scholarly journals Erosive rainfall in Rio do Peixe Valley in Santa Catarina, Brazil: Part I - Determination of the erosivity index

2017 ◽  
Vol 21 (11) ◽  
pp. 774-779
Author(s):  
Álvaro J. Back ◽  
Augusto C. Pola ◽  
Nilzo I. Ladwig ◽  
Hugo Schwalm
Keyword(s):  
Rain Gauge ◽  
The State ◽  
Rainfall Data ◽  
Data Series ◽  
Rainfall Erosivity ◽  
Santa Catarina ◽  
R Factor ◽  
Erosivity Index ◽  
Erosive Rainfall

ABSTRACT This study aimed to determine the rainfall erosivity index in the Valley of Rio do Peixe, in the state of Santa Catarina, Brazil. The data series of three rain gauge stations in the cities of Campos Novos, Videira, and Caçador were used to determine the rainfall erosivity based on the EI30 index and to adjust the equations in order to estimate the EI30 value from the rainfall coefficient. On average, it was observed that erosive rains represents 81.4-88.5% of the annual precipitation. The adjusted equations can be used to estimate rainfall erosivity in locations with only rainfall data. The regional equation specified for the erosivity estimation is EI30 = 74.23 Rc0.8087. The R factor is 8,704.8; 7,340.8; and 6,387.1 MJ mm ha-1 h-1 year-1 for Campos Novos, Videira, and Caçador, respectively. In Campos Novos and Videira, the erosivity was classified as high, while in Caçador, it was classified as average.

scholarly journals Comparing Rainfall Erosivity Estimation Methods Using Weather Radar Data for the State of Hesse (Germany)

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1424 ◽  
Author(s):  
Jennifer Kreklow ◽  
Bastian Steinhoff-Knopp ◽  
Klaus Friedrich ◽  
Björn Tetzlaff
Keyword(s):  
Climate Change ◽  
Rain Gauge ◽  
The State ◽  
Estimation Methods ◽  
Rainfall Erosivity ◽  
Spatiotemporal Resolution ◽  
Rain Gauge Data ◽  
R Factor ◽  
Gauge Data ◽  
Erosive Rainfall

Rainfall erosivity exhibits a high spatiotemporal variability. Rain gauges are not capable of detecting small-scale erosive rainfall events comprehensively. Nonetheless, many operational instruments for assessing soil erosion risk, such as the erosion atlas used in the state of Hesse in Germany, are still based on spatially interpolated rain gauge data and regression equations derived in the 1980s to estimate rainfall erosivity. Radar-based quantitative precipitation estimates with high spatiotemporal resolution are capable of mapping erosive rainfall comprehensively. In this study, radar climatology data with a spatiotemporal resolution of 1 km2 and 5 min are used alongside rain gauge data to compare erosivity estimation methods used in erosion control practice. The aim is to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Our results clearly show that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. Radar climatology data show a high potential to improve rainfall erosivity estimations, but uncertainties regarding data quality and a need for further research on data correction approaches are becoming evident.

scholarly journals Rainfall pattern and erosion potential in the physiographic regions of the state of Pernambuco, Brazil

2018 ◽  
Vol 22 (12) ◽  
pp. 849-853
Author(s):  
Antonio G. Pinheiro ◽  
Thais E. M. dos S. Souza ◽  
Suzana M. G. L. Montenegro ◽  
Abelardo A. de A. Montenegro ◽  
Sérgio M. S. Guerra
Keyword(s):  
The State ◽  
Rainfall Data ◽  
Rainfall Pattern ◽  
Annual Rainfall ◽  
Data Series ◽  
Rainfall Erosivity ◽  
Annual Average ◽  
Erosivity Index ◽  
Weather Stations ◽  
Physiographic Regions

