From the study it was observed that the flood depth of 2–10 cm is not a serious threat
but a flood depth of 10–100 cm presents a significant issue because of the tidal effect.
Apart from the tidal effect, increase in imperviousness, urbanization, and decrease in
flow length have contributed greatly to flood risk. Finally, the study concludes with
appropriate infrastructure and by improving the infiltration capacity of runoff by
optimized drainage systems (Dang and Kumar 2017).
West Bank of Palestine
The most challenging task in the hydrology field is prediction and quantification of
surface runoff. The CN is a key factor in determining surface runoff through the
SCS-based hydrologic method. The traditional method of calculating CN is tedious
and time consuming in hydrologic modeling. Therefore, the SCS method combined
with GIS is used for calculating surface runoff. In this study, the hydrologic
modeling flow of the West Bank catchment is analyzed by the SCS-CN method.
The West Bank of Palestine is an arid to semiarid region with yearly rainfall of
100 mm to 700 mm from the Jordan River to the central part of the region. The
calculated composite CN for the entire West Bank is assumed to be 50 for dry
conditions in the basin. This study clearly proves that combining GIS with the SCS
method is a powerful tool for estimating runoff volume in the basin (Shadeed and
Almasri 2010).
Marand Basin, Iran
The LULC change and its impacts on peak runoff in Marand basin, Iran, were
studied using remote sensing with GIS techniques. The runoff coefficient was
estimated from the LULC extracted satellite images; slope map, hydrologic soil
groups, rainfall intensity, and peak runoff for each subbasin were calculated. In this
study by using linear function in fuzzy logic model and integration of two layers of
peak runoff and elevation line, layers between 0 and 1 were transformed into fuzzy
values. Then, by multiple overlap weights with these two layers, different classes of
flood hazard map were developed. The flood hazard map is compared with participatory Geographic Information System (PGIS) and transferring the information to a
confusing matrix; the accuracy of the flood map generated was 87.83%, these maps
were compared with a LULC map, and the flood extent was determined (Mousavi
et al. 2019).
South Carolina
In this study, the United States Geological Survey (USGS) hydrologic units of three
different data sets were used. In the first data set, rainfall is observed at individual
meteorological gauges, and the second dataset was from the National Centers for
52
S. Natarajan and N. Radhakrishnan
but a flood depth of 10–100 cm presents a significant issue because of the tidal effect.
Apart from the tidal effect, increase in imperviousness, urbanization, and decrease in
flow length have contributed greatly to flood risk. Finally, the study concludes with
appropriate infrastructure and by improving the infiltration capacity of runoff by
optimized drainage systems (Dang and Kumar 2017).
West Bank of Palestine
The most challenging task in the hydrology field is prediction and quantification of
surface runoff. The CN is a key factor in determining surface runoff through the
SCS-based hydrologic method. The traditional method of calculating CN is tedious
and time consuming in hydrologic modeling. Therefore, the SCS method combined
with GIS is used for calculating surface runoff. In this study, the hydrologic
modeling flow of the West Bank catchment is analyzed by the SCS-CN method.
The West Bank of Palestine is an arid to semiarid region with yearly rainfall of
100 mm to 700 mm from the Jordan River to the central part of the region. The
calculated composite CN for the entire West Bank is assumed to be 50 for dry
conditions in the basin. This study clearly proves that combining GIS with the SCS
method is a powerful tool for estimating runoff volume in the basin (Shadeed and
Almasri 2010).
Marand Basin, Iran
The LULC change and its impacts on peak runoff in Marand basin, Iran, were
studied using remote sensing with GIS techniques. The runoff coefficient was
estimated from the LULC extracted satellite images; slope map, hydrologic soil
groups, rainfall intensity, and peak runoff for each subbasin were calculated. In this
study by using linear function in fuzzy logic model and integration of two layers of
peak runoff and elevation line, layers between 0 and 1 were transformed into fuzzy
values. Then, by multiple overlap weights with these two layers, different classes of
flood hazard map were developed. The flood hazard map is compared with participatory Geographic Information System (PGIS) and transferring the information to a
confusing matrix; the accuracy of the flood map generated was 87.83%, these maps
were compared with a LULC map, and the flood extent was determined (Mousavi
et al. 2019).
South Carolina
In this study, the United States Geological Survey (USGS) hydrologic units of three
different data sets were used. In the first data set, rainfall is observed at individual
meteorological gauges, and the second dataset was from the National Centers for
52
S. Natarajan and N. Radhakrishnan