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Streamflow Prediction in Highly Regulated, Transboundary Watersheds Using Multi-Basin Modeling and Remote Sensing

Tien L T Du1,2, Hyongki Lee1, Duong D Bui3

  • 1Department of Civil and Environmental Engineering University of Houston Houston TX USA.

Water Resources Research
|July 22, 2022
PubMed
Summary
This summary is machine-generated.

Remote sensing effectively estimates reservoir storage for within-year and over-year types but struggles with run-of-river. An integrated operational scheme improves streamflow prediction in data-sparse basins.

Keywords:
Mekong and Vietnammulti‐basin modelregulated streamflowremote sensing imageryreservoir operationtransboundary

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Area of Science:

  • Hydrology
  • Remote Sensing
  • Water Resource Management

Background:

  • Reservoir operations significantly impact downstream hydrology.
  • Accurate monitoring of reservoir storage and releases is crucial for water management and hydrological modeling.
  • Existing global reservoir databases have limitations in representing local operational details.

Purpose of the Study:

  • To assess the efficacy of remote sensing for inferring reservoir operations across various scales.
  • To develop and validate a framework for improving reservoir representation in global databases and hydrological models.
  • To enhance streamflow prediction in ungauged cascade reservoir systems.

Main Methods:

  • Evaluated remote sensing imagery for estimating daily storage volumes of 21 reservoirs in the Greater Mekong region.
  • Developed and applied an Integrated Reservoir Operation Scheme within a multi-basin hydrological model.
  • Assessed prediction accuracy using correlation coefficients (CC), normalized root mean squared error (NRMSE), and Kling-Gupta efficiency (KGE).

Main Results:

  • Remote sensing accurately estimated storage for within-year and over-year reservoirs (CC ≥ 0.9, NRMSE ≤ 31%) but not for run-of-river types.
  • The integrated operational scheme significantly improved streamflow prediction in cascade systems.
  • Competent daily regulated streamflow predictions were achieved across reservoir types (median CC=0.65, NRMSE=8%, KGE=0.55) and downstream stations (median CC=0.94, NRMSE=8%, KGE=0.81).

Conclusions:

  • Remote sensing is a valuable tool for monitoring certain reservoir types but has limitations for others.
  • The proposed framework enhances reservoir data for hydrological models, improving human impact assessments.
  • The integrated operational scheme provides a robust solution for streamflow prediction in data-scarce river basins, aiding adaptation to extreme events.