ABSTRACT The objective of the present study was to characterize the spatial and temporal (2000-2015) rainfall pattern variability and erosive potential in the different physiographic regions of the state of Pernambuco, Brazil. Rainfall data series (3 to 12 years) from 25 weather stations of the state were analyzed. Erosive rainfall events (more than 10 mm depth) were considered to evaluate the annual erosivity index, monthly erosivity index (EI30), rainfall erosivity factor (R), and rainfall pattern. The inverse distance weighting (IDW) - inverse of the square of the distance - was used to create spatial interpolation and develop maps. The rainfall data from the weather stations showed average annual rainfall of 827 mm and average erosivity of 4,784 MJ mm ha-1 h-1. The Metropolitan region of Pernambuco presented the highest rainfall erosivity index, with annual average of 9,704 MJ mm ha-1 h-1; and the Sertão do São Francisco region had the lowest, with annual average of 4,902 MJ mm ha-1 h-1. The state of Pernambuco presented advanced (42%), intermediate (38%), and delayed (20%) rainfall patterns.

scholarly journals Erosivity of rainfall in Lages, Santa Catarina, Brazil

2014 ◽  
Vol 38 (6) ◽  
pp. 1890-1905 ◽  
Author(s):  
Jefferson Schick ◽  
Ildegardis Bertol ◽  
Neroli Pedro Cogo ◽  
Antonio Paz González
Keyword(s):  
Temporal Distribution ◽  
Daily Rainfall ◽  
Rainfall Erosivity ◽  
Santa Catarina ◽  
Time Period ◽  
Erosivity Index ◽  
Annual Total ◽  
Erosive Rainfall ◽  
Management And Conservation ◽  
Erosive Capacity

The erosive capacity of rainfall can be expressed by an index and knowing it allows recommendation of soil management and conservation practices to reduce water erosion. The objective of this study was to calculate various indices of rainfall erosivity in Lages, Santa Catarina, Brazil, identify the best one, and discover its temporal distribution. The study was conducted at the Center of Agricultural and Veterinary Sciences, Lages, Santa Catarina, using daily rainfall charts from 1989 to 2012. Using the computer program Chuveros , 107 erosivity indices were obtained, which were based on maximum intensity in 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90, 100, 110, 120, 135, 150, 165, 180, 210, and 240 min of duration and on the combination of these intensities with the kinetic energy obtained by the equations of Brown & Foster, Wagner & Massambani, and Wischmeier & Smith. The indices of the time period from 1993 to 2012 were correlated with the respective soil losses from the standard plot of the Universal Soil Loss Equation (USLE) in order to select the erosivity index for the region. Erosive rainfall accounted for 83 % of the mean annual total volume of 1,533 mm. The erosivity index (R factor) of rainfall recommended for Lages is the EI30, whose mean annual value is 5,033 MJ mm ha-1 h-1, and of this value, 66 % occurs from September to February. Mean annual erosivity has a return period estimated at two years with a 50 % probability of occurrence.

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 Erosive rainfall in the Rio do Peixe Valley: Part III - Risk of extreme events

2018 ◽  
Vol 22 (1) ◽  
pp. 63-68
Author(s):  
Álvaro J. Back ◽  
Augusto C. Pola ◽  
Nilzo I. Ladwig ◽  
Hugo Schwalm
Keyword(s):  
Extreme Events ◽  
Annual Rainfall ◽  
Data Series ◽  
Rainfall Erosivity ◽  
Significance Level ◽  
Control Structures ◽  
Kolmogorov Smirnov ◽  
Runoff Control ◽  
The Mean ◽  
Erosive Rainfall

ABSTRACT Understanding the risks of extreme events related to soil erosion is important for adequate dimensioning of erosion and runoff control structures. The objective of this study was to determine the rainfall erosivity with different return periods for the Valley of the Rio do Peixe in Santa Catarina state, Brazil. Daily pluviographic data series from 1984 to 2014 from the Campos Novos, and Videira meteorological stations and from 1986 to 2014 from the Caçador station were used. The data series of maximum annual rainfall intensity in 30 min, maximum annual erosive rainfall, and total annual erosivity were analyzed for each station. The Gumbel-Chow distributions were adjusted and their adhesions were evaluated by the Kolmogorov-Smirnov test at a significance level of 5%. The Gumbel-Chow distribution was adequate for the estimation of all studied variables. The mean annual erosivity corresponds to the return period of 2.25 years. The data series of the annual maximum individual rainfall erosivity coefficients varied from 47 to 50%.

