Water Volume and 3D Topographic Analysis of Hub Dam, Karachi, Using Remote Sensing and Global Bathymetry Data
 
More details
Hide details
1
Department of Geography, University of Karachi, Karachi, Pakistan
 
2
Department of Environmental Sciences, Sindh Madressatul Islam University Karachi, Karachi, 74000, Pakistan
 
 
Submission date: 2024-09-14
 
 
Acceptance date: 2023-10-24
 
 
Publication date: 2024-10-30
 
 
Corresponding author
Imran Ahmed Khan   

imranak32@uok.edu.pk
 
 
Trends in Ecological and Indoor Environmental Engineering, 2024;2(3):35-41
 
KEYWORDS
ABSTRACT
Background:
Karachi one of the largest and fastest-growing cities in the world, the city faces severe water scarcity and aggravated by climate change, excessive groundwater extraction in dense urban areas and inefficient water distribution systems. The Hub Dam, along with the Keenjhar Lake, is the source of water for Karachi's population as well as for the industrial and urban agricultural sectors. It is therefore important to have current information on the dam's storage capacity, its topographic features and the hydrological dynamics of its catchment area, which remains poorly understood to date.

Objectives:
This study's primary objectives are (1) to assess the hydrography of Hub Dam using global bathymetric and remote sensing data and (2) to create a detailed 3D model of the dam to facilitate analysis of its hydrological features. (3) Also generate data-driven insights to guide improvements in Karachi’s urban water management policies.

Methods:
The methodology integrates global bathymetric data with remote sensing and Google Earth Engine (GEE) for analysing Hub Dam’s hydrological and topographic characteristics. Data processing and visualization were conducted in Google Colab by python codes. This process enabling an efficient workflow for handling and analysing large datasets.

Results:
Hub Dam 3D model is developed. The approaches is enhancing visualization of the dam's water volume and terrain features to support detailed hydrological analysis. 3D volumetric analysis estimates with total water volume at approximately 79.954 · 106 m3. It was found that currently, the Hub Dam reservoir has enough water to fulfil Karachi needs for 503 days, a critical reserve for urban planning, resource management. Using a topographic analysis that includes surrounding slopes and watershed features, key elements affecting dam performance were identified. This result helps to adjust the filling of the dam with water. The Hub Dam's ability to store water was confirmed through a preliminary volume assessment.

Conclusion:
The study successfully achieved its objectives. The results obtained highlight the importance of adaptive well informed strategies for sustaining well managed water resources in response to environmental pressures. The current study further advance rational water management by integrating remote sensing and data-driven methodologies into this area of knowledge. That is, the effectiveness of Using Remote Sensing and Global Bathymetry Data has been repeatedly proven by other studies in different areas of knowledge. However, in the current study, this scientific approach has successfully demonstrated the efficient and rapid acquisition of accurate, up-to-date information on reservoir capacity and hydrological characteristics, which opens up new opportunities for efficient water management.
REFERENCES (31)
1.
Aslam, M. N., Ashraf, S., Shrestha, S., Ali, M., & Hanh, N. C. (2024). Climate change impact on water scarcity in the Hub River Basin, Pakistan. Groundwater for Sustainable Development, 27, 101339. https://doi.org/10.1016/j.gsd.....
 
2.
Begum, A. B. E. D. A., Khan, M. Z., Khan, A. R., Zehra, A. F. S. H. E. E. N., Hussain, B. A. B. A. R., Siddiqui, S. A. I. M. A., & Tabbassum, F. O. Z. I. A. (2013). Current status of mammals and reptiles at Hub Dam area, Sindh/Balochistan, Pakistan. Current World Environment, 8(3), 407–414. http://dx.doi.org/10.12944/CWE....
 
3.
Carr, G., Blöschl, G., & Loucks, D. P. (2012). Evaluating participation in water resource management: A review. Water Resources Research, 48(11), W11401. https://doi.org/10.1029/2011WR....
 
