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Santiago Begueria - One of the best experts on this subject based on the ideXlab platform.
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computation of rainfall Erosivity from daily precipitation amounts
Science of The Total Environment, 2018Co-Authors: Santiago Begueria, Roberto Serranonotivoli, Miquel TomasburgueraAbstract:Abstract Rainfall Erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known Erosivity indices. One issue with this and other Erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall Erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases.
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mapping monthly rainfall Erosivity in europe
Science of The Total Environment, 2017Co-Authors: Cristiano Ballabio, Pasquale Borrelli, Jonathan Spinoni, Katrin Meusburger, Silas Michaelides, Santiago Begueria, Andreas Klik, Saso Petan, Miloslav Janecek, Preben OlsenAbstract:Rainfall Erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall Erosivity both spatially and temporally. During winter months, significant rainfall Erosivity is present only in part of the Mediterranean countries. A sudden increase of Erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall Erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual Erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of Erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different Erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly Erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall Erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.
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monthly rainfall Erosivity conversion factors for different time resolutions and regional assessments
Water, 2016Co-Authors: Panos Panagos, Pasquale Borrelli, Cristiano Ballabio, Jonathan Spinoni, Katrin Meusburger, Silas Michaelides, Santiago Begueria, Andreas Klik, Saso Petan, Michaela HrabalikovaAbstract:As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall Erosivity has been added to REDES. Rainfall Erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall Erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall Erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly Erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall Erosivity and presents the spatial patterns of monthly rainfall Erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall Erosivity.
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trends in rainfall Erosivity in ne spain at annual seasonal and daily scales 1955 2006
Hydrology and Earth System Sciences, 2012Co-Authors: Marta Angulomartinez, Santiago BegueriaAbstract:Rainfall Erosivity refers to the ability of precipitation to erode soil, and depends on characteristics such as its total volume, duration, and intensity and amount of energy released by raindrops. Despite the relevance of rainfall Erosivity for soil degradation prevention, very few studies have addressed its spatial and temporal variability. In this study the time variation of rainfall Erosivity in the Ebro Valley (NE Spain) is assessed for the period 1955–2006. The results show a general decrease in annual and seasonal rainfall Erosivity, which is explained by a decrease of very intense rainfall events whilst the frequency of moderate and low events increased. This trend is related to prevailing positive conditions of the main atmospheric teleconnection indices affecting the West Mediterranean, i.e. the North Atlantic Oscillation (NAO), the Mediterranean Oscillation (MO) and the Western Mediterranean Oscillation (WeMO).
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estimating rainfall Erosivity from daily precipitation records a comparison among methods using data from the ebro basin ne spain
Journal of Hydrology, 2009Co-Authors: Marta Angulomartinez, Santiago BegueriaAbstract:Summary Among the major factors controlling soil erosion, as vegetation cover or soil erodibility, rainfall Erosivity has a paramount importance since it is difficult to predict and control by humans. Accurate estimation of rainfall Erosivity requires continuous rainfall data; however, such data rarely demonstrate good spatial and temporal coverage. Daily weather records are now commonly available, providing good coverage that better represents rainfall intensity behavior than do more aggregated rainfall data. In the present study annual rainfall Erosivity was estimated from daily rainfall records, and compared to data obtained employing the RUSLE R factor procedure. A spatially-dense precipitation database of high temporal resolution (15 min) was used. Two methodologies were applied: (i) daily rainfall Erosivity estimated using several parametric models, and, (ii) annual rainfall Erosivity estimated by regression-based techniques employing several intensity precipitation indices and the modified Fournier index. To determine the accuracy of estimates, several goodness-of-fit and error statistics were computed in addition to a spatial distribution comparison. The daily rainfall Erosivity models accurately predicted annual rainfall Erosivity. Parametric models with few combined parameters and a periodic function simulating intra-annual rainfall behavior provided the best results. Where daily rainfall records were not available, good estimates of annual rainfall Erosivity were also obtained using regression-based techniques based on 5-day maximum precipitation events, the maximum wet spell duration, and the ratio between the lengths of average wet and dry spells. Inherent limitations remain in the use of daily weather records for estimating rainfall Erosivity. Future research should focus on incorporating measures of natural rainfall properties of the particular region, including kinetic energy and intensity, and their effects on the soil.
