4thInternationalConference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
FLOOD INUNDATION MAPPING FROM SATELLITE IMAGES: A CASE STUDY OF SOME FLOOD PRONE DISTRICTS OF BANGLADESH
M. M. Rahman*, R. M. Tumon& R. Chakma
Department of Urban & Regional Planning, Rajshahi University of Engineering & Technology, Rajshahi-6204, Bangladesh. E-mail: [email protected] *Corresponding Author
ABSTRACT Bangladesh is one of the most flood prone countries in the world due to its geographical settings. Every year a major portion of land is inundated by flood. Efficient and timely monitoring of flood events is prerequisite for flood risk management as well as disaster response during and after flood. But near real time flood monitoring is quasi impossible without the use of Earth Observation (EO) data from space. Synthetic aperture radar (SAR) image from space offer opportunity for near real time flood monitoring regardless of time and weather. In this paper Sentinel-1 satellite images have been used in order to delineate flood inundated area. Firstly, the images have been calibrated through radiometric correction the image filtering has been done in 7X7 window using Speckle Filtering method. Finally, Binarization has been applied on filtered images in order to separate water from non-water area selecting a threshold value through iteration process and histogram analysis. The total work has been done in SNAP and ArcGIS software. This study suggests that real time automatic delineation of flood extent from satellite images can be the scope for further research in this field.
Keywords: Flood Inundation, Earth Observation, Satellite Images, Synthetic aperture radar (SAR), Calibration, Sentinel Image.
INTRODUCTION Bangladesh is one of the most flood prone countries in the world with about 21 percent of the country is subject to annual flooding and an additional 42 percent is at risk of flood with varied intensity (Ahmed and Mirza, 2000). Approximately 20% to 25% of its territory is inundated during the monsoon season (Siddiqui & Hossain, 2006). Two-thirds of Bangladesh is less than 5 meters above sea level, making it one of the most flood prone countries in the world. Severe flooding during a monsoon causes significant damage to crops and property, with severe adverse impacts on rural livelihoods (IFAD, 2012). A number of research works have identified that the monsoon flood scenario will be aggravated with future climate change context (WMO & GWP, 2003).
Earth Observation (EO) data facilitate the mapping of flood extent over large areas. In this context, remotely sense data has the potential during the various phases of flood management process of providing an overview of the situation on the ground without direct contact with the flooded area that allows the decision makers to follow the water extent during the disaster (Sghaier et al., 2018).Numerous remote sensing techniques have been used to detect and delineate flooded area based on synthetic aperture radar (SAR) imagery (Liu et al., 2004). SAR delivers an all-weather, all-day tool for flood mapping at near real-time. In the study by Smith (1997), the capability to operate during daytime and night time and in almost all-weather conditions, Synthetic Aperture Radar (SAR) sensors
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The objective of this research is to determine the flood extents using multi-temporal satellite SAR data sets. In this paper Sentinel-1 satellite images have been used in order to delineate flood inundated area. Firstly, the images have been calibrated through radiometric correction the image filtering has been done in 7X7 window using Speckle Filtering method. Finally, Banalization has been applied on filtered images in order to separate water from non-water area selecting a threshold value through iteration process and histogram analysis. The methods are selected which are fast and easy to apply over large areas and images are inexpensive and easily available. Several image are analyzed from the year 2017. The change detection and thresholding was conducted to separate from flood area to non-flood.
METHODOLOGY
Data Used Sentinel-1A images of Sirajganj, Bogra and Tangail have been used in this study to delineate flood inundation area. The 10 Sentinel-1A images of different dates have been downloaded as a zip format from Copernicus website (https://scihub.copernicus.eu/dhus/#/home). The dates of images have been presented in the Table 1.
Table 1: Downloaded Sentinel-1A images Sentinel-1 image Date S1A_IW_GRDH_1SDV 24/04/2017 S1A_IW_GRDH_1SDV 5/7/2017 S1A_IW_GRDH_1SDV 17/07/2017 S1A_IW_GRDH_1SDV 29/07/2017 S1A_IW_GRDH_1SDV 31/07/2017 S1A_IW_GRDH_1SDV 12/8/2017 S1A_IW_GRDH_1SDV 22/08/2017 S1A_IW_GRDH_1SDV 5/9/2017 S1A_IW_GRDH_1SDV 17/09/2017 S1A_IW_GRDH_1SDV 29/09/2017
Data preparation Sentinel-1 images are preserved in manifest.safe format. First, manifest.safe was selected from the image folder to view in the SNAP tool. Then folder was viewed on the left corner. There were two bands for each polarization recorded: Amplitude and Intensity. (The Intensity band is a virtual one. Which is the square of the amplitude). A subset helped creating a smaller portion of image which necessary.
Pre-processing Firstly, image calibration has been done. The Radiometric Calibration provides imagery in which the pixel values can be directly related to the radar backscatter of the scene (ESA).This created a new product with calibrated values of the backscatter coefficient. The radiometric calibration is applied by the following equation which are acquired from ESA: value (i) = , where, depending on the selected Look Up Tables (LUT), value (i) = one of or original . = One of the betaNought (i), sigmaNought(i), gamma(i) or dn(i) Noise (i) = . The de-noise LUT must be calibrated matching the radiometric calibration LUT applied to the DN: Noise (i) = calibrated noise profile for one of one of or original .
= One of the betaNought (i), sigmaNought (i), gamma (i) or dn(i)
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The calibrated noise profile can then be applied to the data to remove the noise by subtraction. Application of the radiometric calibration LUT and the calibrated de-noise LUT can be applied in one step as follows: Value (i)=
Secondly, Speckle filtering has been applied to remove the image noise. Speckle noise was considered as a common phenomenon in all coherent imaging systems like laser, acoustic and SAR imagery (Gan et al., 2012). Here Lee filter was used to reduce the speckle noise with window size 7 by 7. The equation is , L= , is the estimated pixel value (Horritt et al., 2001). Where x is an image pixel corrupted by a stationary multiplicative noise n such that y = nx (Gagnon and Jouan, n.d.). The after image from figure shows that the noise is reduced.
Third, optimal thresholding has been applied to detect flooded area based in the histogram of the image and separate water areas and non-water areas (Ezzini et al, 2018). Histogram thresholding encompasses separating the image into several gray scale ranges based on peaks in the histogram (Townse & Foster 2002). The expression 255*(image name< threshold value) has been applied to separate water from non-water. Fourth, Radiometric Terrain Correction was applied using digital elevation model SRTM 3sec. for geometric correction. Finally, unsupervised classification was conducted to separate the whole area into two classes: Non-Water and Water. A map was prepared to visualize the flood condition of that particular date.
RESULTS AND DISCUSSIONS Flood inundation areas for the month of July and August in the year 2017 have been illustrated by the Figure 2. A reference image during non-flood season is presented in Figure 1 to compare the flooded area. In both figures light yellow colour represent non-flooded area and blue for flooded area. In [Fig.2] it is clearly acknowledged that the blue portion is larger than the Reference image from [Fig.1]. Figure 2 shows the spatial temporal variation of flooded area during July, August and September of 2017 where Figure 3 and Figure 4 show the temporal variation. From the Figure 4 it is seen that highest flood is observed on 17 July 2017 whereas minimum inundated area is observed on 29 September 2017. Actually in the context of Bangladesh July month is considered as Monsoon season. In July average rainfall is too high in comparing with other months.
Fig. 1: Water and Non-Water Area in non-flooded season.
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Fig. 2: Flood inundated area in different dates
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Fig. 3: Percentage of flooded and non-flooded area in the month of July and August 2017. Source: calculated by Author from satellite Images, 2018
Fig. 4: Temporal variation of inundated area in the month of July and August 2017 Source: calculated by Author from satellite Images, 2018
Table 2: Percentages of Flooded area in three districts Bogra Sirajganj Tangail Date Flooded Area % Flooded Area % Flooded Area % 24/4/2017 191.60 5.95 337.31 12.57 160.99 4.23 5/7/2017 482.31 14.98 726.28 27.06 377.90 9.93 17/7/2017 484.32 15.04 898.25 33.47 615.47 16.18 29/7/2017 521.75 16.20 584.22 21.76 381.57 10.03 31/7/2017 522.18 16.22 609.24 22.70 358.68 9.43 12/8/2017 243.83 7.57 581.32 21.66 284.06 7.47 22/8/2017 295.75 9.19 593.27 22.10 365.71 9.61 5/9/2017 353.36 10.97 736.10 27.43 402.37 10.58 17/9/2017 295.75 9.19 593.27 22.10 365.71 9.61 29/09/2017 243.83 7.57 581.32 21.66 284.06 7.47 Source: calculated by Author from satellite Images, 2018
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Table 2 shows the percentage of flooded area by Districts and Dates. For all districts highest flood is observed on 31 July. This table also depicts that Sirajgonj is most flooded area compared to Bogra and Tangail districts.
CONCLUSIONS With the flood mapping it could be easily determined that which area is affected by flood and which are not. Synthetic aperture radar (SAR) image from space offer opportunity for near real time flood monitoring regardless of time and weather. In this paper Sentinel-1 satellite images have been used in order to delineate flood inundated area. Figure 1 is considered as a reference image and this image is taken in April month. All other flooded area has been compared with this image. This study suggests that real time automatic delineation of flood extent from satellite images can be the scope for further research in this field.
ACKNOWLEDGMENTS We would like to express our gratitude to European Space Agency (ESA) to provide Sentinel-1A images and SNAP software with free of charge.
REFERENCES 1. Ahmed, A. U., & Mirza, M. M. (2000). Review of causes and dimension of Floods with Particular Reference to Flood '98. In Q. K. Chowdhuri, A. K. Azad, S. H. Imam, & M. Sarker (Eds.), Prespectives on Flood 1998 (pp. 67-84). Dhaka, Bangladesh: University Press Limited. 2. Ezzine, A; Daragi, F; Rajhi, H. and Ghatassi, A. 2018. Evaluation of Sentinel-1 data for flood mapping in the upstream of Sidi Salem dam (Northern Tunisia). Arabian Journal of Geosciences (2018). 11:170.https://doi.org/10.1007/s12517-018-3505-7 3. Gan, T.Y., Zunic, F., Kuo, C.C. and Strobl, T. (2012) “Flood mapping of Danube River at Romania using single and multi-date ERS2SAR images” Int. J. Appl. Earth Obs. Geoinform.18 69–81 4. Horritt, M. S., D. C. Mason, and A. J. Luckman. 2001. Flood Boundary Delineation from Synthetic Aperture Radar Imagery Using a Statistical Active Contour Model. International Journal of Remote Sensing 22 (13): 2489–507. 5. International Fund for Agricultural Development. (IFAD). (2012). Bangladesh: Coastal Climate Resilient Infrastructure Project (CCRIP).Retrieved from http://www.climatechange.gov.au/sites/climatechange/files/files/IUCN_Infrastructure_Solom on_Islands_case_study.pdf [Accessed on 12.07.2018] 6. Sghaier, M.O., Hammami, I., Foucher, S and Lepage, R. (2018) “Flood Extent Mapping from Time Series SAR Images Based on Texture Analysis and Data Fusion” Remote Sensing. 2018, 10, 237; doi: 10.3390/rs10020237 7. Siddiqui, K. U., & Hossain, A. N. H. A. (2006). Options for Flood Risk and Damage Reduction in Bangladesh. Dhaka: University Press Limited. 8. Smith, L. C. 1997. Satellite Remote Sensing of River Inundation Area, Stage, and Discharge: A Review. Hydrological Processes 11 (10): 1427–39. 9. Townsend, P. A., and J. R. Foster. 2002. A Synthetic Aperture RadarBased Model to Assess Historical Changes in Lowland Floodplain Hydroperiod. Water Resources Research 38 (7): 1115–25. 10. World Meteorological Organization and Global Water Partnership (WMO and GWP), (2003). Integrated flood management. The Associated Programme on Flood Management. Retrieved from http://www.apfm.info/pdf/case_studies/cs_bangladesh.pdf [Accessed on 05.07.18]
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
FEASIBLE AND SUSTAINABLE COASTAL PROTECTION AND ADAPTATION STRATEGIES FOR COASTAL AREAS OF BANGLADESH
R. T. Khan1*& P Das2
1Department of Civil & Water Resources Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh. E-mail: [email protected] 2Department of Water Resources Engineering, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh. E-mail:[email protected]
*Corresponding Author
ABSTRACT Bangladesh has about a 710 km long coastline with the Bay of Bengal at the southernmost part of the country. Due to the dynamic characteristics of the Meghna estuary, geographic location and climatic conditions, the coastal regions of Bangladesh are subjected to a number of challenges every year including coastal erosion, sedimentation, salinity intrusion, tropical cyclone, storm surge, tidal bores etc. This study focuses on formulating a combination of protection and adaptation strategies to mitigate and counteract the current as well as future aggravated coastal challenges due to climate change impacts. The protection strategies include structural interventions such as coastal embankments, hydraulic structures such as regulators, sluice gates, breakwaters, afforestation and bank protection works and soft adaptation measures such as rainwater harvesting, cyclone shelters, etc. The effectiveness of the existing natural protection such as hilly elevated terrain in the southeast coast, mangrove forest in the west, natural forests in the islands at the estuary as well as of the artificial measures such as coastal polders, hydraulic structures, bank protection works, etc. are assessed in this context. The results of this study will be helpful for the policy makers to adopt suitable strategies to counteract the coastal hazards and thus reduce risk to human life, livelihood, agriculture, ecosystem and biodiversity.
Keywords: Coastal Challenges; Coastal Protection; Adaptation; Structural & Non-Structural Measures.
INTRODUCTION The coastal zone of Bangladesh encompasses a total area of 47,201 km2, including 19 districts & 47 upazillas(WARPO, 2006) and the exclusive economic zone in the Bay (Islam, 2004). It is home of multiple vulnerabilities. This coastal zone of the country is prone to multiple natural calamities yearly such as floods, tidal bores, cyclones, salinity intrusion, etc. Statistical analysis reveals that Bangladesh is affected by a major cyclone once every 16 years. Some major cyclones in the recent past include: the cyclone of May, 1991, hitting the southeastern coast and causing deaths of about 136,000 people; Cyclone Sidr of November 15, 2007 attacking the southwestern coast of Khulna division and the Sundarbans. Catastrophic cyclonces caused deaths of nearly 900,000 people during the last 35 years. (Islam, 2004). The frequent natural disasters, salinity intrusion and potential of sea-level rise expose the coastal belt of Bangladesh to multiple challenges and threats to human life, livelihood and economy. Besides some natural protective barriers, the coastline of Bangladesh is also protected by about 139 coastal polders, constructed by EPWAPDA (currently known as BWDB) from 1960 to 1970 (Prosoil Foundation Consultant, 2016). The main objective of the coastal polders was to prevent the low-lying land from periodic inundation of saline water during high tides. These polders protect an area of about
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1.2 million hectares of coastal land (BWDB 2013). These coastal embankments play an important role in shaping and controlling the coastal morphology of Bangladesh, such as lowering the tidal range by as much as 30 percent, checking the saline water intrusion during high tides, and controlling coastal erosion. But these polders also initiated unintended consequences such as drainage congestion within the polders, siltation of natural drainage channels, reduction in upstream and overbank storage, etc. Due to morphological, land and sea-level changes in the following 50 years, and increased intensity and frequency of the storm surges, effectiveness of many of the polders along with their hydraulic structures is now in question. However, rehabilitation and reconstruction of many coastal polders and their hydraulic structures are currently ongoing under various projects (CEIP-I & CEIP-II). The objective of the study is to assess and identify the problems and limitations of existing protective measures, recommend solutions to problems associated with existing measures and recommend additional measures to mitigate vulnerability to coastal challenges.
METHODOLOGY This study is mainly based on analyses of secondary data, published papers, reports of various organizationson coastal management issues of Bangladesh. The resources, threats, challenges and management activities of the coast were analyzed. Secondary data of storm surge, tsunami, sea level rise, erosion, salinity intrusion on coastal zone were used for analysis.
ANALYSIS Coastal Challenges Coastal zone of Bangladesh has been delineated based on the tidal fluctuations, salinity and storm surge risk [Fig. 1].
Fig.1: Coastal Zones of Bangladesh (Hasan, 2014)
Around one-third of country’s total land area belongs to the coastal zone. The country has been divided into three distinct coastal regions, namely the western, central and eastern regions (Hasan, 2014). The western zone consists of flat and low salt marshes and numerous rivers and channels crisscross this region. The central region is the most active one and undergoes continuous accretion and erosion. The eastern region is protected by hilly uplands and comparatively stable. Sundarbans, the largest mangrove forest, acts as the only natural protection across a significant part of the south-west coastal zone. But the other parts of the coastal area lacks any significant natural protective barrier to dissipate the cyclone energy. The main resources of Coastal zone in Bangladesh are: Mangrove forest, Fish resource, Shrimp culture, Salt farming, Natural gas (2 offshore gas fields out of 22 in country’s total), Shipping & water transport (2 sea ports), Ship breaking industry, tourism and recreation. The coastal land use distribution is shown in Fig. 2. Major coastal problems in Bangladesh are salinity intrusion, storm surge, erosion, drainage congestion etc. In the last fifty years more than 70 coastal cyclones hit Bangladesh and more than 900000 people have died. It has been observed that about 17 percent of the 508 cyclones originated in the Bay of
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Bengal hit Bangladesh. (Ahamed et al, 2012). The number of storm surges and its associated death are presented in Fig. 3 and Fig.4 (Rahman & Das, 2016).
Fig.2: Major land use in Coastal Fig.3: Number of Cyclones in Fig. 4: Number of Deaths Zone Bangladesh from Cyclones
Potential sea level rise induced by global climate change places Bangladesh at a very vulnerable situation. World According to a study conducted by World Bank (2000), the sea level rise was predicted as 10 cm, 25 cm and 1m by the years 2020, 2050 and 2100 respectively, affecting about 2%, 4% and 17.5% of total land area of Bangladesh. Rate of sea level rise was estimated as 1 cm per year according to IPCC 2005. Various studies (Hasan 2016, Brammer 2014, Sarwar & Woodroffe 2013) conducted on coastal erosion-accretion phenomenon along the Bangladesh coastline based on analysis of satellite imagery reveal the changing landmass along the Bangladesh coastline. However, these studies produce varying results regarding the total eroded or accreted area. But they agree in the facts that, the western coast consisting of Satkhira, Bagerhat, Patuakhali,Barguna etc. are subjected to net erosion, whereas the central coast consisting of Noakhali, Feni etc. are subjected to net accretion due to coastal deposition at the Meghna estuary. Salinity intrusion is a common problem for all the coastal districts of Bangladesh. However, high amount of fresh river water discharge from the Ganges-Brahmaputra-Meghna basin pushes back the saline seawater in the central coastal region. But during the dry season, in low flow conditions, salinity intrusion towards the land poses a major threat to drinking and irrigation water availability. The freshwater flow is drastically reduced in the dry season due to upstream water diversion and withdrawal from the major river systems. For example, the minimum discharge of the Ganges river decreases by as much as 82% is found during the dry months. (Rahman et al., 2011). As a result, the salinity increases drastically. Salinity intrusion affects the agriculture, natural fish-breeding centres and fresh water supply to the urban and rural areas of coastal zone. Salinity concentration distribution in different parts of the coastal zone is shown in Fig. 5.
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Fig. 5: Salinity Concentration Map in Groundwater in Coastal Areas of Bangladesh (Source: Hossain & Hasan, 2017) Coastal Protection and Adaptation Measures The Storm Warning Center (SWC) of Bangladesh Meteorological Department (BMD) is the responsible authority to provide forecast and early warning of tropical cyclones and storm surges. Various levels of cyclone intensities are indicated by 2 distant signal modules ranging from 1 to 2 and 8 near signal modules ranging from 3 to 10 for sea ports with. The intensity of the cyclones increase from 1 through 10. Two techniques are employed by BMD for tropical cyclone forecasting: (a) Storm Track Prediction (STP), and (b) Steering and Persistence (STEEPER). But neither of these technologies is effective enough to produce fairly accurate forecasts with greater than 12 hours of lead time. For the vulnerable communities, numerous cyclone shelters are constructed to protect against cyclone and associated storm surge. Many schools, community centers and public structures in the coastal areas are designated to be used as cyclone shelters when needed. To protect the coastal community, 775 multi-purpose shelters and 1369 elevated earth mounds for livestock have been proposed to be constructed over the next 15 years in the National Water Management Plan (NWMP). One of the most successful and state of the art initiatives include the Cyclone Preparedness Programme (CPP) undertaken in joint collaboration of Ministry of Food and Disaster Management and the Bangladesh Red Crescent Society. Under this programme, a dedicated team of community volunteers were trained up in coastal and offshore island villages. Eleven coastal districts are covered under the cyclone preparedness programme. Volunteers play a crucial role in the various phases of cyclone disaster management such as: dissemination of cyclone warnings, evacuation, rescue, first aid, emergency relief and usage of radio communication equipment. They are the first line of an early warning system to their communities. As an operational wing of the government’s disaster management bureau, the CPP provides scheduled daily weather reports via an extensive high frequency (HF) radio transmitting system operated by volunteers throughout the coastal region of Bangladesh. In addition, government has constructed about 2,400 cyclone and flood shelters along its coastal belt. There is still a requirement to construct a further 1,500 shelters to serve 3.56 million people residing in the high risk coasts. To promote community participation in the construction and maintenance of cyclone shelters, a complimentary disaster preparedness programme has been initiated by the government. Presently, Bangladesh is working on developing a Tsunami Preparedness Programme as an extension to the Cyclone Preparedness Programme. Coastal afforestation is one of the most useful soft-structural measures. Forests and tree roots provide a protective cover of vegetation that anchors soils, slows and soaks up water runoff. Studies show that coastal forests like mangroves and cypress stands shield the coastlines by reducing wave height and energy. Areas buffered by mangroves were less damaged by the Indian Ocean Tsunami than areas without tree vegetation at 2004. Mangroves trap and stabilize sediment and reduce the risk of shoreline erosion because they dissipate surface wave energy. This attribute makes mangroves a potential natural solution for particular coastal protection problems. (Rahman et. al. 2011).With an aim to enhance coastal environmental sustainability, the Community Based Adaptation to Climate Change through Coastal Afforestation project was undertaken by UNDP from 2009 to 2015. Under this project, total 6372 hectares of land in four coastal districts – Barguna, Noakhali, Bhola and Chittagong were afforested. Such initiative is believed to safeguard life, property and crops from natural disaster, stabilize newly accreted land, promote aquatic ecosystem and habitat. (UNDP 2012) The chief strategy adopted by Bangladesh Government to protect the coastal areas by constructing the raised earthen embankments or polders, parallel to the shoreline. These polders have proven successful in mitigating salinity intrusion, coastal erosion and storm surge inundation. To permit passage of water inside the polders for irrigation purposes, to facilitate stormwater drainage, and to provide communication of water vessels in and out of the polders, various hydraulic structures such as regulators, flushing sluices, irrigation inlets, boat passes, navigation locks, etc. are constructed within the polders. These are gated structures, which allow the regulation of flows in and out of the polders. The local Water Management Groups or communities are mostly responsible for operation of the gates. However, due to lack of maintenance, in many regulators or sluices, the gate operation mechanism is fully damaged. As a result, they cannot be operated and the local communities do not get the full
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benefits of the structures. Moreover, the recent cyclones of SIDR, AILA and Mahasen have washed away or dislodged many gates of the regulators, and the structures require replacement of gate shutters for proper functionality. The current worldwide accepted and adopted concept of structural coastal protection measures include “Living Shorelines”, which involve the use of locally available materials and promote the natural coastal processes.The living coastline may consist of rocks, concrete blocks, sand, and marsh plants. There are various such technologies such as breakwaters, revetments, sills, groins, beach nourishment, vegetation etc. which may be adopted depending on the wave energy, wave height, construction cost, availability of materials, and local needs. In major parts of the Bangladesh coastline, sills, consisting of marsh plants with base protection by rocks as shown in Fig. 6 may be a feasible solution. They are easy and cheap in construction, and may be adopted in conjunction with the existing coastal polders, to dissipate the wave energy and mitigate the coastal erosion.
