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Watershed Planning within a Quantitative Scenario Analysis Framework
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Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins.

Jeonghyeon Choi1, Ungtae Kim2, Sangdan Kim3

  • 1Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang-Si, Gyeonggi-Do 10223, Republic of Korea.

The Science of the Total Environment
|August 30, 2023
PubMed
Summary

Satellite-remote sensed data (SRSD) can improve streamflow prediction in ungauged basins. Leaf area index (LAI) data, used alone, showed robust performance in calibrating hydrologic models for accurate water resource management.

Keywords:
Ecohydrologic modelEvapotranspirationLeaf area indexPrediction in ungauged basinsRemotely sensed dataSoil moisture

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Area of Science:

  • Hydrology
  • Remote Sensing
  • Water Resource Management

Background:

  • Effective water resource management relies on accurate basin water availability data.
  • Many basins lack observed hydrological data due to limited hydrometric station operation, complicating planning.
  • Remote sensing data offers a promising alternative for hydrological information in ungauged basins.

Purpose of the Study:

  • To investigate the applicability of Satellite-Remote Sensed Data (SRSD) for hydrologic model calibration in Prediction in Ungauged Basins (PUB).
  • To assess the performance of different SRSDs (Leaf Area Index (LAI), actual evapotranspiration, soil moisture) for streamflow prediction.
  • To evaluate the potential of LAI-based calibration for improving streamflow prediction in data-scarce regions.

Main Methods:

  • A hydrologic model was calibrated using individual SRSDs (LAI, actual evapotranspiration, soil moisture) for 28 Korean basins.
  • A vegetation module was incorporated into the model to utilize LAI data.
  • Model performance was evaluated by comparing streamflow predictions against traditional streamflow calibration.

Main Results:

  • The hydrologic model calibrated with LAI demonstrated the most robust performance in predicting daily streamflow.
  • LAI-based calibration improved streamflow prediction accuracy, especially during low-flow seasons by accounting for vegetation-hydrology interactions.
  • SRSD-only calibration, particularly with LAI, showed promising results for ungauged basins without requiring data preprocessing.

Conclusions:

  • Satellite-derived LAI is a valuable data source for calibrating hydrologic models in ungauged basins.
  • LAI-based calibration offers a viable alternative to traditional methods where observed streamflow data is scarce.
  • Further research is needed to optimize the use of evapotranspiration and soil moisture data and expand SRSD application across diverse regions.