Water Volume and 3D Topographic Analysis of Hub Dam, Karachi, Using Remote Sensing and Global Bathymetry Data

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.

Keywords

• 3d visualization
• remote sensing
• global bathymetry
• google earth engine (gee)
• topographic analysis
• python
• resource management