Raphael Spiekermann

Early warning systems play a crucial role in mitigating the impacts of severe weather events and related hazards. Traditional systems typically focus on meteorological forecasts and often do not account for the potential consequences that may follow, unlike impact-based approaches. In densely populated mountainous regions, such as the Alps, heavy precipitation frequently causes damaging mass movements. Since mass movement impacts ultimately result from a complex interplay of meteorological, geo-environmental, and socio-economic factors, warnings based solely on precipitation may have limited effectiveness. This study introduces a flexible, dynamic, and spatially explicit modelling framework for supporting impact-based early warning of precipitation-induced mass movement processes. The framework is tailored to three movement types: slides, flows, and falls. It integrates predisposing, preparatory, and triggering conditions, combining geo-environmental, meteorological, and exposure data to estimate daily impact potential for an Alpine core study area covering more than 91 000 km ² (Austria and South Tyrol), and subsequently explores the transferability of these relationships to the wider Alpine region, subject to important limitations. Using Generalized Additive Mixed Models (GAMMs), the approach captures non-linear relationships between impacts and predictors, ensuring interpretability and operational relevance. Further key elements include incorporation of potential runout paths while maintaining a basin-based landscape representation, focusing model training on relevant terrain and time-periods to avoid trivial predictions, generating interpretable outputs, and illustrating potential applicability through time-series predictive maps derived from hindcasting and “what-if” scenarios. Results indicate promising performance of slide- and flow-type models for the core study area, suggesting potential for operational application, while the fall-type model shows limited applicability for early warning, likely due to its lower sensitivity to short-term weather conditions. The primary contribution of this research lies in demonstrating a transferable and generalizable modelling framework, rather than delivering an operational regional warning system. Beyond early warning, the framework shows broad applicability for analysing spatio-temporal patterns, conducting trend analyses, and assessing climate change impacts. This research advances the fields of landslide prediction and impact-based warning by providing a transferable and generalizable approach, offering actionable insights for disaster risk reduction and climate adaptation strategies.

Machine learning based dynamic numerical climate multi-model ensembleweighting for high impact weather affecting the energy infrastructureThe infrastructure for renewable energy production and electrical grid itself is affectedby weather and climate conditions and vulnerable to high impact weather andcascading events. A reliable representation of the meteorological conditions leading tosuch events including their uncertainty is therefore needed for both weather and climatetime scales. Individual numerical weather and climate models exhibit systematicstrengths and weaknesses across scales, and geographic regions, despite thedifferences in model physics and parametrizations. One way to tackle this and avoidrunning single-model ensemble climate simulations are multi-model or poor-manensembles consisting of a set of different climate or weather models combined.Ensemble mixing offers a way to mitigate these weaknesses while providing uncertaintyquantification. Simply ensemble averaging can dilute forecast and climate signals andpenalize outliers and rare extremes. Different approaches have been proposed to tacklethis problem by assigning non-uniform weights to individual model fields andparameters, however, these methods often rely on domain knowledge such as modeldependencies [1,2].Here, we propose a machine learning-based multi-model ensemble model-mixingframework that is domain-agnostic and assigns spatially and temporally dynamicweights, in addition to an error metric. The domain of interest is the Alps, which exhibitchallenging terrain and localized extreme events, e.g. precipitation extremes that aredifficult to capture in conventional climate models. The CERRA reanalysis data at ~5.5km resolution serves as the target grid. We build a multi-model ensemble by combiningdynamically downscaled simulations of 2 m air temperature, precipitation, and windspeed from COSMO-CLM (6 km) and WRF (10 km). Each regional model is driven by twoCMIP6 global climate models (MPI-ESM and EC-Earth) under two scenarios (SSP1-2.6and SSP5-8.5), with an additional historical period used for training. Static informationsuch as orography and seasonal dependencies are considered. We evaluate theensemble’s performance on selected extreme events (e.g., heavy precipitation,windstorms, heatwaves) that can (and did) harm energy infrastructure, such as theEuropean derecho 2022.[1] Christensen, Jh, E Kjellström, F Giorgi, G Lenderink, and M Rummukainen. 2010.‘Weight Assignment in Regional Climate Models’. Climate Research 44 (2–3): 179–94.https://doi.org/10.3354/cr00916.[2] Merrifield, Anna Louise, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and RetoKnutti. 2020. ‘An Investigation of Weighting Schemes Suitable for Incorporating LargeEnsembles into Multi-Model Ensembles’. Earth System Dynamics 11 (3): 807–34.https://doi.org/10.5194/esd-11-807-2020.

