The increased occurrence of multiple cascading and compounding hazards underlines the importance of integrated- and multi-hazard-based assessment approaches for the development of thorough strategies towards disaster resilience. To this purpose, a national-scale multi-hazard risk assessment was conducted between September 2020 and December 2021 for Burundi, focusing on the natural hazards flooding, torrential rains, landslides, earthquakes, and strong winds. This integrated multi-hazard assessment resulted in comparable nationwide provincial and commune-scale Annual Average Loss (AAL) values, further aggregated to provide a preliminary estimate of the resulting overall risk. Historical climatology (1990–2019) was computed, and a preliminary evaluation of the potential effects of climate change in the future period (2020–2049) was carried out. Data availability and reliability were challenging throughout the whole assessment and were tackled by integrating local authoritative sources with international and global resources. An up-to-date exposure model was implemented and complemented by an indicator-based socioeconomic vulnerability assessment. Furthermore, a data-driven statistical susceptibility model for shallow landslides has been derived at national scale. The consequent multi-hazard risk assessment provides an approximate picture of the expected nationwide risk distribution in economic terms. The results should support the identification of priority areas and actions for disaster risk management.From a research perspective, the paper provides a transparent, hazard-wise framework that harmonises heterogeneous hazard, exposure and vulnerability information into comparable risk metrics in data-scarce environments. From a practice perspective, the resulting risk estimates are intended as a pragmatic baseline for multi-hazard comparison and prioritisation, supporting the identification of areas where DRR efforts and data improvements may be most impactful.
Investigating the effects of forest land cover and forest structure on shallow landslide characteristics such as their morphology (e.g., area and mean depth) and topographic profiles could provide a better understanding of how forests affect landslide processes. Landslides located under the forest canopy, which are often overlooked by conventional landslide mapping methods (e.g., using aerial imagery), can be captured using airborne laser scanning (ALS). In this study we investigated forest effects on landslides by developing a well-performing semi-automated workflow for mapping landslide scars and analysing their characteristics in relation to the forest canopy cover, using bi-temporal ALS data and a random forest model. The mapped landslide scars were analysed with a forest canopy cover mask and forest structure parameters, such as the closest tree distance and the number of trees surrounding the scar. The investigated scars within the forest have significantly larger depths, thicknesses and higher pre-failure slope values than scars located outside the forests. Additionally, the closest tree distance showed a clear relationship with the scar volume or landslide magnitude. This enhances our understanding of forest impacts on landslide processes and their protective function. Furthermore, it shows that inventories which neglect landslides in forests also misrepresent their characteristics.
This study investigated the potential of cloud seeding to mitigate extreme rainfall localization (i.e., overseeding) associated with mesoscale convective systems in Japan. Using a numerical weather prediction model, we conducted cloud seeding experiments by artificially increasing ice nuclei concentrations in a double-moment microphysics scheme for the heavy rainfall event in Hiroshima Prefecture, Japan, in August 2014. We examined the sensitivity of rainfall changes to altitude and area of the seeding. The results showed that seeding in the mid–upper troposphere (7.2–8.6 km), where air temperature ranged from −22 to −12 °C, resulted in the most pronounced changes in rainfall amount. At these levels, high supercooled cloud water content and strong updrafts favoured heterogeneous freezing, resulting in a depletion of moisture and suppression of graupel growth. The cloud seeding led to reduced rainfall within the heavy rainfall region and increased rainfall downwind, demonstrating the hypothesized dispersal mechanism of “overseeding”. Expanding the seeding to cover the upstream region of the heavy rainfall area had a greater impact than increasing vertical thickness of the seeding. The most effective seeding configuration (24 km × 24 km area at 7.2 km) achieved an 11.5 % decrease in area-averaged 3-h accumulated rainfall and a maximum reduction of 32 % in 3-h accumulated rainfall over the heavy rainfall region. Future work should consider more realistic representations of seeding substance (i.e., transport, dispersion, and interactions) and explore a wider range of rainfall events to generalize the applicability of this approach.
