Gudidha Gopi
Spatial mapping of flood prone areas and risk assessment of Chalakudy river basin using HEC-HMS and HEC-RAS models - Tavanur Department of Irrigation and Drainage Engineering, Kelappaji college of Agricultural Engineering and Technology 2021 - 228p.
P hD
Floods are one among the most devastating natural disasters that affects
life on the globe. For the planning and design of water resources projects in the
preferred area, planners and engineers usually require reliable estimates of flood
magnitude and frequency. Kerala state in the Indian sub continent received a
catastrophic flood in the year 2018. The present study attempts to model the flood
flows and map the flood prone areas of a river basin in Kerala. The Chalakudy
river basin, one of the worst-affected river basins due to heavy rains and floods
was selected for the present study. This is the fifth largest river in Kerala. The
basin is predominant with agricultural land and falls under the humid tropical
zone, where water resources planning and management is necessary for irrigation
scheduling, flood control and design of various engineering structures.
In order to address the above issues, an attempt was made to calibrate and
validate HEC-HMS model for simulating the flood hydrograph for the Chalakudy
river basin. Flood frequency analysis was carried out to estimate the flood peak
values using frequency distributions in HEC-SSP software. The results were
compared with the estimated flood peak values for different return periods
obtained from the HEC-HMS model. Hydraulic routing was done in HEC-RAS
model and the flood inundation maps were prepared. The cadastral level risk areas
were identified based on water surface profiles of velocity and depth of flood
extent and its characteristics. Food vulnerability maps based on land use patterns
were developed in order to identify the severely affected land uses.
The HEC-HMS model for the basin was developed using SCS-UH, SCS-
CN, Recession and Muskingum methods to find out the loss rate, runoff
transformation and routing of flood respectively. Statistical performance indices
of the model, Nash-Sutcliffe efficiency (NSE) and Coefficient of correlation (R2)
values were obtained above 0.7, Error in Peak Flow (%) and Error in Volume (%)
were figured below 20% and Root Mean Square Error-Standard Deviation Ratio
(RSR) was acquired as 0.5 and below. These values indicated that HEC-HMS
model simulation performed well in both calibration and validation. Thefrequency discharge values calculated using Log Pearson type-III distribution
indicated a high degree of similarity to the HEC-HMS generated values with an
R 2 value of 0.862. The results of the Log Normal and Gumbel distributions are
significantly lower than those of the HEC-HMS model values.
The assessment of the vulnerability due to the flooding was made with
regard to the land use pattern and cadastral level risk map of Chalakudy river
basin was developed for different return periods. Kadukutty Panchayat located in
the downstream of Chalakudy river basin was found to be the maximum flood
inundated area for 10 year return period ( 557 ha) and for 200 year return period
(681 ha). Manjapra Panchayat located in upstream was found to be the least flood
inundated area for 10 year return period (6 ha) and for 200 year return period (9
ha). Annamanada, Kadukutty, Melur and Pariyaram panchayats were under high
risk areas, with depths greater than 20 m. Ayyampuzha, Chalakudy, Mala,
Kuzhur, Parakkadavu and Puthenvelikara panchayats were under medium risk
areas with depths varying
from 10
to 20 m. Athirappilly, Manjapra and
Karukutty panchayats were under low risk areas with depths less than 10 m. The
flood vulnerability maps were generated by intersecting the flood plain land use
map with the flooded area polygons. Paddy land near to the river banks was
found to be the highest inundated by different return period floods, followed by
forest and other vegetation, barren land and other land use classes.
Irrigation and Drainage Engineering
natural disasters
Flood prone areas
631.3 / GUD/SP PhD
Spatial mapping of flood prone areas and risk assessment of Chalakudy river basin using HEC-HMS and HEC-RAS models - Tavanur Department of Irrigation and Drainage Engineering, Kelappaji college of Agricultural Engineering and Technology 2021 - 228p.
P hD
Floods are one among the most devastating natural disasters that affects
life on the globe. For the planning and design of water resources projects in the
preferred area, planners and engineers usually require reliable estimates of flood
magnitude and frequency. Kerala state in the Indian sub continent received a
catastrophic flood in the year 2018. The present study attempts to model the flood
flows and map the flood prone areas of a river basin in Kerala. The Chalakudy
river basin, one of the worst-affected river basins due to heavy rains and floods
was selected for the present study. This is the fifth largest river in Kerala. The
basin is predominant with agricultural land and falls under the humid tropical
zone, where water resources planning and management is necessary for irrigation
scheduling, flood control and design of various engineering structures.
In order to address the above issues, an attempt was made to calibrate and
validate HEC-HMS model for simulating the flood hydrograph for the Chalakudy
river basin. Flood frequency analysis was carried out to estimate the flood peak
values using frequency distributions in HEC-SSP software. The results were
compared with the estimated flood peak values for different return periods
obtained from the HEC-HMS model. Hydraulic routing was done in HEC-RAS
model and the flood inundation maps were prepared. The cadastral level risk areas
were identified based on water surface profiles of velocity and depth of flood
extent and its characteristics. Food vulnerability maps based on land use patterns
were developed in order to identify the severely affected land uses.
The HEC-HMS model for the basin was developed using SCS-UH, SCS-
CN, Recession and Muskingum methods to find out the loss rate, runoff
transformation and routing of flood respectively. Statistical performance indices
of the model, Nash-Sutcliffe efficiency (NSE) and Coefficient of correlation (R2)
values were obtained above 0.7, Error in Peak Flow (%) and Error in Volume (%)
were figured below 20% and Root Mean Square Error-Standard Deviation Ratio
(RSR) was acquired as 0.5 and below. These values indicated that HEC-HMS
model simulation performed well in both calibration and validation. Thefrequency discharge values calculated using Log Pearson type-III distribution
indicated a high degree of similarity to the HEC-HMS generated values with an
R 2 value of 0.862. The results of the Log Normal and Gumbel distributions are
significantly lower than those of the HEC-HMS model values.
The assessment of the vulnerability due to the flooding was made with
regard to the land use pattern and cadastral level risk map of Chalakudy river
basin was developed for different return periods. Kadukutty Panchayat located in
the downstream of Chalakudy river basin was found to be the maximum flood
inundated area for 10 year return period ( 557 ha) and for 200 year return period
(681 ha). Manjapra Panchayat located in upstream was found to be the least flood
inundated area for 10 year return period (6 ha) and for 200 year return period (9
ha). Annamanada, Kadukutty, Melur and Pariyaram panchayats were under high
risk areas, with depths greater than 20 m. Ayyampuzha, Chalakudy, Mala,
Kuzhur, Parakkadavu and Puthenvelikara panchayats were under medium risk
areas with depths varying
from 10
to 20 m. Athirappilly, Manjapra and
Karukutty panchayats were under low risk areas with depths less than 10 m. The
flood vulnerability maps were generated by intersecting the flood plain land use
map with the flooded area polygons. Paddy land near to the river banks was
found to be the highest inundated by different return period floods, followed by
forest and other vegetation, barren land and other land use classes.
Irrigation and Drainage Engineering
natural disasters
Flood prone areas
631.3 / GUD/SP PhD