Estimation of soil loss with RUSLE during a period of increasing rainfall erosivity (1997-2018) in two-contrasted Mediterranean watersheds (southern Spain).

2021 ◽  
Author(s):  
Juan F. Martinez-Murillo ◽  
José A. Sillero-Medina ◽  
José D. Ruiz-Sinoga
Keyword(s):  
Soil Loss ◽  
Mediterranean Climate ◽  
Geographical Area ◽  
Soil Surface ◽  
Point Of View ◽  
Rainfall Regime ◽  
Rainfall Erosivity ◽  
Spatial And Temporal Variability ◽  
Mediterranean Mountains ◽  
Factor C

<p>During the last 25 years, an increasing rainfall erosivity occurred in South of Spain according to recent studies. This fact may rendered in an increment of the derived threatens from water erosion and, consequently, soil loss processes, one of the main geomorphic agent in that geographical area. This study deals with the application of RUSLE equation in two-contrasted Mediterranean mountainous watershed from 1997 to 2018. Both of them are characterised with very common ecogeomorphologic features from Mediterranean mountains but differs in the rainfall regime: one watershed shows an altitudinal gradient from dry-Mediterranean to subhumid Mediterranean climate, and the other one from semiarid to dry-Mediterranean climate.</p><p>From the methodological point of view, RULSE was applied but some modifications were introduced in its calculation: i) rainfall intensity calculated in 10-minutes instead of 30-minutes for Factor R; ii) vegetation cover estimated by means of NDVI for Factor C; and iii) validation using field inventory of soil surface components.</p><p>The results indicated differences between both watersheds given their different ecogeomorphologic conditions. The precision of using I10 let valuate better the soil loss estimation and its spatial and temporal variability. The validation with the soil surface components obtained better results in the rainiest watershed with more biotic ecogeomorphological conditions. This study is of great useful to detect priority areas to carry out revegetation plans to control erosion and floodings.</p>

scholarly journals RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection in Fincha Catchment, Abay River Basin, Ethiopia.

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Landsat 8 ◽  
Land Resources ◽  
Rusle Model ◽  
Factor C ◽  
Significant Factors

Abstract Background: The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. The annual dramatic increment of the depletion of very important soil nutrients exposes the residents of this catchment to high expenses of money to use artificial fertilizers to increase the yield. This paper was conducted in Fincha Catchment where the soil is highly vulnerable to erosion, however, where such studies are not undertaken. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss, where such studies are not undertaken, by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Results: Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations (2010-2019) and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. Conclusion: Finally, it was concluded that having information about the spatial variability of soil loss severity map generated in the RUSLE model has a paramount role to alert land resources managers and all stakeholders in controlling the effects via the implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.

SOIL EROSION MAPPING OF WATERSHED IN MIRZAPUR DISTRICT USING RUSLE MODEL IN GIS ENVIRONMENT

2016 ◽  
Vol 4 (3) ◽  
pp. 56 ◽  
Author(s):  
Deepanshu Agarwal ◽  
Kunal Tongaria ◽  
Siddhartha Pathak ◽  
Anurag Ohri ◽  
Medha Jha
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Uttar Pradesh ◽  
Rainfall Erosivity ◽  
Agricultural Activity ◽  
Preparation Methods ◽  
Rusle Model ◽  
Calculation Of Parameters ◽  
Erosion Prediction ◽  
Factor C

Soil erosion is one of the serious issues threatening the environment. It is a growing problem especially in areas of agricultural activity where soil erosion not only leads to de-creased agricultural productivity but also reduces water availability. This leads to drastic degradation of the agricultural lands. So there is a need to take up conservation and management measures which can be applied to check further soil erosion. Universal Soil Loss Equation (USLE) is the most popular empirically based model used globally for erosion prediction and control. Remote sensing and GIS techniques have become valuable tools for the digitization of the input data and genereation of maps. In the present study, RUSLE model has been adopted to estimate the soil erosion in the Khajuri watershed of Uttar Pradesh, India. This model involves calculation of parameters including runoff-rainfall erosivity factor (R), soil erodability Factor (K), topographic factor  (LS), cropping management factor (C), and support practice factor (P). Layer wise thematic maps of each of these factors were generated using GIS platform using various data sources and data preparation methods. The results of the study indicate that the annual average soil loss within the watershed is about  t/ha/yr (metric ton per hectare per year).