Figure 6: Side View of Proposed Coastal Protection Sill (Source: North Carolina Department of Environmental Quality)
Salinity intrusion problem is significant in the South-West costal region, which has been further aggravated due to Ganges flow diversion upstream by the Farakka barrage. As a result, the major source of freshwater flow in the south-west coast, the Gorai offtake of the Padma river, receives very little flow in the dry period. Several projects (Gorai River Restoration Project Phase-I & II) were undertaken to ensure diversion of adequate flow from Padma into Gorai. But due to unplanned dredging and low water flow in the Padma, these projects have failed to ensure sufficient water flow in Gorai, and lower the salinity intrusion problem. Even after dredging, several ‘Chars’ were seen at the river bed, and very little flow was observed in the river during the dry period (The Independent, 2017). The failure of these projects reveal the fact that, a combination of technical and diplomatic aspects, including revisioning the 1996 Ganges treaty need to be adopted to restore the Gorai River. Although the coastal polders protect, to some extent, against surface water salinity, groundwater salinity is still an unresolved issue. Currently there is no effective regulation or legislation about drilling and extraction from groundwater aquifers. Very little research or data is available regarding groundwater and aquifer characteristics in Bangladesh. Excessive dependency on and extraction of groundwater for agricultural uses promote salinity intrusion. In the current socio-economic and technical setup, conjunctive use, regulation of well extraction, rainwater harvesting could be some potential adaptation measures to groundwater salinity intrusion. For medium term protective measures, well extraction regulations may be adopted based on mathematical model analyses regarding groundwater and saline water interaction. Horizontal well technology, involving injection of fresh/treated wastewater into the saline water interface through a short length of horizontal well, can be a potential technical solution, in changing climate and sea-level rise scenarios.
CONCLUSIONS Due to the dynamic nature of the coastline and tropical disaster-prone climate of the coastal zone, coastal protection is a resource-intensive challenge for Bangladesh Government. Every year, a considerable amount of national budget has to be reserved for coastal protection measures. This study focuses on the structural as well as non-structural and soft protective and adaptive measures to reduce, 1029
mitigate and recover from the various coastal challenges. Community based disaster management practices have become the most effective and feasible strategy to combat the coastal challenges. Also, involving the stakeholders and local representatives the during the planning stages of coastal management projects significantly increases the sustainability of the proposed interventions. Comprehensive Disaster Management Program (CDMP) has been designed to adopt an umbrella programme approach that encompasses all aspects of risk management and in so doing facilitates to move from a single agency response and relief strategy to a whole of government holistic strategy that addresses the issue of community vulnerability. National Disaster Management Plan undertaken by the Government of Bangladesh defines in broad outline the systemic and institutional mechanisms under which disaster risk reduction and emergency response management is undertaken in Bangladesh. It outlines disaster management vision, strategic goals and conceptual framework. It establishes disaster management regulative and planning frameworks, and identifies priority areas for disaster risk reduction and emergency response management. In the recent years, Bangladesh has been appreciably successful in mitigating the coastal hazards and associated human casualties and destruction of property.
REFERENCES Ahamed, S., Rahman, M. M. and Faisal, M.A., 2012.Reducing Cyclone Impacts in the Coastal Areas of Bangladesh: A Case Study of Kalapara Upazila. Journal of Bangladesh Institute of Planners, 5:185-197. Bangladesh Water Development Board. 2013. Final Report, Coastal Embankment Improvement Project, Phase-I. Dhaka. Brammer, H. 2014. Bangladesh’s dynamic coastal regions and sea-level rise. Climate Risk Management. 1:51-62. Economic Relations Division (ERD). 2003. Bangladesh - A National Strategy for Economic Growth Poverty Reduction and Social Development.Ministry of Finance. Government of Bangladesh (GoB), 2008. Cyclone Sidr in Bangladesh: Damage, Loss, and Needs Assessment for Disaster Recovery and Reconstruction. Hasan, M. R. 2015. Groundwater Salinity Zoning in the Southwestern Coastal Region of Bangladesh. Slideshare [online]. Available at: https://www.slideshare.net/CPWE/groundingwater-salinity-and-zoning-in-the-southeastern- coastal-region-of-bangladesh [Accessed 22 March, 2018] Hasan, M. 2016. Landmass Change Along the Bangladesh Coastline. University of Texas at Austin. Hossain, M.A.R. & Hasan, M.R. 2017. An assessment of impacts from shrimp aquaculture in Bangladesh and prospects for improvement. FAO Fisheries and Aquaculture Technical Paper No. 618. Rome, FAO. pp-96 Islam, M. R., 2004.Living in the Coast: Problems, Opportunities and Challenges.Working Paper WP011, Dhaka. 2004, Programme Development Office (PDO) and Integrated Coastal Zone Management Plan (ICZMP), pp 13-15. MoEF, 2008. Bangladesh Climate Change Strategy and Action Plan 2008.Ministry of Environment and Forests, Government of the People's Republic of Bangladesh. Dhaka. North Carolina Department of Environmental Quality. Estuarine Shoreline Stabilization Options. [online]. Available at: https://deq.nc.gov/ [Accessed 01 September, 2018] Prosoil Foundation Consultant. 2016. Classification of Wetlands of Bangladesh, Annexure-5. Dhaka: Department of Bangladesh Haor & Wetlands Development. pp. 5-9. Rahman, M. M., & Biswas, S. K. 2011.Feasible Solution of Protection and Adaptation Strategy for Coastal Zone of Bangladesh. Pakistan Journal of Meteorology, 8(15): 13-15. Rahman, M.A. & Rahman, S. 2015.Natural and traditional defense mechanisms to reduce climate risks in coastal zones of Bangladesh. Weather and Climate Extremes.7:84-95. Rahman, Md. A. & Das, P 2016. Challenge, Vulnerabilities and Management of the Coastal Zone around the World. 3rdInternational Conference on Advances in Civil Engineering. Sarwar, M. & Woodroffe, C. D. 2013. Rates of shoreline change along the coast of Bangladesh. Journal of Coastal Conservation. 17(3):515-526. The Independent, 2017. Tk 930-cr Gorai Dredging Project Fails to Deliver. [online]. Available at:
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http://www.theindependentbd.com/post/88023. [Accessed 01 September, 2018] UNDP 2012.Project Factsheet. Community-based Adaptation to Climate Change through Coastal Afforestation.[online]. Available at: http://www.bd.undp.org/content/dam/bangladesh/docs/. [Accessed 31 August, 2018]
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
EFFECT OF INTERMITTENT WATER SUPPLY SYSTEM IN WATER DISTRIBUTION NETWORK IN RAJSHAHI CITY, BANGLADESH
S.Akter*, A.Hasnat& M. M. Hasan
Department of Civil Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh. E-mail:[email protected]*; [email protected];[email protected]
*Corresponding Author
ABSTRACT Keeping quality controlled in an intermittent stage of a water supply chain is a challenge for developing countries like Bangladesh. So it is an imperative issue to check the water quality at different interval of a day. This paper is redacted to investigate the degree of fluctuation of water quality and the state of water distribution network (WDN) during intermittent water supply (IWS) period in Rajshahi city. Two different times of a day were selected named starting hour (SH) and running hour (RH) for water quality justification that are supplied from Rajshahi WASA. Four samples were assorted in both cases of WASA and their residential consumers for these selected hours. A total of forty samples were accumulated from ten wards for 5 physiochemical quality of water. Physical parameters including turbidity, pH, odor and chemical parameter iron were performed by a standard laboratory test. The results illustrate that piped water supply during the morning or first flush after completion of a long interval, quality deviates from their standard forms (WHO and BDS) tremendously in this time interval. Then the same water from the same location but for the different time was examined when the water supply was running fully named running hour (RH). Water quality for these two different times exhibits a dramatical distinction. A comparatively lower concentration of iron, turbidity, conductivity, and odor were found during the running hour. But pH in both two cases was approximately the same. People endure a lot due to this problem and even they tolerate reddish and black water after just opening the tap in a day after a long interval of supply in a day.
Keywords: intermittent water supply; starting hour; running hour; WHO
INTRODUCTION With the booming rate of increase of population, water use is also increasing twice comparing with the population growth in the last century. This rate becomes hilarious in developing countries. Around 780 million people of the world do not have access to clean drinking water and 2.5 billion people do not have proper sanitation. As a result, 6-8 million people die each year due to water-related diseases and disasters (UN water quality, 2013). Nowadays Clean drinking water is recognized as a fundamental human right of human being. And providing safe and clean drinking water has added an extra feature of a challenge with this. Therefore water quality control is becoming top priority policy agenda of many countries (WHO guideline, 2011). As water is one of the most significant ingredients on earth and human physiology and man’s continued existence depends very much on its availability so providing safe and reliable water supply has created a headache for the producers in the developing countries. Bangladesh is such a developing country and Rajshahi is the fourth largest city of it. To overcome the extreme water crisis in Rajshahi, ‘Rajshahi WASA’ was established in 2013. From then
1032 it provides water to the households of the city. But the nature of water provided by WASA was poor in both physical and chemical aspects. Different types of complains were developed day by day from consumers for that water. And unfortunately, there have not much research works done in this issue. K. Roy et al (2018) has done some works on it but for a few wards. And S. Akter et al (2018) has done with a small number of parameters of some other wards. But in practical cases, it is found that the water quality varies much at starting hour of the pump with the running hour. Rajshahi WASA supplies water twice a day at 6 AM and 3 PM respectively. The quality of water supplied in this intermittent time shows huge variations in this two starting time of the pump with the running time that means after the first flush hour. Though the pH almost remains the same the other parameters change a lot in the meantime. Even the color and odor also have changed. Bambos Charalambous described different effects of this intermittent water supply on water distribution network. To develop the distribution system for quality ensure Fernando Alvarruiz (2016) has proposed some measures. Ze-hua Liu et al (2016) showed how plastic pipe distribution system is changing the quality intermittently. In this paper, we have studied some physical and chemical quality of water of 10 wards of Rajshahi city with 40 samples collecting from both WASA and consumer at starting and running hour of the pump. The locations are selected considering the place where the intermittent water quality deviates more from the standard values.
METHODOLOGY Study location and data collection: Rajshahi city-corporation is located in the north-west part of Bangladesh. It is the 4th largest city of Bangladesh. Geologically it’s position is at 24021/ and 24026/ north latitudes and between 88028/ and 88037/ east longitudes. The total area of it is 47.78 sq km. In opposition with 449756 population, the daily water demand is 9.76 million liters. Where WASA supplies 7.16 million liters which covers 319327 peoples demand daily. The total subscriber of Rajshahi WASA is 40540 (the Year 2017-18). It supplies water through 632.50 km pipeline network (Rajshahi WASA, 2018). The study location and total methodology of research have been shown in Fig. 01 & 02 respectively.
Fig. 1: Study area of the research work (Made by Author)
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•Observation of water quality of Rajshahi WASA through practical condition and from different research works. •Selection of study points based on degree of deviation of intermittent water quality from 01 source to consumer.
•Sample collection from WASA and consumer at starting hour and running hour twice a day. 02 •Lab analysis.
•Problem findings 03 •Result and Discussions.
Fig.2: Flowchart of research methodology. Out of 30 wards, top 10 wards are selected for research which shows more declination of water quality from source to consumer. At starting hour, source samples are collected exactly from the pump and the consumer samples are from neighboring consumers 100-200 m from the source (Tania M. Alarcon Falconi et al, 2016). And the samples of running hour are collected in the same procedure. Thus a total of 40 samples were collected for this research work. All the samples were collected in 1-liter polyethylene (PE) bottles preheated by washing with dilute HCl and later rinsed with distilled water (Etim et al., 2013) and then carried in public health lab of RUET safely.
RESULT AND DISCUSSIONS This work has been conducted with some physical and chemical parameters. Turbidity, pH, conductivity, and odor are physical parameters and Iron concentration is the chemical parameter used in this paper. pH value pH value indicates the acidity or alkalinity of the water (Guettaf, Maoui, & Ihdene, 2014). In the case of Rajshahi WASA, the pH value remains almost the same at source and consumer both in starting hour and running hour. And it stays within the standard limit of BDS (6.5 – 8.5) and WHO (6.5-8.5).
7.4 7.2 7 6.8 6.6 pH value 6.4 6.2 6 3 7 11 12 13 14 21 22 25 27 Ward No.
SH (WASA) SH (consumer) RH (WASA) RH (consumer)
Figure 03: Showing pH value with respect to the ward in 4 different condition.
Turbidity Turbidity alludes the existence of suspended material such as clay, silt, finely divided organic material, and other inorganic material. Higher turbidity may generate possible bacterial contamination (Rajon &
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Bari, 2014). According to WHO & BDS guideline, the allowable turbidity for drinking water are 5 NTU and 10 NTU respectively. Kashiadanga pump (Ward 03) contains a high amount of turbidity at the running hour. Mahisbathan pump is now remain stopped after our complaining for producing a high amount of silt, clay, and stone in water. And Pathanpara pump is producing high turbid water at starting hour but it becomes clean in running hour. In general, it is found that water becomes turbid gradually from source to consumer.
SH (WASA) SH (consumer) RH (WASA) RH (consumer)
25 20 15 10
Turbidity (NTU) Turbidity 5 0 3 7 11 12 13 14 21 22 25 27 Ward No.
Fig. 4: Variation of turbidity value in wards in 4 different conditions.
Conductivity Conductivity is a measure of water’s capability to pass electrical flow. This ability is directly related to the concentration of ions in the water. These conductive ions come from dissolved salts and inorganic materials such as alkalis, chlorides, sulfides, and carbonate compounds. In the case of Rajshahi WASA, the conductivity of water remains the same in all conditions.
SH (WASA) SH (consumer) RH (WASA) RH (consumer)
2000
1500
1000
500 Conductivity (mhos/cm) Conductivity 0 3 7 11 12 13 14 21 22 25 27 Ward No.
Fig. 5: Showing conductivity with respect to every ward in 4 different conditions.
Odor An unwanted odor coming from drinking water is often the sign of bigger issues plaguing the tap or pipes. Often this smell does not signify the presence of a harmful contaminant, but it could indicate that the drinking water is not the cleanest and should be inspected to be restored to its purest state. The water produced by Rajshahi WASA is almost odor free. Some samples collected from the consumer at starting time feels a bit odorous, but it’s not much.
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Iron concentration Iron is one of the most important element of blood in human and another living creature. Iron is an essential need for human nutrition and metabolism. But in excess quantities results in toxic effect like hemochromatosis in tissues (Sagar et al., 2015). Iron enters drinking water supplies from natural deposits in the earth or from agricultural and industrial practice (Fahmida et al., 2013). Our Research said that the iron concentration is more at the starting hour. And it increases at consumers place than the source.
SH (WASA) SH (consumer) RH (WASA) RH (consumer)
7 6 5 4 3
Iron (mg/l) Iron 2 1 0 3 7 11 12 13 14 21 22 25 27 SH (WASA) 0.1 0.2 2 4.5 3 2 0.5 0 6.5 3 SH (consumer) 0.3 2.5 3.4 5 3 4.5 1.6 0 6.6 3 RH (WASA) 2 2 1.5 3 3 2 0.9 0 3 3 RH (consumer) 3.4 2 2.5 3.5 3 3.5 0 0 3.3 3 Ward No.
Fig.6: Variation of iron concentration at different wards in different times
Table 01: Comparison of water quality between sources and consumers. Parameter Max. Deviation (SH) Max. Deviation (RH) pH 9.21% 7.07% Turbidity 38.76% 26.88% Conductivity 47.36% 44.46% Iron 92% 42% Odor 20% 05%
CONCLUSION Research has revealed that pH and conductivity value increases gradually from source to consumer in both starting hour and running hour. That means the acidity or alkalinity or ion concentration increases through the pipeline. Again water becomes turbid in consumers stage. Iron concentration also increases dramatically at consumers place. In every cases starting hour gives more shabby water than running hour. And the water quality decreases gradually from source to consumers. Different materials annexed in steel pipes cause this deterioration. So Rajshahi WASA should take proper steps at the source to flourish their water supply quality and trace the pipelines where a specific parameter deviates more in meeting up with design requisites. In those regards, this paper may aid them a lot.
REFERENCES K. Roy et al, 2018. Assessment of supplied water quality in Rajshahi city, Bangladesh. Proceedings of the 4th International Conference on Civil Engineering for Sustainable Development (ICCESD 2018), 9~11 February 2018, KUET, Khulna, Bangladesh (ISBN-978-984-34-3502-6). S. Akter et al, 2018. Assessment of Water supply system and water quality of Rajshahi WASA in Rajshahi City Corporation (RCC) area, Bangladesh. 1st National Conference on Water Resources Engineering (NCWRE 2018), 21-22 March 2018, CUET, Chittagong, Bangladesh. www.cuet.ac.bd Bambos Charalambous. The Effects of Intermittent Supply on Water Distribution Networks. Water Board of Lemesos, P.O. Box 50225, 3602, Lemesos. Cyprus.
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Fernando Alvarruiz, Fernando Martínez Alzamora, Antonio M. Vidal, 2016. Improving the performance of water distribution systems’ simulation on multicore systems. Springer, DOI 10.1007/s11227-015-1607-5. Ze-hua Liu, Hua Yin, Zhi Dang, 2016. Do estrogenic compounds in drinking water migrating from plastic pipe distribution system pose adverse effects to human? An analysis of scientific literature. Springer, DOI 10.1007/s11356-016-8032-z Etim, EE; Odoh, R; Itodo, AU; Umoh, SD; and Lawal, U. (2013). Water Quality Index for the Assessment of Water Quality from Different Sources in the Niger Delta Region of Nigeria. Frontiers in Science, 3(3), 89-95. doi: DOI: 10.5923/j.fs.20130303.02 Tania M. Alarcon Falconi et al, 2016. Quantifying tap-to-household water quality deterioration in urban communities in Vellore, India: The impact of spatial assumptions. International Journal of Hygiene and Environmental Health, 220 (2017) 29–36, ELSEVIER Guettaf, M; Maoui, A and Ihdene, Z. (2014). Assessment of water quality: a case study of the Seybouse River (North East of Algeria). Appl Water Sci, 295–307, doi: DOI 10.1007/s13201-014-0245-z Rajon, MA. and Bari, DMN, (2014). Surface Water (Pond) quality of Rajshahi city. Rajshahi University of Engineering & Technology, RUET. Fahmida, K; Lemon, MHR; Islam, MS and Kader, MA. (2013). Assessment of Supplied Water Quality of Khulna WASA of Bangladesh Paper presented at the International Conference on Mechanical, Industrial and Materials Engineering Rajshahi. Electronic Online Article UN-Water, An increasing demand, facts and figures, UN-Water, coordinated by UNESCO in collaboration with UNECE and UNDESA, 2013, http://www.unwater.org/water-cooperation- 2013/en/. World Health Organization (WHO), Guidelines for DrinkingWater Quality, WHO Press, Geneva, Switzerland, 4th edition, 2011. Rajshahi Wasa Official, http://rajshahiwasa.org.bd/statistics/daily-water-production/
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
ASSESSMENT OF GROUNDWATER QUALITY OF RAJSHAHI CITY IN BANGLADESH
S.Akter*&A.Hasnat
Department of Civil Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh. E-mail:[email protected]*; [email protected] *Corresponding Author
ABSTRACT Quality of groundwater is a major concern as we consumed it without further treatment. Majority of diseases are caused by poor drinking water in developing countries like Bangladesh. About 60% of Rajshahi residents use groundwater for drinking and domestic purpose. Therefore, understanding the significance of drinking water quality, the present study was carried out to investigate the physiochemical quality of groundwater that was supplied to Rajshahi city in Bangladesh. Total 20 samples were collected from different tubewells of 10 wards among 30 wards to analyze 6 physiochemical parameters. Selected physical parameters are pH, turbidity, odor, conductivity and chemical parameters are iron and hardness respectively. Different standard laboratory tests were conducted to determine the physical and chemical quality of groundwater and compared them with WHO and BDS. By analyzing the test, it was obvious that the majority of samples contain higher concentration of iron and hardness and these samples highly deviated from WHO and BDS standards. Odor was detected about 70% samples. PH of all samples fell within allowable limit. Most of the tubewells contain turbid water. Hardness and conductivity are also high in case of few samples. The higher concentration of iron occurs may be due to geologic formation of aquifer. Excess turbidity causes health risk and provide shelter to pathogenic micro-organism. Odor in water create major aesthetic problems to consumers. Due to higher concentration of these selected parameters, groundwater is not suitable for drinking and domestic purpose. So, alternative measures are suggested.
Keywords: Groundwater; Rajshahi city; Lab analysis; WHO
INTRODUCTION Groundwater is the most precious and renewable resource in the world (Islam, Ahmed, Bodrud-Doza, & Chu, 2017). The health condition, peoples livelihood and socioeconomic activities of a country highly depends on the availability and quality of groundwater as it satisfies the demand of agricultural, industrial and domestic sectors of the country (Saleem A. Salman, Shamsuddin Shahid, Morteza Mohsenipour, & Hamid Asgari, 2018; Sharma, Rishi, & Keesari, 2017). But the natural and anthropogenic activities dominant the groundwater quality directly or indirectly (Verma et al., 2017). By questionnaire survey of Rajshahi city householders, groundwater quality of this city become poorer day by day. So, the peoples concerned about the groundwater quality as there is an intimate relationship between water quality and public health and people suffer from various water borne diseases (Lonergan & Vansickle, 1991). Reference should be added. Several research based projects have been conducted by laboratory test in foreign countries and Bangladesh. Something should be added to correlate between the water quality and literature review. 14 physiochemical
1038 parameter were analyzed for six water samples collected from borehole and spring water sources in Ethiopia (Shigut, Liknew, Irge, & Ahmad, 2017).A study was attempted to check the groundwater quality of Faridpur district in Bangladesh (Bodrud-Doza et al., 2016). A study was made in the selected study area to identify the concentration of different water quality parameter (Rasul & Jahan, 2010). Again, another study was carried out to investigate hydro-geochemistry and groundwater quality in Rajshahi city (Mostafa, Uddin, & Haque, 2017). Lacking should be added. The aim of the present study was to investigate the current situation and updated condition of previous studies of groundwater quality withdrawn from different hand tube wells in Rajshahi city Corporation. A total of 20 samples collected from 10 wards to analyze 5 physical and chemical parameters. Selected physical parameters are pH, odor, turbidity and chemical parameters are iron and hardness respectively. Different standard laboratory tests were conducted to determine the physical and chemical quality of groundwater and compared them with WHO and BDS.
METHODOLOGY Study location and data collection: Rajshahi city-corporation is located in the north-west part of Bangladesh. It is the 4th largest city of Bangladesh. Geologically its position is at 24021/ and 24026/ north latitudes and between 88028/ and 88037/ east longitudes. The total area of it is 47.78 sq km. In opposition with 449756 population the daily water demand is 9.76 million liters. Where WASA supplies 7.16 million liters which covers 319327 peoples demand daily. It covers 80% of total demand in which 77% from underground water and 03% from surface water. Total subscriber of Rajshahi WASA is 40540 (Year 2017- 18). It supplies water through 632.50 km pipeline network (Rajshahi WASA, 2018). The study location and total methodology of research have been shown in Fig. 01 & 02 respectively.
