Sarkar et al. (2025) Taming the non-linearity: An iterative conceptual routing model for improving flood peak prediction

Identification

• Journal: Environmental Modelling & Software
• Year: 2025
• Date: 2025-12-22
• Authors: Ekant Sarkar, Akshay Kadu, S.L.Kesav Unnithan, Basudev Biswal
• DOI: 10.1016/j.envsoft.2025.106843

Research Groups

• Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India
• Department of Civil Engineering, Monash University, Clayton, Australia
• Commonwealth Scientific and Industrial Research Organisation, Canberra, Australia
• Stantec ResourceNet Pvt. Ltd., Pune, India

Short Summary

This study introduces a novel Iterative Routing Model (IRM) to improve flood peak prediction by dynamically updating flow velocity based on streamflow magnitude. Applied to the Godavari River Basin, the IRM significantly outperformed traditional models in accurately simulating flood peak discharge and timing.

Objective

• To develop and evaluate an iterative conceptual routing model that accounts for the non-linear relationship between flow velocity and discharge to improve the prediction of flood peak magnitude and timing in large river basins.

Study Configuration

• Spatial Scale: Seven gauging stations within the Godavari River Basin, India (large river basin).
• Temporal Scale: Flood event scale, focusing on peak discharge and timing, with results reported in days for timing deviation.

Methodology and Data

• Models used: Iterative Routing Model (IRM) (proposed model), compared against two unspecified traditional routing models.
• Data sources: Observed streamflow data from seven gauging stations in the Godavari River Basin.

Main Results

• The Iterative Routing Model (IRM) outperformed two other models in simulating peak discharge and timing.
• IRM achieved the lowest average absolute deviations: 29.98 % for peak discharge and 0.2 days for timing.
• The model yielded the highest median Nash-Sutcliffe Efficiency (NSE) of 0.78 and Kling-Gupta Efficiency (KGE) of 0.79 across all stations.
• Calibrated Manning’s roughness values from the IRM were found to be more realistic compared to those from other models.
• The proposed model demonstrates significant potential for improving flood peak predictions in large river basins.

Contributions

• Introduction of a novel Iterative Routing Model (IRM) that dynamically updates flow velocity as a function of streamflow magnitude, addressing a key limitation of traditional routing models.
• Enhanced accuracy in predicting flood peak discharge and timing by better accounting for the non-linear relationship between flow velocity and discharge.
• Demonstrated superior performance over existing models in a large river basin context, providing more realistic calibrated hydrological parameters (e.g., Manning's roughness).

Funding

• Not specified in the provided text.

Citation

@article{Sarkar2025Taming,
  author = {Sarkar, Ekant and Kadu, Akshay and Unnithan, S.L.Kesav and Biswal, Basudev},
  title = {Taming the non-linearity: An iterative conceptual routing model for improving flood peak prediction},
  journal = {Environmental Modelling & Software},
  year = {2025},
  doi = {10.1016/j.envsoft.2025.106843},
  url = {https://doi.org/10.1016/j.envsoft.2025.106843}
}