4.
Du, X., Fan, X., Tan, J., & Zhu, J. (2010, November). Study on 3D visualization application for the Grand Canal heritage site research. In Sixth International Symposium on Digital Earth: Data Processing and Applications (Vol. 7841, pp. 576-583). SPIE. https://doi.org/10.1117/12.873....
 
5.
Hameed, F., Qureshi, M. A., & Khalil, R. M. Z. (2022). Establishing Level-Area-Volume Relationships of Darawat Reservoir Using Time Series Remote-Sensing Images. https://doi.org/10.21203/rs.3.....
 
6.
Irfan, M., Kazmi, S. J. H., & Arsalan, M. H. (2018). Sustainable harnessing of the surface water resources for Karachi: a geographic review. Arabian Journal of Geosciences, 11, 1–11. https://doi.org/10.1007/s12517....
 
7.
Islam, H., Abbasi, H., Karam, A., Chughtai, A. H., & Ahmed Jiskani, M. (2021). Geospatial analysis of wetlands based on land use/land cover dynamics using remote sensing and GIS in Sindh, Pakistan. Science Progress, 104(2), 00368504211026143. https://doi.org/10.1177/003685....
 
8.
Jain, S., Srivastava, A., Khadke, L., Chatterjee, U., & Elbeltagi, A. (2024). Global-scale water security and desertification management amidst climate change. Environmental Science and Pollution Research, 1–25. https://doi.org/10.1007/s11356....
 
9.
Khan, H. F., & Arshad, S. A. (2022). Beyond water scarcity: Water (in) security and social justice in Karachi. Journal of Hydrology: Regional Studies, 42, 101140. https://doi.org/10.1016/j.ejrh....
 
10.
Kilsedar, C. E., Fissore, F., Pirotti, F., & Brovelli, M. A. (2019). Extraction and visualization of 3D building models in urban areas for flood simulation. The international archives of the photogrammetry, remote sensing and spatial information sciences, 42, 669–673. https://doi.org/10.5194/isprs-....
 
11.
Kumar, K., Ledoux, H., & Stoter, J. (2018). Dynamic 3D visualization of floods: case of The Netherlands. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 83–87. https://doi.org/10.5194/isprs-....
 
12.
Kumar, S., Jaswal, A., Pandey, A., & Sharma, N. (2017). Literature review of dam break studies and inundation mapping using hydraulic models and GIS. International Research Journal of Engineering and Technology, 4(5), 55–61.
 
13.
Lioumbas, J., Christodoulou, A., Katsiapi, M., Xanthopoulou, N., Stournara, P., Spahos, T., ... & Theodoridou, N. (2023). Satellite remote sensing to improve source water quality monitoring: A water utility's perspective. Remote Sensing Applications: Society and Environment, 32, 101042. https://doi.org/10.1016/j.rsas....
 
14.
Marsal, R., & García-Carpallo, J. (2024). Spatial and hydrological analysis of the water supply system in as-Sila'/Sela (Tafila, Jordan) based on a 3D model. Virtual Archaeology Review, 15(30), 1–20. https://doi.org/10.4995/var.20....
 
15.
Mukonza, S. S., & Chiang, J. L. (2024). Machine and deep learning-based trophic state classification of national freshwater reservoirs in Taiwan using Sentinel-2 data. Physics and Chemistry of the Earth, Parts A/B/C, 134, 103541. https://doi.org/10.1016/j.pce.....
 
16.
Nakib, A. M., Luo, Y., Emon, J. H., & Chowdhury, S. (2024). Machine learning-based water requirement forecast and automated water distribution control system. Computer Science & IT Research Journal, 5(6), 1453–1468. https://doi.org/10.51594/csitr....
 
17.
Qi, H., & Altinakar, M. S. (2012). GIS-based decision support system for dam break flood management under uncertainty with two-dimensional numerical simulations. Journal of Water Resources Planning and Management, 138(4), 334–341. https://doi.org/10.1061/(ASCE)....
 
18.
Rajendran, S., Nasir, S., & Jabri, K. A. (2020). Mapping and accuracy assessment of siltation of recharge dams using remote sensing technique. Scientific Reports, 10(1), 10364. https://doi.org/10.1038/s41598....
 
