Paper ID: 159
IDENTIFICATION OF FLOOD HOTSPOTS IN CHATTOGRAM CITY DRAINAGE SYSTEM
1M.A.H. Chowdhury & 2A. Akter
1 Department of Civil Engineering, Chittagong University of Engineering & Technology (CUET), Chattogram 4349, Bangladesh,
ABSTRACT Urban flooding has become an inevitable problem which is worryingly widespread in Chattogram City during monsoon period. Drainage congestion and increasing rainfall pattern are playing a key role in worsening the flooding situation for city dwellers. Based on field survey 17 locations of the city were found most vulnerable to flooding and waterlogging, with a field observed inundation depth of 0.3m to 1.8m. Hydrologic and hydraulic analysis were carried out for the years 2010 to 2016 to assess the flooding extent of the area. The aim of this study is to estimate the volume of peak runoff of 22 subcatchments of the Chattogram City, predicting magnitude of flood peaks using Gumbel’s method for a return period of 10 years, and to identify the flooding hotspots. Hydrologic modeling system of HEC- HMS was used for peak runoff calculation. Flood hotspots were identified using simulated flooded junctions and surcharged conduits. The flooded locations indicated by model simulations match with 17 flood prone areas obtained from field survey and literature review. Flood maps showing flood hotspots were prepared to represent flood affected areas. Lack of proper urbanization, inadequate drainage facilities, and clogging of drains due to human waste disposal were prime causes identified for inefficient disposal of excess runoff from heavy storm events. This study is expected to give proper guidance to development authority and city planners to take measures in identified flood hotspots for improving this flooding situation.
Keywords: Urban flooding, Hydrologic Modeling, Hydraulic Modeling, Flood hotspots mapping
INTRODUCTION Chattogram City in Bangladesh known as the noteworthy seaside seaport city in southeastern part is situated on the bank of the Karnaphuli River. Due to seasonal variations especially in the rainy season, this city faces flooding in the city areas as a result of excessive downpours. Analysis of the possible causes of this frequent waterlogging condition reveals blockage of drains (some of which are natural and some are caused by human interventions) and increased imperviousness because of urbanization in most of the parts of urban area (Anisha and Hossain, 2014). Though clogged drains are cleared occasionally to facilitate the flow of storm water, imperviousness percentage is in constant rise owing to proportional population growth. Increased urban flood is experienced in Chattogram City in recent years and so as the inundated locations. About 60% of the existing drains are also existed in 2006 master plan. With almost 3% of urbanization rate and change in land uses in due course, inundation experiences rather recent years in many parts of the city (CWASA, 2015). In addition to heavy rainfall and urbanization, three major issues involved in increased urban flood experiences: Lack of details knowledge on hydrological cycle due to the change in rainfall patterns as well as land uses. Water in flooded areas remains stagnant for 2 to 3 days. Thus, there is a knowledge gap on the rainfall-runoff relationships those are generating inundations in low lying areas around the city; Absence of hydraulic assessment in terms of conveyance capacity of the existing roadside drains having constant width of 0.3 to 0.9 m. Therefore, critical drain intersects remain unknown; and Knowledge gap on flood hotspots around the city due to absence of flood hazard map. The aim of this study is to estimate the volume of peak runoff of 22 subcatchments of the Chattogram City, predicting magnitude of flood peaks using Gumbel’s method for a return period of 10 years, and to identify the flooding hotspots. A hydrological model simulating rainfall-runoff process and a hydraulic model representing the flooding situations in the city drainage network may help in identifying the flood prone areas.Thus, user friendly tools viz., Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)) are used around the world to feature the generated surface runoff (Haile et al., 2016). To reduce urban floods, these surface runoffs are expected to be disposed properly. If unplanned urbanization happens, the immediate action required for drainage network improvement. In this connection, hydraulic model study can assess the conveyance capacity of proper disposal route. Once the flood hotspots are known to the stakeholders, their decision and action against urban flood can be taken.