Droughts are among the major challenges facing Europe and pose a significant threat to food security on a continental scale. The shifting of climatic zones is forcing societies to adopt measures to cope with climate extremes. However, the pace of adaptation is much slower than the rate of change observed over the past decade. The most developed economies in Europe are already progressing toward implementing strategies aligned with the Sustainable Development Goals. One of the key scientific objectives is to support this adaptation by providing data-driven decision-making tools at both regional and national levels. At the same time, in some European countries, the understanding of drought remains uncertain. Even basic hazard assessments lack coherence, and methodologically sound and systematic vulnerability assessments are often entirely absent. This paper aims to uncover the drought research landscape over the past ten years for nine countries in Southeast Europe. Using a structured query in the SCOPUS database, we attempted to systematize scientific knowledge on drought exposure in the region and identify “white spots” and gaps in knowledge at both country and regional levels. Our findings show a significant increase in the number of research papers focused on various aspects of drought in these nine countries over the last decade. However, for Montenegro, Albania, Slovenia, and North Macedonia, only one or two papers were found. On the other hand, due to the complexity of drought phenomena – including the wide range of indicators, seasonality, methodologies, and aspects studied – it is extremely difficult to form a comprehensive picture for well-represented countries like Romania and Serbia. To enhance understanding of drought trends in the region over the past ten years, our review incorporates the Combined Drought Indicator (CDI) assessment. The CDI v4 dataset, provided by the Copernicus Drought Observatory, serves as the main unified tool for drought monitoring across Europe. The analysis revealed similar temporal patterns across the region, with some differences in outliers, such as historical droughts. As additional context, we included drought impact data gathered from the newly published European Drought Impact Database (EDID) database. This supplementary information helps us understand the “inheritance” of drought impacts along major rivers and their variability.
Moving towards a more holistic approach to disaster risk management, in which a multi-hazard and multi-risk approach is central, offers many opportunities to increase society's resilience. In 2022, we presented a research agenda of six points that could contribute towards this paradigm shift. In this perspective paper we synthesise key learnings from the MYRIAD-EU project – which ran from September 2021 to December 2025 – reflecting on progress and challenges faced in pursuing this research agenda, and share perspectives that may help to further improve multi-hazard and multi-risk assessment and management. Going forward, we point to several avenues for continued scientific research that we feel would benefit the field: continue the mainstreaming and mutual understanding of concepts and definitions; continue developing a strong evidence base of how dynamics in hazard, exposure, and vulnerability in space and time shape multi-risk; further developing methods for providing both current and future multi-hazard and multi-risk scenarios; increasing the availability of appropriate, solutions-oriented, usable tools; more explicitly including equity issues and equitable disaster risk reduction and adaptation; continue extensively testing and coproducing multi-hazard and multi-risk knowledge in in-depth case studies; supporting the development of Multi-Hazard Early Warning Systems;
Izmir, a major city in western Turkey, is located in a highly seismic region, subject to frequent earthquakes due to its proximity to active fault systems. This paper critically evaluates the strong ground motions recorded in Izmir, with a focus on understanding the implications for urban infrastructure and future seismic hazard mitigation. Historically available data is collected and compared with the available ground motion prediction equations (GMPE). Later, the most appropriate prediction equation is selected and used to determine the target response spectrum. 2020 Sisam earthquake is a well-documented seismic event and the data from the stations are then used to further calibrate the 1D site response model. Lastly, possible future events are generated and results are compared with the current Turkish Earthquake Code (TEC). Amplification factors prescribed by code for Izmir Bay have been surpassed by projected future events, highlighting the necessity for reassessment. Therefore, region-specific seismic zoning should be established when standard code practices fall short in accounting for significant site effects. Concrete recommendations about local site modification factors and evaluations on this topic have been provided within the article.
Experiencing severe flooding tends to negatively impact mental health, creating a significant public health issue. Moreover, extreme events can co-occur, magnifying potential impacts. Insights into the combined impact of co-occurring disasters on mental health, such as floods and COVID-19, are, however, largely lacking. We addressed this research gap by conducting 400 face-to-face interviews in October 2023 in Hue City, Vietnam, where residents faced simultaneous flooding and COVID-19 in 2020.The respondents' mental health was assessed using the Kessler psychological distress scale (K6), revealing that 20 % of the respondents experienced mental health distress, while 80 % did not report such distress. Binary logistic regression models demonstrated that among twelve flood stressors, facing “livelihood difficulties”, “seeing dead human bodies”, and “being rescued” relate significantly to mental distress. Meanwhile “impacts on individual health” and “interrupted education” are the two significant COVID-19 stressors. These five factors stay significant when combined. Additionally, a multivariable regression model revealed the combined effects of flood and COVID-19 when comparing the ORs of four groups ranging from “No flood stress and No Covid stress” to “Flood stress and Covid stress”. The effect size is largest for those who experienced flood and COVID-19 impacts, followed by those who suffered only floods and those who faced only COVID-19, with the smallest effect size.These findings underline the need to address public health problems caused by multiple risks, which is still a significant gap in developing countries. Furthermore, psychological impacts could be reduced by providing additional support to at-risk communities, like managing human remains, rehearsing evacuation plans, preventing school closures, and setting up public health infrastructure for psychological assistance.