Fig. 1: Study area of research work (Made by Author)
We have studied on water quality of Rajshahi WASA for these study area (Arif Hasnat, Saleha Akter, 2018). In that study we have seen that in many wards of Rajshahi, people use ground water instead of WASA water for drinking and other purposes. This is because of the better water quality of ground water than WASA water. In other wards people totally depends on WASA water because of poor quality of ground water. Considering this variations we select top 10 wards for our study. A total of 20 samples were collected for this research work. All the samples are collected in 1-litre polyethylene (PE) bottles pre heated by washing
1039 with dilute HCl and later rinsed with distilled water (Etim et al., 2013) and then carried in public health lab of RUET safely.
i) Observation of water i) Lab test i) Presentation
01 02 03 quality in the study area. ii) Data analysis ii) Sample collection. iii) Problem findings
Fig. 2: Flowchart of research methodology.
RESULT AND DISCUSSIONS This work constitutes 3 physical and 2 chemical parameters. Analysis of pH, Turbidity, Conductivity, Odor, Iron concentration and Hardness are described in this section. Analyzed value of these parameters are compared to BDS and WHO standards.
Table 1: Experimental method and allowable limit Selected Parameters Experimental Methods BDS WHO pH pH meter 6.5 to 8.5 6.5 to 8.5 Turbidity Turbidity meter 10 5 Odor Threshold odor number odorless odorless Iron Titration method 0.3-1.0 0.3 Hardness Soda reagent method 200-500 500 pH value pH value indicates the acidity or alkalinity of the water (Guettaf, Maoui, & Ihdene, 2014). In the case of groundwater of Rajshahi, pH value remains almost same in the range between 6.5 to 7.5 and this range satisfies WHO and BDS standard 6.5 to 8.5. 7.6 7.4 7.2 7 6.8 6.6
Value pH of Value 6.4 6.2 6
Sample Location
Fig. 3: pH value of groundwater with respect to wards. Turbidity Turbidity indicates the presence of suspended material such as clay, silt, finely divided organic material, and other inorganic material. Higher turbidity may create possible bacterial contamination (Rajon & Bari, 2014). According to WHO & BDS guideline the allowable turbidity for drinking water are 5 NTU and 10 NTU respectively. The higher and lower concentration of turbidity found were 25.9 NTU and 0.51 NTU in ward 3 which exceed WHO (5NTU) and BDS standard (10NTU). Within 10 samples ward 3, 13, 14, 23 & 24 remain middle ranged of WHO and BDS limit. Rest of the samples lie below WHO and BDS standard [Fig. 04]
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30 25 20 15 10 5
0 Turbidity (NTU) Turbidity
Sample locations Turbidity WHO limit BDS limit
Fig. 4: Showing turbidity value of ground water of 10 wards. Odor An unwanted odor coming from drinking water is often the sign of bigger issues plaguing the tap or pipes. Often this smell does not signify the presence of a harmful contaminant, but it could indicate that the drinking water is not the cleanest and should be inspected to be restored to its purest state. About 70% samples of ground water was found as odorous and rest of the samples found odorless. Hardness Hardness of water can be measured as soft (<75 mg/L), moderately hard (75-150 mg/L), hard (150-300 mg/L) and very hard (>300 mg/L) with respect to the concentration of calcium and magnesium (Akter, Ferdous, & Kafy, 2018; Roy, Akter, & Islam, 2018). 4 samples contain soft water (ward 11 & 22). 2 samples were moderately hard (ward 7). And 6 samples were very hard (ward 12, 3 & 23). In average 50%, 20%, 10% and 10% samples were very hard, hard, moderately hard and soft water respectively [Fig. 07]
700 600 500 400 300 200
100 Hardness Hardness (mg/l) 0
Sample location
hardness of GW BDS(low) BDS(high)
Fig. 5: Showing Hardness condition of wards
20% soft moderately hard 50% 10% hard
20% very hard
Fig. 6: Hardness condition in percentage.
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Iron concentration Iron is one of the most important elements of blood in human and another living creature. Iron is an essential need for human nutrition and metabolism. But in excess quantities results in toxic effect like hemochromatosis in tissues (Sagar et al., 2015). Iron enters drinking water supplies from natural deposits in the earth or from agricultural and industrial practice (Fahmida et al., 2013). In this case maximum and minimum concentration of iron were 5.6mg/l and 0.2mg/l found in ward 3 and ward 21 respectively. About 50% samples contain higher concentration of iron and deviate WHO and BDS standards. Rest of the samples fall within allowable limit or below the allowable limit. 6 5 4 3
Iron, mg/l Iron, 2 1 0 War War War War War War War War War War War War War War War War War War War War d-3 d-3 d-7 d-7 d-11 d-11 d-12 d-12 d-13 d-13 d-14 d-14 d-21 d-21 d-22 d-22 d-23 d-23 d-24 d-24 Iron 5.6 4.5 1.5 1.2 0.35 2.4 1.54 2 2.63 4 0.54 0.32 0.2 0.2 0.3 0.5 0.4 0.3 1 1.2 WHO limit 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 BDS limit(low) 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 BDS limit(high) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Sample locations
Iron WHO limit BDS limit(low) BDS limit(high)
Fig. 7: Condition of iron concentration in wards
CONCLUSION Research revealed that people of Rajshahi city do not get good quality of groundwater. Though the pH of all samples were within allowable limit but other selected parameters deviated from WHO and BDS guideline extremely. About 70% samples contain odor. 50% samples fall within very hard range in hardness. In average 50% samples had higher concentration of iron. Few wards contain extreme level of turbid water. Due to higher concentration of these selected parameters, groundwater is not suitable for drinking and domestic purpose in these wards. So, authority of Rajshahi city Corporation should take necessary steps to overcome these problems. That can be either the betterment of WASA water or purification of groundwater.
REFERENCES Akter, S., Ferdous, L., & Kafy, A. A. (2018). Assessment of Water supply system and water quality of Rajshahi WASA in Rajshahi City Corporation (RCC) area, Bangladesh. Paper presented at the 1st National Conference on Water Resources Engineering, Chittagong, Bangladesh. Bodrud-Doza, M., Islam, A. R. M. T., Ahmed, F., Das, S., Saha, N., & Rahman, M. S. (2016). Characterization of Groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh. Water Science, 19-40. doi: http://dx.doi.org/10.1016/j.wsj.2016.05.001 Hossain, M. L., Nahida, S. K. N., & Hossain, M. I. (2014). Water quality status of recreational spots in Chittagong City Journal of Water Resources and Ocean Science, 3(3), 38-44. doi: doi: 10.11648/j.wros.20140303.12 Islam, A. R. M. T., Ahmed, N., Bodrud-Doza, M., & Chu, R. (2017). Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics. Environ Sci Pollut Res. doi: 10.1007/s11356-017-0254-1
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Lonergan, S., & Vansickle, T. (1991). Relationship between water quality and human health: A case study of the Linggi River Basin in Malaysia. Social Science and Medicine, 33(8), 937-946. doi: https://doi.org/10.1016/0277-9536(91)90264-D Mostafa, M. G., Uddin, S. M. H., & Haque, A. B. M. H. (2017). Assessment of hydro-geochemistry and groundwater quality of Rajshahi City in Bangladesh. Appl Water Sci, 4663–4671. Perlman, H. (2018, 8 August 2018). Water Properties. Retrieved 28 August, 2018, from https://water.usgs.gov/edu/ph.html Rajon, M. A., & Bari, D. M. N. (2014). Surface Water(Pond) quality of Rajshahi city. Rajshahi University of Engineering & Technology, Ruet. Rasul, M. T., & Jahan, M. S. (2010). Quality of Ground and Surface Water of Rajshahi City Area for Sustainable Drinking Water Source Journal Of Scientific Research, 2(3), 577-584. Roy, K., Akter, S., & Islam, M. (2018). Assessment of supplied water quality of rajshahi wasa(RWASA) in Bangladesh. Paper presented at the 4th International Conference on Civil Engineering for Sustainable Development Khulna, Bangladesh. Sagar, S., Chavan, R., Patil, C., Shinde, D., & Kekane, S. (2015). Physico-chemical parameters for testing of water- A review International Journal of Chemical Studies, 3(4), 24-28. Saleem A. Salman, Shamsuddin Shahid, Morteza Mohsenipour, & Hamid Asgari. (2018). Impact of landuse on groundwater quality of Bangladesh. Sustainable Water Resources Management, 1-6. doi: https://doi.org/10.1007/s40899-018-0230-z Sharma, D. A., Rishi, M. S., & Keesari, T. (2017). Evaluation of groundwater quality and suitability for irrigation and drinking purposes in southwest Punjab, India using hydrochemical approach. Appl Water Sci, 3137–3150 doi: 10.1007/s13201-016-0456-6 Shigut, D. A., Liknew, G., Irge, D. D., & Ahmad, T. (2017). Assessment of physico-chemical quality of borehole and spring water sources supplied to Robe Town, Oromia region, Ethiopia. Appl Water Sci, 155– 164 doi: 10.1007/s13201-016-0502-4 Verma, D. K., Bhunia, G. S., Shit, P. K., Kumar, S., Mandal, J., & Padbhushan, R. (2017). Spatial variability of groundwater quality of Sabour block, Bhagalpur district (Bihar, India). Appl Water Sci, 1997–2008 doi: 10.1007/s13201-016-0380-9
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
QUANTITATIVE FLUVIAL ANALYSIS OF BRAHMAPUTRA-JAMUNA BRAIDED RIVER SYSTEMS USING REMOTE SENSING IMAGES
M. H. Haque*&M. Maliha
Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. E-mail: [email protected] *Corresponding Author
ABSTRACT Brahmaputra-Jamuna, one of the complex braided river systems of the world, covers a large swath of Bangladesh. In this study, plan form dynamics of a braided river have been discussed through an amalgamation of Plan Form Index (PFI) and Flow Geometry Index (FGI). PFI shows at what percentage the river is braided or in other words, it strives to represent the fluvial disposition in relation to a certain water level. On the other hand, FGI indicates what the underwater disposition of the sub-channel is and used to depict hydraulic efficiency of braided river systems. Using remote sensing and GIS in the fluvial study of a braided river is a common practice and when multispectral satellite images are used, this kind of analysis is informative. To find out both indices, remote sensing images captured by Landsat MSS, TM, ETM+, and OLI were used. Processing the images of 40 years from 1977-2017, the part of the river from the entry point into Bangladesh to the confluence with the Ganges at Aricha was mapped out using ArcGIS. Full length of the river was divided into several reaches each spanning not more than 10 km for full-fledged analysis. PFI was calculated in this process and different parts of the section were grouped into highly braided, moderately braided and low braided respectively considering different time span for each reach length of the study area.
Keywords: Braided river System; Bramhaputra-Jamuna; Plan Form Index (PFI); Flow Geometry Index (FGI); Remote Sensing.
INTRODUCTION Originated from the Manasarovar Lake of northern Himalayas, Bramhaputra river drains approximately 530,000 Km2 area through its 2900 km long course along which it crosses steep slope mountains of Tibetan Plateau, geologically young and less sloped Assam valley and very flat sloped alluvial plains to Bay of Bengal (Immerzeel, 2008; Sarma, 2005). Large flows and nearly 600M ton/year of sediment loads contribute to the complex morphological configuration of Bramhaputra ranging from straight, sinuous, meandering channel to braided, anabranching, intermediate channels (Goswami, 1985; Sarma, 2005; Sarker et al., 2003). The Brahmaputra enters Bangladesh east of Bhabanipur (India) and northeast of Kurigram district. Bramhaputra is locally called Jamuna after merging with Teesta in Bangladesh. It first flows south and joining Padma, it then turns southeast and travels to meet the Meghna near Chandpur. It is the widest river system in the country. The flow of the Brahmaputra- Jamuna is more erratic than that of the Ganges. Bramhaputra has very dynamic and irregular banks due to severe erosion. Knowledge about the dynamics of the bank can be extracted by quantifying the fluvial and morphological characteristics of this complex braided river system. Different braiding indices have been introduced by Lane (1957), Leopold and Wolman (1957), Henderson (1961) but Antropovskiy (1972) but they failed to identify the control of channel pattern by sedimentology (Sharma, 2004). Considering the fact, Sharma (2004) has introduced two indices, Plan Form Index (PFI) and Flow Geometry Index (FGI) to identify the degree of braiding of the braided river. Advance in remote sensing
1044 has enabled us to observe extensively the channel pattern and bar formation using different satellites images and quantifying the characteristics. In this study, two indices were calculated for nearly 260 Km of Bramhaputra-Jamuna river reach in Bangladesh using remote sensing images of different years to quantify the braiding of Bramhaputra Jamuna braided river system.
METHODOLOGY According to Sharma (2004), Plan Form Index (PFI) represents the percentage of the actual flow width over the overall river width per braid channel and thus reflects the fluvial landform disposition with respect to a given water level. On the other hand, The Flow Geometry Index reflects the underwater sub-channel disposition to depict hydraulic efficiency of braided river systems. Equation [1] and [2] describes the relationship between flow top width, overall river width and number of braid channels with respect to PFI and FGI.
푇 ×100 푃퐹퐼 = 퐵 (1) 푁 ∑ 푑푖.푥푖 퐹퐺퐼 = × 푁 (2) 푅.푇 Fig. 1. Study Area and different reaches
Where, T = Flow top width, B = Overall river width, N = Number of braid channels, R = Hydraulics mean depth of the stream, di and xi are depth and width of submerged subchannel.
In this study, we have followed the methodology described by Sharma (2012) and represented in the flow chart in Fig. 2.
Data Collection: Satellite Images pre- Preparation of NDWI 1. Satellite Images processing, geo-referencing images and delineation of
2. BWDB Observation and analysis, Observed data river shorelines and bars Data processing
Fixing Base line,
River Center Line and Calculation of PFI and dividing river into FGI Indices different reaches
Fig. 2. Flow chart of the processes followed.
Data Collection, Processing and Shoreline Delineation Available images from 1978-2017 of Landsat MSS, TM, ETM+, and OLI were analysed, and high- quality images were selected to process for calculating PFI. Obtained images described in Table 1 were collected from the Global Data Explorer which is maintained by United States Geological Survey (USGS). Collected images were ortho-rectified in UTM (Universal Transverse Mercator) projection
1045 system in datum WGS 84 and Zone 46-47. Finally, images from 1980, 1991, 2001, 2011 and 2017 were selected for analysis. Images were mosaiced and clouds were removed using photogrammetry tools.
Table 1. Acquired Satellite Images for analysis Sl. No. Date Acquired Landsat Mission Name Sensor ID Path No Row No 1 16/01/1980 Landsat 3 MSS 148 042, 043 2 29/12/1991 Landsat 5 TM 138 042,043 3 20/11/2001 Landsat 7 ETM 138 042, 043 4 02/11/2011 Landsat 7 ETM 138 042, 043 5 10/12/2017 Landsat 8 OLI TIRS 138 042, 043
Water level of 46.9L and River cross section data of J13 were collected from Bangladesh Water Development Board for 1979, 1992, 2001, 2013 and 2016. The date and year of the surveyed cross section data did not coincide with the satellite images intended to calculate PFI due to unavailability of the observed data. FGI was calculated near Bahadurabad for the year 1979, 1992, 2001, 2012 and 2016. Water level station 46.9L of BWDB and Jamuna river cross section J13 was used for calculation. For calculating NDWI (Normalized Difference Water Index), Eq. (3) has been used when SWIR band was available, and Eq. (4) was used when SWIR were not available.
푆푊퐼푅−푁퐼푅 푁퐷푊퐼 = (3) 푆푊퐼푅+푁퐼푅
퐺푟푒푒푛−푁퐼푅 푁퐷푊퐼 = (4) 퐺푟푒푒푛+푁퐼푅
Fixing Baseline, Centreline and Dividing into Reaches Line drawn through 90.01E and 25.99N was used as baseline. Through meticulous observation of the centreline of the river shapefile obtained from image analysis, centreline of the river was drawn. Starting for Dhubri (India), then whole river length up to Aricha in Bangladesh was divided into 26 reaches, each spanning not more than 10 Km and successive numbers were assigned to these reaches. Numbering of reaches were done from upstream to downstream. Locations of the different reaches are described in Table 2.
Table 2. Different Reaches and their identifying locations. Reach No Approximate Location Reach No Approximate Location 1 Dhubri/Phulbari (India) 14 Mathurpara-Milanpur 2 Phulbari/Muthakhowa Pt. (India) 15 Nawkhila 3 Muthakhowa Pt./Salapara (India) 16 Dhunot 4 Salapara/Kurigram Border 17 Kazipur 5 Rowmari 18 Sirajganj 6 Ulipur 19 Sirajganj/Bhuapur 7 Chilmari/Haripur 20 Jamuna Bridge 8 Kamarjani 21 Belkuchi 9 Kamarjani/Fulchari 22 Shahzadpur 10 Kristomonichar/Kholabarichar 23 Chauhali 11 Bahadurabad 24 Daulapur/Bera 12 Saghata 25 Mathura 13 Shariakandi 26 Aricha
Each of the 10 Km segment was further divided into approximately 1 Km segment and ten (10) cross sections were drawn in each to calculate PFI. Average PFI value of these 10 cross sections were taken as the PFI value of the reach. Total 260 cross sections were drawn and used to calculate top flow width, overall river width and bar number of the river. Location of the cross sections were fixed for all five
1046 years that we have analysed to enable comparison over the years. FGI was calculated according to Equation (2) for available years near Bahadurabad.
Sample Calculations for Reach 11 In the year 2017, Cross section 1 of Reach 11 (named as C11_1) had the flow top width of 4.55 Km while total width was calculated as 12.40 Km. Total number of braided channels calculated at this section was 4. Using these values in the Equation (1) we get the following result, 4.55 ×100 푃퐹퐼 = 12.40 = 4.59. Averaging the PFI value calculated in other cross sections (C11_2 to C11_10) 8 we found the PFI value of the Reach 11 as 4.29. This calculations were repeated for other reaches (Reach 1 to Reach 26).
Similarly, FGI was calculated for the XS J13 of BWDB which falls in the Reach 11 of our study. In 2017, Hydraulic Mean depth of the cross section was calculated as 2.574Km, Top flow width was 2100Km and Number of braided channels was found 4 throughout this cross section. Depth and width of every braided channel were calculated and ∑dixi was calculated as 14842.57. Using these values in ∑ 푑푖.푥푖 14842.57 the Equation (2) we found the following result, 퐹퐺퐼 = × 푁 = × 4 = 10.88. Similar 푅.푇 2.57 푋 2120 procedure was used to calculate FGI for other years.
RESULTS AND DISCUSSIONS Analysis result of PFI for 1980, 1991, 2001, 2011 and 2017 are incorporated in the Table 3 below.
Table 3. Calculated PFI for different reaches and Comparison over the times. Braiding Braiding Braiding Braiding Reach 1980 1991 between 2001 between 2011 between 2017 between Braiding from 1980 Braiding Notes No 1980 and 1991 and 2001 and 2001 and to 2017 in 2017 PFI PFI 1991 PFI 2001 PFI 2011 PFI 2011 1 8.95 5.42 Increased 4.83 Increased 5.21 Increased 4.73 Increased Slightly Increased M 2 3.51 1.97 Increased 1.87 Increased 2.40 Decreased 2.00 Increased Slightly Increased H 3 2.77 1.71 Increased 1.88 Increased 2.01 Decreased 2.22 Decreased Slightly Increased H L = Low 4 6.40 5.51 Increased 3.72 Increased 3.81 Decreased 3.72 Increased Severely Increased H Braided, 5 8.44 6.52 Increased 6.58 Decreased 5.98 Increased 7.14 Decreased Slightly Increased M [PFI > 19] 6 9.06 7.18 Increased 7.31 Decreased 7.11 Increased 5.75 Increased Increased M 7 9.47 3.13 Increased 4.58 Decreased 4.63 Decreased 5.17 Decreased Increased M 8 6.21 4.35 Increased 4.81 Decreased 4.30 Increased 4.50 Decreased Increased M 9 10.17 6.84 Increased 7.90 Decreased 6.04 Increased 5.86 Increased Increased M 10 6.68 7.09 Increased 8.64 Decreased 7.94 Increased 5.97 Increased Increased M 11 7.63 5.43 Increased 5.01 Increased 4.59 Increased 4.29 Increased Increased M M = 12 7.81 6.54 Increased 5.66 Increased 4.53 Increased 3.72 Increased Severly Increased H Moderately 13 7.43 7.49 Increased 8.06 Decreased 8.19 Decreased 6.78 Increased Slightly Increased M Braided, 14 6.10 6.05 Increased 5.14 Increased 9.08 Decreased 10.64 Decreased Decreased M 4 < PFI < 15 9.01 10.39 Decreased 4.82 Increased 6.67 Decreased 10.91 Decreased Slightly Decreased M 19 16 5.42 8.15 Decreased 7.90 Decreased 6.54 Increased 7.26 Decreased Decreased M 17 6.64 4.29 Increased 3.79 Increased 4.40 Decreased 5.17 Decreased Slightly Increased M 18 7.51 3.86 Increased 4.78 Decreased 4.35 Decreased 3.23 Increased Severly Increased H 19 7.18 4.30 Increased 4.22 Increased 5.24 Decreased 5.92 Decreased Increased M 20 10.27 12.63 Decreased 12.42 Increased 12.43 Stable 15.90 Decreased Decreased M 21 8.93 7.79 Increased 7.55 Increased 7.52 Increased 10.17 Decreased Decreased M H = Highly 22 9.20 6.16 Increased 6.17 Decreased 11.46 Decreased 8.55 Increased Slightly Increased M Braided, 23 8.88 8.38 Increased 7.25 Increased 7.18 Increased 4.88 Increased Severly Increased M PFI < 4 24 10.76 6.82 Increased 6.40 Increased 4.55 Increased 4.21 Increased Severly Increased M 25 8.98 7.21 Increased 8.59 Decreased 7.24 Increased 6.17 Increased Slightly Increased M 26 19.41 19.12 Increased 9.85 Increased 10.13 Decreased 9.48 Increased Severly Increased M
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The result of the analysis shows that the, Bramhaputra river is highly braided at the reaches near India-Bangladesh border. Reach near the Salapara-Kurigram border experienced severe increase of braiding over the years. It can be seen from the obtained values of table 3 that the values of PFI in increased significantly from 1980 to 1991. For the consecutive 10 years, the PFI values did not increase significantly from 1991 to 2001. Then the braiding began increasing and finally in 2017, the PFI of the reach is calculated as 3.72, indicating high braiding. Reach no. 20, where Jamuna Bridge crossed this mighty river, was the only reach that was found stable and braiding Fig. 3. Reach wise Mean PFI over the Years decreased significantly over the times. But the reach only 20Km upstream of it is highly braided and braiding has severely increased since 1980. Fig. 3 above depicts the change in PFI over the times. Reach 26 near Aricha where Jamuna meets Ganges, PFI has been decreased drastically since 1980. In 80’s the reach was low braided with
Fig. 4. (a) Reach wise maximum PFI value and (b) Reach wise minimum PFI value over the years. PFI=19.41 but now it is moderately braided with PFI=9.48. Reach 7 also experienced severe increase in braiding after 1980. Fig. 4 and Fig. 5 below depict the change of extreme values over the years. Both figures show that upstream section of Bramhaputra-Jamuna have not experienced drastic or severe change while downstream section experienced severe change over the years. Maximum value of PFI was found 49.81 for reach no 26 in 80’s but it has decreased in recent years significantly.
Table 4. FGI of Reach 11 (XS J3) Sl Year Month Maximum FGI Braiding Intensity Notes No. Discharge 1 1979 April 4600 10.95 Moderately Braided Moderately Braided: 2 1992 January 7080 2.81 Low Braided 7 < FGI <35 3 2001 February 4772 5.52 Low Braided Highly Braided: 4 2012 February 3521 7.41 Moderately Braided FGI >35 5 2016 March 2036 10.87 Moderately Braided
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Flow Geometry Index of J13 is tabulated in the Table 4 which shows that the reach is moderately braided.
