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1 February 2014 Determining Rainfall Erosivity in Costa Rica: A Practical Approach
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Abstract

Rainfall erosivity (R-factor) is an important variable used in soil erosion estimation models. In Costa Rica, the R-factor was computed for 106 stations across the country by Wilhelm-Günther Vahrson in 1990. These results provided the main input information for this study, which used them to estimate the R-factor for Costa Rica. Regression equations were computed to estimate the R-factor for the Caribbean slope, the Pacific slope, and the country as a whole. Forward stepwise analysis and multiple regression analysis were employed to determine the regression coefficients for each developed equation. Elevation and monthly rainfall had a strong influence on the definition of the R-factor equations. The Modified Fournier Index variable was included only in the national-scale equation as a good proxy for the mean annual precipitation effect at each site. Inclusion of elevation in all equations reflects the importance of the transitional effect of high-intensity convective rainfall in the lowlands and low-intensity orographic rainfall in the highlands. This study provides an easy way to estimate the R-factor using regression equations that require only simple and readily available geophysical information. The use of these equations in conjunction with soil and land-use maps as well as digital elevation models will allow the estimation and evaluation of soil erosion on a watershed scale in Costa Rica. This will also improve the application of other hydrological models that require soil erosion as an input variable to estimate sediment yields.

Introduction

Soil erosion and sediment yield reduce water storage capacity in lakes and reservoirs, modify fluvial and coastal geomorphology, pollute water resources, and affect the hydrology and ecology of downstream ecosystems (Summerfield 1991; Wood and Armitage 1997; Syvitski et al 2005; Zhang et al 2006; Huggett 2007; Bilotta and Brazier 2008). Highlands and steep mountains are very susceptible to soil degradation; this process is triggered by the steep slopes, land-cover change, and high rainfall erosivity capacity (Zhou et al 2008; García-Ruiz 2010). Therefore, information on soil erosion estimations in mountainous regions is highly important in efforts to prevent suspended sediment yield and loss of soil. However, in mountainous regions, particularly in the tropics, there is a lack of physical data and practical hydrological models to help in the design and evaluation of soil conservation programs. On the other hand, it is well known that the quality and geographical coverage of hydrometeorological data in poor countries are obstacles to hydrological studies and water resource planning (Kim et al 2005). This is an important worldwide issue for watershed management, land-use planning, and environmental impact analysis, since soil erosion estimation is an important way to evaluate the impact of soil conservation practices and land-use change.

Soil erosion prediction and conservation have been an important issue worldwide, and the development of empirical methods to estimate annual rates has facilitated estimates of soil loss from specific land units (eg plots, catchments, and watersheds). Soil erosion estimates from regions with scarce physical data have employed the universal soil loss equation (USLE) (Wischmeier and Smith 1978) or the revised version (RUSLE) (Renard et al 1996a; Calvo-Alvarado and Gregory 1997). However, these methods had not been extensively used in tropical regions due to the reduced availability of physical data to calibrate the parameters, such as rainfall erosivity, soil erodibility, land cover, topography, and soil conservation (Calvo 1998).

Rainfall erosivity (the R-factor) expresses the potential capacity of raindrops to cause soil detachment, a process that is strongly dependent on rainfall intensity (Ferro et al 1999; Angulo-Martínez et al 2009). The R-factor is expressed in megajoules (MJ) mm ha−1 h−1 y−1 and is defined as the mean annual sum of individual storm erosion index values: Total storm kinetic energy (MJ) multiplied by the maximum rainfall intensity in 30 minutes (Wischmeier and Smith 1978). The R-factor is particularly difficult to calibrate due to the data set that is necessary and to the required geographical sampling distribution over time and space; such requirements are impossible to fulfill in poor tropical nations. In tropical regions, rainfall intensities of 40 mm h−1 to more than 160 mm h−1 can occur in time intervals of 10 seconds to 20 minutes (Ajayi and Ofoche 1984). Consequently, R-factor estimation requires extensive records of rainfall events at small time intervals (30 minutes or less) to extract needed information such as rainfall duration, intensity, and total volume. In addition, strong rainstorms in the tropics are related to convective precipitation mechanisms, producing from 1% to 8% of the total annual rainfall in the Neotropics (Rickenbach et al 2012); however, in the highlands, the orographic rainfall increases as the result of the condensation of air humidity as temperature decreases with altitude, which has an impact on the definition of the R-factor that has not been fully studied in tropical regions.

