Application of a RUSLE-based soil erosion modelling on Mauritius Island.
Soil erosion is an environmental problem that exacerbates on-site land degradation, while at the same time being a source of sediment and pollutants that adversely affect off-site aquatic ecosystems (Lal 2001). Mauritius Island is potentially at risk to soil erosion because of its rugged topography, extensive sugarcane cultivation, and tropical climate. This makes the land particularly vulnerable to erosion. In addition, other ecosystems can be damaged due to erosion, namely, estuaries, marshes, fish habitats, sea grass, and coral reefs.
Conservation measures are needed to reduce the effects of soil erosion, and successful conservation programs require the concentration of resources on priority action areas. Soil-erosion risk assessment at a regional scale is commonly used to identify such conservation areas (e.g. Vrieling et al. 2002). The first published study to map the erosion risk across the whole island was conducted by Nigel and Rughooputh (2010b), developing a soil-erosion risk-mapping model termed MauSERM (Mauritius Soil Erosion Risk Mapping). Four erosion factors, namely rainfall, land cover, topography, and soil, were considered during the erosion risk mapping using the MauSERM model. Investigations were also made on the best way to combine the factors, and the decision rule methodology was chosen, as it can be easily manipulated to mimic the complex interactions of all four factors (as opposed to factorial scoring).
A second study was later conducted by running the model with new datasets, where slope gradient was replaced with slope length (inclusive of land parcels and drainage structure effects). The results illustrate that erosion sites occur commonly in cultivated areas with steep slopes and erosion has a summer-dominant pattern caused by intensive summer rainfall (Nigel and Rughooputh 2010a). This study was qualitatively based and enabled identification of priority action areas. Now, a more quantitative study is needed, as this can provide assistance in fiver basin management and to indicate the order of magnitude of soil erosion and sediment yield (de Vente and Poesen 2005).
The aim of the present study is to compute soil loss estimates for Mauritius Island and especially for the priority action areas. The specific objectives are: (1) apply the Revised Universal Soil Loss Equation (RUSLE) model for the whole island, (2) compute soil loss estimates for the high-erosion areas (HEA) initially mapped, and (3) define a second approach for mapping of HEA and derive estimates of RUSLE soil loss for these HEA. In this second approach, the HEA can be mapped from only two factors such as land cover and slope gradient, which are readily available, instead of soil erodibility and length of slope, which are generally more difficult to map at large scale as they require more geographical information system (GIS) datasets (see e.g. Vrieling 2006). The RUSLE soil map produced will be the first for Mauritius and will enable regional erosion evaluation for the determination of areas where soil conservation should be emphasised.
Materials and methods
Mauritius is in the south-west of the Indian Ocean (Fig. 1) at latitude 20[degrees]10'S and longitude 57[degrees]30E. The island has an elliptical shape with a major axis 63 km, minor axis 43 km, surface area 1859 [km.sup.2], and highest peak 828 m altitude. The island is of volcanic origin with eruptions lasting from 10 to 0.025 million years Before Present. Two main soil groups exist: mature Latosols originating from highly weathered, basaltic lava rock; and immature Latosolic soils with minerals still in the process of weathering. Most soils have high clay contents and high percentages of stone and organic matter content (organic matter 3-11%). Soil depth is mainly 50-100cm, similar to maximum plant rooting depth (Parish and Feillafe 1965).
The climate is tropical maritime with two seasons: a rainy summer from November to April dominated by cyclone passage, and a dry winter from May to October dominated by the South-East Trade Wind and frontal systems. About 70% of mean annual rainfall is received during summer. February is the wettest and hottest month, whereas October is the driest month. Mean annual rainfall is ~2000 mm, equivalent to ~3700 [Mm.sup.3], of which annual evaporation is 30%, surface runoff 60%, and groundwater recharge 10% (WRU 2007). Surface runoff is confined within 213 river basins, which occupy 83% of the island (Fig. 1) (Nigel and Rughooputh 2010a). Torrential flows with severe bank erosion and turbidity in the lagoon are common during intense rainfall events (Arlidge and Wong You Cheong 1975).