Extreme rainfall events have increased community and asset vulnerability to hazards like flash floods, particularly in mountainous regions. This study adopts SMART principles emphasizing inclusiveness and a bottom-up, community-led approach towards the development of an urban flood early warning system (EWS) in the Lesser Himalayas. We demonstrate how low-cost, community-engaged hydrometeorological monitoring captures the nuances of watershed and storm characteristics to improve urban flood early warning in data-scarce mountain regions. A hydrometeorological monitoring network comprising three LiDAR-based water-level sensors and four rain-gauges was deployed across the Bindal watershed, Uttarakhand, India. Observations reveal pronounced spatial variability, as seen in September 2022, with rainfall differences of 187 mm and inter-station correlations ranging from r = 0.82 to 0.20 over distances of 2.74–8.24 km. A southwestward movement of rainfall systems with an approximate 15 min lag was also observed within the watershed. In contrast, secondary datasets (GPM-IMERG and ERA5) failed to capture the magnitude and heterogeneity of rainfall patterns, raising concerns about their reliability for flash flood studies in mountainous areas. While developing an operational EWS is beyond the scope of this study, the findings provide foundational hydrometeorological insights and practical evidence to inform the implementation of SMART, community-centered urban flood EWS in Himalayan regions.
This study investigates the predictability of rainfall over Equatorial Africa (EA) and evaluates the forecasting performance of the European Centre for Medium-Range Weather Forecasts fifth-generation seasonal forecast version 5.1 (ECMWF-SEAS5.1) for the September–November (SON) period during 1981–2023 (43 years). The analysis considers two lead-times, focusing on initial conditions (ICs) from September and August. Regression, spatiotemporal and composite analyses are applied to highlight the relationship between extreme precipitation events over EA and the various associated atmospheric circulation drivers. The analysis reveals that ECMWF-SEAS5.1 successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over the region for both ICs, with notably better skills for September. In addition, the model effectively captures the teleconnections between EA rainfall and tropical sea surface temperature, including the Indian Ocean dipole and El Niño-Southern Oscillation, for both ICs. Regions with highest potential predictability skills coincide with regions where the model accurately represents strong (weak) composite rainfall anomalies, associated with strong (weak) moisture flux convergence (divergence) values, although the magnitude tends to be underestimated. However, other important observed features, such as the components of the African easterly jet, are well represented by the model for the September IC, but not for August. While many atmospheric mechanisms driving precipitation in the region are well simulated, their underestimation likely explains the model's general tendency to underestimate the magnitude of extreme rainfall events. The results of this study support efforts to improve forecast outputs in the national weather services across the region by integrating ECMWF model outputs into operational weather bulletins.
Extreme hydro-meteorological events can have a substantial impact on vegetation and ecosystems. In particular, with heatwaves and droughts projected to become more frequent due to climate change, understanding their effects on forests is crucial. In this study, we present a novel, large-scale, spatially explicit analysis of forest browning drivers across Europe, using a homogeneous and automated random forest modeling framework. By running independent models at each 0.5° grid point, we enable a region-specific comparison of hydro-meteorological drivers, capturing the diversity of forest responses across the continent. We identify the most relevant hydro-meteorological predictors of low normalized difference vegetation index (NDVI) events at monthly to annual timescales, using NDVI data from the Advanced Very High Resolution Radiometers (AVHRR) and climate variables from ERA5 and ERA5-Land reanalyses. These predictors include maximum temperature at 2 m precipitation, surface latent heat flux, and soil moisture up to 18 months before the observed browning. The random forest model exhibits a high prediction skill over most grid points in Europe, with a critical success index greater than 0.75 for 65 % of grid points. Notably, warm and dry conditions in spring and early summer emerge as essential predictors. We also uncover multi-year influences, with soil moisture and temperature anomalies from the preceding year playing a significant role, especially in Scandinavia and for coniferous forests. The random forest approach further reveals non-linear relationships, such as both positive and negative precipitation anomalies at different lags contributing to browning risk.