Recently, the widespread use of geographical information systems (GIS) in combination with remote-sensing technologies has prompted the use of the USLE or RUSLE to simulate the impact of land-use change on total watershed soil erosion and sediment yields (Calvo-Alvarado and Gregory 1997; Calvo 1998; Jain and Das 2010; Chen et al 2011; Prasannakumar et al 2011; Lin et al 2013). As a consequence, a possibility has arisen of developing alternative methods of estimating the R-factor as a function of geographical variables that are easy to obtain in data-scarce locations. In Honduras, R-factor determination was carried out using linear regressions, obtaining good results based on elevation and mean annual precipitation (Mikhailova et al 1997). However, information related to methods for estimating the R-factor and determining soil erosion in tropical regions is still scarce. Hence, this article provides a simplified regression relationship between the R-factor (dependent variable) and easily obtained geographical data (independent variables) at all locations across Costa Rica.

Methodology

Site description

Costa Rica is located in the Neotropical region (8°–11°N; 82°–86°W), with an annual precipitation of 1300–8500 mm y−1. Costa Rica has special hydrological characteristics due to a combination of several factors. First, the Talamanca, Central, and Guanacaste Mountain Ranges divide the country longitudinally into the Caribbean and Pacific slopes. Because of the steep topography and the prevailing trade winds, orographic precipitation has a strong influence. The northwest–southeast ridges of the Caribbean slope are rainy almost all year-round. The northeast trade winds are active from November through March. From May to October, the entire country is influenced by the passage of the Intertropical Convergence Zone (ITCZ) and by the southwest trade winds. As a result of these influences, only the Pacific slope experiences a dry season between December and April, because the humidity coming from the northeast trade winds is retained on the Caribbean slope. Temporal and spatial distributions of rainfall in Costa Rica are also modulated by changes in oceanic and weather phenomena such as El Niño (warm phase) and its opposite, La Niña (cold phase), which are considered the highest expressions of climatic variability (Calvo 1990; Vahrson 1990; Manso et al 2005; Guzmán and Calvo-Alvarado 2013). Rainwater chemical analysis has detected a strong marine influence on Costa Rica's rains (Hendry et al 1984; Eklund et al 1997; Clark et al 1998), as the result of water vapor transport by the wind from the Caribbean Sea and Pacific Ocean to inland locations.

Data

Rainfall erosivity values (the dependent variable) were obtained from Vahrson (1990) for 115 meteorological stations across Costa Rica (Figure 1). The R-factor was originally reported in 100 foot ton inch acre−1 h−1, and these values were translated into MJ mm ha−1 h−1 y−1 by multiplying each value by 17.02 (Renard et al 1996b). The mean annual rainfall erosivity estimated by Vahrson (1990) made use of Equation 1, proposed by Woodward (1975). This equation calculated rainfall erosivity (R) based on maximum annual rainfall (MAR) events of 6 hours duration and 2 years return period (MAR2;6). Fernández and Salas (1999) define the return period “as the average number of trials (usually years) to the first occurrence of an event of magnitude greater than a predefined critical event.” The MAR2;6 records were obtained from pluviograph data with 10 years of records from each of the selected meteorological stations. As a consequence of Costa Rica's position on the Central American isthmus, extreme events are very common due to the frequent impact of tropical storms and convective storms associated with the passage of the ITCZ. Consequently, extreme rainfall events analysis was performed using the Gumbel (1945) method, which is well applicable to the climatic conditions of Costa Rica (Vahrson and Fallas 1988).