Agricultural drought, defined here as insufficient soil water availability to sustain crop growth during the vegetation period, is an emerging climate risk in Lower Austria. Although the region has historically benefitted from relatively reliable summer precipitation, recent decades have seen an increasing frequency of dry spells and heat periods, with direct consequences for crop yields and farm income. This study presents results from a regional agricultural drought risk assessment that evaluates crop-specific changes in drought risk under current and future climate conditions and explores the potential moderating effects of nature-based solutions in the form of hedgerows.The assessment builds on the CLIMAAX drought risk workflow, which integrates climate variables, soil moisture dynamics, and evapotranspiration into a process-oriented crop production model. For application in Lower Austria, the workflow was adapted to incorporate regionally specific information, including irrigation infrastructure, soil characteristics, and dominant production systems. This allows drought risk to be assessed not only as a function of climate forcing, but also in the context of local agronomic and socio-economic conditions.At the core of the model is a daily simulation of crop water demand and supply throughout the growing season. Gridded climate data (temperature, precipitation, radiation, humidity, and wind) are combined with soil parameters such as available water capacity and rooting depth to determine periods of water stress during critical phenological stages. These deficits are then used to estimate potential yield losses, enabling spatially explicit estimates of drought impacts. Simulations are performed for a historical baseline as well as mid- and end-century climate scenarios under RCP4.5 and RCP8.5. The workflow was adapted to estimate annual yield losses, accounting for extreme drought years which would otherwise be masked by period averages.To evaluate the role of nature-based solutions in mitigating agricultural drought, the current extent of multifunctional hedgerows and a set of hedge scenarios were incorporated into the model. Microclimatic effects of hedgerows on evapotranspiration were represented using empirical change factors derived from field experiments in Lower Austria (Orfánus and Eitzinger, 2010). These factors describe a linear reduction in evapotranspiration in the lee of an 8 m high hedge, ranging from 0.5 at the hedge to 1.0 at a distance of 80 m, and were applied based on prevailing wind direction. High-resolution (10 m) simulations were made for selected drought years to capture local hedge effects on soil moisture and crop water stress.Results indicate declining productivity for maize, sugar beet, soybean, and sunflower across all climate scenarios, while wheat and barley show increasing yield potential compared to the baseline scenario. Hedge scenario simulations demonstrate a measurable reduction in drought stress during dry years, underscoring the potential of multifunctional hedgerows as a supportive landscape-scale adaptation measure that can simultaneously mitigate drought risk and soil erosion. While hedgerows may introduce trade-offs related to shading and competition under average moisture conditions, the modelling framework provides a robust basis for identifying climatic and spatial contexts in which their net effect on crop productivity is positive, thereby supporting targeted and climate-resilient implementation strategies.