Panos Panagos - One of the best experts on this subject based on the ideXlab platform.
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global rainfall Erosivity assessment based on high temporal resolution rainfall records
Scientific Reports, 2017Co-Authors: Panos Panagos, Pasquale Borrelli, Katrin Meusburger, Andreas Klik, Kyoung Jae Lim, Jae E Yang, Chiyuan Miao, Nabansu Chattopadhyay, Seyed Hamidreza Sadeghi, Zeinab HazbaviAbstract:The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall Erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall Erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall Erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global Erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall Erosivity was estimated to be 2,190 MJ mm ha−1 h−1 yr−1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall Erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.
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towards estimates of future rainfall Erosivity in europe based on redes and worldclim datasets
Journal of Hydrology, 2017Co-Authors: Panos Panagos, Cristiano Ballabio, Jonathan Spinoni, Katrin Meusburger, Christine Alewell, Pasquale BorrelliAbstract:Abstract The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall Erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall Erosivity data have the potential to become useful knowledge to predict future rainfall Erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall Erosivity based on climate change scenarios. The mean rainfall Erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha−1 h−1 yr−1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April–September). The output results report a pan-European projection of future rainfall Erosivity taking into account the uncertainties of the climatic models.
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monthly rainfall Erosivity conversion factors for different time resolutions and regional assessments
Water, 2016Co-Authors: Panos Panagos, Pasquale Borrelli, Cristiano Ballabio, Jonathan Spinoni, Katrin Meusburger, Silas Michaelides, Santiago Begueria, Andreas Klik, Saso Petan, Michaela HrabalikovaAbstract:As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall Erosivity has been added to REDES. Rainfall Erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall Erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall Erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly Erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall Erosivity and presents the spatial patterns of monthly rainfall Erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall Erosivity.
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rainfall Erosivity in italy a national scale spatio temporal assessment
International Journal of Digital Earth, 2016Co-Authors: Pasquale Borrelli, Nazzareno Diodato, Panos PanagosAbstract:ABSTRACTSoil erosion by water is a serious threat for the Mediterranean region. Raindrop impacts and consequent runoff generation are the main driving forces of this geomorphic process of soil degradation. The potential ability for rainfall to cause soil loss is expressed as rainfall Erosivity, a key parameter required by most soil loss prediction models. In Italy, rainfall Erosivity measurements are limited to few locations, preventing researchers from effectively assessing the geography and magnitude of soil loss across the country. The objectives of this study were to investigate the spatio-temporal distribution of rainfall Erosivity in Italy and to develop a national-scale grid-based map of rainfall Erosivity. Thus, annual rainfall Erosivity values were measured and subsequently interpolated using a geostatistical approach. Time series of pluviographic records (10-years) with high temporal resolution (mostly 30-min) for 386 meteorological stations were analysed. Regression-kriging was used to interpol...