i0276-4741-34-1-48-e01.gif

FIGURE 1 

Location of the meteorological stations employed in the study. (Map by Programa de Manejo y Conservación de Recursos Naturales-ITCR, 2013)

i0276-4741-34-1-48-f01.tif

Data from the meteorological stations included mean monthly precipitation, mean annual precipitation (MAP), elevation, and the Modified Fournier Index (F index). The F index as proposed by Arnoldus (1980), was used in this analysis as a substitute for precipitation in the R-factor estimation. Ferro et al (1999) concluded that this index is linearly correlated to the R-factor. The F index depends on mean monthly rainfall (pi) and mean annual precipitation (P) as shown in Equation 2.

i0276-4741-34-1-48-e02.gif

Based on data availability, 106 of the 115 meteorological stations were selected for this analysis. The complete data set is presented in the Supplemental data, Table S1 ( http://dx.doi.org/10.1659/MRD-JOURNAL-D-13-00062.S1).

Data analysis

The data were organized into three groups to account for regional effects, as previously discussed by Calvo-Alvarado et al (2009): data from the Pacific slope, the Caribbean slope, and a combination of the two to allow analysis on a national scale. For each group, the R-factor was determined through multiple regression analysis. The selected independent variables are those that are easy to obtain for each site, and hence variables such as rainfall intensity were omitted in this analysis. The nonselection of rainfall intensities is due to the fact that in Costa Rica—and generally in countries in the tropics—rainfall networks with extended and complete records (more than 10 years) usually collect only daily rainfall. The main purpose of our work was to develop equations to estimate the R-factor for any site in Costa Rica that lacks this information, and this can only be accomplished using variables that are easy to obtain.

Forward stepwise analysis was applied to select the best independent variables to estimate the R-factor for each station. This analysis selected the independent variables used to perform the multiple regression analysis obtaining the predictor coefficients. The software STATISTICA 6.0 (StatSoft Inc 2003) was employed in all the analyses, to which a probability value of 0.05 was applied. All the assumptions for normal distribution were considered and tested to verify the validity of the analysis.

Results

The precipitation (P) from the 106 stations is 3077.3 mm y−1. March is the driest month (84.6 mm mo−1), and October is the wettest (408.1 mm mo−1). The Pacific slope has a well-defined dry season from December to March, with precipitation rates lower than 100 mm mo−1, and another short dry season between June and July. The Caribbean slope lacks a dry season due to the constant influence of northeast trade winds responsible for precipitation rates >100 mm mo−1, but rainfall diminishes during March (Figure 2). P is 2725.2 mm y−1 for the Pacific slope (60 stations) and 3536.7 mm y−1 for the Caribbean slope (46 stations). This difference of more than 800 mm y−1 is the result of the humidity carried by the trade winds from the Caribbean Sea.

FIGURE 2 

Monthly rainfall distribution in Costa Rica based on data from 106 stations.

i0276-4741-34-1-48-f02.tif

Vahrson (1990) identified a decreasing trend of R-factor with elevation. The Costa Rican lowlands show higher erosive rains than the uplands. The lowlands have a variety of rains as a consequence of local convective and frontal systems, which lead to a wide range of R-factor values. On the other hand, locations higher than 2000 m above sea level (masl) reported R-factor values lower than 5000 MJ mm ha−1 h−1 y−1 (Figure 3). The characteristic rain of the uplands results from the orographic effect and the presence of relatively few local convective systems. R-factor values decrease at higher elevations on both slopes, the Pacific and the Caribbean.

FIGURE 3 

R-factor in relation to elevation in Costa Rica. (Modified from Vahrson 1990)

i0276-4741-34-1-48-f03.tif

Increasing P magnifies the R-factor on both slopes (Figure 4A). A similar trend is observed with the F index, due to its linear relation with the P value (Figure 4B); however, a wide variation among F index and R-factor values can be observed along the value ranges (Figure 4C).