Recent extreme precipitation events across Europe, including those in autumn 2024, underscore the need to strengthen proactive disaster risk reduction through improved impact-based early warning. In Austria, precipitation-related hazards such as landslides, flash floods and hailstorms repeatedly result in considerable impacts on people, infrastructure and economic assets. These challenges are expected to intensify under ongoing climate and environmental change. In response, European national meteorological and hydrological services are increasingly pursuing a paradigm shift in their warning strategies, from traditional weather warnings towards impact-based warnings (IbW). IbW focus on the consequences of weather events (“what the weather will do”) rather than solely on meteorological conditions (“what the weather will be”). However, data-driven and applicable approaches to predict precipitation-induced impacts at the national scale remain limited.The PRE4IMPACT-AT project is part of the Austrian Climate Research Programme (ACRP) and addresses this gap by developing explainable and user-oriented impact-based predictive models for precipitation-related hazards in Austria. This contribution presents the overall project concept and the methodological framework, exemplified through a recent transferable and generalizable approach (Steger et al., 2025; https://doi.org/10.5194s/egusphere-2025-4940). PRE4IMPACT-AT focuses on processes whose impacts that typically occur in temporal and spatial proximity to precipitation events, namely landslides, flash floods and hailstorms.Adopting a risk-oriented perspective, PRE4IMPACT-AT first conceptualizes impacts as the outcome of interacting atmospheric drivers, biophysical and geomorphological preconditions, and socioeconomic exposure and vulnerability. These relationships are formalized using an impact-chain framework, which supports the systematic identification and prioritization of key impact drivers for each hazard type. In subsequent steps, the selected drivers are parameterized and harmonized using a wide range of national datasets, including meteorological and geo-environmental information, as well as socioeconomic data. Model training relies on available national and international damage databases (landslides, flash floods) and agricultural insurance loss data (hail). Based on these datasets, explainable machine learning is applied to derive spatiotemporal predictive rules linking static and dynamic drivers to observed impacts. The resulting models aim to characterize typical impact conditions, with a strong emphasis on interpretability to enhance transparency and allow plausibility checks. The models are evaluated in hindcast and nowcast settings to assess their suitability for short-term impact-based warning applications. In addition, long-term analyses, synthesizing large numbers of hindcasts, are used to identify trends in critical conditions and emerging patterns. Finally, individual hazard-specific models are combined to provide a multi-hazard impact perspective. A core element of PRE4IMPACT-AT is continuous user engagement through iterative evaluation workshops with stakeholders who hold warning mandates. Overall, the project contributes to advancing impact-based forecasting, early warning and climate impact assessment by providing Austria with a transparent and operationally relevant foundation, while offering transferable insights for national services facing similar challenges across Europe. This project is funded by the Climate and Energy Fund in the course of the Austrian Climate Research Programme (ACRP) and the FFG (www.ffg.at). The FFG is the central national funding agency and strengthens Austria’s innovative capacity.

To improve and automate the shallow landslide component of the already operating Austrian early warning system AMAS (Austrian Multi-Hazard Impact-based Advice Service), regional precipitation thresholds are needed. Both the existing warning system and the precipitation thresholds developed in this study do not target individual landslides, but focus on severe regional events involving multiple landslides. Here, we present preliminary results of precipitation threshold modelling at national scale. Historic regional events were extracted from Austria-wide landslide inventories, including GEORIOS (GeoSphere Austria), the WLK database of the Austrian Torrent and Avalanche Control, as well as landslide inventories from different Austrian federal states. Landslide absence observations were identified by selecting landslide-free precipitation events with more than 20 mm in 24 h for which no indications of landslides were found after screening additional sources such as fire brigade reports, police records, local authorities, and VIOLA – the severe weather database of GeoSphere Austria.Taking into account the diverse environmental conditions under which landslides occur, Austria was divided into 21 geo-climatic regions using hierarchical cluster analysis, which considered geological, geomorphological, pedological and climatic factors, complemented by expert knowledge. While our aim is to model the precipitation thresholds for each of the 21 geo-climatic regions in Austria, we present preliminary results for two study areas of the Fischbacher Alps and the Vorarlberger Molasse. Lessons learned in these areas will be applied to nationwide modeling.Precipitation threshold modeling was performed using two different techniques: (i) a data-driven approach based on generalized additive models (GAMs), which combines triggering and antecedent precipitation, and (ii) a quantile regression approach, which defines the onset of relevant precipitation following a dry period. For both approaches, precipitation data from INCA (Integrated Nowcasting through Comprehensive Analysis, combined radar and station data) were used with hourly resolution. To optimize the results, the durations of triggering and antecedent precipitation in the GAM model, as well as the dry-period duration and the maximum precipitation threshold during the dry period in the quantile regression model, are systematically varied. Additional model variants consider the inclusion of the antecedent precipitation index (API) and the use of different landslide samples (e.g., representatively sampled points across different rainfall events) for both models. The best modeling results are selected via ROC-based cross-validation complemented with expert plausibility checks (e.g., longer antecedent precipitation for fine-grained soils). First GAM results showed very high predictive performance, with mean cross-validation AUROCs exceeding 0.9. Including a third variable in the model, namely peak 1-hour rainfall within the triggering window, alongside cumulative triggering and antecedent precipitation further improved the model, and the modeled relationships appeared plausible. Early quantile-regression estimates of intensity-duration (ID) thresholds are consistent with prior work (e.g., Guzzetti et al., 2008; Marra et al., 2014) but exhibit a steeper power-law decay. These results are sensitive to event-sample representativeness as well as the delineation of triggering rainfall, and they reveal spatial heterogeneity consistent with differing geological and meteorological predisposition.