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spatio temporal analysis of rainfall Erosivity and Erosivity density in greece
Catena, 2016Co-Authors: Panos Panagos, Pasquale Borrelli, Cristiano Ballabio, Katrin MeusburgerAbstract:Rainfall Erosivity considers the effects of rainfall amount and intensity on soil detachment. Rainfall Erosivity is most commonly expressed as the R-factor in the Universal Soil Loss Equation (USLE) and its revised version, RUSLE. Several studies focus on spatial analysis of rainfall Erosivity ignoring the intra-annual variability of this factor. This study assesses rainfall Erosivity in Greece on a monthly basis in the form of the RUSLE R-factor, based on a 30-min data from 80 precipitation stations covering an average period of almost 30 years. The spatial interpolation was done through a Generalised Additive Model (GAM). The observed intra-annual variability of rainfall Erosivity proved to be high. The warm season is 3 times less erosive than the cold one. November, December and October are the most erosive months contrary to July, August and May which are the least erosive. The proportion between rainfall Erosivity and precipitation, expressed as Erosivity density, varies throughout the year. Erosivity density is low in the first 5 months (January–May) and is relatively high in the remaining 7 months (June–December) of the year. The R-factor maps reveal also a high spatial variability with elevated values in the western Greece and Peloponnesus and very low values in Western Macedonia, Thessaly, Attica and Cyclades. The East–West gradient of rainfall Erosivity differs per month with a smoother distribution in summer and a more pronounced gradient during the winter months. The aggregated data for the 12 months result in an average R-factor of 807 MJ mm ha− 1 h− 1 year− 1 with a range from 84 to 2825 MJ mm ha− 1 h− 1 year− 1. The combination of monthly R-factor maps with vegetation coverage and tillage maps contributes to better monitor soil erosion risk at national level and monthly basis.
M A Nearing - One of the best experts on this subject based on the ideXlab platform.
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expected climate change impacts on rainfall Erosivity over iran based on cmip5 climate models
Journal of Hydrology, 2021Co-Authors: Mahmood Azari, Alireza Oliaye, M A NearingAbstract:Abstract Climate change has some important implications for soil resource and sediment loads of rivers which may affect sustainable food production and use of river for water supply. The present study assessed the potential impacts of climate change on rainfall Erosivity over Iran. Climate change data from three GCMs under two RCPs 4.5 and 8.5 were statistically downscaled using the LARS-WG model. Regional R-factor regression equations were applied to derive future rainfall Erosivity. The results of this study revealed that the average annual R-factor for the observed data was 268 MJ mm ha − 1 h − 1 yr − 1. Projections revealed that in the 2040–2060 period under RCP 4.5, except arid regions in the East, other regions (encompassing 66.1%) of Iran will experience an increase in the R-factor by 2.5–22.5 % mainly in the mountain areas in the North and Northwest. Projections for RCP 4.5 in the period of 2060–2080, showed that in the arid zones in the Southeast, Center and East of Iran, the rainfall Erosivity will decrease; however, in RCP 8.5, an increase in rainfall will cause an increase in rainfall Erosivity in most parts of Iran. The results also indicated that soil erosion and sediment load in the 2040–2060 period may have some serious implications; therefore new strategies must be developed to attenuate the adverse effects of climate change on soil erosion and sediment deposition in reservoirs.
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rainfall Erosivity essential historical conceptual and practical perspectives for continued application
2021Co-Authors: Dennis C Flanagan, Ryan P Mcgehee, Puneet Srivastava, M A NearingAbstract:Abstract Although the scientific knowledge behind rainfall Erosivity has been around for nearly a century, the essential theory has not changed much since its inception. However, increasingly diverse precipitation data sources, differences in calculation methods, and both human- and software-induced errors have given rise to discrepancies in Erosivity maps being used today. The original Erosivity index was developed using breakpoint (pluviograph) rainfall data, but it is generally applied to fixed-interval data for national modeling efforts with USLE-based models (and their kin). This chapter provides an overview of Erosivity science today, how the field has evolved from its inception, a brief review of its foundational theory, how information is preserved or lost when applied to different precipitation datasets, and how all this information can be used to leverage better modeling practices and outcomes.
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rainfall Erosivity an overview of methodologies and applications
Vadose Zone Journal, 2017Co-Authors: Shuiqing Yin, M A Nearing, Pasquale Borrelli, Xiaochan XueAbstract:The rainfall Erosivity factor (R factor) is one of six erosion factors in the Universal Soil Loss Equation (USLE), which together reflect the combined effects that cause soil loss by rill and interrill erosion on hillslopes by precipitation. It is defined as the summation of event EI30 (the product of kinetic energy and maximum 30-min intensity) over a year and calculated based on rainfall hyetograph data. The R factor was developed in the various versions of the USLE, including the definition of the individual event and the criterion for selecting events used in the calculation, the equation used to estimate the unit kinetic energy from the rainfall intensity, the estimation of Erosivity from the snowmelt and thaw, and Erosivity mapping. Most research on rainfall Erosivity deals with any of three aspects: developing estimation methods for deriving Erosivity from courser resolution rainfall data (such as daily, monthly, and annual) but with greater spatial and temporal coverages than those from hyetograph data; preparing Erosivity maps including those for annual average, monthly, and 10-yr recurrence Erosivity; and documenting temporal trends in Erosivity. Rainfall Erosivity research on these three aspects is summarized to provide a greater understanding of the R factor.