FIGURE 4 

Relations between mean annual precipitation and R-factor (A), mean annual precipitation and F index (B), and F index and R-factor (C) in Costa Rica, based on data from 106 stations.

i0276-4741-34-1-48-f04.tif

Forward stepwise analysis underlines the effect at the national scale of monthly precipitation registered in January, April, and November, plus the F index and elevation (Equation 3). The choice of monthly precipitation depicts the importance of precipitation seasonality, while elevation introduces topography as a key variable that indirectly considers the effect of orographic rainfall in the R-factor definition. The R-factor on the Caribbean slope is affected by April and July precipitation, as well as elevation (Equation 4), while for the Pacific slope, it is defined by September precipitation and elevation (Equation 5). In these equations, R represents the R-factor; F is the Modified Fournier Index (mm); E is elevation (masl); p with subscripts denotes monthly precipitation (mm) (mm month−1) for January (Jan), April (Apr), July (Jul), September (Sep), and November (Nov); Ns refers to the national scale; Cs refers to the Caribbean slope; and Ps refers to the Pacific slope; and p (no subscript) is the probability value

i0276-4741-34-1-48-e03.gif
i0276-4741-34-1-48-e04.gif
i0276-4741-34-1-48-e05.gif

where i0276-4741-34-1-48-e08.gif, i0276-4741-34-1-48-e09.gif, and i0276-4741-34-1-48-e10.gifare the rainfall erosivity values (MJ mm ha−1 h−1 yr−1) at national scale, Caribbean slope, and Pacific slope, respectively. Figure 5 shows the relationship between the measured and calculated R-factors.

FIGURE 5 

Relationships between measured and calculated R-factor calculations based on Equations 1, 2, and 3.

i0276-4741-34-1-48-f05.tif

Discussion

Hastenrath (1968) noted the marked rainfall differences between the Caribbean and Pacific slopes of Central America. This differentiation is consistent with the pattern in Costa Rica, where the continental divide works as a topographic barrier that affects precipitation patterns (Calvo 1990). This study provided a strong argument to develop regional equations to estimate the R-factor.

Additionally, a strong elevation effect is included in all equations estimating the R-factor across Costa Rica, with negative altitude coefficients in all equations. Goovaerts (1999) remarked on the potential use of elevation to calculate rainfall erosivity in Portugal and underlined the power of digital elevation models for this purpose. Even with high MAP, Costa Rica shows a constant reduction in the R-factor above 1000 masl on both slopes. This pattern is probably associated with the dominant effects of low-intensity orographic rainfall in the highlands, which contrast with the effects of the frequent high-intensity convective storms of the lowlands.

Guswa et al (2007), using water isotopic signatures, showed the presence of orographic precipitation in Monteverde, Costa Rica (850 masl). This type of precipitation accumulates more than 2000 mm y−1 over 3000 masl (Horn 1989, cited by DeForest Safford 1999). High elevations in Costa Rica are characterized by unimodal rainfall seasonality, with 9 months of precipitation greater than 50 mm mo−1 (DeForest Safford 1999). In these highlands, orographic precipitation is registered over long periods with low rainfall intensity, illustrating the prevailing effect of orographic precipitation on mountaintops.

The R-factor for the Pacific slope is strongly affected by September's rainfall due to the high water volume just after the short dry season that takes place between June and July. On the Caribbean slope, April and July have the most influence on the R-factor; this effect is related to the seasonal transition from dry to wet in this region. In April, the rains begin to increase in volume; they reach a peak in July. On a national scale, the R-factor shows a more complex interaction with monthly precipitation. The three months that are included in the national-scale equation mark the edges of the dry season (January and April) and the end of the rainy season (November).