CONCLUSIONS Brahmaputra-Jamuna is the most energetic and has the highest stream power among all the major rivers. Bramhaputra-Jamuna braided river system in Bangladesh is mostly classified as moderately braided. Analyzing the PFI of Brahmaputra-Jamuna for different years ranging between 1980 and 2017, and upon comparison of the results, it is evident that braiding is gradually increasing and approaching to the value where the river system can be classified as highly braided. Highly braided rivers exhibit the tendency of formation of higher number of bars, and potential of erosion and accretion. Besides PFI, another index FGI representing underwater sub-channel disposition and hydraulic efficiency of the braided river system was calculated for different years ranging between 1979 and 2016. The obtained values of FGI classified the Brahmaputra-Jamuna river system to be moderately braided. This study provides broad range of classification of braided river system. It also quantifies the braiding by numerical values with certain threshold and enables us to understand the progressive stages of the braiding of the reach.
REFERENCES Goswami, D. C. 1985. Brahmaputra River, Assam, India: Physiography, basin denudation, and channel aggradation. Water Resources Research, 21,959-978. Immerzeel, W. 2008. Historical trends and future predictions of climate variability in the Brahmaputra basin. International Journal of Climatology, 28(2), 243-254 Sarkar, R. Garg and N. Sharma, 2012. RS-GIS Based Assessment of River Dynamics of Brahmaputra River in India, Journal of Water Resource and Protection, Vol. 4 No. 2, 2012, pp. 63-72. doi: 10.4236/jwarp.2012.42008. Sarker, M. H., Huque, I., Alam, M., & Koudstaal, R. 2003. Rivers, chars and char dwellers of Bangladesh. International Journal of River Basin Management, 1:1, 61-80 Sarma, J. N. 2005. Fluvial process and morphology of the Brahmaputra River in Assam, India. Geomorphology, 70(3), 226-256. Sharma N. 2004. Mathematical Modelling and Braid Indicators. In: Singh V.P., Sharma N., Ojha C.S.P. (eds) The Brahmaputra Basin Water Resources. Water Science and Technology Library, vol 47. Springer, Dordrecht
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
HYDROLOGICAL AND MORPHOLOGICAL ANALYSIS OF THE GORAI RIVER
S. Rahman, T. Islam* & M. A. Rahman
Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. E-mail: [email protected]; [email protected]*; [email protected] *Corresponding Author
ABSTRACT Gorai river is one of the major distributaries of the Ganges river and also the major source of freshwater in the South-western region of Bangladesh. After construction of Farakka Barrage, the flow of the river has reduced significantly. In contrast to the decrease in the (low-water) discharge of the river, the annual sedimentation of sand in the river has increased, hindering the safe passage of flow from Ganges to Gorai river. This contributes to the change in hydrological and morphological characteristics of the river. The aim of this study is to observe the hydrological and morphological characteristics of the river and develop a hydrodynamic model using Delft3D to determine and compare the flow parameters before and after dredging for different flow conditions. This study shows that a flood with a return period of 50 years and 100 years will have a discharge of 10810 cumec and 12050 cumec and a water level of 14.15 mPWD and 14.50 mPWD respectively at Gorai Railway Bridge. The total erosion and deposition in the study area from 2006 to 2016 are found to be 57.34 Ha and 91.89 Ha respectively. The shifting of the banklines provides an idea about the change of the course of the river in between these 10 years. According to the steady hydrodynamic model, for average, minimum and maximum flow in pre-dredged condition diversion of water from Ganges to Gorai are found to be 758 cumec, 0 cumec and 5800 cumec respectively. After dredging, these values increase to 940 cumec, 59 cumec and 7610 cumec respectively. It is hoped that further development of the hydrodynamic model will lead to a stable solution to keep the flow continuous throughout the year.
Keywords: Hydrology; Morphology; Dredging; Steady Hydrodynamic Model; Delft3D
INTRODUCTION Bangladesh is a riverine country with about 405 rivers flowing over it [BWDB, 2011]. Most of Bangladesh lie within the GBM (Ganges-Brahmaputra-Meghna) Basin where the rivers tend to show great deal of changes in long time span (Figure-1). Gorai river is one of the major right-bank distributaries of the Ganges river having length of about 86 kilometers with varying width and 5 centimeters per kilometer gradient for flood flow [BWDB, 2011]. It flows south-eastward gradually becoming Madhumati, Baleswar and finally discharges into the Bay of Bengal as Haringhata River covering five districts which are Kushtia, Rajbari, Jhenaidah, Magura and Faridpur (Figure-2). Gorai river is of great importance for the supply of fresh water for drinking and irrigation purposes for the people living in this region. But this river stores a huge load of sediment and becomes non-navigable in dry season. So it undergoes a massive morphological change due to the storage of huge load of sediment. This rapid change in the bathymetry of the river causes change in its hydrological properties too. The river becomes discontinuous at places in the dry season due to the accretion of sediment in the upper reach of the river which gradually decreases its width. For this, the flow during the rainy season
1050 cannot pass properly resulting flooding on both sides of the river. That is why dredging is performed at the upper reach of the river at a regular interval to keep the river flowing throughout the year.
Fig. 1: Ganges-Brahmaputra-Meghna Basin (GMB) Fig. 2: Gorai River Catchment Area (Islam, (Wikimedia Commons) S.N.)
The main goal of this study is to study the hydrological and morphological characteristics of Gorai river and perform hydrodynamic analysis of the river at the Ganges-Gorai bifurcation using Delft3D model. In order to study the hydrological and morphological characteristics of the Gorai River a reach starting from the Gorai Railway Bridge to 30 kilometers downstream containing 5 BWDB cross sections from GM 6 to GM 10 has been selected (Figure-3a). To perform the hydrodynamic analysis at the Ganges-Gorai bifurcation, a reach of about 50 kilometers in the Ganges and a reach of about 30 kilometers in the Gorai river has been selected (Figure-3b).
(a) (b) Fig. 3: Study Area for (a) Hydrological and Morphological Analysis, (b) Hydrodynamic Analysis
METHODOLOGY For the study different sets of data named hydrologic data (discharge and water level), bathymetric data and satellite images have been used. For the hydrological analysis, discharge data at Hardinge Bridge and Gorai Railway Bridge and the water level data at Hardinge Bridge, Sengram, Gorai Railway Bridge and Kamarkhali Transit for the years 1970-2016 have been collected from Bangladesh Water Development Board (BWDB). For the morphological and hydrodynamic analysis, bathymetric data of Gorai River and a portion of Ganges River have been collected from BWDB for the years 2000-2016. In morphological analysis, satellite images near Gorai Railway Bridge have been used from Google Earth for two years 2006 and 2016 to determine the erosion and deposition in between these 10 years.
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For the purpose of hydrological and morphological study of the Gorai River the collected data were processed using combination of multiple softwares. Among the softwares Microsoft Excel, ArcGIS and Delft3D were used most frequently for analyzing the collected data. Hydrological Analysis Hydrological analysis mainly involves analysis of water level and discharge. Maximum, minimum and average discharge and water level are determined for certain stations. Also water level and discharge of floods with certain return period are determined using Gumbel’s Method (the most common method of frequency analysis). Gumbel’s equation giving the value of the variate X with a recurrence interval T is expressed as-
In this study, maximum, minimum and average water level at Gorai Railway Bridge have been calculated from the water level data using Microsoft Excel. Maximum water levels of every year then used in Gumbel’s equation and the water levels of floods with 50 years and 100 years return period were determined. Then using the calculated data from Gumbel’s method flood frequency curves have been generated for water level at Gorai Railway Bridge. Similar thing has been done for discharge data at Gorai Railway Bridge discharge station.
Morphological Analysis using Cross-Sectional Data Cross-sections of GM 6 to GM 10 generated for 2006 and 2016 from the cross-sectional data using Microsoft Excel were superimposed to observe the changes. From the superimposed cross-sections of 2006 and 2016 the shifting of the lowest bed level at GM 6 to GM 10 were determined.
Morphological Analysis using Bathymetric Data and Satellite Images This analysis involves determination of erosion-deposition and shifting of banklines. Google Earth and ArcGIS have been used to collect and process the data and then perform the analysis. To determine erosion-deposition of the study area for a time period of 10 years of 2006-2016, firstly 2 satellite images of the study area corresponding to years 2006 and 2016 were collected from Google Earth and then processed in ArcGIS. After georeferencing properly the banklines of the study area were digitized and line shape files were created. Polygons were created from the intersections of the baklines for different years and the area of the polygons were calculated and sorted as erosion or deposition. Summing up these areas total erosion-deposition of the study area has been determined for 10 years. To determine the shifting of banklines, the created shape files of the banklines were used. Superimposing the banklines of the years 2006 and 2016 the change has been observed.
Hydrodynamic Analysis using Delft3D Model Hydrodynamic analysis of the Ganges-Gorai bifurcation has been performed using Delft3D model. Using sufficient data, a steady hydrodynamic model was developed to predict the diversion of water from Ganges to Gorai under different flow conditions. Locations of the boundaries and the reaches for this hydrodynamic model are shown in Figure-3(b). To set up the model, firstly in Delft3D-RGFGRID, an xyz file was created using the bathymetry data and sample points were plotted (Figure-4). Then splines are drawn over the sample points carefully, covering the study area which then converted to a grid and further refined according to the need and orthogonalized to create a convenient grid (Figure-5). This grid and xyz file was then imported in
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Delft3D-QUICKIN, using triangular interpolation process a depth file was created and further refined using smoothing process to create a convenient depth file (Figure-6).
Figure 4: Points Plotted Figure 5: Grid Generated Figure 6: Depth Generated Using Delft3D-RGFGRID Using Delft3D-RGFGRID Using Delft3D-QUICKI Grid, Grid Enclosure and Depth files were used to define the domain in Delft3D-FLOW module. Average discharge at Hardinge Bridge was used as the upstream boundary condition. Average water levels at Sengram and Janipur were used as the two downstream conditions. 5 observation points and 2 cross sections were defined for monitoring the conditions throughout Ganges-Gorai river. A steady simulation was performed for a month with a time step of 2 minutes. Same model was run for maximum and minimum discharge and water level and thus diversion of water from Ganges to Gorai was determined for average, maximum and minimum flow. Furthermore, a dredged section of 12 km with a depth of 5 m starting from the bifurcation to the Gorai Railway Bridge was selected to observe the changes in flow and for that similar procedure was applied with modified grid and depth files for the dredged channel and diversion of water was determined for all the cases in dredged condition. The model was calibrated at the Hardinge Bridge and validated at the Gorai Railway Bridge and Talbaria under four different flow conditions of maximum flow, minimum flow, wet period flow and dry period flow with the value of Manning’s n taken as 0.025.
Figure 7: Model Calibration for Water Level at Hardinge Bridge (Left Side), Model Validation for Water Level at Gorai Railway Bridge (Right Side)
Figure 8: Model Validation for Discharge at Gorai Railway Bridge (Left Side), Model Validation for Water Level at Talbaria (Right Side) RESULT AND DISCUSSION Hydrological Analysis For hydrological analysis water level and discharge data were analyzed. Hydrographs were generated and floods were predicted for return period of 50 and 100 years. The results are presented in Table-1 andFigure-9 Table 1: Flood Discharges (Left Side) and Water Levels (Right Side) Corresponding to Return Periods at Gorai RailwayBridge Return Period Magnitude of Flood (Year) (cumec) 5 6540 10 7875 25 9560
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50 10810 Return Period Water Level of Flood 100 12050 (Year) (mPWD) 200 13290 5 12.96 10 13.33 25 13.80 50 14.15 100 14.50 200 14.85
Figure 9: Flood Frequency Curve Using Gumbel’s Method at Gorai Railway Bridge Morphological Analysis From the superimposed plots of GM 6 to GM 10 the changes in the cross-sections within the years 2006 and 2016 are observed (Figure-10 to Figure-14).
Figure 10: Cross-section at Station GM 6 (44m Figure 11: Cross-section at Station GM 7 (No shifting shifting of lowest bed level towards left bank) of lowest bed level towards any bank)
Figure 12: Cross-section at Station GM 8 (4m Figure 13: Cross-section at Station GM 9 (134m shifting of lowest bed level towards right bank) shifting of lowest bed level towards left bank)
Figure 14: Cross-section at Station GM 10 (1m shifting of lowest bed level towards right bank) From the satellite images of the years 2006 and 2016, erosion-deposition and bankline shifting are,
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Figure 15: On 28 November, 2006 (Left Side), On Figure 16: Total Eroded and Deposited 13 April, 2016 (Right Side) Areas in the Study Area
Figure 17: (a) Erosion-Deposition(a) and (b) Shifting of Banklines at Study(b Area) (Gorai Railway Bridge to 30 km downstream) from 28 November, 2006 to 13 April, 2016 Hydrodynamic Analysis In the selected reach of Ganges-Gorai river system steady hydrodynamic simulation was performed for average, minimum and maximum water flow in both pre-dredged and post-dredged conditions. Results are tabulated below and other parameter changes are shown in Figure-18 to Figure-19. Table 2: Diversion of Water from Ganges to Gorai River Discharge in Ganges Discharge in Gorai Condition Diversion Rate (%) (cumecs) (cumecs) Pre-dredged 758 13.46 5633(average) Post-dredged 940 16.69 Pre-dredged 0 0 850(minimum) Post-dredged 59 6.94 Pre-dredged 5800 11.69 49600(maximum) Post-dredged 7610 15.34
Figure 18: Water Depth Before and After Dredging Figure 19: Depth Averaged Velocity Before and After Dredging CONCLUSION In this study, hydrological and morphological characteristics of the Gorai river have been analyzed and steady hydrodynamic analysis of Gorai River at the Ganges-Gorai bifurcation has been performed using Delft3D model. This study reveals that a flood with a return period of 50 years and 100 years will have a discharge of 10810 cumec and 12050 cumec and a water level of 14.15 mPWD and 14.50 mPWD
1055 respectively at Gorai Railway Bridge. The total erosion and deposition in the study area from 2006 to 2016 are found to be 57.34 Ha and 91.89 Ha respectively. Among the cross-sections lowest bed level at GM 9 shifts the most. It shifts 134 m towards left bank from 2006 to 2016. According to the steady hydrodynamic model, for average, minimum and maximum flow in pre-dredged condition diversion of water from Ganges to Gorai are found to be 758 cumec, 0 cumec and 5800 cumec respectively. After dredging, these values increase to 940 cumec, 59 cumec and 7610 cumec respectively.
REFERENCES BWDB (2011). Rivers of Bangladesh, Bangladesh Water Development Board. Deltares (January, 2011) User Manual Delft3D. Talukder, M.A. (June, 2014), Mid-term Evaluation of Gorai River Restoration Project, Phase-II, Individual consultant.
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
2D HYDRODYNAMIC MODELING OF GANGES-GORAI RIVER USING HEC-RAS 2D MODEL
T. Islam*& M. M. Ali
Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. E-mail:[email protected]*; [email protected] *Corresponding Author
ABSTRACT The southwestern region of Bangladesh is extremely dependent on the percentage of flow sharing in the Ganges-Gorai river system to meet requirements for agriculture, fisheries, navigation and domestic and industrial sectors. After construction of Farakka Barrage on the Ganges River, Ganges water flow has reduced significantly in the downstream and due to that the water shortage and sedimentation process in the Gorai basin has become prominent, its width has shrunk and the depth has deduced in different section of the river. This contributes to the change in hydrodynamic characteristics of the river. In this study, a two-dimensional hydrodynamic model for the Ganges-Gorai river system has been set up using HEC-RAS 2D modeling software in order to estimate the average low flow and percentage of flow diversion rate in the Ganges-Gorai bifurcation during dry season under different dredging scenarios. 2D unsteady flow simulation was performed with three different dredging scenarios that varied with changing bed level only, changing bottom width only and changing both the bed level and bottom width combinedly. The results revealed that during the dry season, both the increase in bottom width and lowering of bed level (which can be defined as the most efficient strategy) increase the rate of flow diversion up to 18.62% from Ganges to Gorai and can convey up to 407.85 cumec average low flow through Gorai river. The present hydrodynamic model provides an opportunity for further development to be undertaken considering the river restoration.
Keywords: Ganges-Gorai River; Dredging Scenario; River Restoration; Hydrodynamic Model; HEC-RAS 2D
INTRODUCTION Bangladesh is known as a deltaic plain at the confluence of the Ganges-Padma, Brahmaputra-Jamuna and Meghna rivers basins (GBM) and their tributaries (Figure 1). Being part of these three major river systems of South Asia, the country is bestowed upon by the innumerable resources from the rivers. About 700 rivers including tributaries flow through the country constituting a waterway of total length around 24,140 km. In general, diversion of river flow in the upstream plays an important role in the major characteristics of the rivers. Most rivers in south-western region (SWR) of Bangladesh depend on water flow from Ganges river. Many of the branches of Ganges river dry out during the dry season due to low inflow of Ganges river as well as significant sedimentation. The Gorai river is the major distributary of Ganges river and the main source of fresh surface water in the SWR of Bangladesh. For drinking and irrigation purposes people of this region largely depend on this river. This river is also one of the major regional navigation routes. The economy and environment of this region is very much dependent on this river. It takes off from the Ganges at Talbaria, north of Kushtia town and 17 km downstream from Hardinge Bridge and flows south-eastward gradually becoming Madhumati, Baleswar and finally discharges into the Bay of Bengal as Haringhata River (Figure 2).
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Fig. 1: Ganges-Padma, Brahmaputra-Jamuna Fig . 2 : Ganges - Gorai River System and Meghna Rivers Basins (GBM)
During the dry season (January to May), the flow of Gorai river decreases rapidly. This declining flow through Gorai river is a matter of concern for the SWR of Bangladesh. The hydrodynamic study of the Gorai river is of much importance for sustaining the socio-economic and environmental balance of the south-western region of Bangladesh. In this study, the effect of various dredging conditions in the Gorai river has been analyzed using 2D hydrodynamic model to increase the percentage flow sharing of Ganges-Gorai river and maintain a continuous flow through Gorai river. The main objective is to analyze the percentage of dry seasonal flow diversion from Ganges to Gorai river in different dredging conditions, to set up a two-dimensional hydrodynamic model for the Ganges-Gorai river using HEC-RAS 2D model and to estimate average low flow and flow diversion rate in the Ganges-Gorai bifurcation during dry season under different dredging scenarios. The Gorai river is about 86 kilometers in length with width varying along the length. It has an average width of 280 meters with maximum of 560 meters and minimum of 150 meters. It is a meandering river. Its gradient for flow is 5 centimeters per kilometer [BWDB, 2011].
Fig. 3: Study Area in Satellite View (Left Side) and in Map View (Right Side)
In this study, a 50 km reach of Ganges river starting from Hardinge Bridge to Sengram and 30 km reach of Gorai river starting from Talbaria to Janipur have been taken as the study area, along with that a 12 km long dredged channel of 67 m bottom width and 2.5 mPWD bed level along the thalweg of Gorai river from Gange-Gorai bifurcation to Gorai Railway Bridge has been considered as the existing post-dredged condition of Gorai river (Figure 3).
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METHODOLOGY For the study different sets of data named bathymetric data and hydrologic data (discharge and water level) have been used. For the hydrodynamic analysis, discharge data at Hardinge Bridge and Gorai Railway Bridge and the water level data at Hardinge Bridge, Sengram, Gorai Railway Bridge and Kamarkhali Transit for the years 1970-2016 have been collected from Bangladesh Water Development Board (BWDB). Bathymetry data of Gorai River and a portion of Ganges River have been collected from BWDB for the year of 2016. To set up the model preprocessing was necessary in GIS which included generation of the point shape file from river bathymetry data and creation of bathymetric surface grid using kriging interpolation method. After completion of interpolation, resampling of river bathymetry has been done to grid size of 2 meter. The created topographic grid contains some default topographic data outside the river that are not appropriate but since only the river bathymetric grid is needed for model setup, it can be ignored. The complete bathymetric grid has been shown in Figure 4. This raster file (bathymetric grid) was then exported to TIFF format for the further use in 2D modeling in HEC-RAS for developing a terrain model.
(a) (b) Fig. 4: (a) Generation of Point Shapefile from Bathymetry Data, (b) Creation of Bathymetric Grid from Point Shapefile (Raster File)
For further analysis, many other raster files have been created in ArcGIS for representing the different dredging scenarios by reducing the bed level and changing the width of the existing dredged channel along the Gorai river. This has been done firstly by changing the depth and width of the dredged channel’s raster and then finally merging those rasters with the initial raw raster file. For setting up the 2D hydrodynamic model in HEC-RAS, the raster file (bathymetric grid) created earlier in ArcGIS was imported in Ras Mapper to create Terrain. Using this Terrain model in Geometric Data, 2D computational mesh have been generated within the edges of the Ganges-Gorai river system. In the mesh, cell spacing has been taken as 50m x 50m in Ganges river and around 25m x 25m in relatively narrow Gorai river. Total 57983 cells were obtained in the mesh. Then the flow area has been selected and the boundary lines have been drawn in this Geometric Data (Figure 5). After defining 2D flow area, boundary lines and computational mesh; boundary condition has been provided. Flow hydrograph of the year 2014 at Hardinge Bridge Station has been provided as the upstream boundary condition. For downstream boundary conditions, stage hydrographs of the same year at the two downstream stations (Janipur and Sengram) have been provided. The model has been calibrated hydro-dynamically through the adjustment of Manning’s roughness coefficients. For the post dredged condition analysis of this study, the observed flow data that have been collected were of post-dredged condition but the Fig. 5: Boundary Condition Lines (Red Circled) in bathymetry data was of pre-dredged condition of the 2D Flow Area along with the Mesh
Gorai river. So, some adjustments in the bathymetry data (a 12 km long dredged channel of 67 m bottom width and 2.5 mPWD bed level along the thalweg of Gorai river from Gange-Gorai bifurcation to Gorai Railway Bridge has been added to the Terrain to represent the existing post-dredged condition of the Gorai river) along with the adjustment of Manning’s roughness coefficient, ´n´ have been made to calibrate the model. The model has been simulated using the daily hydrograph for five months from January to May for the year of 2014 and calibrated at the Hardinge Bridge Station and Gorai Railway Bridge Station considering Manning's `n' value as 0.03. It was also validated at the Gorai Railway Bridge Station for the year of 2012 with the calibrated Manning's `n' value as 0.03. Calibration and validation graphs are shown in Figure 6 to 9.
Y = 0.8064x + 1.4154 R² = 0.9603
Fig. 6: Model Calibration (Left Side) and Regression Analysis (Right Side) for Ganges Water Level at Hardinge Bridge Station
Y = 0.757x + 0.9507 R² = 0.9038
Fig. 7: Model Calibration (Left Side) and Regression Analysis (Right Side) for Gorai Water Level at Gorai Railway Bridge Station
Y = 1.0394x – 5.5371 R² = 0.9645
Fig. 8: Model Calibration (Left Side) and Regression Analysis (Right Side) for Gorai Discharge at Gorai Railway Bridge Station
Y = 0.9024x + 0.3675 R² = 0.9902
Fig. 9: Model Validation (Left Side) and Regression Analysis (Right Side) for Gorai Water Level at Gorai Railway Bridge Station
RESULT AND DISCUSSION Under the existing dredged condition, which is a channel of 12km long, 67m bottom width and 2.5mPWD bed level along Gorai river; the average and percentage of flow through Gorai during dry period (January to May) is calculated to be 151.63 cumecs and 6.92% respectively for the year 2014. For analyzing the other different dredging scenarios, following conditions have been considered;
Effect of Different Bed Level For analyzing the effect of changes in bed level of the dredged channel, 2.25 mPWD, 2 mPWD, 1.75 mPWD and 1.5 mPWD bed levels have been considered with bottom width of the dredged channel fixed at 67 m. The results are represented in tabular form in Table-1 and in graphical form in Figure 10. Comparison of flow scenarios is shown in Figure 11.
