2 edition of **Rainfall erosivity and its use for soil loss estimation** found in the catalog.

Rainfall erosivity and its use for soil loss estimation

Piet Van der Poel

- 12 Want to read
- 6 Currently reading

Published
**1980**
by Soil Conservation Section, Division of Land Utilization, Dept. of Agricultural Field Services, Ministry of Agriculture in Gaborone, Republic of Botswana
.

Written in English

- Botswana.,
- Botswana
- Soil erosion prediction -- Botswana.,
- Soil erosion -- Climatic factors -- Botswana -- Mathematical models.,
- Rain and rainfall -- Botswana -- Mathematical models.

**Edition Notes**

Statement | by Piet Van der Poel. |

Classifications | |
---|---|

LC Classifications | S625.B55 V36 1980 |

The Physical Object | |

Pagination | 17, [1] leaves : |

Number of Pages | 17 |

ID Numbers | |

Open Library | OL1075408M |

LC Control Number | 93980908 |

Estimation of Erosivity from Rainfall Data. The rainfall erosivity is related to the kinetic energy of rainfall. The following two methods are widely used for computing the erosivity of rainfall. 1. EI 30 Index method and. 2. KE > 25 Index method. 1. EI 30 Index Method. This method was introduced by Wischmeier (). Introduction. Rainfall erosivity indicates the potential of a storm to erode soil. A single index of rainfall erosivity that can measure the composite effect of various rainstorm characteristics on soil erosion is highly desirable for predicting soil loss [1, 2].It is well known that soil losses are frequently due to a few intense rainfall events [1, 3].Cited by: 9.

An integrated method has been adopted to estimate soil loss in a plateau and plateau fringe river basin where soil erosion is significant. The integration of Revised Universal Soil Loss Equation model and geographical Information technology has been used for soil loss estimation. In GIS platform, the overlay of rainfall-runoff erosivity factor, soil erodibility factor, slope length factor Cited by: • the ratio of soil loss from land under specified crop or mulch conditions to the corresponding soil loss from tilled, bare soil • the C factor reduces the soil loss estimate according to the effectiveness of vegetation and mulch at preventing detachment and transport of soil particles (erosion control)File Size: KB.

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 Cited by: 7. This project used the existing, complete-year rainfall record for 27 sites in the state of Pennsylvania to compare the USDA isoerodent maps to the annual rainfall erosivity, R, values calculated using the USEPA equations for the National Resource Conservation Service Type II rainfall.

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Modern definitions of rainfall erosivity began with the development of the Universal Soil Loss Equation (USLE), where rainfall characteristics were statistically related to soil loss from. The min rainfall erosivity index (EI 30) is commonly used in the Universal Soil Loss Equation for predicting soil loss from agricultural hillslopes.

EI 30 is calculated from the total kinetic energy and the maximum min rainfall intensity of a storm. Normally, EI 30 values are. Version 2 of the Revised Universal Soil Loss Equation (RUSLE2) estimates soil Rainfall erosivity and its use for soil loss estimation book from rill and interrill (sheet and rill) erosion caused by rainfall and its associated overland flow.

RUSLE2 uses six factors for climatic erosivity, soil erodibility, slope length, slope steepness, cover-management, and support practices to compute soil Size: KB. Rainfall erosivity is the capability of rainfall to cause soil loss from hillslopes by water. Modern definitions of rainfall erosivity began with the development of the Universal Soil Loss Equation (USLE), where rainfall characteristics were statistically related to soil loss from thousands of plot-years.

This research established an empirical methodology to estimate potential soil erosion rate based on revised universal soil loss equation (RUSLE) and E30 model. The study was conducted on a highly precipitated, rugged, tropical forested with steep slope watershed during to The fourth (4th) largest river of Papua New Guinea, and its catchment area was considered for this by: 5.

soil loss in tons/acre/year, ‘R’ is the rainfall-runoff erosivity factor, ‘K’ is the soil erodibility factor, ‘LS’ is the slope length and degree, ‘C’ is the land-cover management factor, and ‘P’ is.

Considerable seasonal and inter-annual changes exist in rainfall amount and intensity in New South Wales (NSW), Australia. These changes are expected to have significant effect on rainfall erosivity and soil erosion by water, but the magnitude of the impact is not well quantified because of the non-linear and dynamic nature of the relationship between rainfall amount and rainfall erosivity Cited by: PCRaster GIS soil loss risk maps have been developed for Densu basin using models of Universal Soil Equation.