Seasonal rainfall distribution has been shown to have an important effect on the R-factor in other regions around the world. Wilkes and Sawada (2005) proved the effect of this variable in the Great Lakes region of North America, while the effects of global phenomena such as El Niño and La Niña have been shown to modify the volume and intensity of rainfall in Peru (Romero et al 2007).

The R-factor has been positively correlated with MAP and the F index, but the manner of estimating varies among authors. Mannaerts and Gabriels (2000) suggested a linear relationship between MAP and the R-factor. Equation 6 depicts the procedure used by Ferro et al (1999) and Diodato (2004), employing the F index and MAP as predictor variables. Eltaif et al (2010) worked with MAP as an independent variable to estimate the R-factor on an annual basis in Jordan, as shown in Equation 7.

i0276-4741-34-1-48-e06.gif

where x is the P or F index.

i0276-4741-34-1-48-e07.gif

where x is the P, while a and b represent coefficients that describe the local conditions.

In Costa Rica, P was not a significant variable in the prediction of the R-factor, probably because the R-factor is a combined effect of elevation, the passage of ITCZ, and the trade winds, which leads to a multitude of combinations of orographic, convective, and frontal storms during the year in each site. The F index, on the other hand, was included in the national-scale analysis as the best proxy to gauge the P seasonality.

R-factors have been frequently estimated for mountainous countries such as Switzerland (Meusburger et al 2012), Peru (Romero et al 2007), and Spain and Italy with their Mediterranean rainfall regime (Ferro et al 1991, 1999; Brath et al 2002; Diodato 2004, 2006; Angulo-Martínez et al 2009; Diodato and Bellocchi 2009). Only a few R-factor estimates have been conducted for tropical countries such as Costa Rica (Vahrson 1990) or for arid and semiarid countries such as Jordan (Eltaif et al 2010) and Cape Verde (Mannaerts and Gabriels 2000). Extrapolation and prediction of the R-factor have also been performed through maps in South Africa (Smithen and Schulze 1982) and Costa Rica (Vahrson 1990).

The work performed in Costa Rica by Vahrson (1990) provides a base map of rainfall erosivity, but today's technology allows the spatial estimation of variables such as the R-factor, enabling the use of well-defined equations to interpolate this parameter. The estimation equations presented here can support the production of more accurate maps such as was done for the Great Lakes region by Wilkes and Sawada (2005). These equations can also facilitate the application of the USLE or RUSLE soil erosion equations by using digital elevation models and GIS.

Conclusions

Data availability is an important factor to consider in the analysis of hydrological processes. The lack of meteorological stations with long, reliable records in the tropics precludes the application of many hydrological models used worldwide. Empirical models for estimating soil erosion, such as USLE and RUSLE, require a good series of precipitation data to compute the R-factor. Because highlands are the areas most susceptible to soil erosion, an accurate R-factor estimation is highly necessary. The limited number of meteorological stations in Costa Rica's mountain regions underlines the importance of prediction equations for estimating R-factors.

This study provides an easy way to estimate the R-factor using regression equations that require only simple and easy-to-collect geophysical data. The use of these equations in conjunction with soil maps, land-use and ground-cover maps, and digital elevation models will allow robust estimation and evaluation of soil erosion on a watershed scale. This will also improve the application of other hydrological models to estimation of suspended sediment yields, which requires soil erosion information as the main input variable.

The application of the equations developed in this study to other Central American countries that share a similar climate must be conducted with caution, due to microclimatic differences and the topographical characteristics of the places under evaluation. As a consequence, it is important to generate information on true R-factor for a few stations in order to test these equations and evaluate their precision and applicability.

Open access article: please credit the authors and the full source.

