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.

Rainfall erosivity and its estimation for Australia's tropics

Soil Research ◽  
1998 ◽  
Vol 36 (1) ◽  
pp. 143 ◽  
Author(s):  
B. Yu
Keyword(s):  
Model Performance ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Wet Season ◽  
R Factor ◽  
Different Seasons ◽  
Monthly Distribution ◽  
The Tropics ◽  
Parameter Values

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 300 mm at Jervois (015602) to about 4000 at Tully (032042). The corresponding R-factor ranges from 1080 to 33500 MJ·mm/(ha ·h·year). For these tropical sites, both rainfall and rainfall erosivity are highly seasonal with a single peak in February mostly. Summer months (November–April) typically contribute about 80% of annual rainfall and about 90% of the R-factor. The daily erosivity model performed better for the tropical sites with a marked wet season in summer in comparison to model performance in temperate regions of Australia where peak rainfall and peak rainfall erosivity may occur in different seasons. A set of regional parameters depending on seasonal rainfall was developed so that the R-factor and its seasonal distribution can be estimated for sites without pluviograph data. The prediction error using the regional parameter values is about 20% for the R-factor and 1% for its monthly distribution for these tropical sites.

scholarly journals Rainfall erosivity mapping over mainland China based on high density hourly rainfall records

2021 ◽  
Author(s):  
Tianyu Yue ◽  
Shuiqing Yin ◽  
Yun Xie ◽  
Bofu Yu ◽  
Baoyuan Liu
Keyword(s):  
Soil Erosion ◽  
Average Rate ◽  
Mainland China ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
R Factor ◽  
Hourly Rainfall ◽  
Hourly Data ◽  
Factor Map

Abstract. Rainfall erosivity represents the effect of rainfall and runoff on the average rate of soil erosion. Maps of rainfall erosivity are indispensable for soil erosion assessment using the Universal Soil Loss Equation (USLE) and its successors. To improve current erosivity maps based on daily rainfall data for mainland China, hourly rainfall data from 2381 stations for the period 1951–2018 were collected to generate the R factor and the 1-in-10-year EI30 maps (available at https://dx.doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001; Yue et al., 2020). Rainfall data at 1-min intervals from 62 stations (18 stations) were collected to calculate rainfall erosivities as true values to evaluate the improvement of the new R factor map (1-in-10-year EI30 map) from the current maps. Both the R factor and 1-in-10-year EI30 decreased from the southeastern to the northwestern, ranging from 0 to 25300 MJ mm ha−1 h−1 a−1 for the R factor and 0 to 11246 MJ mm ha−1 h−1 for the 1-in-10-year EI30. New maps indicated current maps existed an underestimation for most of the southeastern areas and an overestimation for most of the middle and western areas. Comparing with the current maps, the R factor map generated in this study improved the accuracy from 19.4 % to 15.9 % in the mid-western and eastern regions, from 45.2 % to 21.6 % in the western region, and the 1-in-10-year EI30 map in the mid-western and eastern regions improved the accuracy from 21.7 % to 13.0 %. The improvement of the new R factor map can be mainly contributed to the increase of data resolution from daily data to hourly data, whereas that of new 1-in-10-year EI30 map to the increase of the number of stations from 744 to 2381. The effect of increasing the number of stations to improve the interpolation seems to be not very obvious when the station density was denser than about 10 · 103 km2 1 station.

scholarly journals Spatial Distribution and Temporal Trends of Rainfall Erosivity in Three Gorges Reservoir Area of China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Huiying Liu ◽  
Guanhua Zhang ◽  
Pingcang Zhang ◽  
Shengnan Zhu
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
Three Gorges Reservoir ◽  
Temporal Trends ◽  
Daily Rainfall ◽  
Kendall Test ◽  
Annual Rainfall ◽  
Three Gorges Reservoir Area ◽  
Rainfall Erosivity ◽  
Three Gorges