19.
Salama, A., ElGabry, M., El-Qady, G., & Moussa, H. H. (2022). Evaluation of Grand Ethiopian Renaissance Dam Lake Using Remote Sensing Data and GIS. Water, 14(19), 3033. https://doi.org/10.3390/w14193....
 
20.
Salman, M. M., Abraheim, A. K., & Al-Attabi, A. M. A. N. (2024). Stability analysis of Hub dam under rapid drawdown. Open Engineering, 14(1), 20220497. https://doi.org/10.1515/eng-20....
 
21.
Sharma, S., Beslity, J. O., Rustad, L., Shelby, L. J., Manos, P. T., Khanal, P., ... & Khanal, C. (2024). Integrating Remote Sensing, GIS, AI, and Machine Learning for Natural Resource Management: Comparative Analysis of Tools and the Critical Role of In‐Situ Validation Data. https://doi.org/10.20944/prepr....
 
22.
Shaukat, S. S., Khan, M. A., Mett, M., & Siddiqui, M. F. (2014). Structure, composition and diversity of the vegetation of Hub dam catchment area, Pakistan. Pakistan Journal of Botany, 46(1), 65–80.
 
23.
Singla, J. G., & Padia, K. (2021). A novel approach for generation and visualization of virtual 3D city model using open source libraries. Journal of the Indian Society of Remote Sensing, 49(6), 1239–1244. https://doi.org/10.1007/s12524....
 
24.
Souza, A. P. D., Teodoro, P. E., Teodoro, L. P. R., Taveira, A. C., de Oliveira-Júnior, J. F., Della-Silva, J. L., ... & da Silva Junior, C. A. (2021). Application of remote sensing in environmental impact assessment: a case study of dam rupture in Brumadinho, Minas Gerais, Brazil. Environmental Monitoring and Assessment, 193(9), 606. https://doi.org/10.1007/s10661....
 
25.
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS journal of photogrammetry and remote sensing, 164, 152–170. https://doi.org/10.1016/j.ispr....
 
26.
Velastegui-Montoya, A., Montalván-Burbano, N., Carrión-Mero, P., Rivera-Torres, H., Sadeck, L., & Adami, M. (2023). Google Earth Engine: a global analysis and future trends. Remote Sensing, 15(14), 3675. https://doi.org/10.3390/rs1514....
 
27.
Vorosmarty, C. J., Green, P., Salisbury, J., & Lammers, R. B. (2000). Global water resources: vulnerability from climate change and population growth. science, 289(5477), 284–288. http://dx.doi.org/10.1126/scie....
 
28.
Yépez-Rincón, F. D., Ferriño Fierro, A. L., Escobedo Tamez, A. N., Guerra Cobián, V. H., Pinedo Sandoval, O. E., Chávez Gómez, J. H., ... & Pirasteh, S. (2024). Mapping Longitudinal and Transverse Displacements of a Dam Crest Based on the Synergy of High‐Precision Remote Sensing. Advances in Civil Engineering, 2024(1), 6220245. https://doi.org/10.1155/2024/6....
 
29.
Zeng, W., Xu, K., Cheng, S., Zhao, L., & Yang, K. (2023). Regional Remote Sensing of Lake Water Transparency Based on Google Earth Engine: Performance of Empirical Algorithm and Machine Learning. Applied Sciences, 13(6), 4007. https://doi.org/10.3390/app130....
 
30.
Zhang, S., Hou, D., Wang, C., Pan, F., & Yan, L. (2020). Integrating and managing BIM in 3D web-based GIS for hydraulic and hydropower engineering projects. Automation in Construction, 112, 103114. https://doi.org/10.1016/j.autc....
 
31.
Zubair, A., Begum, A., Nasir, M. I., & Mahmood, T. (2012). Determination of Trace Elements with respect to its Suitability for Drinking and Irrigation in Hub Dam, Pakistan. European Academic Research, 1(5), 816–830.
 
Journals System - logo
Scroll to top