scholarly journals Development Land Erosion Model Using Model Builder GIS (Case Study: Citepus Watershed)

2018 ◽  
Vol 147 ◽  
pp. 03003
Author(s):  
Dina PA Hidayat ◽  
Sih Andajani
Keyword(s):  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Model Builder ◽  
Watershed Analysis ◽  
Runoff Discharge ◽  
The Impact ◽  
Impervious Areas ◽  
Soil Loss Equation

Land erosion is the impact of increasing runoff discharge and land use conversion to impervious areas. Land erosion usually calculated by formula called USLE (Universal Soil Loss Equation) then modified as MUSLE (Modified Universal Soil Loss Equation). These formula calculate average annual soil loss in tons/areas depends on rainfall erosivity (R), soil erodibility factor (K), topographic factor (LS), cropping and conservation factor (CP). GIS (Geographic Information System) is a system designed to capture, manipulate, and analyze spatial/geographic data. There are some tools related water resources analysis in ArcGIS such as: watershed analysis and also have a tools for user to create their own model called model builder. This research was aim to create a model to calculate land erosion using MUSLE formula by model builder in ArcGIS. The output for this research is the model which can be used to calculate annual soil loss in watershed area based on GIS systems. For the model trial and case study, we use Citepus watershed located on Bandung West Java, that has 5 river branches: Cibogo, Cikakak, Cilimus, Cipedes and Ciroyom. As the result of the model, the value of average annual soil loss in Citepus watershed can be calculated automatically by developed model.

scholarly journals Estimativa da erosividade da chuva por diferentes métodos e seu impacto na equação universal de perdas de solo, no semiárido pernambucano (Estimation of erosivity by different methods and their impact on the universal soil loss equation in Pernambuco semi-arid)

2019 ◽  
Vol 12 (3) ◽  
pp. 859
Author(s):  
Joaquim Pedro de Santana Xavier ◽  
Alexandre Hugo Cezar Barros ◽  
Daniel Chaves Webber ◽  
Luciano José de Oliveira Accioly ◽  
Flávio Adriano Marques ◽  
...  
Keyword(s):  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
R Factor ◽  
Semi Arid Region ◽  
Erosion Loss ◽  
R Factors ◽  
Semi Arid ◽  
Soil Loss Equation

Dentre os diversos métodos indiretos para estimar as perdas de solo por erosão, a Equação Universal de Perdas de Solo (EUPS) é a mais utilizada devido a sua robustez e por ser constituída de uma simples estrutura fatorial, que integra fatores naturais e antrópicos atuantes na perda de solos. A erosão é um dos fenômenos mais danosos ao solo e às atividades humanas e por isso seu estudo é importante. Para o cálculo das perdas de solo por meio da EUPS, a avaliação da erosividade das chuvas (fator R) é essencial, pois estima o fenômeno produzido pelas chuvas. O objetivo deste trabalho foi avaliar três metodologias disponíveis de obtenção da erosividade das chuvas para a região do semiárido pernambucano, avaliando sua influência nos resultados da EUPS. Os três modelos selecionados para estimar o Fator R foram desenvolvidos por Wischmeier e Smith (mais conhecido e utilizado), por Silva que estimou valores para diversas regiões do País e por Cantalice e outros que trabalharam especificamente para cada região climática do estado de Pernambuco. Os resultados indicam que as metodologias de Wischmeier e Smith e Silva obtiveram resultados de erosividade da chuva semelhantes, tendo Silva alcançado valores maiores. Cantalice e outros obtiveram os resultados mais baixos. Os resultados da EUPS indicam que, quantitativamente, os diferentes fatores R geram grande diferença nas perdas de solo, porém, qualitativamente chegam a resultados semelhantes na classificação de áreas de maior erosão, de acordo com a FAO. Logo, as três metodologias são viáveis na identificação de áreas prioritárias para a mitigação da erosão.   A B S T R A C TAmong several indirect methods to estimate soil erosion loss, the Universal Soil Loss Equation (EUPS) is the most used due to its robustness and because it is constituted of a simple factorial structure that integrates natural and anthropic factors which act in the loss of soils. Erosion is one of the most damaging phenomena to the soil and the human activities, evidencing the importance of studying it. The evaluation of rainfall erosivity (R factor) is essential for the calculation of soil loss through the EUPS, since it is possible to estimate how significant rainfall is to the occurrence of this phenomenon. The objective of this work was to evaluate three methodologies to obtain the rainfall erosivity available for the semi - arid region of Pernambuco, evaluating its influence on the results of the EUPS. The three models used to estimate the R-factor were developed by Wischmeier and Smith, the best known and used model, Silva who estimated values for several regions of the country and Cantalice and others who worked specifically for each climatic region of the state of Pernambuco. As a result, very similar results of rainfall erosivity were obtained between Wischmeier and Smith´s and Silva´s methodology, with Silva reaching higher values of energy amplitude, while Cantalice and others obtained the lowest results. The results of EUPS indicate that, quantitatively, the different R factors generate a large difference in soil loss, but qualitatively they reach similar results in the classification of areas where erosion are greater, according to the FAO. Therefore, the three methodologies are feasible in the identification of priority areas for erosion mitigation.Keywords: soil, rainfall erosivity, USLE, GIS