Austria’s expansion of renewable energy generation, together with projected increases in climate variability under climate change, is expected to substantially increase the vulnerability of the energy system to weather-driven disruptions. This challenge is particularly acute in the Alpine region, where complex topography and land–atmosphere interactions drive highly heterogeneous and rapidly evolving meteorological conditions. These Alpine-specific processes give rise to localized extreme events that are difficult to forecast and pose significant challenges for energy system operation, infrastructure planning, and grid stability.The project EnergyProtect aims to identify present and future meteorological risk hotspots, defined as locations of renewable energy infrastructure with elevated exposure to weather conditions that can impair energy production or destabilize the electricity grid. We focus on hazardous meteorological phenomena relevant to wind and hydropower systems, including wind speed ramping, high wind and gust events, and high-precipitation episodes. Rapid wind speed changes can induce mechanical stress on wind turbines and other energy-related infrastructure, reduce operational efficiency, and trigger sudden power fluctuations that challenge grid balancing. Sustained high winds and gusts may lead to turbine cut-outs, structural damage, and pronounced power ramping events. In hydropower systems, extreme precipitation can increase tailwater levels, thereby reducing generation efficiency, while also elevating the risk of electrical faults and infrastructure damage in flood-prone areas.The meteorological hazard assessment combines several advanced modelling approaches. Key components include (i) physics-informed machine learning techniques to detect and classify patterns of adverse weather, (ii) an ensemble of dynamically downscaled climate simulations at convection-permitting resolutions to capture Alpine-scale processes, and (iii) probabilistic estimates of event frequency, return periods, and future changes in intensity. This framework enables a consistent characterization of both present-day and future extreme weather hazards, while explicitly accounting for model and scenario uncertainty.These meteorological datasets are subsequently integrated into a spatio-temporal exposure analysis of renewable energy assets to identify current and projected risk hotspots. We present preliminary results for multiple severity levels of wind speed and storm/gust ramping and high wind events with the potential to cause turbine cut-outs, efficiency losses, or grid destabilization. Using hourly meteorological datasets at spatial resolutions ranging from 1 to 30 km, we map the average annual occurrence of these risk events across Austria and quantify associated uncertainties. The results provide a robust basis for climate-resilient planning and adaptation strategies for Austria’s current and future energy system.

Early warning systems play a crucial role in mitigating the impacts of severe weather events and related hazards. Traditional systems typically focus on meteorological forecasts and often do not account for the potential consequences that may follow, unlike impact-based approaches. In densely populated mountainous regions, such as the Alps, heavy precipitation frequently causes damaging mass movements. Since mass movement impacts ultimately result from a complex interplay of meteorological, geo-environmental, and socio-economic factors, warnings based solely on precipitation may have limited effectiveness. This study introduces a dynamic, spatially explicit modelling framework for impact-based early warning of precipitation-induced mass movement processes, tailored to three movement types: slides, flows, and falls. The framework integrates predisposing, preparatory, and triggering conditions, combining geo-environmental, meteorological, and exposure data to estimate daily impact potential across the Alpine region (450,000 km²). Using Generalized Additive Mixed Models (GAMMs), the approach captures non-linear relationships between impacts and predictors, ensuring interpretability and operational relevance. Beyond accounting for meteorological, geo-environmental, and exposure information, further key elements of the approach include incorporation of potential runout paths while maintaining a basin-based landscape representation, focusing model training on relevant terrain and time-periods to avoid trivial predictions, generating interpretable outputs, and demonstrating applicability through time-series predictive maps derived from hindcasting and "what-if" scenarios. Results highlight the strong operational potential of slide- and flow-type models, while the fall-type model exhibits limited usability for early warning, due to its low sensitivity to short-term weather conditions. Beyond early warning, the framework demonstrates broad applicability for analysing spatio-temporal patterns, conducting trend analyses, and assessing climate change impacts. This research advances the fields of landslide prediction and impact-based warning by providing a transferable and generalizable approach, offering actionable insights for disaster risk reduction and climate adaptation strategies.

