Li et al. (2026) Enhancing Hydrological Model Calibration for Flood Prediction in Dam-Regulated Basins with Satellite-Derived Reservoir Dynamics

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-01-06
• Authors: Chaoqun Li, Huan Wu, Lorenzo Alfieri, Yiwen Mei, Nergui Nanding, Zhijun Huang, Ying Hu, Lei Qu
• DOI: 10.3390/rs18020193

Research Groups

[Information not explicitly available in the provided text.]

Short Summary

This study proposes a synergistic modeling framework for data-scarce dammed basins, integrating satellite-based reservoir operation into a distributed hydrological model and its calibration. The framework enhances basin-internal process representation and flood detection probability by enabling refined model parameterization, even if overall streamflow estimation is similar to models without dam operation.

Objective

• To evaluate the influence of reservoir representation on model calibration and the spatial reliability of distributed hydrological modeling, particularly in data-scarce dammed basins.
• To propose and test a synergistic modeling framework that integrates a satellite-based reservoir operation scheme into a distributed hydrological model and incorporates reservoir processes into the model calibration procedure.

Study Configuration

• Spatial Scale: Nandu River Basin, southern China.
• Temporal Scale: Focused on flood events and flood forecasting.

Methodology and Data

• Models used: DRIVE flood model (specifically, the coupled version DRIVE-Dam).
• Data sources: Satellite altimetry, FABDEM topography.

Main Results

• Reservoir dynamics were effectively reconstructed by combining satellite altimetry with FABDEM topography, successfully supporting the development of the reservoir scheme.
• The calibration configuration with dam operation (CWD) slightly improved overall streamflow estimation (Nash-Sutcliffe Efficiency and Kling-Gupta Efficiency > 0.75) at the calibrated outlet gauge, similar to the configuration without dam operation (CWOD).
• CWD significantly enhanced basin-internal process representation, evidenced by superior peak discharge and flood event capture with reduced bias.
• CWD boosted flood detection probability from 0.54 to 0.60 and reduced false alarms from 0.28 to 0.15.
• These improvements in CWD stem from refined parameterization enabled by a physically complete model structure.
• In contrast, CWOD led to subdued flood impulses and prolonged recession due to spurious parameters that distorted baseflow and runoff response.

Contributions

• Provides a practical and synergistic modeling framework for flood forecasting in data-scarce dam-regulated basins.
• Demonstrates that incorporating explicit reservoir representation into the model calibration procedure significantly enhances model parameterization and internal hydrological process representation.
• Underscores the strong potential of satellite observations (altimetry and topography) for improving hydrological modeling in data-limited regions, particularly for reconstructing reservoir dynamics.
• Addresses the challenge of limited availability of reservoir regulation data by integrating satellite-based operation schemes.

Funding

[Information not explicitly available in the provided text.]

Citation

@article{Li2026Enhancing,
  author = {Li, Chaoqun and Wu, Huan and Alfieri, Lorenzo and Mei, Yiwen and Nanding, Nergui and Huang, Zhijun and Hu, Ying and Qu, Lei},
  title = {Enhancing Hydrological Model Calibration for Flood Prediction in Dam-Regulated Basins with Satellite-Derived Reservoir Dynamics},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18020193},
  url = {https://doi.org/10.3390/rs18020193}
}