The modeling approach includes two types: regression-based and spatial
transition-based models (Lambin 1997). The spatial transition models are also called
cellular automation (CA) simulation models as these predict future land development based on probabilistic estimations such as the Monte Carlo technique (Clarke
and Gaydos 1998). The Markov model is an appropriate tool for simulating future
LULC changes (Logsdon et al. 1996). From the various studies on the Markov
model, the accuracy and the prediction accuracy of the model was 98.5% (Jianping
et al. 2005; Zhang et al. 2011).
SCS-CN Approach
The challenging tasks in the field of hydrology are prediction and quantification of
surface runoff from the catchment area. The curve number (CN) is a critical factor in
defining runoff from the Soil Conservation Service (SCS)-based hydrologic modeling method. The curve number is a function of soil type and land use (Table 3.1).
Rainfall–Runoff Model: HEC-HMS
The HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modeling System) is
an open-source software developed by the U.S. Army Corps of Engineering Hydrologic Engineering Center. It is designed to simulate the rainfall–runoff processes of a
dendritic watershed. The meteorological model in HEC-HMS is the most significant
component because it defines the meteorology boundary conditions for the subbasins. The model includes precipitation, evapotranspiration, and snowmelt methods
to be used in simulations. The control specifications in the model control the time
span of a simulation, including starting and end date and time and a time interval.
The simulation run is created by combining a basin model, a meteorological model,
and control specifications. The simulation results include information on peak flow
and total volume. Model calibration is required to obtain optimal parameters of
different methods used in subbasin and junction elements (USACE 2000).
Table 3.1 SCS value based on soil cover type
Soil type
Land use
Cultivated Pasture Forest
With above average infiltration rates; usually sandy or gravelly
0.20
0.15
0.10
With average infiltration rates; no clay pans; loams and similar
soils
0.40
0.35
0.30
With below-average infiltration rates; heavy clay soils or soils
with a clay pan near the surface; shallow soils above impervious
rock
0.50
0.45
0.40
3 Flood Risk Assessment for a Medium Size City Using Geospatial Techniques with. . .
47
transition-based models (Lambin 1997). The spatial transition models are also called
cellular automation (CA) simulation models as these predict future land development based on probabilistic estimations such as the Monte Carlo technique (Clarke
and Gaydos 1998). The Markov model is an appropriate tool for simulating future
LULC changes (Logsdon et al. 1996). From the various studies on the Markov
model, the accuracy and the prediction accuracy of the model was 98.5% (Jianping
et al. 2005; Zhang et al. 2011).
SCS-CN Approach
The challenging tasks in the field of hydrology are prediction and quantification of
surface runoff from the catchment area. The curve number (CN) is a critical factor in
defining runoff from the Soil Conservation Service (SCS)-based hydrologic modeling method. The curve number is a function of soil type and land use (Table 3.1).
Rainfall–Runoff Model: HEC-HMS
The HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modeling System) is
an open-source software developed by the U.S. Army Corps of Engineering Hydrologic Engineering Center. It is designed to simulate the rainfall–runoff processes of a
dendritic watershed. The meteorological model in HEC-HMS is the most significant
component because it defines the meteorology boundary conditions for the subbasins. The model includes precipitation, evapotranspiration, and snowmelt methods
to be used in simulations. The control specifications in the model control the time
span of a simulation, including starting and end date and time and a time interval.
The simulation run is created by combining a basin model, a meteorological model,
and control specifications. The simulation results include information on peak flow
and total volume. Model calibration is required to obtain optimal parameters of
different methods used in subbasin and junction elements (USACE 2000).
Table 3.1 SCS value based on soil cover type
Soil type
Land use
Cultivated Pasture Forest
With above average infiltration rates; usually sandy or gravelly
0.20
0.15
0.10
With average infiltration rates; no clay pans; loams and similar
soils
0.40
0.35
0.30
With below-average infiltration rates; heavy clay soils or soils
with a clay pan near the surface; shallow soils above impervious
rock
0.50
0.45
0.40
3 Flood Risk Assessment for a Medium Size City Using Geospatial Techniques with. . .
47