Since the early 2000s, the Lake Chad Basin (LCB) has witnessed a rising number of violent attacks from insurgent groups, as well as confrontations among armed militias. Often, civilians and their means of subsistence are the primary targets. While various geographical factors are suspected to influence the timing and location of conflicts, there remains a lack of consensus on what predictors must be considered for conflict modeling efforts. This research explores the importance of socioeconomic and environmental predictors for conflict in the LCB. We present a quantitative assessment of how these variables inform a machine learning model aimed at predicting conflict events in the region. We utilize documented conflicts in the LCB, as recorded in the Armed Conflict Location & Event Data, for both training and testing the model. The model is based on Earth observation-derived environmental and socioeconomic features from time series data spanning the last two decades. We analyze means, anomalies, and trends for each month and across the entire time series of environmental factors, which include air temperature, precipitation, potential and total evapotranspiration, soil moisture, surface water extent, and gross primary productivity in both irrigated and unirrigated areas. Additionally, we incorporate means, anomalies, and trends of socioeconomic factors such as population density, the Subnational Human Development Index, and the number of ethnic claims in specific areas. We also consider the means, anomalies, and trends of prior conflicts as indicators of a region's general instability. All these parameters are used in a random forest regression model to forecast conflict occurrence. We identify which features are significant to the model for each experiment using Shapley Additive Explanations for individual features. Our results indicate that it is crucial to consider both socioeconomic and environmental variables when discussing potential future conflicts. The quantitative insights highlighting the relative importance of factors across various domains can serve as a foundation for developing integrated approaches in future conflict modeling research. Therefore, we believe this information is valuable for researchers and stakeholders in sustainable development.
How to cite: Sogno, P., Höser, T., Fokeng, R. M., and Kuenzer, C.: What drives conflict in the Lake Chad Basin? – Assessing the impact of environmental and socioeconomic factors using Earth observation and machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4852, https://doi.org/10.5194/egusphere-egu25-4852, 2025.
Extreme event attribution (EEA) science estimates the influence of human and natural drivers on extreme weather. Collectively the field has demonstrated that human-caused warming has contributed to an increased likelihood and intensity of a range of extreme weather events across most inhabited regions. The geographically uneven nature of attribution capability globally presents ethical challenges for using attribution science in an equitable way and a range of recommendations on the extent to which EEA can inform decision and policy making have been made.
As an interdisciplinary team of climate attribution scientists and climate knowledge brokers we build on the discussion around the role for EEA across a range of decision-making contexts. We provide a novel ‘use case’ perspective, with a focus on how EEA can inform media and communication, humanitarian applications, adaptation action and risk management, legal challenge, and the Fund for Responding to Loss and Damage.
We explore the relative capabilities and limitations of different EEA methods within these use cases and identify how evidence gaps vary regionally. In particular, we focus on those gaps relevant to countries that face technical, computational or other capacity barriers to conducting and utilising EEA assessments.
We provide an example of an approach for bridging across disciplines to support practitioner and decision-making communities with the utilisation of scientific research relevant to their operating contexts. Ultimately the aim is to support the infrastructure necessary for climate attribution science to inform effective climate adaptation and mitigation action, accounting for the inherent limitations and uncertainties.
How to cite: Waterson, A., Sanderson, M., McCarthy, M., and Wilson, L.: Extreme event attribution: a utilisation perspective for decision-making communities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6509, https://doi.org/10.5194/egusphere-egu25-6509, 2025.
Design flood estimation is the process of calculating either a peak flow, volume, or level with a defined probability of exceedance or average recurrence interval for the purposes of infrastructure design, planning, or decision making. The methods to be used for calculating a design flood are generally prescribed in national-level flood guidance documents. While traditionally these documents have assumed that historical data is stationary and hence representative of the future planning horizon, this assumption is no longer valid. Climate change is affecting various flood risk drivers including increasing extreme rainfalls and changing antecedence moisture conditions, resulting in altered flood exceedance probabilities. There are now mandatory requirements for corporate reporting of climate related risks. The net result is that flood guidance across the world is being updated for climate change.