There is extensive sugarcane cultivation, which occupies 54.1% of the island, while forest covers 26.7%, sparse vegetation 7.1%, urban areas 5.5%, scrub 2.9%, tea 1.6%, water bodies 0.9%, barren lands 0.5%, wetlands 0.4%, vegetables 0.2%, and sand 0.1% (Nigel and Rughooputh 2010b). Sugarcane is harvested each year from July to October, during which the cane ratoon is not removed but left over for regrowth and replaced usually every 5-7 years, at which time the whole field undergoes tillage. Seeruttun et al. (2007) measured soil loss for five sites for four consecutive years, with each site having two plots: one bare and the other planted with sugarcane. The rate of soil loss from bare plots was in the range 0.5 37.6 t [ha.sup.-1] [year.sup.-1], while sugarcane reduced soil loss by 80 99%. However, these studies were conducted only on plots having linear slopes and isolated from upslope contributing areas (soil type and rainfall erosivity did not vary over each plot size).
Estimating soil loss pattern with RUSLE
Like most qualitative models, one of the limitations of MauSERM is that it provides qualitative outputs, which are not linked to quantitative estimates of soil loss. These would have been very useful for planning purposes, particularly for the high erosion areas. Converting erosion risk classes into estimates of soil loss may be done by upscaling erosion measurements for selected sites, as proposed by Vrieling et al. (2002). In the present case, no erosion measurements for selected sites were available to perform such an upscaling.
Another approach would be the use of a quantitative erosion model, such as the RUSLE model (Renard et al. 1997) or its first version, the USLE (Wischmeier and Smith 1978). These models are widely used for modelling soil loss quantitatively, including for large areas by using a GIS (e.g. Lu et al. 2003). It must be noted that the original USLE model is a quantitative model, but application of the model at a regional scale within a G1S requires simplification of the model, and therefore, its use within a GIS is commonly referred to as a semi-quantitative modelling of erosion. The soil loss equation for the RUSLE model is given as follows:
A = R x K x LS x C x P (1)
where A is the spatial average soil loss (t [ha.sup.-1] [year.sup.-1]), R is the rainfall erosivity factor (MJ.mm [ha.sup.-1][h.sup.-1] [year.sup.-1]), K is the soil erodibility factor (t.h [MJ.sup.-1][mm.sup.-1]), LS is the slope length and steepness factors (unitless), C is the cover management factor (unitless, values ranging from 0 to 1), and P is the support practice factor (unitless, values ranging from 0 to 1).
In the present case, we computed rainfall erosivity R using Eqn 2 (Arnoldus 1980), and which was found appropriate for use locally (Le Roux et al. 2005):
R = 0.0302[(MFI)sup.1.9] (2)
where MFI is the Modified Fournier Index, and is given by Eqn 3:
MFI = [1=12.summation over (i=1)][([M.sub.i]).sup.2]/A (3)
where [M.sub.i] is the rainfall amount received in month i and A is the annual rainfall amount.
Values of soil erodibility for soil types (Table 1) were obtained from Le Roux (2005) and Seeruttun and Ah Koon (2006). A map of slope length, LS, was obtained from Nigel and Rughooputh (2010a), computed by taking into account land parcel effects along with the methodology of Desmet and Govers (1996) as specifically implemented in the USLE2D software of Van Oost and Govers (2000).
Further data needed for running the RUSLE model above (Eqn 1) are the C and P values for land cover types. These C and P values are given in Table 2, which were obtained mainly from Le Roux (2005), except for sparse vegetation (which has been set the same as for forest). These values of C and P were then assigned to a 10-m cell-size land cover map, resulting in two raster maps (of C and P) suitable for raster processing of Eqn 1 to produce a soil loss map. The land cover map was, produced mainly from published map series (updated year 2005) (Nigel and Rughooputh (2010a).
