Floods stand out as the most destructive hazards globally, posing a constant threat to human lives and inflicting considerable economic hardships across the planet (Costache, 2019; Sarkar & Mondal, 2019). Between 1995 and 2015, floods accounted for approximately 47% of weather-related disasters, impacting 2.3 billion people and resulting in 157,000 fatalities (Atreya et al., 2017). Bangladesh, situated in one of the most flood-prone regions—the Ganges, Brahmaputra, Meghna (GBM) basins—experiences frequent flooding (Chowdhury & Hassan, 2017; Dasgupta et al., 2011). With 80% of the country designated as floodplain, Bangladesh contends with annual inundation affecting around 20–25% of its land area and in extreme years, this figure can soar to over 60% of the entire country (Kundzewicz et al., 2014; Mirza et al., 2002). According to the Bangladesh Bureau of Statistics, from 2009 to 2014, floods affected about 34% of households in the country, leading to an estimated total loss of approximately 42,807 million Taka (422 million USD) (BBS, 2015).
The monsoon season in Greater Dhaka brings about regular flooding. Over 30% of households in Dhaka, Gazipur, and Munshiganj are affected by floods, while in Narayanganj, the figure stands at approximately 18% (BBS, 2015). The lowlands of Dhaka consistently face inundation, the severity of which depends on both rainfall and upstream flows and along with the fluvial flooding, pluvial flooding is becoming a major concern for the residents of Dhaka (Alam, 2014; Faisal et al., 2003, Stalenberg & Vrijling, 2009).
Greater Dhaka has witnessed devastating floods in various years, including 1954, 1955, 1968, 1971, 1974, 1987, 1988, 1998, 2004, and 2007. The flood of 1988 submerged about 85% of Dhaka for several weeks and the 1998 flood submerged nearly 56% of Dhaka for approximately 10 weeks, causing extensive damage and suffering (Jabeen et al., 2010; Jahan, 2000). The 2004 flood, although of shorter duration than the 1988 and 1998 events, took a longer time to drain from Dhaka city areas. During this flood, 40% of the city and its inhabitants directly suffered due to pluvial flooding, and the ready-made garments sector incurred a loss of $10.3 billion (632 billion TK) (M. Alam & Rabbani, 2007; Mark et al., 2018). The 2007 flood surpassed the duration of the 1988 and 2004 events, but peak water levels for all rivers surrounding Greater Dhaka were lower than those in 1988, 1998, and 2004 (Dasgupta et al., 2011).
Besides property damage and human fatalities, the secondary effect of flooding, such as the spread of water-borne diseases like diarrhea, dysentery, and typhoid, is severely affecting the health of Dhaka residents. During the 1988, 1998, and 2004 flood events, patient visits at the ICDDR,B almost doubled compared to non-flooded times (Schwartz et al., 2006). In the 2007 flood, 43,250 diarrhea patients were admitted to the ICDDR,B hospital (Harris et al., 2008). Prolonged exposure to persistent and toxic chemicals through the use of polluted floodwater for irrigation, washing, and bathing in the floodplain regions of Greater Dhaka that receive toxic contaminants from polluted rivers during monsoon flooding has led to reduced agricultural production and an increase in non-communicable diseases among both children and adults(Hossain et al., 2018). These studies underscore the rising health risks associated with the use of floodwater, posing serious implications for public health in Dhaka.
Effective response during flooding events requires real-time observation and monitoring of the affected areas (Amarnath & Rajah, 2016; Chapi et al., 2017; Martinez & le Toan, 2007). In Bangladesh, the Flood Forecasting and Warning Centre (FFWC) relies on hydrological models for providing flooding information, using inputs like discharge, weather, and digital elevation model (DEM) data. However, accurate DEM and discharge data are often lacking. Remote sensing images, particularly Synthetic Aperture Radar (SAR) sensors, offer a solution by overcoming the limitations of hydrological models, especially in all weather conditions (M. S. Rahman & Di, 2016; Roy et al., 2017; Greifeneder et al., 2014; Rahman & Thakur, 2018). While optical images are effective during good weather conditions, they are limited by cloud interference (Fu et al., 2020). SAR sensors, on the other hand, can penetrate clouds and operate day and night, offering a valuable tool for flood monitoring. The availability of free SAR data through the European Space Agency’s (ESA) Sentinel-1 C-band SAR mission has opened up significant opportunities for flood extent monitoring.
