Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

• DOI: 10.5194/egusphere-egu21-15524
• Publication Date: 2021-03-04T12:43:00Z
• Authors: Jakob Zscheischler; Johannes Vogel; Pauline Rivoire; Cristina Deidda; Leila Rahimi; Christoph Sauter; Elisabeth Tschumi; Karin van der Wiel; Tianyi Zhang
• Keywords: Extreme Weather

<p>Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms cause impacts, such as crop failure, is crucial importance improve their understanding and forecasting. In this study we investigate whether key meteorological drivers be identified using Least Absolute Shrinkage Selection Operator (Lasso) in a model environment, method allows for automated variable selection able handle collinearity between variables. As an example impact, failure annual wheat yield simulated by APSIM driven 1600 years daily data from global climate (EC-Earth) under present-day conditions Northern Hemisphere. We then apply Lasso logistic regression determine which during growing season failure.</p><p>We obtain good performance Central Europe eastern half United States, while regions Asia western States are less accurately predicted. Model correlates strongly with mean variability yields, is, highest relatively large variability. Overall, nearly all grid points inclusion temperature, precipitation vapour pressure deficit predict failure. addition, predictors seasons required prediction. These results illustrate omnipresence compounding effects both different periods creating events. Especially indicators diurnal temperature range number frost days selected statistical relevant at most points, underlining overarching relevance.</p><p>We conclude useful tool automatically detect compound could applied other wildfires or floods. detected relationships purely correlative nature, more detailed analyses establish causal structure impacts.</p>