scholarly journals ESTIMATE THE ANNUAL SOIL LOSS IN KUMMATTIPATTI NADI WATERSHED USING RUSLE MODEL THROUGH GEOSPATIAL TECHNOLOGY

2020 ◽  
Vol 46 (2) ◽  
pp. 75-82
Author(s):  
Suraj Shaikh ◽  
Masilamani Palanisamy ◽  
Abdul Rahaman Sheik Mohideen
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Tamil Nadu ◽  
Management Strategies ◽  
Geographical Information ◽  
Rainfall Erosivity ◽  
Agricultural Activity ◽  
Erosion Risk ◽  
Factor C ◽  
Conservation And Management

Soil erosion and soil loss is one of the common problems threatening the environment. This degrading phenomenon declines the soil fertility and significantly affects the agricultural activity. As a consequence, the productivity of soil is affected unquestionably. In this reason, there is a basic need to take up conservation and management measures which can be applied to check further soil erosion. Even though, soil erosion is a mass process spread cross the watershed, it is not economically viable to implement conservation techniques to the entire watershed. However, a method is a pre-requisite to identify the most vulnerable areas and quantify the soil erosion. In this study, Revised Universal Soil Loss Equation (RUSLE) has been accepted to estimate soil erosion in the Kummattipatti Nadi watershed part of the Coimbatore district of Tamil Nadu, India. This model has several parameters including runoff-rainfall erosivity factor (R), soil erodability Factor (K), topographic factor (LS), cropping management factor (C), and support practice factor (P). All these layers are prepared through geographical information system (GIS) by using various data sources and data preparation methods. The results of the study shows that the annual average soil loss within the watershed is about 6 t/ha/yr (metric ton per hectare per year). Higher soil erosion is observed in the land use classes of gullied wasteland, open scrub forest and degraded plantation. The soil erosion risk is extremely higher on the steep slopes and adjoining foothills. The proper conservation and management strategies has to be implement in this watershed for the development.

Identification of Soil Erosion Prone Areas of Madhya Pradesh using USLE/ RUSLE

2021 ◽  
Vol 58 (02) ◽  
pp. 177-191
Author(s):  
Ashwini Suryawanshi ◽  
Anupam Kumar Nema ◽  
Rahul Kumar Jaiswal ◽  
Sukant Jain ◽  
Saswat Kumar Kar
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Madhya Pradesh ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Rusle Model ◽  
Percentage Area ◽  
Support Practice Factor ◽  
Factor C ◽  
Soil Loss Equation

Soil erosion is caused due to the dynamic action of erosive agents, mainly water, and is a major threat to the environment. Primary aim of the present study was to study the soil loss dynamics, and identify the environmental hotspots in Madhya Pradesh to aid decision-makers to plan and prioritize appropriate conservation measures. Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE) models were applied for erosion rate estimation by generating thematic maps of R (Rainfall erosivity factor), K (Soil erodibility factor), LS (Topographic factor), C (Cover and management factor), and P (Support practice factor) factors by using several input parameters in QGIS software. Subsequently, the different classes of soil erosion and percentage area under these classes were identified. The average annual soil erosion for the entire state as obtained from the USLE and RUSLE model were 5.80 t.ha-1.yr-1 and 6.64 t.ha-1.yr-1, respectively. The areas under severe risk were 1.09 % and 1.80 %, and very severe risk areas were 1.57 % and 1.83 % as estimated by USLE and RUSLE model, respectively. As compared to RUSLE model, USLE model underestimated rate of soil erosion for most river basins of the state as well as for the entire state

scholarly journals Estimation of Soil Erosion Using RUSLE Model and GIS: The Case of Satinskyi Catchment, Western Rwanda