In addition to flooding, surface water pollution is a major environmental concern, especially in developing countries like Bangladesh undergoing rapid industrialization and urbanization (Karn & Harada, 2001). The rivers of Greater Dhaka, heavily impacted by domestic and industrial wastes from thousands of factories, exhibit high pollution levels. Industrial pollution alone contributes to 60% of the total pollution in the Dhaka watershed (Ahmed et al., 2015; Asaduzzaman et al., 2016; Islam et al., 2014, 2015; Tamim et al., 2016). The rivers in the Greater Dhaka Watershed show elevated levels of organic pollution, pathogens, ammonia, heavy metals and exhibit high toxicity(Rampley et al., 2020; P. G. Whitehead et al., 2019). Though exposure to polluted water occurs throughout the year for low-income communities living close to rivers, exposure is higher in monsoon through subsistence usage owing to the perceived low pollution in monsoon(Hoque et al., 2021).
Ensuring water quality is crucial for public health and safety, and to safeguard surface water resources, it is essential to establish a comprehensive water quality monitoring program (Ouyang, 2005). However, analyzing a large number of samples and monitoring various parameters often makes it challenging to evaluate water quality as a single unit (Chapman, 1996). In such cases, a water quality index (WQI) serves as a convenient and useful tool to assess water quality status for different temporal and spatial resolutions. It helps to consolidate information on water quality parameters into a meaningful and easily understandable format, making it valuable for state agencies and the general public (Sutadian et al., 2016). Several water quality indices have been developed by national and international organizations. The Canadian Council of Ministers of the Environment (CCME) adopted Water Quality Index (WQI), usually known by its acronym form ‘CCME-WQI’, stands out due to its advantages, including compliance with legal requirements and diverse water uses, suitability for assessing water quality in specific areas and seasons, flexibility in criteria selection, and tolerance for missing data (Mohebbi et al., 2013; Terrado et al., 2010; Yan et al., 2016).
We understand that flooding adversely affects both living beings and floodplains, and if the floodwater is polluted, it can have even more harmful effects on the surrounding environment. However, previous studies have separately examined river water quality and floods in Greater Dhaka owing to the misconception of considering pollution as a dry season only phenomenon, thus missing the population exposure dimension and the underlying risks to health and well-being from pollution. This study aims to provide a clear overview of both flooding and water quality in the rivers of Greater Dhaka by evaluating flooding and water quality in Greater Dhaka during different time periods in addition to assessing the number of people that are exposed to the floodwater. The study observes the extent of flooding and pollution status during the monsoon and post-monsoon periods of 2019 and 2020.
Study Area:
Figure 1: Landcover of Greater Dhaka (Jalal et al., 2019).
Greater Dhaka is located within the coordinates of 90° E to 90.74° E longitude and 23.37° N to 24.34° N latitude, covering an area of approximately 4929.45 km². The majority of this area is utilized for cropland, accounting for 45.82%. Rural settlements and built-up areas follow with 27.17% and 13.67%, respectively (Fig. 1). Rivers and water bodies constitute 8.26%, while forestry covers 3.45%. Approximately 21% of the study area experiences flooding, primarily with depths ranging from 1.83 to 3.08 meters. This region is situated in the southern part of the Madhupur Tract, characterized as a Pleistocene terrace elevated 1–10 meters above the adjacent floodplains. According to the Köppen climate classification, the area exhibits a Tropical savanna climate with dry-winter characteristics. The monthly average temperature in the Dhaka area ranges from 16°C to 33°C (1953–2018). In January, temperatures average between 18–20°C, while in April and July, they range from 28–29°C. The yearly average rainfall is 2148mm, with the highest monthly recorded rainfall being 856 mm. In the Dhaka city area, the annual average rainfall is approximately 2117 mm (1980–2012).