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rainfall Erosivity an historical review
Catena, 2017Co-Authors: M A Nearing, Shuiqing Yin, Pasquale Borrelli, V O PolyakovAbstract:Abstract 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 data. USLE Erosivity combines the energy of the rainfall and the maximum continuous 30-min intensity in the event. Energy of rainfall is estimated as a function of the storm intensity through the rainfall event. The USLE Erosivity has been used effectively for conservation planning purposes for more than 5 decades. When the USLE was replaced by the Revised Universal Soil Loss Equation (RUSLE), a new energy-intensity equation was adopted. The new equation was not extensively tested prior to adoption, leads to significant under-predictions of Erosivity, and was later replaced in RUSLE2. The RUSLE energy-intensity equation is no longer recommended by the RUSLE and RUSLE2 development teams. RUSLE2 also introduced the concept of Erosivity density, which resulted in significant improvements in the calculations and mapping of rainfall Erosivity. Calculations of Erosivity as a whole are entirely based on rainfall intensities, and Erosivity is an empirically-based index. The science indicates that the direct role of kinetic energy of rainfall as the driver of hillslope erosion in all cases is not warranted by the overall evidence, because many times the kinetic energy of raindrops is not the driving force behind rill erosion. The USLE Erosivity empirically explains much of the variance in the soil loss from natural rainfall erosion plots.
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projected climate change impacts in rainfall Erosivity over brazil
Scientific Reports, 2017Co-Authors: Andre Almagro, M A Nearing, Paulo Tarso Sanches De Oliveira, Stefan HagemannAbstract:The impacts of climate change on soil erosion may bring serious economic, social and environmental problems. However, few studies have investigated these impacts on continental scales. Here we assessed the influence of climate change on rainfall Erosivity across Brazil. We used observed rainfall data and downscaled climate model output based on Hadley Center Global Environment Model version 2 (HadGEM2-ES) and Model for Interdisciplinary Research On Climate version 5 (MIROC5), forced by Representative Concentration Pathway 4.5 and 8.5, to estimate and map rainfall Erosivity and its projected changes across Brazil. We estimated mean values of 10,437 mm ha-1 h-1 year-1 for observed data (1980-2013) and 10,089 MJ mm ha-1 h-1 year-1 and 10,585 MJ mm ha-1 h-1 year-1 for HadGEM2-ES and MIROC5, respectively (1961-2005). Our analysis suggests that the most affected regions, with projected rainfall Erosivity increases ranging up to 109% in the period 2007-2040, are northeastern and southern Brazil. Future decreases of as much as -71% in the 2071-2099 period were estimated for the southeastern, central and northwestern parts of the country. Our results provide an overview of rainfall Erosivity in Brazil that may be useful for planning soil and water conservation, and for promoting water and food security.
Marta Angulomartinez - One of the best experts on this subject based on the ideXlab platform.
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trends in rainfall Erosivity in ne spain at annual seasonal and daily scales 1955 2006
Hydrology and Earth System Sciences, 2012Co-Authors: Marta Angulomartinez, Santiago BegueriaAbstract:Rainfall Erosivity refers to the ability of precipitation to erode soil, and depends on characteristics such as its total volume, duration, and intensity and amount of energy released by raindrops. Despite the relevance of rainfall Erosivity for soil degradation prevention, very few studies have addressed its spatial and temporal variability. In this study the time variation of rainfall Erosivity in the Ebro Valley (NE Spain) is assessed for the period 1955–2006. The results show a general decrease in annual and seasonal rainfall Erosivity, which is explained by a decrease of very intense rainfall events whilst the frequency of moderate and low events increased. This trend is related to prevailing positive conditions of the main atmospheric teleconnection indices affecting the West Mediterranean, i.e. the North Atlantic Oscillation (NAO), the Mediterranean Oscillation (MO) and the Western Mediterranean Oscillation (WeMO).
