scholarly journals Differentiation and regionalization of rainfall erosivity factor values in the Czech Republic

2012 ◽  
Vol 7 (No. 1) ◽  
pp. 1-9 ◽  
Author(s):  
M. Janeček ◽  
V. Květoň ◽  
E. Kubátová ◽  
D. Kobzová
Keyword(s):  
Czech Republic ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
The Czech Republic ◽  
Average Value ◽  
R Factor ◽  
Soil Loss Equation

The rain erosivity R-factor is one of the main parameters in the Universal Soil Loss Equation (USLE). This paper describes the procedure used to update, differentiate and regionalize the rainfall erosivity R-factor. For the Czech Republic it is recommended to use the average value R = 40.

scholarly journals Values of rainfall erosivity factor for the Czech Republic

2013 ◽  
Vol 61 (2) ◽  
pp. 97-102 ◽  
Author(s):  
Miloslav Janeček ◽  
Vít Květoň ◽  
Eliška Kubátová ◽  
Dominika Kobzová ◽  
Michaela Vošmerová ◽  
...  
Keyword(s):  
Czech Republic ◽  
Water Erosion ◽  
Rainfall Erosivity ◽  
The Czech Republic ◽  
Average Value ◽  
R Factor ◽  
The Usa ◽  
Time Division ◽  
R Factors

Abstract The processing of ombrographic data from 29 meteorological stations of the Czech Hydrometeorological Institute (CHMI), according to the terms of the Universal Soil Loss Equation for calculating long term loss of soil through water erosion, erosion hazard rains and their occurrence have been selected, with their relative amount and erosiveness - R-Factors determined for each month and years. By comparing the value of the time division of the R-Factor in the area of the Czech Republic and in selected areas of the USA it has been demonstrated that this division may be applied in the conditions of the Czech Republic. For the Czech Republic it is recommended to use the average value R = 40 based on the original evaluation.

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 Adjusting the CPmax factor in the Universal Soil Loss Equation (USLE): areas in need of soil erosion protection in the Czech Republic

2016 ◽  
Vol 12 (sup1) ◽  
pp. 58-62 ◽  
Author(s):  
Ivan Novotný ◽  
Daniel Žížala ◽  
Jiří Kapička ◽  
Hana Beitlerová ◽  
Martin Mistr ◽  
...  
Keyword(s):  
Czech Republic ◽  
Soil Erosion ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
The Czech Republic ◽  
Erosion Protection ◽  
Soil Loss Equation

scholarly journals A Review on Rainfall Erosivity (R factor) in Universal Soil Loss Equation

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Mohamad Nazuhan ◽  
Zullyadini A. Rahaman ◽  
Zainuddin Othman
Keyword(s):  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
R Factor ◽  
Soil Loss Equation

scholarly journals Rainfall erosivity estimation using gridded daily precipitation datasets

2020 ◽  
Author(s):  
Maoqing Wang ◽  
Shuiqing Yin ◽  
Tianyu Yue ◽  
Bofu Yu ◽  
Wenting Wang
Keyword(s):  
Soil Loss ◽  
Daily Precipitation ◽  
Universal Soil Loss Equation ◽  
Lower Probability ◽  
Rainfall Erosivity ◽  
Correction Factors ◽  
Random Errors ◽  
Gridded Data ◽  
R Factor ◽  
Soil Loss Equation

Abstract. Rainfall erosivity is one of the most important factors incorporated into the empirical soil erosion models USLE (Universal Soil Loss Equation) and RUSLE (Revised Universal Soil Loss Equation). Gridded precipitation datasets have been widely used in the estimation of rainfall erosivity, whereas biases due to scale differences between gridded data and gauge data have been ignored. Based on daily precipitation observations from over 2000 stations in China, as well as four widely used gauge-based gridded daily precipitation datasets, CPC, GPCC, CN05.1 and NMIC, this study compared the probability density functions (PDFs) of the gridded and gauge datasets using the skill score method, quantified the bias of rainfall erosivity (including the R-factor and 1-in-10-year event rainfall erosivity) estimated using the gridded daily precipitation datasets from that estimated using the gauge daily precipitation dataset based on the area reduction factor (ARF) method, and established correction factors for rainfall erosivity maps generated from gridded datasets. The results showed that the gridded daily data reduced the frequency of no-rain days and the intensity of heavy precipitation. In the eastern part of China, the grid-estimated R-factor values were underestimated by 15–40 % compared with the gauge-estimated values, and the grid-estimated 1-in-10-year event rainfall erosivity values were underestimated by 25–50 %, whereas in the western part of China, noticeable random errors were introduced. The lower probability and intensity of the daily precipitation larger than the 90th percentile in the gridded datasets were mainly responsible for the underestimation. CN05.1 was the most-recommended among the four datasets, as it had the lowest mean relative error (MRE), and the accuracy was higher for the eastern part of China than for the western part of China. The MREs were 16.1 % and 25.1 % for the R-factor after applying correction factors of 1.708 and 1.010, respectively, for the eastern and western part of China. The 1-in-10-year event erosivity had larger correction factors and MREs than did the R-factor, with the MREs being 22.1 % and 27.2 % after applying correction factors of 1.959 and 1.880, respectively, for the eastern and western part of China. This study pointed out that in the applications of gridded precipitation datasets, the empirical models established based on gauge precipitation data should not be used directly for the gridded data, or a bias correction process needed to be considered for the model outputs.

