scholarly journals Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

2015 ◽  
Vol 19 (10) ◽  
pp. 4113-4126 ◽  
Author(s):  
S. Yin ◽  
Y. Xie ◽  
B. Liu ◽  
M. A. Nearing
Keyword(s):  
Energy Intensity ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
The World ◽  
Model Efficiency ◽  
Erosivity Index ◽  
Soil Erosion By Water ◽  
Event Energy ◽  
Eastern Half

Abstract. Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event (energy-intensity values – EI30) is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models based on commonly available rainfall data resolutions, such as daily or monthly totals, to calculate rainfall erosivity. Eleven stations with 1 min temporal resolution rainfall data collected from 1961 through 2000 in the eastern half of China were used to develop and calibrate 21 models. Seven independent stations, also with 1 min data, were utilized to validate those models, together with 20 previously published equations. The models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Using symmetric mean absolute percentage errors and Nash–Sutcliffe model efficiency coefficients, we can recommend 17 of the new models that had model efficiencies ≥ 0.59. The best prediction capabilities resulted from using the finest resolution rainfall data as inputs at a given erosivity timescale and by summing results from equations for finer erosivity timescales where possible. Results from this study provide a number of options for developing erosivity maps using coarse resolution rainfall data.

scholarly journals Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

2015 ◽  
Vol 12 (5) ◽  
pp. 4965-4996 ◽  
Author(s):  
S. Yin ◽  
Y. Xie ◽  
B. Liu ◽  
M. A. Nearing
Keyword(s):  
Daily Rainfall ◽  
Rainfall Amount ◽  
Rainfall Data ◽  
Data Availability ◽  
Rainfall Erosivity ◽  
Data Prediction ◽  
Yearly Average ◽  
Model Efficiency ◽  
Erosivity Index ◽  
Better Than

Abstract. Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event, EI30, is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models that relate more commonly available rainfall data resolutions, such as daily or monthly totals, to rainfall erosivity. Eleven stations with one-minute temporal resolution rainfall data collected from 1961 through 2000 in the eastern water-erosion areas of China were used to develop and calibrate 21 models. Seven independent stations, also with one-minute data, were utilized to validate those models, together with 20 previously published equations. Results showed that models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Prediction capabilities, as determined using symmetric mean absolute percentage errors and Nash–Sutcliffe model efficiency coefficients, were demonstrated for the 41 models including those for estimating erosivity at event, daily, monthly, yearly, average monthly and average annual time scales. Prediction capabilities were generally better using higher resolution rainfall data as inputs. For example, models with rainfall amount and maximum 60 min rainfall amount as inputs performed better than models with rainfall amount and maximum daily rainfall amount, which performed better than those with only rainfall amount. Recommendations are made for choosing the appropriate estimation equation, which depend on objectives and data availability.

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.

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 Application of Angot precipitation index in the assessment of rainfall erosivity: Vojvodina Region case study (North Serbia)

2021 ◽  
Vol 61 (2) ◽  
pp. 123-153
Author(s):  
Tin Lukić ◽  
Tanja Micić Ponjiger ◽  
Biljana Basarin ◽  
Dušan Sakulski ◽  
Milivoj Gavrilov ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Precipitation Index ◽  
Mitigation Strategies ◽  
Rainfall Erosivity ◽  
Erosivity Index ◽  
Statistical Approaches ◽  
Soil Erosion By Water ◽  
Precipitation Trends ◽  
Rainfall Erosion

The paper aims to provide an overview of the most important parameters (the occurrence, frequency and magnitude) in Vojvodina Region (North Serbia). Monthly and annual mean precipitation values in the period 1946–2014, for the 12 selected meteorological stations were used. Relevant parameters (precipitation amounts, Angot precipitation index) were used as indicators of rainfall erosivity. Rainfall erosivity index was calculated and classified throughout precipitation susceptibility classes liable of triggering soil erosion. Precipitation trends were obtained and analysed by three different statistical approaches. Results indicate that various susceptibility classes are identified within the observed period, with a higher presence of very severe rainfall erosion in June and July. This study could have implications for mitigation strategies oriented towards reduction of soil erosion by water.

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.

Development of web-based WERM-S module for estimating spatially distributed rainfall erosivity index (EI30) using RADAR rainfall data

CATENA ◽  
2018 ◽  
Vol 161 ◽  
pp. 37-49 ◽  
Author(s):  
Avay Risal ◽  
Kyoung Jae Lim ◽  
Rabin Bhattarai ◽  
Jae E. Yang ◽  
Huiseong Noh ◽  
...  
Keyword(s):  
Rainfall Data ◽  
Rainfall Erosivity ◽  
Radar Rainfall ◽  
Web Based ◽  
Spatially Distributed ◽  
Erosivity Index

scholarly journals Estimativa da erosividade da chuva na bacia hidrográfica do rio Juma com base em dados do satélite TRMM / Estimation of rain erosion in the Juma river basin based on TRMM satellite data

2019 ◽  
Vol 29 (56) ◽  
pp. 45
Author(s):  
Miqueias Lima Duarte ◽  
Eliomar Pereira da Silva Filho
Keyword(s):  
River Basin ◽  
Soil Degradation ◽  
Prevention And Control ◽  
Rainfall Erosivity ◽  
Territorial Planning ◽  
Surface Data ◽  
Historical Series ◽  
Erosivity Index ◽  
And Control ◽  
Rain Erosion