(USLE) and Revised Universal Soil Equation (RUSLE). Soil loss factors such as rainfall erosivity, soil erodibilty, slope and slope length were also mapped for the by: 6. Furthermore, the statistical tool has been used to estimate the amount of soil loss potential and to classify the level of the soil loss risk in the study watershed.

For the matter of management prioritization, soil erosion map of the watershed was classified in to six classes as shown in Table 6 and Fig. 9 by: This increases the capability of the runoff to detach and transport the soil material. For slopes steeper than 4% the factor L is generally computed by the above formula (Eq.

The exponent is for slopes of less than 3% and for 4% slopes. Slope steepness affects both runoff and soil loss.

The universal soil loss equation with its revisions represents the established model and the means for estimating annual long-term average soil loss. An important factor of this equation is the rainfall erosivity, R, which is closely connected to the energy carried by rainfall.

Rainfall data are essential for the estimation of R. In particular, data from pluviographs are more suitable for that : Konstantinos Vantas, Epaminondas Sidiropoulos, Chris Evangelides.

The Revised Universal Soil Loss Equation (RUSLE) model has been adopted to estimate the annual soil loss and the equation (Equation 1) is as: A RXKXLXSXCXP= (1) Where, A is the average annual soil loss per unit area expressed in tones/ha/year (t ha-1 year -1); R is the rainfall– runoff erosivity factor (MJ mm ha-1 h-1); K is soil erodibility.

Rainfall erosivity is the capability of rainfall to cause soil loss from hillslopes by water. Modern definitions of rainfall erosivity began with the development of the Universal Soil Loss Equation (USLE), where rainfall characteristics were statistically related to soil loss from thousands of plot-years of natural rainfall and runoff by: Pluviograph data at 6-min intervals for 41 sites in the tropics of Australia were used to compute the rainfall and runoff factor (R-factor) for the Revised Universal Soil Loss Equation (RUSLE), and a daily rainfall erosivity model was validated for these tropical sites.

Mean annual rainfall varies from about mm at Jervois () to about at Tully (). The corresponding R-factor Cited by: same rainfall data will be mainly associated with factors other than rainfall erosivity. Estimation of rainfall kinetic energy (E) is based on the annual rainfall data.

The kinetic energy has been expressed in terms of rainfall intensity equation developed by Elwell and Stocking () as quoted by Department of Agricultural and Technical. mathematical model, Universal Soil Loss Equation USLE and its revised form RUSLE.

Erosivity is the potential capacity of the raindrops to cause detachment of the soil particles from its location and it depends on rainfall intensity its recurrence. Hence it is important to accurately estimate erosivity for quantitavite estimation of soil Size: 1MB.

alternative procedures to ﬁll this gap for estimation rainfall erosivity index are suggested in RUSLE hand-book (Renard et al., ). Attempts were therefore made to estimate the erosivity index by using a few rainfall data such as storm amount and duration (Istok et al., ; Bagarello and D’Asaro, ; Mannaerts and Gabriels, ).

Why and when you need to calculate Rainfall Erosivity factor. Soil erosion, which is the gradual removal of the top layer of the soil, is a common land degradation problem. Researchers have however noted that soil erosion has increased in the 20 th century.

Soil erosion is a major issue, causing the loss of topsoil and fertility in agricultural land in mountainous terrain. Estimation of soil erosion in Nepal is essential because of its agriculture-dependent economy (contributing 36% to national GDP) and for preparing erosion control plans.

The present study, for the first time, attempts to estimate the soil loss of Nepal through the application Cited by: 1. A future variation of precipitation characteristics, due to climate change, will affect the ability of rainfall to precipitate soil loss.

In this paper, the monthly and annual values of rainfall erosivity (R) in Greece are calculated, for the historical period –, using precipitation records that suffer from a significant volume of missing : Konstantinos Vantas, Epaminondas Sidiropoulos, Athanasios Loukas.

EPA has updated its Rainfall Erosivity Factor Calculator to correct known problems and to use updated data from the Natural Resources Conservation Service’s (NRCS) Revised Universal Soil Loss Equation, Version 2 (RUSLE2) database.The erosivity factor in the universal soil loss equation (USLE) provides an effective means of evaluating the erosivity power of rainfall.

The present study proposes three regression models for estimating the erosivity factor based on daily, monthly, and annual precipitation data of rainfall Author: LeeMing-Hsi, LinHuan-Hsuan.Metadata.

Title: Soil Loss by Water Erosion in Europe Description: At a resolution of m, this is the most detailed assessment yet of soil erosion by water for the study applied a modified version of the Revised Universal Soil Loss Equation (RUSLE) model, RUSLEwhich delivers improved estimates based on higher resolution ( m compared to 1 km) peer-reviewed inputs of rainfall.