Soundharajan et al. (2025) A Meta‐Analysis to Disentangle the Impacts of Climate and Land Use Changes on Streamflow

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Earth s Future
• Year: 2025
• Date: 2025-10-01
• Authors: Bankaru‐Swamy Soundharajan, Paul D. Wagner, Kristin Peters, S. Sreeraj, Nicola Fohrer, P. Athira, Jens Kiesel
• DOI: 10.1029/2024ef005757

Research Groups

Not specified in the abstract, as this is a meta-analysis integrating published datasets.

Short Summary

This meta-analysis quantitatively assesses the individual and combined impacts of precipitation, temperature, and land use/land cover (LULC) changes on streamflow. It finds that precipitation is the dominant driver, explaining nearly half of streamflow variance, followed by nuanced LULC effects (agriculture increases, forests decrease), while temperature has a minimal and inconsistent influence.

Objective

• To disentangle and assess the combined effects and relative importance of precipitation, temperature, and land use/land cover (LULC) changes on streamflow dynamics.

Study Configuration

• Spatial Scale: Broad, multi-regional to global (meta-analysis of diverse published datasets).
• Temporal Scale: Long-term trends (implied by focus on climate change and LULC changes).

Methodology and Data

• Models used: Multiple linear regression, Random Forest models.
• Data sources: Published datasets (integrated through a meta-analysis approach).

Main Results

• Precipitation is the dominant driver of streamflow, showing significant variability and a direct linear correlation. Multiple linear regression indicates precipitation alone explains nearly 50% of the variance in streamflow.
• Land use/land cover (LULC) changes have nuanced effects: conversions to agriculture generally increase streamflow, whereas transitions to forests reduce it. LULC changes contribute an additional, but smaller, percentage to streamflow variance.
• Temperature impacts are inconsistent and have minimal influence on streamflow according to multiple linear regression.
• The Random Forest model achieved R² values of 0.7, confirming precipitation as the most critical predictor, followed by temperature and LULC changes.
• Including catchment area and climate zone in the models did not significantly improve predictive power.

Contributions

• Provides a comprehensive quantitative and qualitative meta-analysis integrating diverse published datasets to assess streamflow drivers.
• Quantifies the relative importance of precipitation, temperature, and LULC changes on streamflow dynamics.
• Offers critical insights for sustainable water resource management and predictive hydrological modeling by highlighting the combined importance of these factors.

Funding

Not specified in the abstract.

Citation

@article{Soundharajan2025MetaAnalysis,
  author = {Soundharajan, Bankaru‐Swamy and Wagner, Paul D. and Peters, Kristin and Sreeraj, S. and Fohrer, Nicola and Athira, P. and Kiesel, Jens},
  title = {A Meta‐Analysis to Disentangle the Impacts of Climate and Land Use Changes on Streamflow},
  journal = {Earth s Future},
  year = {2025},
  doi = {10.1029/2024ef005757},
  url = {https://doi.org/10.1029/2024ef005757}
}