Table 1: Flow Diversion from Ganges to Gorai River (Average Low Flow) Bed Level at Dredged Channel Average Low Flow in Gorai at Rate of Diversion (%) (mPWD) Railway Bridge (cumec) 2.5 151.63 6.92 2.25 165.26 7.55 2.00 174.69 7.98 1.75 186.54 8.52 1.50 198.43 9.06
Fig. 10: Average and Percentage of Flow Diversion Fig. 11: Flow Scenarios of Gorai River at Different from Ganges to Gorai River for Different Bed Levels Bed Levels
Effect of Different Bottom Width For analyzing the effect of changes in bottom width of the dredged channel, 80m, 100m, 125m and 150m bottom widths have been considered with bed level of the dredged channel fixed at 2.5 mPWD. The results are represented in tabular form in Table-2 and in graphical form in Figure 12. Comparison of flow scenarios is shown in Figure 13.
Table 2: Flow Diversion from Ganges to Gorai River (Average Low Flow) Bottom Width at Dredged Average Low Flow in Gorai at Rate of Diversion (%) Channel (m) Railway Bridge (cumec) 67 151.63 6.92 80 220.04 10.05 100 273.27 12.48 125 294.38 13.44 150 326.50 14.91
Fig. 12: Average and Percentage of Flow Diversion Fig. 13: Flow Scenarios of Gorai River at Different from Ganges to Gorai River for Different Bottom Widths Bottom Widths
Combined Effect of Different Bed Level and Bottom Width For analyzing the combined effect of changes in both bottom width and bed level of the dredged channel, certain combinations have been considered and the following results have been obtained; Table 3: Average Flow and Rate of Diversion for Different Combinations of Bottom Width and Bed Level
BW (m) 67 100 150 BL (m) 2.5 151.63cumec, [6.92%] 273.27cumec, [12.48%] 326.50cumec, [14.91%] 2 174.69cumec, [7.98%] 316.88cumec, [14.47%] 370.88cumec, [16.93%] 1.5 198.4291cumec, [9.06%] 355.18cumec, [16.22%] 407.85cumec, [18.62%]
Fig. 14: Average Flow in Gorai River for Different Combinations of Bottom Width and Bed Level
CONCLUSION In this study, 2D hydrodynamic model for Ganges-Gorai river has been established in HEC-RAS along with some pre-processing in ArcGIS. The model has been calibrated for the year 2014 and validated for the year 2012 for both water level and discharge at Hardinge Bridge and Gorai Railway Bridge Station. This study reveals that during dry season, more flow can be obtained continuously in Gorai river by increasing the bottom width of the dredged channel and lowering its bed level combinedly, rather than just lowering the bed level only. It also reveals that a 12km long dredged channel of 67m bottom width with 2mPWD bed level may ensure the minimum flow requirement of 160 cumec whereas a channel of 150m bottom width with 1.5mPWD bed level may ensure the maximum requirement of 400 cumec discharge suggested by the BWDB (2017).
REFERENCES BWDB (2011). Rivers of Bangladesh, Bangladesh Water Development Board. BWDB (2017). Environmental Flow (E-Flow) Assessment of Gorai River, DWRE, Department of Water Development Board. US Army Corps of Engineers. Feb,2016. HEC-RAS 2D Modeling User’s Manual, Institute of Water Resources, Hydrologic Engineering Center.
4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
FLOODPLAIN ENCROACHMENT ANALYSIS USING HEC-RAS 1D MODEL AND 1D-2D COUPLED MODEL
N. M. Pieu*& A T M H. Zobeyer2
Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. E-mail:[email protected]; [email protected] *Corresponding Author
ABSTRACT Floodplains are an important part of a river flow which help reduce the flood peak by providing areas the flood water can spill into during a flood event, thus increasing the flood storage capacity of a river. Almost 40% of Bangladesh is covered by floodplains and with its exceeding population floodplains are being blocked or encroached by structures and landfills in many rivers of Bangladesh disregarding the law or the environment. It is thus important to know how flood flow reacts to different degrees of encroachment to determine the flood level due to encroachment. This study is conducted to determine and analyze the effect on flood level due to varying width and length constriction in the floodplain. For this, an ideal 1-D channel is modeled in HEC-RAS with different degrees and types of encroachment. The results showed encroachment across the width has very significant effect on flood level. The effect was studied further using a 1D/2D coupled HEC-RAS model of Balu River to determine how the water level changes in the coupled model due to encroachments. Preliminary results showed that only 20% encroachment caused a 24cm rise in water level.
Keywords: Floodplain encroachment; HEC-RAS model; 1D-2D coupled model; Balu River
INTRODUCTION Bangladesh is a thriving country with over 200 rivers and tributaries occupying almost 79% of the country. Almost 80% of its landmass is made up of fertile alluvial lowlands or floodplains. With a population of over 163 million and a small area of a little over 147 sq. km. population accommodation is a huge problem. In face of the growing population settlement in the floodplains and wetland is becoming more common, sometimes floodplains and wetlands are fully blocked and filled during construction of infrastructure at or near the floodplains, polluting the river water and reducing a its capacity to store flood water. Floodplain encroachment is the hindrance or blocking of the floodplain of a river that could potentially affect the flood flows. The blocking of the floodplain of a river that could potentially affect the flood flows with obstructions in the form of a bridge, building or landfill that increases the ground elevation in a floodplain encroaches it. This unchecked settlement in the floodplains will raise the flood peak during a flood event causing the floodwater to inundate greater percentage of low-lying areas even at normal floods during monsoon. This problem is rarely addressed but extensive encroachments occur disregarding the law and the environment. The riverbanks at Ashulia, Gabtali, Amin Bazar, Diabari, Chatbari, Birulia, Dour, Basila, Shoalmachi, Jhaochar, Ramchandrapur, and Narayanganj port areas and some parts of Munshiganj have been taken over by ready-mix factories, cement factories, brick-kiln owners, realtors and riverbed material traders. The situation is particularly bad in Dhaka, Narayanganj, Gazipur and Munshiganj, with the respective district administrations failing to protect public lands like riverbanks from encroachment. Major portions (23.33%) of the encroached land were used for industrial development as well as housing and developmental projects. The main adverse effect of encroachments was on Negative health impact.
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Other adverse effects included economic loss through obliteration of fisheries, scarcity of clean water, reduction of aesthetic value of the surroundings, etc. (Mahmood et al., 2017). Balu is one such river having its floodplain encroached in various locations. The Balu river, 24km north of Dhaka, originates from the Lakkhah and the old Brahmmaputra in Mymensingh and flows to Shitalakhya. The encroachers have occupied the Balu River from both side from Rupganj in Narayanganj to Kaliganj in Gazipur district and have already developed its flood basin for different projects even though a High Court order exists (2009) prohibiting construction of infrastructure about 50 meters from a river floodplain to protect the natural flow of a river. Unmonitored encroachments of the floodplains of rivers has led to river pollution in Shitalakhya, Buriganga and Turag River and upstream flood in Nepal. With the ever-growing population, encroachments into the floodplain cannot be avoided but it is imperative to know the extent of encroachment of floodplain should be allowed for particular structures so that the flood levels are not significantly increased. Also, the effect of fully blocking the floodplain across the width of the floodplain, as was done in the Turag River, will be evaluated by using Balu River as the study case. Although 1D–2D coupled models prove not to be satisfactory in the general case because only mass transfer is taken into account, while momentum transfer is often neglected (Pascal et al.,2011), the flow in complex floodplain cannot be accurately simulated using 1D model alone and so a 1D/2D coupled model could better describe a natural floodplain (Vojinovic et al.,2008; Li et al., 2009) This study is done by modeling a prismatic channel in one dimensional HEC-RAS to investigate the effect of encroaching the floodplain across the width and along the length of floodplain for an ideal rectangular channel. It is also done to investigate how this simple model compares to a floodplain encroachment of a 1D-2D coupled model of a natural river. 1D-2D coupled model of Balu River is developed for this. The floodplain of Balu River is encroached by different degrees to check the rise in water level for each case.
METHODOLOGY 1D ideal channel model: The cross section of a simple trapezoidal section is drawn for the prismatic channel for ease of analysis. The channel was of 28km and the main channel was taken to be 30m wide while the floodplains on each bank are taken 50 be 50m wide as shown in the figure 1. The length of the channel was chosen so that the change in water level was gradual. no_encro Plan: 1) Plan 05 7/16/2017
.03 .025 .03 22 Legend
EG 13MAR2017 2400 WS 13MAR2017 2400 Ground 20 Bank Sta
18
16 Elevation Elevation (m)
14
12
10 0 20 40 60 80 100 120 140 Station (m) Figure 1: The cross-section of the channel without encroachment of the floodplain
For the prismatic ideal channel, a discharge value is selected ensuring water floods the whole of the floodplain which has a higher value of manning’s roughness co-efficient compared to the main channel. Normal depth was used as the downstream boundary condition. HEC-RAS user’s manual is followed for encroachment analysis. A steady state run is initiated with encroachments before initiating the unstaedy run. In the steady encroachment, method 1 was used where the right and left encroachment river bank stations were specified. The left bank offset and and the right bank offsets are entered to identify the encroachment along the width of the specified cross section and upstream and downstream river stations (RS) are entered to specify the length of the channel along which encroachments are made. The ecroachments are then transferred to unsteady run where several different encroachment scenario was run in different profiles. There were 22 floodplain encroachment cases considered for the ideal channel and they are shown in table 1. Numbers 1 to 4 represent degrees of floodplain
1064 encroachments along the width of the channel while alphabets A to D represent encroachments made along the length of the channel. Two extra cases were considered for 50 and 25% width encroachment to analyse the changes in water better.
Table 1: The floddplain encroachment cases considered in the 1D run. % of floodplain % of floodplain length %of floodplain area case width encroached encroached encroached 1. 100 A. 25 25 1-A B. 20 20 1-B C. 15 15 1-C D. 10 10 1-D 2. 85 A. 25 21.25 2-A B. 20 17 2-B C. 15 12.75 2-C D. 10 8.5 2-D 3. 75 A. 25 18.75 3-A B. 20 15 3-B C. 15 11.25 3-C D. 10 7.5 3-D 4. 50 Extra 50 25 4-extra A. 25 12.5 4-A B. 20 10 4-B C. 15 7.5 4-C D. 10 5 4-D 5. 25 Extra 50 12.5 5-extra A. 25 6.25 5-A B. 20 5 5-B C. 15 3.75 5-C D. 10 2.5 5-D
1D-2D coupled model: Balu is a tidal river that is its flow and water level are influenced by tides. It has a length of about 22km an average width of 190m. It is located at the eastern border of the Dhaka metropolitan, the capital of Bangladesh. Its upstream station is located at Pubail at 23⁰55’N and 90⁰30’E. It is a very important river for drainage of flood water from Turag and Shitalakshya river during flood season and also for navigational purposes. It has only two hydrologic stations one in Pubail which is the upstream station and one in Demra which is the downstream station. Its right floodplain is at the eastern edge of Dhaka and its left floodplain is in Naryangonj. Over the past few years the left floodplain has been encroached by projects such as the Jalsiri project for Army housing, Purbachal project and the Zinda Park. The figure 2 shows the boundaries of the study area. The floodplain elevation data and river bathymetry data was obtained a secondary source, the department of Water Resources Engineering (WRE) of Bangladesh University of Engineering and Technology (BUET). The discharge and water level data was obtained from the two stations Demra and Pubail, also from BUET, was from1983 to 2012. A rating curve was generated to obtain the upstream discharge data for upstream boundary condition using known data in Demra using gage method. The flow chart shown in figure 3 describes the pre-processing done for the set up of the coupled model.
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(a)
( b )
Figure 2: (a) a map of the study area. (b) satellite image of the study area.
Figure 3: Flow chart of the pre-processing and set-up of the 1D/2D coupled model.
Flow hydrograph and stage hydrograph was used as the upstream and downstream boundary condition respectively. The model was run for 5 months from June to October, calibrated for the year 1988 and validated for the year 1985. The results of calibration and validation are shown in figure 4 and 5 respectively. The coefficient of determination for calibration was 0.988 while for validation was 0.97 which was achieved at n=0.04 for the floodplain section. The calibrated and validated model was then used to model the 2D floodplain encroached DEMs. Encroachements on the 2D plain was done by raising the DEM values at specific locations in the floodplain. This was done by merging the original raster file with a raster file created with a higher
elevation (16m), creating various 2D floodplain encroachment scenario as shown in figure 6. Figure 4: calibration of 1D/2D coupled model Figure 5: validation of 1D/2D coupled model 1066
(a) (b) (c) Figure 6: 2D floodplain having various degrees of floodplain encroachments, the blocked area is red with 16m elevation. Here (a)case1 has 29% (b) case2 has 20% (c) and case3 has 10% of its floodplain encroached.
RESULTS AND DISCUSSIONS Water level changes were calculated for highest discharge in the unsteady run just upstream of the encroachment to see the most effect of the encroachments and the results are shown in figure 7. As seen in figure 7, highest water level changes were observed when the whole width of the floodplain was blocked (case 1-A to 1-D) and the lowest change in water level was observed in cases where only 25% of the width was blocked (case 5-A to 5-D) conforming with the findings of Ogawa et al. (1986).
(a) (b) Fig 7: (a) the rise in water level in various floodplain encroachment cases compared to un-tampered floodplain. (b) the water level change at the cross section upstream of the encroachment for cases 1-A to 1-D
In fig 7(a), the points in the same line have the same amount of encroached width but increasing amount of length of floodplain is blocked. The results follow the general trend that as encroachment increases, the flood level rises since greater area of flood flow is reduced and water level rises to make up for the loss in volume. As seen in the figure flood level rises steeper when the width of the floodplain is blocked than when length of the floodplain is blocked. Figure 7(b) is the water level change observed in the 1D unsteady model when the width of the floodplain was blocked by 100% at various 25, 20, 15 and 10% of encroachments made along the floodplain length. From the figure, rise in water level is more prominent when the discharge is high, that is the abrupt rise in water level will be felt more during pre-monsoon and monsoon season. For the 1D/2D coupled model, the water level obtained upstream of the river was taken for each encroachment case was compared to the un-tampered floodplain scenario in fig 8 (a). The change in water level at different cases was found and was compared with the results obtained from the 1D run analysis in fig 8.
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Fig 8: the water level observed upstream of the river at different encroachment cases presented above.
In figure 8, case1, case 2 and case 3 represents 29, 20 and 10% floodplain encroachments respectively. It is seen from fig 8(a) that the general trend is followed and the water level rises as greater percentage of the floodplain is encroached. The flow was highest in September and it is seen that the change in water level was highest at the time as well producing 25 and 24cm at case1 and case 2 respectively. But further analysis is required for using a different method to generate a better rating curve upstream exploring further encroachment scenarios for the coupled model for an accurate comparison.
CONCLUSIONS The results showed that 1D-2D coupled model of Balu River was made successfully as the model was calibrated and validated with good correlation between the real and modeled data. Physical floodplains are complex and the coupled model can be utilized to determine the flood map around the area prior to the construction of a structure in the floodplain to determine its feasibility and effect on the surrounding area. A greater area of the floodplain may be encroached if the encroachments are made along the length rather than encroaching across the width of the floodplains (that is closer to the river banks). In actuality further analysis is required in the coupled model, flood maps can be created to determine the extent of different levels of encroachment and climate model can be used to feed future discharge data to see how same level of encroachments will affect the long term flooding in the area.
REFERENCES Finaud-Guyot, P., Delenne, C., Guinot, V. and Llovel, C., 2011. 1D–2D coupling for river flow modeling. Comptes Rendus Mécanique, 339(4), pp.226-234. Li, W., Chen, Q. and Mao, J., 2009. Development of 1D and 2D coupled model to simulate urban inundation: an application to Beijing Olympic Village. Chinese Science Bulletin, 54(9), pp.1613-1621. Mahmood, S., Nourin, F.T.J., Siddika, A. and Khan, T.F., 2017. Encroachment of the Buriganga River in Bangladesh. Journal of Minerals and Materials Characterization and Engineering, 5(05), p.266. Ogawa, H. and Male, J.W., 1986. Simulating the flood mitigation role of wetlands. Journal of Water Resources Planning and Management, 112(1), pp.114-128. Subramanya, K., 2013. Engineering Hydrology, 4e. Tata McGraw-Hill Education, pp 147 to 150 Vojinovic, Z. and Tutulic, D., 2009. On the use of 1D and coupled 1D-2D modelling approaches for assessment of flood damage in urban areas. Urban water journal, 6(3), pp.183-199.
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
ASSESSMENT OF RAINFALL TREND USING MANN– KENDALL TEST AND THE SEN’S SLOPE ESTIMATOR IN BANGLADESH
S. D. Shubro1, N. Ahasan1& R.I. Esha2*
1Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. E-mail:[email protected];[email protected] 2Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, Australia. E-mail:[email protected]
*Corresponding Author
ABSTRACT Rainfall is one of the indispensable Hydrological factors that can point out climate change. Assessment of rainfall trends is of paramount importance, in studying the impacts of climate change, in order to discern the alteration that has already taken place and future prediction will be the easy job to do. Trends in the annual and seasonal rainfall of Rangpur, Chapai Nawabganj, Gazipur, and Panchagarh district, Bangladesh using 48 years (1965-2012) monthly rainfall data at different rain- gauge stations are investigated by this study. The trend analysis is carried out by using Mann-Kendall test and Sen’s slope estimator. The statistics on the alteration of standard deviation, mean and coefficient of variation of rainfall in different seasons are obtained from the statistical and temporal trend analysis of four districts. It is easy to surmise that the monsoon rainfall is going to be escalating with the passing year and it will become more erratic too if we look at the average rainfall over time throughout the country. As there is an increased rainfall in premonsoon season, the mean rainfall increases in Rangpur, Chapai Nawabganj, Gazipur, and Panchagarh indicating the retreat of mean rainfall from monsoon to the premonsoon season. Mann-Kendall Z statistics and Sen’s Slope estimator with linear regression analysis determine the annual and seasonal rainfall trends. The outcomes of this study put forward to readdressing the derivation of model precipitation and crave for developing an integrated scheme for its water resources forethought and risk assessment of vulnerable areas.
Keywords: Trend,;Mann-Kendall Test; Sen’s Slope Estimator; Regression.
INTRODUCTION The appraisal of climate change has become imperative as protracted climate change is the most minacious topic around the earth. The spatial discrepancies and temporal changes for different parameters associated with climate are resolved by Trend analysis (Swain et al.,2015). Rainfall trend is crucial for agriculture in Bangladesh where its larger percentage of the economy depends on husbandry. Any drastic change in precipitation can be a huge menace and annihilate the economy of Bangladesh. With an intention to prognosticate how much the rainfall swerves from mean with the rise of years, and identify the probability of precariousness in term of depth of rainfall, standard deviation, mean and coefficient of variation of four districts with respect to year are analyzed. (Farhana and Rahman,2011). The main objective of this paper takes into account the mensuration of the significance change using Man-Kendall test and evaluation of trend magnitude and the slope with the help of Python
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STUDY AREA Daily rainfall data (1965 to 2012 ) are monitored at four distinct districts from environmental point of view across Bangladesh. The study area is 7157.08 km2, which covers 4 districts of Bangladesh. The districts positions are 24.0958° N and 90.4125° E (Gazipur), 25.7468° N and 89.2508° E(Rangpur), 26.2709° N and 88.5952° E(Panchagarh ), 24.7413° N and 88.2912° E( Chapainwabganj).
Fig. 1: Location of four districts with a distinction of Mean annual rainfall
METHODOLOGY The statistical non-parametric Mann-Kendall test and Sen’s Slope estimator test are performed for trend analysis on the daily rainfall data of four districts for about 48 years in the paper. Non- parametric tests are chosen over parametric tests (Swain et al.,2015). With a view to scrutinising the spatial and temporal alterations in precipitation for the four districts the analytical parameters like mean, maximum, standard deviation, coefficient of variation for precipitation data have been reckoned (THENMOZHI and KOTTISWARAN,2016).The Mann–Kendall statistics (S) is defined as
and ,
; where xj and xk are the successive rainfall values in months j and k (j > k) ( Addisu et al.,2015).To examine the statistical significance of the increasing or decreasing trend of mean rainfall values, a test statistic (Z) is applied -
; When Z >0, it implies a positive trend and whereas a negative value designates a decreasing course. The null hypothesis Ho: there is no notable drift of the monthly rainfall is discarded at 5 % if |Z| >1.96 ( Diop et al., 2016).
RESULTS AND DISCUSSIONS Standard deviation, mean and co-efficient of variation of rainfall are represented with respect to year from 1965 to 2012 for four districts of Bangladesh in the following graphs.
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RANGPUR DISTRICT 150
100
50 Standard Standard
Deviation(mm) 0 1970-74 1975-78 1979-83 1984-88 1989-93 1994- 2008-12 Year 2007 RANGPUR DISTRICT 230 210 190 170 Mean(mm) 150 1970-74 1975-78 1979-83 1984-88 1989-93 1994-2007 2008-12 Year RANGPUR DISTRICT 0.6 0.4 0.2
Variation 0
Coefficient Of Coefficient 1970-74 1975-78 1979-83 1984-88 1989-93 1994-2007 2008-12 Year Fig. 2: Variation of Standard deviation(SD), Mean and Coefficient of variation(CV) of Rangpur District PANCHAGARH DISTRICT 100 80 60 40
Standard Standard 20 Deviation(mm) 0 1965-69 1970-75 1976-80 1981-85 1986-90 1991-95 1996-2000 2001-05 2006-13 Year PANCHAGARH DISTRICT 250
200
150 Mean(mm) 100 1965-69 1970-75 1976-80 1981-85 1986-90 1991-95 1996-2000 2001-05 2006-13 Year Fig. 3: Variation of Standard deviation(SD), Mean of Panchagarh District( CV is not graphed as SD, Mean both are downward and CV is insignificant)
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GAZIPUR DISTRICT 120 100 80 60 40 20 0
Standard Deviation(mm) 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09 2010-13 Year GAZIPUR DISTRICT 230
210
190
170 Mean(mm) 150 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09 2010-13 Year Fig. 4: Variation of Standard deviation (SD), Mean of Gazipur District ( Coefficient of Variation(CV) is not graphed as SD, Mean both are downward and CV is insignificant)
CHAPAINWABGANJ DISTRICT 100
50 Standard
0 Deviation(mm) 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09 2010-13 Year CHAPAINWABGANJ DISTRICT 200
150
Mean(mm) 100 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09 2010-13 Year CHAPAINWABGANJ DISTRICT 0.6 0.4 0.2
Variation 0 Coefficient of 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09 2010-13 Year
Fig. 5: Variation of Standard deviation (SD), Mean and Coefficient of variation(CV) of Chapainwabganj District
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Except for Rangpur and Chapainwabganj, rest two districts almost have a downward slope of SD, Mean and CV which indicates the possibility of deviation of rainfall with respect to mean is bland. The causation of rainfall gets closer to mean and is more anticipated with the increment of year.
