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Assessment of erosion hazard with the USLE and GIS: A case study of the Upper Ewaso Ng'iro North basin of Kenya
JAG
Volume
l
2 - Issue 2 - 2000
Assessment of erosion hazard with the USLE and GIS: A case study of the Upper Ewaso Ng’iro North basin of Kenya Bancy M Mati I, Royston P C Morgan2, Francis N Gichuk?, John N Quintor+, Tim R Brewer2 and Hans P Liniger4 1 Jomo Kenyatta University of Agriculture 2 Cranfield 3
and Technology,
University, Silsoe, Bedfordshire,
P.O. BOX 62000, Nairobi, Kenya
MK45 4DT, England, UK
University of Nairobi, P.O. BOX 30197, Nairobi, Kenya
4 Centre for Development
soil erosion
KEYWORDS:
Kenya,
and Environment
Upper
hazard
Ewaso Ng’iro
University of Bern, Hallerstrassl2,
assessment,
land use, it became necessary to map and quantify soil erosion more extensively, covering the entire basin, with
USLE, GIS,
basin
the aim of providing a tool for planning soil conservation strategies at the regional level. Due to the complexity of the variables involved, and since they interact in a wide spatial domain, simplicity of data management and the ability to transfer from data-rich to data-poor areas became very important. The use of GIS techniques and
ABSTRACT The Universal Soil Loss Equation (USLE) was used to predict soil erosion hazard in the Upper Ewaso Ng’iro data from
erosion
(rainfall erosivity) was determined amount-erosivity
North basin of Kenya using
plots and reconnaissance
relationship,
surveys. The R-factor
by extrapolation
was determined
records.
The K-factor
(soil erodibility)
by laboratory
analysis of soil samples collected from 83 test sites in
the basin. The LS-factors (slope length mined from a digital elevation C-factor
from
factor (conservation servation, Geographic
was determined
from vegetation
SPOT imagery and field surveys. The P-
from the Ministry
Information
from maps of soil con-
of Agriculture.
A raster-based
System (GIS) was used to interactively
culate soil loss and map erosion hazard. The results obtained comparable
calwere
UPPER EWASO
to measured soil loss values from erosion plots. About
36 percent of the Upper Ewaso Ng’iro basin was predicted high
at coarse resolutions most commonly quantified using annual soil loss rates. The Universal Soil Loss Equation (USLE) [Wischmeier & Smith, 19781 and Arc/Info GIS [ESRI, 19971 were used in this study to determine these loss rates as it was possible to determine the factors of the equation and use GIS techniques to extrapolate for the ungauged areas in the Upper Ewaso Ng’iro basin.
and steepness) were deter-
practice) was estimated
obtained
using data obtained
model (DEM) of the basin, while the
(crop and management)
cover data, obtained
erosion models is one way of achieving this [Petersen et al, 19981. According to Herweg [ 19961, erosion hazard is
from the rainfall
using data from autographic
risk of erosion,
rangelands.
most of this land being
Land use and management
were
CH-3012 Berne, Switzerland
The Upper
to have
in the overgrazed
between
the
longitudes
major
factors
36”
decrease
phy. Due to the reconnaissance
Nyandarua
tations
in determining
addition, factors
there is a need for research to determine for
fanya-juu
reliable LS, C and P factors for the USLE. In
local soil conservation
practices,
appropriate
such as trash
Most
P-
acterised
the
Upper
Kenya has been identified 1943;
Ministry
38”
Mt.
m) and
region Plateau,
the extreme
1700
00”
east.
and
Altitudes
Kenya
(5199
the
Nyambene
m), the
by the
Hills. gently
of
1700-
of the basin are chartopography,
m to
north-eastern
km2,
north
at an elevation
to rolling
about
15,251
1” 15”
is occupied
and north-east
by undulating from
and
and
where
less than
region
alti-
1000
[Gichuki
m
et a/,
1998al.
INTRODUCTION erosion
central
basin covers
south
from
Laikipia
drop
towards
east (3999
1800 m. The north
terraces and stone lines.
tudes
Soil
10”
Range
of the
North
00 20”
northwards,
undulating
lines,
BASIN
Ewaso Ng’iro latitudes
associated with soil erosion, and - to a lesser extent - the topograscale of this study there were limi-
NG’IRO
of Water
Ewaso
Ng’iro
as a major Development,
North
problem
basin
of Due to the position of the Upper Ewaso Ng’iro basin on the lee slopes of Mt. Kenya and the Nyandarua Range, most of the basin is semi-arid. Annual rainfall ranges from over 2000 mm on the Nyandarua Range to less than 365 mm per annum in the drier north eastern areas [Jaetzold & Schmidt, 1983; Thomas & Liniger, 1994;
[Edwards,
19921. However,
soil erosion research in the basin has only been done on localised areas [Kihara, 1998; Liniger, 1991, Liniger et al, 1998; Mutunga, 1994; Wanjogu, 1994 I. As land degradation became more evident with increasing changes in
78
Erosion hazard
in the Upper Ewaso Ng’iro North basin of Kenya
JAG
Gichuki et al, 1998b]. Mean annual rainfall averages about 700 mm in the basin. Despite this relatively high
l
Volume
2 - Issue 2 - 2000
pixel was viewed as a single slope plane for which the USLE could be applied individually. It was not possible to route runoff and soil loss within a given sub-catchment in the basin. Therefore, the soil loss values predicted in this study represent soil erosion generated within each pixel treated in isolation, but they do not indicate sediment yields in small catchments or over hillslope units of a larger area. This data gives a first approximation of the likely sediment sources in the basin.
figure, rainfall distribution is such that the seasonal amounts are insufficient for proper crop growth in most parts of the basin [Thomas & Liniger, 19941. In the western and north-western areas, continental rains fall between April and August. The eastern region has a clear bimodal distribution, with rainfall maxima in April and October [Berger, 19891. The central region is a transition zone, where the two patterns overlap. This is the driest zone. Rainfall intensities are usually high, averaging about 20 to 40 mm h-1 and higher intensity storms of up to 96 mm h-1 have been recorded [Liniger, 19911.
GOVERNING
EQUATION
The Universal Soil Loss Equation (USLE] [Wischmeier & Smith, 19781 was applied in a GIS environment to determine the average annual soil loss and its distribution in the basin. The USLE predicts soil loss for a given site as a product of six major factors [Equation I], whose values at a particular location can be expressed numerically. The values of these erosion factors vary considerably about their means from storm to storm, but the effects of these fluctuations average out in the long run. Thus the USLE is suitable for predicting long-term averages; the soil erosion is calculated as follows:
The predominant soils include chromic-humic Cambisols and humic Andosols (FAO classification) on Mt. Kenya, the Nyandarua Range and the Nyambene Hills above 2000 m, with the eutric Nitisols and luvic Phaeozems at lower altitudes on the mountain footridges. On the plateaux, chromic-pellic Vertisols dominate with pockets of eutric Planosols. In the central highlands eutric Regosols dominate the slopes of the hills and minor scarps, while most of the northern lowlands comprise calcic-chromic Cambisols and ferric-chromic Luvisols [Sombroek et al, 1980; Liniger et al, 19981. About 70 percent of the basin comprises rangelands. Large-scale commercial ranches are found in the central plateau, while the drier northern regions are used by pastoral communities for communal grazing [Thurow & Herlocker, 19931. Although wildlife is found in most parts of the basin, there are several game reserves run privately or by public institutions. Of the small proportion of the basin occupied by croplands, large scale wheat/barley farms are found in thin strips around the slopes of Mt. Kenya and the Nyandarua Range, as well as small-scale settlements operated as mixed farms, where maize is the predominant crop. In addition, there are forests at the higher altitudes on the mountains and major hills.
A=RxKxLxSxCxP
111
Where: A = annual soil loss in t ha-1 yr-1. R = Rainfall erosivity factor (J mm.m-z h-1) K = Soil erodibility factor (t J-1 mm-l) L = Slope length factor 5 = Slope steepness factor C = Crop and management factor P = Conservation supporting practices factor DETERMINING
USLE FACTOR VALUES
Rainfall erosivity (R) factor
Rainfall erosivity was determined by first calculating storm erosivity indices using data from six stations (Embori, Kalalu, Karuri, Mukogodo, Sirima and Teleswani), for which comprehensive autographic records were available. The storm erosivities were then accumulated for each year to give the USLE rainfall erosivity index, the R-factor, for each station. In total, 17 plot years of rainfall erosivity data were obtained for the six stations.
METHODOLOGY The overall methodology involved use of a soil erosion model, the Universal Soil Loss Equation (LISLE) [Wischmeier & Smith, 19781 in a GIS (Geographic Information System), with data obtained from weather stations, reconnaissance surveys, runoff plots and topographic maps. Individual GIS files were built for each factor in the USLE and combined by cell-grid modelling procedures in Arc/Info GIS [ESRI, 19971 to predict soil loss in the spatial domain. The grid-cells were set to 100 m by 100 m, which was the resolution possible with the available data and computer facilities. Although the USLE is recommended for small plots and fields [Wischmeier & Smith, 19781, the 1 ha cell sizes used in this study were considered to give adequate detail because this study was of reconnaissance scale [Goodchild, 19931. Each
To obtain R-factors for the whole basin, rainfall-erosivity relationships (Table 1) were determined by regression analysis of annual rainfall data from 75 geo-referenced manual gauging stations [Berger, 1989; Thomas & Liniger, 19941 spread in and around the Upper Ewaso Ng’iro basin. It was found necessary to separate the data into two groups as per the agro-climatic zone location of each station, in order to get better correlation coefficients. The two regression equations obtained (shown in Table 1) were respectively applied to the rainfall map of the basin in Ardlnfo GRID, thus producing the erosivity map of the basin (Figure 1).
