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USLE-Based Assessment of Soil Erosion by Water in the Nyabarongo River Catchment, Rwanda

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Soil erosion has become a serious problem in recent decades due to unhalted trends of unsustainable land use practices. Assessment of soil erosion is a prominent tool in planning and conservation of soil and water resource ecosystems. The Universal Soil Loss Equation (USLE) was applied to Nyabarongo River Catchment that drains about 8413.75 km² (33%) of the total Rwanda coverage and a small part of the Southern Uganda (about 64.50 km²) using Geographic Information Systems (GIS) and Remote Sensing technologies. The estimated total annual actual soil loss was approximately estimated at 409 million tons with a mean erosion rate of 490 t·ha(-1)·y(-1) (i.e., 32.67 mm·y(-1)). The cropland that occupied 74.85% of the total catchment presented a mean erosion rate of 618 t·ha(-1)·y(-1) (i.e., 41.20 mm·y(-1)) and was responsible for 95.8% of total annual soil loss. Emergency soil erosion control is required with a priority accorded to cropland area of 173,244 ha, which is extremely exposed to actual soil erosion rate of 2222 t·ha(-1)·y(-1) (i.e., 148.13 mm·y(-1)) and contributed to 96.2% of the total extreme soil loss in the catchment. According to this study, terracing cultivation method could reduce the current erosion rate in cropland areas by about 78%. Therefore, the present study suggests the catchment management by constructing check dams, terracing, agroforestry and reforestation of highly exposed areas as suitable measures for erosion and water pollution control within the Nyabarongo River Catchment and in other regions facing the same problems.
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International Journal of
Environmental Research
and Public Health
Article
USLE-Based Assessment of Soil Erosion by Water in
the Nyabarongo River Catchment, Rwanda
Fidele Karamage 1,2,3, Chi Zhang 1, 4,*, Alphonse Kayiranga 1,2,3, Hua Shao 1,2, Xia Fang 1,2,
Felix Ndayisaba 1,2,3, Lamek Nahayo 1,2,3, Christophe Mupenzi 1,2,3 and Guangjin Tian 5
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography,
Chinese Academy of Sciences, Urumqi 830011, China; fidelekaramage@yahoo.com (F.K.);
kayiranga2020@yahoo.co.uk (A.K.); shaohua@ms.xjb.ac.cn (H.S.); sun@163.com (X.F.);
davfelix@yahoo.fr (F.N.); lameknahayo@gmail.com (L.N.); mupenzic@gmail.com (C.M.)
2University of Chinese Academy of Sciences, Beijing 100049, China
3Faculty of Environmental Studies, University of Lay Adventists of Kigali (UNILAK), P.O. 6392,
Kigali, Rwanda
4School of Resources Environment Science and Engineering, Hubei University of Science and Technology,
Xianning 437000, China
5
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University,
Beijing 100875, China; tianguangjin@bnu.edu.cn
*Correspondence: zc@ms.xjb.ac.cn; Tel.: +86-991-7823127
Academic Editor: Yu-Pin Lin
Received: 12 July 2016; Accepted: 12 August 2016; Published: 20 August 2016
Abstract:
Soil erosion has become a serious problem in recent decades due to unhalted trends
of unsustainable land use practices. Assessment of soil erosion is a prominent tool in planning
and conservation of soil and water resource ecosystems. The Universal Soil Loss Equation (USLE)
was applied to Nyabarongo River Catchment that drains about 8413.75 km
2
(33%) of the total
Rwanda coverage and a small part of the Southern Uganda (about 64.50 km
2
) using Geographic
Information Systems (GIS) and Remote Sensing technologies. The estimated total annual actual soil
loss was approximately estimated at 409 million tons with a mean erosion rate of 490 t
·
ha
1·
y
1
(i.e., 32.67 mm·y1).
The cropland that occupied 74.85% of the total catchment presented a mean
erosion rate of 618 t
·
ha
1·
y
1
(i.e., 41.20 mm
·
y
1
) and was responsible for 95.8% of total annual soil
loss. Emergency soil erosion control is required with a priority accorded to cropland area
of 173,244 ha,
which is extremely exposed to actual soil erosion rate of 2222 t
·
ha
1·
y
1(i.e., 148.13 mm·y1)
and contributed to 96.2% of the total extreme soil loss in the catchment. According to this
study, terracing cultivation method could reduce the current erosion rate in cropland areas by
about 78%. Therefore, the present study suggests the catchment management by constructing check
dams, terracing, agroforestry and reforestation of highly exposed areas as suitable measures for
erosion and water pollution control within the Nyabarongo River Catchment and in other regions
facing the same problems.
Keywords:
soil erosion; water pollution; cropland; land-cover and land-use; USLE; GIS; remote
sensing; Nyabarongo River Catchment; Rwanda
1. Introduction
The global freshwater is only 2.5% of total global water of which a little more than 1.2% is
surface water [
1
]. However, every day, 2 million tons of sewage, industrial and agricultural waste are
discharged into the world’s water [
2
]. Humans have increased the sediment transport by global rivers
through soil erosion (by 2.3
±
0.6 billion metric tons per year) [
3
]. Pollution can be so severe that the
fresh water is no longer usable without incurring unacceptably high cleanup costs [
4
]. In addition,
Int. J. Environ. Res. Public Health 2016,13, 835; doi:10.3390/ijerph13080835 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2016,13, 835 2 of 16
depending on the type and level of pollution, the water body may become also unsuitable for fishing,
swimming, or even for aquatic animals to survive in [5].
Presently, land cover and land-use change is a critical world problem. There was a net decrease
in global forest area of 1.7% between 1990 and 2005. Africa experienced net annual forest area losses
of 1.1 million ha between 1990 and 2000 and 2.7 million ha between 2000 and 2005 [
6
]. In most of
sub Saharan Africa, more than 50% of the populations rely on agriculture for their livelihood [
7
].
Just 25 years ago (in 1990), forest covered about 44% of Rwanda’s territory, while the cropland only
occupied 28%. In 2015, more than 56% of the country’s land areas have been converted to croplands to
meet the food demands, at the extent of massive deforestation [
8
]. However, these land conversions
mainly for agricultural land use are associated with severe environmental problems including soil
erosion by water and its water pollution [
8
]. Around 80% of the pollution in seas and oceans comes
from land-based activities [9,10].
Lake Victoria, which drains the water from the Nyabarongo River through the Akagera River
has been listed among the top 10 most highly polluted water bodies in the world [
11
]. This pollution
is accelerated by intensive agriculture due to rapid population growth and industrialization in their
riparian communities [
12
]. As a consequence, nutrients from erosion have caused the infestation of
water hyacinth in Lake Victoria and the Akagera river basin [13,14].
In Rwanda, the actual soil erosion by water was estimated at approximately 595 million tons per
year with a mean soil erosion rate of 250 t
·
ha
1·
y
1
[
8
]. Erosion causes soil nutrients losses estimated
at 945,200 t of organic materials, 41,210 t of nitrogen, 200 t of phosphorus and 3055 t of potash
annually [
15
]. Soil erosion caused by land reclamation seriously threatens the water quality in
Rwanda [
16
18
], where a households-based survey indicated its contribution to about 61.8% for
water pollution within the Nyabarongo River system [
19
]. The water in the Nyabarongo River
system is polluted as far as physical parameters are concerned with high Turbidity of
737.28 ±571.03
Nephelometric Turbidity Units (NTU), Nitrate–Nitrogen (NO
3
–N) of 28.79
±
20.94 mg/L compared
to 1 NTU and 11 mg/L, respectively, set as the standard limits by the WHO Drinking Water
Guideline [
20
]. Previous studies have focused on the analysis of water quality in Nyabarongo River
and quasi-unanimously pointed out soil erosion as the main causal agent. However, few attempts
have dealt with elucidating the causes of soil erosion and quantitatively assessing the soil erosion
status in the Nyabarongo River Catchment; however, such information is critical for the effective
management of the catchment. The objectives of this study are, therefore, (1) to delineate the extent of
the Nyabarongo River Catchment; (2) assess the causes of soil erosion in the catchment in the form
of Land Cover and Land use (LCLU) and geomorphology and (3) estimate the actual soil erosion
rate in the catchment. To achieve the research objectives, this study used the Universal Soil Loss
Equation (USLE) model developed by the United States Department of Agriculture (USDA) to predict
the longtime average annual inter-rill and rill soil loss under various effects such as rainfall, soil types,
topography, and land cover type and land use [21].