A second approach to identify high-erosion risk areas
Another approach that can be used to establish the high-erosion areas of the island is the use of only two factors that can be directly mapped. A land cover map is one such factor. The other is a slope gradient map, which requires less processing and fewer datasets compared with a 'land cultivation suitability' map, which in addition to topography requires information on soil type. Initially, a land cultivation suitability map was used in Nigel and Rughooputh (2010a), and it required (i) slope gradient, (ii) length of slope, (iii) soil erodibility, and (iv) a pre-existing land suitability classification scheme [the one defined for Mauritius by Arlidge and Wong You Cheong (1975) was used]. Additionally, land cultivation suitability would be more difficult to directly visualise in the field compared with steep slope gradients alone.
As such, based on land cover and slope gradient, a more straightforward definition of HEA can be obtained. This alternative HEA will henceforth be termed HEA-B. In essence, HEA-B can be mapped based on the occurrences of crop cultivation on slopes >20%. In a GIS, this is achieved by extracting land cover classes that show cultivation (sugarcane, tea, and vegetables) and intersecting this dataset with a slope gradient map that shows only values >20%.
Results and discussion
Here, results of the second approach are presented first, followed by results of the RUSLE modelling. Results of the mapping of HEA-B show that 58 [km.sup.2] of land has slopes >20% and is being cultivated (with sugarcane 98.8%, vegetables 0.06%, and tea 1.14%). The extent of HEA-B is thus about four times less than that of the original HEA (58 v. 251 [km.sup.2]).
The spatial distribution of the established HEA (original HEA and HEA-B) is shown in Fig. 2. In the event that not all of the 251 [km.sup.2] of the original HEA will be the focus for conservation efforts (due to financial constraints, for instance), then at least the areas defined as HEA-B, and particularly the sugarcane fields, must be targeted for conservation efforts.
The map of soil loss produced using the RUSLE model is shown in Fig. 3. The soil loss estimates were classified using the scheme of Le Roux et al. (2005) for Mauritius. The highest soil-loss values are mostly distributed within the steep, cultivated hilly areas. The total soil loss on the island is estimated at 298 259 t [year.sup.-1]. Accordingly, based on RUSLE relative modelling, concerning the alternative definition of 'high-erosion areas' (HEA-B), it is seen that total soil loss from the sugarcane fields of HEA-B is 63 673 t [year.sup.-1], which is 66% of total soil loss from all sugarcane fields on the island. Thus, while sugarcane on steep slopes occupies only 58 [km.sup.2] of the 1006 [km.sup.2] of land cultivated with sugarcane, it is nonetheless contributing to 66% of total soil loss from all sugarcane fields and contributing to 21% of overall soil loss on the island. If at least these sugarcane fields of HEA-B were afforested, their total soil loss would be reduced to 7641 t [year.sup.-1], which would reduce soil loss from all sugarcane fields by 58% and reduce overall soil loss by 19% (Table 3).
Nonetheless, the first proposal for conservation efforts remains the conversion of 251 [km.sup.2] of HEA into forest or other natural vegetation, which would reduce the total soil loss from sugarcane fields by 74% and that of the island by 25%. Indeed, based on RUSLE modelling of alternative practices, if all of the HEA were converted to forest, their soil loss would be reduced from 84 780 to 10264 t [year.sup.-1] i.e. a reduction of 88% for the HEA and a reduction of 25% for the island. Specifically, sugarcane in HEA contributes 81 389 t[year..sup.-1] of soil loss, which is 84% of the annual soil loss from all sugarcane fields on the island (estimated at 96 865 t [year.sup.-1]).
According to Morgan (2005), predictive soil erosion models such as the RUSLE do not, in general, yield true deterministic results of erosion rates. In the absence of measured soil loss or sedimentation data for model calibration and validation, such erosion models are more wisely used for comparing different land management systems, while their use for calculation of absolute soil loss and sediment yield remains a bad practice and, at best, speculative (Morgan 2005).
No sedimentation data have been available in the present case to validate the RUSLE soil loss estimates. Field measurement of soil loss is time-consuming, as at least 2 3 years are needed before reliable measurements can be obtained (Lal 1990). Thus, in the present case, the use of the estimated soil loss on a relative basis is favoured, and this would give an idea of the relative impact of afforestation or other revegetation scenarios before they are implemented.
