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Enhanced autumn phenology model incorporating agricultural drought

  • 0State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing 100049, China.
The Science of the Total Environment +

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August 2, 2024

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August 2, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Agricultural drought significantly impacts autumn plant phenology more than meteorological drought. Enhanced models show agricultural drought delays the end of season, crucial for understanding climate change effects.

Area Of Science

  • Ecology
  • Climate Science
  • Agricultural Science

Background

  • Autumn phenology, specifically the end of season (EOS), is influenced by various environmental factors.
  • The distinct impacts of different drought types (agricultural vs. meteorological) on autumn phenology remain underexplored.
  • Understanding these impacts is vital for predicting vegetation responses to climate change.

Purpose Of The Study

  • To investigate the influence of pre-season agricultural and meteorological droughts on autumn phenology in the Northern Hemisphere.
  • To develop and evaluate enhanced autumn phenology models that incorporate drought factors.
  • To compare the predictive capabilities of different drought indices within phenology models.

Main Methods

  • Development of enhanced autumn phenology models, including Cooling Degree Days (CDD) models integrated with Standardized Soil Moisture Drought Index (SSMI) and Standardized Precipitation Index (SPI).
  • Analysis of satellite-derived Solar-Induced Fluorescence (SIF) estimates from 2001-2020 to assess trends in autumn phenology.
  • Evaluation of model performance using various metrics and comparison of future EOS projections under different Shared Socioeconomic Pathways (SSPs).

Main Results

  • No significant trend in the advancement or delay of autumn EOS was observed across the Northern Hemisphere from 2001-2020.
  • Pre-season agricultural drought exhibited more pronounced cumulative and delayed impacts on EOS than meteorological drought.
  • The CDD<sub>SSMI</sub> model improved EOS simulation by over 14%, and CDD<sub>SPI</sub> by over 5.6%, compared to the standard CDD model.
  • Future projections indicated that CDD models, particularly CDD<sub>SSMI</sub> and CDD<sub>SPI</sub>, projected earlier EOS compared to the standard CDD model.

Conclusions

  • Drought type significantly influences autumn plant phenology, with agricultural drought having a greater impact on the end of season.
  • Integrating drought indices like SSMI and SPI into phenology models substantially enhances their accuracy in simulating EOS.
  • Accurate parameterization of drought impacts in phenology models is essential for reliable predictions of vegetation responses to climate change.

Keywords:

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