2. Materials and Methods
2.1. Description of the Study Area
The Nyabarongo River (Figure 1) has an estimated length of 151.5 km and drains a total area
of 8478.24 km
2
(8413.75 km
2
or 33% of the total Rwanda coverage plus 64.50 km
2
, a small part of the
Southern Uganda). The catchment is mostly formed by a mountainous terrain with a mean slope
of 30% and elevation ranging from 1341 m to 4491 m. The underlying geology consists of 59.3% Acrisols,
19.2% Regosols, 9.2% Andosols, 6.7% Ferralsols, 2.8% Cambisols, 0.8% Histosols, 0.4% Gleysols,
and other 1.5% is covered by water [
22
]. This catchment experiences a tropical climate with two
rainfall seasons a year, March to May and September to December [
23
], an annual mean temperature
of 17
C and mean precipitation of 1231 mm/y [
24
]. Precipitation increases with elevation,
which varies from about 864 mm in the central plateau across the Lake Muhazi and Kigali City
Int. J. Environ. Res. Public Health 2016,13, 835 3 of 16
to 2258 mm in the mountain ranges over Nyungwe forest and volcano areas. The Nyabarongo River
starts from the confluence of Mbirurume and Mwogo rivers and ends up at its confluence with the
Akanyaru River, emptying into the Akagera River, one of the largest rivers that drains into Lake
Victoria [
25
]. The Nyabarongo River serves as a tributary of the Nile River and provides goods and
services such as a source of drinking water, irrigation and fishery to the Rwandese communities;
however, it is currently facing heavy pollution from mining, encroachment, landslides, unsustainable
agriculture and domestic and industrial wastes [26].
Int. J. Environ. Res. Public Health 2016, 13, 835 3 of 16
the Akanyaru River, emptying into the Akagera River, one of the largest rivers that drains into Lake
Victoria [25]. The Nyabarongo River serves as a tributary of the Nile River and provides goods and
services such as a source of drinking water, irrigation and fishery to the Rwandese communities;
however, it is currently facing heavy pollution from mining, encroachment, landslides,
unsustainable agriculture and domestic and industrial wastes [26].
(a)
(b)
Figure 1. (a) Location map of the Nyabarongo River Catchment; and (b) an aerial view of the
Nyabarongo River, with the water looking muddy brown due to pollution [26].
2.2. Delineation of the Catchment
The Nyabarongo River Catchment (Figure 1a) was delineated from the Advanced Space borne
Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM)
version 2 (30 m resolution) acquired from the United States Geological Survey (U.S.G.S.)
EarthExplorer (EE) database [27], using the hydrology toolset of the ArcGIS software version 10.2
(Environment Systems Research Institute (Esri) Inc., Redlands, CA, USA).
Figure 1.
(
a
) Location map of the Nyabarongo River Catchment; and (
b
) an aerial view of the
Nyabarongo River, with the water looking muddy brown due to pollution [26].
Int. J. Environ. Res. Public Health 2016,13, 835 4 of 16
2.2. Delineation of the Catchment
The Nyabarongo River Catchment (Figure 1a) was delineated from the Advanced Space borne
Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM)
version 2
(30 m resolution) acquired from the United States Geological Survey (U.S.G.S.) EarthExplorer
(EE) database [
27
], using the hydrology toolset of the ArcGIS software version 10.2 (Environment
Systems Research Institute (Esri) Inc., Redlands, CA, USA).
2.3. Preparation of LCLU Map for the Nyabarongo River Catchment
A land cover and land use (LCLU) map of the Nyabarongo River Catchment was developed
from four Landsat 8 images (path; row: 173/61; 173/62 acquired on 21 September 2015 and 172/61;
172/62 acquired on 12 July 2015) from the U.S.G.S. global visualization tool [
28
]. ENVI software
version 5.2 (Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation, Boulder, CO,
USA) was used to develop the Land Cover and Land Use map of the Nyabarongo River Catchment.
Firstly, these images were radiometrically corrected and the cloud-shadows were masked and the gap
filling algorithm was used to obtain a cloud-free image [
29
]. The supervised maximum likelihood
classification method [
30
] used in this study is the most common supervised classification method
used with remote sensing image data, and it was found to be more applicable and reliable for the
satellite image classification purposes [
30
,
31
]. Six LCLU classes (Settlement, Cropland, Forestland,
Grassland, Wetland and Water Bodies) within the study area were identified based on the U.S.G.S.
classification system level 1 [
32
] and are enough to analyze the influence of land use on soil erosion
for our case study. Referring to the accuracy assessment methods [
33
35
], 60 points were randomly
selected on the original Landsat image for each land cover category and, thereafter, the overlay of
classified image on the Google Earth program (Google Inc., Amphitheatre Parkway Mountain View,
San Jose, CA, USA) was applied for accuracy verification.
The overall accuracy for the LCLU classification of the Nyabarongo River Catchment was 91%
(Table 1). This accuracy is acceptable compared to the recommended overall classification accuracy of
at least 85% [
32
,
36
,
37
] and 70% for each land use category accuracy [
36
]. The LCLU map for the study
area indicates that cropland comprised about 75% of the total catchment (Figure 2).
Table 1.
Confusion Matrix for 2015 Land Cover and Land Use (LCLU) for the Nyabarongo
River Catchment.
1 2 3 4 5 6 User Accuracy Commission Error
1.Settlement 53 5 0 0 0 0 58 91% 9%
2.Cropland 753 0 3 0 0 63 84% 16%
3.Forestland 0 0 53 2 1 0 56 95% 5%
4.Grassland 0 2 7 52 2 0 63 83% 17%
5.Wetland 0 0 0 2 57 0 59 97% 3%
6.Water Bodies 0 0 0 0 1 60 61 98% 2%
60 60 60 59 61 60 360 – –
Producer Accuracy 88% 88% 88% 88% 93%
100%
– –
Omission Error 12% 12% 12% 12% 7% 0%
Overall Accuracy 91% – – – – – –
Kappa 89% – – – – – –
Int. J. Environ. Res. Public Health 2016,13, 835 5 of 16
Int. J. Environ. Res. Public Health 2016, 13, 835 5 of 16
Figure 2. Land Cover and Land Use (LCLU) for the Nyabarongo River Catchment in 2015.
2.4. Development of the USLE Factors
The ArcGIS software has become a prominent tool for the USLE (Equation (1)) modeling. This
approach has been used successfully in various studies to assess soil loss and its planning
management [8,38–44]. Using the nearest-neighbor method, all the datasets utilized in this study
were resampled to the same spatial resolution of 30 × 30 m and reprojected to the World Geodetic
System (WGS) 1984_Universal Traverse Mercator (UTM) zone 35 south because they were acquired
from different sources with different spatial resolutions.
A = R × K × LS ×C×P (1)
where A: average annual soil loss per unit area (t·ha
1
·y
1
); R: rainfall-runoff erosivity factor
(MJ·mm·ha
1
·h
1
·y
1
); K: erodibility factor (t·h·MJ
1
·mm
1
); LS: slope length (L) and the slope steepness
(S) factor; C: cover and management factor; and P is the support and conservation practices factor [45].
2.4.1. Rainfall Erosivity Factor (R)
Rainfall erosivity contributes to about 80% of soil loss [46] and has significant impacts on soil
erosion [47]. Because of the paucity of R measurement data worldwide [48] and the lack of rain
gauged meteorological data in the study area [8], we have been constrained to use R factor derived
from the Global raster dataset provided by the Global Land Degradation Information System
(GLADIS) database provided by the Food Agriculture Organization (FAO) [49]. Given that in
tropical areas the transformation of the Fournier index into the R factor works less well, R factor has
been generated using an alternative index related to annual rainfall (Equation 2) developed by
Lo et al. [48,50]:
R = 38.46 +3.48 ×P (2)
where P is the Average annual rainfalls.
The R factor for Nyabarongo River Catchment has the value ranging from
4741.21 MJ·mm·ha
1
·h
1
·y
1
to 5958.94 MJ·mm·ha
1
·h
1
·y
1
(Figure 3a).
