Heatwaves can greatly impact societies, underscoring the need to extend current heatwave prediction lead times. This study investigates multiple machine learning (ML) model approaches for heatwave occurrence prediction with long lead times of one to five months. Five ML classifiers, built using Google Earth Engine remote sensing datasets, are developed and tested for heatwave prediction for the national scale (case example of Sweden) over time period 1989–2019. The ML modelling is based on 13 final explanatory atmospheric and landscape features. The balanced random forest model exhibits the consistently best performance among the tested ML models, stable across all investigated lead times (from one to five months) with balanced accuracy of around 0.77, even though not overall identifying actual heatwave occurrence (decreased recall for heatwave occurrence from 0.87 to 0.81). Application of SHapley Additive exPlanations technique for model interpretation shows increasing importance of model output with increasing lead time for landscape features such as runoff and soil water. Overall, more frequent heatwave occurrence emerges for places characterized by lower values of geopotential height, evaporation, precipitation, and topographical slope, and higher values of temperature, runoff, and sea level pressure. The study also exemplifies how the developed ML modelling approach could be used to identify and warn for early signs of forthcoming heatwave occurrence, and further step-wise improve the identification and warning toward less uncertainty for shorter lead times. This can facilitate earlier warning in support of better planning of measures to mitigate adverse heatwave impacts, up to several months ahead of their possible occurrence.

Decentralized grid solutions could be a feasible alternative to improve resilience and mitigate cascading effects in island states. Our study explores approaches that reduce the risk of infrastructure failures and promote decentralized utility planning in islands. A novel framework is proposed to conduct a power system resilience assessment by integrating vulnerability assessments and energy system modelling approaches through network analysis. The framework is applied to an island context, where vulnerability to hydroclimatic hazards, geographic isolation, restricted access to energy sources, small population bases inadequate for substantial infrastructure investments, dependence on imported energy, lack of energy source diversification, and fragile ecosystems have exacerbated energy insecurity. As a case study, we have applied the framework to Cuba. We simulate disruptions in vulnerable network nodes in Cuba to determine the municipalities that are most impacted by the simulated cascading failures. We designed and optimized the lowest cost decentralized solutions to increase resilience either by acting as the baseload electricity source or as a complementary backup system to complement in case of a power outage. Then, the resilience of the designed system was assessed using power system resilience metrics. The study results show Regla municipality in Cuba as the most vulnerable hotspot for electricity distribution. Upon the different system comparisons, ancillary systems outperform backup systems in enhancing power system resilience, especially in the context of a disruptive event, supplying up to 53 MWh/day more, although they have higher investment costs. Based on this research, resource planners and policymakers can understand vulnerable node points and prioritize the necessary investments for the preferred system choice to alleviate impacts of energy insecurity on the Island States.

In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWI $$_\textrm{S}$$ S ) and anomalous temperatures (HWI $$_\textrm{E}$$ E ), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWI $$_\textrm{S}$$ S , SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types.