While state-of-the-art regional climate modelling is invaluable for developing projections of extreme rainfall and other flood risk drivers, there are limitations associated with any single line of evidence that suggest a structured approach for evidentiary synthesis is needed. This issue is compounded in Australia, a large geographic area with relatively low population density, meaning that high-resolution regional climate modelling is only feasible in high-priority regions. Moreover, the purpose of design flood guidance is to inform flood estimation practice, and thus care is needed to ensure information is presented in a form that can be integrated into standard flood estimation practice. To this end, an approach to updating Australian flood guidance was developed to include the following elements: (1) expert elicitation (2) stakeholder engagement (3) a scientific review of literature relevant to design flood estimation, and (4) guidance preparation with stakeholder engagement to close the feedback loop. The methodology included a meta-analysis to aggregate information on extreme rainfall changes across multiple lines of evidence. The meta-analysis concluded that hourly extreme rainfalls intensify by 15% per degree of global warming while daily rainfall intensify by 8% per degree of global warming.
The updated guidance resulted in several novel outcomes. Uplift factors are recommended to be applied to design rainfalls up to and including the Probable Maximum Precipitation (PMP), and factors are provided to adjust loss rates and temporal patterns used in hydrological modelling. To estimate current flood risks it is recognised that the intensity-duration-frequency (IDF) curves based on historic data needs to be adjusted upwards to account for the embedded trend due to global warming. While not user requests could be met, for example additional guidance on the choice of temperature projection, overall, the adopted methodology ensured that the update met the user needs while being consistent with the current science.
How to cite: Nathan, R., Wasko, C., and Westra, S.: Updating Australia’s Flood Guidance for Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7850, https://doi.org/10.5194/egusphere-egu25-7850, 2025.
Italy is a world leader in viticulture and wine business. However, the sector is facing challenges due to climate change, underscoring the necessity for reliable localised data on the future impacts of climate change on viticulture. The km-scale climate models, known as convection-permitting models (CPMs), are proven to provide a more reliable representation of atmospheric fields in high-resolution compared to coarser resolution models, but their use for impact studies is still limited. Here, we fill this gap by exploring the use of climate models, including CMP, in simulating wine grape productivity at a local scale in Italy.
In particular, the study utilises a range of temperature- and precipitation-based bioclimatic indices to analyse the potential impact of climate variability on viticulture. The indices are derived from the E-OBS dataset, the high-resolution climate reanalysis product SPHERA, the CNRM climate model at both regional (CNRM-ALADIN) and convection-permitting (CNRM-AROME) scale. The analysis employs both single and multiple regression approaches to establish the correlation between the productivity data provided by two Italian wine consortia and the bioclimatic indices over the period 2000-2018. The findings indicate a robust correlation between productivity and temperature-based bioclimatic indices, particularly within the context of northern Italy, with the multiple regression approach explaining between 45% and 64% of the total variability in productivity, depending on the case.
Climate models appear to be a useful tool for explaining productivity variance. The added value of CPM is evident when precipitation-based indices are relevant in controlling the yield variability. Moreover, one of the main advantages of using climate models, rather than re-analysis or observational data, is the possibility to examine future scenarios. Therefore, the CNRM-AROME simulation, driven by ERA-Interim, is used to build a multiple regression model for wine grape productivity in Italy in the period 1986-2005. The statistical model is then used to predict the future yield (2090-2099) under the RCP 8.5 emission scenario. The results are expected to provide valuable insights that will be useful for future adaptation strategies in the viticultural sector and pave the way for more widespread use of the CPMs in impact studies.
