Compound weather and climate events: dependence and causality issues

Composition of multiple climate drivers or/and hazards characterizes compound weather and climate events. Understanding this kind of events needs to analyze the complex causal chain, which could cause extreme impacts. Estimation of the dependence between characteristics/drivers (random variables) of these kind of events, is in its infant step and of significant importance in the field of hydrology, meteorology and risk assessment. To do this estimation, the combination of multiple climate drivers have to be considered, because the composition of them could push an event to extreme levels by a factor of up to some points, compared with variables being independent. Specially spatial and temporal dependencies as main classes of dependencies are respectively considered to estimate the dependencies of random variables at the same time, and inter-temporal situation. In addition, complex interacted physical processes, cause weather/climate related extreme events in multiple temporal and spatial scales. When drivers combine the impacts of events intensify, especially when they occur in succession / simultaneous (such as drought and heat wave, heavy rainfall and saturated soils or global or regional synchronized floods or heatwaves). Local and short timescales of compound events are felt in different spatial and temporal scales, in addition climate change and local-scale changes are significant issues to how deal with and model non-stationarity of compound events. To model and analyze compound events, understanding the components of compound events is an essential critical issue. Compound events may have modulators, drivers, hazards and impacts. Hazards do not need to be extreme in statistical senses to make an extreme impact. Generally, compound events are distinguished in four classes: 1) Preconditioned, 2) Multivariate, 3) Spatially Compounding and 4) Temporally Compounding, but it is not always easy to identify a strict bound to fit an compound event in a specific class. Separation of each type of compound events is a challenging issue. In this study for each class of compound events a case study research has been done to detect and analyze multiple drivers. To consider a “Preconditioned Events”, the effect of the rainfall intensity on the unsaturated (pre-existing condition) 3D corner slope under historical (1961-2005) and future (2006 and 2100) conditions (under climate change) has been investigated. To do this investigation two types of soil (clayey and sandy) have been considered. Rainfall caused an increment of pore water pressure u in the unsaturated corner slope which led to a considerable reduction in the soil suction and the safety factor. By increasing the daily rainfall intensity, the slopes with high friction angle and low cohesion are more at risk, especially after increasing rainfall intensity under climate change, and safety factor of slopes reduced remarkable. This work is very useful to manage the infrastructure in downstream of slopes, also when climate change also in view. In the “Multivariate class of compound events”, the statistical dependence between flood peak Q, flood volume V and flood duration D has been investigated using a worldwide database of daily discharge. Results of this chapter shed light on the compound nature of flood events rainfall-driven, where the dependence between flood event characteristics (Q, V, D) emerges as a consequence of the relation of such characteristics to the rainfall input variables (I, W) that control the hydrographs. In addition, this result puts light also on the multivariate modeling of flood event characteristics (Q, V, D) stating that there is not a causal priority among these variables to be used in conditional analysis and modeling. From the modeling activity, on the U.S. sub-dataset, we obtained that the conceptual hydrological model is able to represent the observed dependencies between each couple of variables for rainfall-driven flood events, and for such events, the pairwise dependence of each couple is not causal, is of spurious kind, coming from the “Principle of Common Cause”. The third class of compound events, Spatially Compounding has been highlighted in United Kingdom (UK) catchments to extract the “Causes of dependence between extreme floods”. The similarity of catchment characteristics and also climatology information help to deal with synchronization in specific time window between stations. We considered the co-occurrence of annual maxima, and then in a more general framework, introduced three dissimilarity indices: asynchrony index, climatological and hydrological dis-similarity indices. Asynchrony index represents the synchronized flood events that can be the result of meteorological reasons (heavy rain) or to hydrological reasons (snow melting). We considered the climatological dissimilarity index which accounts for the annual precipitation, and the hydrological index which takes in one several information about catchments characteristics. As well as these indices we considered also the distances of couple stations to the assessment of conditional statistical dependencies. These three indices are interrelated and have less dissimilarity for the near couples, and dependent-dissimilarity maps could help to more explanation of the dependence and independence between couples. This work could be done between different kind of climate regions for flood alerting. Finally for the last class of compound events, Temporal Compounding, sequences of different types of meteorological and hydrological droughts in snow-influenced catchments have been addressed. Three variables such as “Precipitation” (sum of Rainfall and Snowfall), “Rainfall”, and “Runoff” (Snow melting as well as Rainfall-Runoff, estimated by HyS model in monthly scale to detect multi-temporal compound drought events from 1950-2015, have been considered. Detecting snow drought using SPI and SRI in preceding winter could make an alarm for SMI index in spring or summer. Using different indices as a complementary index of SPI, help to better deal with drought risk. It is notable that the distinction among the different types of compound classes is not easy. In this study I tried to make an effort in making clear this distinction.