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.

Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall–runoff erosivity R factor

2002 ◽  
Vol 153 (1-2) ◽  
pp. 143-155 ◽  
Author(s):  
Guangxing Wang ◽  
George Gertner ◽  
Vivek Singh ◽  
Svetlana Shinkareva ◽  
Pablo Parysow ◽  
...  
Keyword(s):  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Rainfall Runoff ◽  
Temporal Prediction ◽  
R Factor ◽  
Soil Loss Equation

scholarly journals Socioeconomic modifications of the universal soil loss equation

Solid Earth ◽  
2015 ◽  
Vol 6 (3) ◽  
pp. 1025-1035 ◽  
Author(s):  
A. Erol ◽  
Ö. Koşkan ◽  
M. A. Başaran
Keyword(s):  
Socioeconomic Factors ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Physical Factors ◽  
Simulation Methods ◽  
Human Influence ◽  
Social Scientists ◽  
Socioeconomic And Demographic Factors ◽  
The Impact ◽  
Soil Loss Equation

Abstract. While social scientists have long focused on socioeconomic and demographic factors, physical modelers typically study soil loss using physical factors. In the current environment, it is becoming increasingly important to consider both approaches simultaneously for the conservation of soil and water, and the improvement of land use conditions. This study uses physical and socioeconomic factors to find a coefficient that evaluates the combination of these factors. It aims to determine the effect of socioeconomic factors on soil loss and, in turn, to modify the universal soil loss equation (USLE). The methodology employed in this study specifies that soil loss can be calculated and predicted by comparing the degree of soil loss in watersheds, with and without human influence, given the same overall conditions. A coefficient for socioeconomic factors, therefore, has been determined based on adjoining watersheds (WS I and II), employing simulation methods. Combinations of C and P factors were used in the USLE to find the impact of their contributions to soil loss. The results revealed that these combinations provided good estimation of soil loss amounts for the second watershed, i.e., WS II, from the adjoining watersheds studied in this work. This study shows that a coefficient of 0.008 modified the USLE to reflect the socioeconomic factors, such as settlement, influencing the amount of soil loss in the studied watersheds.

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

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 A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

Soil Systems ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 62 ◽  
Author(s):  
Kinnell
Keyword(s):  
Soil Loss ◽  
Agricultural Land ◽  
Universal Soil Loss Equation ◽  
Physical Situation ◽  
Rill Erosion ◽  
The Impact ◽  
Soil Losses ◽  
Soil Loss Equation ◽  
Rainfall Erosion

Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.

Sign in / Sign up

Export Citation Format

Share Document