How to cite: Fosser, G., Massano, L. T., Gaetani, M., and Caillaud, C.: Assessing the Impact of Climate and climate Change on Wine Grape Productivity in Italy: The Role of Convection-Permitting Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10936, https://doi.org/10.5194/egusphere-egu25-10936, 2025.
Apart from the rainfall depth, the impact of an extreme precipitation event is influenced by its temporal profile, including the timing, magnitude, and duration of the peak intensity, which often occur on sub-hourly time scales. It is therefore crucial to accurately represent this time scale in climate models to increase the confidence in projected climate change signals of extreme precipitation.
High-resolution climate projections at the convection-permitting (CP) scale have been shown to improve the representation of precipitation intermittency, intensity, and diurnal cycle, and this greatly improves their representation of extreme precipitation at sub-daily time scales. However, previous studies of CP simulations have often been limited to hourly model outputs, and little is known about their representation of sub-hourly extreme precipitation.
Our study investigates sub-hourly precipitation in the KIT-KLIWA ensemble - a CP climate model ensemble over Germany with a resolution of 2.8 km. It is driven by 3 CMIP5 GCMs that are coupled to the regional climate model COSMO-CLM. We use a novel event-based approach to compare modelled extreme precipitation events at a temporal resolution down to 5 mins with station and radar observation networks in Germany for the historical period (1971-2000).
Our results show the benefit of using an event-based analysis for the understanding of modelled precipitation biases in CP climate model simulations. Moreover, we find that key features of the temporal precipitation event profiles - including the 5-min peak intensity and the timing of the bulk precipitation - are reproduced by the CP climate model simulations.
How to cite: Hundhausen, M., Fowler, H. J., Feldmann, H., and Pinto, J. G.: Precipitation event profiles in a sub-hourly convection-permitting climate model ensemble, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12151, https://doi.org/10.5194/egusphere-egu25-12151, 2025.
The Katari River Basin (KRB) is increasingly vulnerable to climate change, which affects water availability, water quality, and ecosystems. Economic activities are amplifying these issues by increasing water demand and pollution. Local indigenous communities are particularly impacted by these challenges, which arise from a combination of climate change effects, pollution, and poor water management practices. The absence of clear strategies for adaptation or mitigation further exacerbates these vulnerabilities. This study integrates impact modelling with a participatory framework for water resource management, the Climate Risk Informed Decision Analysis (CRIDA). It combines climate projections from regional climate models of the Coupled Model Intercomparison Project (CMIP), hydrological modelling using the Soil and Water Assessment Tool (SWAT+), and stakeholder engagement across diverse sectors of the basin. This approach allows to identify present and future challenges in the KRB and establishes adaptation pathways to reduce vulnerabilities. The first phase of the implementation of the CRIDA framework involved a workshop where maps were created by stakeholders highlighting challenges such as droughts, floods, water pollution, erosion, and solid waste transport. Collaborative discussions fostered empathy and a shared commitment to identifying solutions. Furthermore, modelling results indicate drying trends during the dry season and intensified wet periods, heightening risks of droughts, floods, and water scarcity. These findings, shared with stakeholders, enabled them to anticipate how current challenges may evolve and to develop informed strategies for resilience. This work establishes a critical foundation for adaptive water management by incorporating stakeholder insights and informed decision-making. Future discussions as part of CRIDA between local communities, municipal governments, and Bolivia’s Ministry of Environment and Water will benefit from this shared understanding of the KRB’s climate risks, challenges, and potential adaptation solutions. Moreover, the developed hydrological model will serve as a ‘’stress-testing’’ tool, whereby proposed solutions can be evaluated to find the most effective one.
How to cite: Teran Orsini, J. P., Agramont Akiyama, A., Villafuerte, L., and Peres-Cajias, G.: Integrating climate change impact modelling and local stakeholder participation for water resources management on the Katari River Basin, Bolivia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12334, https://doi.org/10.5194/egusphere-egu25-12334, 2025.
