Rainfall Erosivity

Rainfall erosivity concerns the ability of rainfall to cause erosion on the surface of the earth. The difficulty in modeling the distribution, the size, and the terminal velocity of raindrops in relation to the detachment of soil particles led to the use of more tractable rainfall indices. Thus, in the universal soil loss equation (USLE), the coefficient of rainfall erosivity, R, was introduced. This coefficient is based on the product of the rainfall kinetic energy of a storm and its maximum 30-minute intensity. An important problem in the application of USLE and its revisions in various parts of the world concerns the computation of R, which requires pluviograph records with a length of at least 20 years. For this reason, empirical equations have been developed that are based on coarser rainfall data, such as daily, monthly, or yearly, which are available on larger spatial and temporal extents. However, the lack of denser data is dealt more effectively by means of machine learning methods. Computational systems for this purpose were recently developed based on feed-forward neural networks, yielding significantly better results.

One major controlling factor of water erosion is rainfall erosivity, which is quantified as the product of total storm energy and a maximum 30 min intensity (I 30). Rainfall erosivity is often expressed as R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). As rainfall erosivity is closely correlated with rainfall amount and intensity, the rainfall erosiv-ity of Switzerland can be expected to have a regional characteristic and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity. So far only national seasonal means and regional annual means exist for Switzerland. We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to calculate long-term monthly mean R-factors. Stepwise generalized linear regression (GLM) and leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal pat

Soil erosion is a very serious problem from agricultural as well as environmental point of view. Various computer models have been used to estimate soil erosion and assess erosion control practice. Universal Soil Loss Equation (USLE) is one of the most frequently used soil loss estimation models which have been used in many countries around the world. Erosivity (USLE R-factor) is one of the USLE input parameters to reflect impacts of rainfall in computing soil loss. R factor for a specific rainfall event depends upon maximum rainfall intensity of specific period and kinetic energy of that event. Annual R factor is calculated as the sum of erosivities of such rainfall events that occurred. It is usually calculated from rainfall data having higher temporal resolution but the process of calculation is very tedious and also the higher temporal resolution data are not readily available in many parts of the world. Various regression models have been developed to estimate monthly R factor as well as annual R factor using monthly/yearly rainfall amount. However, it is rarely allowed to estimate R factor with higher accuracy using these models since they were developed from obsolete dataset and also only the rainfall amount was used for an input parameter without rainfall intensity. In this study, a web-based Erosivity estimation system (Web ERosivity Module-WERM) was developed to compute R factor using 10 min interval rainfall data. The model was then tested for 75 different cities in Korea using the rainfall data of 15 to 18 years from 1997 to 2014 obtained from Korea Meteorological Administration (KMA). Using the monthly rainfall data and R factor values obtained from the model, regression equation for 25 cities was developed to estimate monthly R factor from the monthly rainfall with amount and intensity of rainfall considered. The coefficient of determination (R 2) of the regression equation ranged from 0.75 to 0.92. This indicated that these regression equations can be used to estimate the value of R-factor from the monthly rainfall data with more than 75% accuracy. The WERM is very simple to use and it can be a very effective tool to compute R factor using higher temporal resolution rainfall data. Along with this, it is possible to calculate R factor using local daily rainfall with the help of regression equations which are available for 25 cities in South Korea till now.

"Declining productivity of lateritic soil of India indicates land degradation, which occurs
mostly through soil erosion and physical or chemical deterioration of soil. In the tropical wetdry type of morphoclimatic region (western Birbhum and Eastern Jharkhand districts of India), the primary process of soil erosion usually takes place when falling raindrops beat thebare soil surface in heavy storm. Rainsplash erosion, sheet and interrill erosion (overlandflow), rill and gully erosion are considered as major forms of water erosion. Due to high erodibility of lateritic soil, bare soil cover (deforestation), high erosivity of monsoonal rainfall, low clay, moisture and organic matter content of soil, the study area (border area between Rampurhat I block of Birbhum district, West Bengal and Shikaripara block of Dumka district, Jharkhand) is very much susceptible to rill and gully erosion. So the present article is an attempt to quantify the soil loss and sediment yield of catchments of sample gullies under different forms of water erosion.
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Purpose: This study gives a critical assessment of the rainfall erosivity factor (R) for selected sites in the Majha region, representing different locations use of mean monthly rainfall data.
Methodology: By applying empirical methods, the rainfall intensity for all the locations was obtained and was further determined at three different intervals of 30-minutes, 45-minutes, and 60-minutes, respectively. The rainfall erosivity factor (R) was calculated by the revised universal soil loss equation (RUSLE).
Main Findings: Using RUSLE, the rainfall erosivity factor (R) for each of the locations was measured as follows; EI = 3878.49 (MJmmha-1hr-1), EI = 4013.71 (MJmmha-1hr-1), EI = 4302.24 (MJmmha-1hr-1) for Majha region of Amritsar, Tarntaran and Pathankot respectively. A close observation of the data obtained revealed that as rainfall intensity increased with the duration, the rainfall erosivity index reduced or decreased.
Implications of study: Nevertheless, it is expected that if proper cover crop and management practices are applied despite the region, the study area falls within, rainfall erosivity can be cushioned, thus reducing further erosion tendencies and enhancing food production chances from productive lands within the area.
The novelty of the study: The rainfall erosivity factor (R) was calculated by the revised universal soil loss equation (RUSLE).

