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Katharina Klehmet, Peter Berg, Denica Bozhinova, Louise Crochemore, Yiheng Du, Ilias Pechlivanidis, Christiana Photiadou, Wei Yang
DOI
Abstract
Water and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re-forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re-forecasts into 1000-year-long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub-samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re-forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re-forecasts will diverge into independent samples. The meteorological seasonal re-forecasts are taken from the SEAS5 system, and hydrological re-forecasts are generated with the E-HYPE process-based model for the pan-European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100-year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes.
Monique M. Kuglitsch, Jon Cox, Jürg Luterbacher, Bilel Jamoussi, Elena Xoplaki, Muralee Thummarukudy, Golestan Sally Radwan, Soichiro Yasukawa, Shanna N. McClain, Rustem Arif Albayrak, David Oehmen & Thomas Ward
Artificial intelligence can help to reduce the impacts of natural hazards, but robust international standards are needed to ensure best practice.
M. Girons Lopez, T. Bosshard, L. Crochemore, I.G. Pechlivanidis,
Journal of Hydrology, Volume 650, 2025, 132504,
Abstract
Seasonal hydrological forecasts are vital for managing water resources and adapting to climate change, aiding in diverse planning and decision-making processes. Currently it is unknown how different forecasting methods, considering initial hydrological conditions and dynamic meteorological forcing, perform across the Swedish river systems, despite the significant socio-economic implications. Here we explore the drivers that mostly impact streamflow predictions and attribute the added quality of these predictions to local hydrological regimes. We compare the accuracy of seasonal streamflow forecasts driven by dynamic GCM-based meteorological forecasts with those generated by the Ensemble Streamflow Prediction (ESP) method. The analysis spans across about 39,500 sub-catchments in Sweden encompassing various climatic, geographical and human-influenced factors. Results show that the streamflow predictability varies in space due to the country’s diverse hydrological regimes. Regardless of the regime, updating the models to achieve the best possible initial conditions is crucial for enhancing forecast skill across all seasons for up to 4 months. GCM-based meteorological forcing notably improves short-term streamflow accuracy, showing significant impact particularly up to 4–8 weeks lead time depending on the local hydrological regime. In the snow-driven northern regions, ESP demonstrates superior performance over GCM-based streamflow forecasts in winter. Conversely, in the southern regions, where conditions are predominantly influenced by rainfall, GCM-based forecasts show higher performance up to 4–6 weeks ahead, regardless of the season. In river systems with high human influences, streamflow climatology outperforms ESP and GCM-based forecasts underscoring the challenges of accurately modelling artificial reservoir management and the need for better access to management data. These insights guide the development of an advanced national seasonal hydrological forecasting service, and highlight the need for region-specific forecasting strategies indicating areas where predictability is enhanced by improved monitoring, hence initial conditions, and/or meteorological forcings. Finally, we discuss the applicability of these forecasting methods to other regions worldwide, thereby placing our new insights within a global context.
Van Loon, A. F., Kchouk, S., Matanó, A., Tootoonchi, F., Alvarez-Garreton, C., Hassaballah, K. E. A., Wu, M., Wens, M. L. K., Shyrokaya, A., Ridolfi, E., Biella, R., Nagavciuc, V., Barendrecht, M. H., Bastos, A., Cavalcante, L., de Vries, F. T., Garcia, M., Mård, J., Streefkerk, I. N., Teutschbein, C., Tootoonchi, R., Weesie, R., Aich, V., Boisier, J. P., Di Baldassarre, G., Du, Y., Galleguillos, M., Garreaud, R., Ionita, M., Khatami, S., Koehler, J. K. L., Luce, C. H., Maskey, S., Mendoza, H. D., Mwangi, M. N., Pechlivanidis, I. G., Ribeiro Neto, G. G., Roy, T., Stefanski, R., Trambauer, P., Koebele, E. A., Vico, G., and Werner, M.: Review article: Drought as a continuum – memory effects in interlinked hydrological, ecological, and social systems, Nat. Hazards Earth Syst. Sci., 24, 3173–3205, https://doi.org/10.5194/nhess-24-3173-2024, 2024.
