibility. An extended version of the Revised Universal Soil Loss Equation (RUSLE) has been implemented merging different empirical rainfall-erosivity equations within a climatic ensemble model and adding a new factor for a better consideration of soil stoniness within the model. Pan-European soil erosion rates by water have been estimated through the use of publicly available datasets and locally reliable empirical relationships. The accuracy of the results is corroborated by a visual plausibility check (63% of a random sample of grid cells are accurate, 83% at least moderately accurate, bootstrap p ≤ 0.05). A comparison with country level statistics of pre-existing European maps of soil erosion by water is also provided.

scholarly journals Geomorphic Control on Soil Erosion – a Case Study in the Subarnarekha Basin, India

2021 ◽  
Vol 54 (1) ◽  
pp. 1
Author(s):  
Amar Kumar Kathwas ◽  
Nilanchal Patel
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Principal Component ◽  
Soil Surface ◽  
Distant Location ◽  
The Individual ◽  
Degree Of Association ◽  
Soil Erosion By Water ◽  
Qualitative And Quantitative Characteristics

<p>Geomorphology depicts the qualitative and quantitative characteristics of both terrain and landscape features combined with the processes responsible for its evolution. Soil erosion by water involves processes, which removes soil particles and organic matter from the upper sheet of the soil surface, and then transports the eroded material to distant location under the action of water. Very few studies have been conducted on the nature and dynamics of soil erosion in the different geomorphologic features. In the present investigation, an attempt has been made to assess the control of geomorphologic features on the soil loss. Universal Soil Loss Equation (USLE) was used to determine soil loss from the various geomorphological landforms. Principal component analysis (PCA) was implemented on the USLE parameters to determine the degree of association between the individual principal components and the USLE-derived soil loss. Results obtained from the investigation signify the influence of the various landforms on soil erosion. PC5 is found to be significantly correlated with the USLE-derived soil loss. The results ascertained significant association between the soil loss and geomorphological landforms, and therefore, suitable strategies can be implemented to alleviate soil loss in the individual landforms.</p>

scholarly journals Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

2015 ◽  
Vol 19 (10) ◽  
pp. 4113-4126 ◽  
Author(s):  
S. Yin ◽  
Y. Xie ◽  
B. Liu ◽  
M. A. Nearing
Keyword(s):  
Energy Intensity ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
The World ◽  
Model Efficiency ◽  
Erosivity Index ◽  
Soil Erosion By Water ◽  
Event Energy ◽  
Eastern Half

Abstract. Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event (energy-intensity values – EI30) is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models based on commonly available rainfall data resolutions, such as daily or monthly totals, to calculate rainfall erosivity. Eleven stations with 1 min temporal resolution rainfall data collected from 1961 through 2000 in the eastern half of China were used to develop and calibrate 21 models. Seven independent stations, also with 1 min data, were utilized to validate those models, together with 20 previously published equations. The models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Using symmetric mean absolute percentage errors and Nash–Sutcliffe model efficiency coefficients, we can recommend 17 of the new models that had model efficiencies ≥ 0.59. The best prediction capabilities resulted from using the finest resolution rainfall data as inputs at a given erosivity timescale and by summing results from equations for finer erosivity timescales where possible. Results from this study provide a number of options for developing erosivity maps using coarse resolution rainfall data.

scholarly journals Database of rainfall erosivity factor for 141 locations in Brazil.

