Simulation of Runoff for Subarnarekha …
165
3.2 Hydro-Meteorological Data
The model uses daily weather data of rainfall, maximum and minimum temperature,
solar radiation, relative humidity and wind speed for modeling and simulation. Here
11 years’ (1976 to 2005) daily rainfall data has been collected from Indian Meteorological Department (IMD) at an interval of 0.5° for 90 stations. In ArcSWAT, the user
is allowed to load the spatial location of each weather station and allocate the data
to the sub-catchments. The other weather parameters, i.e., maximum and minimum
temperature, wind speed, solar radiation, and humidity, have been acquired through
the weather generator tool of ArcSWAT model while weighted average of the gridded
rainfall data over the catchment area was fed into the model.
Prediction of runoff in SWAT model can be performed either using the Soil Conservation Services Curve Number method (SCS, 1972) or Green and Ampt infiltration
method (1911). In the present study, SCS CN method has been applied. Finally,
SWAT simulation was performed to accomplish the input database for ArcSWAT
model and run the model for monthly as well as yearly basis.
4 Results and Discussion
The relationship between hydrologic process like a rainfall event and its relationship
with the runoff resulting from it is a complex one because of the presence of a
number of climatic and catchment factors affecting the transformation of runoff from
rainfall. This paper aims to represent the framework of SWAT model for modeling
of the rainfall-runoff process. 11 years (2005–2015) of 0.25 gridded daily rainfall
data of 90 stations is used in this study. SWAT simulation was performed in yearly
and monthly basis. The annual average runoff for given annual average rainfall is
shown in Table 1. In the present study, the maximum runoff has been found in the
year 2007 and the minimum runoff in the year 2010. The monthly average rainfall
for the 11 years data and the corresponding runoff has also been estimated with
the help of this model is shown in Table 2. The graphical representation of the
annual and monthly average rainfall-runoff values has been shown in ‘Figs. 6 and 7.’
The correlation between rainfall-runoff has been performed with the 11 years data
resulting in a remarkable correlation with r
2 value of 0.9716.
5 Conclusion
SWAT model produced good simulation results of the study area for both monthly
and annual runoff. Assessment of the model performance has been accomplished
in conjunction with the statistical coefficients with observed correlation coefficient
of 0.971. In this study, remote sensing data helped a lot by serving the input data
165
3.2 Hydro-Meteorological Data
The model uses daily weather data of rainfall, maximum and minimum temperature,
solar radiation, relative humidity and wind speed for modeling and simulation. Here
11 years’ (1976 to 2005) daily rainfall data has been collected from Indian Meteorological Department (IMD) at an interval of 0.5° for 90 stations. In ArcSWAT, the user
is allowed to load the spatial location of each weather station and allocate the data
to the sub-catchments. The other weather parameters, i.e., maximum and minimum
temperature, wind speed, solar radiation, and humidity, have been acquired through
the weather generator tool of ArcSWAT model while weighted average of the gridded
rainfall data over the catchment area was fed into the model.
Prediction of runoff in SWAT model can be performed either using the Soil Conservation Services Curve Number method (SCS, 1972) or Green and Ampt infiltration
method (1911). In the present study, SCS CN method has been applied. Finally,
SWAT simulation was performed to accomplish the input database for ArcSWAT
model and run the model for monthly as well as yearly basis.
4 Results and Discussion
The relationship between hydrologic process like a rainfall event and its relationship
with the runoff resulting from it is a complex one because of the presence of a
number of climatic and catchment factors affecting the transformation of runoff from
rainfall. This paper aims to represent the framework of SWAT model for modeling
of the rainfall-runoff process. 11 years (2005–2015) of 0.25 gridded daily rainfall
data of 90 stations is used in this study. SWAT simulation was performed in yearly
and monthly basis. The annual average runoff for given annual average rainfall is
shown in Table 1. In the present study, the maximum runoff has been found in the
year 2007 and the minimum runoff in the year 2010. The monthly average rainfall
for the 11 years data and the corresponding runoff has also been estimated with
the help of this model is shown in Table 2. The graphical representation of the
annual and monthly average rainfall-runoff values has been shown in ‘Figs. 6 and 7.’
The correlation between rainfall-runoff has been performed with the 11 years data
resulting in a remarkable correlation with r
2 value of 0.9716.
5 Conclusion
SWAT model produced good simulation results of the study area for both monthly
and annual runoff. Assessment of the model performance has been accomplished
in conjunction with the statistical coefficients with observed correlation coefficient
of 0.971. In this study, remote sensing data helped a lot by serving the input data