Table 1: Monthly rainfall Sen estimator of slope and Mann-Kendall’s trend test (mm/yr) results Mann–Kendall’s test, (HO: there is no trend) RANGPUR Jan Feb Mar April May June July Aug Sept Oct Nov Dec Sen -0.664 -0.57 -0.948 0.875 -0.138 0.115 -0.27 0.065 0.646 0.293 0.186 0.299 Slope P 0.634 0.552 0.735 0.341 0.967 0.818 0.511 0.950 0.127 0.706 0.445 0.505 CHAPAINWABGANJ Sen 0.155 -0.92 -0.284 0.209 0.568 0.363 0.513 0.285 0.279 0.192 0.434 0.868 Slope P 0.577 0.132 0.991 0.151 0.708 0.537 0.448 0.816 0.723 0.903 0.221 0.544 GAZIPUR Sen 0.01 0.055 -0.689 -0.18 0.11 0.098 -0.23 0.469 0.205 0.533 -0.45 -0.65 Slope P 0.172 0.882 0.883 0.952 0.936 0.919 0.407 0.194 0.586 0.272 0.657 0.370 PANCHAGARH Sen 0.142 0.15 0.001 0.655 0.02 0.888 0.356 0.045 0.669 0.207 0.126 0.348 Slope P 0.023* 0.013* 0.027* 0.211 0.019* 0.003* 0.205 0.993 0.028* 0.055 0.649 0.948 α 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
Sen Slope Estimator negative values for few months are representing a downward trend. If the p-value is greater than the significance level (α) = 0.05, we cannot reject the null hypothesis H0 and the p- value (*) is lower than the significance level(α) = 0.05, we can reject the null hypothesis H0. Only for the months of January, February, March, May, June, September in Panchagarh district there is a rainfall trend as the p-value is less than (alpha, α) 0.05.
CONCLUSION If we want to confront dynamic weather conditions, assessment of its fluctuation is inevitable. Under this research, according to the trend analysis for four districts of Bangladesh with monthly precipitation data for the session of 1965-2012 using non-parametric Mann-Kendall and Sen estimator of slope test, we get a downward trend for the maximum months. A rising precipitation trend has been observed at Panchagarh district with a 95% confidence level. The largest of the drifts were trivial at this confidence level in the session and a negative trend of few months can affect agriculture largely of the specific region.
REFERENCES Addisu,S. ; Selassie,YG; Fissha,G. and Gedif,B.2015. Time series trend analysis of temperature and rainfall in lake Tana Sub‑basin, Ethiopia. Environmental Systems Research ,4: 25.DOI 10.1186/s40068-015-0051-0. Diop,L. ; Bodian,A. and Diallo,D.2016. Spatiotemporal Trend Analysis of the Mean Annual Rainfall in Senegal. European Scientific Journal April, doi: 10.19044/esj.2016.v12n12p231. Farhana,S. and Rahman,MM.2011. Characterizing rainfall trend in Bangladesh by temporal statistics Analysis. 4th Annual Paper Meet and 1st Civil Engineering Congress, December 22-24, Dhaka, Bangladesh. Swain,S. ; Verma,M. and Verma,MK. 2015. STATISTICAL TREND ANALYSIS OF MONTHLY RAINFALL FOR RAIPUR DISTRICT, CHHATTISGARH. International Journal of Advanced Engineering Research and Studies/IV/II/Jan.-March,87-89.
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THENMOZHI, M. and KOTTISWARAN, SV.2016. ANALYSIS OF RAINFALL TREND USING MANN– KENDALL TEST AND THE SEN’S SLOPE ESTIMATOR IN UDUMALPET OF TIRUPUR DISTRICT IN TAMIL NADU. Vol. 6, Issue 2, 131-138.
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
WATER FEATURE EXTRACTION BY USING MULTI-TEMPORAL LANDSAT IMAGERY AND HYDROLOGICAL ASSESSMENT OF CHALAN BEEL
M. S. Ahmed *, U. K. Navera& M. Islam
Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. E-mail:[email protected]*; [email protected];[email protected] *Corresponding Author
ABSTRACT The wetlands of Bangladesh are considered as one of the most productive areas for thousands of people. Chalan Beel is the one of the main wetland in the North-Western region of Bangladesh. It is considered to be one of the main sources of water for agriculture, fisheries and domestic use. In recent years, rising temperature, less rainfall, human interventions (mainly encroachment) into this Beel are increasing in such a rate that the overall existence of this wetland during dry season has fall in a severe crisis. A study has been conducted to assess the temporal changes and water feature extraction of Chalan Beel for the period of 1990-2016 by using the multi-temporal Landsat TM & OLI images and also by using the hydrological data. From the Landsat image analysis, it has been found that the extension of Chalan Beel has been decreased for both Monsoon and Dry Period and the retention period has also been significantly reduced. Along with Landsat image analysis, hydrologic data within the Chalan Bill area especially along the Karatoa-Atrai-Gur-Gumani-Hurasagar (KAGGH) River network have been analyzed. Discharge, water level data have been used to observe the present hydrologic condition. The water level at Atrai Railway Bridge, Singra, Chanchkair stations has dropped. Discharge at the Mohadebpur stations also showed decreasing trend within this period. Flow from the upstream of KAGGH River network has decreased which affected the extension of Chalan Beel.
Keywords: Chalan Beel; Wetland; Landsat imagery; remote sensing; geographic information system; NDWI.
INTRODUCTION Wetlands are playing a vital role in the ecological system of Bangladesh as they are sources of fresh water, fish, and aquatic resources. The Bangladesh Water Act, (2013) defines wetlands as: “Wetland means any land where water remains at the level of surface or close to it and which inundates with shallow water from time to time and where grows such plants that may usually grow and survive in marsh land”. In Bangladesh, rivers, floodplains, lakes, haors, baors, beels, jheels, ponds, low-lying areas etc. are generally perceived as wetlands. Normally, the Haors are full of water in the wet seasons and they dry up during winter. But there remain some deep pockets within the Haors that do not dry up even in the winter. These deep points within the Haors are known as Beels. Beels are sustained from groundwater to a large extent (DBHWD, 2016). According to the definition of Alam and Hossain, 2012, Beels as small saucer-like depressions of a marshy character and they dry up in the winter but during the rains expand into broad and shallow sheets of water, which may be described as fresh water lagoons. Bangladesh has a number of wetlands which are called Haor, Baor, Beel, Jheel, Dighi and Lake. Among them Tangua Haor, Hakaluki Haor, Marjat Baor, Chalan Beel, Bikka Beel, Boro Beel, Arial Beel, Hatir Jheel Ram Sagar Dighi and Kaptai Lake are the major wetlands. Chalan Beel is the largest water body
1075 and main wetland in the North-Western region of Bangladesh. It is the major source of ground water recharge, deep percolation and interflow (several rivers are crisscrossing this wetland and act as connectors) of this vast region which contributes to the ground water irrigation system, aquaculture and surface water flow. Chalan Beel comprises a series of depressions interconnected by various channels to form more or less one continuous water body. According to Hossain et al. (2009) covers an area of about 375 km2 during the monsoon season. The Beel extends over four adjacent districts, Rajshahi, Pabna, Sirajganj and Natore. Figure 1 shows the extention of Chalan Beel. The major parts of it cover an extensive area of Raiganj upazila of Sirajganj district and Chatmohar upazila of Pabna district. It lies between Singra upazila of Natore district and the north bank of the river Gumani. The southeastern extremity of the Beel is at Astamanisha in Pabna district, Close to Nunnagar, where the Gumani and the Baral meet. The greatest breadth of the Beel is about 13 km from Tarash at the northeast to Narayanpur, near the north bank of the Gumani. Its greatest length is about 24 km from Singra to Kachikata on the Gumani (Alam and Hossain, 2012).
Fig. 1. Location Map of Chalan Beel
Chalan Beel supplies fresh water as well as abundant of aquatic resources and plays a vital role to keep the environment of the surrounding vast region balanced. It makes the land fertile, alluvial and also a large reservoir of biological diversity of this region (Rahman et al., 2010). The Beel area serves about 5 million people, predominantly through fisheries and agricultural activities. The Chalan Beel area incorporates 21 rivers and 93 smaller seasonal Beels of varying size. Most of the rivers and Beels are at risk of partial or total degradation, as a result of agricultural encroachment, siltation and other anthropogenic activities. Almost 83% of the rivers, and 68% of the Beels in the lean season, shrunk to 0–5% of their maximum (monsoon) water-spread area during the dry season. In 2005–2006, the annual fish production in Chalan Beel was 12,217 tonnes, being less than half of the production observed in 1982 of about 24,000 tonnes (Hossain et al., 2009). Along with lot of threat still there is very little concern noticed about this sensitive issue. Very few adequate researches are found in this regards. Therefore a Study to assess the temporal changes of Chalan Beel area has been conducted for the period of 1990-2016 by using the multi-temporal Landsat 4 & 5-TM and Landsat 8- OLI images. There are different satellite-derived indexes for the extraction of surface water from Landsat data. According to Rokni et al. (2014) the Normalized Difference Water Index (NDWI) is found superior to other indexes and hence it was used to detect the spatiotemporal changes of the Chalan Beel.
METHODOLOGY Satellite imagery of Landsat 4 & 5 - TM and Landsat 8 - OLI has been used to extract water features and detect the changes in water surface area within the study region between year 1990 and 2016 (Table 1). All satellite imageries used in this study were obtained from the website of USGS (http://earthexplorer.usgs.gov/) and Land Viewer (https://lv.eosda.com/)
Table1. Landsat dataset properties used for the study
Year Satellite Data Spatial Resolution Band Number 1990, 1995, 2000, 2005, 2010, 2011 Landsat 4 & 5-TM 30 m 2 & 4 2016 Landsat 8-OLI 30 m 3 & 5
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Water level and discharge data from Bangladesh Water Development Board (BWDB) was also collected to justify the satellite the image analysis. The study has been conducted to assess the temporal changes and water feature extraction of Chalan Beel for the period of 1990-2016 by using the multi-temporal Landsat images. The Study is carried out by following the steps shown in figure 2. The analysis has been started with the selection of study area. The Landsat images are then processed by using ArcGIS. The processing starts with the atmospheric correction for the calculation of radiance and then the reflectance has been calculated. Finally the Normalized Difference Water Index (NDWI) was calculated.Radiance is calculated from the Landsat 4 & 5 imagery using the following equation:
푳훌 = (Gain) × (Digital Number) + (Bias) Where, Gain and Bias values are obtained from the .MTL file provided with the data set. Once the radiation is calculated by the above equation then reflectance is calculated by the following equation:
2 π Lλ d ρ = ° (1) (퐸푆푈푁∗Sin휃퐸) Where, Esun = Mean exoatmospheric solar irradiance θ ̊π θ = E 180 ° 휽푬= Local sun elevation angle. d = Earth sun distance For Landsat 8 OLI image the reflectance is calculated using the following equation: Conversion to TOA Reflectance: ′ ρλ = Mρ ∗ Qcal + Aρ (2) Where: ′ 훒훌=TOA planetary reflectance, without correction for solar angle. Mρ=Band-specific multiplicative rescaling factor from the metadata Aρ=Band-specific additive rescaling factor from the metadata. Qcal= Quantized and calibrated standard product pixel.
′ ρλ ρλ = (3) sinθE
Where, 훒훌 = TOA planetary reflectance ° 휽푬= Local sun elevation angle. NDWI is used to monitor changes related to water content in water bodies, using Green and Near-Infrared (NIR) wavelengths, defined by Mc Feeters, 1996. ρ −ρ 푁퐷푊퐼 = Green NIR (4) ρGreen+ρNIR Where, the Water feature has positive value. After calculating the NDWI the images were then classified into two categories. The values, greater than zero, have been classified as water feature and the others as land area and other features.
RESULTS AND DISCUSSIONS The assessment of temporal changes of Chalan Beel has been performed using the Landsat satellite images. Normalized Difference Water Index (NDWI), one of the satellite-derived indices, has been calculated by equation (4) for the detection of change of wet area in Chalan Beel. The Study has been conducted according to the procedures explained in the methodology section and by using equations (1), (2), (3) and (4). The extracted water surface areas have been shown in Table 2 for different time of
1077 the year. Also, NDWI has been calculated for Chalan Beel area for different months for the year 2011 to observe the changes of the water body throughout the year. By analyzing different years, it can be seen that the water surface area has been decreased about 75% in dry period and 40-50% in monsoon period for this study period. Currently, the extent of Chalan Beel is smaller during the months of March, April and May which is approximately 2% of the monsoon period. On the other hand it has the larger surface area during the months of August to November and is around 900 Km2 within the Chalan Beel area. The average water surface area of Chalan Beel round the year is about 634 Km2 which was previously 1424 Km2 according to The Imperial Gazetteer of India, 1919. This indicates that the rivers connectivity is becoming narrower day by day due to siltation and encroachment (Sayed et al., 2014).
(a) (b) (c)
(d) (e) (f) Fig. 3. (a) NDWI-(April and October) 1990, (b) NDWI-(April and October) 1995, (c) NDWI-(April and October) 2000, (d) NDWI-(April and October) 2005, (e) NDWI-(April and November) 2010, (f) NDWI-(April and October) 2016
Figure 3(a) to 3(f) show the NDWI value for the Chalan Beel area during the lean period (April) and monsoon period (October/November) for the year 1990, 1995, 2000, 2005, 2010 and 2016 respectively.
(a) (b) (c) (d) Fig. 4. (a) NDWI-(February and March) 2011, (b) NDWI-(April and June) 2011, (c) NDWI - (August and September) 2011, (d) NDWI-(October and November) 2011 NDWI value in Figure 4 (a) to 4(d) show the Chalan Beel area during the months of February, March, April, June, August, September, October, and November, for the year 2011 respectively. From this analysis it has been observed that the water of Chalan Beel disappears within 3 months. This
1078 phenomenon is a matter of concern and the reasons should be identified by adequate research. Otherwise the agriculture and ecology of Chalan Beel area will be affected.
Table 2: Chalan Beel area derived from Landsat TM & OLI images
Year Month Area of Land (Km2) Area of Chalan Beel & Water Bodies(Km2) April 6950.99 42.33 1990 October 5979.80 1013.56 April 6966.82 27.20 1995 October 4616.80 2376.50 April 6971.40 21.92 2000 October 5862.40 1120.93 April 6981.36 11.96 2005 November 5488.84 1504.50 April 6975.20 18.13 2010 October 6278.07 715.41 February 6595.02 398.31 March 6962.21 31.11 April 6973.42 19.71 June 6909.66 83.67 2011 August 5189.02 1808.40 September 5917.74 1075.58 October 5857.42 1135.91 November 6468.98 524.34 April 6983.30 10.03 2016 October 6155.50 737.86
Above analysis has also been justified using Trend Analysis of the existing gage data from BWDB. The water level at Atrai Railway Bridge, Singra, Chanchkair stations has dropped around 1.6 m, 1.2 m, 1 m respectively during the period 1990 to 2016.
Fig.5: Atrai Railway Water Level Trend Analysis Fig.6: Singra Water Level Trend Analysis
Discharge at the Mohadebpur stations also showed decreasing trend within this period. The peak discharge in Mohadebpur was 822m3/s in 1990 where it has attenuated to 180 m3/s in the year 2000. The reduced flow also affected the duration of water retention in the Chalan Beel area. The indiscriminate use of land, the changes in hydrological parameters results in degradation of Chalan Beel which has been observed by the Landsat image analysis.
Fig.7: Chanchkair Water Level Trend Analysis Fig.8: Mohadebpur Discharge & Water Level Trend Analysis
The above analysis shows that the area of Chalan Beel has significantly decreased with years and the water of the Beel flows/percolated within a short period of time. The area is still decreasing day by day.
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Previously it was a vast water body which acted as a reservoir but now it has become several disconnected small static water bodies. The Chalan Beel area has been decreased and duration of existence of water in the Beel has become shorter. Water of Chalan Beel almost disappears during the dry period. Over pumping of water during dry season is also a cause to deteriorate the environmental balanced of this wetland (Rahman et al., 2010). Chalan Beel is no longer able to contribute to the ground water recharge like before as its extent has been reduced significantly.
CONCLUSIONS It can be observed from the study that the natural extensive wet area of Chalan Beel has already been significantly reduced and its connectivity has been lost due to both natural and anthropogenic interventions. The rate of drying up is very high in the dry season which has an adverse impact on the surrounding area. (1) During the last two decades the overall Beel area has been decreased due to human interventions. The connectivity of the water body during the dry season has been interrupted due to heavy siltation and encroachment. So dredging of the connecting rivers is required to maintain this vast wetland system. (2) Among the 21 rivers in Chalan Beel, the connectivity of the main rivers like Atrai, Baral, Gur, Gumani and other connecting water bodies are getting narrower because of siltation and encroachment. (3) Chalan Beel almost disappears within a short period during the dry season and it is found from this study that the extended water area exists only for 3 months. (3) The major source of irrigation in the surrounding area of Chalan Beel is ground water. Due to excessive extraction of ground water, the effect on reduction of Chalan Beel area is visible. (4) Retention of water for lean period can be obtained through suitable engineering interventions. (5) Proper hydrologic, hydrodynamic and morphological analysis of the Chalan Beel area is required to use the water body as a reservoir. So, it is high time to take necessary steps for safeguarding the Chalan Beel area for future water security.
REFERENCES Alam, M. S., and Hossain, M. S. (2012). "Beel" National Encyclopedia of Bangladesh (Second ed.). Asiatic Society of Bangladesh. Bangladesh Water Act, December, 2013. Government of the People’s Republic of Bangladesh, Ministry of Law, Justice and Parliamentary Affairs Bangladesh Water Development Board (BWDB), Water Level and Discharge Data Chalan Beel – Wikipedia, https://bn.wikipedia.orgnote-Banglapedia-2 Classification of Wetlands of Bangladesh, Main Report, Volume I, DBHWD, December, 2016. Submitted to Government of The People’s Republic of Bangladesh, Ministry of Water Resources Hossain, M. A. R., Nahiduzzaman, M., Sayeed, M. A., Azim, M. E., Wahab M. A., and Olin P. G. 2009. The Chalan Beel in Bangladesh: Habitate and biodiversity degradation, and implications for future management. ISSN: 1320-5331, 1440-1770, DOI:10.1111/j.1440-1770.2009.00387.x Komeil R., Ahmad, A., Selamat A., and Hazini, S. 2014, Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. ISSN 2072-4292, doi: 10.3390/rs6054173 Landsat 7 Science Data Users Handbook « Landsat Science, https://landsat.gsfc.nasa.gov/landsat-7-science-data-users-handbook/ Master Plan of Haor Area, Summary Report, Volume I, BHWDB, April, 2012. Submitted to Government of The People’s Republic of Bangladesh, Ministry of Water Resources Mcfeeters, S. K., 1996 “The Use of Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features” Rahman, M., Rahman, M., and Asaduzzaman, M. 2010. The Aggression of Human Activities on Chalan Beel a Threat on Wetland Environment: Study on Natore - Rajshahi Region of Bangladesh. ISSN 1728-7855, 8(1&2): 151-159. Sayeed, M. A., Hashem, S., Salam, M. A., Hossain, M. A. R., and Wahab, M. A. 2014. Assessment of Chalan beel Ecosystem Diversity through Remote Sensing and Geographical Information Systems. ISSN 2028-9324 Vol. 7 No. 1 July 2014, pp. 353-365 The Imperial Gazetteer of India, 1919. New ed. New York: Oxford University Press.
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
A CASE STUDY ON OVERFLOWING OF KHAL IN CHITTAGONG CITY
K. F. B. Mahmud1*, D. Majumder2 & S. K. Palit3
1Lecturer, Department of Civil Engineering, Port City International University, Chittagong, Bangladesh 1, 2Ex-student, 3Professor, Department of Civil Engineering, Chittagong University of Engineering & Technology, Bangladesh [email protected], [email protected]
*Corresponding Author
ABSTRACT Chittagong is the prime port city as well as commercial capital of Bangladesh. The economic and financial growth of the country mostly depends upon the development of Chittagong city as about more than 90% export-import business activities are performed through this city. But every monsoon, about one-third of the city goes under water that affects the areas in various forms which in turns affect the whole country. Overflow from canals is mainly responsible for water logging. Chaktai Khal, considered to be the life-line of Chittagong city for serving various socio-economic activities. Moreover, it acts as the main drainage system for removing rain water along with usual sewage outlet, has been choked with solid waste and filth along with encroachments causing overflow of water flooding both sides from Bahaddarhat to Chaktai areas. The present investigation reports the identification of the causes and bad impacts of overflowing of Chaktai khal on daily life. The geometrical shape and causes of overflow are observed physically and RL of canal bed & section top are measured. Then a longitudinal bed profile has been drawn. It has been found that the reduced level (RL) of the starting point (Bahaddarhat Police Box) and the confluence point of the khal is almost same. So, the canal cannot flash out water from Bahaddarhat to Karnaphuli river. Moreover, tidal water flows towards Bahaddarhat from Karnaphuli river and creates overflowing problem. Inadequate bed slope also reduces the drainage capacity. Siltation, vegetation, disposed solid waste, land grabbing formation of excessive bends etc. are also the causes of overflowing of the canal. Overflowing problem mainly effects on environment, social life, business sector, roads and communication systems. Re-profiling the bed slope, increasing the canal top, increasing the road level at water- logging prone area & installing a tidal regulator near the confluence point can be some remedial measures to eradicate the overflowing problem.
Keywords: Chaktai Khal; Overflowing; Drainage Capacity; Canal Bed Slope.
INTRODUCTION Bangladesh is a rapidly developing country and the country’s second largest city Chittagong is the centre of all development activities and also home for the country’s largest port. But every Monsoon, about one-third of the city goes under water that affects the areas in various forms which in turns affect the whole country. Chaktai Khal is one of the most important natural drainage systems in Chittagong City. Chaktai, Khatunganj & Asadganj are the main seat of trade and commerce in Chittagong. It’s a wholesale market dealing with food items like rice, pulse, onion, ginger, powder milk, sugar, edible oil, dry fish etc and building materials like C. I. Sheet, paint etc. Retailers from greater Chittagong area frequently visit these areas for collecting their merchandise. Fig. 1 shows some of the regular
1081 activities on the Chaktai khal. But nowadays, Chaktai-Khatunganj areas, unlike in the past are losing their importance and failing to attract new business due to overflowing of Chaktai khal. Some of the occurrences in the recent past have shown in Fig. 2. City dwellers lamented that it now become a history that once high tide and flash flood passed and swept away all waste through Chaktai khal linked with the Karnaphuli river and goods-laden boats operated in the canals. The khal once served the business hub of Chittagong is now called the grief of Chittagong City (New Age, 2017). People of surrounding areas face a lot of problems due to flooding created from overflowing of Chaktai khal.
Fig. 1: Regular activities on Chaktai khal
Fig. 2: Distresses due to overflow of Chaktai Khal at Khatunganj
Siltation, vegetation, excessive bends, encroachment, poor management system, lack of public awareness etc. are responsible for overflowing. Overflow causes various problems like disruption of traffic movement, disruption of normal life of the urban inhabitants, damage of structure, water pollution and increase of water borne diseases. Hence, Department of Civil Engineering, CUET, has taken an initiative to find out the causes of overflowing of Chaktai khal and to provide some remedial measures.
METHODOLOGY The investigation is carried out on the study area in the following manner which is shown in Fig. 3. Map & Data Site Causes of Remedial Impacts Collection Investigation overflow Proposals
Fig. 3: Work plan of the present investigation
In this investigation, site has to be observed physically. During site investigation- canal top width, silt depth, water depth, water to retaining wall top height, latitude & longitude, RL of canal top have to be measured. Width, depth & height have to be measured manually. Latitude, longitude and RL have to be measured by using My Elevation mobile apps. The places of excessive bend formation, siltation, vegetation, disposed solid waste, land grabbing etc. have also to be observed.
STUDY AREA Chaktai khal is one of the most important natural drainage systems in Chittagong city. In this study, drainage system mainly covers the area like Bahaddarhat, Chawkbazar, Bakalia, Chaktai, Asadganj, Khatunganj and up to the confluence point of the river Karnaphuli.