METHODOLOGY The whole Chattogram City was divided into several sub catchments. Spatial heterogeneity of the subcatchments developed were captured as input parameters (e.g., land use, slope, percent impervious, drainage network, etc.) for different GIS layers. The generated parameters were incorporated in modeling software to perform hydrologic simulation using HEC-HMS and the model performance was evaluated by comparing the simulated output with the observed data. Output of hydrologic simulation was then instilled in a hydraulic model to analyze flow through drainage networks and to identify flood hotspots. Actual drainage networks of the selected flood locations were used in the model studies. [Fig. 1]: Identified flood prone areas
For model setup flooded areas in Chattogram City were identified through field survey as well as from literatures [Fig. 1]. The daily precipitation data was collected from the Bangladesh Meteorological Department (BMD) during 2010 to 2016. A 30m Digital Elevation Model (DEM) was acquired from the Shuttle Radar Topography Mission. Drainage network of Chattogram City area was collected and upgraded in flood prone locations. During field visits drainage information were accumulated for instance drainage physical condition, cross-sections, newly constructed drains and flooding depth of the waterlogged areas. Field survey was conducted during 2016 and 2017 for identifying the flood inundated areas. Based on the flood intensity, duration and inundated areas, flood prone areas were identified. As per literatures and experiences there are four identified reasons of floods in Chattogram, i.e.: waterlogging in urban areas, overflow from the khals, overflow from roadside drains and tidal induced floods.
Table 1: Input data for Modeling Parameter Duration Details Source Rainfall 2010-2016 Daily precipitation data BMD Temperature 2010-2016 Daily temperature data BMD DEM - 30m resolution USGS (2017) Drainage 2006 Upgraded according to field survey CDA Network Ward boundary, contour, roads, different Spatial data 2006 structures, water bodies, drains, open CDA and CCC area, rivers etc.
HYDROLOGIC MODEL SETUP The terrain preprocessing tools of Arc Hydro and HEC-GeoHMS, extensions of ArcGIS 10.1 was used to delineate the subcatchments and the stream network of the HEC-HMS model from the DEM. HEC-HMS model including subcatchment properties, meteorological model, time series data, control specifications were provided to estimate stream flow runoff for each subcatchments using hydrologic parameters extracted through terrain processing (ESRI, 2012). After completion of the preprocessing in GIS, the exported basin model was transported in HEC-HMS environment to complete the remaining parameters inclusion and simulate for the rainfall-runoff process [Fig. 2]. Analyzing the soil profile data collected from CDA, 13 subcatchments out of 22 in the whole study area have presence of sand with different compositions like fine sand with little silt, sandy clay loam, medium to fine sand little silty and fine sand with silt. Setting up the model in HEC-HMS requires editing of four basic components, i.e. basin model, meteorological model, control specifications and time series data (Mandal and Chakrabarty, 2016). As transform method, SCS unit hydrograph method was chosen for simulation and the lag times were calculated in this regard. The calculation of the lag time for basin was based on the following equation:
0.7 1000 2.587 L0.8 9 CN t (hour) (Schwab et al. lag 1900 H 0.5 1993) (1) Where, L (m) = Hydraulic watershed length= 110A0.6, A (ha) = Subcatchment area, CN= Curve number, H (%) = Average subcatchment land slope Lag time for each reach element in the catchment was calculated using the following equation: l0.65 Lag time, T1 (hr.) = (Schwab et al. 1993) (2) 83.4 [Fig. 2]: HEC-HMS model setup
Where, l(m) = largest hydraulic length and this was determined by clipping the sub-basins for natural streams using GIS.
The subcatchments properties extracted using GIS processing tool and the meteorological files i.e. precipitation data from the year 2010 to 2016 and the final schematized HEC-HMS model was used for hydrologic simulations in this study. The simulation of the HEC-HMS model is carried out for years 2010 to 2016. The calibration of the model was performed for three different reaches i.e. Chaktai khal, Rajakhali khal, and Mahesh khal comparing the observed daily flow data of year 2014 to the simulated data of that year with reasonable responses of R2 as 0.782, 0.768 and 0.719 respectively [Fig.3a and b]. Model validations were performed for the same three reaches for the measured flow data of 2016. Reasonable peak flow data in the calibration and validation graphs indicate a high-level relation between them (Dodge 2008). R2 value of 0.78 corresponds to 78% of the monitored data can be reproduced by the model for all the rainfall events. The results were obtained exhibit few deviations from the measured field data which seems fairly satisfactory.