To support public safety and risk management in snow-covered mountains, regional avalanche forecasts must deliver reliable information on avalanche conditions, including regional danger levels representing the avalanche danger across warning regions. To promote greater transparency and consistency in avalanche danger level assessment across European avalanche warning services, a revised version of the EAWS Matrix was developed based on expert elicitation. The Matrix, a structured decision support tool that combines the Matrix input factors snowpack stability, the frequency of snowpack stability, and avalanche size, is used to determine the regional danger level. To support the development of the Matrix described in detail in the companion paper (Müller et al., 2025), we analyzed its operational use over the first three winters following implementation by 26 European avalanche warning services. Our aim was to identify inconsistencies in Matrix application and to provide empirically based guidance for further refinement. In operational use, forecasters predominantly assigned a consistent single danger level to most Matrix input factor combinations. However, two factor combinations (poor-some-size 2very poor-some-size3) were commonly assigned to one of two adjacent danger levels, indicating that these combinations function as transition zones between danger levels. Analyses based on finer-grained assessments of the input factors, that is, using sub-classes of the predefined coarse factor categories, revealed systematic tendencies within these classes. While application of the Matrix was relatively consistent for avalanche problems relating to dry-snow conditions, pronounced inconsistencies emerged in the classification of snowpack stability for wet-snow and gliding snow avalanche problems. These findings underscore the need for community-wide discussion and harmonization in Matrix application, particularly with respect to stability assessment practices. Assessing input factors at a finer scale shows potential for preserving important nuances in expert judgment and may enable more targeted guidance on when to assign the higher or lower of two danger levels indicated by the Matrix. However, because neither the danger level nor its input factors can be measured independently, a formal validation of Matrix logic and operational application is not possible. Despite some inconsistencies, our results suggest that European forecasters generally align with the Matrix logic, supporting its operational utility.
On 15 and 16 September 2022, a large area of the Marche region in central Italy experienced an exceptionally heavy rainfall event, with nearly 420 mm of rain falling in just six hours. The intense rains, in addition to causing 13 fatalities, triggered a large number of landslides in the mountain areas and flood events, mainly concentrated along the valleys of the hydrographic basins of the Metauro, Cesano, Misa, and Esino Rivers. The physiographic setting of the territory and the poor maintenance of both the main and secondary hydrographic network, often insufficient or entirely absent, exacerbated an already exceptional event. Although extraordinary, the natural event occurred in an area already affected by intense meteoric events in the past, the most recent of which had taken place just eight years earlier, in 2014.This study presents the results of the systematic and detailed surveys conducted in several sites affected by the storm, also providing detailed case studies. These surveys highlighted the critical issues detected during the disaster and identified appropriate intervention measures for reducing hydraulic risk in the Region. Many of these measures are innovative and will serve as guidelines for future land-use planning and for improving public education and awareness in flood-prone areas.
Mitigation of carbon dioxide diffuse degassing hazards remains underexplored in comparison to other volcanic hazards such as eruptions, despite their persistent and deadly impacts on communities living in active volcanic regions. This study uses a mixed-methods approach – combining quantitative surveys and qualitative interviews – to assess household perceptions of the implementation and effectiveness of risk mitigation measures against mazuku, a locally known hazard caused by emissions of carbon dioxide in the western part of Goma, Virunga Volcanic Province. Data were collected across three sampling zones, capturing demographic characteristics, eruption risk experiences, and perceptions regarding the implementation of mazuku risk mitigation measures.Findings reveal three locally recognised categories of mitigation measures: (1) emission-limiting measures, such as blocking gas with waste materials; (2) adaptive measures, such as house ventilation or living on upper floors; and (3) awareness measures based on orally transmitted local knowledge such as avoiding mazuku zone early morning. Financial resources, gender and prior risk experience – often linked to length of residence – emerged as significant positive determinants of both motivation and perceived efficacy for the first two categories. Perceptions of awareness measures showed no significant variation across zones even between demographic profile groups. Spatial patterns in perceived implementation and perceived efficacy appear to reflect collective community mitigation implementation rather than based on individual risk mitigation assessment, with some measures perceived as effective despite limited physical evidence of reduced gas concentration.The study supports the importance of co-creating mitigation strategies with local communities, adapting interventions to socio-economic realities and avoiding the importation of external mitigation measures that may lack contextual relevance. It also calls for complementary research measuring the actual effectiveness of these measures through physical monitoring of mazuku