2.4.2. Soil Erodibility Factor (K)
The K factor (Equation (3)) is a quantitative value that is experimentally determined taking into
consideration the soil texture and structure, the organic matter content and the permeability [21]:
Figure 2. Land Cover and Land Use (LCLU) for the Nyabarongo River Catchment in 2015.
2.4. Development of the USLE Factors
The ArcGIS software has become a prominent tool for the USLE (Equation (1)) modeling.
This approach has been used successfully in various studies to assess soil loss and its planning
management [
8
,
38
44
]. Using the nearest-neighbor method, all the datasets utilized in this study
were resampled to the same spatial resolution of 30
×
30 m and reprojected to the World Geodetic
System (WGS) 1984_Universal Traverse Mercator (UTM) zone 35 south because they were acquired
from different sources with different spatial resolutions.
A=R×K×LS ×C×P (1)
where A: average annual soil loss per unit area (t
·
ha
1·
y
1
); R: rainfall-runoff erosivity factor
(MJ
·
mm
·
ha
1·
h
1·
y
1
); K: erodibility factor (t
·
h
·
MJ
1·
mm
1
); LS: slope length (L) and the slope
steepness (S) factor; C: cover and management factor; and P is the support and conservation practices
factor [45].
2.4.1. Rainfall Erosivity Factor (R)
Rainfall erosivity contributes to about 80% of soil loss [
46
] and has significant impacts on soil
erosion [
47
]. Because of the paucity of R measurement data worldwide [
48
] and the lack of rain gauged
meteorological data in the study area [
8
], we have been constrained to use R factor derived from
the Global raster dataset provided by the Global Land Degradation Information System (GLADIS)
database provided by the Food Agriculture Organization (FAO) [
49
]. Given that in tropical areas the
transformation of the Fournier index into the R factor works less well, R factor has been generated
using an alternative index related to annual rainfall (Equation 2) developed by Lo et al. [48,50]:
R=[38.46 +(3.48 ×P)] (2)
where Pis the Average annual rainfalls.
The R factor for Nyabarongo River Catchment has the value ranging from 4741.21 MJ·mm·ha1·h1·y1
to 5958.94 MJ·mm·ha1·h1·y1(Figure 3a).
Int. J. Environ. Res. Public Health 2016,13, 835 6 of 16
Int. J. Environ. Res. Public Health 2016, 13, 835 7 of 16
erosion in forested regions [45]. Wet seasons associated with the low vegetation coverage are most
frequently the optimum period for estimating an annual soil loss whenever land is highly exposed to
soil erosion risk [56]. The normalized vegetation index (NDVI) is the source of C factor values. The
accuracy of these values can be maximized taking into account their quantitative assessment such as
cover type, arboreal height, percentage of tree cover and shrubs and thickness of grass cover and
humus [57]. However, in some places, mostly in tropical areas including the area under
investigation in this study, the abundance of thick clouds [29] remains a challenge for quality remote
sensing data. In spite of these challenges, the present study established the C factor map (Figure 3e)
by attributing representative C factor values recommended by Wischmeier and Smith (1978) [21] to
LCLU classes; 0, 0.003, 0.09, and 0.63 for the wetland, forest, settlement and grassland, and cropland,
respectively, in accordance with the methodology adopted by a number of similar studies [8,40,52,58].
2.4.5. Support Practice Factor (P)
The P factor plays an important role in the form of conservation practices [44]. According to the
value of the P factor observed over Rwanda [50], the current study established the P factor map with
0 value for water and wetland and 0.75 for the rest of the LCLU classes (Figure 3f).
Figure 3. Maps of the Universal Soil Loss Equation (USLE) factors for the Nyabarongo River
Catchment: (a) rainfall erosivity; (b) soil erodibility; (c) slope length and slope steepness; (d) the
Slope angle; (e) land cover management; (f) conservation support practice.
Figure 3.
Maps of the Universal Soil Loss Equation (USLE) factors for the Nyabarongo River Catchment:
(
a
) rainfall erosivity; (
b
) soil erodibility; (
c
) slope length and slope steepness; (
d
) the Slope angle;
(e) land cover management; (f) conservation support practice.
2.4.2. Soil Erodibility Factor (K)
The K factor (Equation (3)) is a quantitative value that is experimentally determined taking into
consideration the soil texture and structure, the organic matter content and the permeability [21]:
K=2.1 ×106×M1.14 ×(12 OM)+0.0325 ×(P2)+0.025 ×(S3)(3)
where M: (% silt + % very fine sand) (100% clay); OM = percentage of organic matter; P: permeability
class; and S: structure class.
Int. J. Environ. Res. Public Health 2016,13, 835 7 of 16
Erosion models often use secondary data available in a geographic information system as an
alternative approach mostly when assessing soil erosion for a large area because the measurement
of soil erosion is expensive and time consuming due to extensive laboratory analysis of samples
of different soil profiles [
8
,
42
,
51
]. Therefore, the K factor used in this study with values ranging
from 0.05 t
·
h
·
MJ
1·
mm
1
to 0.21 t
·
h
·
MJ
1·
mm
1
(Figure 3b) was derived from Global raster datasets
in grid format as provided by the Global Land Degradation Information System (GLADIS) database of
the Food Agriculture Organization (FAO) [
49
]. This project obtained the global K factor dataset by
applying the soil erodibility classification for water erosion FAO 1984 to the soil units of the soil map
of the world, both FAO 1974 and 1990 legends. Furthermore, the reference factor was adjusted for
topsoil texture characteristics and soil phase characteristics influencing resistance to rainfall impact
such as surface stoniness or influencing soil permeability such as lithic, plastic, anthraquic soil phases
or other impermeable layers. Saline and sodic soil phases reduce soil permeability of subsoil and
therefore indirectly increase soil erodibility. The modifiers (topsoil texture, protection impact and
reduced permeability) have consecutively been applied to the reference soil erodibility factor of each
soil unit and a weighted average for each soil mapping unit has been calculated [50].
2.4.3. Slope Length and Steepness Factor (LS)
The slope length factor (L) represents the effect of slope length on erosion, and the slope steepness
factor (S) reflects the influence of slope gradient on erosion [
21
]. The LS factor (Figure 3c) for this
study was estimated from the ASTER GDEM of 30 m resolution [
27
] using Equation (4) [
52
] computed
with the Raster Calculator tool from the Spatial Analyst extension of ArcMap (Environment Systems
Research Institute (Esri) Inc., Redlands, CA, USA). The Flow Accumulation tool available in the Spatial
Analyst Hydrology toolset of ArcMap was used to calculate the Flow Accumulation grid. The Grid
slope in percentage was calculated with the Slope tool in the Spatial Analyst Surface toolset of ArcMap:
LS =QaM
22.13 y
×0.065 +0.045 ×Sg+0.0065 ×S2
g(4)
where
LS
: topographical factor;
Qa
: Flow Accumulation grid;
Sg
: Grid slope in percentage; M: Grid
size (Vertical length times horizontal length), y: a constant dependent on the value of the slope gradient:
0.5 if the slope is greater than 4.5%, 0.4 on slopes of 3% to 4.5%, 0.3 on slopes of 1% to 3%, and 0.2 on
slopes less than 1%. Therefore, in our study a constant (y) of 0.5 was used in Equation (4) due to the
mean slope of 30% observed for the Nyabarongo River Catchment. The ASTER GDEM was chosen to
be used in this study due to its finer spatial resolution that closely matches that of the Geo-referenced
Landsat images and at comparable accuracies [
29
,
53
] and it was found to be reliable for LS factor
generation [
44
]. Accuracies for this global product were estimated with 20 m at 95% confidence for
vertical data and 30 m at 95% confidence in horizontal data [
54
]. The LS factor (Figure 3c) has values
ranging from 0 to 230.21. This range indicates an area with very steep slopes where 40% of the land
catchment has very steep slopes ranging from 30% to 205.5% gradient (Figure 3d). Generally, the
range of slope values in degrees is from 0 to 90. For percent rise, the range is 0 for near infinity. A flat
surface is 0%, a 45 degree surface is 100%, and as the surface becomes more vertical, the percentage rise
becomes increasingly larger [
55
]. This expresses that the cells with slopes >100% in the Nyabarongo
River Catchment are for the extremely steep surface >45 degrees gradient.
