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.

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 understanding and forecasting. In this study we investigate whether key meteorological drivers of extreme impacts can be identified using 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 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 the logistic Lasso regression to predict 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 both between 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.

Compound events, like compound floods, have rapidly aroused interest due to the strong impacts associated with them. The spatial dependence has a fundamental role in the dynamics of these events, and causative investigations of their origins could contribute to elucidate their dynamics. Here, we addressed the pairwise spatial dependence between annual maximum (instantaneous) discharges occurring in river stations located in the United Kingdom. First, we tested the hypothesis that the dependence comes from the co-occurrence of annual maxima using Kendall’s tau measure of association and its conditional version, calculated from the non-co-occurrent values. This hypothesis, commonly accepted in literature, would attribute to the co-occurrence of the origin of the spatial dependence between extreme floods. The analysis showed how there is also dependence between annual maxima pertaining to catchments located very far from one another, and where the co-occurrence of annual maxima is small, if not zero. We formulated a general hypothesis to explain the spatial dependence between annual maxima: dependence is the compound result of co-occurrences, and climatological and hydrological similarities. The origin of dependence is more complex than what is presently stated in the literature. Thus, not only is synchronization a cause, but similarities in climate and hydrological response may also play a role. We introduced three dissimilarity indices and dependence-dissimilarity maps to illustrate this general hypothesis.

In a network of binarized precipitation (i.e., wet or dry value), the connection or dependence between each pair of nodes can occur following one or more of the following conditions: wet-wet, dry-dry, wet-dry, or dry-wet. Here, we firstly investigate the different types of dependence, year by year, within a precipitation network of binarized variables. We compare the sample estimate of the probability of co-occurrence (or occurrence with a lag time within ±3 days) of each of the four possible combinations with respect to the correspondent confidence interval in hypothesis of independence. We develop a procedure to efficiently assess the dependence behavior of all couples of nodes within the network and apply the methodology to a network of rain gauges covering Europe and north Africa. Plain Language Summary Meteorological connections are important issues of the climate system because they can help identifying geographic areas where compound weather events could occur. These are natural hazards characterized by extreme and devastating impacts. The typology of compound events (e.g., the co-occurrence, or subsequence, of two floods, or two droughts, or a flood and a drought, in two geographic areas, among others) is a function of the type of dependence between the variables involved. Here, we investigate the different types of pairwise dependence, year by year, within a precipitation network with respect to wet-wet, dry-dry, wet-dry, and dry-wet co-occurrences (or occurrences with a lag time within ±3 days), introducing a general methodology based on probability measures. This helps identifying situations which could lead respectively to compound floods or compound droughts, as well as connections between floods and droughts. We apply the methodology to a network of rain gauges covering Europe and North Africa. In order to optimize the computational times, we develop also a procedure to efficiently calculate the significance of the dependence between each pair of nodes within the network. The results show how it is possible to identify geographic areas where the wet-wet and/or dry-dry occurrences are frequent, so where we should expect compound floods and/or compound drought/low flow conditions, and where wet-dry and/or dry-wet conditions occur: mainly wet conditions in south and dry conditions in north.

Despite the several sources of inaccuracy, commercial microwave links (CML) have been recently exploited to estimate the average rainfall intensity along the radio path from signal attenuation. Validating these measurements against “ground truth” from conventional rainfall sensors, as rain gauges, is a challenging issue due to the different spatial sampling involved. Here, we assess the performance of a network of CML as opportunistic rainfall sensors in a challenging mountainous environment located in Northern Italy. The benchmark dataset was provided by an operational network of rain gauges and by three disdrometers. Moreover, disdrometer data were used to establish an accurate relationship between path attenuation and rainfall intensity. A new method was developed for assessing CML: time series of rainfall occurrence and rainfall depth, representative of CML radio path, were derived from the nearby rain gauges and disdrometers and compared with the same quantities gathered from the CML. It turns out that, over the very short integration times considered (10 min), CML perform well in detecting rainfall, whereas quantitative rainfall estimates may have large discrepancies.