Conhecer o potencial da chuva em causar erosão do solo é de fundamental importância para a compreensão da fragilidade de uma região, essas informações podem ser utilizadas na prevenção e controle da degradação do solo, auxiliando o planejamento territorial. Este estudo tem por objetivo estimar a erosividade da chuva na bacia hidrográfica do rio Juma, no sul do estado do Amazonas. Foram utilizados dados mensais pluviométricos do produto 3B42-V7 do sensor TRMM obtidos na plataforma Giovanni e comparados com dados de superfície, para a série histórica de 1998 a 2016. O índice de erosividade da chuva foi obtido a partir de um modelo proposto por Oliveira Jr e Medina (1990) desenvolvido para a região. Os resultados obtidos apontam que a variação espacial do índice de erosividade da chuva ao longo da bacia do rio Juma foi pequena (média de 11,66 MJ.mm.ha-1.h-1.ano-1), as maiores variações estão relacionadas a sazonalidade regional, sendo que o mês de julho apresenta o menor índice de erosividade médio (47,74 MJ.mm.ha-1.h-1.ano-1), enquanto que o mês de fevereiro apresentou o maior índice de erosividade (145,73 MJ.mm.ha-1.h-1.ano-1).Palavras–chave: Potencial erosivo da chuva, Degradação do solo, Sensor orbital.Abstract Knowing the potential of rain to cause soil erosion is of fundamental importance to understand the fragility of a region, this information can be used in the prevention and control of soil degradation, assisting the territorial planning. This study aims to estimate the rainfall erosivity in the river basin of the Juma, in the south of the state of Amazonas. Monthly rainfall data from the 3B42-V7 TRMM sensor product obtained from the Giovanni platform and compared with surface data were used for the historical series from 1998 to 2016. The rainfall erosivity index was obtained from a model proposed by Oliveira Jr and Medina (1990) developed for the region. The results indicate that the spatial variation of the rainfall erosivity index along the Juma river basin was small (mean of 11.66 MJ.mm.ha-1.h-1.year-1), the most significant variations are related to regional seasonality, and the month of July It has the lowest mean erosivity index (47.74 MJ.mm.ha-1.h-1.year-1), while February presented the highest erosivity index (145.73 MJ.mm.ha-1.h-1.year-1).Keywords: Erosive potential of rain, Soil degradation, Orbital Sensor.

scholarly journals Probing the carbon emissions in 30 regions of China based on symbolic regression and Tapio decoupling

2021 ◽  
Author(s):  
Haiying Liu ◽  
zhiqun zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Symbolic Regression ◽  
Car Ownership ◽  
Emissions Reduction ◽  
Policy Recommendations ◽  
The World ◽  
Urbanization Rate

Abstract Against the background of energy shortages and severe air pollution, countries around the world are aware of the importance of energy conservation and emissions reduction; China is actively achieving emissions reduction targets. In this study, we use a symbolic regression to classify China's regions according to the degree of influencing factors, and calculate and analyze the inherent decoupling relationship between carbon emissions and economic growth in each region. Based on our results, we divided the 30 regions of the country into six categories according to the main influencing factors: GDP (13 regions), energy intensity (EI; 7 regions), industrial structure (IS; 3 regions), urbanization rate (UR; 3 regions), car ownership (CO; 2 regions), and household consumption level (HCL; 2 regions). Then, according to the order of the average carbon emissions in each region from high to low, these regions were further categorized as type-EI, type-UR, type-GDP, type-IS, type-CO, or type-HCL regions. The decoupling index of each region showed a downward trend; EI and GDP regions were the most notable contributors to emissions, based on which we provide policy recommendations.

scholarly journals Decomposition Analysis of CO2 Emissions for Turkey and Iran over 1990-2010

2014 ◽  
Author(s):  
Hasan Rüstemoğlu ◽  
Sevin Uğural
Keyword(s):  
Co2 Emissions ◽  
Scale Effect ◽  
Energy Intensity ◽  
Decomposition Analysis ◽  
Carbon Intensity ◽  
Composition Effect ◽  
Economic Sectors ◽  
Intensity Effect ◽  
The World ◽  
Laspeyres Index

There exists an important awareness for reduction of CO2 emissions to obtain a sustainable world. Together with this, there is a great deal of interest for decomposition analysis to see the accelerating and decelerating factors of CO2 emissions. The aim of this project is to decompose CO2 emissions in economic sectors for the two superpowers of Middle East, Iran and Turkey, over the time period between 1990 and 2010, for Turkey obtained a rapid growth performance in recent years and Iran which is the energy superpower of the world. Refined Laspeyres Index decomposition method and a consistent data gathered from the World Bank’s and UN’s databases have been used during the analysis. Five main sectors (agriculture, manufacturing, transportation, construction and other service sectors) and four main impacts (scale effect, composition effect, energy intensity effect and carbon intensity effect) have been considered to see the increasing and decreasing factors of CO2 emissions. Various interesting results are observed for both of the countries, for each of the economic sectors. Generally scale effect and energy intensity effect are the dominant impacts for all sectors of both countries. However composition effect and carbon intensity effect are also important contributors for economic activities of these two countries. Overall, our analysis showed that these two countries should pay attention for energy intensity and sustainable economic growth.

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