INTRODUCTION
Soil erosion, continues to be a global constraint
to economic development. Despite decades of efforts to arrest soil erosion,
many farmers are reluctant or unable to adopt appropriate land-use practices.
Often these practices fail to combine high productivity, increased soil
fertility, reduced soil erosion and enhanced welfare. Still, soil conservation
is proposed as a viable route to obtain these objectives (Ekbom 1998).
The negative changes in soil quality is a worldwide concern, especially
in developing countries where soil erosion is becoming a limiting factor
in increasing or even sustaining agricultural production. Heenrink et
al. (2001) describe that 38% of the world`s agricultural land is degraded,
while in Africa and Central America, the share of degraded land in total
agricultural land is as high as 65 and 74%, respectively. South America`s
agricultural degraded land is around 45%.
It is estimated that in India out of a total geographic
area of 328 million hectare, about 187 million hectare are subject to
varying degrees of water erosion problems (Anonymous, 1996). It has been
observed that the trap efficiency of Indian reservoirs is about 90%. The
heavy sedimentation of these reservoirs/dams has not only resulted into
drastic reduction in their live storage capacity but also resulted into
a reduction of their lifespan and carrying capacity to just 1/4th of what
was assumed at the time these were designed thereby leading to an equally
alarming situation of serious floods in the country. Floods hit India
almost every year. It has been estimated that about 5334 million tones
of soil is lost annually due to runoff and associated activities thereby
leading to an annual average national soil erosion rate of about 16.75
t ha-1 year-1 against the permissible level of 7.5
to 12.5 tones for various regions (Bhan, 1997). Further, it has been assessed
that annually about 8.4 metric tones of soil nutrient lost due to soil
erosion problem are much greater than the quantity we are using at present
in Indian agriculture (Singh and Poonia, 2003). Due to this, in terms
of annual food grain production, soil erosion accounts for a total productivity
loss of about 40 million tones. Thus, accurate soil loss estimates are
extremely important for designing appropriate resource managing and soil/water
conserving measures.
Well-validated watershed scale hydrologic models are
excellent predictive tools for obtaining accurate sediment yield estimates
from agro-ecologically diverse watersheds. In this context Universal Soil
Loss Equation (USLE) (Wischmeier and Smith, 1978), due to its dependence
on easily available soil, topographic and vegetation data, has emerged
as the most commonly used soil loss estimating model. As USLE predicts
gross soil loss, it needs to be multiplied by a sediment delivery ratio
to give sediment yield estimates. It has been shown that delivery ratios
for determining sediment yields from soil loss predictions suffer from
uncertainty due to considerable variations in rainfall distributions with
time. Due to uncertainty in delivery ratio and inability of USLE model
to give direct sediment yield estimates, Modified Universal Soil Loss
Equation (MUSLE) was proposed by Williams and Berndt (1977). However,
in contrast to USLE, MUSLE model has been generally used to predict sediment
yields on single storm basis. Although there are some reports that it
can also be used to predict sediment yields on annual basis by determining
soil loss for events at varying return periods (Simons and Senturk, 1992).
Yet, it has not been widely applied for obtaining long term annual sediment
yield estimates. Further, it is observed that USLE model generally gives
highly overestimated soil loss for small sized drainage basins (Weggel
and Rustom, 1992). However whether the same holds true for the MUSLE model
also has not been tested widely.
Thus with this in background the present investigation
was mainly aimed at Testing long term annual sediment yield estimating
potential of MUSLE model on two hilly micro-watersheds in Almora district
of Uttaranchal, India.
MATERIALS AND METHODS
To meet the proposed objective two gauged micro-watersheds viz.,
Salla Rautella (0.47 km2) and Naula (0.42 km2),
with continuous rainfall and discharge records for 6 years (i.e., 1991-93
and 1996-98), were selected. These micro-watersheds, extending between
29°35’05” N to 29° 35’30” N latitudes
and 79°33’10” E to 79°33’ 33” longitudes,
are situated about 32-38 km northwest of Almora town of Uttaranchal (Fig.
1a). Dense (reserved) pine (Pinus roxburghii) and oak (Banj
sp.) forests are the major land use types for the Salla Rautella and Naula
micro-watersheds, respectively. Their absolute relief and average slope
are about 1650 and 2190 m above m.s.l. and 26.4° and 25.0°, respectively.