Abstract
Droughts are often long-lasting phenomena, without a distinct start or end and with impacts cascading across sectors and systems, creating long-term legacies. Nevertheless, our current perceptions and management of droughts and their impacts are often event-based, which can limit the effective assessment of drought risks and reduction of drought impacts. Here, we advocate for changing this perspective and viewing drought as a hydrological–ecological–social continuum. We take a systems theory perspective and focus on how “memory” causes feedback and interactions between parts of the interconnected systems at different timescales. We first discuss the characteristics of the drought continuum with a focus on the hydrological, ecological, and social systems separately, and then we study the system of systems. Our analysis is based on a review of the literature and a study of five cases: Chile, the Colorado River basin in the USA, northeast Brazil, Kenya, and the Rhine River basin in northwest Europe. We find that the memories of past dry and wet periods, carried by both bio-physical (e.g. groundwater, vegetation) and social systems (e.g. people, governance), influence how future drought risk manifests. We identify four archetypes of drought dynamics: impact and recovery, slow resilience building, gradual collapse, and high resilience–big shock. The interactions between the hydrological, ecological, and social systems result in systems shifting between these types, which plays out differently in the five case studies. We call for more research on drought preconditions and recovery in different systems, on dynamics cascading between systems and triggering system changes, and on dynamic vulnerability and maladaptation. Additionally, we advocate for more continuous monitoring of drought hazards and impacts, modelling tools that better incorporate memories and adaptation responses, and management strategies that increase societal and institutional memory. This will help us to better deal with the complex hydrological–ecological–social drought continuum and identify effective pathways to adaptation and mitigation.
Anastasiya Shyrokaya et al 2024 Environ. Res. Lett. 19 014037
Abstract
Despite the scientific progress in drought detection and forecasting, it remains challenging to accurately predict the corresponding impact of a drought event. This is due to the complex relationships between (multiple) drought indicators and adverse impacts across different places/hydroclimatic conditions, sectors, and spatiotemporal scales. In this study, we explored these relationships by analyzing the impacts of the severe 2018–2019 central European drought event in Germany. We first computed the standardized precipitation index (SPI), the standardized precipitation evaporation index (SPEI), the standardized soil moisture index (SSMI) and the standardized streamflow index (SSFI) over various accumulation periods, and then related these indicators to sectorial losses from the European drought impact report inventory (EDII) and media sources. To cope with the uncertainty associated with both drought indicators and impact data, we developed a fuzzy method to categorize them. Lastly, we applied the method at the region level (EU NUTS1) by correlating monthly time series. Our findings revealed strong and significant relationships between drought indicators and impacts over different accumulation periods, albeit in some cases region-specific and time-variant. Furthermore, our analysis established the interconnectedness between various sectors, which displayed systematically co-occurring impacts. As such, our work provides a new framework to explore drought indicators-impacts dependencies across space, time, sectors, and scales. In addition, it emphasizes the need to leverage available impact data to better forecast drought impacts.
Abstract
In a changing climate, the growing frequency and intensity of wildfires requires innovative services in order to efficiently remediate against their catastrophic socioeconomic threat. Under the framework of MedEWSa project, we capitalise upon the reliability of the FireHub platform to further enhance its capability and features along the full spectrum of pre-event to post-event time scales, catering: (i) prevention and preparedness, (ii) detection and response, as well as (iii) restoration and inducement of cascading effects.
During the pre-event stage, the fire risk over Attica Region is denoted on a daily basis in 5 risk levels over a detailed 500m grid spacing through a combination of high resolution numerical weather predictions, advanced ML models that utilize historic wildfire record analysis as well as a number of associated atmospheric parameters (temperature, wind speed and direction, precipitation, dew point) and datasets (DEM, land use / land cover) from 2010 onwards. During the event, continuous monitoring is provided through MSG/SEVIRI image acquisitions every 5 minutes from NOA’s in-house antenna, while the spatiotemporal fire-spread information is simulated through a dynamic modelling of the evolving fire. This feature is currently being further developed in order to be capable of performing “hot” starts along the incident and re-estimate based upon new hotspot retrievals from VIIRS imagery. Finally, the procedure of post-event burnt-scar mapping is currently being automated, to provide rapid footprints of the affected areas by utilising MODIS, VIIRS and Sentinel imagery and examine potential cascading effects through hazard assessment maps on landslides, soil erosion and floods. The whole suite will be hosted on a brand new fully responsive user interface that will provide detailed yet straightforward and easy to adopt information in order to enhance the decision making of policy makers and public bodies.