Water erosion is one of the major threats to soils in Tunisia, affecting 49% of the total area. Annually, the Tunisian water capacity storage decreases by 25 Mm3 due to dam siltation. The Universal Soil Loss Equation (USLE) is a powerful tool that is widely used by scientists in many countries to provide estimates of soil loss and sediment yields from specified land surface areas. Rainfall erosivity is one of the five factors considered in the USLE to quantify the climate effect on the soil loss rate. This paper analyses and compares rainfall erosivity values that were compiled with ten different regression equations using the annual precipitation and the Modified Fournier Index (MFI). Those equations were applied on 55 stations in Tunisia and using 115 years of data. The results showed that the equations developed by Bolinne et al., overestimate the rainfall erosivity. In contrary, the Renard&Freimun and the Yu &Rossewell equations underestimate the rainfall erosivity. In the other hand the Apaydinthe Irvem and Belaid equations can be highly dependent on the rainfall amount and the Modified Fournier Index (MFI) values. The Roose and Arnoldus SI and Ferro equations provide statistically equivalent erosivity values to those that were computed using I30. On average, the difference in the erosivity estimates using those equations are 22% and 18% and 15% respectively. The Rango& Arnoldus SI equation provides the closer erosivity values to those computed using Wischmeier equation with a 2% difference on average. The Arnoldus SI equation provides the most statistically equivalent values for all the sites with a linear regression and an R2of 0.98. Based on those results, the Arnlodus SI equation was adapted and used to construct rainfall erosivity map In Tunisia using GIS. Finally, the rainfall erosivity values may vary significantly depending on the equations used due to the original condition in which they were developed. It is crucial to select a proper equation that can be adapted to local conditions, especially based on climatic regions. On the other hand, the spatial distribution of rainfall erosivity can be highly dependent on the number and the distribution of stations.

Characterization of simulated rainfall at macro- and micro-structural levels represents a major research task in order to realistically extrapolate laboratory results to the field scale. Different rainfall simulation systems produce different erosivity relationships. However, Meshesha et al. (2016) identified additional new sources of variation, which are poor simulator maintenance and the simulator’s experimental settings, and showed that these factors may give rise to disagreements and conflicts in experimental results. Understanding raindrop generation under certain simulated conditions in the laboratory is key for attribution of simulated raindrops characteristics to different simulator designs. In this comment, issues related to the attribution of kinetic energy and drop size to rainfall simulator design are discussed, in order to shed further light on Meshesha et al.’s (2016) work, and its wider context.

L’erosione idrica del suolo dipende sia da fattori naturali sia da azioni antropiche ed è notevolmente influenzata dalla aggressività della precipitazione (la cosiddetta erosività, R). Per valutazioni accurate di questo fattore climatico è necessario disporre di ampie serie storiche di dati di pioggia con aggregazione sub-oraria che, purtroppo, sono invece disponibili solo per un numero ridotto di stazioni, peraltro spesso distribuite in modo poco uniforme su un territorio di interesse. L’ammodernamento delle reti pluviometriche con sensori automatici ha reso più agevole disporre di tali informazioni, ma solo per gli ultimi 10-15 anni. Pertanto, soprattutto per scopi tecnico-applicativi, è necessario ricorrere a metodi di stima semplificati che utilizzano principalmente dati di pioggia giornaliera, solitamente più numerosi e rilevati in stazioni ben distribuite spazialmente. Dal punto di vista operativo, è altresì necessario fornire indicazioni circa il grado di incertezza associata all’uso di relazioni empiriche semplificate rispetto a metodi più rigorosi. In questo studio sono stati utilizzati dati di pioggia giornaliera in Regione Campania aggiornati al 2017 e sono state utilizzate tre diverse espressioni empiriche per stimare l’indice R. I risultati ottenuti sono stati confrontati con quelli che derivano da un metodo di stima più accurato e hanno confermato l’importanza di analizzare i caratteri di erosività in termini stagionali. Le analisi hanno anche consentito di identificare i periodi dell’anno in cui sono da attendersi maggiori incertezze nelle stime semplificate dell’aggressività della precipitazione.

