Datta, S., Karmakar, S., Mezbahuddin, S., Hossain, M. M., Chaudhary, B. S., Hoque, M. E., ... & Baul, T. K. (2022). The limits of watershed delineation: implications of different DEMs, DEM resolutions, and area threshold values. Hydrology Research, 53(8), 1047-1062. https://doi.org/10.2166/nh.2022.126
Abstract
Identifying and demarcating watershed areas provides a basis for designing and planning for water resources. In this study, DEMs-based estimates of watershed characteristics of three rivers of Bangladesh – Halda, Sangu, and Chengi – were derived using eight Digital Elevation Models (DEMs) of 30 m, 90 m, and 225 m resolution in the Soil and Water Assessment Tool (SWAT). We have assessed watershed characteristics concerning DEMs, resolutions, and Area Threshold Values (ATVs). Though the elevation data differed, high correlation values among DEMs and resolutions confirm the negligible effect of elevation in the watershed delineation. However, the slope and watershed delineation vary for different DEMs and resolutions. The 90 m DEMs estimated larger areas for Halda and Chengi and lower perimeter values for all three rivers. In watershed delineation, the area near the mouth and flat terrain did not coincide with DEMs. The common intersected area by DEMs can be used as the focal area of watershed management. ATV ≤ 40 km2 significantly influences sub-basin counts and stream network extraction for these watershed areas. Though watershed size and shape were independent of the different ATVs, the DEM-based watershed delineation process in SWAT needs optimum ATV values to represent the stream network precisely.
Datta, S., Karmakar, S., Islam, M. N., Karim, M. E., Kabir, M. H., & Uddin, J. (2022). Assessing landcover and water uses effects on water quality in a rapidly developing semi-urban coastal area of Bangladesh. Journal of Cleaner Production, 336, 130388. https://doi.org/10.1016/j.jclepro.2022.130388
Abstract
We have studied the urbanization effect on water resources of a developing semi-urban area on the southeast side of Chattogram (Chittagong) city, enduring industrialization and subsequent urbanization. The landcover comprising water bodies, vegetation, and agricultural lands were 9.36%, 40.55%, and ∼37%, respectively. Domestic water demand was estimated by interviewing households, while water samples were collected using the grab sampling method from rivers, canals, ponds, and groundwater for laboratory analysis. Daily average water consumption increases with growing family size and income. 52.14 million liters per day (MLD) water is needed for households in the study area, with a per capita demand of 123.80 L per day. The daily maximum water demand range of households was 78.20 MLD – 104.27 MLD, with the peak hourly demand being 5.87 MLD. However, available aquifer recharge is estimated as low as 11 million m3 per year. Except for some points, the average pH, TDS, and Cl− values were acceptable, though EC, SS, and COD values were exceeding the water quality standards with a small amount of HCO3− and no CO3− values in the water samples, making the water of the study area less compatible for domestic purposes. The primary causes were waste disposals, saltwater intrusion, and a few instances of industrial discharge. The settlement landcover directly influences water quality parameters pH, TDS, and conductivity in compositional analysis. We believe that expanding the build-up area to the vegetated area will eventually induce water quality degradation, mainly saline water intrusion.
As Co-author
Hossen, S., Ali, Y., Chakma, S., Datta, S., & Hoque, A. S. M. R. (2025). Spatiotemporal analysis of land cover change, projection, and fragmentation: an application of Google Earth Engine and machine learning approach on Baraitali Forest, Bangladesh. Geology, Ecology, and Landscapes, 9(3), 907–920. https://doi.org/10.1080/24749508.2024.2359776
Abstract
Understanding the spatiotemporal dynamics of land cover/use change and forest fragmentation from 1981 to 2021 across the Baraitali Forest was very crucial for modeling future land cover, formulation of sustainable and robust forest/land-management strategies, and policy. The study adopted a mixed modern tool of Google Earth Engine platform, Machine Learning Algorithms, ArcGIS software, Land Change Modeller of TerrSet software, and FRAGSTATS programs. The study revealed that total land cover from 1981 to 2021 found a negative change in vegetative coverage, and a positive change in settlement which was more visible within the 6 land cover classes. Projections of future land cover for the next 10 years anticipate that vegetation coverage will be reduced by 65.96 ha, while settlement will be enhanced by 41.91 ha in 2031. The study also found conversions/transition of land cover throughout the research area. Overall change of spatiotemporal patterns was leading to substantial forest fragmentation. The overall accuracy and Kappa statistics for all the supervised land cover classifications were satisfactory. The study will fill an information gap by providing the revealed information at national and regional scales and may contribute to understanding of global change.
