Dey et al. (2026) Catchment’s Resilience to Flood Generation Mechanism: The Role of Soil Moisture

Identification

• Journal: Water Resources Management
• Year: 2026
• Date: 2026-03-31
• Authors: Pankaj Dey, N. K. Goel, Mohd Talha
• DOI: 10.1007/s11269-026-04612-4

Research Groups

• Mehta Family School of Sustainability, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
• Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
• International Centre of Excellence for Dams, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
• Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

Short Summary

This study evaluates the drivers and mechanisms of catchment resilience to dominant flood-generating mechanisms under varying antecedent soil moisture conditions across 191 catchments in peninsular India. It reveals that soil properties, rainfall characteristics, land cover, and meteorological variables during dry spells systematically influence this resilience and the spatial variation of antecedent soil moisture.

Objective

• To evaluate the drivers and mechanisms of catchment resilience to dominant flood-generating mechanisms under different antecedent soil moisture (ASM) conditions.
• To understand the co-evolutionary relationship between flood seasonality and ASM, and the spatial variation of ASMs.

Study Configuration

• Spatial Scale: 191 catchments across peninsular India, covering major river basins including Brahmani, Subarnarekha, Mahanadi, Sabarmati, Mahi, Narmada, Godavari, Krishna, Pennar, and Cauvery.
• Temporal Scale: Multi-decadal analysis using daily hydrometeorological time series data, with specific data sources spanning from 1901 to 2010 for rainfall and 1969 to 2005 for temperature, implying a long-term historical perspective.

Methodology and Data

• Models used:
  • Flood event extraction: Framework by Zhang et al. (2021).
  • Flood seasonality quantification: Circular statistics framework.
  • Flood generation mechanism classification: Modified decision tree scheme based on Stein et al. (2020), incorporating antecedent soil moisture saturation.
  • Modeling spatial variation of antecedent soil moisture: XGBoostRegression (machine learning model).
  • Driver importance analysis: SHAP (Shapley Additive exPlanations) values.
• Data sources:
  • Hydrometeorological observations: Daily streamflow from 191 gauging stations and daily gridded soil moisture dataset (spatially averaged at catchment scale) for Peninsular India.
  • Soil characteristics: Soil texture (% clay, % sand, % silt at 30 cm depth) from SoilGrids250m dataset; mean porosity from Global Hydrogeology Maps v.2 (GLHYMPS) data.
  • Event characteristics: Duration of event wet spell, duration of dry spell before the event wet spell, soil moisture transition ratio.
  • Land cover: Fraction of built-up area.
  • Meteorological covariates: Mean dry spell shortwave radiation, relative humidity, wind speed, mean temperature, and mean elevation of catchments.

Main Results

• Flood peaks and durations spatially align with the southwest monsoon season, with most floods occurring in August. Southern regions also experience floods from October to December due to carry-over moisture and northeast monsoon rainfall.
• Long-rain floods are the dominant flood generation mechanism in over 94% of the catchments, followed by short-rain floods (less than 20%).
• Catchments exhibiting long-rain floods for higher antecedent soil moisture content are predominantly in the northern region and show stronger flood seasonality. Conversely, southern catchments exhibit long-rain floods at smaller antecedent soil moisture thresholds and have weaker seasonality.
• Catchment resilience to long-rain floods is driven by a complex interplay of soil properties (% sand, % silt, % clay, porosity), event rainfall characteristics (dry spell duration, wet spell duration, soil moisture transition ratio), and land cover (fraction of built-up area).
• The spatial variation of antecedent soil moisture is accurately modeled (R² = 0.7, RMSE = 0.03) and primarily driven by meteorological variables during the dry spell, with mean dry spell shortwave radiation being the most significant factor, suggesting a soil moisture-radiation feedback.

Contributions

• Provides a novel understanding of catchment resilience to dominant flood-generating mechanisms by integrating antecedent soil moisture states, flood seasonality, and spatial variations.
• Identifies the specific regional controls (soil properties, rainfall characteristics, land cover, and meteorological variables) that modulate catchment resilience to long-rain floods in peninsular India.
• Highlights the critical role of soil moisture-radiation feedback in driving the spatial variability of antecedent soil moisture, offering insights for improving hydrological models.
• Enhances the process-based understanding of flood generation, which is crucial for improving the efficiency and accuracy of flood risk management and forecasting, particularly in regions with strong seasonal regimes.

Funding

• DST INSPIRE Faculty Fellowship (Faculty Reg. No: IFA22-EAS 114; Application Reference No.: DST/INSPIRE/04/2022/001952)
• MoJS-supported International Centre of Excellence for Dams, IIT Roorkee
• SPARK Internship from IIT Roorkee (funded through the DST INSPIRE Faculty Research Grant)

Citation

@article{Dey2026Catchments,
  author = {Dey, Pankaj and Goel, N. K. and Talha, Mohd},
  title = {Catchment’s Resilience to Flood Generation Mechanism: The Role of Soil Moisture},
  journal = {Water Resources Management},
  year = {2026},
  doi = {10.1007/s11269-026-04612-4},
  url = {https://doi.org/10.1007/s11269-026-04612-4}
}