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Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is challenging, which is attributable to the complex nature of natural hazard interactions and the limited availability of multi-hazard observations. This study presents an approach for identifying multi-hazard events during the past 123 years (1900-2023) using the EM-DAT global disaster database. Leveraging the 'associated hazard' information in EM-DAT, multi-hazard events are detected and assessed in relation to their frequency, impact on human lives and assets, and reporting trends. The interactions between various combinations of natural hazard pairs are explored, reclassifying them into four categories: preconditioned/triggering, multivariate, temporally compounding, and spatially compounding multi-hazard events. The results show, globally, approximately 19 % of the 16,535 disasters recorded in EM-DAT can be classified as multi-hazard events. However, the multi-hazard events recorded in EM-DAT are disproportionately responsible for nearly 59 % of the estimated global economic losses. Conversely, single hazard events resulted in higher fatalities compared to multi-hazard events. The largest proportion of multi-hazard events are associated with floods, storms, and earthquakes. Landslides emerge as the predominant secondary hazards within multi-hazard pairs, primarily triggered by floods, storms, and earthquakes, with the majority of multi-hazard events exhibiting preconditioned/triggering and multivariate characteristics. There is a higher prevalence of multi-hazard events in Asia and North America, whilst temporal overlaps of multiple hazards predominate in Europe. These results can be used to increase the integration of multi-hazard thinking in risk assessments, emergency management response plans and mitigation policies at both national and international levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

The Horn of Africa faces an ongoing multi-year drought due to five consecutive failed rainy seasons, a novel climatic event with unpreceded impacts. Beyond the starvation of millions of livestock, close to 23 million individuals in the region are currently facing high food insecurity in Kenya, Somalia and Ethiopia alone. The severity of these impacts calls for the urgent upscaling and optimisation of early action for droughts. However, drought research focuses mainly on meteorological and hydrological forecasting, while early action triggered by forecasts is seldom addressed. This study investigates the potential for early action for droughts by using seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 system for the March-April-May (MAM) and October-November-December (OND) rainy seasons. We show that these seasonal rainfall forecasts reflect major on-the-ground impacts, which we identify from drought surveillance data from 21 counties in Kenya. Subsequently, we show that the SEAS5 drought forecasts with short lead times have substantial potential economic value (PEV) when used to trigger action before the OND season across the region (PEV max  = 0.43). Increasing lead time to one or two months ahead of the season decreases PEV, but the benefits persist (PEV max  = 0.2). Outside of Kenya, MAM forecasts have limited value. The existence of opportunities for early action during the OND season in Kenya and Somalia is demonstrated by high PEV values, with some regions recording PEV max values close to 0.8. To illustrate the practical value of this research, we point to a dilemma that a pastoralist in the Kenyan drylands faces when deciding whether to adopt early livestock destocking. This study underscores the importance to determine the value of early actions for forecast users with different action characteristics, and to disseminate this value alongside the standard forecasts themselves. This allows users to trigger effective actions before drought impacts develop., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Fiscal resilience against disasters is vital for the recovery in the aftermath of climate hazards. Without swift access to available funds for disaster relief, damages to human and the economy would be further exacerbated. How insurance may influence fiscal performance over time and can increase fiscal resilience for today and under a future climate has not been looked at yet in detail. Focusing on the Caribbean region and on the fiscal performance of governments after disaster events, we empirically analyze the effectiveness of the Caribbean Catastrophe Risk Insurance Facility (CCRIF) regarding the reduction of short-term fiscal effects. We embed this analysis within a novel climate impact storyline approach where we produce past plausible events and investigate the usefulness of insurance under such events. The storylines were modified according to global and climate change related boundary conditions to address the issue whether the CCRIF is fit for purpose or will need to be adapted in the future. We found that both hurricane strikes and the CCRIF affect fiscal outcomes of Caribbean countries. Furthermore, there are indications that CCRIF can counteract the negative fiscal consequences over the short term period induced by the disaster. Our analysis should shed some light on the current discussions on how development related assistance can be structured to enhance climate resilience in highly exposed countries for both direct and fiscal impacts of disasters., Supplementary Information: The online version contains supplementary material available at 10.1007/s41885-023-00126-0., Competing Interests: Competing InterestsThe authors declare no competing interests., (© The Author(s) 2023.)

Conventional green roofs have often been criticised for their limited water buffer capacity during extreme rainfall events and for their susceptibility to droughts when additional irrigation is unavailable. One solution to these challenges is to create an extra blue water retention layer underneath the green layer. Blue-green roofs allow more stormwater to be stored, and the reservoir can act as a water source for the green layer throughout capillary rises. An automated valve regulates the water level of the system. It can be opened to drain water when extreme precipitation is expected. Therefore, the water buffer capacity of the system during extreme rainfall events can be maximised by integrating precipitation forecasts as triggers for the operation of the valve. However, the added value of this forecast-based operation is yet unknown. Accordingly, in this study, we design and evaluate a hydrological blue-green roof model that utilises precipitation forecasts. We test its performance to capture (extreme) precipitation and to increase evapotranspiration and evaporative cooling under a variety of precipitation forecast-based decision rules. We show that blue-green roofs can capture between 70 % and 97 % of extreme precipitation (>20 mm/h) when set to anticipate ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). This capture ratio is considerably higher than that of a conventional green roof without extra water retention (12 %) or that of a blue-green roof that does not use forecast information (i.e., valve always closed; 59 %). Moreover, blue-green roofs allow for high evapotranspiration rates relative to potential evapotranspiration on hot summer days (around 70 %), which is higher than from conventional green roofs (30 %). This serves to underscore the higher capacity of blue-green roofs to reduce heat stress. Using the city of Amsterdam as a case study, we show the high upscaling potential of the concept: on average, potentially suitable flat roofs cover 13.3 % of the total area of the catchments that are susceptible to pluvial flood risk. If the 90th percentile of the ECMWF forecast is used, an 84 % rainfall capture ratio can translate into capturing 11 % of rainfall in flood-prone urban catchments in Amsterdam., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