Haque, M. B., Karmakar, S., Datta, S., Sajid, A. P., Mamun, M. A. A., Hoque, M. E., ... & Alam, M. S. (2024). Discharge and sediment load modelling using rating curve-based missing data management. Hydrology Research, 55(10), 959-975. https://doi.org/10.2166/nh.2024.165
Abstract
Hydrological models are vital for water management to determine in-stream flow, irrigational water, domestic water supply, and biodiversity conservation. This study formulates a hydrological model with a novel approach for streamflow and sediment load in the QGIS-supported Soil and Water Assessment Tool for the Halda River catchment, a unique ecological habitat for natural carp spawning and freshwater sources. The daily simulation uses an innovative stage–discharge relationship technique from available 15-day interval flow data. The model evaluation parameters R2 values 0.80 and 0.62, and NS values 0.81 and 0.61 for calibration and validation of streamflow suggested excellent agreement in the seasonal cycle and most of the monsoon peak flow. The streamflow/precipitation ratio indicates a significant influence of groundwater through infiltration. The baseflow shows a decreasing trend. The sediment load based on suspended sediment concentration at a downstream location is 1,625 tons/day. On the contrary, the model prediction is 30 times lower. The scattered sediment load data support the model estimate by considering relatively lower intervention or land use change in its upstream. This model provides a baseline for daily flow and sediment load for scenario modeling (e.g., climate change, land use change) for environmental flow estimation of the fish habitat, freshwater supply, irrigation, and salinity intrusion.
Karim, M. E., Hoque, M. E., Islam, M. N., Hossain, M. M. Bhuiyan., Datta, S., & Karmakar, S. (2020). Land-use and Land Cover Characterisation and Spatial-temporal Analysis of the Matamuhuri River Watershed. The Chittagong University Journal of Marine Sciences and Fisheries, 1(1), 49-65.
Link: https://cu.ac.bd/cujms/assets/paperfile/21_paperfile_LG3AESI77Z_(4)%20.pdf
Abstract
Changes in physiographic, meteorological, and biogeographic processes affect the river basin process. These natural processes are usually overridden by anthropogenic land use, and land cover changes henceforth affecting basin run-off and sediment load. The Matamuhuri River basin is one of the pristine watersheds of Bangladesh, where settlement density is among the lowest. During the last two decades, rapid land cover changes have become eminent in many regional watersheds of Bangladesh due to population growth and a changing perception of resources. Hence, we have studied the land cover change in the Matamuhuri River basin by selecting Landsat images from the Earth Explorer database managed by USDA for 1989, 2004, and 2019. Four land classes were selected for this study. This study revealed that during 1989 (the base year), the dominant land cover was forest or vegetation, which covered 138,526.29 ha (67.74%) and settlement covered 19,782.72 ha (9.67%). In 2004, the settlement controlled nearly 16.85% of the total land, and vegetation cover had decreased (59.22%). In 2019, the vegetation cover regained its stronghold (66.06%), which may be due to government policy to conserve forest land though a remarkable increase, in settlement also observed. Agriculture land is decreased in this case. It holds nearly 11.85% of the total land (24,231.87 ha). This data shows that as infrastructure development downstream of the watershed increases, the catchment becomes more vulnerable to change. Again, the upstream area's land-cover change is very rapid, requiring adequate conservation attention. The overall accuracy of the classification was calculated at 76.6%, 81.25% and 90.24%, with Kappa coefficient values of 0.69, 0.75 and 0.87, respectively, for 1989, 2004 and 209 image classification. The overall Kappa accuracy test shows the strength of the supervised classification performed in this study.