2021 ◽  
Vol 1 (3) ◽  
pp. 95-101
Author(s):  
Roberto Avelino Cecílio ◽  
João Paulo Bestete de Oliveira ◽  
David Bruno de Sousa Teixeira ◽  
Fernando Falco Pruski ◽  
Sidney Sara Zanetti
Keyword(s):  
Soil Erosion ◽  
Best Practices ◽  
Regression Models ◽  
Environmental Problem ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
R Factor ◽  
Lack Of Information ◽  
Erosivity Index

Soil erosion is a serious agricultural and environmental problem considered as a threat to sustainable development around the world. Rainfall is the primary cause of soil erosion, what leads the knowledge of its potential to cause soil erosion (rainfall erosivity – R-factor) to be a valuable tool for the design of land conservation best practices. As Brazil has a lack of information about rainfall erosivity, the present paper has determined the R-factor of 141 pluviographic stations distributed over Brazilian territory. Initially, erosive rainfalls were identified, and then the EI30 erosivity index was used to obtain the rainfall erosivity values. Regression models for the estimation of rainfall erosivity using daily rainfall data were established based on the correlation between the monthly average values of erosivity and the modified Fournier index. Results showed that the annual rainfall erosivity in the Brazilian stations analyzed ranged from 368.7 to 16,850.6 MJ mm ha-1 h-1 year-1. The results presented help to expand information about the spatial distribution of rainfall erosivity in Brazil, contributing to better conservation planning of land use.

scholarly journals ASSESSMENT OF SOIL EROSION BY RUSLE MODEL USING GIS: A CASE STUDY OF CHEMORAH BASIN, ALGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 70-78
Author(s):  
Khanchoul K. ◽  
Balla F. ◽  
Othmani O.
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Agricultural Land ◽  
Plant Cover ◽  
Rainfall Erosivity ◽  
Productive Capacity ◽  
Rusle Model ◽  
Erosion Loss ◽  
Gis Software ◽  
Soil Erosion By Water

Soil erosion by water is one of the major sources of land degradation. Erosion contributes to the temporary or permanent lowering of the productive capacity of agricultural land and sedimentation of dams. The purpose of this study is to assess soil loss rate using a GIS/RUSLE approach at the Chemorah basin by focusing on two catchments, namely, Reboa and Soultez. The assessment of soil erosion aims thus to identify the lands more prone to erosion which are vital for erosion management process. RUSLE model supported by GIS software is to predict the spatial variability of erosion occurring in the Chemorah basin and its sub-basins. Five inputs such as rainfall erosivity, soil erodibility, slope and length of slope, plant cover and anti-erosion practices, are used in the model to compute the erosion loss rates. The mean annual soil loss in Chemorah river basin is estimated at 7.52 T/ha/year, and varying between 3.78 T/ha/year in Soultez catchment and 6.06 T/ha/year in Reboa sub-basin. The study shows that low erosion (≤ 7 T/ha/year) covers 52% and high to very high erosion (> 7 T/ha/year) which does not exceed 23% of the Chemorah basin area. The results indicate that Reboa catchment faces the greatest risk of soil erosion compared to Soultez one, with contributions of 44 % and 32 % of their basin areas respectively. Use of the erosion factors’ information coupled with GIS/RUSLE program can help to design the appropriate land management to minimize soil erosion in the basin.

scholarly journals Modelling potential climate change impacts on sediment yield in the Tsitsa River catchment, South Africa

Water SA ◽  
2021 ◽  
Vol 47 (1 January) ◽  
Author(s):  
Simone Norah Theron ◽  
Harold Louw Weepener ◽  
Jacobus Johannes Le Roux ◽  
Christina Johanna Engelbrecht
Keyword(s):  
Climate Change ◽  
South Africa ◽  
Soil Erosion ◽  
Sediment Yield ◽  
Mitigation Strategies ◽  
Sub Saharan Africa ◽  
Future Climate Change ◽  
Rainfall Erosivity ◽  
Climate Change Scenarios ◽  
Negative Effects