Rainfall erosivity is a key factor to predict soil erosion rate in universal soil loss equation (USLE) and revised USLE (RUSLE). Understanding rainfall erosivity characteristics, especially its spatial distribution and temporal trends, is essential for soil erosion risk assessment and soil conservation planning. In this study, the spatial-temporal variation of rainfall erosivity in the Three Gorges Reservoir Area (TGRA) of China during 1960–2010, at annual and seasonal scales, was explored based on daily rainfall data from 40 stations (26 meteorological stations and 14 hydrologic stations). The Mann–Kendall test and Co–kriging interpolation method were applied to detect the temporal trends and spatial patterns. The results showed that TGRA’s annual rainfall erosivity increased from west, south, and east to the north-central, ranging from 3647.0 to 10884.8 MJ·mm·ha−1·h−1 with an average value of 6108.1 MJ·mm·ha−1·h−1. The spatial distribution of summer and autumn rainfall erosivity was similar to the pattern of annual rainfall erosivity. Summer is the most erosive season among four seasons, accounting for 53% of the total annual rainfall erosivity, and winter is the least erosive season. July is the most erosive month with an average of 1327.3 MJ·mm·ha−1·h−1, and January is the least erosive month. Mean rainfall erosivity was 5969.2 MJ·mm·ha−1·h−1 during 1960–2010, with the lowest value of 3361.0 MJ·mm·ha−1·h−1 in 1966 and highest value of 8896.0 MJ·mm·ha−1·h−1 in 1982. Mann–Kendall test showed that the annual rainfall erosivity did not change significantly across TGRA. Seasonal rainfall erosivity showed a significant decrease in autumn and insignificant decrease in summer and winter. Monthly rainfall erosivity in TGRA showed insignificant increases from Jun to Jul and then underwent decreases from Aug to Nov. and from Dec to Feb and it rose again in Feb reaching a 0.01 level significance. The daily rainfall data of supplemental stations is very useful to interpolate rainfall erosivity map, which could help to find the credible maximum and minimum value of TGRA. In total, the findings could provide useful information both for soil erosion prediction, land management practices, and sediment control project of TGRA.

Rainfall erosivity estimation using daily rainfall amounts for South Australia

Soil Research ◽  
1996 ◽  
Vol 34 (5) ◽  
pp. 721 ◽  
Author(s):  
B Yu ◽  
CJ Rosewell
Keyword(s):  
Model Performance ◽  
South Australia ◽  
Daily Rainfall ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
The South ◽  
Winter Rainfall ◽  
R Factor

The rainfall erosivity model relating storm erosivity to daily rainfall amounts was tested for 4 sites in South Australia where seasonal rainfall erosivity is generally out of phase with seasonal rainfall because of the predominant winter rainfall. The model worked reasonably well, with the coefficient of efficiency varying from 0.54 to 0.77, and the average discrepancy between actual and estimated monthly distribution was no more than 3%. The model performance in the winter rainfall area is similar to that in the uniform and summer rainfall areas. A set of regional parameter values estimated using a combined dataset is recommended for other sites in the agricultural and viticultural areas of South Australia where the mean annual rainfall ranges from 300 to 500 mm. The R-factor and its seasonal distribution were estimated for 99 sites in South Australia using long-term daily rainfall data. The R-factor varies mostly between 250 and 500 MJ . mm/(ha . h . year). Rainfall erosivity peaks in winter in the southern part of the western agricultural area and the south-east corner of the State, while it peaks in summer in the inland area east of the South Flinders and Mount Lofty Ranges.

A Rainfall Erosivity database for Brazil from MSWEP rainfall dataset

2021 ◽  
Author(s):  
Ingrid Petry ◽  
Fernando Mainardi Fan
Keyword(s):  
Temporal Resolution ◽  
Soil Loss ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Regression Equations ◽  
R Factor ◽  
Thiessen Polygons ◽  
Erosivity Index ◽  
Factor Map