The soils of these test watersheds are in general of sandy loam to loamy
sand texture with average organic matter content of 0.74-1.40%, saturated
hydraulic conductivity of 134-235 mm h-1 and volumetric soil
moisture contents at field capacity and saturation as 15 and 30%, respectively
(Kaur et al., 2002). In general, the climate of this region is
sub-temperate with moderate summers (18-22°C), a short spell (about
2 months) of chilling winter and general dryness, except during southwest
monsoon season. Average annual rainfall for the Salla Rautella (Pine forest)
and Naula (Oak forest) micro-watersheds stands at 927 and 981 mm, respectively
(Rawat et al., 1999).
MUSLE model input data generation: The sediment yield (Y, tones) in Modified Universal Soil
Loss Equation (MUSLE) is in general expressed as (Williams and Berndt,
1977):
| Q |
= |
Total runoff volume (in m3) |
| qp |
= |
Peak runoff rate in (m3 sec-1) |
| K |
= |
Soil erodibility factor |
| LS |
= |
Slope length and gradient factor |
| Table 1: |
Annual total run-off
volumes and peak run-off rates for Salla Rautella (Pine forest)
and Naula (Oak forest) micro-watersheds |
 |
C=Cropping management
factor
P=Erosion control practice factor |
These input parameters were estimated as per the following
procedures.
Run-off volume (Q) and peak run-off rate (qp) estimation:
Annual run-off volume (Q, m3) and peak runoff rate (qp,
m3 sec-1) for 1991-93 and 1996-98 years was obtained
from weekly stage hydrograph data obtained through water level stage recorder
installed at the mouth of each watershed (Rawat et al., 1999).
The annual total run-off and peak run-off rates for Salla Rautella (Pine
forest) and Naula (Oak forest) micro-watersheds was show in Table
1.
Soil erodibility factor (K) estimation: The soil erodibility factor (K) represents average soil
loss from a specific area of soil in cultivated continuous fallow with
a standard plot length as 22.13 meters and a standard percentage slope
as 9%. It varies from 0.70 for the most fragile soil to 0.01 for the most
stable soil. The K factor was determined through a particle size, organic
matter, soil structure and permeability data based soil erodibility nomograph
(Johnson et al., 1984). The following formula was used to evaluate
the nomograph readings:
| M |
= |
particle size diameter = {(%silt + %very fine sand)x(100 - %clay)} |
| a |
= |
Percent organic matter |
| b |
= |
Soil structure code |
| c |
= |
Profile permeability class |
In the present study, the above %sand, %silt, %clay, %organic matter,
soil structure and soil permeability data for the test watersheds (Table
2) were obtained from Kaur et al. (2002).
GIS based topographic factor (LS) estimation: The slope length
and gradient factor (LS) is defined as the ratio of soil loss from any
slope length and gradient to soil loss
 |
| Fig 1: |
Location of test Salla Rautella (Pine
forest) and Naula (Oak forest) micro-watersheds and their drainage
networks |
| Table 2: |
General soil characteristics of test micro-watersheds |
 |
from a 22.13 m plot with 9% slope and same soil type
and other conditions. It varies from 0.1 to 5 in the most frequent farming
contexts in West Africa and may reach 20 in mountainous areas. This factor
is defined by the multiplication of the L and S-factors (Moore and Burch,
1986), where:
Here, Lai is the mean overland flow length
(in m) of an ith order test watershed and is computed probabilistically
(Rodriguez-Iturbe and Valdes, 1979), based on the GIS interface derived
total watershed area, its stream number, order and length, in the following
manner:
| A |
= |
Test watershed area (in m2), PoAi
(i.e., initial state probability) is the probability of a raindrop
to (initially) fall on an ith order overland region and is equal to
the ratio of the total area of ith order overland region to the total
watershed area |
| Ni |
= |
No. of ith order streams |
| Lci |
= |
Mean ith order channel length (m) and m is a slope dependent exponent
computed as (McCool et al., 1989) |
Where, θ = slope of test watershed in degrees =
tan-1 (watershed slope in %/100). While, slope gradient (S)
factor was computed as
s = Mean slope (in %) of test watersheds
In the present study, this topographic data for each test watershed was
obtained through avenue programming of above procedures in an Arc View/Arc
View Spatial Analyst interface (ESRI, 1999). For this, firstly the two
micro-watersheds and their sub-watersheds and drainage networks were digitally
delineated from the test area`s digital elevation model (Kaur and Dutta,
2002). Figure 1b gives a pictorial depiction of the delineated test watersheds
and their drainage networks. Each sub
| Table 3: |
GIS interfaced geomorphologic parameters Salla Rautella
and Naula micro-watersheds |
 |
| Table 4: |
Observed VS MUSLE model simulated sediment
yields for Salla Rautella and Naula micro-watersheds |
 |
watershed within any test watershed was identified by the order of the
stream flowing through it. Streams were ordered as per Strahler`s (1952)
stream ordering method. Table 3 shows these GIS interface
derived geomorphologic parameters for the two test micro-watersheds.
Crop management and conservation practice factor (CP)
estimation: The cropping management factor (C) represents the
ratio of soil loss from land with specific cropping and management to
that from tilled and fallow conditions on which the K factor is evaluated.
The C-factor, also called the cover and management factor, generally varies
from 1 for bare soil to 1/1000 for forest, 1/100 for grasslands and cover
plants and 1 to 9/10 for root and tuber crops. The erosion-control-practice
factor (P), on the other hand, represents the effect of conservation practices.
The P factor is determined as the ratio of soil loss using one of the
conservation practices to the soil loss using straight row farming. The
P factor for straight row farming is always equal to unity. It generally
varies from 1 for bare soil with no erosion control to about 1/10 for
tied ridging on a gentle slope.
In the present study, average CP-factors for the two
micro-watersheds were determined through inverse modelling on annual hydrologic
and sediment yield records for 1991 and 1992. This resulted into CP values
of 0.008 and 0.03 for the Salla Rautella and Naula micro-watersheds, respectively.
As the two test watersheds with pine and oak forests had no conservation
practices, therefore the above CP values can also be treated as C-factor
values for the two test watersheds. These crop management factor values,
obtained through inverse modelling technique, for the two test watersheds
were found to be quite close to those given by Jianguo (2001) for the
mixed pine and oak tree (coniferous) -forests.
MUSLE model validation and evaluation: The sediment yields estimated through above procedures
were validated on four years (i.e., 1993 and 1996-1998) annual sediment
yield records for Salla Rautella (Pine forest) and Naula (Oak forest)
micro-watersheds. Long term annual sediment yield estimating potential
of MUSLE model on micro-watersheds was evaluated in terms of mean relative
errors (Green and Stephenson, 1986). Relative Errors (RE) were expressed
as:
| S |
= |
MUSLE simulated sediment yield (tons) |
| O |
= |
Observed sediment yield (tons) |
RESULTS AND DISCUSSION
Long term annual sediment yield estimates through MUSLE: The above
analysis showed that Salla Rautella is first order micro-watershed while
Naula is second order micro-watershed (Table 3). It was
further observed that as expected Naula micro-watershed, with higher LS-factor
value (28.71) than Salla Rautella micro-watershed (25.40), was associated
with higher sediment yields (Table 4). It could be clearly
observed from Table 4 that MUSLE model estimated sediment yields were
associated with about (-) 12 to 14% overall mean relative errors. Comparison
of actually observed mean and standard deviation values for annual sediment
yields from test micro-watersheds (i.e., Salla Rautella: mean = 9.58 tons;
STD = 4.10 tons and Naula: mean = 23.89 tons; STD = 5.10 tons) with their
corresponding simulated values (i.e., Salla Rautella: mean = 10.92 tons;
STD = 4.67 tons and Naula: mean = 26.61tons; STD = 6.37 tons) further
showed that long term annual sediment yields could be quite realistically
simulated by the MUSLE model. The present study could thus clearly show
that MUSLE model can give accurate long-term average annual sediment yield
estimates for micro-watersheds.
CONCLUSIONS
In Indian, there are about 3297 watersheds, of which
about 70% are un-gauged of remaining 30% watersheds that are gauged, very
few have functional gauging stations. As most of the Indian watersheds
are un-gauged or inadequately gauged, therefore a successful application
of the MUSLE model for long-term annual sediment yield estimation on two
Indian micro-watersheds in fact projected its tremendous application potential
for the evolution of a decentralized planning process, at micro-watersheds
scales, in the country.
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