2.4.4. Cover Management Factor (C)
The C factor reflects the effects of cropping and management practices on soil erosion rates in
agricultural lands and the effects of vegetation canopy and ground covers on reducing the soil erosion
in forested regions [
45
]. Wet seasons associated with the low vegetation coverage are most frequently
the optimum period for estimating an annual soil loss whenever land is highly exposed to soil erosion
risk [
56
]. The normalized vegetation index (NDVI) is the source of C factor values. The accuracy of
these values can be maximized taking into account their quantitative assessment such as cover type,
Int. J. Environ. Res. Public Health 2016,13, 835 8 of 16
arboreal height, percentage of tree cover and shrubs and thickness of grass cover and humus [
57
].
However, in some places, mostly in tropical areas including the area under investigation in this study,
the abundance of thick clouds [
29
] remains a challenge for quality remote sensing data. In spite of these
challenges, the present study established the C factor map (Figure 3e) by attributing representative C
factor values recommended by Wischmeier and Smith (1978) [
21
] to LCLU classes; 0, 0.003, 0.09, and
0.63 for the wetland, forest, settlement and grassland, and cropland, respectively, in accordance with
the methodology adopted by a number of similar studies [8,40,52,58].
2.4.5. Support Practice Factor (P)
The P factor plays an important role in the form of conservation practices [
44
]. According to
the value of the P factor observed over Rwanda [
50
], the current study established the P factor map
with 0 value for water and wetland and 0.75 for the rest of the LCLU classes (Figure 3f).
The soil erosion map was overlaid with the slope map (%) with four slope angle classes
(<5% as very gentle to flat, 5% to 15% as gentle, 15% to 30% as steep and >30% as very steep) [
59
] to
assess the influence of the slope steepness on the soil erosion rates. To assess the potential impact of
terracing cultivation method, the P factor was estimated based on the slope and terracing cultivation
method (Table 2) [60].
Table 2. Support Practice Factor (P) for terracing.
Slope (%) 0–7 7–11.3 11.3–17.6 17.6–26.8 >26.8
P Factor 0.1 0.12 0.16 0.18 0.2
As recommended by Wischmeier and Smith [
21
], to identify the areas of greatest vulnerability
in this study, a potential soil erosion map (Figure 4a) that considers only the natural factors if a field
were continuously in fallow conditions and the actual soil erosion map (Figure 4b) that considers both
natural and LCLU management practices have been generated by the multiplication of the R, K, LS
factor maps and the R, K, LS, C, P factor maps respectively using the raster calculator function tool
of the ArcGIS. Actual soil loss from the cropped field is usually much less than the potential soil loss
depends on the particular combination of cover, crop sequence, and management practices [
21
]. The
statistics of soil erosion rates presented in Tables 39were computed using the Zonal Statistics as Table
Tool available in the Spatial Analyst Zonal Toolset of the ArcGIS software version 10.2 (Environment
Systems Research Institute (Esri) Inc., Redlands, CA, USA).
3. Results
Soil erosion maps are presented with double units where 15 t
·
ha
1·
y
1
= 1 mm
·
y
1
according to
FAO recommendation [
57
,
61
]. Erosion rates were classified into three categories, notably Moderate
(0 t·ha1·y1–100 t·ha1·y1), High (100 t·ha1·y1–300 t·ha1·y1) and Extreme (300 t·ha1·y1) [58].
The results from natural soil erosion factors (rainfall erosivity, soil erodibility and slope length and
slope steepness) indicate that the Nyabarongo river Catchment is naturally vulnerable to soil erosion
by water with a rate of 1397 t
·
ha
1·
y
1
or 93.13 mm
·
y
1
. The areas of greatest vulnerability with
extreme potential erosion rate of 4487 t
·
ha
1·
y
1
or 299.13 mm
·
y
1
comprised 31% of the catchment
and contributed up to 99.54% of the total annual soil loss (Figure 4a and Table 3). When taking
into accounts land cover and land use (C) and support practice (P) factors in 2015, the catchment is
associated with an actual soil erosion rate of 490 t
·
ha
1·
y
1
or 32.67 mm
·
y
1
and 22% of the catchment
is exposed to extreme soil erosion of 2178 t
·
ha
1·
y
1
or 145.2 mm
·
y
1
that contributed 97.8% of the
total annual soil loss (Figure 4b and Table 4).
Int. J. Environ. Res. Public Health 2016,13, 835 9 of 16
Int. J. Environ. Res. Public Health 2016, 13, 835 8 of 16
The soil erosion map was overlaid with the slope map (%) with four slope angle classes
(<5% as very gentle to flat, 5% to 15% as gentle, 15% to 30% as steep and >30% as very steep) [59] to
assess the influence of the slope steepness on the soil erosion rates. To assess the potential impact of
terracing cultivation method, the P factor was estimated based on the slope and terracing cultivation
method (Table 2) [60].
Table 2. Support Practice Factor (P) for terracing.
Slope (%) 0–7 7–11.3 11.3–17.6 17.6–26.8 >26.8
P Factor 0.1 0.12 0.16 0.18 0.2
As recommended by Wischmeier and Smith [21], to identify the areas of greatest vulnerability
in this study, a potential soil erosion map (Figure 4a) that considers only the natural factors if a field
were continuously in fallow conditions and the actual soil erosion map (Figure 4b) that considers
both natural and LCLU management practices have been generated by the multiplication of the R, K,
LS factor maps and the R, K, LS, C, P factor maps respectively using the raster calculator function
tool of the ArcGIS. Actual soil loss from the cropped field is usually much less than the potential soil
loss depends on the particular combination of cover, crop sequence, and management practices [21].
The statistics of soil erosion rates presented in Tables 3–9 were computed using the Zonal Statistics
as Table Tool available in the Spatial Analyst Zonal Toolset of the ArcGIS software version 10.2
(Environment Systems Research Institute (Esri) Inc., Redlands, CA, USA).
3. Results
Soil erosion maps are presented with double units where 15 t·ha1·y1 = 1 mm·y1 according to
FAO recommendation [57,61]. Erosion rates were classified into three categories, notably Moderate
(0 t·ha1·y1–100 t·ha1·y1), High (100 t·ha1·y1–300 t·ha1·y1) and Extreme ( 300 t·ha1·y1) [58]. The
results from natural soil erosion factors (rainfall erosivity, soil erodibility and slope length and slope
steepness) indicate that the Nyabarongo river Catchment is naturally vulnerable to soil erosion by
water with a rate of 1397 t·ha1·y1 or 93.13 mm·y1. The areas of greatest vulnerability with extreme
potential erosion rate of 4487 t·ha1·y1 or 299.13 mm·y1 comprised 31% of the catchment and
contributed up to 99.54% of the total annual soil loss (Figure 4a and Table 3). When taking into
accounts land cover and land use (C) and support practice (P) factors in 2015, the catchment is
associated with an actual soil erosion rate of 490 t·ha1·y1 or 32.67 mm·y1 and 22% of the catchment
is exposed to extreme soil erosion of 2178 t·ha1·y1 or 145.2 mm·y1 that contributed 97.8% of the total
annual soil loss (Figure 4b and Table 4).
Figure 4.
Maps of the Nyabarongo River Catchment: (
a
) potential soil erosion; and (
b
) actual soil
erosion, 2015.
Table 3.
Estimated potential soil erosion rates in the entire Nyabarongo River Catchment excluding
water bodies (Figure 4a).
Soil Erosion Class
(t·ha1·y1)Area (ha) Area (%) Mean Erosion
(t·ha1·y1)Annual Soil
Loss (t)
Annual Soil
Loss (%)
Moderate (0–100) 551,114 66 0.6 ±7 350,050 0.03
High (100–300) 25,051 3 200 ±57 5,017,380 0.43
Extreme (300) 258,857 31 4487 ±5310 1,161,465,050 99.54
Entire Catchment 835,022 100 1397 ±3611 1,166,832,480 100
Table 4.
Estimated actual soil erosion rates in the entire Nyabarongo River Catchment excluding water
bodies, 2015 (Figure 4b).
Soil Erosion Class
(t·ha1·y1)Area (ha) Area (%) Mean Erosion
(t·ha1·y1)Annual Soil
Loss (t)
Annual Soil
Loss (%)
Moderate (0–100) 609,566 73 2 ±12 1,227,482 0.3
High (100–300) 41,751 5 186 ±58 7,774,055 1.9
Extreme (300) 183,705 22 2178 ±2327 400,159,243 97.8
Entire Catchment 835,022 100 490 ±1413 409,160,780 100
LCLU is represented by the values of C factor. The cropland that is associated with the highest C
factor value of 0.63 comprised 76% of the total land catchment and is exposed to the highest estimated
mean erosion rate of 618 t
·
ha
1·
y
1
compared to other land use classes. 95.8% of the total soil loss was
observed in cropland area (Table 5).