<p>In erosion studies the behavior of rainfall is primordial, since rain is responsible for the first stage of the erosion process: the detachment of soil particles. The erosive potential of rainfall, erosivity, is considered in the universal soil loss equations (R)USLE family through the parameter R, or R factor. This factor is calculated from the rainfall erosivity index, which is the product of kinetic energy of the rain by the maximum intensity of the rain of 30 minutes of duration. As sub-hour rainfall data is not always available, there are in the literature a series of equations obtained from regression, which use monthly and annual rainfall and present a good estimate of erosivity for your study site. In Brazil, in addition to limitations regarding the temporal resolution of rainfall data, there are also spatial limitations. Monitoring stations are concentrated mostly in urbanized areas, usually near the coast. The other regions, such as agricultural and forest areas, are poorly monitored, and these areas are of great interest for monitoring erosion, not only because they are periodically exposed soil areas, but also because of the high rainfall rates that humid forests like Amazon have. MSWEP is a rainfall database that combines observed, satellite and reanalysis data. It has global coverage, temporal resolution of 3 hours, spatial 0.1º and data from 1979 to 2016. Databases like this have great potential to be used in areas such as Brazil, due to its spatial and temporal resolution. In this context, considering the relevance that the soil loss equations still present today, this work developed a rainfall erosivity database entitled REDB-BR (Rainfall Erosivity Database for Brazil). It provides the R factor in a 0.1º resolution grid, developed with 37 years of rainfall data from the MSWEP dataset. The R factor was calculated trough 73 erosivity index regression equations, which mostly uses the Modified Fournier Index (MFI), a relation between monthly precipitation and annual precipitation. Thiessen polygons were used in order to spatialize and define the areas of each equation. Over the Brazilian territory, the R factor ranges from 1.200 to 20.000 MJ mm ha-1 h-1 year-1, with the higher values in the North region, and the lowest values in the Northeast. The spatial patterns of erosivity are very similar to the climatic zones of Brazil. The R factor map takes advantage of MSWEP dataset and presents a spatial resolution very detailed to a country with continental scale such as Brazil. The database includes the equations shapefile and table, Thiessen Polygons shapefile and the R factor map in raster format, which allows more possibilities of application. The database can be accessed at <https://zenodo.org/record/4428308#.X_hxsOhKiUk>. We identified sudden changes in behavior between the delimited areas, which suggests a need for more regression equations in order to better represent the behavior of the erosivity in the Brazilian territory.</p>

scholarly journals Approach of the annual erosivity in North Chile

2021 ◽  
Author(s):  
Bastian Saez ◽  
Jose Vargas Baecheler ◽  
Alfonso Gutierrez-Lopez
Keyword(s):  
Regression Models ◽  
Arid Regions ◽  
Maximum Intensity ◽  
Spatial Behavior ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Spatial And Temporal Variability ◽  
R Factor ◽  
Hourly Data ◽  
Factor Data

The Norte Grande of Chile (17°S-29°S) features arid regions, where rainfall is generally convective with high spatial and temporal variability, which is the cause of floods with large amounts of sediments due to water erosion. The most relevant factor in erosive processes in arid regions is erosivity, which can be quantified by the RUSLE R-factor, but precipitation data are required every 30 minutes, however, these records are limited. The ones that are available are not enough to characterize it spatially. Consequently, the objective of this study is to evaluate regression models of the annual erosivity using rainfall aggressiveness indices as an explanatory variable, with the aim of analyzing the spatial behavior of erosion. Correlations were made between the maximum intensity in 30 minutes (I30) to the maximum intensity in one hour (I60), which were useful for calculating the R-factor for stations with hourly data by applying a correction factor to I60 determined by the correlations. Four regression models were established for each of the six aggressiveness indices and a relationship was selected through validation, using stations with few years of continuous recording. The selected equation allowed generating 103 spatially distributed erositivy-values, which served to make a subsequent co-kriging, in order to make a spatial analysis of rainfall erosivity. Results showed that there are under-estimations of I30, however, they are considered acceptable due to the efficiency obtained (Nash-Sutcliffe = 0.787). The calculated R-factor data-points allowed selection of the best-fit potential equation, which uses the mean annual rainfall as a predictor

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.

Spatial and seasonal distribution of rainfall erosivity in Australia

Soil Research ◽  
2002 ◽  
Vol 40 (6) ◽  
pp. 887 ◽  
Author(s):  
Hua Lu ◽  
Bofu Yu
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Seasonal Distribution ◽  
Rainfall Erosivity ◽  
Continental Scale ◽  
R Factor ◽  
Monthly Distribution ◽  
The Mean

Spatially distributed rainfall erosivity and its seasonal distribution are needed to use the revised universal soil loss equation (RUSLE) for erosion risk assessment at large scale. An erosivity model and 20-year daily rainfall data at 0.05° resolution were used to predict the R-factor and its monthly distribution for RUSLE in Australia. Predicted R-factor values were compared with those previously calculated using pluviograph data for 132 sites around Australia. The daily erosivity model was further evaluated for 43 sites where long-term pluviograph data were available. Predicted and calculated monthly distributions of the R-factor were compared for these 43 sites. For the 132 sites where R-factor values were compiled from previous investigations, the model efficiency was 0.81 with root mean squared error (rmse) of 1832 MJ.mm/(ha.h.year), or 47.5% of the mean for the 132 sites. For the additional 43 sites, the coefficient of efficiency was 0.93 with a 12.7 mm rainfall threshold, and 0.94 when all storms were included in the calculations. The rmse was 908 MJ.mm/(ha.h.year), or 28.6% of the mean for the 43 sites with a zero rainfall threshold. The prediction error for monthly distribution of the R-factor was 2.3% with a zero threshold and 2.5% with 12.7�mm threshold. This and previous studies have shown that the daily rainfall erosivity model can be used to accurately predict the R-factor and its seasonal distribution in Australia. Digital maps were produced showing the spatial and seasonal distribution of the R-factor at 0.05° resolution in Australia. These maps have been used to assess rill and sheet erosion rate at the continental scale.