FIELD INVESTIGATION
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The site of the Chaktai khal has been observed physically and various dimensions like canal width, depth, silt depth, RL of the canal bed & top have been measured at various distances starting from Bahaddarhat junction to confluence point of the Karnaphuli River. This information has been shown in Table 1.
Table 1: Essential information of Chaktai khal collected from field observation Location Distance CDA1969 Present Depth(ft) Total Canal Canal (ft) Master Width Water Avg. Water Depth Top Bed Plan (ft) Depth Silt to Wall (ft) RL RL Width (ft) Top (ft) (ft) Starting 31 21.50 1.30 1.00 6.56 8.86 23 14.14 300 38 40.00 0.80 0.80 6.40 8.00 26 18.00 590 43 45.00 0.80 0.55 6.56 7.91 27 19.1 Bahaddarh 850 75 46.00 0.50 0.40 5.90 6.80 27 20.20 at*(Police 1250 48 39.40 0.90 0.60 6.56 8.12 20 11.88 Box) 1640 45 36.10 0.90 1.40 4.84 7.06 23 16.74 1950 46 34.50 1.00 0.70 5.56 7.25 18 10.75 2100 46 33.70 2.00 1.30 3.61 6.91 20 13.09 2430 44 43.00 2.00 0.85 5.00 8.31 20 11.69 Hares 2820 50 44.00 1.00 0.70 5.00 6.61 23 16.39 Shah 3150 57 43.00 1.30 0.65 4.90 6.85 20 13.15 Mazar 3520 68 46.00 3.00 1.30 5.00 9.24 27 17.76 Lane 3880 69 43.30 3.50 1.40 4.30 9.32 20 10.68 Bakalia 4350 68 41.00 3.50 1.40 4.22 9.12 23 13.88 4790 67 39.40 3.60 1.00 3.94 8.54 27 18.46 Dhuniar 5000 60 41.00 3.28 1.35 4.00 8.63 23 14.37 Pool, 5350 57 43.00 3.00 1.65 3.28 8.01 28 19.99 Chawkbaz 5700 57 40.00 2.70 2.00 4.00 8.70 28 19.3 ar 6150 50 42.00 3.50 1.80 3.40 8.70 28 19.3 6550 44 40.00 4.00 0.70 4.00 8.70 27 18.30 Chandanp 7060 42 41.00 3.50 1.20 4.34 9.04 25 15.96 ura 7560 40 43.00 3.20 1.50 4.84 9.54 23 13.46 8070 37 44.30 3.28 1.00 5.90 10.22 23 12.78 Dewanbaz 8200 39 46.00 4.10 0.85 5.00 10.03 25 14.97 ar 8430 37 36.00 2.79 1.15 6.00 10.00 27 17.00 8990 50 40.00 2.46 1.35 6.60 9.60 27 17.40 9250 61 59.00 1.50 0.80 6.56 8.86 23 14.14 Master 9850 65 38.00 2.00 0.70 4.30 7.00 23 16.00 Pool 10500 72 41.00 2.63 1.35 4.92 8.90 23 14.10 11710 80 42.70 2.62 1.65 6.54 10.81 23 12.19 Miakhan 11840 89 52.50 2.62 1.65 6.54 10.81 25 14.19 Nogor 12340 101 42.70 2.00 0.85 8.20 11.05 27 15.95 12830 110 43.00 0.66 1.35 6.54 8.58 28 19.42 Chaktai 13270 102 60.00 0.33 1.70 7.50 9.54 27 17.46 *(Conflue 13710 105 75.00 0.30 1.20 8.50 10.00 25 15.00 nce Point) 14140 101 99.00 …. …. …. …. 23 …. *14470 --- 187.0 …. …. …. …. 17 ….
CAUSES OF OVERFLOW Overflowing occurs mainly during the period of heavy rainfall. The drainage system can’t drain out of excess water. Also during monsoon period, tidal flows enter into the natural drainage systems which are connected with the Karnaphuli River. Inadequate drainage system, excessive bending, inadequate
1083 bed slope, siltation, vegetation, inadequate maintenance, lacking of interconnectivity of various drainage systems etc. are responsible for overflowing. Excessive Rainfall Heavy rainfall is one of the main reasons for water logging in Chittagong City. Relatively low intensity of rainfall also causes serious water logging problems for certain areas of the city. The capacity of Chaktai khal is not sufficient to drain out huge amount of rain water.
Siltation & vegetation Problem Siltation mainly occurs at the concave side of bending due to lower velocity at that side. Siltation problem reduces the width of the canal which is shown in the following Fig. 5. So Chaktai khal lost its capacity due to decrease in canal section because of siltation. Many places in the canal are covered by the vegetation after siltation takes place along the Chaktai khal.
Disappearance of natural drainage system & formation of excessive bends Rapid population growth, unplanned development & land filling to develop new residential areas and encroachment on khals with unauthorized construction are the summarized general man made physical and social activities related to the disappearance of natural drainage system. And these illegal man-made activities have been created numerous numbers of excessive bends (Fig. 6) at various points of the canal.
Solid Waste Disposal Municipal solid waste consists of household waste, construction and demolition debris, sanitation residue and waste from streets along the Chaktai khal is creating obstacles in the flow path of water is shown in the Fig. 7.
Inadequate Maintenance Inadequate maintenance of existing natural drains due to lack of planned maintenance program, equipments, adequate budget, staffing, proper monitoring program and institutional set up to effectively operate and maintain the drainage network. There is often poor communication and co- ordination between the different urban authorities responsible for operating and maintaining the various components of the drainage network. This is shown in Fig. 8.
Fig. 5: Siltation Fig. 6: Excessive bends Fig. 7: Solid waste Fig. 8: Inadequate Maintenance
Inadequate Bed Slope Inadequate bed slope is one of the main causes of overflowing of Chaktai Khal. It is the main reason for the capacity loss & tidal effect on the khal. Using the data from Table 1, a longitudinal bed profile is drawn which is shown in Fig. 9. It is seen that RL at the starting point & the confluence point (end point) is almost same. Some places the canal bed elevation is high and some places it is low. For this reason, the canal cannot flash out water from Bahaddarhat to Karnaphuli River efficiently. Moreover, tidal water flows towards Bahaddarhat from Karnaphuli River and creates overflowing problem.
IMPACTS OF OVERFLOW In the city, natural water courses are almost spoiled, and existing drainage system can’t drain excess water, results overflow. The overflow problems create various bad impacts at its surrounding areas.
Social impacts
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Overflow invades various social sectors. People endure the brunt of bad drainage through direct flood damage, pollution of water supplies and the aquatic environment, the breeding of vectors and soil erosion. It also disrupts normal traffic movement which is shown in Fig. 10 & 11.
Fig. 9: Longitudinal bed & top profile of Chaktai Khal with proposed bed slope
Physical impacts Overflow accelerates the damage of structures, infrastructures and underground service lines. It contributes to ground heave, subsidence and dampness. Metalloid pipes of various underground utility services are damaged and lose longevity. Water logging causes damage of roads (both paved and unpaved) in rainy season which interrupts journey. Serious damage of road has occurred in many locations near Chaktai Khal. Roads are greatly damaged which are shown in Fig. 12.
Fig. 10: Social impacts Fig. 11: Communication impacts Fig.12: Road damaged by water logging
Environmental impacts Overflow attacks the environment by polluting water, spreading water borne diseases. Storm water gets polluted as it mixed with solid waste, clinical waste, silt contaminants and sewage from overflowing latrines and sewers causing pollution and wide range of problems associated with water borne diseases. People cannot get pure potable water because surface water and shallow groundwater sources are polluted due to water logging. Contamination of groundwater also leads to adverse health impacts.
Economic impacts Water is an economic burden. Water logging reduces the life span and damages the roads and metalloid pipes of various underground utility services such as water, telephone, sewerage etc. It requires a huge cost to replace these facilities. The city authority had to spend about taka 7 to 8 billion every year to replace and maintain infrastructures damaged by water logging. Damage of sub structures, brick foundations, houses in slums due to water logging means the huge economic losses for the inhabitants.
Business impacts Water logging causes serious havoc at the port city’s main business hubs Chaktai and Khatungong damaging essential commodities inside the ware houses. Due to heavy downpour and water logging in June 2017 causes a loss over taka 100 crores. Khatungong and Chaktai alone faced a financial loss of about taka 70 crores. Above scenario shows the burden of overflow in business sector which leads to serious economic loss to the country.
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Remedial Proposals Remedial proposals to minimize the overflowing problem are given below: Providing sufficient bed slope so that the water can easily flash out from Bahaddarhat to Karnaphuli River which is shown in Fig. 9. Excessive bends should be reduced by providing artificial cut-off at the proper locations so that siltation & vegetation can be minimized. Interconnectivity among the existing drains to be developed. Proper disposition of solid waste has to be ensured. Illegal construction works/land grabbing near the bank of the khal have to be stopped. Regular and careful maintenance to be ensured. Silt traps can be constructed at appropriate locations to minimize the siltation and vegetation problem. A tidal regulator with the provision of a navigation gate at the mouth of the khal should be installed. Raising of canal top and/ or road level can be done as shown in Fig. 13.
Fig. 13: Raising of canal top
CONCLUSION . Inadequate drainage system, excessive bending, inadequate bed slope, siltation, vegetation, inadequate maintenance, lacking of interconnectivity of various drainage systems etc are mainly responsible for overflowing of 57 khals (According to CDA, 1969 drainage master plan) in Chittagong City. . In case of Chaktai khal, the amount of encroachment is up to 50 feet. The concerned authority should ensure proper monitoring to stop illegal construction works. . There is insufficient interconnectivity between the existing drainage systems of areas surrounding the Chaktai khal. . Overflowing effects on environment, social life, business sector, roads and communication systems etc. . Providing sufficient bed slope, reducing excessive bends, construction of silt trap, tidal regulator etc. are some remedial measures to eradicate overflowing problem. . Moreover, coordination among the related authorities of the city is required to control this problem.
REFERENCES 1. Chow, V.T. 1959. “OPEN-CHANNEL HYDRAULICS”. International Edition: McGraw-Hill Book Company, Inc, United States of America. 2. Garg, S. K. 2007. “IRRIGATION ENGINEERING AND HYDRAULIC STRUCTURES”. Twenty First Revised Edition: Khanna Publisher, 2-B Nath Market, Nai Sarak, Delhi-110006. 3. Khanna, S.K. & Justo, C. E. G. 2001. “HIGHWAY ENGINEERING”. Eighth Edition: New Chand & Bros, Civil Lines, Roorkee 247 667, India. 4. Hoque, M. A.; Nandi, k. & Palit, S. K. 2012. Overflowing of Mirza Khal and Its Effect on Road & Drainage System, B.Sc. Engg. Thesis, Civil Engineering Department, CUET, Chittagong-4349, Bangladesh. 5. Ara, F. 2017. “Encroachments eat into port city canals”. New Age. [Online Report]. Available at: http://www.newagebd.net [Accessed 10 January 2018].
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4thInternational Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
APPLICATION OF PALMER METHOD IN FUTURE DROUGHT ANALYSIS OF THE NORTH WEST REGION OF BANGLADESH USING CORDEX DATA
S. Chowdhury*&N. Jahan
Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. E-mail: [email protected]*; [email protected]
*Corresponding Author
ABSTRACT Climate change is the most concerned topic by this time and frequent with severe drought events are the result of it. In this study drought features are quantified by Palmer Method assessing PDSI (Palmer Drought Severity Index) for the Northwestern part of Bangladesh. The appraisement of Palmer method has been conducted by comparing drought features demonstrated by PDSI with historic drought events. The result shows that Palmer method performs surprisingly well by capturing historic drought events like 1981, 1982, 1989, 1994-95, 2000, 2006 and 2009 using historic data for the control period 1980-2010. MIROC5, GCM data provided by CORDEX has been evaluated and bias corrected by regression analysis comparing with observed data and drought features for the control period. Later projected GCM data of Representative Concentration Pathways (RCP) 8.5 are bias corrected for the period 2011-2100. Future drought features are calculated in three categories as 2020s, 2050s and 2080s. Trend analysis of future rainfall and temperature has been done for the superficial idea about future showing increase in temperature and precipitation. This illustrates drier winter with heavy rainfall in monsoon. The study concluded with prolong drought events in early 21st century with severe but short duration and frequent drought events in mid and late 21st century.
Keywords: Drought; PDSI; North-West Region; CORDEX; RCP 8.5.
1. INTRODUCTION Bangladesh is called the land of six seasons but in meteorological viewpoint grossly four season exists namely Winter, Pre-Monsoon, Monsoon and Post-Monsoon. Having tropical monsoon climate, she faces extreme high rainfall during monsoon resulting in high flood. But the winter is so dry that every year the northeastern part has to attend severe droughts. The northwestern region is one of the main sources of staple food cultivation. Drought causes immense damage to the crop as well as agro based economy of Bangladesh. That is why it is needed to analysis drought as well as its pattern and it will be beneficial to the resources planners if a good prediction can be done. Moreover, throughout the world various indices are available to quantify drought. In this study Palmer method is used to evaluate the PDSI index about its suitability, accuracy and simplicity to use. Mainly US army and other U.S Government agencies use this index and other countries like Switzerland, Russia, Canada, Australia etc use this index. In Bangladesh comparatively, less study has been conducted about drought with PDSI index where as SPI and SPEI indices are most common and popular methods to estimate drought. So proper and wide range of studies are needed to conduct. The main objectives of this study is to appraise the PDSI index in assessing meteorological drought in purpose of good prediction in future periods to avoid immense damage to our crop production and take remedial measures against drought in future.
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The 1st section of this study contains introduction including background, objectives, organizations of paper, literature reviews and study area. The 2nd section contains a elaborate description of methodology and the 3rd section presents the results and elaborate discussion over the result. Conclusion, Acknowledgement and References are listed in the 4th, 5th and 6th section respectably.
1.1 Literature Reviews Various indices are there to quantify drought but among them Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Crop Moisture Index (CMI), Palmer Drought Severity Index (PDSI) etc are popular indices. In Bangladesh mostly, SPI and SPEI is used in drought analysis where as PDSI is attempted to be widely studied. In 1965, Palmer developed PDSI for quantifying the severity of meteorological drought. Latter Wells et al. (2004) proposed a self- calibrating PDSI algorithm (scPDSI) designed to automatically calibrate the PDSI parameters from historical climate data at the location of interest. Later different studies conducted with ScPDSI and more satisfying results been found. Datta (2005) identified the historic droughts using PDSI of selected districts of Bangladesh and compared the result of PDSI with other indices SPEI and SPI and found a realistic result.
1.2 Study Area The north-western part of Bangladesh is the most drought prone region. Due to its geographical position the El Nino phenomenon led to a severe drought in north-western part and a severe shortage of rainfall through the region.
Fig. 1: Study Area, The Northwestern Region of Bangladesh and four selected stations.
2. METHODOLOGY
2.1 Data Collection In this study observed data of monthly precipitation, temperature and available soil moisture of the selected stations are collected from Bangladesh Meteorological Department (BMD), Bangladesh Water Development Board (BWDB) and Soil Research Development Institution (SRDI). Global Climate Model (GCM), MIROC5 data of same parameters are collected from CORDEX (The Coordinated Regional Downscaling Experiment),a project of The World Climate Research Programme (WCRP) for the control period 1981-2010 and for the projected period 2011-2100. For the projected period, meteorological data of Representative Concentration Pathways (RCP) 8.5 is used to estimate future drought.
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2.2 Bias Correction While comparing the GCM data with the observed data some bias is identified and that needed to be corrected. In this study GCM data is bias corrected and calibrated by Regression analysis method. Calibrated results are evaluated by comparing pre and post Root Mean Square Errors (RMSE) for the control period 1980-2010. Later with the calibrated regression equation of every parameter future data were corrected.
70 R² = 0.4885 35 y = 0.1817x + 8.5578 60 30 50 25 40 20 30 15 20 10 10 5
0 0 PrCORDEX (mm/day) 0 10 20 30 40 (mm/day)BMD Pr 0 20 40 60 80 Pr BMD (mm/day) Pr CORDEX>20 mm/day
25 R² = 0.5304 20 15 10
5 (mm/day) 0
Corrected Corrected PrCORDEX 0 20 40 Pr BMD (mm/day) Fig 2: Regression Analysis of Bias Correction Erroneous Pr CORDEX Station: Thakurgaon Pr BMD (1980-2010) Corrected Pr CORDEX 70 60 50 40 30 20 10 0
Precipitation (mm/day) Precipitation 1979 1984 1989 1994 1999 2004 2009 Time( year) Fig 3: Precipitation data comparison before and after bias correction
By the same process of regression analysis bias from temperature data was mitigated. The comparison, before and after bias correction of temperature data is shown by graphical presentation at [Fig 4]. In this analysis threshold is estimated differently at each station by graphical representation and eye estimation. Precipitation is found one sided bias whereas temperature shows two-sided biasness like temperature greater than 30ºC and less than 16ºCare erroneous. Erroneous Temp CORDEX Station: Thakurgaon Temp BMD (1980-2010) Corrected Temp CORDEX 34 31
C) 28 ° 25 22 19 16 13 Temperature ( Temperature 10 1979 1984 1989 1994 1999 2004 2009 Time (year)
Fig 4: Temperature data comparison before and after bias correction
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2.3 Palmer Method In 1965, Palmer introduced an index named Palmer Drought Severity Index (PDSI) to quantify droughts for the nine different locations of seven states of United States to measure drought and make some constant Table 1: PDSI Classification values to quantify drought. Later at 2004 Wells et al. PDSI Value Weather Condition +4.0 or more Extremely wet proposed a modified PDSI named Self Calibrated Palmer +3.0 to +3.99 Very wet Drought Severity Index (ScPDSI) to assess drought for any +2.0 to +2.99 Moderately wet area of interest eliminating the constants that was +1.0 to +1.99 Slightly wet calculated for USA with the location parameters so that +0.5 to +0.99 Incipient wet spell location variability can be taken in consideration. PDSI +0.49 to -0.49 Near normal index varies between -4 to +4 where the positive sign -0.5 to -0.99 Incipient dry spell -1.0 to -1.99 Mild drought indicated the wet weather condition and the negative values -2.0 to -2.99 Moderately drought represent the dry condition. Palmer categorized this range -3.0 to -3.99 Severe drought in several weather conditions, shown on [Table 1]. In -4.0 or less Extreme drought calculation of departure moisture various estimation methods of potential evapotranspiration (PET) can be done like Thornwaite (PE_th) or Penman– Monteith (PE_pm) method can be used to estimate PET. In this study Thornwaite method is used for the four selected meteorological stations. Dai (2011a) has shown that using PE_th over-estimated the impact of global warming, resultingin much lower PDSI than using PE_pm (Mesgana,2016), which was therefore chosen to estimate PDSI for projected climatein the 2020s, 2050s and 2080s in this study.
3. RESULT AND DISCUSSION
Drought features determined by Palmer method are able to capture the historic drought events like 1981, 1982, 1989, 1994-95, 2000, 2006 and 2009. From the comparative study of drought features found from BMD and CORDEX data, both data give similar results like the number of drought events and drought duration. In [Fig 5] no of drought events found from both BMD and CORDEX for four meteorologicalstations has been shown.
50 40 30 20 10
0 No of Drought Events Drought of No BMD CORDEX BMD CORDEX BMD CORDEX BMD CORDEX Rajshahi Dinajpur Thakurgaon Naogaon
Mild Drought Moderate Drought Severe Drought Extreme Drought . Fig 5: Comparative Study of No of Drought Events found from BMD & CORDEX
From the trend analysis it has been seen that both the future temperature and precipitation data set show a positive slope. That reveals the increase in temperature and precipitation in coming century. In Rajshahi district it is assumed that at the end of 21st century for the 90 years from 2011 temperature will rise about 2.241º C and Thakurgaon temperature may increase about 1.94ºC. Due to global warming it is quite
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Future Trend of precipitation Station: Thakurgaon (2011-2100) y = 0.0073x - 8.5779 25 20 15 10 5 0 Precipitation (mm/day) Precipitation 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Time(year) Future Trend of Temperature y = 0.0215x - 19.283 Station: Thakurgaon (2011-2100)
33 C) ° 28
23
18
Temperature ( Temperature 13 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Time(year) Fig 6: Trend of Future precipitation and temperature predicted that temperature will rise. But in sense of global warming, precipitation should decrease. Trend analysis shows a positive upward slope for the precipitation and that may indicate heavy rainfall in rainy season but drier winter so that winter drought will be more extreme and rainy season will introduce devastating rainfall with flood. It is seen that in Dinajpur about 44.79% bias in precipitation is mitigated by regression analysis process. Again, correlation of determination also increases after the bias correction.
Table 2: Bias Correction result No. Precipitation Temperature RMSE RMSE % of Bias RMSE RMSE % of Bias Stations Before After Mitigated Before After Mitigated Correction Correction Correction Correction 1. Thakurgaon 8.32 4.85 41.71 3.00 2.36 21.33 2. Rajshahi 4.74 3.59 24.26 2.33 1.99 14.59 3. Naogaon 5.30 3.80 28.30 2.63 2.18 17.11 4. Dinajpur 8.44 4.66 44.79 3.00 2.36 21.33
Later future drought is estimated by PDSI drought index. Future droughts are quantified into three categories of same time period (30 years) and that is the early 21st century or 2020s (2011-2040), mid of 21st century or 2050s (2041-2070) and the late of 21st century that is 2080s (2071-2100).
Station: Rajshahi Station: Dinajpur 40 30 30 20 20 10 10 0 0 Mild Moderate Severe Extreme Mild Moderate Severe Extreme Drought Drought Drought Drought Drought Drought Drought Drought 2020s 2050s 2080s 2020s 2050s 2080s
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Station: Naogaon Station: Thakurgaon 40 40 30 30 20 20 10 10 0 0 Mild Moderate Severe Extreme Mild Moderate Severe Extreme Drought Drought Drought Drought Drought Drought Drought Drought
2020s 2050s 2080s 2020s 2050s 2080s
Fig 7: No of drought events in future period in four meteorological stations.
Thakurgaon may show the most vulnerability due to long duration and more severe droughts. 2080s may show extreme but short duration frequent drought events where as 2050s may come with severe and long duration drought events.
4. CONCLUSION Bangladesh is a developing country with lots of seasonal variability and has to attend climatic hazards in every season. Northwest region of the country is greatly affected by drought every year causing huge damage to crop production. Being agro-based economy and a vital region of staple food production, drought prediction can be a great help to resources planner and policy makers. For this vision this study is conducted and finds a good possibility to predict by Palmer Method. This method assumes to have severe drought in coming century with the increase of temperature about (2-3)°C and drier winter with severe drought whereas monsoon with huge rainfall resulting subversive flood is predicted. More accurate result can be obtained by calculating ScPDSI as this index takes regional climate variability under consideration without taking climate parameters as constant. While using GCM data bias correction is a must and more accurate correction method can be used to obtain more accurate result. Different GCM takes different things under consideration and updated their versions focused on different prospective. So, depends on which purpose and which scale the results are going to use, more intensive calculation can be done to achieve the accuracy.
5. ACKNOWLEDGEMENT We are thankful to the Department of Water Resources Engineering of Bangladesh University of Engineering and Technology to provide every help to accomplish the work. We are thankful to every staffs of WRE Department of BUET for their kind assistance. We like to thank our family to encourage us at every step. We acknowledge our gratitude towards the Almighty to make us worthy and capable to complete the work.