Knutzen, F., Averbeck, P., Barrasso, C., Bouwer, L. M., Gardiner, B., Grünzweig, J. M., Hänel, S., Haustein, K., Johannessen, M. R., Kollet, S., Müller, M. M., Pietikäinen, J.-P., Pietras-Couffignal, K., Pinto, J. G., Rechid, D., Rousi, E., Russo, A., Suarez-Gutierrez, L., Veit, S., Wendler, J., Xoplaki, E., and Gliksman, D.: Impacts on and damage to European forests from the 2018–2022 heat and drought events, Nat. Hazards Earth Syst. Sci., 25, 77–117, https://doi.org/10.5194/nhess-25-77-2025, 2025.
Abstract
Drought and heat events in Europe are becoming increasingly frequent due to human-induced climate change, impacting both human well-being and ecosystem functioning. The intensity and effects of these events vary across the continent, making it crucial for decision-makers to understand spatial variability in drought impacts. Data on drought-related damage are currently dispersed across scientific publications, government reports, and media outlets. This study consolidates data on drought and heat damage in European forests from 2018 to 2022, using Europe-wide datasets including those related to crown defoliation, insect damage, burnt forest areas, and tree cover loss. The data, covering 16 European countries, were analysed across four regions, northern, central, Alpine, and southern, and compared with a reference period from 2010 to 2014.
Findings reveal that forests in all zones experienced reduced vitality due to drought and elevated temperatures, with varying severity. Central Europe showed the highest vulnerability, impacting both coniferous and deciduous trees. The southern zone, while affected by tree cover loss, demonstrated greater resilience, likely due to historical drought exposure. The northern zone is experiencing emerging impacts less severely, possibly due to site-adapted boreal species, while the Alpine zone showed minimal impact, suggesting a protective effect of altitude.
Key trends include (1) significant tree cover loss in the northern, central, and southern zones; (2) high damage levels despite 2021 being an average year, indicating lasting effects from previous years; (3) notable challenges in the central zone and in Sweden due to bark beetle infestations; and (4) no increase in wildfire severity in southern Europe despite ongoing challenges.
Based on this assessment, we conclude that (i) European forests are highly vulnerable to drought and heat, with even resilient ecosystems at risk of severe damage; (ii) tailored strategies are essential to mitigate climate change impacts on European forests, incorporating regional differences in forest damage and resilience; and (iii) effective management requires harmonised data collection and enhanced monitoring to address future challenges comprehensively.
Xoplaki, E., Ellsäßer, F., Grieger, J., Nissen, K. M., Pinto, J. G., Augenstein, M., Chen, T.-C., Feldmann, H., Friederichs, P., Gliksman, D., Goulier, L., Haustein, K., Heinke, J., Jach, L., Knutzen, F., Kollet, S., Luterbacher, J., Luther, N., Mohr, S., Mudersbach, C., Müller, C., Rousi, E., Simon, F., Suarez-Gutierrez, L., Szemkus, S., Vallejo-Bernal, S. M., Vlachopoulos, O., and Wolf, F.: Compound events in Germany in 2018: drivers and case studies, Nat. Hazards Earth Syst. Sci., 25, 541–564,
Abstract
Europe frequently experiences a wide range of extreme events and natural hazards, including heatwaves, extreme precipitation, droughts, cold spells, windstorms, and storm surges. Many of these events do not occur as single extreme events but rather show a multivariate character, known as compound events. We investigate the interactions between extreme weather events, their characteristics, and changes in their intensity and frequency, as well as uncertainties in the past, present, and future. We also explore their impacts on various socio-economic sectors in Germany and central Europe. This contribution highlights several case studies with special focus on 2018, a year marked by an exceptional sequence of compound events across large parts of Europe, resulting in severe impacts on human lives, ecosystems, and infrastructure. We provide new insights into the drivers of spatially and temporally compound events, such as heat and drought, and heavy precipitation combined with extreme winds, and their adverse effects on ecosystems and society, using large-scale atmospheric patterns. We also examine the interannual influence of droughts on surface water and the impact of water scarcity and heatwaves on agriculture and forests. We assess projected changes in compound events at different current and future global surface temperature levels, demonstrating the need for improved quantification of future extreme events to support adaptation planning. Finally, we address research gaps and future directions, stressing the importance of defining composite events primarily in terms of their impacts prior to their statistical characterisation.
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