scholarly journals Precipitation Patterns and Rainfall Erosivity Return Period Under Savanna Conditions in Formosa, Goiás, Brazil

2020 ◽  
Vol 8 (4) ◽  
pp. 554
Author(s):  
Aline Franciel de Andrade ◽  
Roriz Luciano Machado ◽  
Cássia Cristina Rezende ◽  
Elizabete Alves Ferreira ◽  
Daniel Fonseca de Carvalho ◽  
...  
Keyword(s):  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Return Periods ◽  
Tropical Regions ◽  
Average Annual Rainfall ◽  
Physical Attributes ◽  
Erosivity Index ◽  
Rainfall Patterns ◽  
Agricultural Areas ◽  
Intermediate Pattern

Losses of soil and nutrients affect a large part of agricultural areas in tropical regions, regardless of the level of technology adopted. This study evaluated the physical attributes and erosivity indices associated with rainfall patterns and return periods in the region of Formosa, State of Goiás, Brazil. Using series of pluviographic (2002-2008) and pluviometric (1975-1998) data from a station located at municipality of Formosa, the erosive potential (EI30 and KE>25), rainfall patterns (advanced, intermediate and delayed) and the erosivity associated with the rainfall return periods were determined. The average annual rainfall of the region was 1,391.6 mm with 87.4% of the rains concentrated in October to March. The average annual values of EI30 and KE>25 corresponded to 8,041.6 MJ mm ha-1 h-1 year-1 and 125.7 MJ ha-1 year-1, respectively. The months of the year did not differ based on rainfall pattern. The advanced hydrological pattern had the highest frequency of occurrence, followed by the delayed and intermediate patterns. The highest EI30 and KE>25 indices for individual rainfall seasons occurred under the intermediate and the advanced patterns in February and under the intermediate pattern in October for the index KE>25. The average annual erosivity index (R factor of USLE) (8041.6 MJ mm ha-1 h-1 year-1) is expected to occur at least once every 1.89 years, corresponding to a probability of occurrence of 52.84%. The average annual values of EI30 estimated for the return periods of 2, 5, 10, 25, 50 and 100 years were 8,230, 10,225, 10,889, 11,222, 11,421 and 11,488 MJ mm ha-1 h-1 year-1, respectively.

scholarly journals Estimating Rainfall Erosivity Factor Using Future Climate Projection in the Myjava Region (Slovakia)

2021 ◽  
Vol 24 (s1) ◽  
pp. 31-36
Author(s):  
Peter Valent ◽  
Roman Výleta
Keyword(s):  
Soil Erosion ◽  
Climate Models ◽  
Regional Climate ◽  
Multiple Linear Regression Model ◽  
Rain Gauge ◽  
Rainfall Erosivity ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
R Factor ◽  
Geostatistical Approach

Abstract Rainfall erosivity factor (R) of the USLE model is one of the most popular indicators of areas potentially susceptible to soil erosion. Its value is influenced by the number and intensity of extreme rainfall events. Since the regional climate models expect that the intensity of heavy rainfall events will increase in the future, the currently used R-factor values are expected to change as well. This study investigates possible changes in the values of R-factor due to climate change in the Myjava region in Slovakia that is severely affected by soil erosion. Two rain gauge stations with high-resolution 1-minute data were used to build a multiple linear regression model (r 2 = 0.98) between monthly EI 30 values and other monthly rainfall characteristics derived from low-resolution daily data. The model was used to estimate at-site R-values in 13 additional rain gauge stations homogeneously dispersed over the whole region for four periods (1981–2010, 2011–2040, 2041–2070, 2071–2100). The at-site estimates were used to create R-factor maps using a geostatistical approach. The results showed that the mean R-factor values in the region might change from 429 to as much as 520 MJ.mm.ha−1.h−1.yr−1 in the second half of the 21st century representing a 20.5% increase.

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