The general recommendation is thus to convert the cultivation of the 58 or 251[km.sup.2] of HEA (HEA-B or original HEA, respectively) into planted vegetation or forest in order to reduce their soil loss and downstream sedimentation. Consequently, the measurement and modelling of sediment yield for the HEA, as well as the establishment of suitable riparian vegetated filters, are also recommended for future research.
Using the RUSLE map to build a 'tolerable soil loss-rate' concept
The steeplands of tropical islands have long been recognised as being affected by soil erosion. For example, various USLE erosion modelling studies conducted for Hawaiian Islands have reported accelerated erosion rates and high sediment yield for agricultural cultivations (e.g. Calhoun and Fletcher 1999). For the Virgin Islands, Ramos-Scharrrn and Macdonald (2007) studied erosion for all land-use types with an empirical GIS-based erosion model and found that the combination of steep slopes, small drainage areas, shallow soils, anthropogenic activities, and high-intensity rainstorms resulted in high sediment yield ~3-9 times higher than under natural conditions.
In Mauritius, the current research is on studying erosion patterns and estimating soil loss. The next step is to research the rates of soil loss that would be 'tolerable' for the island, i.e. not exceeding the rate of soil production. In Australia, Bui et al. (2011) reviewed three levels of 'tolerable' soil loss that can be adopted for environmental conservation (T1 for soil conservation, T2 for agricultural productivity, and T3 for water conservation). A comparison of the work of Bui et al. (2011) with the current study gives an insight into the way forward for soil erosion studies on Mauritius, where soil conservation services, regulations, and policies do not even exist. Thus, drawing on a 'tolerable soil loss-rate' concept, it may be said that soil erosion rates of 5-12 t [ha.sup.-1] [year.sup.-1] for Mauritian soils will not exceed a 'tolerable T2' soil reduction rate of 0.4-l mm [year.sup.-1], assuming a bulk density of 1200kg [m.sup.-3] (based on USDA T2 estimates described by Montgomery 2007). Accordingly, for ease of interpretation, the soil loss estimates in Fig. 3 are classified as very low (<5 t [ha.sup.-1] [year.sup.-l]), low (5 12 t [ha.sup.-1] [year.sup.-1]), and moderate-high (>12t [ha.sup.-1] [year.sup.-1]). In Australia, Bui et al. (2011) reported a tolerable soil reduction rate of ~0.015 mm [year.sup.-1], which is similar to those in Europe but lower than those adopted by the USA, China, and India (~0.4-1 mm [year.sup.-1]). In the current work for Mauritius, similar higher values are adopted, mainly based on USDA values, and such values are being adopted in the tropical volcanic islands of Hawaii, albeit this range may have been set too high (Montgomery 2007; Bui et al. 2011).
Because accelerated soil erosion rates exceed soil production rates, a point can be reached where there is no soil left, even if this takes 50-5000 years for a soil that had an original depth of l m (Montgomery 2007). In Mauritius, soils have relatively good depths, being derived from highly weathered basalts (Parish and Feillafe 1965), but all soils, no matter how great their initial depth, are at risk of unsustainable, accelerated soil erosion rates driven by non-conservative agriculture (Montgomery 2007). Therefore, it is very important for agricultural countries where lands are sensitive to erosion, notably tropical islands with steep cultivated land and intense rainstorms, to at least assess soil erosion patterns and to estimate soil loss, for instance, by using the RUSLE model as done in this work. One of the key challenges to the production of a nationwide RUSLE soil loss map is to be able to integrate all of the necessary information into a G1S in order to run the model. As described here, this can be achieved by leveraging on methodological approaches that employ available data, such as rainfall depths (instead of rainfall intensity measurements as needed for the original empirical RUSLE). The RUSLE map produced in this way can be used on a relative basis, for comparing proposed agricultural conservation practices. Producing and using the RUSLE map in this way will facilitate further research, such as, as mentioned above, for assessing 'tolerable' soil reduction rate and discussion of national soil conservation strategies. Such an approach was adopted by Bui et al. (2011) for Australia based on an earlier RUSLE soil loss map produced by Lu et al. (2003). This approach can be followed by other countries affected by accelerated soil erosion processes and, for Mauritius, is definitely a recommended way forward.
