Estimating the rainfall erosivity factor from monthly precipitation data in the Madrid Region (Spain)

Open access


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 (RICONA). 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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Angulo-Martínez M. Beguería S. 2009. Estimating rainfall erosivity from daily precipitation records: a comparison among methods using data from the Ebro Basin (NE Spain). J. Hydrol. 379 111-121.

  • Arnoldus H.M.J. 1980. An approximation of the rainfall factor in the Universal Soil Loss Equation. In: De Boodt M. Gabriels D. (Eds.): Assessment of Erosion. John Wiley & Sons Chichister pp. 127-132.

  • Bonilla C.A. Vidal K.L. 2011. Rainfall erosivity in Central Chile. J. Hydrol. 410 1-2 126-133.

  • Brown L.C. Foster G.R. 1987. Storm erosivity using idealized intensity distributions. Trans. ASAE 30 2 379-386.

  • Colotti E. 2004. Aplicabilidad de los datos de lluvia horaria en el cálculo de la erosidad. [Applicability of hourly rainfall data to erosion analysis]. Fondo Editorial de Humanidades y Educación. Departamento de Publicaciones. Universidad Central de Venezuela Caracas. (In Spanish.)

  • Diodato N. 2004. Estimating RUSLE’s rainfall factor in the part of Italy with a Mediterranean rainfall regime. Hydrol. Earth Syst. Sci. 8 1 103-107.

  • Diodato N. Bellochi G. 2007. Estimating monthly (R)USLE climate input in a Mediterranean region using limited data. J. Hydrol. 345 224-236.

  • Ferro V. Giordano G. Iovino M. 1991. Isoerosivity and erosion risk map for Sicily. Hydrol. Sci. J. 36 6 549-564.

  • Ferro V. Porto P. Yu B. 1999. A comparative study of rainfall erosivity estimation for southern Italy and southeastern Australia. J. Hydrol. Sci. 44 1 3-24.

  • Fournier F. 1960. Climat et érosion. La relation entre l'érosion du sol par l'eau et les précipitations atmosphériques. [Relationship between soil erosion by water and rainfall]. Presses Universitaires de France Paris. (In French.)

  • Hudson N. 1971. Soil Conservation. Cornell University Press Ithaca.

  • ICONA 1988. Agresividad de la lluvia en España. Valores del factor R de la ecuación universal de pérdidas de suelo. [Rainfall erosivity in Spain. R-factor values for the Universal Soil Loss Equation]. Ministerio de Agricultura Pesca y Alimentación Madrid. (In Spanish.)

  • Lal R. 1976. Soil erosion on alfisols in Western Nigeria III- Effects of rainfall characteristics. Geoderma 16 389-401.

  • Lee J.H. Heo J.H. 2011. Evaluation of estimation methods for rainfall erosivity based on annual precipitation in Korea. J. Hydrol. 409 1-2 30-48.

  • Loureiro N. Coutinho M. 2001. A new procedure to estimate the RUSLE EI30 index based on monthly rainfall data and applied to the Algarve region Portugal. J. Hydrol. 250 1-4 12-18.

  • Oliver J.E. 1980. Monthly precipitation distribution: a comparative index. Professional Geographer 32 3 300-309.

  • Onchev N.G. 1985. Universal index for calculating rainfall erosivity. In: El-Swaify S.A. Moldenhauer W.C. Lo A. (Eds.): Soil erosion and conservation. Soil Conservation Society of America Ankeny pp. 424-431.

  • Petkovšek G. Mikoš M. 2004. Estimating the R factor from daily rainfall data in the sub-Mediterranean climate of southwest Slovenia. Hydrol. Sci. J. 49 5 869-877.

  • Renard K.G. Freimund J.R. 1994. Using monthly precipitation data to estimate the R-factor in the revised USLE. J. Hydrol. 157 287-306.

  • Salako F.K. 2008. Rainfall variability and kinetic energy in Southern Nigeria. Climatic Change 86 151-164.

  • Smithen A.A. Schulze R.E. 1982. The spatial distribution in Southern Africa of rainfall erosivity for use in the Universal Soil Loss Equation. Water SA 8 2 74-78.

  • Van der Knijff J.M. Jones R.J.A. Montanarella L. 2000. Soil erosion risk. Assessment in Europe. Office for Official Publications of the European Communities Luxemburg.

  • Wischmeier W.H. 1959. A rainfall erosion index for a Universal Soil-Loss Equation. Soil Sci. Soc. Am. Proc. 23 3 246-249.

  • Wischmeier W.H. Smith D.D. 1961. A universal equation for predicting rainfall-erosion losses - An aid to conservation farming in humid regions. ARS Special Report 22-66. U.S. Department of Agriculture Washington D.C.

  • Wischmeier W.H. Smith D.D. 1965. Predicting rainfall erosion losses from cropland East of the Rocky Mountains. Agriculture Handbook No. 282. U.S. Department of Agriculture Washington D.C.

  • Wischmeier W.H. Smith D.D. 1978. Predicting rainfall erosion losses. A guide to conservation planning. Agriculture Handbook No. 537. U.S. Department of Agriculture Washington D.C.

  • Yu B. Rosewell C.J. 1996. A robust estimator of the R factor for the Universal Soil Loss Equation. Trans. ASAE 39 559-561.

  • Yu B. Hashim G.M. Eusof Z. 2001. Estimating the R-factor with limited rainfall data: a case study from peninsular Malaysia. J. Soil Water Conserv. 56 2 101-105.

Journal information
Impact Factor

IMPACT FACTOR 2018: 2.023
5-year IMPACT FACTOR: 2.048

CiteScore 2018: 2.07

SCImago Journal Rank (SJR) 2018: 0.713
Source Normalized Impact per Paper (SNIP) 2018: 1.228

Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 451 294 16
PDF Downloads 475 386 47