79
Erosion
hazard
TABLE I:
in the Upper
Ewaso Ng’iro
North
JAG
basin of Kenya
l
Volume
2 - Issue 2 - 2000
Regression equations relating annual rainfall with erosivity
Agro-climatic
zone
Stations
1
I - III
Embori,
2
IV - VI
Kalalu,
3
Basin
All stations
Karuri, Sirima,
Erosivitylrainfall Teleswani Mukogodo
r2
relationship
e
n 8
RE = 0.649 Rp - 94.2
0.70
85
RE = 1.17 Rp - 258.5
0.71
124
9
RE = 0.574 Rp - 18.4
0.46
134
17
r2 = Coefficient of linear regression; n = Number of observations; e = Standard error of estimate, RE = Average annual ralnfall eroslvity (J m-2 mm h-1); R, = Annual rainfall amount (mm)
Soil erodibility
fiscal constraints could not permit a full-scale soil survey of the area. The map obtained thus contained 36 major soil mapping units (FAO classification). By assuming that the resolution of the K values matches that of the soil mapping units, the mean K factors obtained for each soil type were consequently used to reclassify the soil map. These were the K-values used in the USLE. The soil erodi-
(K) factor
Basic data for estimating erodibility were obtained by collecting soil samples from 83 test sites, representative of the major soil mapping units [Sombroek et al, 19801, as indicated in Figure 2. Test sites were representative sample areas where data on soils, vegetation, climate and land use were recorded at greater detail during the field surveys. Additional information was also obtained from reports of detailed soil surveys carried out in the basin [van den Berg, 1992; Kironchi et a/, 1992; Mainga & Mbuvi, 19941. The percentage stone and gravel cover were estimated in the field using a metre rule, while rock cover was estimated visually. The soil samples were taken to the laboratory and analysed for percentage fine sand, silt, clay, and organic matter [Kenya Soil Survey, 1987; Page et a/, 19821. These values were used to estimate the erodibility (K) factors, using the USLE soil erodibility nomograph [Wischmeier et a/, 19711. As 43 percent of the sampled sites contained various amounts of stone and gravel, a correction factor for this effect was included in the calculation of the K-factor, based on the equations described by van den Berg [1992].
expressed
Topographic
S)
factors
(L and
in class ranges,
is
Digital topographic data for the Upper Ewaso Ng’iro basin were obtained by digitizing 32 sheets of topographic maps of scale I:50 000. The contours and the drainage system were digitized separately and used to build up the DEM (Digital Elevation Model) of the basin. The contour interval used was 200 m, taking into account available computer memory and technology at the time in Kenya. A grid cell of 100 m was used in building the DEM, as this was considered to be less than the maximum slope length, based on reconnaissance surveys. The resulting DEM was used to determine the slope steepness (q) and slope length (I) in Arc/Info GRID. The USLE slope steepness (5) and length (L) factors were determined as follows [McCool et a/, 19871. 5 = 10.8 sin8 + 0.03 for slopes < 9% PI 5 = 16.8 sin0 - 0.50 for slopes > 9% [31 1 = (UZ2.13)m [41 Where: m = an exponent that depends on slope steepness, being 0.5 for slopes exceeding 5 percent, 0.4 for 4 percent slopes and 0.3 for slopes less than 3 percent
To enable the mapping of the K-factor in the spatial domain, the soil map of the Upper Ewaso Ng’iro basin was first prepared by digitising the Exploratory Soil Map of Kenya [Sombroek et al, 19801 of scale 1 :I ,OOO,OOO. This was because there were no soil survey maps of finer resolution covering the entire basin, while technical and
FIGURE I: Rainfall erosivity distribution Ng’iro North basin
bility map of the basin, shown in Figure 2.
in the Upper Ewaso
FIGURE2: Soil erodibility
80
in the Upper Ewaso Ng’iro North basin
JAG
Erosion hazard in the Upper Ewaso Ng’iro North basin of Kenya
sensed
The flow length as estimated with GIS gave the length of slope planes from the hillcrest to the streambed. A maximum length of 200 m was set in order to get realistic Lfactor values at the upper limits. As the topography of the Upper Ewaso Ng’iro basin is generally flat to gently undulating, limiting the slope lengths to 200 m seemed reasonable in the absence of better information. Due to limitations of computer facilities and data resolution in this work, it was not possible to separate concave, convex or straight slopes. The LS-factor was consequently determined by multiplying the 5 and L-factors in Arc/Info Grid to obtain the map of topographic factors shown in Figure 3. As most of the basin is covered by the plateau, the L’Sfactors are generally low.
data
multispectral
was obtained
from
l
Volume 2 - Issue 2 - 2000
visual
interpretation
of
SPOT 1 HRV imagery, as well as topograph-
ic maps of scale I:50 000. Ground-truthing and vegetation cover monitoring were simultaneously undertaken during the dry seasons of 1996-97, to coincide with the season the imagery had been taken. This covered 83 test sites selected to represent the major land cover types and stratified to include all the geographic zones of the basin, so as to sample as wide a diversity in soils, vegetation,
land use, slope and erosion hazard as possible
The vegetation cover classification developed for East African rangeland conditions by Pratt & Gwynne [I9771 was used and modified to include such classes as smallscale cropland (all crops), large-scale croplands, swamps and urban settlements. Eleven major land cover types were identified: moorland, forest, bushland, bush grassland, grassland, shrub grassland, shrubland, scarpline vegetation, small scale cropland, large scale cropland and swamps [Pratt & Gwynne, 19771. The USLE cover and management factors (C-factors) corresponding to each crop/vegetation condition were estimated from USLE guide tables [Morgan, 1995; Wischmeier & Smith, 19781. These values were used to re-classify the land cover map to obtain the C-factor map of the basin (Figure 4). Determining
conservation practices (P) factor
To determine
FIGURE3: Topographic
the areas covered by soil conservation activities, maps of the catchments conserved through the “Catchment Approach” program of the Soil and Water Conservation Branch, Ministry of Agriculture (1988-97) were used. The Catchment Approach Program ideally conserves an entire catchment. These maps were digitized and annual reports from the Ministry of Agriculture (Republic of Kenya, 1996) were used to provide summarised information on the types of structures found in each district. However, the commonly used structures
factors (LS) in the Upper Ewaso Ng’iro
North basin
Determining the LS factor from grid-based DEM has its limitations. Other than the low resolution of the input data, slope lengths as generated by the DEM were based on the assumption that each slope plane consists of homogeneous soil and vegetation cover. Ideally, the LS factor should be relevant for the runoff-producing sections of the hillslope [Desmet & Govers, 19961. In reality, this may not be the case, due to the difficulties of separating these factors. However, as soil types in the Kenyan highlands are characterised by catenas [Muchena, 19821, for a given slope soils tend to be similar. Crop and management
factor (C)
To determine the C-factor values for the Upper Ewaso Ng’iro basin, it was first necessary to prepare a land cover map of the basin. This was achieved using remote sensing techniques and field surveys. The remotely
FIGURE 4: Cover and management Ewaso Ng’iro basin
81
(C) factors in the Upper
Erosion hazard
were found bunds
in the Upper Ewaso Ng’iro North basin of Kenya
to be fanyajuu
and
grass
structures
were
[Wischmeier P-factor
& Smith,
by assuming
structures terraces,
and
conserved
the
19951,
was
the P-factor
EROSION
hazard
map of
map of the
USLE factors grouped
into five classes using a frequency
analysis,
resulting
shown
and climatic
of the
basin was performed
data
was
through
sion analysis
was done
with
plot data.
erosion
Ewaso
First, linear
the combined
plots
poor
vegetation
with
good
while
cover;
(Table
basin
proviede
Ng’iro regres-
45 plot-years
TABLE 2: Results of linear regression for the validation
expressed
hazard
could in the
in areas
be applied data
classes and ranging
average
annual
were
into account
the wide
it was found
over 25
spatial that
diversity in general
soil loss in the basin,
hazard in the Upper Ewaso Ng’iro North
basin
of USLEwith plot data
Forest
5, = 1.3895,
Grass
5, = 0.6935,
Kalalu,
Maize,
N
frz)
e
+ 0.109
6
0.99
0.08
+ 0.883
14 0
69
3.68
equation
S, = 0.41 IS,
+ 8.673
12
0.53
8.10
Shrubland
Bushland
5 ,, = 0.4275,
+ 4.695
13
0.71
3.34
Combined
data
S” = 0.5355,
+ 2.947
45
0.65
5.48
potatoes
Where, S, = PredIcted soil loss (t ha-1 yr-1); S, = Observed soil loss (t ha-’ yr-1); N = Number of observations; r2 = Coefficient of linear regression; e = Standard error of estimate
82
from
soil loss rates
1 .O t ha-l yr-1 to high risk areas with
be used to predict
Kalalu, Teleswani Naro Moru Met, Naro Moru Gate,
All stations
in
in the Upper Ewaso Ng’iro
in five broad
in the data,
Naro Moru Gate, Teleswani
Sirima
erosion
limitations
and limitations
Regression
Karuri Sirima
soil
that,
soil loss in areas with
the model
the
loss values
3). It was found
the USLE could
Cover
Mukogodo,
as shown
DISCUSSION
FIGURE 6: Soil erosion
catchments in the Upper Ewaso Ng’iro basin (P = 0.18 in the soil conservation catchments and 1 .O elsewhere)
name
soil
it overestimated
otherwise
t ha-1 yr-1. Taking
FIGURE 5: Soil conservation
Station
to
and the measured
the USLE underestimated
are less than
Upper
again
of each ero-
correlation, with
GIS techniques
Figure 6 shows erosion
map
PLOT DATA
USLE for the with
done
according
conditions
This gave a better
very low risk areas, where The validation
was
groups
compared
basin,
THE USLE WITH
four
of
to obtain
the results of the USLE simulation
plot
RESULTS AND
dis-
hazard
in Figure 6.
VALIDATING
analysis
into
error
In order
GRID
sequently
erosion
the standard
(95
were con-
tribution
in the
while
of 0.645
kept in mind.
the
in Arc/Info
coefficient
regression
the data
2 - Issue 2 - 2000
using
to the
interactively
Volume
in Table 2. In addition,
general,
as per
by multiplying
the
in type
loss from
scale of this
1. The soil loss values obtained
using Equation
results,
interval),
l
t ha-1 yr-1) was high.
separating
obtained
HAZARD
was determined
(5.475
sion plot station.
and crop type.