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
R. V Byizigiro ◽  
G Rwanyiziri ◽  
M. Mugabowindekwe ◽  
C. Kagoyire ◽  
M. Biryabarema
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Surface ◽  
Soil Loss ◽  
Slope Angle ◽  
Rainfall Erosivity ◽  
Land Use Patterns ◽  
Rusle Model ◽  
Factor C ◽  
Loss Rates

The problem of soil erosion in Rwanda has been highlighted in previous studies. They have shown that half of the country’s farmland suffers moderate to severe erosion, with the highest soil loss rates found in the steeper and highly rainy northern and western highlands of the country. The purpose of this study was to estimate soil loss in Satinskyi, one of the catchments located in Ngororero District of Western Rwanda. This has been achieved using the Revised Universal Soil Loss Equation (RUSLE) model, which has been implemented in a Geographic Information Systems (GIS) environment. The methods consisted of preparing a set of input factor layers including Slope Length and Steepness (LS) factor, Rainfall Erosivity (R) factor, Soil Erodibility (K) factor, Support Practice (P) factor, and Land Surface Cover Management Factor (C) factor, for the model. The input factors have been integrated for soil loss estimates computation using RUSLE model, and this has enabled to quantitatively assess variations in the mean of the total estimated soil loss per annum in relation to topography and land-use patterns of the studied catchment. The findings showed that the average soil loss in Satinskyi catchment is estimated at 38.4 t/ha/year. It was however found that about 91% of the study area consists of areas with slope angle exceeding 15°, a situation which exposes the land to severe soil loss rates ranging between 31 t/ha/year and 41 t/ha/year. Apart from the steep slope, changes in land use also contribute to high rates of soil loss in the catchment. Keywords: Soil Erosion Estimation, GIS, RUSLE, Satinskyi Catchment, Rwanda

scholarly journals Estimating RUSLE’s rainfall factor in the part of Italy with a Mediterranean rainfall regime

2004 ◽  
Vol 8 (1) ◽  
pp. 103-107 ◽  
Author(s):  
N. Diodato
Keyword(s):  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Critical Examination ◽  
Rainfall Regime ◽  
Rainfall Erosivity ◽  
Rainfall Runoff ◽  
Erosion Index ◽  
Index Rainfall ◽  
Soil Loss Equation

Abstract. The computation of the erosion index (EI), which is basic to the determination of the rainfall-runoff erosivity factor R of the Revised Universal Soil Loss Equation (RUSLE), is tedious and time-consuming and requires a continuous record of rainfall intensity. In this study, a power equation(r2 = 0.867) involving annual erosion index (EI30-annual) in the Mediterranean part of Italy is obtained. Data from 12 raingauge stations are used to derive and then test a regional relationship for estimating the erosion index from only three rainfall parameters. Erosivity rainfall data derived from 5 additional stations are used for validation and critical examination. The empirical procedures give results which compare satisfactorily with relationships calibrated elsewhere. Keywords: erosion index, rainfall, erosivity, Revised Universal Soil Loss Equation

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 Rainfall erosivity in subtropical catchments and implications for erosion and particle-bound contaminant transfer: a case-study of the Fukushima region

2015 ◽  
Vol 12 (7) ◽  
pp. 7225-7266 ◽  
Author(s):  
J. P. Laceby ◽  
C. Chartin ◽  
O. Evrard ◽  
Y. Onda ◽  
L. Garcia-Sanchez ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Soil Surface ◽  
Annual Rainfall ◽  
Spatial And Temporal Variation ◽  
Rainfall Regime ◽  
Rainfall Erosivity ◽  
Fine Soil ◽  
Run Off ◽  
The Mean ◽  
The Impact

