Qadri et al. (2025) Quantifying Flood Impacts on Ecosystem Carbon Dynamics Using Remote Sensing and Machine Learning in the Climate-Stressed Landscape of Emilia-Romagna

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Identification

• Journal: Water
• Year: 2025
• Date: 2025-10-18
• Authors: Jibran Qadri, Francesca Ceccato
• DOI: 10.3390/w17203001

Research Groups

Not specified in the paper.

Short Summary

This study evaluates flood impacts on ecosystem carbon dynamics (Net Primary Productivity and Above-Ground Biomass) in Emilia-Romagna, Italy, using remote sensing and machine learning, revealing significant short-term localized carbon losses and widespread long-term ecological degradation.

Objective

• To evaluate flood impacts on ecosystem carbon dynamics (Net Primary Productivity and Above-Ground Biomass) in the Emilia-Romagna region, Italy, using remote sensing and machine learning techniques, and to quantify short-term changes and long-term ecological degradation.

Study Configuration

• Spatial Scale: Emilia-Romagna region, Italy; localized flood-affected areas; wider regional scale; high-hazard flood zones.
• Temporal Scale: Short-term (before and after 2023 flood events); Long-term (2014-2024 for RSEI assessment, with specific changes noted after 2019).

Methodology and Data

• Models used: Machine learning techniques; Remote Sensing Ecological Index (RSEI).
• Data sources: Remote sensing (implied for NPP, AGB, RSEI).

Main Results

• Short-term analysis of flood-affected areas showed a net deficit of 7.0 kg C yr⁻¹ in Net Primary Productivity (NPP) and 500 Mg C in Above-Ground Biomass (AGB).
• The wider region experienced a gain of 670 kg C yr⁻¹ in NPP but a major loss of 3.3 × 10⁸ kg C in AGB, indicating widespread biomass decline.
• Long-term ecological assessment using RSEI revealed accelerating degradation: the "Fair" ecological class decreased from 90% (2014) to just over 50% (2024), while the "Poor" class expanded. "Good" and "Very Good" classes nearly disappeared after 2019.
• High-hazard flood zones contain significant carbon stocks: 9.0 × 10⁹ kg C in AGB and 1.1 × 10¹⁰ kg C in soil organic carbon, highlighting their vulnerability.

Contributions

• Provides an integrated approach for evaluating flood impacts on ecosystem carbon dynamics using remote sensing and machine learning.
• Quantifies clear, localized, and regional carbon losses due to flood events, which are crucial for regional carbon budgets and post-flood ecosystem assessments.
• Highlights the importance of integrating flood modeling with ecosystem monitoring for climate-adaptive land management and carbon conservation strategies.
• Demonstrates accelerating long-term ecological degradation in the Emilia-Romagna region.

Funding

Not specified in the paper.

Citation

@article{Qadri2025Quantifying,
  author = {Qadri, Jibran and Ceccato, Francesca},
  title = {Quantifying Flood Impacts on Ecosystem Carbon Dynamics Using Remote Sensing and Machine Learning in the Climate-Stressed Landscape of Emilia-Romagna},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17203001},
  url = {https://doi.org/10.3390/w17203001}
}