The effects of climate change on water resources could be numerous and widespread, affecting water quality and water security across the globe. Variations in rainfall erosivity and temporal patterns, along with changes in biomass and land use, are some of the impacts climate change is projected to have on soil erosion. Sedimentation of watercourses and reservoirs, especially in water-stressed regions such as sub-Saharan Africa, may hamper climate change resilience. Modelling sediment yield under various climate change scenarios is vital to develop mitigation strategies which offset the negative effects of erosion and ensure infrastructure remains sustainable under future climate change. This study investigated the relative change in sediment yield with projected climate change using the Soil and Water Assessment Tool (SWAT) for a rural catchment in South Africa for the period 2015–2100. Data from six downscaled Coupled Global Climate Models (CGCM) were divided into three shorter time periods, namely, 2015–2034, 2045–2064 and 2081–2100. Results were then compared with a control scenario using observed data for the period 2002–2017. The results show that, if left unmanaged, climate change will likely lead to greater sediment yield, of up to 10% more per annum. Peak sediment yield will also increase almost three-fold throughout the century. The study shows that projected climate change will have multiple negative effects on soil erosion and emphasised the need for changes in climate to be considered when embarking on water resource developments.

scholarly journals Modelling soil erosion at European scale: towards harmonization and reproducibility

2015 ◽  
Vol 15 (2) ◽  
pp. 225-245 ◽  
Author(s):  
C. Bosco ◽  
D. de Rigo ◽  
O. Dewitte ◽  
J. Poesen ◽  
P. Panagos
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Regional Scale ◽  
Computational Modelling ◽  
Mitigation Strategies ◽  
Soil Parameters ◽  
Rainfall Erosivity ◽  
Nutrient Contents ◽  
Continental Scale ◽  
Country Level

Abstract. Soil erosion by water is one of the most widespread forms of soil degradation. The loss of soil as a result of erosion can lead to decline in organic matter and nutrient contents, breakdown of soil structure and reduction of the water-holding capacity. Measuring soil loss across the whole landscape is impractical and thus research is needed to improve methods of estimating soil erosion with computational modelling, upon which integrated assessment and mitigation strategies may be based. Despite the efforts, the prediction value of existing models is still limited, especially at regional and continental scale, because a systematic knowledge of local climatological and soil parameters is often unavailable. A new approach for modelling soil erosion at regional scale is here proposed. It is based on the joint use of low-data-demanding models and innovative techniques for better estimating model inputs. The proposed modelling architecture has at its basis the semantic array programming paradigm and a strong effort towards computational reproducibility. An extended version of the Revised Universal Soil Loss Equation (RUSLE) has been implemented merging different empirical rainfall-erosivity equations within a climatic ensemble model and adding a new factor for a better consideration of soil stoniness within the model. Pan-European soil erosion rates by water have been estimated through the use of publicly available data sets and locally reliable empirical relationships. The accuracy of the results is corroborated by a visual plausibility check (63% of a random sample of grid cells are accurate, 83% at least moderately accurate, bootstrap p ≤ 0.05). A comparison with country-level statistics of pre-existing European soil erosion maps is also provided.

scholarly journals Assessment Erosion 3D Hazard with USLE and Surfer Tool: A Case Study of Sumani Watershed in West Sumatra Indonesia

2013 ◽  
Vol 18 (1) ◽  
pp. 81 ◽  
Author(s):  
. Aflizar ◽  
Roni Afrizal ◽  
Tsugiyuki Masunaga
Keyword(s):  
Soil Erosion ◽  
Soil Conservation ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Conservation Practices ◽  
Slope Length ◽  
R Factor ◽  
K Factor ◽  
West Sumatra