DETERMINING
confidence
similarities
terraced.
the P-factors
percent
after
bench
the
This gave a correlation
better
catch-
sloping
of data. estimate
an average
5). Due to the reconnaissance
slope gradient
of
tables
soil conservation
catchment
to separate
types
all conserved
outward
obtaining
it was not possible
respective
these
was used to classify
catchments
Soil erosion
to
entire
P-factor
basin (Figure
for
lines, contour
USLE guide
the available
similarly
that
This constant
for
the
Morgan,
adopted that
perform
from
1978;
was
stone
As P-values
not available
of 0.18
ments,
study,
strips.
terraces,
JAG
JAG Volume 2 - Issue 2 - 2000
Erosion hazard in the Upper Ewaso Ng’iro North basin of Kenya
l
TABLE3: Comparison of USLEpredicted and observed soil loss values Range of soil loss rates (t ha-l yr1) Station
Treatment
Min
Embori Kalalu Karuri Mukogodo Naro Moru Gate Naro Moru Met. Sirima Teleswani
Barley Maize Potatoes Shrubland Forest Forest Potatoes Forest
Soil loss predicted with LISLE and GIS (t ha-l)
Observed
soil loss (t ha-l)
Max
Min
Mean
Max
Min
Mean
Max 102.6
Soil loss predicted with the LISLE with plot data (t ha-l) Mean
15.0
18.2
32.4
21.4
31.3
39.7
0
51.3
5.7
22.2
48.5
1.6
8.2
18.1
1.1
14.8
25.4
5.7
14.8
15.2
19.7
35.4
49.9
0.5
24.5
69.4
8.9
24.2
2.2
18.3
35.2
12.2
14.0
38.2
52.4
0
0.1
0.1
0
0.8
1.2
0
0
0
0
0.3
0
0.9
1.5
0
0
1.3
16.2
5.2
27.3
0.9
3.7
8.3
0
0.4
0.7
0
0.1
1.0
13.3
9.5
23.1
0.1
0.1
0.1
1.0
managed vegetation cover was present. This was supported by plot data, since annual soil loss rates rarely exceeded 1.5 t ha-1 under forest cover at Naro Moru Met, Naro Moru Gate and Teleswani (Table 3). Topography played a secondary role, since on steep slopes, erosion was very low under natural forests and grasslands, but high under cultivated land.
as an overall correlation coefficient of 0.65 (95 percent confidence interval) was obtained, although the standard error of estimate (5.48 t ha-l yr-1) was high (Table 2). However, better results were obtained when the data was split into four groups according to similarities in type and the climatic conditions of each erosion plot station. The USLE underestimated soil loss in areas with poor vegetation while it overestimated erosion in areas with good cover. Otherwise, the soil loss values estimated by the USLE were considered realistic when compared with plot data, reconnaissance surveys and sediment yields from the major rivers in the basin [Mati, 1999; Kihara, 19981. However, as the USLE does not predict deposition, the results obtained in this study only predict the susceptibility of a hectare of land to yield sediment.
About 36 percent of the Upper Ewaso Ng’iro basin area was predicted to suffer from erosion risk in excess of a tolerable rate of 9.0 t ha-1 yr-1. This choice of tolerable rates was derived from tables by McCormack & Young [ 19811, which were based on local estimates by Dunne et a/ [I9781 and by consideration of the average soil depth in the basin. The relatively low soil loss values can be attributed to the low LS factors (Figure 3), since most of the basin is plateau. By spatial extent, most of these areas cover much of the semi-arid North and north-east, where communal grazing is predominant. The most severely eroded areas were those covered by the dry shrubland, on which vegetation cover was generally below 40 percent [Mati, 19991. Field observations have shown that the highest erosion damage is through gullies. The rate of gully erosion is not measured in small test plots, which are only used to measure sheet and interrill erosion. Therefore the overall erosion rate can be much higher than predicted by USLE or test plot studies. The Mukogodo catchment study [Mutunga, 19941 showed that higher erosion rates were measured for the whole catchment than those on the test plots, even though there was still some deposition in the catchment. As gullies are localised on areas lying below hills and scarps, rill and interill erosion still remain the dominant processes in most parts of the basin, which are less steep, making the USLE results realistic. In this study, land management was identified as the main factor contributing to high erosion hazard as the poor cover in the shrublands was associated with overgrazing, which could be attributed to a lack of well-organised grazing management practices. In these environments, other factors such as the long-term effects of overgrazing on soil
Table 3 shows the maximum, mean and minimum soil loss predicted at plot (USLE- plot) and regional scales (USLE-GIS), as compared to measured values from the available runoff plot data. The use of class ranges has been recommended [Wischmeier & Smith, 19781 as a better method of checking the applicability of the USLE than comparing absolute values. These results show that the model predicted soil loss with similar ranges that were comparable to observed values, and therefore, it can be applied to the Upper Ewaso Ng’iro basin. However the range from minimum to maximum values in all three data groups (predicted and observed) are high and the predictions less certain at the extremes. By overlaying the erosion hazard map on maps of land cover, soil erodibility, topographic factors and rainfall erosivity, it was possible to deduce those factors associated with high erosion hazard. In general, erosion hazard was low under vegetation cover exceeding 70 percent, where soil loss rates of less than 1 .O t ha-1 yr-1 were predicted. These areas are mostly mountain forests and the well-managed commercial ranches of the Laikipia Plateau. It was found that low erosion risks were predicted even for areas with high rainfall erosivity factors and soils of relatively high erodibility - so long as a well-
83
Erosion hazard in the Upper Ewaso Ng’iro North basin of Kenya
JAG
l
Volume
2 - Issue 2 - 2000
catchments the P-Factor of 1 .OO is true. There could have been other management practices outside the ‘conserved catchments’, but these were not spatially mapped and it was difficult to account for them other than within the C-factor. Thus, the true P-value requires further investigation in this region. Otherwise, this study has at least been able to show the impact of conservation practices in a large watershed, based on the most common conservation practices. These limitations notwithstanding, the USLE has many advantages that make it possible to predict plot-level soil loss and, in combination with GIS techniques, transform these to map erosion at reconnaissance scales.
crusting and compaction [Wanjogu, 19941, burning of rangelands [Thurow & Herlocker, 19931, and soils with sodic or surface sealing properties [Muchena, 19821 may cause more erosion than predicted by the USLE. Croplands were predicted to experience high erosion hazard, although qualitative assessments during the reconnaissance surveys would suggest that the USLE values were too high. This is because there was not much evidence of severe interill, rill or gully erosion on most cultivated lands as compared to overgrazed rangelands. However, erosion plot studies indicated that the values predicted by the USLE were realistic. For instance, soil loss rates of 69.4 t ha-1 were recorded from a potato crop at Karuri in 1994, while at Embori a total of 102.6 t ha-1 was recorded from a barley field in the same year. At Kalalu, soil loss rates exceeding 25 t km-z yr-1 have been recorded in a maize field [Liniger, 1992 a,b]. Conversely, negligible soil loss values have been obtained from the same plots in years with lower rainfall erosivities [Mati, 19991. Therefore, it appears that, depending on rainfall characteristics, many croplands are at a high risk of soil erosion. This illustrates the advantages of having some quantitative data for comparison with qualitative assessments in erosion studies.
CONCLUSIONS This study developed and used a simple methodology to collect representative data quickly and simply, showing that in a GIS environment the USLE can be applied to determine field-scale soil loss quantitatively and spatially, to predict erosion hazard over large watersheds. The study yielded useful thematic data for the Upper Ewaso Ng’iro basin of Kenya that had not been previously available, such as rainfall erosivity, soil types and erodibility, slope steepness, vegetation cover and the spatial distribution of soil conservation works, as well as the erosion hazard map of the basin. In addition, the main factors associated with soil loss in the basin were identified to provide results that can be used for preparation of soil conservation master plans. The USLE was found to predict soil loss from erosion plots in the Upper Ewaso Ng’iro basin quite well, obtaining an overall coefficient of linear regression of 0.65 with observed data, albeit with a high standard error of estimate. In general, the soil loss values estimated by the USLE were considered realistic after comparison with plot data, reconnaissance surveys and sediment yields from the major rivers in the basin. Over 36 percent of the Upper Ewaso Ng’iro basin was predicted to experience soil erosion in excess of an estimated tolerable rate of 9.0 t ha-1 yr-1. The highest erosion hazard was predicted in the semi-arid rangelands associated with communal grazing that occupy the northern and eastern parts of the basin. Forests and wellmanaged rangelands gave the lowest erosion hazard rates, where less than 1 t ha-1 yr-1 was predicted. As the problem of soil erosion is extensive in the overgrazed rangelands of the region, a multi-disciplinary approach is required to identify solutions that are applicable at a reconnaissance scale for the rehabilitation of degraded lands in the Upper Ewaso Ng’iro basin. There are problems with using the USLE in a study of this scale, especially determining reliable LS-, C- and P-factors. Thus, there is a need for research to determine appropriate Pfactors for local soil conservation practices such as trash lines, fanya-juu terraces and stone lines, which are not available from the USLE guide tables.
This study faced several limitations, such as the classification of soil erodibility using the soil mapping units, and the use of a low resolution contour coverage input for the preparation of the basin DEM. The capabilities and limitations of the USLE have been well documented by Wischmeier [1976]. It is also important to note that in modelling with the USLE, each of the six erosion factors in the equation is a function of numerous secondary variables and their interactions. These must be considered when computing local values of the factors. In addition, each factor is derived independently, while in reality, the factors interact in a dynamic system and several assumptions have to be made to adapt the model to a given set of conditions, which is achieved by using annual average values. In this study, modelling with the USLE in conditions of multiple land covers required extrapolation of the C-factors to very different conditions. As a result, the model may not always give satisfactory predictions because other long-term factors, such as changes in vegetation, may become operative. There was also the problem of the choice of P-factors for conservation structures such as trash lines, fanya-juu terraces and stone lines, whose values are not represented in the USLE guide tables [Wischmeier & Smith, 19781. In addition, although the conservation-practice maps used in this study had been updated by the time they were digitised, it was assumed that all the structures were operational. This of course raises many questions, but so, too, does the assumption that outside the conserved
84
Erosion
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in the Upper
Ewaso Ng’iro
North
basin of Kenya
JAG
ACKNOWLEDGEMENTS The authors appreciate the material and logistical support provided by the Natural Resource Monitoring, Modelling and Management Project (NRMs) of Nanyuki, Kenya; the Rockefeller Foundation, Nairobi Office; Centre for Development and Environment, University of Bern, Switzerland; and Silsoe College, Cranfield University, United Kingdom. We would also like to thank Mr. J. Ndungu, Mr. J. K. Mitugo, Mr. Kungu, Mr. G. Githu, Mr. K. Gitari and Mr. C. Mwangi, all of NRMa, for their assistance with data acquisition.
