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

Abstract

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

Impacts of hot-dry compound extremes on US soybean yields

“…A reduced set of predictor variables where shared information between variables is minimized provides an easily-interpretable and robust model for assessing sensitivity of soybean crops to climate and weather variability (Ben-Ari et al, 2018;Gornott and Wechsung, 2016;Lobell and Burke, 2010;Schauberger et al, 2017b). It is possible to use more complex machine learning models such as random forests although these often tend to obscure result interpretation and do not always yield better predictions (Vogel et al, 2019(Vogel et al, , 2021. Note that nonclimatic seasonal influences on crop yields are ignored in this study.…”

“…in the case of hot-dry conditions in the growing season affecting crop yields (Feng and Hao, 2019;Matiu et al, 2017). One way to identify such drivers is through the use of statistical methods that empirically associate drivers to impacts (Vogel et al, 2021). Easily interpretable linear regressions in that context can be useful tools, in particular when fitted with alternative methods that allow for the consideration of a large number of potential predictors (i.e.…”

Guidelines for Studying Diverse Types of Compound Weather and Climate Events

“…We use large-ensemble simulations that consist of 1600 growing seasons and associated annual wintertime wheat yields from Vogel et al (2021). Wheat yields were simulated with the APSIM-Wheat model (version 7.10) (Zheng et al, 2014) driven by meteorological data from the EC-Earth global climate model (Hazeleger et al, 2010;Van der Wiel, Wanders, et al, 2019) under present-day (2011 climate conditions.…”

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

“…Compound hydrometeorological extremes (e.g., hot and drought compound) exert profound impacts on agriculture and water irrigation demand (Zampieri et al, 2017;Lu et al, 2018;Ribeiro et al, 2020b;Haqiqi et al, 2021;Vogel et al, 2021). For example, the compound drought and heatwave events may affect socioecological systems (Mukherjee et al, 2020), wildfires (Abatzoglou and Williams, 2016;AghaKouchak et al, 2020;Sutanto et al, 2020), air pollution (Tressol et al, 2008;Zhang H. et al, 2017;Wang et al, 2017;Lin et al, 2020), heat-related deaths (D'Ippoliti et al, 2010;Mitchell et al, 2016).…”