Even if soil erosion can be easily defined as the displacement of soil particles from one location to another, its quantification in terms of soil loss for a respective area could be very complex due to five characteristics: the intensity of rainfall (erosivity), the soil type (erodibility), the land cover, the slope length and the slope steepness [1
]. In the tropics, soil erosion mainly occurs as sheet and rill erosion triggered by overland flow [4
Usually, soil erosion can be calculated by field measurements. Based on erosion plots at sites of different land use, soil patterns, and slope angles, precipitation and the amount of eroded material is measured over a minimum period of two years (for an example of an experimental setup at sites in Southern Cameroon, see Ambassa-Kiki and Nill [5
]; for a detailed overview of erosion in the humid tropics, see Labrière et al. [6
]). The measured erosion rates are extrapolated to catchment size using models based on high resolution soil maps, Digital Elevation Models (DEMs) and precipitation data [7
A broad number of such models exist which model soil erosion as a whole or as a part of more complex models which are the Universal Soil Loss Equation (USLE; [1
]) and its variations (Revised [R]USLE, Modified [M]USLE), the Agricultural Non-Point Source model (AGNPS), the Areal Non-point Source Watershed Environment Response System (ANSWERS) and the Chemicals, Runoff and Erosion from Agricultural Management Systems (CREAMS), both of which were discussed by Wu et al. [8
]; the Groundwater Loading Effects of Agricultural Management Systems (GLEAMS), the Erosion Productivity Impact Calculator (EPIC) and the Water Erosion Prediction Project (WEPP), which were compared with RUSLE by Reyes et al. [9
]. Chandramohan et al. [10
] tested the Unit Sediment Graph (USG), the WEPP, and the RUSLE on small-scale Indian catchments of around 40 km² each. Their main objective was to predict the soil loss for six different extreme events with the respective parameters measured in the field. Four further events were measured to validate the model. As a result they recommend the USG model which fits best into their set up. However, the more general approach with freely available (global) data was not considered by them. Hrissanthou [11
] figured out that the MUSLE is the best indicator for predicting the sediment yield and the Agriculture Research Service of the US (ARS) have recommended the RUSLE to assist public policy development all over the world [12
]. It seems that USLE and its related concepts are powerful but controversially discussed tools.
Since 1978, USLE has been used by several researchers to estimate soil erosion and sediment yield [13
] “because of its simplicity” as stated by Sotiropoulou et al. [14
], although it considers also the “spatial heterogeneity of soil erosion” [15
]. It is an equation that considers the main parameters influencing erosion, such as rainfall (R factor), soil (K factor), topography (LS factor), land cover (C factor), and land management (P factor), and was developed by Wischmeier and Smith [1
]. Due to the complexity of the data incorporated in the model, different approaches are used for factor calculations: e.g., the R factor can be based on mean annual precipitation [16
], mean monthly precipitation [15
], or single daily events [13
], though the use of mean annual precipitation probably leads to under-estimations of soil loss when there are distinct differences between rainy and dry season; the K factor uses predefined values according to soil types and/or colors [17
] or on detailed grain size and carbon amount data [16
]; or the C factor can be based on calculations of the Normalized Difference Vegetation Index (NDVI) [16
] or again based on predefined values matched with land cover classes [17
] (see Table A1
, Table A2
and Table A3
for selected calculations and [3
] for a (R)USLE review). The differences between USLE, RUSLE, and MUSLE depend on the calculation of the single factors, which have to be adapted to different climate, topography, and also research objectives [3
]. For example in MUSLE, the rainfall factor is replaced by a runoff factor reflecting the “variability of deliver ratio” and therefore detailed precipitation data and outflow measurements are necessary [13
]. Unfortunately, in most developing countries, erosion plot data are missing and weather or sediment load measurements are inconsistently dependent on the political situation in those countries and budget decisions, respectively. Considering that the recent history of the study area in Eastern Congo / the South Kivu Province is characterized by civil war and atrocities (e.g., the genocide in Rwanda and its following conflicts at the border to the DRC) bad data availability from these areas is understandable.