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.

Influence of systematic errors from tipping bucket rain gauges on recorded rainfall data

1998 ◽  
Vol 37 (11) ◽  
pp. 121-129 ◽  
Author(s):  
Rolf Fankhauser
Keyword(s):  
Depth Resolution ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Urban Hydrology ◽  
Data Series ◽  
Data Recording ◽  
Time Step ◽  
Recording Time ◽  
Rain Gauges ◽  
Evaporation Losses

Tipping bucket rain gauges (TBR) are widely used in urban hydrology. The present study investigated the uncertainties in recorded rainfall intensity induced by the following properties of the TBR: depth resolution i.e. the bucket volume, calibration parameters, wetting and evaporation losses and the method of data recording (time between tips or tips per minute). The errors were analysed by means of a TBR simulator i.e. a simulation program that models the behaviour of a TBR. Rainfall data disaggregated to 6 seconds from measured 1-min data and randomly varied were taken as input to the simulator. Different TBR data series were produced by changing the properties of the simulated rain gauge. These data series together with the original rainfall events were used as input to a rainfall-runoff model. Computed overflow volume and peak discharge from a combined sewer overflow (CSO) weir were compared. Errors due to depth resolution (i.e. the bucket size) proved to be small. Therefore TBRs with a depth resolution up to 0.254 mm can be used in urban hydrology without inducing significant errors. Wetting and evaporation losses caused small errors. The method of data recording had also little influence. For larger bucket volumes variable time step recording induced smaller errors than tips per minute recording.

scholarly journals Erosive rainfall in the Rio do Peixe Valley in Santa Catarina, Brazil: Part II - Characteristics and temporal distribution pattern

2017 ◽  
Vol 21 (11) ◽  
pp. 780-784
Author(s):  
Álvaro J. Back ◽  
Augusto C. Pola ◽  
Nilzo I. Ladwig ◽  
Hugo Schwalm
Keyword(s):  
Kinetic Energy ◽  
Distribution Pattern ◽  
Precipitation Amount ◽  
Temporal Distribution ◽  
Maximum Intensity ◽  
Santa Catarina ◽  
Average Precipitation ◽  
Erosivity Index ◽  
Energy Maximum ◽  
Erosive Rainfall

ABSTRACT Exploring the characteristics of erosive rain is an important aspect of studying erosive processes, and it allows researchers to create more natural and realistic hydrological simulations. The objective of this study was to analyse the characteristics of erosive rain and to determine the temporal distribution pattern of erosive rainfall in the Valley of Rio do Peixe in the state of Santa Catarina, Brazil. Daily pluviograms from the meteorological stations located in the cities Campos Novos, Videira, and Caçador in Santa Catarina from 1984 to 2014 were utilized for this study. By studying rainfall that is classified as erosive, the values of kinetic energy, maximum intensity in thirty minutes, and the value of EI30 erosivity index were determined. The rainfall was also classified according to the temporal distribution of rainfall in advanced, intermediate, and delayed patterns. Erosive rainfalls occur at a frequency of 53.3% advanced, 31.1% intermediate, and 15.6% delayed patterns. Erosive rainfall has an average precipitation amount of 25.5 mm, duration of 11.1 h, kinetic energy of 5.6 MJ ha-1, maximum intensity of 30 min of 17.7 mm h-1, and erosivity of 206.4 MJ mm ha-1 h-1. The highest frequency of erosive rainfall occurred in rainfalls lasting from 6 to 12 h (36.1%), followed by rainfalls lasting from 4 to 6 h (22.4%).

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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