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estimating rainfall Erosivity from daily precipitation records a comparison among methods using data from the ebro basin ne spain
Journal of Hydrology, 2009Co-Authors: Marta Angulomartinez, Santiago BegueriaAbstract:Summary Among the major factors controlling soil erosion, as vegetation cover or soil erodibility, rainfall Erosivity has a paramount importance since it is difficult to predict and control by humans. Accurate estimation of rainfall Erosivity requires continuous rainfall data; however, such data rarely demonstrate good spatial and temporal coverage. Daily weather records are now commonly available, providing good coverage that better represents rainfall intensity behavior than do more aggregated rainfall data. In the present study annual rainfall Erosivity was estimated from daily rainfall records, and compared to data obtained employing the RUSLE R factor procedure. A spatially-dense precipitation database of high temporal resolution (15 min) was used. Two methodologies were applied: (i) daily rainfall Erosivity estimated using several parametric models, and, (ii) annual rainfall Erosivity estimated by regression-based techniques employing several intensity precipitation indices and the modified Fournier index. To determine the accuracy of estimates, several goodness-of-fit and error statistics were computed in addition to a spatial distribution comparison. The daily rainfall Erosivity models accurately predicted annual rainfall Erosivity. Parametric models with few combined parameters and a periodic function simulating intra-annual rainfall behavior provided the best results. Where daily rainfall records were not available, good estimates of annual rainfall Erosivity were also obtained using regression-based techniques based on 5-day maximum precipitation events, the maximum wet spell duration, and the ratio between the lengths of average wet and dry spells. Inherent limitations remain in the use of daily weather records for estimating rainfall Erosivity. Future research should focus on incorporating measures of natural rainfall properties of the particular region, including kinetic energy and intensity, and their effects on the soil.
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mapping rainfall Erosivity at a regional scale a comparison of interpolation methods in the ebro basin ne spain
Hydrology and Earth System Sciences, 2009Co-Authors: Marta Angulomartinez, Manuel Lopezvicente, Sergio M Vicenteserrano, Santiago BegueriaAbstract:Abstract. Rainfall Erosivity is a major causal factor of soil erosion, and it is included in many prediction models. Maps of rainfall Erosivity indices are required for assessing soil erosion at the regional scale. In this study a comparison is made between several techniques for mapping the rainfall Erosivity indices: i) the RUSLE R factor and ii) the average EI30 index of the erosive events over the Ebro basin (NE Spain). A spatially dense precipitation data base with a high temporal resolution (15 min) was used. Global, local and geostatistical interpolation techniques were employed to produce maps of the rainfall Erosivity indices, as well as mixed methods. To determine the reliability of the maps several goodness-of-fit and error statistics were computed, using a cross-validation scheme, as well as the uncertainty of the predictions, modeled by Gaussian geostatistical simulation. All methods were able to capture the general spatial pattern of both Erosivity indices. The semivariogram analysis revealed that spatial autocorrelation only affected at distances of ~15 km around the observatories. Therefore, local interpolation techniques tended to be better overall considering the validation statistics. All models showed high uncertainty, caused by the high variability of rainfall Erosivity indices both in time and space, what stresses the importance of having long data series with a dense spatial coverage.
Pasquale Borrelli - One of the best experts on this subject based on the ideXlab platform.
