Deficits or excesses of moisture transport associated with the main moisture transport mechanisms, such as atmospheric rivers, low-level jets, monsoons and tropical cyclones, are in most cases ultimately responsible for hydrometeorological hazards such as droughts or floods. In order to establish a linkage between these deficits or excesses and the occurrence, development and termination of these disastrous events, a wide range of statistical techniques must be employed. These include spatial statistics, extreme value theory, regression analysis and causality or copulas. The aforementioned diversity is exemplified by recent papers employing extreme value theory and copula functions to examine the relationship between excess moisture transport and extreme precipitation, and to investigate the linkage between moisture transport deficits and droughts. A considerable number of papers in meteorological, climatic and multidisciplinary high impact journals employ a wide range of statistical techniques to analyze and model the characteristics of the principal modes of moisture transport and their impact on extreme events.

This Collection is devoted to original and innovative applications of recent statistical methodologies to the study of the major mechanisms of atmospheric moisture transport of atmospheric rivers, low- level jets, tropical cyclones and monsoons. This includes both the study of the characteristics of these systems – structure, spatial distribution, trends in their occurrence, analysis of extremes – as well as the assessment of their impacts mainly extreme precipitation events, floods and droughts.

For this Collection we invite cutting-edge contributions related to methods and applications of spatio-temporal statistics, data mining, data visualization, automatic algorithms, clustering and classification analysis of temporal series, extreme value modelling and analysis of dependence structures on topics including, but not limited to:

• The occurrence and structure of major mechanisms of atmospheric moisture transport: atmospheric rivers, low- level jets, tropical cyclones and monsoons

• The role of atmospheric moisture transport in extreme precipitation events and droughts

• Mechanisms and Impacts of the moisture transport associated to atmospheric rivers, low- level jets, tropical cyclones and monsoons

• Analysis of the nexus oceanic evaporation-moisture transport- extreme events

• Assessment of the influence of changes in land use and vegetation cover in the atmospheric moisture transport and its associated impacts

• Prediction and predictability of atmospheric moisture transport mechanisms and their associated impacts on a variety of time scales

• Compound events associated with extreme precipitation and moisture transport

Environmental processes are influenced by complex, nonlinear interactions, resulting in high-dimensional and ample data sets. Traditional statistical models, which have been widely used to analyze these phenomena, have nevertheless proven to be often insufficient for effectively analyzing such high-dimensional state spaces, without integrating machine-learning approaches. In contrast, machine-learning algorithms operate under relatively weak assumptions regarding the underlying structure and interdependence within the data, allowing them to capture hidden patterns and nonlinear relationships. This Special Issue is therefore dedicated to publishing advances related to the use of Statistics in environmental models, including machine-learning algorithms and any innovative techniques in the analysis of environmental data.

Contributions are welcome on a broad spectrum of related subjects, including:

• Ecohydrology in the context of climate change

• Risk and hazard analysis of natural and environmental hazards

• Remote sensing integration in civil engineering

• Potential impact risk of natural hazards on infrastructure

• Risk assessment of health-related hazards

The Special Issue aims to bring together research that bridges traditional statistical analysis and modern data-driven methods, highlighting their role in improving the understanding, prediction, and management of environmental systems.