In this context, Karamage et al. [16
] used USLE to model soil erosion for Lake Kivu catchment without any plot measurements in the field. According to the high relief energy, they modified the original equation for the topography (LS) developed by Desmet and Govers [26
] to also consider the upstream contribution area for complex slopes. For the slope steepness factor, they used the formula based on McCool et al. [27
], who differentiated between slope angles that were less and more than 9% to also consider steep angles along the East African Rift. For validation, they compared their results with a work of Bewket and Teferi [17
], who used the model in an Ethiopian catchment in the upper Nile Basin. Karamage et al. [16
] tested the model to examine different conservation practices along the steep slopes of Lake Kivu. They calculated a mean annual erosion risk of 3 kt/km² (30 t/ha) and only 33% of their study area has a tolerable soil loss of ≤ 1 kt/km². The main contributor to soil loss in this area is the cropland; therefore, they suggested adjusting the land use techniques to consider terracing, strip-cropping, and contouring.
For the Bukavu region, mainly gray literature deals with soil erosion (e.g., [28
]) and its anthropogenic impacts (e.g., [29
]) conducted for short periods in the framework of student projects. An area-wide quantification of soil erosion does not currently exist.
The main goal of this paper is therefore to (15) quantify the potential soil loss in the Ruzizi sub-catchments of hydropower plants Ruzizi I and II. In addition to the rates of loss, (2) the main sources of sediment input will be identified. We decided to use the (R)USLE model with K, LS, and P factors based on Karamage et al. [16
] but modified R and C factors adjusted to the seasonality [15
] and the heterogeneous and small scale land use practices in the region [16
]. The study area of Karamage et al. [16
] also covers small areas of the Ruzizi catchment directly at the outlet of Lake Kivu; therefore, it could be used for validation. Additionally, (3) aims to prove whether there are possibilities to validate the model beyond their work.
The results of soil loss quantification will be incorporated into the project “Environmental flow requirements of two dammed tropical rivers of the Congo Basin (Eastern Democratic Republic of Congo)” dealing with the impact of Ruzizi I and II hydroelectric dams on the freshwater ecosystem of Ruzizi River. This project aims to assess environmental flow requirements [30
] in order to suggest optimum water resource use and obtain insights to enhance the sustainability of future dam construction. The central goal of the geomorphological sub-project is to estimate the sediment budget of the sub-catchments, and the (R)USLE results are an important component of these estimations.
The value of the R factor is the highest within the (R)USLE model ranging between 762 and 916 MJ mm/ha h yr (Equation (2), Table 5
, Figure 2
A). The logarithmic term changes the areal distribution of R factor in comparison to the precipitation pattern, with slightly higher rates in the southern part of the study area and lower rates in the eastern part. The K factor calculated for the study area ranges between 0.13 and 0.15. In the areas surrounding Bukavu, there is a high K factor value (Figure 2
B) that is mainly triggered by low clay and organic carbon content while the amount of high sand (
) content also leads to a higher K factor. Obviously, the low clay content prevails, or conversely high clay content leads to less erosion and to a lower K factor. Further west and in the extreme east, there is also a high K factor value with medium clay content but high silt content (Figure 2
B, Table 5
, Equations (3)–(7)).
The L factor and flow accumulation show nearly the same spatial pattern, highlighting the main input into L factor calculation. Low values for slope length are occurring at the steep but short slopes on both sides of Ruzizi River. The L factor has a lower range and lower values in contrast to the S factor: the L factor varies between 1 and 2.3, while the S factor varies between 0 and 13.4 (Table 5
, see Figure 2
C for LS factor).
The assignment of C factor values from Huey The [23
] to the different land cover classes lead finally to min and max C factors of 0 (for water areas) and 1 (for pits, Table 3
) for both the January and August calculations. Slightly different values considering rainy and dry season with the respective growth state of the vegetation lead to mean values of 0.27 and 0.28, respectively. The maps highlight the most affected areas along the central Ruzizi valley, mainly consisting of crop land, but also some grasses and bushes (Table 5
, Figure 2
D,E). Only a small number of forest residuals lead to small C values. The high resolution satellite images from the Sentinel 2a MSI sensor displays the small parcels of the mostly small-holder farmers in the region as observed during field work. Also, C factor values (Figure 2
D,E) and (R)USLE results (Figure 3
) reflect this kind of land use.
