Short communication
Rainfall erosivity in Cape Verde
C.M. Mannaerts
a,*, D. Gabriels
b,1aDivision of Water Resources and Environmental Studies, International Institute of Aerospace Surveys and Earth Sciences ITC,
PO Box 6, 7500 AA, Enschede, Netherlands
bDepartment of Soil Management and Soil Care, Faculty of Applied Biological and Agricultural Sciences,
University of Ghent, Coupure Links, 653, B-9000 Ghent, Belgium
Received 24 September 1999; received in revised form 8 February 2000; accepted 24 February 2000
Abstract
This paper presents rainfall erosivity values derived from a 7-year rainfall recording in the Cape Verde islands, Central East Atlantic. The data set consisted of 63 storm events, continuously registered in 15-min intervals. Kinetic energy of storm rainfall corresponded to established values in other tropical locations. Two algorithms to estimate erosivity, expressed as energy times intensity, using daily rainfall or storm depth and duration as predictor variables are derived. Erosivity of design storms for various return frequencies is calculated for some locations in Santiago island. An indicative range for the annual rainfall erosion R-index is given. Data analysis further showed the extreme seasonal concentration of precipitation and erosivity at this location, with a very high fraction of total annual erosivity contained in the annual maximum 24-h rainfall. #2000 Elsevier Science B.V. All rights reserved.
Keywords:Rainfall; Erosivity; Semiarid; Cape Verde
1. Introduction
The Cape Verde islands are located off West Africa between 14 and 188N and 22±268W in the Atlantic
Ocean (Fig. 1). They are about 500 km from the western most point of the African continent (Dakar, Senegal). The islands constitute an archipelago of volcanic origin composed of nine inhabited islands, which together have a land surface of 4033 km2. They are conventionally classi®ed into two groups, the windward islands of San Antao, San Vincente, Santa Lucia, San Nicolau, Sal, and the leeward island group of Boa Vista, Maio, Santiago, Fogo and Brava. The
climate of Cape Verde is governed by the respective positions of the Azores anticyclone, the more equa-torial Inter Tropical Convergence Zone (ITCZ) and the macro-scale mid-Atlantic air mass movements induced by their seasonal changes of location. The annual cyclical movement of the ITCZ around the equator and its migration to the 10±208 northern
latitudes during the months of July±October brings a temporary southwest monsoonal climate to the Cape Verde during summer. The presence of the ITCZ over the Cape Verde latitudes (15±178N) is, however,
negatively affected by pressure ¯uctuations of the Azores anticyclone and other high altitude air mass ¯uxes in the northern Central Atlantic. An extremely variable rainfall regime results from the oscillations of this regional high and low pressure zones (Babau et al., 1981). Local rainfall amounts also largely depend on elevation above sea level and range from a mere 150 mm annually for the arid coastal zones to around *Corresponding author. Tel.:31-53-487-42-10;
fax:31-53-487-43-36.
E-mail addresses: mannaerts@itc.nl (C.M. Mannaerts), donald.gabriels@rug.ac.be (D. Gabriels)
1Tel.:32-9-264-60-50; fax:32-9-264-52-47.
800 mm in the mountainous areas above 1000 m elevation a.m.s.l. Although annual and monthly rain-fall amounts in Cape Verde are invariably low, daily or storm precipitation amounts can be very high (Da Rocha Faria, 1971). The erosivity of rainfall is a major driving force of many hydrological and erosion pro-cesses. No quantitative data were available for deter-mining rainfall erosivity indices for the Cape Verde islands. Roose (1975) extensively analysed the ero-sivity of the West African rainfall climate, but excluded Cape Verde from his investigations due to lack of data. It is inadvisable to extrapolate from erosivity indices of African continental rainfall sta-tions to the Cape Verde because physiographic con-ditions are very different (Mannaerts, 1985). In view of the lack of published values, it was judged impor-tant, in spite of the short monitoring period, to present erosivity values of the Cape Verdean rainfall climate, based on measured short duration rainfall data.