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rainfall Erosivity an overview of methodologies and applications
Vadose Zone Journal, 2017Co-Authors: Shuiqing Yin, M A Nearing, Pasquale Borrelli, Xiaochan XueAbstract:The rainfall Erosivity factor (R factor) is one of six erosion factors in the Universal Soil Loss Equation (USLE), which together reflect the combined effects that cause soil loss by rill and interrill erosion on hillslopes by precipitation. It is defined as the summation of event EI30 (the product of kinetic energy and maximum 30-min intensity) over a year and calculated based on rainfall hyetograph data. The R factor was developed in the various versions of the USLE, including the definition of the individual event and the criterion for selecting events used in the calculation, the equation used to estimate the unit kinetic energy from the rainfall intensity, the estimation of Erosivity from the snowmelt and thaw, and Erosivity mapping. Most research on rainfall Erosivity deals with any of three aspects: developing estimation methods for deriving Erosivity from courser resolution rainfall data (such as daily, monthly, and annual) but with greater spatial and temporal coverages than those from hyetograph data; preparing Erosivity maps including those for annual average, monthly, and 10-yr recurrence Erosivity; and documenting temporal trends in Erosivity. Rainfall Erosivity research on these three aspects is summarized to provide a greater understanding of the R factor.
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rainfall Erosivity an historical review
Catena, 2017Co-Authors: M A Nearing, Shuiqing Yin, Pasquale Borrelli, V O PolyakovAbstract:Abstract 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 data. USLE Erosivity combines the energy of the rainfall and the maximum continuous 30-min intensity in the event. Energy of rainfall is estimated as a function of the storm intensity through the rainfall event. The USLE Erosivity has been used effectively for conservation planning purposes for more than 5 decades. When the USLE was replaced by the Revised Universal Soil Loss Equation (RUSLE), a new energy-intensity equation was adopted. The new equation was not extensively tested prior to adoption, leads to significant under-predictions of Erosivity, and was later replaced in RUSLE2. The RUSLE energy-intensity equation is no longer recommended by the RUSLE and RUSLE2 development teams. RUSLE2 also introduced the concept of Erosivity density, which resulted in significant improvements in the calculations and mapping of rainfall Erosivity. Calculations of Erosivity as a whole are entirely based on rainfall intensities, and Erosivity is an empirically-based index. The science indicates that the direct role of kinetic energy of rainfall as the driver of hillslope erosion in all cases is not warranted by the overall evidence, because many times the kinetic energy of raindrops is not the driving force behind rill erosion. The USLE Erosivity empirically explains much of the variance in the soil loss from natural rainfall erosion plots.
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global rainfall Erosivity assessment based on high temporal resolution rainfall records
Scientific Reports, 2017Co-Authors: Panos Panagos, Pasquale Borrelli, Katrin Meusburger, Andreas Klik, Kyoung Jae Lim, Jae E Yang, Chiyuan Miao, Nabansu Chattopadhyay, Seyed Hamidreza Sadeghi, Zeinab HazbaviAbstract:The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall Erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall Erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall Erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global Erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall Erosivity was estimated to be 2,190 MJ mm ha−1 h−1 yr−1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall Erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.
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towards estimates of future rainfall Erosivity in europe based on redes and worldclim datasets
Journal of Hydrology, 2017Co-Authors: Panos Panagos, Cristiano Ballabio, Jonathan Spinoni, Katrin Meusburger, Christine Alewell, Pasquale BorrelliAbstract:Abstract The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall Erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall Erosivity data have the potential to become useful knowledge to predict future rainfall Erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall Erosivity based on climate change scenarios. The mean rainfall Erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha−1 h−1 yr−1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April–September). The output results report a pan-European projection of future rainfall Erosivity taking into account the uncertainties of the climatic models.
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mapping monthly rainfall Erosivity in europe
Science of The Total Environment, 2017Co-Authors: Cristiano Ballabio, Pasquale Borrelli, Jonathan Spinoni, Katrin Meusburger, Silas Michaelides, Santiago Begueria, Andreas Klik, Saso Petan, Miloslav Janecek, Preben OlsenAbstract:Rainfall Erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall Erosivity both spatially and temporally. During winter months, significant rainfall Erosivity is present only in part of the Mediterranean countries. A sudden increase of Erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall Erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual Erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of Erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different Erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly Erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall Erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.