Table 5.
LCLU 2015 and Estimated soil erosion rates in the entire Nyabarongo River Catchment
excluding water bodies.
LCLU Class Area (ha) Area (%) Mean Erosion
(t·ha1·y1)Annual Soil
Loss (t)
Annual Soil
Loss (%)
Settlement 23,400 2.8 105 ±493 2,454,965 0.6
Cropland 634,596 76.0 618 ±1569 391,976,027 95.8
Forestland 124,630 14.9 49 ±508 6,137,412 1.5
Grassland 42,561 5.1 202 ±744 8,592,376 2.1
Wetland 9835 1.2 0 0 0
Entire Catchment 835,022 100 490 ±1413 409,160,780 100
Int. J. Environ. Res. Public Health 2016,13, 835 10 of 16
Agricultural land use occupied a big portion of 94.3% in the extreme erosion areas
(300 t·ha1·y1) and accounted for 96.2% of the soil loss in these extreme erosion areas (Table 6).
Table 6. LCLU 2015 and estimated extreme soil erosion rates in the Nyabarongo River Catchment.
LCLU Category Area (ha) Area (%) Mean Erosion
(t·ha1·y1)Annual Soil
Loss (t)
Annual Soil
Loss (%)
Settlement 1722 0.9 1162 ±1445 2,000,796 0.5
Cropland 173,244 94.3 2222 ±2340 384,953,192 96.2
Forestland 2066 1.1 2717 ±2820 5,602,229 1.4
Grassland 6673 3.6 1139 ±1556 7,603,026 1.9
Wetland 00000
Extreme Erosion 183,705 100 2178 ±2327 400,159,243 100
The catchment area was classified into four categories according to the slope angle: Very Gentle
to Flat, Gentle, Steep and Very Steep [
59
]. The very steep slope area with a slope angle >30% occupied
44% of the land area within the catchment and is responsible for 73.5% of the total soil loss and is
associated with high mean soil erosion rate of 819 t
·
ha
1·
y
1
(i.e., 54.6 mm
·
y
1
) compared to the other
slope angle classes (Table 7).
Table 7.
Slope (%) and Estimated soil erosion rates in the entire Nyabarongo River Catchment excluding
water bodies.
Description Slope Angle (%) Area (ha) Area (%) Mean Erosion
(t·ha1·y1)Annual Soil
Loss (t)
Annual Soil
Loss (%)
Very Gentle to Flat
<5 41,751 5 49 ±39 2,045,804 0.5
Gentle 5–15 167,004 20 113 ±169 18,821,396 4.6
Steep >15–30 258,857 31 338 ±563 87,560,407 21.4
Very Steep >30 367,410 44 819 ±2015 300,733,173 73.5
Entire Catchment 0–205.5 835,022 100 490 ±1413 409,160,780 100
4. Discussion
Extremely high population density in Rwanda coupled with heavy dependency on agriculture
of 83.4% [
15
] exerts enormous pressure on ecosystems and natural forests [
62
]. Watersheds upstream
and unplanned occupation of land result in severe erosion, causing a serious soil degradation [
8
,
63
,
64
].
Due to the dominant agricultural land use of 74.85% within the Nyabarongo River Catchment in 2015,
the catchment exposed to mean soil erosion rate of 490 t
·
ha
1·
y
1
or 32.67 mm
·
y
1
(Figure 4b and
Table 4), an erosion rate that can occur in cropland areas on steep slopes and heavy precipitation as
previously revealed by Karamage et al. while estimating soil erosion risk in Rwanda with a mean
erosion rate (421 t·ha1·y1or 28.07 mm·y1) for the cropland cell [8].
The moderate and high actual erosion rates were estimated over approximately 78% of the
catchment area excluding water bodies (Table 4). The findings of this study are congruent with the
early findings that estimated the erosion rate of
300 t
·
ha
1·
y
1
for other areas in the
tropics [58,65]
.
Furthermore, the rest of the catchment area (22%) is subjected to an extreme soil erosion rate
of 2178 t
·
ha
1·
y
1
(Table 4). The majority of the upland soils in the humid and subhumid tropics is
grouped as low activity clay (LAC) soils characterized by fragile soils due to its low Effective Cation
Exchange Capacity (ECEC) of
16 meq/100 g clay in the subsoil [
66
,
67
]. Extreme soil erosion rates
occurring in Rwanda including the Nyabarongo River Catchment are associated with very fragile
soils derived from physico-chemical alteration of schistose, quartzite, gneissic, granite and volcanic
rocks [
68
], steep slopes and high rainfall intensity as previously discussed while estimating the soil
erosion in Nethravathi basin located in the middle region of Western Ghats, western India where soil
erosion rate was ranging from 0 t·ha1·y1to 1,907,287 t·ha1·y1[52].
As previously indicated by various researches [
8
,
43
,
50
,
52
,
58
,
59
], clearance of natural biomass
for agricultural land use on steep slopes is the main instigator of soil erosion. This is in agreement
with the findings of this study where the cropland alone, occupying 76% of the land area within the
Int. J. Environ. Res. Public Health 2016,13, 835 11 of 16
catchment, was associated with a high erosion rate of 618 t
·
ha
1·
y
1
or 41.20 mm
·
y
1
and comprised
about 95.8% of the soil loss within the catchment (Table 5). In this study, the USLE model revealed
that the slope gradient plays a significant role on soil erosion within the catchment of Nyabarongo
River, where the soil erosion rate increased in compatibility with the slope angle. The area with a very
steep slope (>30%) was associated with a mean soil erosion rate of 819 t
·
ha
1·
y
1
or 54.6 mm
·
y
1
and
accounted for 73.5% of the total soil loss within the catchment (Table 7).
The present study found that the cropland largely extended to regions with extreme soil erosion,
represented a big portion (94.3%) of the areas linked with extreme erosion rates, and was responsible
for 96.2% of soil loss in these areas (Table 6). A percentage nearly equivalent to the percentage of
extreme soil loss (96.9%) estimated for the agricultural land use in the Rio Lempa, Central America,
indicating that agricultural land use is an important contributing factor in nearly all areas with extreme
and high erosion rates [58].
The government of Rwanda, through the Organic Law number 04/2005, has established protection
measures to protect river shores and wetlands in general; this has obliged the removal of agricultural
crops in the 10 riparian meters and specific crops to be put on those 10 m. If this law is followed, it
would lead to the improvement of water quality without water hyacinths and would be favorable for
transport activities, biodiversity conservation and tourism. On the river shores, the natural vegetation
will constitute a good habitat for reproduction of fish and increase its productivity. Agroforestry,
reeds and bamboos on river shores will constitute another source of food and income for the owners.
The survey showed that 63.8% households knew the Organic Law No. 04/2005 and among the
total 359 households surveyed, 75.2% (270 households) agreed that the mean household’s maximum
willingness to pay for the protection of Nyabarongo River system was 486.4 Rwandan francs (RWF)
per household per month over the proposed five years (US$1 = RWF607) [
19
]. However, as indicated
by this study, failure of extreme erosion control within the entire catchment rather than considering
the buffer zone alone of the river might be the cause of high pollution due to soil sediments from
various watersheds of the Nyabarongo River Catchment. Radical terraces that fight soil erosion have
also had the potential to increase farm productivity and alleviate poverty. However, many difficulties
such as sticky soils that are hard to work on, lack of equipment (i.e., farm tractors), steep slopes,
lack of financial means and qualified supervisors constitute a monumental challenge to achieving
the aims of radical terraces in Rwanda [
15
]. The water pollution is associated with degradation of
natural biomasses where 14.1% of the country of Rwanda vegetation is degraded, from slight (7.5%)
to substantial (6.6%) deterioration. A recent study on vegetation dynamics in Rwanda revealed that
several types of vegetation were seriously endangered: the mosaic grassland/forest or shrubland was
severely degraded, followed by sparse vegetation, grassland or woody vegetation regularly flooded
on water logged soil [69].