Acknowledgments

This article is part of a research project funded by the US National Science Foundation (Grant DEB 0516516) for support of LTREB: Dynamics of Stream Ecosystem Responses across Gradients of Reforestation and Changing Climate in a Tropical Dry Forest, a study conducted by the Stroud Water Research Center. This paper is also part of the research project: Tropi-Dry (Human, Ecological and Biophysical Dimension on Tropical Dry Forest) a collaborative research network sponsored by the Inter-American Institute for Global Change Research (IAI) CRN II # 021, which was funded by the US National Science Foundation (Grant GEO-0452325). We also acknowledge the financial and logistical support of the Vicerectoría de Investigación y Extensión, Instituto Tecnológico de Costa Rica.

REFERENCES

1.

GO Ajayi EBC Ofoche 1984. Some tropical rainfall rate characteristics at Ile-Ife for microwave and millimeter wave applications. Journal of Climate and Applied Meteorology 23:562–567. Google Scholar

2.

M Angulo-Martínez M López-Vicente SM Vicente-Serrano S Beguería 2009. Mapping rainfall erosivity at a regional scale: A comparison of interpolation methods in the Ebro Basin (NE Spain). Hydrology and Earth System Sciences 13:1907–1920. Google Scholar

3.

HMJ Arnoldus 1980. An approximation of the rainfall factor in the universal soil loss equation. In: M de Boodt D Gabriels editors. Assessment of Erosion. Chichester, United Kingdom: John Wiley & Sons, pp 127–132. Google Scholar

4.

GS Bilotta RE Brazier 2008. Understanding the influence of suspended solids on water quality and aquatic biota. Water Research 42:2849–2861. Google Scholar

5.

A Brath A Castellarin A Montanari 2002. Assessing the effects of land-use changes on annual average gross erosion. Hydrology and Earth System Sciences 6:255–265. Google Scholar

6.

JC Calvo 1990. Water resources development in Costa Rica 1970–2000. Hydrological Sciences Journal 35:185–196. Google Scholar

7.

JC Calvo 1998. Suspended sediment yield prediction models for Costa Rican watersheds. Hydrology in the Humid Tropic Environment 253:27–33. Google Scholar

8.

JC Calvo-Alvarado JD Gregory 1997. Predicting Mean Annual Runoff and Suspended Sediment Yield in Rural Watersheds in North Carolina. Report No. 307. Raleigh, NC: Water Resources Research Institute of the University of North Carolina. Google Scholar

9.

JC Calvo-Alvarado C Jiménez-Rodríguez V Jiménez 2009. Ecuación para estimar el factor de erosividad de lluvia R de la EUPS en Costa Rica. In: XVIII Congreso Latinoamericano de la Ciencia del Suelo. 16–20 November 2009. San José, Costa Rica: Sociedad Latinoamericana de la Ciencia del Suelo/Asociación Costarricense de la Ciencia del Suelo. Google Scholar

10.

T Chen R Niu P Li L Zhang B Du 2011. Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: A case study in Miyun Watershed, North China. Environmental Earth Sciences 63:533–541. Google Scholar

11.

KL Clark NM Nadkarni D Schaefer HL Gholz 1998. Cloud water and precipitation chemistry in a tropical montane forest, Monteverde, Costa Rica. Atmospheric Environment 32:1595–1603. Google Scholar

12.

H DeForest Safford 1999. Brazilian Páramos II. Macro- and mesoclimate of the campos de altitude and affinities with high mountain climates of the tropical Andes and Costa Rica. Journal of Biogeography 26:713–737. Google Scholar

13.

N Diodato 2004. Estimating RUSLE's rainfall factor in the part of Italy with a Mediterranean rainfall regime. Hydrology and Earth System Sciences 8:103–107. Google Scholar

14.

N Diodato 2006. Predicting RUSLE (revised universal soil loss equation) monthly erosivity index from readily available rainfall data in Mediterranean area. Environmentalist 26:63–70. Google Scholar

15.

N Diodato G Bellocchi 2009. Assessing and modelling changes in rainfall erosivity at different climate scales. Earth Surface Processes and Landforms 34:969–980. Google Scholar

16.