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Liniger, H.P., 1992a. Soil cover and management - attractive water and soil conservation for the drylands in Kenya. Proceedings, 7th international Soil Conservation Conference, Sydney, 27-30 September 1992. Volume 1, pp. 130-139. Liniger, H.P., 1992b. Water and soil resource conservation and utilization on the northwestNorthwest side of Mount Kenya. Mountain Research and Development 12(4): 363-373. Liniger, H.P. & D.B.Thomas, 1998. GRASS: Ground cover for the Restoration of the Arid and Semi-arid Soils. In: Advances in GeoEcology. CATENA. Special Issue 31: 1167-I 178. Liniger, H.P., F.N. Gichuki, G. Kironchi & L. Njeru, 1998. Pressure on land: The search for sustainable use in a highly diverse environment. In: Resources, actors and policies - towards sustainable regional development in the highland - lowland system of Mount Kenya. Eastern and Southern Africa Journal, Vol. 8, Special Issue, pp. 29-44.
REFERENCES
Mainga, P.M. & J.P. Mbuvi, 1994. Preliminary soil conditions of Embori, Kalalu and Mukogodo sites. Laikipia Research Programme, Nanyuki, Kenya. Laikipia-Mount Kenya papers, B-10, 39 pp.
Berger, P.,1989. Rainfall and agroclimatology of the Laikipia Plateau, Kenya. Institute of Geography, University of Berne, Berne, 102 pp. Desmet, P.J.J. & G. Govers, 1996. A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. Journal of Soil and Water Conservation 51(5): 427433.
Mati, B.M., 1999. Erosion hazard assessment in Upper Ewaso Ng’iro basin of Kenya: Application of GIS, USLE and EUROSEM. PhD thesis, Silsoe College, Cranfield University, United Kingdom, 239
Dunne, T., W.E. Dietrich & M.J. Brunengo, 1978. Recent and past rates of erosion in semi-arid Kenya. Geomorphology Supplement 29: 99-100.
McCool, D.K., L.C. Brown, G.R. Foster, C.K. Mutchler & L.D. Meyer, 1987. Revised slope steepness factor for the Universal Soil Loss equation. Transactions of the American Society of Agricultural engineers 30(5): 1387-l 396.
PP.
Edwards, D.C., 1943. Report on the grazing areas of the Northern Frontier District of Kenya. Ministry of Agriculture, Nairobi.
McCormack, D.E. & K.K. Young, 1981. Technical and societal implications of soil loss tolerance. In: R.P.C. Morgan (Ed.), Soil Conservation: Problems and Prospects. John Wiley & Sons, Chichester, pp. 365-376.
ESRI, 1997. Understanding GIS. The ARC/INFO method. Self-study workbook. Version 7.1 for UNIX and Windows NT. 4th edn. Geoinformation International, Cambridge, UK.
Ministry of Water Development, 1992. The study on the National Master Water Plan - Sectoral Report (B); Hydrology. Ministry of Water Development, Nairobi.
Gichuki, F.N., H.P. Liniger & G. Schwilch, 1998a. Knowledge about highland - lowland interactions: The role of a natural resource information system. In: Resources, actors and policies - towards sustainable regional development in the highland - lowland system of Mount Kenya. Eastern and Southern Africa Journal, Vol. 8, Special Issue, pp. 5-14.
Morgan, R.P.C., 1995. Soil Erosion and Conservation. 2nd Edn. Longman Group, John Wiley &Sons Inc. New York., NY, 298 pp. Muchena, F.N., 1982. The role of soil surveys and land evaluation in assessing soil erosion hazard. In: D.B. Thomas & W.M. Senga (Eds), Proceedings, 2nd Soil and Water Conservation Workshop, University of Nairobi, Nairobi, 10-16 March 1982. Occasional Paper No. 42, Institute of Development Studies, University of Nairobi, pp. 79-86.
Gichuki, F.N., H.P. Liniger, L. MacMillan, G. Schwilch & G. Gikonyo, 1998b. Scarce water: Exploring resource availability, use and improved management. In: Resources, actors and policies towards sustainable regional development in the highland lowland system of Mount Kenya. Eastern and Southern Africa Journal, Vol. 8, Special Issue, pp. 15-28.
Mutunga, C.N., 1994. The Influence of Vegetative Cover on Runoff and Soil Loss - A Study in Mukogodo, Laikipia District. MSc thesis, Department of Agricultural Engineering, University of Nairobi, Nairobi.
Goodchild, M.F., 1993. Data models and data quality: Problems and prospects. In: M.F. Goodchild, L.T. Steyaert & B.O. Parks (Eds), Environmental Modelling with GIS. Oxford University Press, Oxford, pp. 94-103.
Page, A.L., R.H. Miller & D.R. Keeney, 1982. Methods of soil analysis. Part 2. Chemical and microbiological properties. 2nd Edn. Agronomy No. 9, Part 2. American Society of Agronomy Inc. and Soil Society of America Inc. Madison, WI.
Herweg, K., 1996. Assessment of Current Erosion Damage. Field manual. Soil Conservation Research Program, Ethiopia and Centre for Development and Environment, University of Berne, Berne, 69 pp.
Petersen, G.W., E. Nizeyimana & B.M. Evans, 1998. Applications of Geographic Information Systems in soil degradation assessments. In: R. Lal, W.H. Blum, C. Valentino & B.A. Stewart (Eds), Advances in Soil Science: Methods for Assessment of Soil Degradation. CRC Press, New York, NY, pp. 377-391.
Jaetzold, R. & H. Schmidt, 1983. Farm Management Handbook of Kenya, Vol. II/B, Central Kenya. Ministry of Agriculture, Nairobi. Kenya Soil Survey, 1987. Manual for Soil Survey and Land Evaluation, Volume I- Soil Survey. National Agricultural Laboratories, Ministry of Agriculture, Nairobi, 76pp.
Pratt, D.J. 8 M.D. Gwynne, 1977. Rangeland Management and Ecology in East Africa. Robert E. Krieger Publishing Company, New York, NY.
Kihara, F.I., 1998. An Investigation into the Soil Loss Problem in the Upper Ewaso Ng’iro basin. Msc thesis, Department of Agricultural Engineering, University of Nairobi, Nairobi, 135~~.
Republic of Kenya, 1996. Annual report 1995/96. Promoting soil/water conservation and agroforestry. Soil and Water Conservation Branch, Land Development Division, Ministry of Agriculture, Livestock and Marketing, Nairobi, 82 pp.
Kironchi, G., J.P. Mbuvi & H.P. Liniger, 1992. Soil type and land use effects on infiltration in Sirima and Mukogodo Catchments, Laikipia District. Laikipia-Mount Kenya Papers, D-15. Laikipia Research Programme, Nanyuki, Kenya 32 pp.
Sombroek, W.G., H.M.H. Braun & B.J.A. van der Pouw, 1980. The explanatory soil map and agro-climatic zone map of Kenya. Report No. E.I. Kenya Soil Survey, Nairobi, 56 pp.
Liniger, H., 1991. Water conservation for rainfed farming in the semi-arid footzone North West of Mt. Kenya (Laikipia Highlands). PhD thesis. Laikipia-Mount Kenya Papers, D-3. Laikipia Research Programme, Nanyuki, Kenya 174 pp
Thomas, M.K. 8 H. Liniger, 1994. Rainfall data: Additional stations of good quality in the Upper Ewaso Ng’iro basin and Mt. Kenya
85
Erosion
hazard
in the Upper
Ewaso Ng’iro
North
areas. Laikipia-Mount Kenya Papers, B-6. Geography, University of Berne, Berne, 145 pp.
basin of Kenya
Institute
JAG
of
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- 2000
Van den Berg, M., 1992. SWEAP. A computer program for water erosion assessment applied to SOTER: Documentation Version 1.4. SOTER Report No. 7. International Society of Soil Science, Wageningen, Netherlands.
sentant un risque eleve d’erosion, la plupart des terres etant dans des pdturages surexploites. L’utilisation et la gestion ont ete les principaux facteurs associes avec I’erosion du sol, et, a un degre moindre, la topographie. Du fait de I’echelle de la reconnaissance de cette etude il y a eu des limitations dans la determination fiable des facteurs LS, C, et P pour I’equation universelle de perte de sol (USLE). En plus, il est necessaire de faire des recherches pour determiner des facteurs P appropries pour des pratiques de conservation de sol local, telle que lignes vagues, bes terrasses fanya-juu et des lignes de pierres.
Wanjogu, N.S., 1994. The soils of Sirima and Mukogodo catchments, Laikipia District. Laikipia-Mount Kenya Papers, D-19. Laikipia Research Programme, Nanyuki, Kenya, 31 pp.
RESUMEN
Thurow, T.L. & D.J. Herlocker, 1993. Range Management Handbook of Kenya. Volume III, 5. Users Guide for the Range Management Handbook of Kenya. Ministry of Agriculture, Livestock Development and Marketing, Nairobi, 59 pp.
