Castro-Melgar et al. (2025) Assessment of the October 2024 Cut-Off Low Event Floods Impact in Valencia (Spain) with Satellite and Geospatial Data

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-06-22
• Authors: Ignacio Castro-Melgar, Triantafyllos Falaras, Eleftheria Basiou, Issaak Parcharidis
• DOI: 10.3390/rs17132145

Research Groups

• GIS Laboratory, Escuela Nacional de Estudios Superiores Morelia, Universidad Nacional Autónoma de México, Mexico
• Soil Erosion and Degradation Research Group, Departament de Geografia, Universitat de València, Spain
• El Tossal cartografies, Spain

Short Summary

This study systematically reviews and critically analyzes the application of geospatial technologies in forecasting, documenting, and managing the catastrophic 2024 Valencia floods. It demonstrates that while geospatial tools are crucial for rapid disaster response, effective mitigation ultimately depends on robust prior territorial planning and prompt institutional decision-making.

Objective

• To reconstruct and critically analyze how geospatial technologies were used for forecasting, documenting, and managing the 2024 Valencia floods.
• To evaluate the effectiveness and usefulness of these tools, propose lines of improvement for future events, and advocate for greater participation in future territorial planning, especially in flood-susceptible urban areas.

Study Configuration

• Spatial Scale: Valencia metropolitan area, Horta Sud, Valencia province, Castellón province, and Albufera National Park, Spain.
• Temporal Scale: October 29, 2024 (event onset) through November 2024 (immediate response and mapping) and into June 2025 (systematic review and follow-up studies).

Methodology and Data

• Models used:
  • Flood Mud Index (FMI) for automated flood mapping.
  • Models for identifying flood impact (e.g., using Sentinel and MODIS images).
  • GIS-based multi-criteria decision making (GIS-MCDA) and neural networks (mentioned as general applications in pre-impact phases).
• Data sources:
  • Satellite Imagery: Meteosat-11 (MSG-11), Landsat-8 (OLI sensor, 30 m resolution), GeoEye-1 (2 m resolution), WorldView-2 (0.5 m resolution), Pléiades-1 A/B (0.5 m resolution), Sentinel-2 (10 m, 20 m, 60 m resolutions), Sentinel-1 (SAR sensor, 20 m resolution), MODIS (Aqua, 250 m resolution), ICEYE OY (2.5 m resolution), COSMO-SkyMed (15 m resolution), SPOT-6 (1.5 m resolution).
  • Aerial Data: Drone orthophotos (5 cm resolution), SPASA photogrammetric flights (5 cm resolution).
  • Meteorological Data: AEMET meteorological datasets, C-band Doppler weather radars, ground station networks.
  • Hydrological Data: Automatic Hydrological Information System (SAIH), Agrometeorological Information System for Irrigation (SIAR).
  • Geographic Information Systems (GIS) Outputs: Copernicus Emergency Management Service (CEMS) Rapid Mapping (event code EMSR773), International Charter: Space and Major Disasters (ICSMD), MapDANA (University of Valencia), street-level flood map (Polytechnic University of Valencia), Cartographic Institute of Valencia (ICV) web map viewer, HERMES GIS web platform (Spanish Ministry of Transport).
  • Field Data: Topographic rods and GPS for surveying watermarks, LiDAR data (2023).
  • Other: Institutional reports, academic research, press reports, anonymized mobile phone big data.

Main Results

• Geospatial technologies, including satellite imagery, weather radars, and GIS, were indispensable for forecasting, documenting, and managing the 2024 Valencia floods, especially in the immediate aftermath when official information was scarce.
• International coordination platforms (Copernicus EMS and International Charter: Space and Major Disasters) enabled rapid access to satellite imagery and expedited mapping of flooded areas and damage assessment.
• Local academic institutions (University of Valencia, Polytechnic University of Valencia) developed highly accurate and publicly utilized maps (e.g., MapDANA, street-level flood maps) and innovative tools (e.g., Flood Mud Index) to refine flood extent and impact assessments.
• The disaster exposed significant institutional shortcomings in territorial planning and the protection of socioeconomically vulnerable populations, with many affected homes built in identified flood-prone areas despite existing risk prevention plans.
• The event stimulated debate contrasting climate change-based explanations with historical-geographic interpretations of flood risk, underscoring the need for a comprehensive, geographically grounded approach to future risk management.
• Geospatial products provided crucial analytical and documentary value, directly influenced emergency operational management, and significantly raised public awareness of the disaster's magnitude.

Contributions

• Provides a systematic and critical review of the real-world application of geospatial technologies in response to a major, recent hydrometeorological disaster (2024 Valencia DANA).
• Details the chronological phases of geospatial data utilization, from early warning to post-event assessment, highlighting the crucial role of international and local initiatives in filling information gaps.
• Contrasts and integrates climate change and historical-geographic perspectives to explain the disaster, advocating for a holistic understanding of vulnerability and risk.
• Identifies specific institutional and governance failures in territorial planning that exacerbated the disaster's impact, offering lessons for future policy and practice.
• Documents the development and utility of novel spatial analysis tools and high-resolution mapping products created in response to the event, demonstrating innovation in disaster management.

Funding

This research received no external funding.

Citation

@article{CastroMelgar2025Assessment,
  author = {Castro-Melgar, Ignacio and Falaras, Triantafyllos and Basiou, Eleftheria and Parcharidis, Issaak},
  title = {Assessment of the October 2024 Cut-Off Low Event Floods Impact in Valencia (Spain) with Satellite and Geospatial Data},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17132145},
  url = {https://doi.org/10.3390/rs17132145}
}