A produção de sedimentos provocada pelo impacto das gotas da chuva e o seu transporte pelo escoamento superficial têm consequências diretas sobre a qualidade e a fertilidade do solo. O uso de simuladores de chuva e de lisímetros volumétricos vêm sendo utilizados para compreender os processos erosivos do solo. Este trabalho teve por objetivo avaliar a produção e o transporte de sedimentos em três formas de manejo de solo (plantio convencional sem e com mobilização do solo e plantio direto) a partir de chuvas simuladas em lisímetros volumétricos no município de Pejuçara-RS, Brasil. A erosividade da chuva apresentou correlação (0,97) com a energia cinética da chuva. O plantio direto apresentou menor concentração de sedimentos (0,18 a 0,37 mg L-1) e menor perda de solo (0,12 kg ha-1). O plantio convencional sem revolvimento apresentou concentração de sedimentos de 0,35 a 0,91 mg L-1 e perda de solo de 0,25 kg ha-1. O plantio convencional com revolvimento apresentou as maiores concentrações de sedimentos (1,82 a 5,59 mg L-1) e perdas de solo (2,75 kg ha-1). ABSTRACT Soil losses in different cropping systems thought simulated rain in volumetric lysimiters. Sediment production by the rain drop impact and transport caused by runoff have direct

This paper presents certain characteristics of trends in rainfall erosivity density (ED), that have not been so far investigated in depth in the current literature. Global warming is expected to increase the intensity of rainfall in Europe and consequently to increase the soil erosion rates. This potential may have a significant impact, especially on Greece, which is inflicted by the phenomenon of desertification. Raw pluviograph data were acquired from the Greek National Bank of Hydrological and Meteorological Information for 108 stations. Precipitation time series values were cleared from noise and errors and the ratio of missing values was computed. Erosive rainfalls were identified, their return period was determined using Intensity–Duration–Frequency (IDF) curves and erosivity values were computed. A Monte Carlo method was utilized to assess the impact of missing values ratio to the computation of annual erosivity (R) and ED values. It was found that the R values are underestimated in a linear way, while ED is more robust against the presence of missing precipitation values. Indicatively, the R values are underestimated by 49%, when only 50% of the erosive rainfall events are used while at the same time the estimation error of ED is 20%. Using predefined quality criteria for coverage and time length a subset of 18 stations was selected. Their annual ED values, as well as the samples' autocorrelation and partial autocorrelation functions were computed, in order to investigate the presence of stochastic trends. Subsequently, Kendall's Tau was used in order to yield a measure of the monotonic relationship between annual ED values and time. Finally, the hypothesis that ED values are affected by elevation was tested. In conclusion: a) it is suggested to compute ED for the assessment of erosivity in Greece instead of the direct computation of R, b) stationarity of ED was found for the majority of the selected stations, in contrast to reported precipitation trends for the same time period and c) the hypothesis that ED values are not correlated to elevation could not be rejected.