Se implemento la ecuacion universal de perdida de suelo (Universal Soil Loss Equation, USLE) para predecir el riesgo de erosion de suelos en la cuenca del Alto Ewaso Ng’iro North de Kenya, a partir de datos de parcelas de erosion y levantamientos de reconocimiento. Se determine el factor R (erosividad de la Iluvia) por extrapolation a partir de la relation entre cantidad de lluvia y erosividad, en base a datos de registro automatico. Se determine el factor K (erodabilidad del suelo) en base a datos obtenidos por analisis de laboratorio de muestras de suelo tomadas en 83 sitios de ensayo en la cuenca. Se determinaron 10s factores L y S (longitud e inclination de la pendiente) a partir de un modelo digital de elevacidn de la cuenca, mientras que el factor C (cultivos y manejo de las tierras) fue determinado en base a datos de la cobertura vegetal, obtenidos a partir de imagenes SPOT y levantamientos de campo. Se estimo el factor P (practicas de conservation) a partir de mapas de conservation de suelos obtenidos del Ministerio de Agricultura. Se use un Sistema de lnformacion Geografica (SIG) basado en celdas, para calcular la perdida de suelo y mapear el riesgo de erosion en forma interactiva. Los resultados obtenidos fueron similares a 10s valores de perdida de suelo medidos en parcelas de erosion. Aproximadamente 36% de la cuenca del Alto Ewaso Ng’iro tiene alto riesgo de erosion; estas tierras se encuentran mayormente bajo pastos naturales sobrepastoreados. Los principales factores asociados con la erosion de suelos fueron el uso y el manejo de las tierras y, en un grado menor, la topografia. La escala de reconocimiento de este estudio cause limitaciones para determinar valores confiables de 10s factores L, 5, C y P para la ecuacion de USLE. Adicionalmente, se requiere mas investigation para determinar valores del factor P apropiados para las prdcticas locales de conservation de suelos, tales coma lineas de desechos organicos, terrazas del tipo fanya-juu y lineas de piedras.
Wischmeier, W. H., 1976. Use and misuse of the Universal Soil Loss Equation. Journal of Soil and Water Conservation 31: 5-9. Wischmeier, W.H. & D.D. Smith, 1978. Predicting rainfall erosion losses. USDA Agricultural Research Service Handbook 537. USDA, Washington, DC, 57 pp. Wischmeier, W.H., C.B. Johnson & B.V. Cross, 1971. A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water conservation 26: 189-93.
RESUME L’equation universelle de perte de sol (USLE) a ete utilisee pour predire le risque d’erosion du sol dans le bassin Ewaso Ng’iro Superieur au Kenya, en utilisant des donnees de traces d’erosion et des leves de reconnaissance. Le facteur R (erosivite de la pluie) a ete determine par extrapolation a partir de la relation quantite de pluie - erosivite, en utilisant des don&es d’enregistrements autographiques. Le facteur K (erodibilite du sol) a ete determine en utilisant des donnees obtenues a partir d’analyses d’echantillons de sols preleves sur 83 sites tests dans le bassin. Les facteurs LS (longueur des pentes et raideur) ont ete determines a partir d’un modele numerique du terrain (MNT) du bassin, alors que le facteur C (recolte et gestion)a ete determine a partir des donnees de couverture vegetale, obtenues a partir d’images SPOT et de leves terrain. Le facteur P (pratique de conservation) a ete estime a partir de cartes de conservation des sols, obtenues du Ministere de I’Agriculture. Un systeme d’information geographique (SIG) raster a ete utilise pour calculer interactivement le risque de perte de sol et des cartes d’erosion. Les resultats obtenus ont ete comparables aux valeurs de perte de sol mesurees a partir de traces d’erosion. A peu pres 36 pour-cent du bassin Ewaso Ng’iro Superieur a ete considere comme pre-
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Assessment of erosion hazard with the USLE and GIS: A case study of the Upper Ewaso Ng’iro North basin of Kenya Bancy M Mati I, Royston P C Morgan2, Francis N Gichuk?, John N Quintor+, Tim R Brewer2 and Hans P Liniger4 1 Jomo Kenyatta University of Agriculture 2 Cranfield 3
and Technology,
University, Silsoe, Bedfordshire,
P.O. BOX 62000, Nairobi, Kenya
MK45 4DT, England, UK
University of Nairobi, P.O. BOX 30197, Nairobi, Kenya
4 Centre for Development
soil erosion
KEYWORDS:
Kenya,
and Environment
Upper
hazard
Ewaso Ng’iro
University of Bern, Hallerstrassl2,
assessment,
land use, it became necessary to map and quantify soil erosion more extensively, covering the entire basin, with
USLE, GIS,
basin
the aim of providing a tool for planning soil conservation strategies at the regional level. Due to the complexity of the variables involved, and since they interact in a wide spatial domain, simplicity of data management and the ability to transfer from data-rich to data-poor areas became very important. The use of GIS techniques and
ABSTRACT The Universal Soil Loss Equation (USLE) was used to predict soil erosion hazard in the Upper Ewaso Ng’iro data from
erosion
(rainfall erosivity) was determined amount-erosivity
North basin of Kenya using
plots and reconnaissance
relationship,
surveys. The R-factor
by extrapolation
was determined
records.
The K-factor
(soil erodibility)
by laboratory
analysis of soil samples collected from 83 test sites in
the basin. The LS-factors (slope length mined from a digital elevation C-factor
from
factor (conservation servation, Geographic
was determined
from vegetation
SPOT imagery and field surveys. The P-
from the Ministry
Information
from maps of soil con-
of Agriculture.
A raster-based
System (GIS) was used to interactively
culate soil loss and map erosion hazard. The results obtained comparable
calwere
UPPER EWASO
to measured soil loss values from erosion plots. About
36 percent of the Upper Ewaso Ng’iro basin was predicted high
at coarse resolutions most commonly quantified using annual soil loss rates. The Universal Soil Loss Equation (USLE) [Wischmeier & Smith, 19781 and Arc/Info GIS [ESRI, 19971 were used in this study to determine these loss rates as it was possible to determine the factors of the equation and use GIS techniques to extrapolate for the ungauged areas in the Upper Ewaso Ng’iro basin.
and steepness) were deter-
practice) was estimated
obtained
using data obtained
model (DEM) of the basin, while the
(crop and management)
cover data, obtained
erosion models is one way of achieving this [Petersen et al, 19981. According to Herweg [ 19961, erosion hazard is
from the rainfall
using data from autographic
risk of erosion,
rangelands.
most of this land being
Land use and management
were
CH-3012 Berne, Switzerland
The Upper
to have
in the overgrazed
between
the
longitudes
major
factors
36”
decrease
phy. Due to the reconnaissance
Nyandarua
tations
in determining
addition, factors
there is a need for research to determine for
fanya-juu
reliable LS, C and P factors for the USLE. In
local soil conservation
practices,
appropriate
such as trash
Most
P-
acterised
the
Upper
Kenya has been identified 1943;
Ministry
38”
Mt.
m) and
region Plateau,
the extreme
1700
00”
east.
and
Altitudes
Kenya
(5199
the
Nyambene
m), the
by the
Hills. gently
of
1700-
of the basin are chartopography,
m to
north-eastern
km2,
north
at an elevation
to rolling
about
15,251
1” 15”
is occupied
and north-east
by undulating from
and
and
where
less than
region
alti-
1000
[Gichuki
m
et a/,
1998al.
INTRODUCTION erosion
central
basin covers
south
from
Laikipia
drop
towards
east (3999
1800 m. The north
terraces and stone lines.
tudes
Soil
10”
Range
of the
North
00 20”
northwards,
undulating
lines,
BASIN
Ewaso Ng’iro latitudes
associated with soil erosion, and - to a lesser extent - the topograscale of this study there were limi-
NG’IRO
of Water
Ewaso
Ng’iro
as a major Development,
North
problem
basin
of Due to the position of the Upper Ewaso Ng’iro basin on the lee slopes of Mt. Kenya and the Nyandarua Range, most of the basin is semi-arid. Annual rainfall ranges from over 2000 mm on the Nyandarua Range to less than 365 mm per annum in the drier north eastern areas [Jaetzold & Schmidt, 1983; Thomas & Liniger, 1994;
[Edwards,
19921. However,
soil erosion research in the basin has only been done on localised areas [Kihara, 1998; Liniger, 1991, Liniger et al, 1998; Mutunga, 1994; Wanjogu, 1994 I. As land degradation became more evident with increasing changes in
78
Erosion hazard
in the Upper Ewaso Ng’iro North basin of Kenya
JAG
Gichuki et al, 1998b]. Mean annual rainfall averages about 700 mm in the basin. Despite this relatively high
l
Volume
2 - Issue 2 - 2000
pixel was viewed as a single slope plane for which the USLE could be applied individually. It was not possible to route runoff and soil loss within a given sub-catchment in the basin. Therefore, the soil loss values predicted in this study represent soil erosion generated within each pixel treated in isolation, but they do not indicate sediment yields in small catchments or over hillslope units of a larger area. This data gives a first approximation of the likely sediment sources in the basin.
figure, rainfall distribution is such that the seasonal amounts are insufficient for proper crop growth in most parts of the basin [Thomas & Liniger, 19941. In the western and north-western areas, continental rains fall between April and August. The eastern region has a clear bimodal distribution, with rainfall maxima in April and October [Berger, 19891. The central region is a transition zone, where the two patterns overlap. This is the driest zone. Rainfall intensities are usually high, averaging about 20 to 40 mm h-1 and higher intensity storms of up to 96 mm h-1 have been recorded [Liniger, 19911.
GOVERNING
EQUATION
The Universal Soil Loss Equation (USLE] [Wischmeier & Smith, 19781 was applied in a GIS environment to determine the average annual soil loss and its distribution in the basin. The USLE predicts soil loss for a given site as a product of six major factors [Equation I], whose values at a particular location can be expressed numerically. The values of these erosion factors vary considerably about their means from storm to storm, but the effects of these fluctuations average out in the long run. Thus the USLE is suitable for predicting long-term averages; the soil erosion is calculated as follows:
The predominant soils include chromic-humic Cambisols and humic Andosols (FAO classification) on Mt. Kenya, the Nyandarua Range and the Nyambene Hills above 2000 m, with the eutric Nitisols and luvic Phaeozems at lower altitudes on the mountain footridges. On the plateaux, chromic-pellic Vertisols dominate with pockets of eutric Planosols. In the central highlands eutric Regosols dominate the slopes of the hills and minor scarps, while most of the northern lowlands comprise calcic-chromic Cambisols and ferric-chromic Luvisols [Sombroek et al, 1980; Liniger et al, 19981. About 70 percent of the basin comprises rangelands. Large-scale commercial ranches are found in the central plateau, while the drier northern regions are used by pastoral communities for communal grazing [Thurow & Herlocker, 19931. Although wildlife is found in most parts of the basin, there are several game reserves run privately or by public institutions. Of the small proportion of the basin occupied by croplands, large scale wheat/barley farms are found in thin strips around the slopes of Mt. Kenya and the Nyandarua Range, as well as small-scale settlements operated as mixed farms, where maize is the predominant crop. In addition, there are forests at the higher altitudes on the mountains and major hills.