Large magnitude snow avalanches (destructive size ≥ D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a two-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. However, placing this 2019 large magnitude avalanche cycle in historic context requires data prior to the instrumental record. Here, we paired tree disturbance data from dendrochronology (1698 to 2020) with meteorological data from the modeled and instrumental record (1901 to 2020) to understand the frequency and climate drivers of large magnitude snow avalanche cycles. The extensive number of downed trees from the 2019 avalanche cycle allowed us to collect 1,188 cross-sections and cores from 1023 individual trees within 24 avalanche paths across the state. From these samples we identified 4135 avalanche-related growth disturbances. We employed a strategic nested sampling design to spatially aggregate avalanche frequency from individual avalanche paths, to counties, to three major sub-regions (i.e., north, central, and south), and across the entire region (i.e., state of Colorado). Over a period spanning more than three centuries (1698 to 2020), we identified 76 avalanche years within 24 individual avalanche paths. Large magnitude avalanche event frequency varied across paths and sub-regions with several notable region-wide avalanche cycles. Both tree-ring and historical written records highlighted 1899 as a year with widespread and large magnitude avalanche activity similar to the March 2019 avalanche cycle. Since the early-20th century (1900 to 2020) regional avalanche probability declined significantly in parallel with decreasing snowpack throughout Colorado. Similarly, dominant avalanche regimes shifted from large magnitude regional cycles driven by above average snowfall years over most of the record, to regional avalanche cycles occurring more commonly in average to low snow years since 1988. In recent decades, a lack of December precipitation and above average March precipitation characterized years with regional large magnitude avalanche activity. Even with declining snow water equivalent, truly extreme regional large magnitude avalanche cycles remain possible – as demonstrated by the 2019 cycle. This underscores that rare but high-impact events are not eliminated by long-term trends. Understanding the changing snow and weather drivers and subsequent behavior of large magnitude avalanche cycles across multiple spatial scales may improve avalanche forecasting and the products and mitigations strategies developed by structural engineers to mitigate avalanche danger. This can decrease the avalanche risk to the public and improve infrastructure design in avalanche terrain.
The southern rim of the Indian Himalayas is highly susceptible to floods during the summer monsoon, making accurate streamflow modelling critical yet difficult due to complex terrain, climate variability, and sparse ground observations. This study uses a conceptual, semi-distributed hydrological model – enhanced with both static and dynamic glacier modules – to reproduce streamflow into the Alaknanda River at Rudraprayag gauge (∼8600 km2), a representative basin in northern India. The model was calibrated using multi-variable data, including satellite-based glacier water loss and actual evapotranspiration in addition to streamflow, also to address bias in the precipitation input. Despite inherent data uncertainties and simplified process conceptualization, the tailored hydrological modelling captured key features of observed streamflow and produced internally consistent water balance estimates. Multi-variable calibration provided a more plausible representation of hydrological processes and highlighted the value of using complementary satellite-based information in data-poor mountain regions. Parsimonious precipitation adjustment approaches are proven effective for hydrological applications. However, input data errors such as unaccounted-for heavy precipitation events limited short-term streamflow prediction accuracy. The study demonstrates that a viable, parsimonious modelling strategy can still be developed for data-scarce, monsoon-dominated Himalayan basins, offering insights into the spatiotemporal dynamics of streamflow generating processes, the inter-seasonal redistribution of precipitation, the role of cryosphere contributions, and flood simulation. The approach is transferable to other monsoon-dominated, glacier-influenced, and data-limited mountain catchments facing increasing hydroclimatic risks.
The Potenza earthquake of 5 May 1990 (Mw 5.77) was a significant event for southern Italy, despite its moderate magnitude and limited damage. Previous macroseismic studies of this earthquake contained inconsistent and often exaggerated macroseismic intensity values, particularly in areas far from the epicentre. Our analysis reveals that some overestimated intensities were caused by the overlapping damage patterns from previous earthquakes, due to the tendency to emphasize pre-existing or unrepaired damage, or attribute them to the most recent earthquake. In this respect, we re-evaluate all available data from original sources and compile a new and robust dataset comprising 1393 macroseismic data points (MDPs), assessed using both MCS and EMS-98 scales. This updated dataset shows a general decrease in higher intensity values compared to previous assessments, especially within 150 km of the epicentre. We also identify new data sources and remove unreliable entries. Recalculated macroseismic epicentres are in agreement with the instrumental estimate (i.e., 7.3 km using MCS data), while macroseismic magnitudes (Mw 5.05–5.19) are lower than the instrumental one. Additionally, we collect extensive observations of seismically-induced hydrological changes. These hydrological effects provide independent magnitude estimates ranging from M 4.9 to 5.7 for liquefaction and M 5.2 for streamflow responses. This comprehensive re-evaluation significantly enhances the accuracy and usefulness of the macroseismic and environmental data for future seismological research.