Construction of Check dams, reservoirs and terraces might be suitable for sustainable soil
erosion control measures within the Nyabarongo River Catchment based on the well-established
importance of Check dam systems and natural vegetation rehabilitation [
70
], reservoirs that reduced
the flux of sediment reaching the world’s coasts (by 1.4
±
0.3 billion metric tons per year) and
where 100 billion metric tons of sediment and 1 billion to 3 billion metric tons of carbon are
now sequestered in reservoirs within the past 50 years [
3
], and terraces [
8
,
41
,
45
,
60
,
71
73
] on soil
erosion mitigation reduction. Terracing has shown to be the best land conservation support practice
when compared, for example, with strip-cropping and contouring cultivation methods [
60
]. The
current study indicated that terraces could reduce the current erosion rate in cropland areas by
about 78%
, a reduction of mean soil erosion rate from 618 t
·
ha
1·
y
1
to 134 t
·
ha
1·
y
1
or 41.20 mm
·
y
1
to 8.93 mm·y1(Tables 8and 9, and Figure 5).
Int. J. Environ. Res. Public Health 2016,13, 835 12 of 16
Table 8.
Estimated actual soil erosion rates and Slope (%) for cropland cell (C factor = 0.63) of the
Nyabarongo River Catchment and minor support practice of 0.75 (Figure 5a).
Agricultural
Land Use
Suitability
Erosion
Rates
(t·ha1·y1)
Area
(ha)
Area
(%)
Mean
Erosion
(t·ha1·y1)
Annual
Soil Loss (t)
Annual Soil
Loss (%)
Mean
Slope (%)
Suitable <300
463,255
73 15 ±53 7,022,835 2 28 ±19
Unsuitable 300
173,244
27 2222 ±2340 384,953,192 98 29 ±15
Cropland Cell
634,596
100 618 ±1569 391,976,027 100 29 ±18
Table 9.
Estimated actual soil erosion rates and slope (%) for cropland cell (C factor = 0.63) within
the Nyabarongo River Catchment and assumed terracing cultivation method as a sustainable support
practice factor (Table 2and Figure 5b).
Agricultural
Land Use
Suitability
Erosion
Rates
(t·ha1·y1)
Area
(ha)
Area
(%)
Mean
Erosion
(t·ha1·y1)
Annual
Erosion (t)
Annual
Erosion (%)
Mean
Slope (%)
Suitable <300
545,753
86 12 ±59 6,549,036 8 27 ±18
Unsuitable 300
88,843
14 885 ±746 78,588,369 92 40 ±13
Cropland Cell
634,596
100 134 ±411 85,137,405 100 29 ±18
Int. J. Environ. Res. Public Health 2016, 13, 835 12 of 16
Cropland Cell 634,596 100 134 ± 411 85,137,405 100 29 ± 18
Figure 5. Maps of the agricultural land use suitability for the 2015 cropland; (a) agricultural land use
suitability without terraces or with minor support practice (p = 0.75); and (b) agricultural land use
suitability if terraces were applied on the cropland area.
Potentially suitable areas comprised actual erosion rate of <300 t·ha
1
·y
1
or <20 mm·y
1
, while
probably unsuitable areas are characterized by erosion rate of 300 t·ha
1
·y
1
or 20 mm·y
1
under C
factor value of 0.63 for agricultural land use [58].
5. Conclusions
The present study assessed soil erosion by water in the Nyabarongo River Catchment in
Rwanda. The USLE model and GIS datasets were employed. The results indicated that the
Nyabarongo River Catchment is extremely exposed to soil erosion by water due to unmanaged
intensive cropland expansion over steep slopes that cleared a vast part of biomass cover by 76% of
the land catchment in an area predominated by high rainfall intensity and fragile soil with high
sensitivity to erosion. Consequently, an estimated 409 million tons of soils are lost every year in the
catchment and pollute the water of Nyabarongo River. This study suggests the emergency need for
water and soil conservation practices such as terracing cultivation methods, agroforestry,
establishing biomass cover mostly on the land with very steep slopes, and construction of Check
dams which could reduce the current cropland soil erosion and sediment loads and improve the
water quality of the Nyabarongo River. Although this study identified the situation of land use and
their related soil erosion rates, further research is suggested for this area such as a feasibility analysis
of terracing implementation and a suitability analysis of Check dam locations within different
watersheds of the Nyabarongo River Catchments.
Acknowledgments: We thank the anonymous reviewers and the Editor whose comments and suggestions
helped improve the clarity of this manuscript. This study was supported by the National Natural Scientific
Foundation of China (#U1503301) and the National Basic Research Programs of China (#2014CB954204).
Author Contributions: Chi Zhang supervised this study. Fidele Karamage, Felix Ndayisaba and Hua Shao
modeled the USLE and wrote the manuscript. Alphonse Kayiranga, Xia Fang, Lamek Nahayo and Christophe
Mupenzi performed the statistical analysis. Guangjin Tian helped in discussion.
Conflicts of Interest: The authors declare no conflicts of interest.
Figure 5.
Maps of the agricultural land use suitability for the 2015 cropland; (
a
) agricultural land use
suitability without terraces or with minor support practice (P = 0.75); and (
b
) agricultural land use
suitability if terraces were applied on the cropland area.
Potentially suitable areas comprised actual erosion rate of <300 t
·
ha
1·
y
1
or <20 mm
·
y
1
, while
probably unsuitable areas are characterized by erosion rate of
300 t
·
ha
1·
y
1
or
20 mm
·
y
1
under
C factor value of 0.63 for agricultural land use [58].
5. Conclusions
The present study assessed soil erosion by water in the Nyabarongo River Catchment in Rwanda.
The USLE model and GIS datasets were employed. The results indicated that the Nyabarongo River
Catchment is extremely exposed to soil erosion by water due to unmanaged intensive cropland
expansion over steep slopes that cleared a vast part of biomass cover by 76% of the land catchment
in an area predominated by high rainfall intensity and fragile soil with high sensitivity to erosion.
Int. J. Environ. Res. Public Health 2016,13, 835 13 of 16
Consequently, an estimated 409 million tons of soils are lost every year in the catchment and pollute the
water of Nyabarongo River. This study suggests the emergency need for water and soil conservation
practices such as terracing cultivation methods, agroforestry, establishing biomass cover mostly on the
land with very steep slopes, and construction of Check dams which could reduce the current cropland
soil erosion and sediment loads and improve the water quality of the Nyabarongo River. Although
this study identified the situation of land use and their related soil erosion rates, further research
is suggested for this area such as a feasibility analysis of terracing implementation and a suitability
analysis of Check dam locations within different watersheds of the Nyabarongo River Catchments.
Acknowledgments:
We thank the anonymous reviewers and the Editor whose comments and suggestions helped
improve the clarity of this manuscript. This study was supported by the National Natural Scientific Foundation of
China (#U1503301) and the National Basic Research Programs of China (#2014CB954204).
Author Contributions:
Chi Zhang supervised this study. Fidele Karamage, Felix Ndayisaba and
Hua Shao modeled the USLE and wrote the manuscript. Alphonse Kayiranga, Xia Fang, Lamek Nahayo and
Christophe Mupenzi performed the statistical analysis. Guangjin Tian helped in discussion.
Conflicts of Interest: The authors declare no conflicts of interest.
Abbreviations
MJ Megajoule
milliequivalents meq
US$ U.S. dollar
RWF Rwandan Franc
References
1.
Gleick, P.H. Water in Crisis: A Guide to the World
'
s Fresh Water Resources; Oxford University Press: New York,
NY, USA, 1993.
2.
World Water Assessment Programme. The United Nations World Water Development Report: Water for People,
Water for Life; UNESCO: Paris, France, 2003.
3.
Syvitski, J.P.; Vörösmarty, C.J.; Kettner, A.J.; Green, P. Impact of humans on the flux of terrestrial sediment to
the global coastal ocean. Science 2005,308, 376–380. [CrossRef] [PubMed]
4.
Fry, A.; Martin, R. Water Facts and Trends. Available online: http://www.unwater.org/downloads/Water_
facts_and_trends.pdf (accessed on 15 May 2016).
5.
Musy, A. Hydrologie Générale. Available online: http://echo2.epfl.ch/e-drologie/ (accessed on 12 July 2016).
6.