A=RxKxLxSxCxP
111
Where: A = annual soil loss in t ha-1 yr-1. R = Rainfall erosivity factor (J mm.m-z h-1) K = Soil erodibility factor (t J-1 mm-l) L = Slope length factor 5 = Slope steepness factor C = Crop and management factor P = Conservation supporting practices factor DETERMINING
USLE FACTOR VALUES
Rainfall erosivity (R) factor
Rainfall erosivity was determined by first calculating storm erosivity indices using data from six stations (Embori, Kalalu, Karuri, Mukogodo, Sirima and Teleswani), for which comprehensive autographic records were available. The storm erosivities were then accumulated for each year to give the USLE rainfall erosivity index, the R-factor, for each station. In total, 17 plot years of rainfall erosivity data were obtained for the six stations.
METHODOLOGY The overall methodology involved use of a soil erosion model, the Universal Soil Loss Equation (LISLE) [Wischmeier & Smith, 19781 in a GIS (Geographic Information System), with data obtained from weather stations, reconnaissance surveys, runoff plots and topographic maps. Individual GIS files were built for each factor in the USLE and combined by cell-grid modelling procedures in Arc/Info GIS [ESRI, 19971 to predict soil loss in the spatial domain. The grid-cells were set to 100 m by 100 m, which was the resolution possible with the available data and computer facilities. Although the USLE is recommended for small plots and fields [Wischmeier & Smith, 19781, the 1 ha cell sizes used in this study were considered to give adequate detail because this study was of reconnaissance scale [Goodchild, 19931. Each
To obtain R-factors for the whole basin, rainfall-erosivity relationships (Table 1) were determined by regression analysis of annual rainfall data from 75 geo-referenced manual gauging stations [Berger, 1989; Thomas & Liniger, 19941 spread in and around the Upper Ewaso Ng’iro basin. It was found necessary to separate the data into two groups as per the agro-climatic zone location of each station, in order to get better correlation coefficients. The two regression equations obtained (shown in Table 1) were respectively applied to the rainfall map of the basin in Ardlnfo GRID, thus producing the erosivity map of the basin (Figure 1).
79
Erosion
hazard
TABLE I:
in the Upper
Ewaso Ng’iro
North
JAG
basin of Kenya
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Regression equations relating annual rainfall with erosivity
Agro-climatic
zone
Stations
1
I - III
Embori,
2
IV - VI
Kalalu,
3
Basin
All stations
Karuri, Sirima,
Erosivitylrainfall Teleswani Mukogodo
r2
relationship
e
n 8
RE = 0.649 Rp - 94.2
0.70
85
RE = 1.17 Rp - 258.5
0.71
124
9
RE = 0.574 Rp - 18.4
0.46
134
17
r2 = Coefficient of linear regression; n = Number of observations; e = Standard error of estimate, RE = Average annual ralnfall eroslvity (J m-2 mm h-1); R, = Annual rainfall amount (mm)
Soil erodibility
fiscal constraints could not permit a full-scale soil survey of the area. The map obtained thus contained 36 major soil mapping units (FAO classification). By assuming that the resolution of the K values matches that of the soil mapping units, the mean K factors obtained for each soil type were consequently used to reclassify the soil map. These were the K-values used in the USLE. The soil erodi-
(K) factor
Basic data for estimating erodibility were obtained by collecting soil samples from 83 test sites, representative of the major soil mapping units [Sombroek et al, 19801, as indicated in Figure 2. Test sites were representative sample areas where data on soils, vegetation, climate and land use were recorded at greater detail during the field surveys. Additional information was also obtained from reports of detailed soil surveys carried out in the basin [van den Berg, 1992; Kironchi et a/, 1992; Mainga & Mbuvi, 19941. The percentage stone and gravel cover were estimated in the field using a metre rule, while rock cover was estimated visually. The soil samples were taken to the laboratory and analysed for percentage fine sand, silt, clay, and organic matter [Kenya Soil Survey, 1987; Page et a/, 19821. These values were used to estimate the erodibility (K) factors, using the USLE soil erodibility nomograph [Wischmeier et a/, 19711. As 43 percent of the sampled sites contained various amounts of stone and gravel, a correction factor for this effect was included in the calculation of the K-factor, based on the equations described by van den Berg [1992].
expressed
Topographic
S)
factors
(L and
in class ranges,
is
Digital topographic data for the Upper Ewaso Ng’iro basin were obtained by digitizing 32 sheets of topographic maps of scale I:50 000. The contours and the drainage system were digitized separately and used to build up the DEM (Digital Elevation Model) of the basin. The contour interval used was 200 m, taking into account available computer memory and technology at the time in Kenya. A grid cell of 100 m was used in building the DEM, as this was considered to be less than the maximum slope length, based on reconnaissance surveys. The resulting DEM was used to determine the slope steepness (q) and slope length (I) in Arc/Info GRID. The USLE slope steepness (5) and length (L) factors were determined as follows [McCool et a/, 19871. 5 = 10.8 sin8 + 0.03 for slopes < 9% PI 5 = 16.8 sin0 - 0.50 for slopes > 9% [31 1 = (UZ2.13)m [41 Where: m = an exponent that depends on slope steepness, being 0.5 for slopes exceeding 5 percent, 0.4 for 4 percent slopes and 0.3 for slopes less than 3 percent
To enable the mapping of the K-factor in the spatial domain, the soil map of the Upper Ewaso Ng’iro basin was first prepared by digitising the Exploratory Soil Map of Kenya [Sombroek et al, 19801 of scale 1 :I ,OOO,OOO. This was because there were no soil survey maps of finer resolution covering the entire basin, while technical and
FIGURE I: Rainfall erosivity distribution Ng’iro North basin
bility map of the basin, shown in Figure 2.
in the Upper Ewaso
FIGURE2: Soil erodibility
80
in the Upper Ewaso Ng’iro North basin
JAG
Erosion hazard in the Upper Ewaso Ng’iro North basin of Kenya
sensed
The flow length as estimated with GIS gave the length of slope planes from the hillcrest to the streambed. A maximum length of 200 m was set in order to get realistic Lfactor values at the upper limits. As the topography of the Upper Ewaso Ng’iro basin is generally flat to gently undulating, limiting the slope lengths to 200 m seemed reasonable in the absence of better information. Due to limitations of computer facilities and data resolution in this work, it was not possible to separate concave, convex or straight slopes. The LS-factor was consequently determined by multiplying the 5 and L-factors in Arc/Info Grid to obtain the map of topographic factors shown in Figure 3. As most of the basin is covered by the plateau, the L’Sfactors are generally low.
data
multispectral
was obtained
from
l
Volume 2 - Issue 2 - 2000
visual
interpretation
of
SPOT 1 HRV imagery, as well as topograph-
ic maps of scale I:50 000. Ground-truthing and vegetation cover monitoring were simultaneously undertaken during the dry seasons of 1996-97, to coincide with the season the imagery had been taken. This covered 83 test sites selected to represent the major land cover types and stratified to include all the geographic zones of the basin, so as to sample as wide a diversity in soils, vegetation,
land use, slope and erosion hazard as possible
The vegetation cover classification developed for East African rangeland conditions by Pratt & Gwynne [I9771 was used and modified to include such classes as smallscale cropland (all crops), large-scale croplands, swamps and urban settlements. Eleven major land cover types were identified: moorland, forest, bushland, bush grassland, grassland, shrub grassland, shrubland, scarpline vegetation, small scale cropland, large scale cropland and swamps [Pratt & Gwynne, 19771. The USLE cover and management factors (C-factors) corresponding to each crop/vegetation condition were estimated from USLE guide tables [Morgan, 1995; Wischmeier & Smith, 19781. These values were used to re-classify the land cover map to obtain the C-factor map of the basin (Figure 4). Determining
conservation practices (P) factor
To determine
FIGURE3: Topographic
the areas covered by soil conservation activities, maps of the catchments conserved through the “Catchment Approach” program of the Soil and Water Conservation Branch, Ministry of Agriculture (1988-97) were used. The Catchment Approach Program ideally conserves an entire catchment. These maps were digitized and annual reports from the Ministry of Agriculture (Republic of Kenya, 1996) were used to provide summarised information on the types of structures found in each district. However, the commonly used structures
factors (LS) in the Upper Ewaso Ng’iro
North basin
Determining the LS factor from grid-based DEM has its limitations. Other than the low resolution of the input data, slope lengths as generated by the DEM were based on the assumption that each slope plane consists of homogeneous soil and vegetation cover. Ideally, the LS factor should be relevant for the runoff-producing sections of the hillslope [Desmet & Govers, 19961. In reality, this may not be the case, due to the difficulties of separating these factors. However, as soil types in the Kenyan highlands are characterised by catenas [Muchena, 19821, for a given slope soils tend to be similar. Crop and management
factor (C)
To determine the C-factor values for the Upper Ewaso Ng’iro basin, it was first necessary to prepare a land cover map of the basin. This was achieved using remote sensing techniques and field surveys. The remotely
FIGURE 4: Cover and management Ewaso Ng’iro basin
81
(C) factors in the Upper
Erosion hazard
were found bunds
in the Upper Ewaso Ng’iro North basin of Kenya
to be fanyajuu
and
grass
structures
were
[Wischmeier P-factor
& Smith,
by assuming
structures terraces,
and
conserved
the
19951,
was
the P-factor
EROSION
hazard
map of
map of the
USLE factors grouped
into five classes using a frequency
analysis,
resulting
shown
and climatic
of the
basin was performed
data
was
through
sion analysis
was done
with
plot data.