Looking at the spatial pattern of (R)USLE results, it is obvious that the highest soil loss values occur mainly in the central southern part of the study area linked to the broad extend of agricultural area at the steep slopes of the deep incised Ruzizi River (Figure 3
A). This is different to the situation at the eastern border of the river where there is more heterogeneous land cover and, hence, no such large scale estimated soil loss occurs. The smallest rates of loss can be found along the thalwegs in the valleys. This loss is distinctly linked to the topography and the respective low LS factor values. On Rwandan side in the catchment of Ruzizi I, there are some rice paddies with a small amount of erosion that also follow the floodplain of two tributaries of the Ruzizi River. The (R)USLE values of these areas are characterized again by a combination of both land cover and topography reflected by C and LS factors, respectively. Finally, the area of Bukavu has low values of erosion due to having sealed surfaces (Figure 3
The mean value of (R)USLE calculations for catchments Ruzizi I and II is 48 t/ha/yr with a sum of 577,124 t/yr. As precipitation and land cover were gathered in 2016, these calculations can be matched to this year (Table 6
). The P factor used for (R)USLE distinctly reduces the results of soil loss calculations, reflecting an improvement of land management practices. Conservation support practices like terracing can be partially observed in the region, mainly on the Rwandan side. If such techniques can be established in the whole area with high slope angles, then soil erosion can be reduced by 41.5%, which is a mean soil loss of 28 t/ha/yr or a total of 337,710 t/yr for both catchments (Table 6
; Figure 3
The comparison of the catchments shows a slightly higher estimated mean soil loss in Ruzizi II catchment, which is mainly linked to the high rates of crop land at the slope of Ruzizi River in contrast to a more heterogeneous landscape in Ruzizi I catchment that is mainly characterized by less incised tributaries of the Ruzizi River (Figure 2
C). Additionally, the amount of crop land is higher in the Ruzizi II catchment (Table 6
). However, the reduction of mean soil loss by P factor is nearly the same in both catchments (81.7% in Ruzizi I; 81.3% in Ruzizi II), pointing to a comparable distribution of crop land shares in regard to the slope (Table 6
For validation reasons the results dataset of Karamage et al. [16
] was kindly provided by the first author. Before comparison with our own results, this dataset was clipped with the catchment area of Ruzizi I, and very high and obviously incorrect values for soil loss estimates of around 100.000 t/ha/yr were replaced by the value 1000 (see Max values in Table 7
and Table 8
). However, the USLE results from our own calculations exactly following the paper of Karamage et al. [16
] led to results distinctly higher for the Ruzizi I sub-catchment.
Therefore, it is advisable to also consider erosion plots for ground validation. There was a field study done by König [50
] over several years close to Butare in southern Rwanda less than 100 km east of Bukavu. It was conducted using the framework of PASI (Projet Agricole et Social Interuniversitaire) and co-initiated by Koblenz-Landau University. This site is established on a ferralitic soil with a mean annual precipitation rate of 1280 mm and fits well to the study area near Bukavu, respectively. An erosion plot was characterized by bare soil and another by traditional farming of different cultures, both on a slope of 28% with mean annual soil loss rates of >400 and 120–250 t/ha/yr, respectively. Comparing the results of soil loss estimations with König’s [50
], the results of RUSLE model following the C factor of Huey The [23
] and the R factor of Prasannakumar et al. [15
] fit best, as well as when considering the maximum value of soil loss, which is 361 t/ha/yr. The dataset of Karamage et al. [16
] has an output of 24.68 t/ha/yr for the slope gradient of 25%–30% (Table 8
Finally, a regional proxy can be used for validation. Muvundja et al. [51
] gathered turbidity data from September 2016 to October 2017 at the site of Ruzizi II. They transferred the measurements via a linear model to total suspended solids (TSS) of 12.9 t/ha/yr at this location. For Ruzizi II, an area normalized value of 50.58 t/ha/yr can be calculated based on (R)USLE results (Table 6
, Ruzizi II) which is four times higher than TSS; hence, it is reasonable when considering the deposition on the slopes, along the river bank, and in the river bed during transport [1
]. Eisenberg and Muvundja [53
] also tried to estimate turbidity based on the Normalized Difference Turbidity Index (NDTI). When comparing Ruzizi I and II catchments, it becomes obvious that Ruzizi I has lower mean estimated soil loss than Ruzizi II. This observation was also confirmed by NDTI measurements of Ruzizi I and II dam reservoirs, the latter being the most turbid. Overall, the results of the soil loss model matched well to NDTI and turbidity measurements in the study area, while also hinting towards the potential source area of most of the sediments [51
Due to a high range of C and LS values, these factors led to high (R)USLE results, as R and K factors are characterized by only slight differences in the study area (Table 5
, Figure 2
and Figure 3
A). However, e.g., high sand content leads to a low K factor and therefore to reduced erosion as the infiltration is high due to the high void space (c.f. [20
]). It can also be observed that silt content is responsible for higher K factor values leading to higher erosion. Higher content of organic carbon usually leads to reduced susceptibility to erosion; hence, it also shows its peak in the western region because it has a lower weight in K factor calculations than silt content.
