Articles | Volume 12, issue 1
https://doi.org/10.5194/esd-12-151-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-12-151-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Feb 2021
Research article |
| 10 Feb 2021
| 10 Feb 2021
Identifying meteorological drivers of extreme impacts: an application to simulated crop yields
Johannes Vogel
CORRESPONDING AUTHOR
Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Institute of Ecology, Technical University of Berlin, Berlin, Germany
Pauline Rivoire
CORRESPONDING AUTHOR
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Institute of Geography, University of Bern, Bern, Switzerland
Cristina Deidda
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
Leila Rahimi
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
Department of Water Engineering, University of Tabriz, Tabriz, Iran
Christoph A. Sauter
Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, UK
Elisabeth Tschumi
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Climate and Environmental Physics, University of Bern, Bern, Switzerland
Karin van der Wiel
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Tianyi Zhang
CORRESPONDING AUTHOR
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Jakob Zscheischler
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Climate and Environmental Physics, University of Bern, Bern, Switzerland
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
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- Extreme lows of wheat production in Brazil R. Nóia Júnior et al. 10.1088/1748-9326/ac26f3
- Machine-learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany M. Peichl et al. 10.5194/hess-25-6523-2021
- Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields Q. Sun et al. 10.3390/agriculture12111791
- Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks A. Marcolongo et al. 10.1017/eds.2022.1
- Increase of Simultaneous Soybean Failures Due To Climate Change H. Goulart et al. 10.1029/2022EF003106
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- Two machine learning approaches for predicting cyanobacteria abundance in aquaculture ponds M. Zhang et al. 10.1016/j.ecoenv.2023.114944
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- Extreme and compound ocean events are key drivers of projected low pelagic fish biomass N. Le Grix et al. 10.1111/gcb.16968
- Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods W. Zhang et al. 10.3389/feart.2021.673495
- Machine learning in crop yield modelling: A powerful tool, but no surrogate for science G. Lischeid et al. 10.1016/j.agrformet.2021.108698
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- Uncovering the Dynamics of Multi‐Sector Impacts of Hydrological Extremes: A Methods Overview M. de Brito et al. 10.1029/2023EF003906
- Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al. 10.1017/eds.2024.2
- Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al. 10.1016/j.earscirev.2022.104241
- Advancing research on compound weather and climate events via large ensemble model simulations E. Bevacqua et al. 10.1038/s41467-023-37847-5
- Current and future risk of unprecedented hydrological droughts in Great Britain W. Chan et al. 10.1016/j.jhydrol.2023.130074
- Compound dry and hot extremes: A review and future research pathways for India R. Guntu & A. Agarwal 10.1016/j.jhydrol.2024.131199
- Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere M. Anand et al. 10.1088/1748-9326/ad627d
30 citations as recorded by crossref.
- A Study on Meteorological Recognition With a Fusion Enhanced Model Based on EVA02 and LinearSVC C. Cheng et al. 10.1109/ACCESS.2024.3367428
- Extreme lows of wheat production in Brazil R. Nóia Júnior et al. 10.1088/1748-9326/ac26f3
- Machine-learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany M. Peichl et al. 10.5194/hess-25-6523-2021
- Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields Q. Sun et al. 10.3390/agriculture12111791
- Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks A. Marcolongo et al. 10.1017/eds.2022.1
- Increase of Simultaneous Soybean Failures Due To Climate Change H. Goulart et al. 10.1029/2022EF003106
- The effects of varying drought-heat signatures on terrestrial carbon dynamics and vegetation composition E. Tschumi et al. 10.5194/bg-19-1979-2022
- Accounting for Weather Variability in Farm Management Resource Allocation in Northern Ghana: An Integrated Modeling Approach O. Adelesi et al. 10.3390/su15097386
- Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks Y. Boulaguiem et al. 10.1017/eds.2022.4
- Synchronous climate hazards pose an increasing challenge to global coffee production D. Richardson et al. 10.1371/journal.pclm.0000134
- Impacts of compound hot–dry extremes on US soybean yields R. Hamed et al. 10.5194/esd-12-1371-2021
- Critical Climate Periods Explain a Large Fraction of the Observed Variability in Vegetation State A. Kern et al. 10.3390/rs14215621
- Two machine learning approaches for predicting cyanobacteria abundance in aquaculture ponds M. Zhang et al. 10.1016/j.ecoenv.2023.114944
- Greater Flash Flood Risks From Hourly Precipitation Extremes Preconditioned by Heatwaves in the Yangtze River Valley Y. Chen et al. 10.1029/2022GL099485
- On the Systematic Occurrence of Compound Cold Spells in North America and Wet or Windy Extremes in Europe G. Messori & D. Faranda 10.1029/2022GL101008
- Soil properties modulate actual evapotranspiration and precipitation impacts on crop yields in the USA M. Suliman et al. 10.1016/j.scitotenv.2024.175172
- A comparison of climate drivers’ impacts on silage maize yield shock in Germany F. Stainoh et al. 10.1007/s00704-024-05179-z
- Guidelines for Studying Diverse Types of Compound Weather and Climate Events E. Bevacqua et al. 10.1029/2021EF002340
- Extreme and compound ocean events are key drivers of projected low pelagic fish biomass N. Le Grix et al. 10.1111/gcb.16968
- Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods W. Zhang et al. 10.3389/feart.2021.673495
- Machine learning in crop yield modelling: A powerful tool, but no surrogate for science G. Lischeid et al. 10.1016/j.agrformet.2021.108698
- Storylines of weather-induced crop failure events under climate change H. Goulart et al. 10.5194/esd-12-1503-2021
- The KNMI Large Ensemble Time Slice (KNMI–LENTIS) L. Muntjewerf et al. 10.5194/gmd-16-4581-2023
- Uncovering the Dynamics of Multi‐Sector Impacts of Hydrological Extremes: A Methods Overview M. de Brito et al. 10.1029/2023EF003906
- Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al. 10.1017/eds.2024.2
- Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al. 10.1016/j.earscirev.2022.104241
- Advancing research on compound weather and climate events via large ensemble model simulations E. Bevacqua et al. 10.1038/s41467-023-37847-5
- Current and future risk of unprecedented hydrological droughts in Great Britain W. Chan et al. 10.1016/j.jhydrol.2023.130074
- Compound dry and hot extremes: A review and future research pathways for India R. Guntu & A. Agarwal 10.1016/j.jhydrol.2024.131199
- Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere M. Anand et al. 10.1088/1748-9326/ad627d
Latest update: 01 Nov 2024
Short summary
We present a statistical approach for automatically identifying multiple drivers of extreme impacts based on LASSO regression. We apply the approach to simulated crop failure in the Northern Hemisphere and identify which meteorological variables including climate extreme indices and which seasons are relevant to predict crop failure. The presented approach can help unravel compounding drivers in high-impact events and could be applied to other impacts such as wildfires or flooding.
We present a statistical approach for automatically identifying multiple drivers of extreme...
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