Hazard and risk from glacial lake outburst floods (GLOFs) in Bhutan have traditionally been assessed with limited consideration of the downstream exposure and vulnerability associated with individual lakes. However, exposure and vulnerability are key components of risk, and when explicitly attributed to each lake, can provide a more robust basis for prioritising hazard investigations and mitigation efforts. We modelled hypothetical GLOF scenarios for all glacial lakes with an area greater than 0.05 km2> 11 000 people, > 2500 buildings, > 250 km of road, > 400 bridges and ∼ 20 km2
Extratropical windstorms pose a major hazard in Northern Europe, with damage primarily arising from the combined effects of high sustained near-surface winds and extreme gusts. While future changes in mid-latitude storms are expected under climate warming, the implications for wind-related impacts remain uncertain. In this study, we investigate the response of thermodynamic warming to an intense historical storm using Storm Anatol, which severely affected Denmark on 3 December 1999, as a representative case.The storm is simulated using the convection-permitting numerical weather prediction model HARMONIE-AROME within a pseudo-global warming framework. Uniform temperature perturbations are applied throughout the atmosphere, sea surface, and skin layers, while specific humidity is adjusted to maintain relative humidity. Changes in wind speed, gusts, and the spatial and temporal extent of damaging wind conditions are analysed across a range of warming scenarios. To quantify integrated wind exposure, we employ a new cumulative metric applicable to both wind speed and gust diagnostics, referred to as the Cumulative Wind Exposure Index.The simulations show a systematic intensification of near-surface wind and gust speeds with increasing temperature, accompanied by an expansion in the spatial footprint and duration of extreme wind conditions. The cumulative wind exposure increases markedly in the warmer scenarios relative to the historical simulation. When interpreted in the context of established wind–damage relationships, these changes imply substantially enhanced potential for wind-related impacts.Overall, the results demonstrate that thermodynamic warming alone can significantly amplify windstorm exposure, highlighting the importance of considering compound wind characteristics, when assessing future wind hazards and their impacts in Northern Europe.
In the Kyrgyz Range of the northern Tien Shan, Central Asia, glacial lakes have been a focus of monitoring due to the increasing concern over glacial lake outburst floods (GLOFs) in the context of notable glacier recession. This study investigates (1) the historical evolution in the number and area of glacial lakes larger than 0.00045 km22. Over the same period, 190 of the 274 lakes that existed in 1968 had disappeared by 2000, while 154 new lakes had formed. Between 2000 and 2021, an additional 142 lakes disappeared, and 175 new lakes appeared. One lake that had vanished by 2000 reappeared by 2021. Rapid lake formation was associated with a 31 % reduction in glacier area over the past 50 years and with GMC evolution. The expansion of GMCs and melting of buried ice within GMCs produced new surface depressions (thermokarst features), which subsequently filled with water to form lakes, resulting in continuous glacial lake renewal. Thus, the continuous renewal of glacial lakes in the Kyrgyz Range results from the combined effects of glacier retreat, GMC expansion, and buried-ice melt.
Droughts in Europe are becoming increasingly frequent and severe, with the 2022 drought surpassing previous records and causing widespread socio-economic impacts. Using a Europe-wide survey (n = 481 across 30 countries) combined with hydroclimatic data (i.e., Standardized Precipitation Evapotranspiration Index; SPEI), we quantify how forecasting systems and Drought Management Plans (DMPs) affected response timing and perceived effectiveness. It specifically assesses the role of forecasting systems and Drought Management Plans (DMPs) in improving preparedness and in facilitating more effective and timely responses. Our findings show that organisations with forecasting systems or DMPs in place implemented drought response measures on average two and one months earlier respectively than those without, and rated their effectiveness higher. Additionally, the study investigates how drought management practices and awareness have evolved as a consequence of the 2018 European drought and how recent experiences shape water managers' perceptions, with 35 % of the respondents indicating introducing or updating their DMPs after the 2018 drought. The findings emphasize the necessity of a standardized, continent-wide drought risk management coordination to address the multifaceted nature of drought risk by integrating climatic and societal factors, and advocates for a Drought Directive as a means to achieve this. This research aims to inform policy development towards sustainable and holistic drought risk management, highlighting the crucial roles of preparedness, awareness, and adaptive strategies in mitigating future drought impacts.This study and its companion paper The 2022 drought needs to be a turning point for European drought risk management are the result of a study carried out by the Drought in the Anthropocene (DitA) network, an IAHS initiative.