TJ Eklund WH McDowell CM Pringle 1997. Seasonal variation of tropical precipitation chemistry: La Selva, Costa Rica. Atmospheric Environment 31:3903–3910. Google Scholar

17.

NI Eltaif MA Gharaibeh F Al-Zaitawi MN Alhamad 2010. Approximation of rainfall erosivity factors in north Jordan. Pedosphere 20:711–717. Google Scholar

18.

B Fernández JD Salas 1999. Return period and risk of hydrologic events. I: Mathematical formulation. Journal of Hydrologic Engineering 4:297–307. Google Scholar

19.

V Ferro G Giordano M Iovino 1991. Isoerosivity and erosion risk map for Sicily. Hydrological Sciences Journal (Journal des Sciences Hydrologiques) 36:549–564. Google Scholar

20.

V Ferro P Porto B Yu 1999. A comparative study of rainfall erosivity estimation for southern Italy and southeastern Australia. Hydrological Sciences Journal 44:3–24. Google Scholar

21.

JM García-Ruiz 2010. The effects of land uses on soil erosion in Spain: A review. Catena 81:1–11. Google Scholar

22.

P Goovaerts 1999. Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 34:227–242. Google Scholar

23.

EJ Gumbel 1945. Floods estimated by the probability method. Engineering News Record 134:833–837. Google Scholar

24.

AJ Guswa AL Rhodes SE Newell 2007. Importance of orographic precipitation to the water resources of Monteverde, Costa Rica. Advances in Water Resources 30:2098–2112. Google Scholar

25.

I Guzmán J Calvo-Alvarado 2013. Planning and development of Costa Rica water resources: Current status and perspectives. Tecnología en Marcha 26:52–63. Google Scholar

26.

S Hastenrath 1968. Fourier analysis of Central American rainfall. Archiv für Meteorologie, Geophysik und Bioklimatologie B 16:81–94. Google Scholar

27.

CD Hendry CW Berish ES Edgerton 1984. Precipitation chemistry at Turrialba, Costa Rica. Water Resources Research 20:1677–1684. Google Scholar

28.

SP Horn 1989. Postfire vegetation development in the Costa Rican paramos. Madroño 36:93–114. Google Scholar

29.

RJ Huggett 2007. Fundamentals of Geomorphology. New York, NY: Routledge. Google Scholar

30.

M Jain D Das 2010. Estimation of sediment yield and areas of soil erosion and deposition for watershed prioritization using GIS and remote sensing. Water Resources Management 24:2091–2112. Google Scholar

31.

J Kim P Saunders J Finn 2005. Rapid assessment of soil erosion in the Rio Lempa Basin, Central America, using the universal soil loss equation and geographic information systems. Environmental Management 36:872–885. Google Scholar

32.

S Lin C Jing N Coles V Chaplot N Moore J Wu 2013. Evaluating DEM source and resolution uncertainties in the Soil and Water Assessment Tool. Stochastic Environmental Research and Risk Assessment 27:209–221. Google Scholar

33.

CM Mannaerts D Gabriels 2000. Rainfall erosivity in Cape Verde. Soil and Tillage Research 55:207–212. Google Scholar

34.

P Manso W Stolz JC Fallas 2005. El régimen de la precipitación en Costa Rica. Ambientico September: 7–8. Google Scholar

35.

K Meusburger A Steel P Panagos L Montanarella C Alewell 2012. Spatial and temporal variability of rainfall erosivity factor for Switzerland. Hydrology and Earth System Sciences 16:167–177. Google Scholar

36.

EA Mikhailova RB Bryant SJ Schwager SD Smith 1997. Predicting rainfall erosivity in Honduras. Soil Science Society of America Journal 61:273–279. Google Scholar

37.

V Prasannakumar R Shiny N Geetha H Vijith 2011. Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: A case study of Siruvani River watershed in Attapady valley, Kerala, India. Environmental Earth Sciences 64:965–972. Google Scholar

38.

