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A process-based recovery indicator for anthropogenically disturbed river system.

C Pradhan1, S K Padhee2, Rishikesh Bharti3

  • 1Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India. c.pradhan@iitg.ac.in.

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Summary
This summary is machine-generated.

This study reveals how river systems in India

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Area of Science:

  • Hydrology and Geomorphology
  • Environmental Science
  • Remote Sensing and GIS

Background:

  • Assessing river system dynamics and recovery potential is crucial for effective water resource management.
  • Traditional methods often lack the spatial and temporal resolution needed for large river basins.
  • Entropy theory offers a novel approach to quantify system complexity and state.

Purpose of the Study:

  • To investigate the system state and river recovery potential in Mahanadi River sub-basins using entropy theory.
  • To develop and apply novel indicators for assessing river health and temporal changes.
  • To understand the relationship between river recovery and underlying system dynamics.

Main Methods:

  • Utilized Google Earth Engine cloud computing for large-scale geospatial analysis.
  • Computed Cross-Sectional Intensity Entropy (CIE) for post-monsoon river reaches.
  • Formulated a Normalized River Recovery Indicator (NRRI) and related it to Low Flow Exceedance (LFE).

Main Results:

  • Identified distinct system states (threshold-modulated and filter-dominated) based on CIE and NRRI.
  • Observed an 'event-driven' state linked to declining CIE and stabilized vegetation, where floods impact recovery.
  • Demonstrated that instream vegetation influences energy dissipation and morphological continuum.

Conclusions:

  • Entropy theory and cloud computing provide powerful tools for river health assessment.
  • River recovery is influenced by system dynamics, flood events, and the presence of instream vegetation.
  • Findings offer insights into managing large river systems and predicting their evolutionary adjustments.