6. REFERENCES
Bhuiyan, M. A., Khan, M. K. and Datta, S.K. 2002, 'Meteorological Drought Analysis for Northwest Region of Bangladesh', Bangladesh Journal of Water Resources Research, Vol. 19, Pp. 81- 99. Chowdhury, M. H. K. & Hussain, M. A., 1983, "On the Aridity and Drought Conditions of Bangladesh", Mausam, Vol. 34, No. 1,71-76. Dai, A. 2011a, 'Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008', Journal of Geophysical Research, Atmosphere, Vol.116, SN. 0148-0227. Datta, A. R. June 2005, 'Application of Palmer Method in Analyzing Drought in Northwest Region of Bangladesh', M. Engg. Project Work, Dept. of WRE, Bangladesh University of Engineering and Technology, Dhaka. McKee, T. B., Doeskin, N. J., and Kleist, J. 1993, ‘The relationship of drought frequency and duration to time scales', Eighth Conference on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., Pp. 179–184,viewed 20 December 2017,
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4th International Conference on Advances in Civil Engineering 2018 (ICACE 2018) 19 –21 December 2018 CUET, Chittagong, Bangladesh www.cuet.ac.bd
A STUDY ON GROUND WATER LEVEL FLUCTUATION IN SELECTED AREA OF CHITTAGONG CITY
S. Akter1, M. A. Hossen*2& S. Das3
1Department of Civil Engineering, Southern University Bangladesh, Chittagong, Bangladesh. E-mail: [email protected] 2Department of Civil Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh.E-mail: [email protected] 3Faculty of Science, Memorial University of Newfoundland, Newfoundland, Canada. E-mail:[email protected]
*Corresponding Author
ABSTRACT Southern part of Bangladesh is now facing water scarcity problems in both agriculture and secured livelihood. Ground water forms the major portion of earth’s fresh water source and it is almost safe to drink. Depletion of ground water table due to continuous pumping is causing scarcity of water in the city area of Bangladesh. So, information about ground water table is required for future recommendation of ground water supply to general people. For the investigation purpose, depth of water table has been determined in one season with respect to mean sea level. 41wards of Chittagong City Corporation along north-south direction of 91˚47´ longitude and east-west direction of 22˚20´ latitude have been selected for this purpose where depth of water table has been measured from the shallow and deep tube well. The present investigation includes field investigation with the aim of measuring water level from ground surface. From this investigation, it has been established that, water table with respect to mean sea level is different at different wards. During field investigation, 82 shallow tube wells and 82 deep tube wells in 41 wards were found. The overall view shows that, in almost every ward, deep tube well is must to find fresh water as shallow tube well can’t frequently pump water. From the investigation, it is also clear that ground water table is lowering day by day. At the beginning of rainy season when it started to rain the water table comes up. Ground water through shallow tube well is not sufficient to fulfill the required demand for the general people as it is becoming out of reach through shallow well day by day. Depth of water table with respect to mean sea level is quite lower in ward 37& depth of water table with respect to mean sea level is quite higher in ward 13. The study was also carried out to assess groundwater table of Chittagong city. From this analysis, it is found that the GW level is lowering in almost all the region of the study. So, alternative water source should be ensured to mitigate the problem.
Keywords: Depletion; scarcity; lowering; water table; investigation
INTRODUCTION The major portion of earth’s fresh water supply generates from Ground water source. About 97% of the earth’s fresh water supply is stored in the underground (Gleick 1993, 1996). For human consumption, we need wholesome water - water that is free from disease organisms, poisonous substances and excessive amounts of mineral and organic matter; and palatable water – water that is free from colour, turbidity, taste and odour, and is well aerated (Ekpo & Inyang 2000, Fair et al. 1966). Ground water can be used as a reliable earth’s fresh water supply is stored in the underground formation with the increase
1094 in population, the design for water system is essential to meet the increasing demand for water is also increasing throughout the world. Ground water in Bangladesh, except in some places, is available at a shallow depth. Ground water levels are at or near ground level during the period January-May. Ground water rises as a result of recharge during January-May. There are several areas of Bangladesh where ground water withdrawals are causing a large deceive in ground water level during dry seasons. The ground water withdrawal and recharge characteristics suggest that the actual recharge can be increased approaching the potential limits by creating addition storage through increased abstraction during the dry season. Ground water table in Chittagong City is at present in a position from where it is tough to pump ground water by shallow tube well. Deep tube well is required almost in every position to find the fresh water from ground. According to Chittagong WASA they have 85 deep tube wells permanently in all city area. Location & depth of all the deep tube well has not been found as the authority of CWASA provides location and depth of some deep tube well. In this study, therefore the main focus will be found out the variation of the groundwater table of Chittagong City Corporation Area.
METHODOLOGY Investigation of ground water table in Chittagong City is overall a tough ask. Chittagong Water Supply and Sewerage Authority (CWASA) which is the authority for water supply and sewerage, only supply water to one-third of the city dwellers. Rest of people depends on the shallow tube well and deep tube well. Although the water level in Chittagong City is in a critical state, an investigation has been done with great consciousness.
Investigation of Shallow and Deep Tube Well in Chittagong City Corporation Area At the beginning of the work first task is to find out the shallow and deep tube well in the 41 ward’s City Corporation. The ward’s map of Chittagong City is shown in [Fig.1].
Figure 1: Ward’s Map of Chittagong City Corporation Area
Determination of the Reduce Level in Well Location Reduced level of ground surface in well location can be easily found by mobile GPS. Mobile GPS is a space based global navigation satellite system that provides reliable location and time information in all weather & all times anywhere. On the well location, switch on the mobile GPS by using internet. Then wait for five minutes to set its location properly. Then it will give value of longitude latitude & elevation of that specific location with respect to mean sea level.
Depth of Water Table in Different Season
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Depth of water table has been measured from the ground level at well location by opening the head of well. After opening the head of the well, wait for 10-15 minutes to drop the water in the well pipe. Then using a thick wire with a small steal at its bottom, entering into the well pipe until the wire is in a state of less weight. Then by using tape, find the depth from the weir.
RESULTS AND DISCUSSIONS
Ground Water Table Investigation
Table 1: Well locations, class and RL of ground surface w.r. to MSL from ward (1 to 20) Well Tube (RL of Wa Well location Tube (RL of No. Well location Well groun Well rd Well groun Wa Class d No. No. Class d rd surfac surfac No. e w.r. e w.r. Longitude Latitude Longitude Latitude to to MSL) MSL) 1. 91º59'11.64" 22º25'40.06" Deep 7.6 m 41. 91º44'48.68" 22º30'5.54" Deep 7.6 m 2. 91º44'48.68" 22º30'5.54" Shallow 7.0 m 42. 91º56'3.26" 22º22'43.65" Deep 7.0 m 1 11 3. 91º56'3.26" 22º22'43.55" Deep 7.0 m 43. 91º50'8.23" 22º24'45.43" Shallow 7.3 m 4. 91º50'8.23" 22º24'45.43" Shallow 7.3 m 44. 91º40'13.2' 22º11'58.79" Shallow 7.9 m 5. 91º40'13.2' 22º11'58.79" Deep 8.5 m 45. 91º45'10.0" 22º05'21.5" Deep 6.7 m 6. 91º41'10.66" 22º05'20.56" Deep 9.4 m 46. 91º79'8.10" 22º18'40.30" Shallow 7.6 m 2 12 7. 91º59'8.50" 22º18'40.30" Shallow 8.5 m 47. 91º89'42.61" 22º38'5.33" Deep 7.0 m 8. 91º49'41.6" 22º38'5.33" Shallow 10.1m 48. 91º71'3.26" 22º22'35.51" Shallow 6.4 m 9. 91º71'3.56" 22º22'33.5" Deep 7.6 m 49. 91º50'8.33" 22º24'45.33" Deep 20.7 m 10. 91º50'8.23" 22º24'45.43" Deep 8.5 m 50. 91º42'3.02' 22º51'8.79" Deep 17.4 m 3 13 11. 91º40'13.2' 22º11'58.79" Shallow 8.8 m 51. 91º45'10.0" 22º05'21.5" Shallow 15.5 m 12. 91º47'31.2" 22º20'49.96" Shallow 11.0m 52. 91º79'8.10" 22º18'40.30" Shallow 16.8 m 13. 91º47'56.54" 22º22'15.28" Deep 7.3 m 53. 91º81'6.26" 22º35'2.43" Deep 31.1 m 14. 91º58'44.34" 22º19'36.37" Shallow 6.1 m 54. 91º40'13.2' 22º11'58.79" Deep 29.9 m 4 15. 91º47'31.2" 22º20'49.96" Shallow 9.1 m 55. 14 91º68'14.4" 22º29'36.7" Shallow 14.6 m 16. 91º47'56.54" 22º22'15.28" Deep 8.2 m 56. 91º59'40.04" 22º29'36.41" Shallow 14.0 m 17. 91º50'4.34" 22º19'36.37" Shallow 6.1 m 57. 91º83'1.39" 22º34'71.80" Deep 34.1 m 18. 91º47'13.31" 22º19'13.58" Shallow 4.9 m 58. 91º78'6.23" 22º31'45.43" Shallow 26.5 m 5 15 19. 91º46'40.04" 22º16'43.18" Deep 6.1 m 59. 91º88'13.20' 22º31'58.79" Shallow 20.1 m 20. 91º47'37.97" 22º15'36.51" Deep 10.1 m 60. 91º76'57.42" 22º26'38.22" Deep 18.0 m 21. 91º53'3.26" 22º23'43.55" Shallow 8.5 m 61. 91º83'3.26" 22º23'43.55" Deep 7.9 m 22. 91º51'6.23" 22º21'45.43" Deep 7.0 m 62. 91º51'6.23" 22º32'45.41" Deep 11.6 m 6 16 23. 91º50'13.2' 22º21'58.79" Shallow 7.9 m 63. 91º50'13.2' 22º21'58.79" Shallow 8.2 m 24. 91º46'37.41" 22º30'38.22" Deep 8.2 m 64. 91º46'37.42" 22º20'38.22" Shallow 8.8 m 25. 91º47'31.2" 22º20'49.96" Shallow 11.0m 65. 91º40'13.2' 22º11'58.79" Shallow 7.6 m 26. 91º47'56.54" 22º22'15.28" Deep 9.8 m 66. 91º47'31.2" 22º20'49.96" Shallow 8.5 m 7 17 27. 91º50'4.34" 22º19'36.37" Shallow 11.3m 67. 91º47'56.54" 22º22'15.28" Deep 6.7 m 28. 91º47'31.2" 22º20'49.96" Deep 8.2 m 68. 91º88'24.04" 22º49'26.17" Shallow 6.1 m 29. 91º49'29.20" 22º25'8.50" Shallow 8.8 m 69. 91º77'11.22" 22º51'41.16" Deep 7.0 m 30. 91º57'55.14" 22º14'25.8" Shallow 7.9 m 70. 91º47'56.54" 22º22'15.28" Deep 6.1 m 8 18 31. 91º51'4.4" 22º29'26.37" Deep 9.8 m 71. 91º55'4.34" 22º19'36.37" Shallow 7.3 m 32. 91º77'13.1" 22º49'13.08" Deep 9.1 m 72. 91º67'13.31" 22º29'13.58" Shallow 6.1 m 33. 91º81'11.2" 22º22'41.0" Shallow 11.9m 73. 91º66'10.14" 22º19'43.18" Deep 8.5 m 34. 91º77'56.04" 22º28'35.18" Shallow 12.8m 74. 91º47'77.07" 22º14'33.51" Deep 7.9 m 9 35. 91º80'44.4" 22º13'30.7" Deep 13.7m 75. 19 91º76'57.42" 22º26'38.2" Shallow 9.1 m 36. 91º67'23.31" 22º29'13.18" Deep 19.8m 76. 91º83'3.26" 22º23'41.15" Shallow 8.8 m 37. 91º77'95.1" 22º36'85.10" Shallow 11.9m 77. 91º50'34.2' 22º20'24.19" Deep 9.1 m 38. 91º63'3.16" 22º33'40.15" Shallow 7.9 m 78. 91º45'19.0" 22º25'21.50" Deep 10.4 m 10 20 39. 91º71'6.23" 22º31'45.43" Deep 8.8 m 79. 91º49'81.14" 22º18'20.3" Shallow 8.2 m 40. 91º77'7.60' 22º36'83.8" Deep 9.1 m 80. 91º55'42.21" 22º38'5.13" Shallow 9.4 m
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Table 2: Well locations, class and RL of ground surface w.r. to MSL from ward (21 to 41) Well Tube (RL of Wa Well location Tube (RL of No. Well location Well groun Well rd Well groun Wa Class d No. No. Class d rd surfac surfac No. e w.r. e w.r. Longitude Latitude Longitude Latitude to to MSL) MSL) 81. 91º47'16.41" 22º24'25.20" Deep 12.8 m 123. 91º55'4.34" 22º19'36.37" Deep 7.3 m 31 82. 91º55'84.14" 22º46'36.47" Deep 11.9 m 124. 91º67'13.31" 22º29'13.58" Deep 8.8 m 21 83. 91º77'73.31" 22º49'73.8" Shallow 13.7 m 125. 91º66'10.14" 22º19'43.18" Shallow 8.5 m 84. 91º80'8.63" 22º44'55.43" Shallow 12.2 m 126. 91º47'77.07" 22º14'33.51" Shallow 11.0 m 32 85. 91º82'94.4" 22º33'81.0" Shallow 15.8 m 127. 91º50'4.34" 22º19'36.37" Deep 8.8 m 86. 91º55'4.04" 22º29'36.47" Deep 15.5 m 129. 91º47'13.31" 22º19'13.58" Deep 8.5 m 22 87. 91º82'29.10" 22º33'9.10" Deep 14.9 m 130. 91º50'8.23" 22º24'5.43" Shallow 6.1 m 88. 91º83'14.60" 22º33'83.9" Shallow 17.1 m 131. 91º44'13.2' 22º21'58.79" Deep 5.8 m 33 89. 91º60'4.34" 22º19'36.37" Deep 8.8 m 132. 91º61'10.6" 22º15'20.56" Deep 6.1 m 90. 91º47'13.31" 22º29'13.18" Deep 10.7 m 133. 91º59'8.50" 22º18'4.30" Shallow 7.0 m 23 91. 91º67'4.14" 22º19'30.0" Shallow 9.4 m 134. 91º49'41.6" 22º38'5.33" Deep 10.1 m 92. 91º55'4.24" 22º28'46.17" Shallow 8.2 m 135. 91º50'27.2" 22º14'31.5" Deep 12.8 m 34 93. 91º79'0.34" 22º32'75.70" Deep 6.1 m 136. 91º76'57.42" 22º26'38.2" Shallow 9.8 m 94. 91º40'13.2' 22º31'8.79" Shallow 6.7 m 137. 91º30'3.26" 22º23'41.15" Shallow 8.8 m 24 95. 91º77'4.4" 22º19'30.0" Deep 6.1 m 138. 91º29'34.2' 22º42'24.19" Shallow 11.9 m 96. 91º55'4.04" 22º28'46.47" Shallow 7.6 m 139. 91º45'9.20" 22º15'25.50" Deep 9.1 m 35 97. 91º89'42.61" 22º38'5.33" Deep 7.0 m 140. 91º77'56.14" 22º24'15.28" Shallow 10.1 m 98. 91º71'3.26" 22º22'35.51" Shallow 6.4 m 141. 91º50'4.34" 22º19'36.37" Deep 8.8 m 25 99. 91º50'8.33" 22º24'45.33" Shallow 8.5 m 142. 91º47'13.31" 22º19'13.58" Deep 9.8 m 100. 91º42'3.02' 22º51'8.79" Deep 7.9 m 142. 91º61'10.6" 22º15'20.56" Deep 7.3 m 36 101. 91º55'7.3" 22º19'36.3" Deep 8.2 m 143. 91º59'8.50" 22º18'4.30" Deep 8.2 m 102. 91º67'13.11" 22º29'33.51" Deep 7.6 m 144. 91º49'41.6" 22º38'5.33" Shallow 7.6 m 26 103. 91º66'10.14" 22º19'43.18" Shallow 7.0 m 145. 91º50'8.23" 22º24'45.43" Shallow 6.1 m 104. 91º88'57.27" 22º14'43.51" Shallow 7.9 m 146. 91º40'13.2' 22º11'58.79" Shallow 5.5 m 37 105. 91º76'57.42" 22º26'38.2" Shallow 7.6 m 147. 91º47'4.4" 22º19'36.0" Deep 7.6 m 106. 91º83'3.26" 22º23'41.15" Shallow 8.2 m 148. 91º55'4.04" 22º29'36.47" Deep 7.6 m 27 107. 91º29'34.2' 22º42'24.19" Deep 7.0 m 149. 91º47'31.2" 22º20'49.96" Deep 10.1 m 108. 91º45'9.20" 22º15'25.50" Deep 6.4 m 150. 91º47'56.54" 22º22'15.28" Shallow 8.2 m 38 109 91º77'56.14" 22º24'15.28" Deep 8.2 m 151. 91º50'4.34" 22º19'36.37" Shallow 8.8 m 110. 91º50'4.34" 22º19'36.37" Deep 7.0 m 152. 91º47'13.31" 22º19'13.58" Deep 10.7 m 28 111. 91º47'13.31" 22º19'13.58" Shallow 9.8 m 153. 91º50'8.23" 22º24'5.43" Deep 9.1 m 112. 91º56'40.04" 22º26'43.18" Shallow 7.6 m 154. 91º44'13.2' 22º21'58.79" Shallow 8.8 m 39 113. 91º83'3.26" 22º23'43.55" Shallow 7.9 m 155. 91º61'10.6" 22º15'20.56" Shallow 7.3 m 114. 91º51'6.23" 22º32'45.41" Shallow 9.4 m 156. 91º59'8.50" 22º18'4.30" Deep 8.2 m 29 115. 91º50'13.2' 22º21'58.79" Deep 8.2 m 157. 91º49'41.6" 22º38'5.33" Shallow 10.1 m 116. 91º46'37.42" 22º20'38.22" Deep 7.3 m 158. 91º50'8.23" 22º24'45.43" Shallow 9.8 m 40 117. 91º40'13.2' 22º11'58.79" Shallow 7.6 m 159. 91º40'13.2' 22º11'58.79" Deep 10.4 m 118. 91º47'31.2" 22º20'49.96" Deep 7.9 m 160. 91º47'4.4" 22º19'36.37" Deep 8.5 m 30 119. 91º47'56.54" 22º22'15.28" Deep 6.7 m 161. 91º50'8.23" 22º24'45.43" Shallow 6.1 m 120. 91º88'24.04" 22º49'26.17" Shallow 6.1 m 162. 91º55'4.04" 22º29'36.47" Shallow 7.9 m 41 121. 91º77'11.22" 22º51'41.16" Shallow 7.0 m 163. 91º58'2.30" 22º39'26.3" Deep 7.0 m 31 122. 91º47'56.54" 22º22'15.28" Shallow 8.2 m 164. 91º47'14.24" 22º19'26.23" Deep 7.6 m The well locations, well class whether it is shallow or deep and the Reduce Level (RL) with respect to Mean Sea Level (MSL) of individual tube wells of every wards from No. 1 to No. 41 are presented in the Table 1 and Table 2.
Depth of Water Table in Dry Seasons from MSL The maximum, minimum and average depth of water table in every ward is shown in Table 3, from which one can easily understand about the conditions of water table in every respective wards.
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Table 3: Max. , Min. and Mean water table in Ward No. (1 to 41) Ward Max. Min. Mean Ward Max. Min. Mean No. No. 1 3.5 m 1.8 m 2.7 m 22 5.2 m -1.8 m 1.4 m 2 3.0 m -1.2 m 0.7 m 23 2.4 m 0.3 m 0.0 m 3 4.0 m -1.2 m 2.1 m 24 3.2 m 0.9 m 1.7 m 4 5.2 m 2.1 m 4.0 m 25 3.0 m 0.6 m 2.0 m 5 1.5 m -5.5 m -2.9 m 26 2.4 m -2.0 m 1.1 m 6 4.6 m 1.2 m 2.7 m 27 2.4 m -3.0 m 1.6 m 7 4.9 m -1.2 m 2.3 m 28 3.0 m 1.8 m 0.5 m 8 3.7 m 0.9 m 2.5 m 29 1.7 m -0.6 m 0.8 m 9 6.7 m 3.0 m 4.4 m 30 3.4 m 0.0 m 1.7 m 10 3.4 m 0.9 m 2.0 m 31 1.5 m -0.6 m 0.8 m 11 4.0 m 1.5 m 0.6 m 32 2.4 m -2.1 m 1.0 m 12 1.8 m -2.7 m 0.2 m 33 2.7 m -2.7 m 0.4 m 13 7.0 m 3.4 m 5.2 m 34 3.4 m 0.0 m 1.4 m 14 1.1 m 3.3 m 6.3 m 35 3.4 m 0.6 m 2.0 m 15 8.0 m 5.0 m 6.9 m 36 1.8 m 0.0 m 0.4 m 16 3.3 m -1.5 m 0.2 m 37 0.3 m -2.4 m -1.1 m 17 1.5 m -1.2 m -1.7 m 38 3.0 m 0.0 m 1.8 m 18 4.5 m 0.9 m -2.0 m 39 4.3 m 2.4 m 3.4 m 19 3.1 m 1.2 m 2.0 m 40 4.4 m 1.6 m 3.1 m 20 3.0 m 2.7 m 2.7 m 41 2.9 m 0.6 m 1.7 m 21 2.4 m -1.8 m 0.7 m
Fluctuations of water table along the co-ordinates Since most of the tube wells are found along the longitude 91º47' and latitude 22º20' that’s why graphical presentation of water level fluctuation are shown along these respective co-ordinates in [Fig.2] and [Fig.3].
Figure: Water Level Fluctuation along with Longitude 91º47'
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Figure: Water Level Fluctuation along with Longitude 22º20'
CONCLUSIONS The concluding remarks that can be derived from the investigation have been presented below: i. During field investigation, it was found that within 164 well locations in 41 wards; the numbers of shallow and deep tube wells are same. ii. Water tables are different in different wards. But nearly similar in adjacent wards. iii. In almost most of the ward’s level of water table is lowering during dry seasons. iv. Depth of water table with respect to mean sea level is quite lower in well No. 20 of ward No. 5 and quite higher in well No. 54 of ward No. 14 The limitation of this study is not investigating the shallow and deep tube wells separately.
REFERENCES Ahmed, M. F. & Rahman, M. M. (2000). Water Supply and Sanitation. ITN – Bangladesh, Dhaka- 1000.pp: 331 – 338. Ahmed, A. (1990). Safe water supply in Rural Himalayas: Environmental Problems and Strategies. In the proceedings of the 18th National Health Conference on Health Problems of the Nineties, Lahore, Pakistan, 2-5 December 1990. pp: 123-138. Ekpo, N. M. & Inyang, L. E. D. (2000). Radioactivity, physical and chemical parameters of underground and surface waters in Qua Iboe river estuary, Nigeria. Environmental Monitoring and Assessment, 60(1), 47-55. Fair, G. M., Geyer, J. C. & Okun, D. A. (1966). Water and Wastewater Engineering. New York: Wiley. Gleick, P. H. (1993). Water in crisis: a guide to the world’s fresh water resources. New York: Oxford University Press. Gleick, P. H. (1996). Water resources. In S. H. Schneider (ed.), Encyclopedia of climate and weather. New York: Oxford University Press. Groundwater Resources Development in Bangladesh: (Contribution to Irrigation for Food and Constraints to Sustainability). Anwar Zahid and Syed Reaz Uddin Ahmed, Ground Water Hydrology Division, Bangladesh Water Development Board, Dhaka, Bangladesh. Md. A. H. Mirdad, S. K. Palit; Investigation of Ground Water Table in the South-East (Chittagong) Part of Bangladesh. American Journal of Civil Engineering. Vol. 2, No. 2, 2014, pp. 53-59. doi: 10.11648/j.ajce.20140202.17.
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