Llano (2025) Classic and innovative trend analysis of long term annual precipitation in Argentina

Identification

• Journal: Journal of Southern Hemisphere Earth System Science
• Year: 2025
• Date: 2025-08-27
• Authors: María Paula Llano
• DOI: 10.1071/es24049

Research Groups

• Departamento de Ciencias de la Atmósfera y los Océanos (DCAO), Universidad de Buenos Aires (UBA), Buenos Aires, Argentina.
• Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.

Short Summary

This study analyzed long-term annual precipitation trends across 49 stations in Argentina (1959-2020) using Mann-Kendall and Innovative Trend Analysis (ITA), finding an overall increase in precipitation and demonstrating ITA's superior ability to detect significant trends, especially in extreme values.

Objective

• To assess long-term annual precipitation trends across Argentina (1959-2020) by comparing the results of the Mann–Kendall (MK) test and the Innovative Trend Analysis (ITA) method.

Study Configuration

• Spatial Scale: 49 meteorological stations distributed across the entire territory of Argentina.
• Temporal Scale: 1959–2020 (62 years).

Methodology and Data

• Models used: Mann–Kendall (MK) test, Sen’s slope test, Innovative Trend Analysis (ITA) method, Pettitt test, Standard Normal Homogeneity Test (SNHT), Coefficient of Variation (CV).
• Data sources: Long-term annual precipitation data from the National Meteorological Service (Servicio Meteorológico Nacional, SMN) of Argentina.

Main Results

• An overall increase in annual precipitation was observed across Argentina during the 1959–2020 period, with a mean increase of 2 mm per year and a maximum of 6 mm per year.
• The Mann–Kendall test identified positive trends at 38 stations, with 9 being statistically significant (95% confidence level). Only one of the 11 stations with negative trends was significant.
• The Innovative Trend Analysis (ITA) method detected a greater number of significant trends compared to the MK test, with 33 stations showing significant positive slopes (average increase of 2.11 mm per year) and 5 stations showing significant negative slopes (average decrease of 1.68 mm per year).
• ITA's percentile-based analysis revealed that 60% of stations exhibited the largest variation in the highest precipitation values (fourth quartile), with changes up to a 36% increase and a 24% decrease. The most pronounced relative increases and decreases were found in the lowest precipitation values (first quartile), with changes up to a 50% increase and a 12% decrease.
• The ITA method proved particularly valuable for identifying trends across different parts of the precipitation distribution, including non-monotonic trends, and revealed significant trends in cases where the MK test did not (e.g., Pilar and Iguazú stations).

Contributions

• This study is the first to apply the Innovative Trend Analysis (ITA) method and a percentile-based approach to a comprehensive network of stations across the entire Argentine territory for long-term annual precipitation trend analysis.
• It provides a more detailed and spatially comprehensive characterization of precipitation trends than previous works that focused on specific regions or solely used traditional methods.
• It demonstrates the superior capability of the ITA method, particularly its graphical interpretation and ability to detect significant trends within different precipitation categories (low, medium, high), which traditional methods like Mann–Kendall might obscure.
• The findings offer crucial insights for water resource management, agriculture, and climate adaptation strategies in Argentina, emphasizing the importance of using multiple analytical approaches for robust climate change assessment.

Funding

• Universidad de Buenos Aires, project reference: 20020190100090BA
• CONICET, project reference: 11220210100282CO

Citation

@article{Llano2025Classic,
  author = {Llano, María Paula},
  title = {Classic and innovative trend analysis of long term annual precipitation in Argentina},
  journal = {Journal of Southern Hemisphere Earth System Science},
  year = {2025},
  doi = {10.1071/es24049},
  url = {https://doi.org/10.1071/es24049}
}