A soil erosion risk mapping model (MauSERM) was developed previously for Mauritius. The model enabled an assessment of the spatial and temporal variations in erosion patterns and the identification of high-erosion areas of the island. One of the limitations of the MauSERM model was its output qualitative classes, which could not be linked to quantitative classes of soil loss.
However, as shown in this work, by using the RUSLE model, it is possible to compute soil loss for the island and, most importantly, for the high-erosion areas. The soil losses from these areas were then analysed for different land conversion scenarios, principally sugarcane to forest in steep areas. Such a land conversion scenario (sugarcane to forest on steep slopes) is proposed as one means of reducing soil erosion by 19-25% overall on Mauritius.
As confirmed in this work and as reported for other tropical islands with steep cultivated lands, there are areas of accelerated erosion that definitely need to be conserved. The methodological approach used in this work to quantitatively and to cartographically estimate the existence of such erosion-prone areas can be adopted in other countries where there are needs to assess, map, and estimate soil loss on a nationwide basis in order to better inform environmental policy needs for soil and water conservation.
Received 4 July 2012, accepted 19 December 2012, published online 31 January 2013
The authors acknowledge the Cartographic Section of the Ministry of Housing and Lands and the Mauritius Meteorological Services for the provision of data. Dr KH Mueller while at University of Marburg provided other base data. This work was supported by a Tertiary Education Commission (TEC) scholarship for the first author at the University of Mauritius. We also thank the two anonymous reviewers for their comments and suggestions made on earlier versions of this manuscript.
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Nigel (A,C) and Soonil D. D. V. Rughooputh (B)
(A) Stagiaire Postdoctoral, Bureau 5310, INRS Centre Eau Terre Environnement, 490 rue de la Couronne, Quebec, QC G1 K 9A9, Canada.
(B) Faculty of Science, University of Mauritius, R6duit, Mauritius.
(C) Corresponding author. Email: Rody.Nigel@ete.inrs.ca
Table 1. RUSLE K soil erodibility values Soil order Soil type RUSLE erodibility K (t.h [MJ.sup.-1] [mm.sup-1] Zonal Low Humic Latosols, L 0.10 Humic Latosols, H 0.10 Humic Ferruginous Latosols, F 0.15 Intrazonal Latosolic Red Prairie Soils, P 0.20 Latosolic Brown Forest Soils, B 0.20 Dark Mag Clay, M 0.10 Grey Hydromorphic Soils, D 0.10 Low Humic Gleys, G 0.20 Ground Water Laterite, W 0.30 Mountain Slope Complexes, S 0.15 Azonal Lithosols, T 0.30 Alluvial Soils, A 0.15 Regosols, C 0.05 Table 2. RUSLE C and P values for the different land cover types Land cover type C P Sugarcane 0.011 0.975 Sparse vegetation 0.001 1.000 Scrub 0.001 1.000 Barren lands 0.011 1.000 Tea 0.003 1.000 Vegetables 0.204 0.625 Forest 0.001 1.000 Urban areas, water bodies, Nil 1.000 wetlands and sand Table 3. Results summary for soil loss in high-erosion areas (HEA) Area Soil loss ([km.sup.2]) (t [year.sup.-1]) Soil loss (t [year.sup.-1]) Sugarcane 1006 96865 -- fields HEA 251 84780 10264 HEA-B 58 63673 7641 Conservation scenario (conversion to forest) % Soil loss % Soil loss reduction for reduction agriculture island-wide Sugarcane -- -- fields HEA 88 25 HEA-B 58 19
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|Author:||Nigel, Rody; Rughooputh, Soonil D.D.V.|
|Date:||Nov 1, 2012|
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