2.3.2 WATEM/SEDEM input data
The rainfall erosivity factor (R) was derived from the relationship
between annual rainfall and annual erosivity expressed in MJ mm
m−2 h−1 y−1. R
expresses the aggressivity of rainfall and kinetic energy (KE) is
generally suggested to indicate the ability of a raindrop to detach soil
particles from a soil mass (Nearing et al., 2005; Renard, 1997). R was
calculated using the following formula proposed by Nearing et al.
(2005); Renard (1997):
R=\(\frac{\sum_{i=1}^{j}{\left(E*I30\right)i}}{N}\) ( 8)
Where,
E = the total storm energy (MJ)
I30 = the maximum 30-minute intensity (mm
h-1) of daily rainfall i,
j = the number of rain events in an N year period
N = the number of observation years.
Total storm energy was determined using:
E = 1.213 + 0.89 log I ( 9)
Where,
E = is kinetic energy of each rainstorm KJ m-2mm-1
I = is average intensity of daily rainfall i.
The erodibility (t h MJ-1 mm-1) of
the soil was calculated from soil properties using the following
equation proposed by N. Hudson (1993); Renard (1997); Wischmeier and
Smith (1978):
K = [(2.1 * 10-4 M1.14 (12-OM)) +
3.25 (S-2) + 2.5 (P-3)] / 7.59 (10)
Where,
M (the textural factor) = (%silt + %sand) * (100 - %clay);
OM= % organic matter
S and P are field-determined average values of aggregate structure and
permeability classes described as follows:
S = Aggregate/structural class with values (1-4): 1 for very fine, 2 for
fine, 3 for medium coarse, 4 for massive structure respectively, and
P = Permeable class with values (1-6)]: 1 for fast, 2 for fast to
moderate, 3 for moderate, 4 for low to moderate, 5 for low, and 6 for
very low permeability respectively.
The remaining RUSLE parameters, crop management (C) factor and erosion
control factor (P) factor, were consulted from the literature (H Hurni,
1985; Renard, 1997). Annual values of the C-factor were determined based
on the land use types defined by a previous study (H Hurni, 1985), and
spatially attributed based on the current land cover (Renald, 1997). The
P–factor values were determined based on the types of SWC measures
implemented in different areas (Table 1). The P-factor represents the
ratio of soil loss with conservation measures to a reference plot
without conservation measures: a value of one refers to a cultivated
land without conservation practice.
Table 1 approximately here
The P-factor considers the interaction of LS (i,j) attributes and
vegetation cover (C-factor), as well as the direction of flow and TC,
which depends on the type and effectiveness of physical conservation
measures (Foster, 2002; Renard, 1997). Therefore, P–factor and C-factor
values were used in the WATEM/SEDEM model to verify the responses of
different SWC scenarios to soil erosion and sediment yield in the study
sub-watersheds. The P-factor values for the different supporting
conservation practices were adoptable to local study sites environmental
contexts (Foster, 2002; Renard, 1997; Wischmeier & Smith, 1978) and
thus the compounding P-factor for different scenarios of SWC measures
were calculated as follows:
P =PB * PCC * PSC *
PGS (11)
Where PB, PCC, PSC and
PGS are conservation practice sub-factors for bund
structures, contour cultivation, strip cropping and grass strips
respectively (Table 1). The Parcel trap efficiency in WATEM/SEDEM, Ptef,
refers to the way each pixel’s runoff contribution to the upstream
contributing area is reduced. This means that for different land use
types, less runoff is simulated, thereby decreasing downstream LS (i,j)
values and erosion rate by Ptef (Van Oost et al., 2000; A. J. Van
Rompaey et al., 2001). Ptef values of 10 for cultivated lands and 75 for
pasture and forests were chosen based on the optimal or KTC(h) during
model calibration.
A DEM with 20m resolution was derived from global SRTM topographic data
by resampling 30 m resolution and the land use map was created from
Landsat GLSTM_2000 image. Besides, a parcel map was created by the
model by combining the DEM, stream, road, forest, arable land, pasture
and catchment area delineations to account for the effect of landscape
structure on soil erosion and sedimentation processes (Van Oost et al.,
2000). A parcel map is a reclassified land use map that takes a
distinction between, arable land, forest, pasture, roads,
infrastructures, rivers and build-up areas. This makes it possible to
incorporate the effect of field borders on runoff diversion, runoff
interception, erosion and sediment deposition (A. Van Rompaey, Krasa, &
Dostal, 2007; A. J. Van Rompaey et al., 2001).
2.4 SWC scenarios
The SWC scenarios evaluated in this study were developed based on a
biophysical inventory, farmers’ perception (Jemberu, Baartman, Fleskens,
& Ritsema, 2018) and the regional government’s five year strategy
program in the study sub-watersheds to select promising conservation
strategies. Three conservation strategies, including physical (bund
structures), agronomic (strip cropping and contour cultivation) and
vegetative (grass strips) measures were created for the three
sub-watersheds of Koga catchment to determine where each type of or
combination of SWC measures can be implemented. Consequently, soil
erosion, sediment deposition and sediment yields were simulated for five
alternative scenarios of SWC measures described as follows. Scenario I:
a baseline condition (present-day situation) including existing bund
structures; Scenario II: existing bund structures and contour
cultivation; Scenario III: combination of bunds, contour cultivation and
strip cropping; Scenario IV: integrated use of bunds, contour
cultivation, strip cropping and grass strips; and Scenario V: a scenario
without SWC practices (Table 2).
Table 2 Approximately here
2.5 Model calibration
We calibrated and validated WATEM/SEDEM for this study based on sediment
yields measured at the three study sub-watersheds of Koga catchment from
2016 to 2017. The sediment yield data of 2016 was used for calibration
and that of 2017 to validate the performance of the model. The model was
calibrated based on area-specific and absolute sediment yield (by
minimizing the difference between measured and simulated values) since
the objective of the study was to assess the effect of SWC measures on
soil loss and sedimentary processes for various SWC scenarios at
sub-watershed level. The model was first calibrated for the baseline
scenario (scenario I) by changing the maximum and minimum values of the
KTC or KTC (h) and KTC (l). The model was sensitive to KTC (h) and less
sensitive to KTC(l) whereas the model was insensitive to the threshold
KTC(t) value. The KTC(t) was set at 0.1 for arable and 0.01 for
non-arable land uses for all sub-watersheds. After calibration, the
model was run for the four remaining SWC scenarios.
The Nash-Sutcliff efficiency (NSE) statistic was applied to evaluate the
efficiency of the model. NSE is a normalized statistic determining the
relative magnitude of residual variance (between predicted and observed
values) compared with the measured data variance. NSE values between 0
and 1.0 demonstrate model efficiency; the closer the value of NSE
approaches 1, the more efficient is the model. A satisfactory model
should have NSE >0.50 (Baartman, Jetten, Ritsema, & Vente,
2012). The accuracy of the model in predicting soil erosion for these
scenarios was also assessed qualitatively by relating to previous
studies on soil erosion (Jemberu et al., 2018) and available literature
for similar areas (Bewket & Sterk, 2003; Herweg & Ludi, 1999; Mitiku
et al., 2006; Nyssen et al., 2010).