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Efficient Segmentation Algorithm for Estimation of Revised Reservoir Capacities in Google Earth Engine

Author

Listed:
  • Rahul Kumar Jaiswal

    (National Institute of Hydrology)

  • Gunja Dhruw

    (Indira Gandhi Krishi Vishwavidyalaya)

  • Sukant Jain

    (National Institute of Hydrology)

  • Ravi V. Galkate

    (National Institute of Hydrology)

  • Anil Kumar Lohani

    (National Institute of Hydrology)

  • Akhilesh Verma

    (Govt. of CG)

Abstract

The periodic assessment of the revised capacity is essential and conventional techniques through bathymetric surveys and inflow-outflow techniques are time and resource intensive. The application of optical remote sensing data is practiced for a long time to compute the revised capacity of the reservoir but has the limitation of selecting threshold values and inefficacy of the method during the cloudy season. This research proposed the application of the Otsu segmentation technique in Google Earth Engine (GEE) to determine revised water spread using microwave remote sensing data. The Otsu technique is efficient in classifying the image into two distinct classes using within and between-class variances. An application has been developed using Sentinel data in GEE, which has the capacity of cloud-based computing and applied to the four reservoirs of Mahanadi Reservoir Project (MRP) namely Ravishankar Sagar, Murumsilli, Dudhawa, and Sondur reservoir in the Chhattisgarh state of India. The original capacities of these reservoirs vary from 910.51 MCM of Ravishankar (RS) Sagar to 165.89 MCM of Murumsilli reservoir. The revised water spreads computed in the GEE were further used to compute revised volume and in turn the revised cumulative capacities at different levels. The analysis suggested that 17.65% of dead storage (118.26 MCM from 143.00 MCM) and 9.25% of gross storage (85.26 MCM from 910.52 MCM) of RS Sagar has been lost in 42 years (1978 to 2020). Similarly, 10.33% gross capacity of Murumsilli, 13.12% of Sondur, and 12.37% of Dudhawa reservoirs have been lost due to the deposit of sediments from the catchments, and results were found comparable with bathymetric survey results of three reservoirs. The proposed application has been developed in GEE so it can be used in any part of the world in cloudy weather with no human interference.

Suggested Citation

  • Rahul Kumar Jaiswal & Gunja Dhruw & Sukant Jain & Ravi V. Galkate & Anil Kumar Lohani & Akhilesh Verma, 2023. "Efficient Segmentation Algorithm for Estimation of Revised Reservoir Capacities in Google Earth Engine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3795-3812, August.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:10:d:10.1007_s11269-023-03511-2
    DOI: 10.1007/s11269-023-03511-2
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    References listed on IDEAS

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    1. Sanjay Jain & Arun Saraf & Ajanta Goswami & Tanvear Ahmad, 2006. "Flood inundation mapping using NOAA AVHRR data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 949-959, December.
    2. Jean-François Pekel & Andrew Cottam & Noel Gorelick & Alan S. Belward, 2016. "High-resolution mapping of global surface water and its long-term changes," Nature, Nature, vol. 540(7633), pages 418-422, December.
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