Lei et al. (2025) Spatially Explicit Relationships Between Urbanization and Extreme Precipitation Across Distinct Topographic Gradients in Liuzhou, China

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

Identification

• Journal: Water
• Year: 2025
• Date: 2025-12-23
• Authors: Chaogui Lei, Yaqin Li, Chaoyu Pan, J. Y. Zhang, Siwei Yin, Yuefeng Wang, Kebing Chen, Qin Yang, Longfei Han
• DOI: 10.3390/w18010047

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This study quantifies the evolution of extreme precipitation (EP) characteristics and investigates the spatially non-stationary effects of urbanization on EP in Liuzhou, China, revealing that urbanization impacts on EP vary significantly with elevation and temporal scales.

Objective

• To quantify the evolution of extreme precipitation (EP) intensity, magnitude, duration, and frequency on different temporal scales and to identify spatially varying urbanization effects on EP in a complex topographic context, using Liuzhou, China as a case study.

Study Configuration

• Spatial Scale: City of Liuzhou, China, analyzed on a 5 km grid, considering different elevations.
• Temporal Scale: 2009 to 2023, with analyses on daily, daytime, nighttime, and 14-hour scales.

Methodology and Data

• Models used: Innovative Trend Analysis (ITA), Local Indicators of Spatial Association (LISA) maps, Geographically Weighted Regression (GWR).
• Data sources: Not explicitly detailed, but includes extreme precipitation event data and urbanization metrics (population, gross domestic product, urban area percentage).

Main Results

• From 2009 to 2023, extreme precipitation events in Liuzhou became more intense, persistent, and frequent, particularly for higher-grade events and in the steeper northern regions.
• Spatial correlations between comprehensive urbanization (CUB) and extreme precipitation indices exhibited four distinct types (high-high, high-low, low-low, low-high) across individual temporal scales, despite overall negative global correlations.
• Geographically Weighted Regression (GWR) effectively explained the response of most extreme precipitation characteristics to urbanization (population, economy, urban area percentage) with adjusted R² values ranging from 0.5 to 0.8. The predictive accuracy and the local influential strength and direction of urbanization on extreme precipitation were spatially non-stationary and varied significantly with elevation and temporal scales, with urban area percentage showing positive relationships in more areas compared to population and economy.

Contributions

• Quantified the evolution of extreme precipitation intensity, magnitude, duration, and frequency using Innovative Trend Analysis (ITA) across multiple temporal scales (daily, daytime, nighttime, 14 hours).
• Innovatively identified and detailed spatially varying urbanization effects on extreme precipitation, considering different elevations and a fine 5 km spatial grid.
• Demonstrated the utility of Geographically Weighted Regression (GWR) for explaining complex, non-stationary relationships between urbanization and extreme precipitation.
• Provided actionable insights for urban planning and location-specific flood risk management in complex topographic contexts.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Lei2025Spatially,
  author = {Lei, Chaogui and Li, Yaqin and Pan, Chaoyu and Zhang, J. Y. and Yin, Siwei and Wang, Yuefeng and Chen, Kebing and Yang, Qin and Han, Longfei},
  title = {Spatially Explicit Relationships Between Urbanization and Extreme Precipitation Across Distinct Topographic Gradients in Liuzhou, China},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w18010047},
  url = {https://doi.org/10.3390/w18010047}
}