Abstract. The Fukushima Dai-ichi nuclear power plant (FDNPP) accident in March 2011 resulted in a significant fallout of radiocesium over the Fukushima region. After reaching the soil surface, radiocesium is almost irreversibly bound to fine soil particles. Thereafter, rainfall and snow melt run-off events transfer particle-bound radiocesium downstream. Erosion models, such as the Universal Soil Loss Equation (USLE), depict a proportional relationship between rainfall and soil erosion. As radiocesium is tightly bound to fine soil and sediment particles, characterizing the rainfall regime of the fallout-impacted region is fundamental to modelling and predicting radiocesium migration. Accordingly, monthly and annual rainfall data from ~ 60 meteorological stations within a 100 km radius of the FDNPP were analysed. Monthly rainfall erosivity maps were developed for the Fukushima coastal catchments illustrating the spatial heterogeneity of rainfall erosivity in the region. The mean average rainfall in the Fukushima region was 1387 mm yr−1 (σ 230) with the mean rainfall erosivity being 2785 MJ mm ha−1 yr−1 (σ 1359). The results indicate that the majority of rainfall (60 %) and rainfall erosivity (86 %) occurs between June and October. During the year, rainfall erosivity evolves positively from northwest to southeast in the eastern part of the prefecture, whereas a positive gradient from north to south occurs in July and August, the most erosive months of the year. During the typhoon season, the coastal plain and eastern mountainous areas of the Fukushima prefecture, including a large part of the contamination plume, are most impacted by erosive events. Understanding these rainfall patterns, particularly their spatial and temporal variation, is fundamental to managing soil and particle-bound radiocesium transfers in the Fukushima region. Moreover, understanding the impact of typhoons is important for managing sediment transfers in subtropical regions impacted by cyclonic activity.

scholarly journals Assessment of Soil Erosion Hazard in Prambanan District Using RUSLE (Revised Universal Soil Loss Equation)

2021 ◽  
Vol 884 (1) ◽  
pp. 012010
Author(s):  
S. A Mulya ◽  
N. Khotimah
Keyword(s):  
Soil Loss ◽  
Hazard Index ◽  
Universal Soil Loss Equation ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Erosion Hazard ◽  
Erosion Processes ◽  
Factor C ◽  
Soil Loss Equation

Abstract Prambanan District which located in Daerah Istimewa Yogyakarta Province has the potential for land degradation due to erosion processes. With the characteristics of annual rainfall more than 2000 mm / year, topography with a slope of more than 20% in upland areas, as well as the conversion of upland to dryland agriculture are factors that can trigger the erosion process more quickly. If the rate of erosion speed exceeds the ability of the soil to regenerate the soil body, its productivity will be disrupted and accelerate the formation of critical soil. Therefore, it is necessary to know the estimated rate of erosion, tolerable distribution of erosion, and the potential danger of erosion that occurs. The purpose of this study was to (1) predict the rate of erosion, (2) calculate the permissible erosion value, (3) identify the rate & index of erosion hazard. Data were collected using field surveys and soil sampling using stratified random sampling techniques with land units as the unit of analysis. The value of erosion was predicted using the Revised Universal Soil Loss Equation (RUSLE) method. The RUSLE method is described by the following equation, A=R*K*L*S*C*P, where; A as estimated averages annual loss of soil, R is the rainfall erosivity factor, K is the soil erodibility factor, LS is the slope length factor, C is the cover management factor, & P is the conservation practice factor. The results showed that the erosion value ranged from 0.39 - 268.55 tons/ha/year. Permissible erosion ranges from 8.4 – 15 tons/ha/year for Latosol and 27.4 ton/ha/year for Regosol. The Rate of Erosion Hazard is dominated by moderate erosion, covering an area of 1330.7 ha or 31.8% of the total area. The Erosion Hazard Index is dominated by the low class (<1.0) which is covered over 2703.1 ha or 64.61% of the total area.

scholarly journals Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

2014 ◽  
Vol 49 (3) ◽  
pp. 215-224 ◽  
Author(s):  
Daniel Fonseca de Carvalho ◽  
Valdemir Lucio Durigon ◽  
Mauro Antonio Homem Antunes ◽  
Wilk Sampaio de Almeida ◽  
Paulo Tarso Sanches de Oliveira
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Central Area ◽  
Remote Sensing Data ◽  
High Water ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
R Factor ◽  
Factor C ◽  
Soil Losses

The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0.13 Mg ha-1 on May 24, 2009 (dry season) to 62.0 Mg ha-1 on March 11, 2007 (rainy season). In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha-1 , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha-1 , but the central area, with a loss of nearly 300.0 Mg ha-1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.

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