Regarding LS and C factors, the steep slopes along Ruzizi valley covered by crop and grass land heavily contribute to high rates of soil loss. A more detailed look on the spatial pattern along the valley highlights the highest soil loss rates on the DRC’s part of the study area. It is assumed that the people of neighboring Bukavu use the land intensively by conducting grazing and cropping activities due to LULC classification based on high resolution satellite images and personal observations in the field. The next town on Rwandan side is Kamembe/Cyangugu (Ruzizi District), which is situated north of the sub-catchments. During field work several terraces could be observed on the Rwandan border, hinting at improved land management practices in that region limiting soil loss. Even if these shallow sections are not visible in the DEM due to their small sizes in comparison to the spatial resolution of the DEM (Table 1
), they contribute to lower (R)USLE results: Along the steps between the terraces trees and bushes are growing which protect against erosion. In the classification process for C factor calculation, these areas were mainly classified as bushland with a distinct lower value as crop land (Table 3
) and therefore had a lower (R)USLE value. This way of processing probably led to proper results; hence, when adding the P factor, bushland will not be considered. Karamage et al. [16
] suggested using data of high spatial resolution to consider the heterogeneous land use in the study area, but even the Sentinel 2a MSI data (Table 1
) cannot always delineate the small parcels.
Considering erosion plots and turbidity measurements the (R)USLE results fit fine reflecting a high rate of soil loss in the study area mainly linked to crop land along the steep slopes of Ruzizi River. The validation following Karamage et al. [16
] did not match. Their results seem to be correct; however, their R factor was originally used for soil loss modeling in Hawaii [54
] with roughly the half of the mean annual precipitation compared to Bukavu. When modifying their R factor equation following Prasannakumar et al. [15
] the results of soil loss estimations fitted better, even if C factors were based on NDVI or on predefined values for different land cover classes [33
], (Table 2
); hence, it seems that it is not a problem with R factor but with K factor calculations. Karamage et al. [16
] listed K factors from 0.009–0.11 in the table of contents of the respective figure with only small areas with the highest value and broad areas with values from 0.009–0.01 t ha h/ha MJ mm. They also listed several soils like Acrisols, Andosols, and Ferralsols covering the Lake Kivu region. The K factor calculated for Ruzizi I and II sub-catchments following the terms used by Karamage et al. [16
] are partially higher by the factor 10 in a regional soil environment, which is comparable to the surroundings of Lake Kivu. According to tables of Ahmad Ali and Hagos [18
], Bewket and Teferi [17
], or Hurni [21
], the brown to red soils can be matched to K factor values of 0.15–0.25. Due to their small K factor, perhaps the incongruous R factor term from Lo et al. [54
] was selected to increase the results to fit the assumptions better (Table 7
However, even if the validation matches the results, (R)USLE calculates only the soil loss in the respective scale of the input data. It is an approximation. E.g., along Ruzizi I reservoir some gullies were identified in the field linked to sewage channels from near Bukavu. USLE was originally developed for sheet and rill erosion, not for considering deeply incised linear features like gullies [1
]. Additionally, as Ruzizi River is following a tectonically active region, landslides also occur regularly, adding huge amounts of sediments to the system [35
]. These processes do not happen periodically, so it is difficult to add a distinct amount of sediment input to the model.
To reduce soil loss rates, the P factor was introduced to (R)USLE considering contouring, strip-cropping, and terracing as land management techniques. For the steepest topography along Ruzizi valley, terracing is mainly suggested. However, as the study area is situated in naturally occurring montane forest zone management techniques like agro-forestry should also be introduced to the people. König [50
] tested such systems on PASI erosion plots in Rwanda, leading to a distinct reduction of soil loss. These systems could use a respective C factor for forest instead of cropland (Tabs. 2, 3). During field work, reforestation activities could already be observed in the central southern part of the study area along the reservoir of Ruzizi II to improve the protection against erosion. However, these activities are in conflict with current land use practices of the local population. Agro-forestry needs to be established in a sustainable and appropriate manner to the riparian population, though it must be used by the dam operators to combat reservoir siltation and water quality degradation.