KG Renard GR Foster GA Weesies DK McCool DC Yoder 1996a. Rainfall-runoff erosivity factor (R). In: Predicting Rainfall Erosion Losses: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). USDA Agriculture Handbook, pp 19–64. Google Scholar

39.

KG Renard GR Foster GA Weesies DK McCool DC Yoder 1996b. Appendix A. Conversion to SI metric system. In: Predicting Rainfall Erosion Losses: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). USDA Agriculture Handbook, pp 325–328 . Google Scholar

40.

TM Rickenbach R Nieto-Ferreira RP Barnhill SW Nesbitt 2012. Seasonal and regional differences in the rainfall and intensity of isolated convection over South America. International Journal of Climatology 33:2002–2007. Google Scholar

41.

C Romero G Baigorria L Stroosnijder 2007. Changes of erosive rainfall for El Niño and La Niña years in the northern Andean highlands of Peru. Climatic Change 85:343–356. Google Scholar

42.

AA Smithen RE Schulze 1982. The spatial distribution in southern Africa of rainfall erosivity for use in the universal soil loss equation. Water SA 8:74–78. Google Scholar

43.

StatSoft Inc. 2003. STATISTICA 6.1 (data analysis software system). Version 6.  www.statsoft.comGoogle Scholar

44.

MA Summerfield 1991. Global Geomorphology: An Introduction to the Study of Landforms. Ann Arbor, MI: Longman Scientific & Technical. Google Scholar

45.

JPM Syvitski CJ Vörösmarty AJ Kettner P Green 2005. Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science 308:376–380. Google Scholar

46.

W-G Vahrson 1990. El potencial erosivo de la lluvia en Costa Rica. Agronomía Costarricense 14:15–24. Google Scholar

47.

W-G Vahrson J Fallas 1988. Evaluación de Tres Métodos para Estimar Periodos de Retorno de Lluvias Máximas en 24 Horas en la Estación San José, Costa Rica. Nota de Investigación No. 7. San José, Costa Rica: Instituto Meteorológico Nacional. Google Scholar

48.

G Wilkes M Sawada 2005. Geostatistically derived Great Lakes USLE monthly rainfall erosivity factors. Journal of Great Lakes Research 31:155–165. Google Scholar

49.

WH Wischmeier DD Smith 1978. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning. Washington DC: U.S. Department of Agriculture. Google Scholar

50.

PJ Wood PD Armitage 1997. Biological effects of fine sediment in the lotic environment. Environmental Management 21:203–217. Google Scholar

51.

DE Woodward 1975. Discussion of “estimation of rainfall erosion index.”. American Society of Civil Engineers Journal of Irrigation and Drainage Division 101:245–247. Google Scholar

52.

Q Zhang C Xu S Becker T Jiang 2006. Sediment and runoff changes in the Yangtze River basin during past 50 years. Journal of Hydrology 331:511–523. Google Scholar

53.

P Zhou O Luukkanen T Tokola J Nieminen 2008. Effect of vegetation cover on soil erosion in a mountainous watershed. Catena 75:319–325. Google Scholar

Appendices

Supplemental data

TABLE S1 Data employed in the study.

Found at DOI:  http://dx.doi.org/10.1659/MRD-JOURNAL-D-13-00062.S1 (10.1659_MRD-JOURNAL-D-13-00062.S1.pdf) (243.3 KB PDF).

International Mountain Society
Julio César Calvo-Alvarado, César Dionisio Jiménez-Rodríguez, and Vladimir Jiménez-Salazar "Determining Rainfall Erosivity in Costa Rica: A Practical Approach," Mountain Research and Development 34(1), 48-55, (1 February 2014). https://doi.org/10.1659/MRD-JOURNAL-D-13-00062.1
Accepted: 1 December 2013; Published: 1 February 2014
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