Food and Agriculture Organization; Joint Research Centre. Global Forest Land-Use Change 1990–2005;
Lindquist, E.J., D’annunzio, R., Gerrand, A., Macdicken, K., Achard, F., Beuchle, R., Brink, A., Eva, H.D.,
Mayaux, P., San-Miguel-Ayanz, J., et al., Eds.; FAO: Rome, Italy, 2012.
7. Van Straaten, P. Rocks for Crops: Agrominerals of Sub-Saharan Africa; Icraf: Nairobi, Kenya, 2002.
8.
Karamage, F.; Zhang, C.; Ndayisaba, F.; Shao, H.; Kayiranga, A.; Fang, X.; Nahayo, L.; Muhire Nyesheja, E.;
Tian, G. Extent of cropland and related soil erosion risk in Rwanda. Sustainability 2016,8, 609. [CrossRef]
9.
Nezlin, N.P.; DiGiacomo, P.M.; Stein, E.D.; Ackerman, D. Stormwater runoff plumes observed by seawifs
radiometer in the Southern California Bight. Remote Sens. Environ. 2005,98, 494–510. [CrossRef]
10.
Korkmaz, N.; Avci, M. Evaluation of water delivery and irrigation performances at field level: The case of
the menemen left bank irrigation district in Turkey. Indian J. Sci. Technol. 2012,5, 2079–2089.
11.
Grinning Planet. Polluted Seas: Major Bodies of Water/Areas with Serious Water Pollution Problems.
Available online: http://grinningplanet.com/2005/07-26/polluted-seas.htm (accessed on 20 January 2015).
12.
Nhapi, I.; Wali, U.; Usanzineza, D.; Banadda, N.; Kashaigili, J.; Kimwaga, R.; Gumindoga, W.; Sendagi, S.
Heavy metals inflow into Lake Muhazi, Rwanda. Open Environ. Eng. J. 2012,5, 56–65. [CrossRef]
13.
Williams, A.E.; Hecky, R.E. Invasive aquatic weeds and eutrophication: The case of water hyacinth in Lake
Victoria. In Restoration and Management of Tropical Eutrophic Lakes; Reddy, M.V., Ed.; CRC Press: Boca Raton,
FL, USA, 2005.
14.
Albright, T.; Moorhouse, T.; McNabb, T. The Abundance and Distribution of Water Hyacinth in Lake Victoria
and the Kagera River Basin, 1989–2001. Available online: http://nilerak.hatfieldgroup.com/english/nrak/
EO/USGS_CLI_WH_LakeVictoria.pdf (accessed on 12 July 2016).
Int. J. Environ. Res. Public Health 2016,13, 835 14 of 16
15.
De la Paix, M.J.; Lanhai, L.; Jiwen, G.; de Dieu, H.J.; Gabriel, H.; Jean, N.; Innocent, B. Radical terraces in
Rwanda. East Afr. J. Sci. Technol. 2012,1, 53–58.
16.
Murekatete, E. Controls of Denitrification in Agricultural Soils, Wetlands, and Fish Ponds in the Migina Catchment,
Rwanda; Unesco-IHE: Delft, The Netherlands, 2013.
17.
Food and Agriculture Organization of the United Nations (FAO). Rwanda: Ressources En Eau. Available
online: http://www.fao.org/nr/water/aquastat/countries_regions/Profile_segments/RWA-WR_eng.stm
(accessed on 20 April 2015).
18.
UNEP. Rwanda State of Environment and Outlook: Our Environment for Economic Development. Available
online: http://www.unep.org/publications/contents/pub_details_search.asp?ID=4089 (accessed on
20 October 2015).
19.
Sylvie, N. An Assessment of Farmers’ Willingness to Pay for the Protection of Nyabarongo River System,
Rwanda. Master ’s Thesis, University of Nairobi, Nairobi, Kenya, 2012.
20.
Nhapi, I.; Wali, U.; Uwonkunda, B.; Nsengimana, H.; Banadda, N.; Kimwaga, R. Assessment of water
pollution levels in the Nyabugogo Catchment, Rwanda. Open Environ. Eng. J. 2011,4, 40–53. [CrossRef]
21.
Wischmeier, W.H.; Smith, D.D. Predicting Rainfall Erosion Losses—A Guide to Conservation Planning; United
State Department of Agriculture: Washington, DC, USA, 1978.
22.
Van Engelen, V.; Verdoodt, A.; Dijkshoorn, K.; Van Ranst, E. Soil and Terrain Database of Central Africa—DR of
Congo, Burundi and Rwanda; ISRIC Report; World Soil Information: Wageningen, The Netherlands, 2006.
23.
Ntwali, D.; Ogwang, B.A.; Ongoma, V. The impacts of topography on spatial and temporal rainfall
distribution over rwanda based on wrf model. Atmos. Clim. Sci. 2016,6, 145. [CrossRef]
24.
Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very high resolution interpolated climate
surfaces for global land areas. Int. J. Clim. 2005,25, 1965–1978. [CrossRef]
25.
Bosnjakovic, B. UN/ECE strategies for protecting the environment with respect to international watercourses:
The Helsinki and Espoo conventions. World Bank Tech. Paper 1998, 47–64.
26.
NewTimes. Govt, Private Sector Pledge to Conserve R. Nyabarongo. Available online: http://www.
newtimes.co.rw/section/article/2015-12-24/195536/ (accessed on 25 January 2016).
27.
U.S. Geological Survey (USGS). U.S. Geological Survey Earthexplorer. Available online: http://earthexplorer.
usgs.gov/ (accessed on 20 September 2015).
28.
U.S. Geological Survey (USGS). USGS Global Visualization Viewer: Earth Resources Observation and Science
Center (EROS). Available online: http://glovis.usgs.gov/index.shtml (accessed on 20 September 2015).
29.
Basnet, B.; Vodacek, A. Tracking land use/land cover dynamics in cloud prone areas using moderate
resolution satellite data: A case study in Central Africa. Remote Sens. 2015,7, 6683–6709. [CrossRef]
30.
Otukei, J.R.; Blaschke, T. Land cover change assessment using decision trees, support vector machines and
maximum likelihood classification algorithms. Int. J. Appl. Earth Obs. Geoinf.
2010
,12 (Suppl. S1), 27–31.
[CrossRef]
31.
Akgün, A.; Eronat, A.H.; Türk, N. Comparing different satellite image classification methods: An application
in Ayvalik District, Western Turkey. In Proceedings of the 4th International Congress for Photogrammetry
and Remote Sensing, Istanbul, Turkey; 2004. Available online: http://cartesia.org/geodoc/isprs2004/
comm4/papers/505.pdf (accessed on 12 July 2016).
32.
Anderson, J.R.; Hardy, E.E.; Roach, J.T.; Witmer, R.E. A Land Use and Land Cover Classification System for Use
with Remote Sensor Data; U.S. Government Printing Office: Washington, DC, USA, 1976; Volume 964.
33. Mather, P.M. Computer Processing of Remotely-Sensed Images, 3rd ed.; Wiley: Chichester, UK, 2004.
34.
Long, J.B.; Giri, C. Mapping the philippines’ mangrove forests using landsat imagery. Sensors
2011
,11,
2972–2981. [CrossRef] [PubMed]
35.
Bishop, Y.M.; Fienberg, S.E.; Holland, P.W. Discrete Multivariate Analysis: Theory and Practice; Springer Science
& Business Media: New York, NY, USA, 2007.
36.
Thomlinson, J.R.; Bolstad, P.V.; Cohen, W.B. Coordinating methodologies for scaling landcover classifications
from site-specific to global: Steps toward validating global map products. Remote Sens. Environ.
1999
,70,
16–28. [CrossRef]
37.
Manandhar, R.; Odeh, I.O.; Ancev, T. Improving the accuracy of land use and land cover classification of
landsat data using post-classification enhancement. Remote Sens. 2009,1, 330–344. [CrossRef]
38.
Angima, S.; Stott, D.; O’neill, M.; Ong, C.; Weesies, G. Soil erosion prediction using RUSLE for central kenyan
highland conditions. Agric. Ecosyst. Environ. 2003,97, 295–308. [CrossRef]
Int. J. Environ. Res. Public Health 2016,13, 835 15 of 16
39.
Lu, D.; Li, G.; Valladares, G.; Batistella, M. Mapping soil erosion risk in Rondonia, Brazilian Amazonia:
Using RUSLE, remote sensing and GIS. Land Degrad. Dev. 2004,15, 499–512. [CrossRef]
40.
