Christopher John White

Operational extreme weather forecasts and early warnings are generally limited to timescales of up to around 10 days and to predicting single events, such as flooding or a heatwave. However, experimental ‘extended-range’ weather predictions that extend up to 46 days have been developed over the last decade by the world’s leading meteorological centres. A key motivation of exploring this prediction timescale is to bridge the gap between timescales, incorporate the latest ‘multi-hazard’ approaches, and improve early warnings and anticipatory actions. Currently, however, the extended-range prediction and the multi-hazard research and operational communities are largely disconnected. To date, there has been no coordinated effort to build a network that connects these disciplines and communities towards the development of operational systems. However, it is essential that these communities come together to explore windows of opportunity and instigate a step-change in the way forecasts are designed, produced and used. To address this challenge, here we present the ANTICIPATE COST Action (CA24144) that has created the first pan-European network focused on extended-range multi-hazard predictions and warnings. Over the next 4 years, ANTICIPATE will bring together existing but largely disconnected disciplines, operational practitioners and stakeholders (including extreme weather forecasting, extended-range prediction and climate dynamics, disaster risk reduction, multi-hazards, and communications) to drive forward advancements in the science, training, communication and application that will support next generation of effective early warnings that enable preparedness and action across hazards and forecasting lead times. In this talk, we will explore upcoming events and activities, and share how ANTICIPATE will provide vital leadership in multi-hazard predictions and warnings, address gaps and challenges, and help educate the next generation of forecasters and communicators for societal benefit. Further details about the ANTICIPATE COST Action are available here: https://www.cost.eu/actions/CA24144/.

The development of indicators in disaster risk management has only recently started to explicitly include a multi-hazard and multi-risk approach. However, undertaking a natural hazard or risk assessment from a single hazard approach can be considered incomplete where the interactions between, and impacts from, multiple hazards and risks are not considered. Indicators contain observable and measurable characteristics to simplify information to understand the state of a concept or phenomenon, and/or to monitor it over time. To understand how indicators are being used in this context, using a systematic review, we identified 192 publications that mention indicators within either multi-hazard or multi-risk contexts, including hazards, vulnerability, and risk/impact. We found that most studies exploring indicators focused on multi-layer single hazards and risks, where multiple single hazards or risks within a given location were analysed individually and their outcomes presented in an overlaid format. The results also demonstrate a predominance of studies on hazard indicators (88 %) versus risk indicators, with a dominance of hydrometeorological indicators. Only 20 % of the studies integrated hazard, vulnerability and risk/impact. Based on the findings, we propose a set of actionable recommendations to enable the development and uptake of multi-hazard and multi-risk indicators.

This paper describes the role of operational meteorology from a single disciplinary standpoint through to multidisciplinary, interdisciplinary and, more recently, transdisciplinary approaches and what this means for future forecasting systems. We start from the foundation of a single meteorological discipline: in this case, the forecasting of wind speed and direction that led to the creation of the first gale warning service in the wake of the ‘Royal Charter Storm’ in 1859. We build on this, giving further examples of where multidisciplinary and interdisciplinary methods have enhanced our understanding of observed meteorological and hydrometeorological phenomena from multiple perspectives, to produce a more holistic understanding of the effect of weather and climate on the ‘whole of society’. An exemplar of this is the integration of meteorology and hydrology to create the UK’s first hydrometeorological profession through the Flood Forecasting partnership between the Met Office and the Environment Agency. We end our discussion by exploring the move to a transdisciplinary approach through living labs: we believe this is vital to increase societal resilience to ever-more extreme weather events driven by anthropogenic warming. This article is part of the Royal Society Science+ meeting issue ‘Hydrology in the 21st century: challenges in science, to policy and practice’.

To mark the 40th anniversary of the British Hydrological Society, a landmark Discussion Meeting was held at the Royal Society in June 2024, bringing together a transdisciplinary community, including hydrologists, policymakers and practitioners, to reflect on four decades of progress and chart future directions for hydrology. This special issue presents a collection of papers from that meeting, addressing advances in data, modelling, forecasting and decision-making in the context of intensifying climate and hydrological extremes. Key themes include the need for open, reproducible science, greater integration of machine learning, AI and convection-permitting models and a shift towards transdisciplinary, co-produced knowledge that better supports adaptation, resilience and policy impact. The issue highlights the critical roles of education, collaboration and equity in shaping a hydrology that is not only technically advanced but socially and environmentally responsive to the challenges of the 21st century. This article is part of the Royal Society Science+ meeting issue ‘Hydrology in the 21st century: challenges in science, to policy and practice’.

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 presentation, focused on a recently published article in Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2023.169120), 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.

Undertaking a natural hazard or risk assessment from a single hazard approach can be considered incomplete where the interactions between and impacts from multiple hazards and risks are not considered. However, the development of indicators in disaster risk management has only recently started to explicitly include multi-hazard and multi-risk approach. Indicators contain observable and measurable characteristics to simplify information to understand the state of a concept or phenomenon, and/or to monitor it over time. To date, there have been limited efforts to understand how indicators are being used in this context. Using a systematic review, 194 publications were identified that mention indicators, covering hazards, vulnerability, and risk/impact. We find that the majority of studies exploring indicators are multi-layer single hazards and risks; in other words, they did not include the interactions between hazards. The results also demonstrate a predominance of studies on hazard indicators (88 %) versus risk indicators, with a dominance of hydro-meteorological indicators. Only 20 % of the studies integrated hazard, vulnerability and risk/impact. Based on the findings, we propose 12 recommendations to enable the uptake of indicators, from advancing research into multi-hazard and multi-risk indicator frameworks, to enabling partnerships to ensure the inclusion of stakeholder needs in indicator development.

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

Inadequate climate data stations often make hydrological modelling a rather challenging task in data-sparse regions. Gridded climate data can be used as an alternative; however, their accuracy in replicating the climatology of the region of interest with low levels of uncertainty is important to water resource planning. This study utilised several performance metrics and multi-criteria decision making to assess the performance of the widely used gridded precipitation and temperature data against quality-controlled observed station records in the Lake Chad basin. The study’s findings reveal that the products differ in their quality across the selected performance metrics, although they are especially promising with regards to temperature. However, there are some inherent weaknesses in replicating the observed station data. Princeton University Global Meteorological Forcing precipitation showed the worst performance, with Kling–Gupta efficiency of 0.13–0.50, a mean modified index of agreement of 0.68, and a similarity coefficient SU = 0.365, relative to other products with satisfactory performance across all stations. There were varying degrees of mismatch in unidirectional precipitation and temperature trends, although they were satisfactory in replicating the hydro-climatic information with a low level of uncertainty. Assessment based on multi-criteria decision making revealed that the Climate Research Unit, Global Precipitation Climatology Centre, and Climate Prediction Centre precipitation data and the Climate Research Unit and Princeton University Global Meteorological Forcing temperature data exhibit better performance in terms of similarity, and are recommended for application in hydrological impact studies—especially in the quantification of projected climate hazards and vulnerabilities for better water policy decision making in the Lake Chad basin.