130 130
120 /s 120
3 /s 3 y = 0.9217x + 7.7971
y = 0.9204x + 7.4039 m R² = 0.782 R² = 0.765 110 110
100 100 Chaktai khal Chaktai khal 90 90
Observed Discharge, Observed Discharge, 80 Observed Discharge, Observed Discharge, m 80 70 70 70 80 90 100 110 120 130 70 80 90 100 110 120 130 Simulated Discharge, m3/s Simulated Discharge, m3/s (a) (b) [Fig. 3]: (a) Calibration and (b) Validation of Chaktai Khal Gumbel’s method comprises of following equations:
푇 (Garg, 2005) (3) 푦(푇) = −[푙푛. l n( ) 푇 ˗ 1 (Garg, 2005) (4) 푦(푇) ˗ 푦푛̅ 퐾 = 푆푛 (Garg, 2005) (5) ̅ 푋푇 = 푋 + 퐾푆
푋푇 = value of X for a return period of T 푋̅ = Mean of X K = Frequency factor N = sample size 푦푛̅ =Reduced mean depending on N 푆푛 = Reduced standard deviation depending on N The hydraulic model was set up was done on the existing drainage network system. For each drains in the network, three cross-sections were collected. Lengths of the drainage channels are upgraded from 2006 drainage network collected from CDA. The reach elements in the HEC-HMS model convey the stream flow downstream of the basin and hydraulic analysis was done on the channels of the drainage network. Parameters such as %impervious, slope and curve number in hydraulic model were same as HEC-HMS model. Manning’s n for conduits which are artificial open channel of brick in cement mortar was taken as 0.015. For earth bottom and rubble sided drains, 0.030 was assigned as Manning’s n.
Table 2: Flood peaks for 10 year return period Flood Peaks Flood Peaks Flood Peaks Subcatchment Subcatchment Subcatchment m3/s m3/s m3/s W1 92.24 W9 71.35 W17 69.8 W2 68.95 W10 78.04 W18 44.92 W3 80.77 W11 43.5 W19 74.3 W4 74.57 W12 48.27 W20 7368 W5 83.4 W13 72.79 W21 79.8 W6 77.45 W14 67.96 W22 78.86 W7 50.58 W15 88.52 W8 60.5 W16 79.32
Dynamic wave routing was considered for simulation which solves Saint-Venant equations i.e. the continuity and momentum equations because in this routing it takes into account the water level in both nodes and conduits (James et al., 2010). This processes can also perform simulation for multiple looped network. The peak discharge values for all 22 subcatchments were used for hydraulic simulation to obtain the node flooding and conduit surcharging representing the flooding conditions of Chattogram City.
RESULTS AND DISCUSSION During 2016-2017, total 760 drains were surveyed. Due to siltation and clogged condition, connectivity loss of various drains was observed in various areas like Chawkbazar, Probortok circle, Bahaddarhat, Kapasgola, Khatunganj, Chaktai, Agrabad Access Road, Halishahar and Patenga. While performing the survey for collecting the drain cross-sections, the depth of the portion Chaktai khal in Chawkbazar was found around 1.524m near the bank area which was constructed with a depth almost 4.572m.
The results presenting the location of flood hotspots specifically Bakalia, Chawkbazar, Rahattarpul, Bahir Signal to Kaptai Rastar Matha, Bahaddarhat Bus Terminal, Khaja Road, Muradpur, Muradpur to Sholoshohor Road, North Kattoli, South Kattoli, North Halishahar, Agrabad Access Road- Bepari para, Chotopol, Boropol, CDA Bank Colony, North-Middle Halishahar, South-Middle Halishahar, North Patenga in Chattogram City that were obtained from the hydraulic simulation [Fig. 4], these were influenced by factors included rainfall pattern, topography, existing drainage condition, and urbanization rate.
Locations closed to Chaktai khal and Rajakhali khal like Bahaddarhat, Badurtala, Kapasgola, Chawkbazar, West Bakalia, South Bakalia and Chaktai are prone to flooding as low-lying areas. Chaktai, Majhirghat, and Madarbari face flooding due to tidal effect from Karnaphuli River. Simulated flooded areas of the city matches with the field observed flood prone areas. [Fig. 4]: Flood hotspots
CONCLUDING REMARKS To predict the future flood hazard in Chattogram City, this research project was conducted in three phases included flied investigation, a hydrologic model study and a hydraulic model study. Simulated hydrographs from HEC-HMS model is compared with the observed ones to analyze the model performance. The comparison of simulated and observed hydrographs gives a positive indication of the accuracy and reliability of the developed methodology. Flood peaks for 10 year return period was calculated using Gumbel’s method and used for hydraulic simulation. Finally, the 17 flood hotspots in the drainage system were acquired and these represent similar outcomes experiencing citywide as per the field observations. As the output result is based on analysis of a simplified drainage network of the Chattogram city, it partially represents the actual scenario of flooding in the drainage system.
ACKNOWLEDGMENTS The authors would like to appreciate the financial and logistic supports for this study from the department of Civil Engineering, Chittagong University of Engineering and Technology (CUET). Appreciation extended to Chittagong Development Authority (CDA) and Chittagong City Corporation (CCC) for their kind support during field survey and providing relevant information.
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