Abstract:The need for continuous recording rain gauges makes it difficult to determine the rainfall erosivity factor (Rfactor) of the Universal Soil Loss Equation in regions without good spatial and temporal data coverage. In particular, the R-factor is only known at 16 rain gauge stations in the Madrid Region (Spain). The objectives of this study were to identify a readily available estimate of the R-factor for the Madrid Region and to evaluate the effect of rainfall record length on estimate precision and accuracy. Five estimators based on monthly precipitation were considered: total annual rainfall (P), Fournier index (F), modified Fournier index (MFI), precipitation concentration index (PCI) and a regression equation provided by the Spanish Nature Conservation Institute (R ICONA ). Regression results from 8 calibration stations showed that MFI was the best estimator in terms of coefficient of determination and root mean squared error, closely followed by P. Analysis of the effect of record length indicated that little improvement was obtained for MFI and P over 5-year intervals. Finally, validation in 8 additional stations supported that the equation R = 1.05·MFI computed for a record length of 5 years provided a simple, precise and accurate estimate of the R-factor in the Madrid Region.
The Universal Soil Loss Equation (USLE) has been widely used to predict the long-term average annual soil loss associated with sheet and rill erosion caused by rainfall and runoff. Initially developed for agricultural purposes, it was later modified and extended for estimating soil loss on cut and fill slopes. In addition to this valuable equation, management of cut and fill slopes also requires a classification system in order to prioritize erosion control measures and maintenance operations based on estimated soil loss. Currently available erosion classifications (focused on soil productivity and sustainable agriculture) may not be relevant to transportation infrastructure slopes, in which soil loss rates are dramatically higher and primarily concern operational service conditions. Therefore, the objective of this study was to develop a classification system based on soil loss rates computed by the USLE model that can be valuable for management of cut and fill slopes. It was assumed that erosion classifications developed for agriculture could be used in cut and fill slopes as long as the ratio between respective soil loss rates was applied. It was found that the topographic factor, which accounts for the length and steepness of the slope, may explain most of the difference in respective soil loss rates. For typical values of the topographic factor, soil loss rates on cut and fill slopes were found to be roughly ten times greater than those on agriculture. Based on this finding, a new classification with six erosion levels was developed. Finally, validation analysis showed that the proposed classification successfully ranked soil loss rates reported in the literature into different categories.
Calculation of the rainfall erosivity factor (R-factor) of the (R)USLE model requires continuous recording rain gauges, which may limit its use in areas without good temporal data coverage. In mainland Spain, the Nature Conservation Institute (ICONA) determined the Rfactor at few selected pluviographs, so simple estimates of the R-factor are definitely of great interest. The objectives of this study were: (1) to identify a readily available estimate of the R-factor for mainland Spain; (2) to discuss the applicability of a single (global) estimate based on analysis of regional results; (3) to evaluate the effect of record length on estimate precision and accuracy; and (4) to validate an available regression model developed by ICONA. Four estimators based on monthly precipitation were computed at 74 rainfall stations throughout mainland Spain. The regression analysis conducted at a global level clearly showed the modified Fournier index (MFI) ranked first among all assessed indexes. Applicability of this preliminary global model across mainland Spain was evaluated by analyzing regression results obtained at a regional level. It was found that three contiguous regions of Eastern Spain (Catalonia, Valencian Community and Murcia) could have a different rainfall erosivity pattern, so a new regression analysis was conducted by dividing mainland Spain into two areas: Eastern Spain and Plateau-lowland area. A comparative analysis concluded that the bi-areal regression model based on MFI for a 10-year record length provided a simple, precise and accurate estimate of the R-factor in mainland Spain. Finally, validation of the regression model proposed by ICONA showed that R-ICONA index overpredicted the R-factor by approximately 19%.Keywords: rainfall erosivity, R-factor, Universal Soil Loss Equation, modified Fournier index, soil erosion, Spain Resumen La necesidad de disponer de un registro continuo de la precipitación dificulta el cálculo del índice de erosión pluvial (factor R) del modelo (R)USLE en zonas sin un buen registro temporal. En la España peninsular, el Instituto para la Conservación de la Naturaleza (ICONA) determinó el factor R en un reducido número de pluviógrafos, por lo que es de gran interés disponer de una herramienta que permita estimar el factor R de manera sencilla. Los objetivos de este estudio fueron: (1) identificar un estimador del factor R en la España peninsular; (2) discutir la aplicabilidad de un único modelo de estimación global a partir de los resultados obtenidos a nivel regional; (3) analizar el efecto de la longitud del intervalo de cálculo en la precisión y exactitud de las estimaciones; y (4) evaluar el modelo de regresión disponible propuesto por ICONA. Para ello se calcularon cuatro estimadores basados en la precipitación mensual en 74 estaciones pluviométricas repartidas por la geografía peninsular. El análisis de regresión llevado a cabo demostró que el índice de Fournier modificado (MFI) es el mejor estimador. La aplicabilidad del modelo global generado inicialmente se eva...
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