La erosión del suelo por efecto de la escorrentía superficial es una de las principales preocupaciones ambientales en las cuencas hidrográficas ecuatorianas. Los modelos empíricos de erosión del suelo están basados en procesos que utilizan el factor R como índice de erosividad de la lluvia. Para obtener valores de los factores condicionantes de la erosión del suelo mediante el modelo Revised Universal Soil Loss Equation (RUSLE), se requieren mediciones de lluvia de alta resolución en un pequeño intervalo de tiempo; cuando estos datos no están disponibles, se utiliza información ráster satelital. El objetivo de este trabajo fue analizar las interrelaciones entre los factores condicionantes de la erosión del suelo en cuencas hidrográficas de Ecuador, como insumo para la construcción de un modelo espacial de pérdida del suelo. La metodología consistió en la delimitación de las cuencas y el cálculo de los factores de elevación y pendiente mediante un Modelo de Elevación Digital (DEM) de 1 km de resolución, que fue mejorado mediante los Softwares R y QGIS a una resolución de 4 km. La información de (a) cobertura del suelo se obtuvo desde Global Land Cover Database, a través del componente global del Servicio de Monitoreo Terrestre de Copernicus; el factor (b) Litología se recuperó desde la World Global Lithological Map Database; y los datos del (c) factor R (erosividad de la lluvia) se procesaron a partir del Global Rainfall Erosivity Database. Los resultados indicaron que el promedio de elevación de las cuencas ecuatorianas oscila entre 0 a 2200 msnm. El promedio de las pendientes va decreciendo conforme las cuencas se van alejando de las proximidades a la Cordillera de Los Andes, evidenciándose una mayor variación en las cuencas que drenan hacia el Pacífico. En cuanto a la cobertura vegetal del suelo, la mayor parte corresponde a bosques cerrados y vegetación herbácea. Desde el punto de vista geológico, hay una gran variedad de afloramientos litológicos correspondientes a rocas de distintas naturalezas, lo cual le confiere a cada unidad hidrográfica en particular, distintos niveles de erodabilidad. El factor R mostró un comportamiento regular en las áreas próximas a la Cordillera de Los Andes mientras que, en los sectores próximos al océano Pacífico, los valores se mostraron más irregulares. La información raster satelital recuperada y geoprocesada mediante herramientas y software propios de las Tecnologías de la Información Geográfica, permitieron reclasificarla para un mejor análisis de cada uno de los factores condicionantes de la pérdida del suelo por erosión en cuencas hidrográficas ecuatorianas, así como de las interrelaciones existentes entre ellos. Esto permitirá construir un modelo espacial de la erosión del suelo en Ecuador, herramienta de gran valor para la gestión y conservación de los recursos naturales que coexisten en sus cuencas hidrográficas.

The susceptibility of physical environment to the occurrence of mass movements in a mountainous catchment is mainly linked to the impact of rainfall on the soil as a trigger element. The aim of this research is to analyze the erosive potential of rainfalls on the Chichiriviche River catchment, in order to check if their characteristics are related with the activation of erosive processes. The methodology considered the processing of monthly and annual total and mean rainfall data, and rainfall annual maximum sheeting, with which the following analyzes were conducted: spatio-temporal distribution of rainfall; frequency analysis of extreme events; and estimate of the Annual Fournier Index (AFI), Modified Fournier Index (MFI), Rainfall Concentration Index (RCI), Total Erosivity Index (TEI), and the Rain Erosive Potential Index (R). The spatial distribution of rainfall in the area is controlled mainly by the altitude (relief), and its annual regime is characterized by a greater proportion of pluviometric amounts at year end. The rainfall maximum intensities ranging between 56.43 mm/h and 81.95 mm/h for events of 1 hour and 100-year return period. In general terms the AFI, MFI, RCI, TEI and R indicate a low to moderate rainfall erosive potential, so it is concluded that the occurrence of mass removal processes is more related to extraordinary rainfall as isolated punctual events.

In this article, a comparison is presented of empirical equations to machine learning methods for the estimation and imputation of rainfall erosivity values, associated with significant amounts of rainfall measurements that are missing in the available recording rain gauge data of the Greek Hydroscope database. The empirical equations are mainly based on exponential relations between erosivity and rainfall, while the machine learning methods employed in this paper are feed-forward neural networks with Bayesian regularization and ridge regression with nonlinear transformation. The data came from 81 measuring stations of the Ministry of the Environment and Energy. In the employed algorithms, the output was the weekly cumulative erosivity value, which resulted from processing the data of all rain gauges and pluviographs, while the input data consisted of the weekly cumulative rainfall, the month, the co-ordinates and the elevation of the station, as well as the number of days for which...

L’erosione idrica del suolo dipende sia da fattori naturali sia da azioni antropiche ed è notevolmente influenzata dalla aggressività della precipitazione (la cosiddetta erosività, R). Per valutazioni accurate di questo fattore climatico è necessario disporre di ampie serie storiche di dati di pioggia con aggregazione sub-oraria che, purtroppo, sono invece disponibili solo per un numero ridotto di stazioni, peraltro spesso distribuite in modo poco uniforme su un territorio di interesse. L’ammodernamento delle reti pluviometriche con sensori automatici ha reso più agevole disporre di tali informazioni, ma solo per gli ultimi 10-15 anni. Pertanto, soprattutto per scopi tecnico-applicativi, è necessario ricorrere a metodi di stima semplificati che utilizzano principalmente dati di pioggia giornaliera, solitamente più numerosi e rilevati in stazioni ben distribuite spazialmente. Dal punto di vista operativo, è altresì necessario fornire indicazioni circa il grado di incertezza associata all’uso di relazioni empiriche semplificate rispetto a metodi più rigorosi. In questo studio sono stati utilizzati dati di pioggia giornaliera in Regione Campania aggiornati al 2017 e sono state utilizzate tre diverse espressioni empiriche per stimare l’indice R. I risultati ottenuti sono stati confrontati con quelli che derivano da un metodo di stima più accurato e hanno confermato l’importanza di analizzare i caratteri di erosività in termini stagionali. Le analisi hanno anche consentito di identificare i periodi dell’anno in cui sono da attendersi maggiori incertezze nelle stime semplificate dell’aggressività della precipitazione.