Early warning systems play a crucial role in mitigating the impacts of severe weather events and related hazards. Traditional systems typically focus on meteorological forecasts and often do not account for the potential consequences that may follow, unlike impact-based approaches. In densely populated mountainous regions, such as the Alps, heavy precipitation frequently causes damaging mass movements. Since mass movement impacts ultimately result from a complex interplay of meteorological, geo-environmental, and socio-economic factors, warnings based solely on precipitation may have limited effectiveness. This study introduces a dynamic, spatially explicit modelling framework for impact-based early warning of precipitation-induced mass movement processes, tailored to three movement types: slides, flows, and falls. The framework integrates predisposing, preparatory, and triggering conditions, combining geo-environmental, meteorological, and exposure data to estimate daily impact potential across the Alpine region (450,000 km²). Using Generalized Additive Mixed Models (GAMMs), the approach captures non-linear relationships between impacts and predictors, ensuring interpretability and operational relevance. Beyond accounting for meteorological, geo-environmental, and exposure information, further key elements of the approach include incorporation of potential runout paths while maintaining a basin-based landscape representation, focusing model training on relevant terrain and time-periods to avoid trivial predictions, generating interpretable outputs, and demonstrating applicability through time-series predictive maps derived from hindcasting and "what-if" scenarios. Results highlight the strong operational potential of slide- and flow-type models, while the fall-type model exhibits limited usability for early warning, due to its low sensitivity to short-term weather conditions. Beyond early warning, the framework demonstrates broad applicability for analysing spatio-temporal patterns, conducting trend analyses, and assessing climate change impacts. This research advances the fields of landslide prediction and impact-based warning by providing a transferable and generalizable approach, offering actionable insights for disaster risk reduction and climate adaptation strategies.

Effectively communicating uncertainties inherent to statistical models is a challenging yet crucial aspect of the modelling process. This is particularly important in applied research, where output is used and interpreted by scientists and decision-makers alike. In disaster risk reduction, susceptibility maps for natural hazards are vital for spatial planning and risk assessment. We present a novel type of landslide susceptibility map that jointly visualizes the estimated susceptibility and the corresponding prediction uncertainty, using an example from a mountainous region in Carinthia, Austria. We also provide implementation guidelines to create such maps using popular free and open-source software packages.

European weather services are currently transitioning from traditional weather warnings to impact-based warnings (i.e., from "what the weather will be" to "what it will do"). To inform on what impacts can be expected, meteorological data must be integrated with data on potential hazards and elements at risk.In this study, we developed three impact models on a daily scale to predict the impact of mass movement across the entire Alpine region (450,000 km²). The models focused on three major process classes (slide-types, flow-types, and fall-types) that impact infrastructure, such as buildings and roads. The study area was first divided into ~18,000 sub-basins, with potential process areas (PPAs) delineated in each basin using the angle of reach principle and random walk routing. PPAs enabled a tailored preparation of data describing environmental drivers (e.g., morphometry, land cover, lithology), dynamic meteorological data (e.g., antecedent precipitation, short-term precipitation, temperature effects), and exposure (e.g., number/density of buildings/roads within the PPA). The impact data consisted of precipitation-induced mass movements in Austria and northern Italy, covering more than 3600 basins. This training area was considered sufficiently representative of diverse Alpine environmental conditions to allow for spatial model transferability. Additional steps involving data sampling and the reclassification of predictor variables further supported the extension of model predictions beyond the training area. For example, lithology and land cover data was reclassified to ensure that each unit within the Alpine Space was adequately represented in the training data.Generalized additive mixed models (GAMMs) with automated variable selection were used to link binary impact data to driving factors. Rigorous evaluations, including cross-validation and feature importance assessments, showed high predictive performance (e.g., AUROCs > 0.8) and plausible relationships between drivers and impacts. For example, impact probabilities for slide-types were modeled to be highest when intense short-term precipitation followed high antecedent rainfall, particularly in drier regions that are less "adapted" to such events. Further, a higher number/density of buildings or roads within PPAs also increased impact likelihood, while effects related to morphology, temperature, lithology, land cover, and seasonality further supported model plausibility. The applicability of the model is presented from three perspectives: (i) "What-if" scenarios to explore how hypothetical changes in drivers (e.g., precipitation) affect impact probabilities; (ii) hindcasting to validate model predictions for past events and demonstrate potential for impact-based early warning; and (iii) trend analysis, using ~6,000 daily hindcasts (2005–2021) to reveal spatio-temporal trends through the lens of climate change.The research leading to these results has received funding from Interreg Alpine Space Program 2021-27 under the project number ASP0100101, “How to adapt to changing weather eXtremes and associated compound and cascading RISKs in the context of Climate Change” (X-RISK-CC).