Dasari et al. (2025) Decision support system for climate-resilient runoff estimation

Identification

• Journal: Plant Science Today
• Year: 2025
• Date: 2025-12-31
• Authors: O Dasari, S Selvakumar, S Pazhanivelan, M Raju, KP Ragunath, V. Ravikumar
• DOI: 10.14719/pst.9877

Research Groups

• Department of Soil and Water Conservation Engineering, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
• Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
• Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

Short Summary

This study develops a cloud-based Decision Support System (DSS) for climate-resilient runoff estimation, integrating a modified SCS-CN method with high-resolution geospatial data and CMIP6 climate projections. Validated across two contrasting Indian watersheds, the DSS accurately simulates historical and future runoff, enabling real-time flood forecasting and adaptive water resource management.

Objective

• To develop a cloud-based Decision Support System (DSS) for climate-resilient runoff estimation that integrates a modified Soil Conservation Service Curve Number (SCS-CN) method with high-resolution geospatial datasets and CMIP6 climate projections.
• To validate the DSS across contrasting hydrological environments to demonstrate its robustness and applicability for flood forecasting and adaptive water resource management, especially in data-poor regions.

Study Configuration

• Spatial Scale: Two Indian watersheds: Salebhata catchment (4588.9 km²) and Venkatapur sub-watershed (685 km²). Pixel-based analysis with resolutions ranging from 10 meters (LULC) to 25 kilometers (projected rainfall).
• Temporal Scale: Historical, near-real-time, and future runoff estimations using CMIP6 climate projections (SSP245 scenario). Validation performed using monthly observed runoff data.

Methodology and Data

• Models used: Modified Soil Conservation Service Curve Number (SCS-CN) method, incorporating Antecedent Moisture Condition (AMC) adjustments and slope correction.
• Data sources:
  • Sentinel-2 imagery (ESA Copernicus) for Land-Use/Land-Cover (LULC) classification (10 m resolution).
  • OpenLandMap/USDA for soil texture and hydrological groups (250 m resolution).
  • Shuttle Radar Topography Mission (SRTM)-Digital Elevation Model (DEM) (NASA SRTM) for topography (30 m resolution).
  • Climate Hazards Group Infrared Precipitation with Station (CHIRPS) for historical precipitation (5 km resolution).
  • CMIP6 climate projections (NASA NEX-GDDP IITM-ESM model under SSP245 scenario) for future rainfall (25 km resolution).
  • Observed runoff data from Central Water Commission (CWC) offices for validation.

Main Results

• The DSS achieved strong agreement between simulated and observed runoff, with R² values of 0.82 for the Salebhata catchment and 0.78 for the Venkatapur sub-watershed.
• It accurately captured critical hydrological signatures, including intense monsoon-driven runoff peaks and seasonal runoff volumes in the Salebhata catchment.
• The system successfully replicated complex bimodal runoff patterns typical of semi-arid climates in the Venkatapur watershed.
• The user-friendly, web-based application enables stakeholders to create real-time, location-specific runoff estimates through polygon-based analysis, directly facilitating flood forecasting and adaptive water resource management.
• The framework is reproducible and scalable, demonstrating applicability across diverse hydrological environments and spatial scales, from micro-watersheds to large catchments.

Contributions

• Development of a novel cloud-based Decision Support System (DSS) that integrates a modified SCS-CN method with high-resolution, globally available geospatial datasets and CMIP6 climate projections.
• Enhanced accuracy and robustness of runoff estimation through the incorporation of dynamic LULC, detailed soil properties, SRTM topography, and slope-adjusted CN values, addressing limitations of traditional SCS-CN.
• Provides a user-friendly, web-based platform (Google Earth Engine) for real-time and future runoff estimation, democratizing adaptive water management for stakeholders with varying expertise.
• Validated across diverse hydrological environments (monsoon-dominated and semi-arid Indian watersheds), demonstrating broad applicability and scalability for flood forecasting and water resource planning in data-scarce regions.
• Offers a reproducible platform for climate-resilient water resource planning by integrating climate projections, addressing a key gap in traditional methods.

Funding

Not explicitly mentioned in the provided paper.

Citation

@article{Dasari2025Decision,
  author = {Dasari, O and Selvakumar, S and Pazhanivelan, S and Raju, M and Ragunath, KP and Ravikumar, V.},
  title = {Decision support system for climate-resilient runoff estimation},
  journal = {Plant Science Today},
  year = {2025},
  doi = {10.14719/pst.9877},
  url = {https://doi.org/10.14719/pst.9877}
}