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 1971-2000, using precipitation records that suffer from a significant volume of missing values. In order to overcome the data limitations, an intermediate step is applied using the calculation of monthly erosivity density, which is more robust to the presence of missing values. Spatial Quantile Regression Forests, a data driven algorithm that imitates kriging without the need of strict statistical assumptions, was utilized and validated, in order to create maps of R and its uncertainty using error propagation. The monthly average precipitation for the historical period 1971-2000 estimated by five (5) Global Circulation Models-Regional Climatic Models were validated against observed values and the one with the best performance was used to estimate projected changes of R in Greece for the future time period 2011-2100 and two different greenhouse gases concentration scenarios. The main findings of this study are: (a) the mean annual R in Greece is 1039 MJ·mm/ha/h/y, with a range between 405.1 and 3160.2 MJ·mm/ha/h/y. The highest values are calculated at the mountain range of Pindos and the lowest at central Greece; (b) the monthly R maps adhere to the spatiotemporal characteristics of precipitation depth and intensities over the country; (c) the projected R values, as an average over Greece, follow the projected changes of precipitation of climatic models, but not in a spatially homogenous way.

Declining productivity of lateritic soil of India indicates land degradation, which occurs mostly through soil erosion and physical or chemical deterioration of soil. In the tropical wetdry type of morpho-climatic region (western Birbhum and Eastern Jharkhand districts of India), the primary process of soil erosion usually takes place when falling raindrops beat the bare soil surface in heavy storm. Rainsplash erosion, sheet and inter-rill erosion (overland flow), rill and gully erosion are considered as major forms of water erosion. Due to high erodibility of lateritic soil, bare soil cover (deforestation), high erosivity of monsoonal rainfall, low clay, moisture and organic matter content of soil, the study area (border area between Rampurhat I block of Birbhum district, West Bengal and Shikaripara block of Dumka district, Jharkhand) is very much susceptible to rill and gully erosion. So the present article is an attempt to quantify the soil loss and sediment yield of catchments o...

Uno de los principales problemas ambientales atribuidos al uso de la tierra en la cuenca del río Chichiriviche (estado Vargas, Venezuela), está vinculado con la actividad agrícola que, origina condiciones para la activación de procesos erosivos en el suelo, frente al impacto de la lluvia. Bajo este contexto, se analizaron el potencial erosivo y las intensidades extremas de las precipitaciones, como indicadores de erosividad en la cuenca montañosa del río Chichiriviche. Se analizaron la distribución espacio-temporal de las precipitaciones, la frecuencia de los eventos extremos de las intensidades y se estimaron los Índices de Fournier Anual, Modificado de Fournier, Concentración de las Precipitaciones, Erosividad Total y Potencial Erosivo de las Lluvias. La distribución espacial de las lluvias está controlada principalmente por la altitud; su régimen anual se caracteriza por una mayor concentración de precipitaciones a finales de año. Las intensidades máximas oscilaron entre 56,43 mm/h y 81,95 mm/h para eventos de 1 hora de duración y 100 años de período de retorno. Los índices estimados, indicaron bajo a moderado potencial erosivo de las lluvias. La ocurrencia de procesos erosivos en el área estudiada, está relacionada con la intensidad de las precipitaciones extraordinarias como eventos puntuales aislados.

In this paper, we review the erosivity studies conducted in Chitrakoot to verify the quality and representativeness of the results generated and to provide a greater understanding of the rainfall erosivity in Chitrakoot. We searched the Google Scholar databases and in recent journals and dissertations to obtain the following information: latitude, longitude, city, states, length of records (15-years from 1999 to 2013), precipitation (daily based), equations calculated and respective determination coefficient .

The daily rainfall erosivity in Chitrakoot ranged from 39.846 to 61.841 MJ mm/ha/h. Rainfall erosivity indices, based on intensity and the amount of rainfall, were computed for all precipitations. The lowest values were found in June and the highest values were found in the August in the Chitrakoot region. These equations can be useful to map rainfall erosivity for the entire area.