erosion
Ewaso
First, linear
the combined
plots
poor
vegetation
with
good
while
cover;
(Table
basin
proviede
Ng’iro regres-
45 plot-years
TABLE 2: Results of linear regression for the validation
expressed
hazard
could in the
in areas
be applied data
classes and ranging
average
annual
were
into account
the wide
it was found
over 25
spatial that
diversity in general
soil loss in the basin,
hazard in the Upper Ewaso Ng’iro North
basin
of USLEwith plot data
Forest
5, = 1.3895,
Grass
5, = 0.6935,
Kalalu,
Maize,
N
frz)
e
+ 0.109
6
0.99
0.08
+ 0.883
14 0
69
3.68
equation
S, = 0.41 IS,
+ 8.673
12
0.53
8.10
Shrubland
Bushland
5 ,, = 0.4275,
+ 4.695
13
0.71
3.34
Combined
data
S” = 0.5355,
+ 2.947
45
0.65
5.48
potatoes
Where, S, = PredIcted soil loss (t ha-1 yr-1); S, = Observed soil loss (t ha-’ yr-1); N = Number of observations; r2 = Coefficient of linear regression; e = Standard error of estimate
82
from
soil loss rates
1 .O t ha-l yr-1 to high risk areas with
be used to predict
Kalalu, Teleswani Naro Moru Met, Naro Moru Gate,
All stations
in
in the Upper Ewaso Ng’iro
in five broad
in the data,
Naro Moru Gate, Teleswani
Sirima
erosion
limitations
and limitations
Regression
Karuri Sirima
soil
that,
soil loss in areas with
the model
the
loss values
3). It was found
the USLE could
Cover
Mukogodo,
as shown
DISCUSSION
FIGURE 6: Soil erosion
catchments in the Upper Ewaso Ng’iro basin (P = 0.18 in the soil conservation catchments and 1 .O elsewhere)
name
soil
it overestimated
otherwise
t ha-1 yr-1. Taking
FIGURE 5: Soil conservation
Station
to
and the measured
the USLE underestimated
are less than
Upper
again
of each ero-
correlation, with
GIS techniques
Figure 6 shows erosion
map
PLOT DATA
USLE for the with
done
according
conditions
This gave a better
very low risk areas, where The validation
was
groups
compared
basin,
THE USLE WITH
four
of
to obtain
the results of the USLE simulation
plot
RESULTS AND
dis-
hazard
in Figure 6.
VALIDATING
analysis
into
error
In order
GRID
sequently
erosion
the standard
(95
were con-
tribution
in the
while
of 0.645
kept in mind.
the
in Arc/Info
coefficient
regression
the data
2 - Issue 2 - 2000
using
to the
interactively
Volume
in Table 2. In addition,
general,
as per
by multiplying
the
in type
loss from
scale of this
1. The soil loss values obtained
using Equation
results,
interval),
l
t ha-1 yr-1) was high.
separating
obtained
HAZARD
was determined
(5.475
sion plot station.
and crop type.
DETERMINING
confidence
similarities
terraced.
the P-factors
percent
after
bench
the
This gave a correlation
better
catch-
sloping
of data. estimate
an average
5). Due to the reconnaissance
slope gradient
of
tables
soil conservation
catchment
to separate
types
all conserved
outward
obtaining
it was not possible
respective
these
was used to classify
catchments
Soil erosion
to
entire
P-factor
basin (Figure
for
lines, contour
USLE guide
the available
similarly
that
This constant
for
the
Morgan,
adopted that
perform
from
1978;
was
stone
As P-values
not available
of 0.18
ments,
study,
strips.
terraces,
JAG
JAG Volume 2 - Issue 2 - 2000
Erosion hazard in the Upper Ewaso Ng’iro North basin of Kenya
l
TABLE3: Comparison of USLEpredicted and observed soil loss values Range of soil loss rates (t ha-l yr1) Station
Treatment
Min
Embori Kalalu Karuri Mukogodo Naro Moru Gate Naro Moru Met. Sirima Teleswani
Barley Maize Potatoes Shrubland Forest Forest Potatoes Forest
Soil loss predicted with LISLE and GIS (t ha-l)
Observed
soil loss (t ha-l)
Max
Min
Mean
Max
Min
Mean
Max 102.6
Soil loss predicted with the LISLE with plot data (t ha-l) Mean
15.0
18.2
32.4
21.4
31.3
39.7
0
51.3
5.7
22.2
48.5
1.6
8.2
18.1
1.1
14.8
25.4
5.7
14.8
15.2
19.7
35.4
49.9
0.5
24.5
69.4
8.9
24.2
2.2
18.3
35.2
12.2
14.0
38.2
52.4
0
0.1
0.1
0
0.8
1.2
0
0
0
0
0.3
0
0.9
1.5
0
0
1.3
16.2
5.2
27.3
0.9
3.7
8.3
0
0.4
0.7
0
0.1
1.0
13.3
9.5
23.1
0.1
0.1
0.1
1.0
managed vegetation cover was present. This was supported by plot data, since annual soil loss rates rarely exceeded 1.5 t ha-1 under forest cover at Naro Moru Met, Naro Moru Gate and Teleswani (Table 3). Topography played a secondary role, since on steep slopes, erosion was very low under natural forests and grasslands, but high under cultivated land.
as an overall correlation coefficient of 0.65 (95 percent confidence interval) was obtained, although the standard error of estimate (5.48 t ha-l yr-1) was high (Table 2). However, better results were obtained when the data was split into four groups according to similarities in type and the climatic conditions of each erosion plot station. The USLE underestimated soil loss in areas with poor vegetation while it overestimated erosion in areas with good cover. Otherwise, the soil loss values estimated by the USLE were considered realistic when compared with plot data, reconnaissance surveys and sediment yields from the major rivers in the basin [Mati, 1999; Kihara, 19981. However, as the USLE does not predict deposition, the results obtained in this study only predict the susceptibility of a hectare of land to yield sediment.
About 36 percent of the Upper Ewaso Ng’iro basin area was predicted to suffer from erosion risk in excess of a tolerable rate of 9.0 t ha-1 yr-1. This choice of tolerable rates was derived from tables by McCormack & Young [ 19811, which were based on local estimates by Dunne et a/ [I9781 and by consideration of the average soil depth in the basin. The relatively low soil loss values can be attributed to the low LS factors (Figure 3), since most of the basin is plateau. By spatial extent, most of these areas cover much of the semi-arid North and north-east, where communal grazing is predominant. The most severely eroded areas were those covered by the dry shrubland, on which vegetation cover was generally below 40 percent [Mati, 19991. Field observations have shown that the highest erosion damage is through gullies. The rate of gully erosion is not measured in small test plots, which are only used to measure sheet and interrill erosion. Therefore the overall erosion rate can be much higher than predicted by USLE or test plot studies. The Mukogodo catchment study [Mutunga, 19941 showed that higher erosion rates were measured for the whole catchment than those on the test plots, even though there was still some deposition in the catchment. As gullies are localised on areas lying below hills and scarps, rill and interill erosion still remain the dominant processes in most parts of the basin, which are less steep, making the USLE results realistic. In this study, land management was identified as the main factor contributing to high erosion hazard as the poor cover in the shrublands was associated with overgrazing, which could be attributed to a lack of well-organised grazing management practices. In these environments, other factors such as the long-term effects of overgrazing on soil
Table 3 shows the maximum, mean and minimum soil loss predicted at plot (USLE- plot) and regional scales (USLE-GIS), as compared to measured values from the available runoff plot data. The use of class ranges has been recommended [Wischmeier & Smith, 19781 as a better method of checking the applicability of the USLE than comparing absolute values. These results show that the model predicted soil loss with similar ranges that were comparable to observed values, and therefore, it can be applied to the Upper Ewaso Ng’iro basin. However the range from minimum to maximum values in all three data groups (predicted and observed) are high and the predictions less certain at the extremes. By overlaying the erosion hazard map on maps of land cover, soil erodibility, topographic factors and rainfall erosivity, it was possible to deduce those factors associated with high erosion hazard. In general, erosion hazard was low under vegetation cover exceeding 70 percent, where soil loss rates of less than 1 .O t ha-1 yr-1 were predicted. These areas are mostly mountain forests and the well-managed commercial ranches of the Laikipia Plateau. It was found that low erosion risks were predicted even for areas with high rainfall erosivity factors and soils of relatively high erodibility - so long as a well-
83
Erosion hazard in the Upper Ewaso Ng’iro North basin of Kenya
JAG
l
Volume
2 - Issue 2 - 2000
catchments the P-Factor of 1 .OO is true. There could have been other management practices outside the ‘conserved catchments’, but these were not spatially mapped and it was difficult to account for them other than within the C-factor. Thus, the true P-value requires further investigation in this region. Otherwise, this study has at least been able to show the impact of conservation practices in a large watershed, based on the most common conservation practices. These limitations notwithstanding, the USLE has many advantages that make it possible to predict plot-level soil loss and, in combination with GIS techniques, transform these to map erosion at reconnaissance scales.
crusting and compaction [Wanjogu, 19941, burning of rangelands [Thurow & Herlocker, 19931, and soils with sodic or surface sealing properties [Muchena, 19821 may cause more erosion than predicted by the USLE. Croplands were predicted to experience high erosion hazard, although qualitative assessments during the reconnaissance surveys would suggest that the USLE values were too high. This is because there was not much evidence of severe interill, rill or gully erosion on most cultivated lands as compared to overgrazed rangelands. However, erosion plot studies indicated that the values predicted by the USLE were realistic. For instance, soil loss rates of 69.4 t ha-1 were recorded from a potato crop at Karuri in 1994, while at Embori a total of 102.6 t ha-1 was recorded from a barley field in the same year. At Kalalu, soil loss rates exceeding 25 t km-z yr-1 have been recorded in a maize field [Liniger, 1992 a,b]. Conversely, negligible soil loss values have been obtained from the same plots in years with lower rainfall erosivities [Mati, 19991. Therefore, it appears that, depending on rainfall characteristics, many croplands are at a high risk of soil erosion. This illustrates the advantages of having some quantitative data for comparison with qualitative assessments in erosion studies.
