Hidalgo et al. (2025) Coupled Cellular automata – Snowmelt Runoff Model: A Novel Framework for Assessing Climate Change Impacts on Streamflow

Identification

• Journal: Earth Systems and Environment
• Year: 2025
• Date: 2025-12-22
• Authors: Jose David Hidalgo Hidalgo, David Pulido‐Velazquez, Antonio-Juan Collados-Lara
• DOI: 10.1007/s41748-025-00975-7

Research Groups

• Department Water and Global Change Research, Spanish Geological Survey (IGME-CSIC), Granada, Spain.
• Department of Civil Engineering, University of Granada, Granada, Spain.

Short Summary

This study develops a novel coupled modeling framework (CAM–SRM) to simulate snow cover area and streamflow under climate change scenarios. The results project a significant reduction in annual streamflow (19.4–32.9%) and a two-month shortening of the snow season in Mediterranean mountainous regions.

Objective

• To develop and validate a coupled modeling framework integrating a cellular automata model (CAM) with the Snowmelt Runoff Model (SRM) to assess the impacts of climate change on snow dynamics and water availability in snow-dominated catchments.

Study Configuration

• Spatial Scale: Canales catchment, Sierra Nevada mountain range, southern Spain (176.6 km²; elevation range: 812–3478 m.a.s.l.).
• Temporal Scale: Historical reference period (1976–2005); calibration and validation period (2000–2020); future projections based on 1.5 °C and 3.0 °C warming levels for Spain.

Methodology and Data

• Models used: Cellular Automata Model (CAM) for daily spatiotemporal snow cover area (SCA) simulation; Snowmelt Runoff Model (SRM) for daily streamflow simulation.
• Data sources: AEMET 5 km gridded precipitation and temperature product; MODIS Terra Snow Cover (MOD10A1) for SCA validation; 9 Regional Climate Models (RCMs) from the EURO-CORDEX project (RCP 8.5 scenario).
• Downscaling/Correction: First- and second-order statistical corrections using Bias Correction (BC) and Delta Change (DC) approaches.

Main Results

• Snow Cover: Projected decrease in SCA ranging from 22.1% to 57.5% under the 1.5 °C scenario, with more severe reductions under 3.0 °C warming. The snow season is expected to shorten by approximately two months.
• Streamflow: Mean annual streamflow is projected to decline by 19.4% to 32.9%.
• Seasonality: An earlier onset of snowmelt peak flows is expected, advancing by approximately one month.
• Climate Drivers: Future scenarios indicate a reduction in mean annual precipitation of 14.7% to 22.0% and temperature increases of 1.4 °C to 3.0 °C.
• Model Performance: The coupled CAM–SRM framework achieved satisfactory daily Nash-Sutcliffe Efficiency (NSE) values of 0.60–0.64 and monthly NSE of 0.70–0.76.

Contributions

• Integration of a cellular automata model with the SRM to enable streamflow projections in the absence of future snow cover observations.
• Provides a parsimonious, data-efficient modeling approach for assessing climate change impacts in snow-dominated Mediterranean catchments.
• Quantifies the uncertainty associated with different downscaling techniques (BC vs. DC) and warming levels for water resource planning.

Funding

• STAGES-IPCC (TED2021-130744BC21/AEI/10.13039/501100011033/Union Europea NextGenerationEU/PRTR).
• SIGLO-PRO (PID2021-128021OB-I00/AEI/10.13039/501100011033/FEDER, UE).
• SER-PM (2908/22; Organismo Autónomo Parques Nacionales).
• SIERRA-CC (PID2022-137623OA-I00) funded by MICIU/AEI/10.13039/501100011033 and ERDF, UE.

Citation

@article{Hidalgo2025Coupled,
  author = {Hidalgo, Jose David Hidalgo and Pulido‐Velazquez, David and Collados-Lara, Antonio-Juan},
  title = {Coupled Cellular automata – Snowmelt Runoff Model: A Novel Framework for Assessing Climate Change Impacts on Streamflow},
  journal = {Earth Systems and Environment},
  year = {2025},
  doi = {10.1007/s41748-025-00975-7},
  url = {https://doi.org/10.1007/s41748-025-00975-7}
}

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