Quantitative evaluation of soil erosion rate is an important basic to investigate and improve land use system, which has not been sufficiently conducted in Indonesia.  The Universal Soil Loss Equation (USLE) and Erosion Three Dimension (E3D) in Surfer were used to identify characteristic of dominant erosion factors in Sumani Watershed in West Sumatra, Indonesia using data soil survey and monitoring sediment yield in outlet watershed.  Climatology data from three stations were used to calculate Rainfall erosivity (R) factor. As many as101 sampling sites were used to investigate soil erodibility (K-factor) with physico-chemical laboratory analysis. Digital elevation model (DEM) of Sumani Watershed was used to calculate slope length and Steepness (LS-factor). Landsat TM imagery and field survey were used to determine crop management (C-factor) and conservation practices (P-factor). Calculating soil loss and map of USLE factor were determined by Kriging method in Surfer 9. Sumani Watershed had erosion hazard in criteria as: severe to extreme severe (26.23%), moderate (24.59%) and very low to low (49.18%).  Annual average soil loss for Sumani watershed was 76.70 Mg ha-1 y-1 in 2011. Upland area was designated as having a severe to extreme severe erosion hazard compared to lowland which was designated  as having very less to moderate.  On the other land, soil eroded from upland were deposited in lowland. These results were verified by comparing one year’s sediment yield observation on the outlet of the watershed. Land use (C-factor), rainfall erosivity (R- factor), soil erodibility (K-factor), slope length and steepness (LS-factor) were dominant factors that affected soil erosion. Traditional soil conservation practices were applied by farmer for a long time such as terrace in Sawah.  The USLE model in Surfer was used to identify specific regions susceptible to soil erosion by water and was also applied to identify suitable sites to conduct soil conservation planning in Sumani Watershed.[How to Cite : Aflizar, R Afrizal, T Masunaga. 2013. Assessment Erosion 3D Hazard with USLE and Surfer Tool: A Case Study of Sumani Watershed in West Sumatra Indonesia. J Trop Soils, 18 (1): 81-92. doi: 10.5400/jts.2013.18.1.81][Permalink/DOI: www.dx.doi.org/10.5400/jts.2013.18.1.81]

scholarly journals Applications of remote sensing and GIS for watershed characterization and soil loss assessment of tons watershed in Dehradun, Garhwal Himalaya

2020 ◽  
Vol 1 (1) ◽  
pp. 56-67
Author(s):  
P.C. Chanyal ◽  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Watershed Management ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Garhwal Himalaya ◽  
Rainfall Erosivity ◽  
Loss Assessment ◽  
Factor C

Watershed characterization is the most important part of watershed management which includes soil loss, soil loss assessment indicates the amount of soil loss or erosion in ton/hectare/ year through applying to Geospatial techniques as Remote sensing and GIS. The agricultural land is being lost by manmade as well natural whereas manmade or anthropogenic factor accelerates erosion of soil. It is a worldwide phenomenon leading to loss of decrease of water table availability for plants, increases runoff from the more impermeable subsoil, and loss of nutrients from the soil. Watershed management and assessment of soil loss are most helpful for planning and batter management in a watershed and planning units. Remote sensing and GIS along with the satellite image-based model approach provides a scientific, quantitative, and applied result. It can compute a consistent outcome of soil erosion and sediment yield for a wide range of areas under all climatic circumstances. Revised Universal Soil Loss Equation (RUSLE) apply to soil loss, which is integrated with Remote Sensing and GIS in Tons watershed lies between 77°56’05” E to 78°01’01” East longitude and 30° 21’05” N to 30°26’51” North latitude, having 97.02 km2 area (9,702 hectares) under the sub-tropical climatic region of Uttarakhand. The present case study based on computational with software and geospatial technologies results come i.e. A = is the computed soil loss per unit area, R = is the rainfall erosivity, K = is the soil erodibility factor, L = is the slope-length factor, C = is the cover and management factor, P = is the support practice factor. The rainfall erosivity (R=87.5 + 0.375 × R), C P is under range 0.006-0.8, Soil Erosion Risk range is slight to High 51.40% and 0.85% total area of the study region. Average annual soil loss ton/ha/year indicated in different land-use classification as lowest soil loss found in River bed (0.17 ton/ha/year) and highest shown in the open forest (56.58 ton/ha/year) in 2016. The study area comes under a low probability zone and partially comes under a moderate and moderate-high zone. The case study can be highly recommended and will help to implementation of management of soil loss and soil conservation practice in the Tons watershed as well as Himalayan regions. Keywords: RUSLE, Tons Watershed, Soil Loss, Remote Sensing & GIS, Garhwal Himalaya.

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