Nam, P.T.; Yang, D.; Kanae, S.; OKI, T.; MUSIAKE, K. Global soil loss estimate using RUSLE model: The use
of global spatial datasets on estimating erosive parameters. Geol. Data Process 2003,14, 49–53. [CrossRef]
41.
Fathizad, H.; Karimi, H.; Alibakhshi, S.M. The estimation of erosion and sediment by using the RUSLE
model and RS and GIS techniques (Case study: Arid and semi-arid regions of Doviraj, Ilam province, Iran).
Int. J. Agric. Crop Sci. 2014,7, 303.
42.
Claessens, L.; Van Breugel, P.; Notenbaert, A.; Herrero, M.; Van De Steeg, J. Mapping potential soil erosion in
East Africa using the Universal Soil Loss Equation and secondary data. IAHS Publ. 2008,325, 398–407.
43.
Grimm, M.; Jones, R.; Montanarella, L. Soil Erosion Risk in Europe; European Communities: Napoli,
Italy, 2001.
44.
Biswas, S.S.; Pani, P. Estimation of soil erosion using RUSLE and GIS techniques: A case study of Barakar
River Basin, Jharkhand, India. Model. Earth Syst. Environ. 2015,1, 1–13. [CrossRef]
45.
Renard, K.G.; Foster, G.; Weesies, G.; McCool, D.; Yoder, D. Predicting Soil Erosion by Water: A Guide to
Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE); U.S. Department of Agriculture:
Washington, DC, USA, 1997; Volume 703.
46.
Renard, K.G.; Freimund, J.R. Using monthly precipitation data to estimate the R-factor in the revised USLE.
J. hydrol. 1994,157, 287–306. [CrossRef]
47. Yu, B.; Rosewell, C. A robust estimators of the R-reaction for the universal soil loss eequation. Trans. ASAE
1996,39, 559–561. [CrossRef]
48.
Lo, A.; El-Swaify, S.; Dangler, E.; Shinshiro, L. Effectiveness of EI 30 as an Erosivity Index in Hawaii. Available
online: http://agris.fao.org/agris-search/search.do?recordID=US8639059 (accessed on 12 July 2016).
49.
Land Degradation Assessment in Drylands. Global Land Degradation Information System (GLADIS)
Database. Available online: http://www.fao.org/nr/lada/gladis/gladis_db/ (accessed on 10 December 2015).
50.
Land Degradation Assessment in Drylands. Global Land Degradation Information System—Beta Version.
Available online: http://www.fao.org/nr/lada/index.php?option=com_content&view=article&id=161&
Itemid=113&lang=en (accessed on 10 December 2015).
51.
Martínez-Graña, A.; Goy, J.L.; Zazo, C. Cartographic procedure for the analysis of aeolian erosion hazard in
natural parks (central system, Spain). Land Degrad. Dev. 2015,26, 110–117. [CrossRef]
52.
Ganasri, B.; Ramesh, H. Assessment of soil erosion by RUSLE model using remote sensing and GIS—A case
study of Nethravathi Basin. Geosci. Front. 2015. [CrossRef]
53.
Meigh, J.; McKenzie, A.; Sene, K. A grid-based approach to water scarcity estimates for Eastern and Southern
Africa. Water Resour. Manag. 1999,13, 85–115. [CrossRef]
54.
Tachikawa, T.; Kaku, M.; Iwasaki, A.; Gesch, D.B.; Oimoen, M.J.; Zhang, Z.; Danielson, J.J.; Krieger, T.;
Curtis, B.; Haase, J.; et al. Aster Global Digital Elevation Model Version 2—Summary of Validation Results; NASA:
Washington, DC, USA, 2011.
55.
ESRI. ArcGIS Desktop 9.3 Help. Available online: http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?
TopicName=slope (accessed on 15 January 2016).
56.
Alexandridis, T.K.; Sotiropoulou, A.M.; Bilas, G.; Karapetsas, N.; Silleos, N.G. The effects of seasonality in
estimating the c-factor of soil erosion studies. Land Degrad. Dev. 2015,26, 596–603. [CrossRef]
57.
Martínez-Graña, A.; Goy, J.; Zazo, C. Water and wind erosion risk in natural parks—A case study in “las
batuecas–sierra de francia” and “quilamas” protected parks (central system, Spain). Int. J. Environ. Res.
2014
,
8, 61–68.
58.
Kim, J.B.; Saunders, P.; Finn, J.T. Rapid assessment of soil erosion in the Rio Lempa Basin, Central America,
using the Universal Soil Loss Equation and Geographic Information Systems. Environ. Manag.
2005
,36,
872–885. [CrossRef] [PubMed]
59.
European Environment Agency. CORINE Soil Erosion Risk and Important Land Resources in the Southern
Regions of the European Community. Available online: http://www.eea.europa.eu/publications/COR0-soil
(accessed on 31 December 1994).
60.
Kim, H.S. Soil Erosion Modeling Using RUSLE and GIS on the Imha Watershed, South Korea.
Master’s Thesis, Colorado State University, Fort Collins, CO, USA, 20 April 2006.
61.
Roose, E. Land Husbandry: Components and Strategy; Food and Agriculture Organization of the United Nations
(FAO): Rome, Italy, 1996; Volume 70.
Int. J. Environ. Res. Public Health 2016,13, 835 16 of 16
62.
Habiyaremye, G.; Jiwen, G.; de la Paix Mupenzi, J.; Balogun, W.O. Demographic pressure impacts on forests
in Rwanda. Afr. J. Agric. Res. 2011,6, 4533–4538.
63.
Food and Agriculture Organization of the United Nations (FAO). Rwanda: Géographie, Climat et Population.
Available online: http://www.fao.org/nr/water/aquastat/countries_regions/RWA/index.stm (accessed
on 10 June 2015).
64.
Nahayo, L.; Li, L.; Kayiranga, A.; Karamage, F.; Mupenzi, C.; Ndayisaba, F.; Nyesheja, E.M. Agricultural
impact on environment and counter measures in Rwanda. Afr. J. Agric. Res. 2016,11, 2205–2212.
65.
Carcamo, J.A.; Alwang, J.; Norton, G.W. On-site economic evaluation of soil conservation practices in
Honduras. Agric. Econ. 1994,11, 257–269. [CrossRef]
66.
Atta-Krah, K.; Sanginga, N. The Afneta Alley Farming Training Manual: Source Book for Alley Farming Research;
Tripathi, B.R., Psychas, P.J., Eds.; Alley Farming Network for Tropical Africa: Ibadan, Nigeria, 1992;
Volume 2.
67. Juo, A.; Adams, F. Chemistry of LAC soils. Low Act. Clay (LAC) Soils 1984,37, 14.
68. Twagiramungu, F. Environmental Profile of Rwanda; European Commission: Kigali, Rwanda, 2006.
69.
Ndayisaba, F.; Guo, H.; Bao, A.; Guo, H.; Karamage, F.; Kayiranga, A. Understanding the spatial temporal
vegetation dynamics in Rwanda. Remote Sens. 2016,8, 129. [CrossRef]
70.
Zhao, G.; Mu, X.; Wen, Z.; Wang, F.; Gao, P. Soil erosion, conservation, and eco-environment changes in the
Loess Plateau of China. Land Degrad. Dev. 2013,24, 499–510. [CrossRef]
71.
Wischmeier, W.H.; Smith, D.D. Predicting Rainfall-Erosion Losses from Cropland East of the Rocky Mountains:
Guide for Selection of Practices for Soil and Water Conservation; U.S. Government Printing Office: Washington,
DC, USA, 1965.
72.
Farhan, Y.; Nawaiseh, S. Spatial assessment of soil erosion risk using RUSLE and GIS techniques. Environ.
Earth Sci. 2015,74, 4649–4669. [CrossRef]
73.
Jones, R.J.; Le Bissonnais, Y.; Bazzoffi, P.; Sanchez Diaz, J.; Düwel, O.; Loj, G.; Øygarden, L.; Prasuhn, V.;
Rydell, B.; Strauss, P. Nature and Extent of Soil Erosion in Europe. Available online: http://eusoils.jrc.ec.
europa.eu/ESDB_Archive/pesera/pesera_cd/sect_h1.htm (accessed on 12 July 2016).
©
2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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