2. Data analysis
2.1. Storm erosivity
The long-term rainfall climate of the Cape Verde islands is documented for only a few stations (Da Rocha Faria, 1971; Babau, 1983). Historical informa-tion on short durainforma-tion depths and intensity is almost
non-existing. A short record of measured data (Man-naerts, 1992) was therefore used to analyse rainfall erosivity. The location of the recording gauge was San Felipe, Achada das Vacas (latitude: 148590N, long-itude: 238320W, elevation: 220 m a.m.s.l.) in close proximity (8 km) of Praia city (Fig. 1). A record length of 84 months (1980±1987) consisting of 63 recorded storms was analysed at a 15-min interval basis. The series was truncated at 9 mm rainfall, being the lowest rainstorm depth producing measurable runoff and soil loss at 20 m2plot scale (Mannaerts, 1992). The kinetic energy versus storm rainfall depth was evaluated using both the rainfall energy±intensity relationships of Wischmeier and Smith (1978) and Brown and Foster (1987). As shown in Fig. 2, the kinetic energy of storm rainfall in Cape Verde corresponds well to published values in other tropical locations, e.g., the Philippines (White, 1990) or Zambia (Pauwelyn et al., 1988). Erosivity expressed as kinetic energy times maximum 30-min intensity (EI30), was then derived for all
erosive storms (P>9 mm) using a spreadsheet techni-que. A least squares regression model of erosivity on daily rainfall amount was then constructed after log± log transformation of the data points. The bivariate model can be represented by Eq. (1a) or (1b):
logEI30 1:58 logP24ÿ1:14 (1a)
EI30 0:0723 P24
1:58 (1b)
Fig. 1. Location map of the Cape Verde islands and study area.
where EI30 is equal to the rainfall erosivity in
kJ mÿ2mm hÿ1andP24the daily rainfall amount in
mm. The regression relationship (Eq. (1b)) is plotted in Fig. 3 using logarithmic scales. The coef®cient of determination ofR20.90 indicated good correlation between the proposed model and the observed data points in a log±log space. The reliability of the regression line was evaluated by building the 95% con®dence intervals, using the residual estimation variance of the linear regression model. Con®dence bands shown in Fig. 3, illustrate that the accuracy of the regression model is more pronounced around the highest density (mean) of the data points. A single future prediction of storm erosivity from any rainfall depth by this bivariate regression model is, however, very tentative, as illustrated by the 90% con®dence bands for individual prediction, also shown in Fig. 3. This prediction interval is a representation of the range of values that an individual storm erosivity might take for a given rainfall amount. It combines the effects parameter uncertainty as well as the residual or unexplained variance of the regression. Development of a reliable bivariate statistical predic-tion model for rainfall erosivity, based on rainfall amount alone, is therefore quite unrealistic. Daily rainfall amount will always remain an approximate predictor of erosivity and large under or
overestima-tion can be expected at, respectively, low and high storm depths. This can be explained by the inherent natural variation in properties (e.g., duration, high and low intensity periods, maximum intensity) of individual storm rainfall. A multiple regression ana-lysis was then conducted, including besides rainfall amount, also storm duration as second independent predictor variable in the analysis. Here, rain showers separated by a dry spell of 6 h were considered as different events. Erosivity can be estimated using Eq. (2), with knowledge of storm rainfall amount and storm duration.
EI30 0:06 P
1:81Dÿ0:36 (2)
where EI30 is equal to the rainfall erosivity in kJ
mÿ2mm hÿ1,P the storm rainfall in mm andD the rainfall duration in hours. The coef®cient of determi-nation for this multiple regression was R20.95. Eq. (2) was visualised in Fig. 4 for ®ve different durations ranging from 1 to 24 h. It can be seen from Fig. 4 that this envelope of model estimates derived from Eq. (2) ®ts well to the observed data points.
In Cape Verde, it is common practise to use design storms in watershed soil and water conservation engi-neering. Eqs. (1a) and (1b) were used for a ®rst approximation of the erosivity of established design
Fig. 3. Daily rainfall amounts versus rainfall erosivity (expressed asEI30units) and bivariate regression model.
storms in Cape Verde (INIDA, 1995). Table 1 sum-marises design storm depths, erosivities with 90% con®dence band for some locations in Santiago island. An approximate con®dence interval (e) was used here
as (Yÿt(1ÿa)s; Yt(1a)s), whereYequals the design
storm erosivity, predicted with the regression model (Eqs. (1a) and (1b)), s the standard error of the regression andt(1ÿa)the value of Student'st
-distribu-tion with Nÿa27 degrees of freedom and a
con-®dence interval of 90% (Maidment, 1993). We can observe that theoretically a more accurate prediction of design storm erosivity could be made if storm erosivities of the same rainfall series from which the design storms were derived are used, i.e., the annual series of maximum 24-h rainfalls. The length of record of observed short duration data of rainfall did not permit us to perform such a statistical analysis for Cape Verde now.