COVID-19 has revealed how challenging it is to manage global, systemic and compounding crises. Like COVID-19, climate change impacts, and maladaptive responses to them, have potential to disrupt societies at multiple scales via networks of trade, finance, mobility and communication, and to impact hardest on the most vulnerable. However, these complex systems can also facilitate resilience if managed effectively. This review aims to distil lessons related to the transboundary management of systemic risks from the COVID-19 experience, to inform climate change policy and resilience building. Evidence from diverse fields is synthesised to illustrate the nature of systemic risks and our evolving understanding of resilience. We describe research methods that aim to capture systemic complexity to inform better management practices and increase resilience to crises. Finally, we recommend specific, practical actions for improving transboundary climate risk management and resilience building. These include mapping the direct, cross-border and cross-sectoral impacts of potential climate extremes, adopting adaptive risk management strategies that embrace heterogenous decision-making and uncertainty, and taking a broader approach to resilience which elevates human wellbeing, including societal and ecological resilience., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Flood risk can be reduced at various stages of the disaster management cycle. Traditionally, permanent infrastructure is used for flood prevention, while residual risk is managed with emergency measures that are triggered by forecasts. Advances in flood forecasting hold promise for a more prominent role to forecast-based measures. In this study, we present a methodology that compares permanent with forecast-based flood-prevention measures. On the basis of this methodology, we demonstrate how operational decision-makers can select between acting against frequent low-impact, and rare high-impact events. Through a hypothetical example, we describe a number of decision scenarios using flood risk indicators for Chikwawa, Malawi, and modelled and forecasted discharge data from 1997 to 2018. The results indicate that the choice between permanent and temporary measures is affected by the cost of measures, climatological flood risk, and forecast ability to produce accurate flood warnings. Temporary measures are likely to be more cost-effective than permanent measures when the probability of flooding is low. Furthermore, a combination of the two types of measures can be the most cost-effective solution, particularly when the forecast is more skillful in capturing low-frequency events. Finally, we show that action against frequent low-impact events could more cost-effective than action against rare high-impact ones. We conclude that forecast-based measures could be used as an alternative to some of the permanent measures rather than being used only to cover the residual risk, and thus, should be taken into consideration when identifying the optimal flood risk strategy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

The net greenhouse gas benefits of wind turbines compared to their fossil energy counterparts depend on location-specific wind climatology and the turbines' technological characteristics. Assessing the environmental impact of individual wind parks requires a universal but location-dependent method. Here, the greenhouse gas payback time for 4161 wind turbine locations in northwestern Europe was determined as a function of (i) turbine size and (ii) spatial and temporal variability in wind speed. A high-resolution wind atlas (hourly wind speed data between 1979 and 2013 on a 2.5 by 2.5 km grid) was combined with a regression model predicting the wind turbines' life cycle greenhouse gas emissions from turbine size. The greenhouse gas payback time of wind turbines in northwestern Europe varied between 1.8 and 22.5 months, averaging 5.3 months. The spatiotemporal variability in wind climatology has a particularly large influence on the payback time, while the variability in turbine size is of lesser importance. Applying lower-resolution wind speed data (daily on a 30 by 30 km grid) approximated the high-resolution results. These findings imply that forecasting location-specific greenhouse gas payback times of wind turbines globally is well within reach with the availability of a high-resolution wind climatology in combination with technological information.

A new set of climate change scenarios for 2050 for the Netherlands was produced recently. The scenarios span a wide range of possible future climate conditions, and include climate variables that are of interest to a broad user community. The scenario values are constructed by combining output from an ensemble of recent General Climate Model (GCM) simulations, Regional Climate Model (RCM) output, meteorological observations and a touch of expert judgment. For temperature, precipitation, potential evaporation and wind four scenarios are constructed, encompassing ranges of both global mean temperature rise in 2050 and the strength of the response of the dominant atmospheric circulation in the area of interest to global warming. For this particular area, wintertime precipitation is seen to increase between 3.5 and 7% per degree global warming, but mean summertime precipitation shows opposite signs depending on the assumed response of the circulation regime. Annual maximum daily mean wind speed shows small changes compared to the observed (natural) variability of this variable. Sea level rise in the North Sea in 2100 ranges between 35 and 85 cm. Preliminary assessment of the impact of the new scenarios on water management and coastal defence policies indicate that particularly dry summer scenarios and increased intensity of extreme daily precipitation deserves additional attention in the near future.

Simulations with regional climate models (RCMs), carried out for the Rhine basin, have been analyzed in the context of implications of the possible future discharge of the Rhine river. In a first analysis, the runoff generated by the RCMs is compared to observations, in order to detect the way the RCMs treat anomalies in precipitation in their land surface component. A second analysis is devoted to the frequency distribution of area averaged precipitation, and the impact of selection of various driving global climate models.