Table 1
River | Length (km) | Length in study area(km) | Average width (m) | Surrounding landcover/landuse |
Balu | 44 | 23 | 79 | Mainly rural setup, Urbanization started |
Bangshi | 239 | 22 | 49 | Semi-urban set up at upstream and downstream, rest rural setup |
Bangshi Savar | 13 | 13 | 73 | Semi urban setup |
Buriganga | 29 | 29 | 302 | Highly urbanized |
Dhaleswari | 292 | 60 | 144 | Mainly rural setup with several industries at different locations |
Shitalakhya | 108 | 60 | 228 | Upstream urban setup, downstream highly urbanized |
Tongi Khal | 15 | 15 | 55 | Highly urbanized |
Turag | 62 | 50 | 82 | Upstream urban setup, downstream highly urbanized |
The Greater Dhaka area boasts some of the country's vital rivers, with the Padma flowing in the southwest direction and the Meghna in the southeast. Dhaka city is enveloped by four rivers: the Turag and Buriganga to the west, Tongi Khal to the north, and Balu to the east. To the northwest, the Bangshi and Kaliganga flow, while the Dhaleswari is situated to the south, and the Shitalakhya lies in the eastern part. All these rivers traverse Greater Dhaka, ultimately merging into the Meghna. Description of all the rivers in the study area can be found in Table 1. This region has developed into an economic hub due to its extensive river network, with Dhaka contributing 40% to Bangladesh's gross domestic product. Industries in Greater Dhaka predominantly include textiles, apparels, metals, FMCG (Fast-Moving Consumer Goods), electronics, and construction materials. Although agro-based industries, once common in areas like Narayanganj and Demra, are now lacking. Currently, 18 Export Processing Zones (EPZs) operate in Greater Dhaka, and an additional 8 EPZs are planned to launch in the coming years, foreseeably increasing production and impacting the surrounding environment. Many of these zones are located alongside rivers, with multiple outlets to the water bodies. Economic hubs are strategically formed around waterways, leading to various impacts on them, particularly due to inadequate waste management and drainage systems.
Data used:
This analysis utilized the Ground Range Detected (GRD) product of Sentinel-1 data for flood mapping. Sentinel-1 is a space mission funded by the European Union and conducted by the European Space Agency (ESA) under the Copernicus Programme. It captures C-band synthetic aperture radar (SAR) imagery at various polarizations and resolutions. The GRD products are amplitude images without phase information, and they are projected from slant range to ground range using an Earth ellipsoid. The resulting product features square pixels with reduced speckle. The acquisition employed the Interferometric Wide (IW) swath mode, covering a 250 km swath. Sentinel-1 data from specific dates were selected for this analysis and they are listed in Table 2.
Table 2
Data | Season | Date |
Sentinel-1 GRD | Monsoon | 07/07/2019, 09/07/2019, 19/07/2019, 21/07/2019, 25/07/2019 |
03/08/2020, 06/08/2020, 08/08/2020, 15/08/2020, 18/08/2020, 20/08/2020, 27/08/2020, 30/08/2020 |
Post-monsoon | 01/10/2019, 08/10/2019, 11/10/2019, 13/10/2019, 20/10/2019, 23/10/2019, 25/10/2019 |
02/10/2020, 05/10/2020, 07/10/2020, 14/10/2020, 17/10/2020, 19/10/2020, 26/10/2020, 29/10/2020 |
For calculating permanent waterbody, seasonality data of JRC Global Surface Water Mapping Layers, v1.3 dataset from Earth Engine Data Catalog have been used. This data set is created by using three million Landsat satellite over the past 32 years at 30-meter resolution (Pekel et al., 2016).
For population exposure analysis, 100m resolution population data from WorldPop has been used (Stevens et al., 2015; Tatem, 2017). For this analysis population data of 2019 and 2020 have been used which is adjusted to match United Nations national population estimates. This data has been extracted from recent census-based population counts matched to their associated administrative units are disaggregated to ~ 100x100m grid cells through machine learning approaches. For landcover analysis, Land Cover Map 2015 of Bangladesh has been used in this study(Jalal et al., 2019).
Water samples have been collected from 64 locations of Greater Dhaka River system which includes Balu, Bangshi, Bangshi Savar, Buriganga, Dhaleswari, Shitalakhya, Tongi khal and Turag River (Fig. 2). Samples were collected from a boat at 2m depth. Temperature, pH, dissolved oxygen, redox potential, electrical conductivity, and total dissolved solids of the samples were measured in the filed using HACH HQ40d multiparameter device and turbidity was measured with VELP Scientifica TB-1 portable Turbidimeter. Laboratory analysis of Color, Alkalinity, Iron, Ammonia-Nitrogen, Nitrate, Phosphate, Sulfide, Sulfate and Chloride were conducted by spectrophotometric method and measuring range of the parameters are 15–500 mg/L Pt-Co, 0.02 to 3.0 mg/L, 0.02 to 2.50 mg/L, 0.3 to 30 mg/L, 0.3 to 45.0 mg/L, 5 to 800 µg/L and 2 to 70 mg/L, respectively. Analysis of Alkalinity and chloride were performed by titrimetric method where the method ranges were 10- 4000 mg/L (as CaCO3) and 10 to 10000 mg/L respectively. Arsenic, Zinc, Lead, Cobalt, Cadmium, Nickel, Iron, Chromium and Copper were measured in ICP (Inductively coupled plasma) machine. All the laboratory analyses were conducted at The Soil and Water Analysis Laboratory at Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET).