conditions for a suitable data collection system and is time
cónsuming. Remote sensing as an alternate data collection
system, has the following advantages:
1. It produces areal measurements instead of point
measurements
All information is collected and stored at one place
It can offer high resolution in space and/or time
Data are available in digital form
Data acquisition does not interfere with data observation
Data can be gathered for remote areas that are otherwise
inaccessible
SD MP oly
Erosion hazard estimation can be done in both qualitative &
quantitative way. The qualitative erosion estimation is intended
to model soil erosion on the basis of the geomorphologic
processes and the relationship between landscapes and soil
erosion. However, the quantitative erosion prediction models
are based on physical laws. It includes the process of
detachment by raindrop impact, infiltration, runoff, detachment
by flow, transport by raindrop impact, transport, sediment and
deposition by flow (Suryana, 1996). These physical based
models are classified on the basis of empirical based and
process based:
* Empirical lumped model based on statistical observation
and experiments not taking into account spatial variability
of the variables and parameters used e.g. USLE
* Empirical distributed model based on statistical
observations and experiments considering spatial
variability e.g. SEDIMOT
* Conceptual lumped models based on physical laws not
taking into account spatial variability of the variables and
parameters used e.g. CREAMS
* Conceptual distributed model based on physical laws
considering spatial variability from place to place. These
models are based on the assumption that the soil
parameters differ from place to place. Furthermore these
models are based on physical laws what makes
extrapolation to other areas possible e.g. ANSWERS,
WEPP and LISEM
‘
Choosing a model must be based on the level of application
(national level, watershed or sub watershed level), the required
accuracy, and the availability of data for the models (Eppink,
1995). The category of conceptual distributed model is of
interest to our study since they are based on physical principles
and spatial dimension. Furthermore they can be extrapolated to
a range of conditions where testing is very hard to implement
and economically not feasible.
Spatially _ distributed models of watershed hydrological
processes have been developed to incorporate the spatial
patterns of terrain, soils and vegetation as estimated with the
use of remote sensing and Geographic information system
(GIS) (Band et al., 1993; Famiglietti and Wood, 1991; 1994;
Moore and Grayson, 1991; Moore et al., 1993; Wigmosta et al.,
1994; Star et al., 1997). Land surfaces attributes are mapped
into the watershed structure as estimated from remote sensing
imagery (e.g. canopy leaf area index), digital data (slope,
aspect, contributing drainage area) or from digitised soil maps,
such as soil texture or hydraulic conductivity assigned by soil
series. The optical data sets may be used to distinguish
vegetation types but not soils due to the exception that remote
sensing often cannot provide critical information about soil
properties especially if the soil is obscured by a vegetation
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India, 2002
742
canopy (Band and Moore, 1995). Microwave data seems to be
. best suited for the estimation of the hydrological state of the
soil mainly because of the all weather capability and the
sensor’s sensitivity to the dielectric properties of the surface,
which is linked to the moisture content (Van Oevelen, 2000).
Substantial progress has been made in estimating near- surface
and profile soil water content with active and passive
microwave sensors and in the estimation of hydraulic properties
by model inversion e.g., Entekabi er al., 1994) However, in
general soil spatial information is the least known of the land
surface attributes relative to its well-known spatial variability
(Nielsen and Bouma, 1985). In addition microwaves are °
sensitive to vegetation structure and moisture, which could
allow the estimation of vegetation parameters. Since
topography is one of the main parameters to derive the
conceptual based distributed model, SAR interferometry can
play important role to derive precise accuracy Digital Elevation
Model. An interferometric radar technique for topographic
mapping of surfaces promises a high-resolution approach for
the generation of DEM (Zebker et al, 1994).
After extensive reviews and evaluation of existing hydrologic
models used in the analysis of the soil erosion studies on small
scale, it was decided that KINEROS model is the best-suited
model for hydrological modelling. It is suitable for a smaller
scale and more focussed. It helps in detailed investigations of
runoff and soil erosion because it is distributed, event-oriented,
physically based model describing the processes of surface
runoff and erosion from small watersheds. However, the greater
complexity of KINEROS also entails greater data requirements.
In addition, KINEROS has a specially developed space-time
rainfall interpolator that allows it accurate treatment of highly
variable thunderstorm rainfall. KINEROS infiltration and
erosion parameters are primarily derived through soil
characteristics with modifications made for surface cover
conditions.
The experimental site set up in the study area will provide the
required data for hydrological modelling. Different parameters
derived from the field as well as from the remote sensing data
(optical and microwave) will be included in the dataset.
2. STUDY AREA
The project area commonly known as Sitla Rao Watershed lies
in Lesser Himalaya of India, which is a part of Northern India.
It receives an annual rainfall of 1600mm to 2200 mm/year,
which varies on different elevations and most of the rain
received in the monsoon season (June to August). Three field
sites has been selected with respect to the elevation zoné i.e.
high, low and medium for the installation of rain gauges to
record rainfall data on hourly basis. The geomorphology of the
area constitutes a chain of erosion hills, extensive piedmonts
and river terraces. The landuse/landcover is characterised by
forest, agriculture, barren land, rivers and fallows etc. The soil
distribution varies from gravelly loam to loamy sand. The
majority of soil is loamy and gravelly sandy loam. The
geographical dimension extends from 30? 24' 39" N to 30?
29'05"' N latitude and 77245'33"' E to 7755746" E longitude
covering an area of about 57.59 km?
3. PROBLEM DEFINITION
It is a widely accepted fact that in order to perform any
modeling, following crises do occur (Stoosnijder, 2000)
e adata crisis
e amodelcrisis and