2.2. Annual and monthly distribution of erosivity
The limited 7-year data set did not permit statistical analysis of annual erosivity. Recorded annual erosivity for the individual years in the monitoring period is therefore only given in Table 2. Annual erosion index values were obtained by addition ofEI30values of all
erosive storms occurring in each rainfall year. Table 2 reveals the extreme interannual variability of erosivity in Cape Verde, ranging from very low to almost zero values during dry years (R-factor9.0 in 1982), toR -factor values from 150 to 400 KJmÿ2mm hÿ1during wet years. Although very tentative, an average annual rainfall erosionR-factor value ofR200 metric units or R120 US customary units seems a fair estimate for this location. Recorded values are in correspon-dence with published rainfall erosion index values for West Africa (Roose, 1975). The erosivity content of
Table 1
First approximation of design storm erosivity for some locations in Santiago island, Cape Verde
Return period (year) Location
Praiaa Trindade San Jorge dos Orgaos
P(mm)b EI30ec P(mm) EI30e P(mm) EI30e
1.58 41 2623 53 3824 52 3724
2 55 4125 67 5628 64 5127
5 98 10140 112 12447 114 12948
10 127 15255 142 18162 149 19666
25 154 20768 179 26283 190 28889
aPraia 1485503000N±2383001500W±altitude 67 m; Trindade 148570N±238340W±altitude 210 m; San Jorge dos Orgaos 1686504000N±
23837'W±altitude 317 m.
bDesign storm depth derived from GuÈmbel extreme value statistical distribution estimates (INIDA, 1995).
cDesign storm erosivity in kJ mÿ2mm hÿ1using regression model (Eqs. (1a) and (1b)) and indicative 90% con®dence interval ( e).
Table 2
Annual rainfall, erosivity and derived indices at Achada das Vacas (148590N±238320W±220 m a.m.s.l.), Santiago island, Cape Verde
Data Year
1980 1981 1982 1983 1984 1985 1986
Pan(mm) 319.0 98.8 82.4 129.4 254.9 173.4 303.0
P24max(mm) 99.8 38.0 34.1 91.8 179.5 58.7 110.0
PCIa 30 42 42 58 55 44 32
R-factorb 218.6 49.0 9.0 98.1 239.0 97.9 350.6
EI30±24maxb 98.9 22.0 7.5 87.6 223.6 47.7 179.6
R/Pan-ratioc 0.68 0.50 0.11 0.82 0.94 0.56 1.16
aPCI or precipitation concentration index after Oliver (1980), computed according to Michiels and Gabriels (1996). bAnnual erosivityR-factor and annual maximum 24-h stormEI30values are in kJ mÿ2mm hÿ1.
rainfall, as represented by theR/Pratio orR-factor to annual rainfall (Table 2), compares, however, higher than many other semiarid locations in the world with similar or higher mean annual rainfall, e.g., Northern Burkina Faso (Roose, 1975), Southern Spain (ICONA, 1989) or Arizona, US, (Renard et al., 1996). The monthly distribution of rainfall and erosivity is shown in Fig. 5. The long-term average monthly rainfall distribution of Trindade station (148570N±238340W± 210 m), located in close proximity of the study area, was added for better comparison. The very strong concentration of precipitation in a short rainy season from August to October, also shown by the precipita-tion concentraprecipita-tion index (Oliver, 1980) or PCI values derived from the data and shown in Table 2, is obvious from Fig. 5. An important ®nding derived from the data set was the share of the annual maximum 24 h storm in the total annual rainfall amount and erosivity. The annual maximum 24 h storm represented an average of 45% of the annual rainfall total and con-tained 63% of annual erosivity in the 1980±1986 data set. This phenomenon is explained by the very limited number of rainfall days (observed range from 3 to 20 days a year), combined with important daily rainfall amounts. This issue and its consequence when pre-dicting rainfall soil erosion losses is dealt with in another research paper by the same authors (Man-naerts and Gabriels, 2000).
3. Conclusions
This short paper aims to contribute to the quanti-tative assessment of rainfall erosivity in a data scarce semiarid region. Knowledge of design storm erosivity, the annual R-factor and the seasonal distribution of erosivity, permit soil and water conservationists to make improved designs for erosion control, water harvesting or small hydraulic structures. Although a limited measured data set was used in the analysis, veri®cation with established rainfall kinetic energy and erosivity values from other tropical locations, suggest validity of the ®gures presented herein. It is recommended to update the kinetic energy and ero-sivity predictor equations and R-factor estimates as more experimental data become available.
Acknowledgements
The authors wish to acknowledge the Ministry of Agriculture, Forest Service and the Institute of Agricultural Research and Development (INIDA) of Cape Verde for providing ancillary precipitation data.
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