CONCLUSIONS This study developed and used a simple methodology to collect representative data quickly and simply, showing that in a GIS environment the USLE can be applied to determine field-scale soil loss quantitatively and spatially, to predict erosion hazard over large watersheds. The study yielded useful thematic data for the Upper Ewaso Ng’iro basin of Kenya that had not been previously available, such as rainfall erosivity, soil types and erodibility, slope steepness, vegetation cover and the spatial distribution of soil conservation works, as well as the erosion hazard map of the basin. In addition, the main factors associated with soil loss in the basin were identified to provide results that can be used for preparation of soil conservation master plans. The USLE was found to predict soil loss from erosion plots in the Upper Ewaso Ng’iro basin quite well, obtaining an overall coefficient of linear regression of 0.65 with observed data, albeit with a high standard error of estimate. In general, the soil loss values estimated by the USLE were considered realistic after comparison with plot data, reconnaissance surveys and sediment yields from the major rivers in the basin. Over 36 percent of the Upper Ewaso Ng’iro basin was predicted to experience soil erosion in excess of an estimated tolerable rate of 9.0 t ha-1 yr-1. The highest erosion hazard was predicted in the semi-arid rangelands associated with communal grazing that occupy the northern and eastern parts of the basin. Forests and wellmanaged rangelands gave the lowest erosion hazard rates, where less than 1 t ha-1 yr-1 was predicted. As the problem of soil erosion is extensive in the overgrazed rangelands of the region, a multi-disciplinary approach is required to identify solutions that are applicable at a reconnaissance scale for the rehabilitation of degraded lands in the Upper Ewaso Ng’iro basin. There are problems with using the USLE in a study of this scale, especially determining reliable LS-, C- and P-factors. Thus, there is a need for research to determine appropriate Pfactors for local soil conservation practices such as trash lines, fanya-juu terraces and stone lines, which are not available from the USLE guide tables.
This study faced several limitations, such as the classification of soil erodibility using the soil mapping units, and the use of a low resolution contour coverage input for the preparation of the basin DEM. The capabilities and limitations of the USLE have been well documented by Wischmeier [1976]. It is also important to note that in modelling with the USLE, each of the six erosion factors in the equation is a function of numerous secondary variables and their interactions. These must be considered when computing local values of the factors. In addition, each factor is derived independently, while in reality, the factors interact in a dynamic system and several assumptions have to be made to adapt the model to a given set of conditions, which is achieved by using annual average values. In this study, modelling with the USLE in conditions of multiple land covers required extrapolation of the C-factors to very different conditions. As a result, the model may not always give satisfactory predictions because other long-term factors, such as changes in vegetation, may become operative. There was also the problem of the choice of P-factors for conservation structures such as trash lines, fanya-juu terraces and stone lines, whose values are not represented in the USLE guide tables [Wischmeier & Smith, 19781. In addition, although the conservation-practice maps used in this study had been updated by the time they were digitised, it was assumed that all the structures were operational. This of course raises many questions, but so, too, does the assumption that outside the conserved
84
Erosion
hazard
in the Upper
Ewaso Ng’iro
North
basin of Kenya
JAG
ACKNOWLEDGEMENTS The authors appreciate the material and logistical support provided by the Natural Resource Monitoring, Modelling and Management Project (NRMs) of Nanyuki, Kenya; the Rockefeller Foundation, Nairobi Office; Centre for Development and Environment, University of Bern, Switzerland; and Silsoe College, Cranfield University, United Kingdom. We would also like to thank Mr. J. Ndungu, Mr. J. K. Mitugo, Mr. Kungu, Mr. G. Githu, Mr. K. Gitari and Mr. C. Mwangi, all of NRMa, for their assistance with data acquisition.
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Volume
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Liniger, H.P., 1992a. Soil cover and management - attractive water and soil conservation for the drylands in Kenya. Proceedings, 7th international Soil Conservation Conference, Sydney, 27-30 September 1992. Volume 1, pp. 130-139. Liniger, H.P., 1992b. Water and soil resource conservation and utilization on the northwestNorthwest side of Mount Kenya. Mountain Research and Development 12(4): 363-373. Liniger, H.P. & D.B.Thomas, 1998. GRASS: Ground cover for the Restoration of the Arid and Semi-arid Soils. In: Advances in GeoEcology. CATENA. Special Issue 31: 1167-I 178. Liniger, H.P., F.N. Gichuki, G. Kironchi & L. Njeru, 1998. Pressure on land: The search for sustainable use in a highly diverse environment. In: Resources, actors and policies - towards sustainable regional development in the highland - lowland system of Mount Kenya. Eastern and Southern Africa Journal, Vol. 8, Special Issue, pp. 29-44.
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areas. Laikipia-Mount Kenya Papers, B-6. Geography, University of Berne, Berne, 145 pp.
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of
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Van den Berg, M., 1992. SWEAP. A computer program for water erosion assessment applied to SOTER: Documentation Version 1.4. SOTER Report No. 7. International Society of Soil Science, Wageningen, Netherlands.
sentant un risque eleve d’erosion, la plupart des terres etant dans des pdturages surexploites. L’utilisation et la gestion ont ete les principaux facteurs associes avec I’erosion du sol, et, a un degre moindre, la topographie. Du fait de I’echelle de la reconnaissance de cette etude il y a eu des limitations dans la determination fiable des facteurs LS, C, et P pour I’equation universelle de perte de sol (USLE). En plus, il est necessaire de faire des recherches pour determiner des facteurs P appropries pour des pratiques de conservation de sol local, telle que lignes vagues, bes terrasses fanya-juu et des lignes de pierres.
Wanjogu, N.S., 1994. The soils of Sirima and Mukogodo catchments, Laikipia District. Laikipia-Mount Kenya Papers, D-19. Laikipia Research Programme, Nanyuki, Kenya, 31 pp.
RESUMEN
Thurow, T.L. & D.J. Herlocker, 1993. Range Management Handbook of Kenya. Volume III, 5. Users Guide for the Range Management Handbook of Kenya. Ministry of Agriculture, Livestock Development and Marketing, Nairobi, 59 pp.
Se implemento la ecuacion universal de perdida de suelo (Universal Soil Loss Equation, USLE) para predecir el riesgo de erosion de suelos en la cuenca del Alto Ewaso Ng’iro North de Kenya, a partir de datos de parcelas de erosion y levantamientos de reconocimiento. Se determine el factor R (erosividad de la Iluvia) por extrapolation a partir de la relation entre cantidad de lluvia y erosividad, en base a datos de registro automatico. Se determine el factor K (erodabilidad del suelo) en base a datos obtenidos por analisis de laboratorio de muestras de suelo tomadas en 83 sitios de ensayo en la cuenca. Se determinaron 10s factores L y S (longitud e inclination de la pendiente) a partir de un modelo digital de elevacidn de la cuenca, mientras que el factor C (cultivos y manejo de las tierras) fue determinado en base a datos de la cobertura vegetal, obtenidos a partir de imagenes SPOT y levantamientos de campo. Se estimo el factor P (practicas de conservation) a partir de mapas de conservation de suelos obtenidos del Ministerio de Agricultura. Se use un Sistema de lnformacion Geografica (SIG) basado en celdas, para calcular la perdida de suelo y mapear el riesgo de erosion en forma interactiva. Los resultados obtenidos fueron similares a 10s valores de perdida de suelo medidos en parcelas de erosion. Aproximadamente 36% de la cuenca del Alto Ewaso Ng’iro tiene alto riesgo de erosion; estas tierras se encuentran mayormente bajo pastos naturales sobrepastoreados. Los principales factores asociados con la erosion de suelos fueron el uso y el manejo de las tierras y, en un grado menor, la topografia. La escala de reconocimiento de este estudio cause limitaciones para determinar valores confiables de 10s factores L, 5, C y P para la ecuacion de USLE. Adicionalmente, se requiere mas investigation para determinar valores del factor P apropiados para las prdcticas locales de conservation de suelos, tales coma lineas de desechos organicos, terrazas del tipo fanya-juu y lineas de piedras.
Wischmeier, W. H., 1976. Use and misuse of the Universal Soil Loss Equation. Journal of Soil and Water Conservation 31: 5-9. Wischmeier, W.H. & D.D. Smith, 1978. Predicting rainfall erosion losses. USDA Agricultural Research Service Handbook 537. USDA, Washington, DC, 57 pp. Wischmeier, W.H., C.B. Johnson & B.V. Cross, 1971. A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water conservation 26: 189-93.
RESUME L’equation universelle de perte de sol (USLE) a ete utilisee pour predire le risque d’erosion du sol dans le bassin Ewaso Ng’iro Superieur au Kenya, en utilisant des donnees de traces d’erosion et des leves de reconnaissance. Le facteur R (erosivite de la pluie) a ete determine par extrapolation a partir de la relation quantite de pluie - erosivite, en utilisant des don&es d’enregistrements autographiques. Le facteur K (erodibilite du sol) a ete determine en utilisant des donnees obtenues a partir d’analyses d’echantillons de sols preleves sur 83 sites tests dans le bassin. Les facteurs LS (longueur des pentes et raideur) ont ete determines a partir d’un modele numerique du terrain (MNT) du bassin, alors que le facteur C (recolte et gestion)a ete determine a partir des donnees de couverture vegetale, obtenues a partir d’images SPOT et de leves terrain. Le facteur P (pratique de conservation) a ete estime a partir de cartes de conservation des sols, obtenues du Ministere de I’Agriculture. Un systeme d’information geographique (SIG) raster a ete utilise pour calculer interactivement le risque de perte de sol et des cartes d’erosion. Les resultats obtenus ont ete comparables aux valeurs de perte de sol mesurees a partir de traces d’erosion. A peu pres 36 pour-cent du bassin Ewaso Ng’iro Superieur a ete considere comme pre-
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