Rainfall Erosivity and Erodibility of Inceptisols in the Southwest Colombian Andes

1996 ◽  
Vol 32 (1) ◽  
pp. 91-101 ◽  
Author(s):  
M. Ruppenthal ◽  
D. E. Leihner ◽  
T. H. Hilger ◽  
J. A. Castillo F.
Keyword(s):  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Clay Soils ◽  
Rainfall Erosivity ◽  
Soil Erodibility ◽  
Study Region ◽  
The Third ◽  
R Factor ◽  
Colombian Andes ◽  
Soil Loss Equation

SUMMARYThe rainfall erosivity (R) and soil erodibility (K) factors of the Universal Soil Loss Equation (USLE) were determined on two sites in the Colombian Cauca Department over a five year period when rainfall was mostly lower than average. The results showed that the high erosion potential of the soils can be attributed more to high rain erosivity than soil erodibility. The R factor explained between 59 and 81% of the variation in soil loss recorded on continuously clean-tilled fallow plots. The erodibility of Inceptisols in the study region is classified as low. Values for soil erodibility (K) ranged from 0.012 to 0.015 (measured in SI units) in the fifth year of permanent bare fallowing. K factors were higher in the rainy than in the dry season. Soils, previously under grass vegetation, were very resistant to erosion in the first two years of bare fallowing. In the third year erodibility increased sharply and continued to increase steadily until the sixth year. K factors predicted by the USLE nomograph underestimated the empirically-determined erodibility of these highly aggregated clay soils.

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.

scholarly journals The rainfall erosivity factor in the Czech Republic and its uncertainty

2016 ◽  
Vol 20 (10) ◽  
pp. 4307-4322 ◽  
Author(s):  
Martin Hanel ◽  
Petr Máca ◽  
Petr Bašta ◽  
Radek Vlnas ◽  
Pavel Pech
Keyword(s):  
Czech Republic ◽  
Spatial Interpolation ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
The Czech Republic ◽  
Interpolation Methods ◽  
R Factor ◽  
Record Length ◽  
Spatial Coverage ◽  
Spatial Interpolation Methods

Abstract. In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10 min data for 96 stations and corresponding R factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance weighting, standard, ordinary, and regression kriging with parameters estimated by the method of moments and restricted maximum likelihood, and a generalized least-squares (GLS) model. For the regression-based methods, various statistics of monthly precipitation as well as geographical indices are considered as covariates. In addition to the uncertainty originating from spatial interpolation, the uncertainty due to estimation of the rainfall kinetic energy (needed for calculation of the R factor) as well as the effect of record length and spatial coverage are also addressed. Finally, the contribution of each source of uncertainty is quantified. The average R factor for the area of the Czech Republic is 640 MJ ha−1 mm h−1, with values for the individual stations ranging between 320 and 1520 MJ ha−1 mm h−1. Among various spatial interpolation methods, the GLS model relating the R factor to the altitude, longitude, mean precipitation, and mean fraction of precipitation above the 95th percentile of monthly precipitation performed best. Application of the GLS model also reduced the uncertainty due to the record length, which is substantial when the R factor is estimated for individual sites. Our results revealed that reasonable estimates of the R factor can be obtained even from relatively short records (15–20 years), provided sufficient spatial coverage and covariates are available.

scholarly journals Evaluation of discrepancies in spatial distribution of rainfall erosivity in the Czech Republic caused by different approaches using GIS and geostatistical tools

2017 ◽  
Vol 12 (No. 2) ◽  
pp. 117-127 ◽  
Author(s):  
J. Brychta ◽  
M. Janeček
Keyword(s):  
Spatial Distribution ◽  
Czech Republic ◽  
Temporal Resolution ◽  
Agricultural Land ◽  
Interpolation Method ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
Yearly Variation ◽  
The Czech Republic ◽  
R Factor

The study presents all approaches of rainfall erosivity factor (R) computation and estimation used in the Czech Republic (CR). A lot of distortions stem from the difference in erosive rainfall criteria, time period, tipping rain gauges errors, low temporal resolution of rainfall data, the type of interpolation method, and inappropriate covariates. Differences in resulting R values and their spatial distribution caused by the described approaches were analyzed using the geostatistical method of Empirical Bayesian Kriging and the tools of the geographic information system (GIS). Similarity with the highest temporal resolution approach using 1-min rainfall data was analyzed. Different types of covariates were tested for incorporation to the cokriging method. Only longitude exhibits high correlation with R and can be recommended for the CR conditions. By incorporating covariates such as elevation, with no or weak correlation with R, the results can be distorted even by 81%. Because of significant yearly variation of R factor values and not clearly confirmed methodology of R values calculation and their estimation at unmeasured places we recommend the R factor for agricultural land in the Czech Republic R = 40 MJ/ha·cm/h +/– 10% depends on geographic location.

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