Climate change solutions rely on data from numerical models, remote sensing, and ground observations. Improvements in modeling (such as convection-permitting models) and measurement technology (such as new remote sensing instruments) lead to an ever growing confidence in our understanding in processes and changes in the climate system. However, all data have---and will remain to have---an associated uncertainty, and it is crucial that these uncertainties are taken into account when designing data-informed climate change solution.
Data producers usually strive to provide reliable uncertainty estimates alongside their products that should help inform decisions that are based on these products. However, data users often struggle to make sense of uncertainty information, because it is usually expressed as the statistical spread in the observations (for example, as random error standard deviation), which does not relate to an intended use of the data. That is, data and their uncertainty are usually expressed as something like “x plus/minus y”, which does not answer the really important question: How much can I trust “x”, or any use of or decision based upon “x”? As a consequence, uncertainties are often ignored altogether, and model predictions or observational data taken at face value.
In this talk, we demonstrate how looking at deterministic estimates from models or Earth observations alone can be misleading, and that any decisions based on these estimates are unlikely to be the best course of action. We then show how typical data representations like “the state of this variable is “x plus/minus y” can be transformed into more meaningful, actionable information, i.e., statements such as “the data and their uncertainties suggest that we can be “z” \% confident that…”. Finally, we discuss how such an approach can help data users make better decisions and design more reliable climate change solutions, thus maximizing the socioeconomic merit of Earth system science data. Adopting such an approach will be a transdisciplinary endeavour that requires close dialogues between data producers and decision makers.
How to cite: Gruber, A., Bulgin, C. E., Dorigo, W., Embury, O., Formanek, M., Merchant, C., Mittaz, J., Muñoz-Sabater, J., Pöppl, F., Povey, A., and Wagner, W.: Making sense of uncertainties: Ask the right question, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15968, https://doi.org/10.5194/egusphere-egu25-15968, 2025.
The Netherlands’ Southwest Delta (SW-Delta) faces complex challenges as climate change and sea level rise (SLR) intensify conflicts between flood protection infrastructures, ecological health, and economic activities. Consequently, integrating multiple disciplines and across sectors to address competing needs and interconnected challenges is becoming crucial. The Delta Wealth project, funded by the Netherlands Organization for Scientific Research (NWO), aims to develop adaptive climate adaptation strategies that enhance the long-term SW-Delta’s resilience by balancing a safe, ecologically healthy, and economically prosperous. This study aims to identify and evaluate the approach employed by the Delta Wealth project to bridge scientists, policymakers, and stakeholders in developing resilience strategies that balance ecology, safety, and economy, resulting in co-creating adaptive, scientifically sound, practical, and socially accepted resilience measures. We employed a literature review, interviews with researchers, biweekly meetings, expert meetings, project documentation analysis, and storyline communication to evaluate the opportunities and limitations of the collaborative methods applied by the Delta Wealth project. Our findings reveal that the Delta Wealth project applies a transdisciplinary approach, an approach that integrates diverse disciplines, practitioners, and stakeholders, and utilizes methods like co-creation processes, stakeholder engagement, and digital storyline tools to balance ecology, safety, and economy in the SW-Delta. They establish a science-policy-society interface (Learning Community), iteratively integrating knowledge produced by ongoing PhD students from different universities with multiple disciplines, including 1) flood risk management, 2) freshwater availability and salinization, 3) ecology, 4) social welfare, and 5) societal support. Research organizations like Deltares collaborate on expertise in freshwater, hydraulic, and flood risk modeling. Governmental institutions, including the Province of Zeeland, Rijkswaterstaat, and Waterboard Scheldestromen, provide insights into regional environmental management, national water management, flood defenses, and coastal protection. Private sector companies like HKV and Boskalis offer inputs on technical expertise in hydraulic engineering and flood defense design. Non-governmental organizations such as Het Zeeuwse Landschap and Bureau Waardenburg provide perspectives on environmental consultancy, ecological impacts, and landscape conservation. Stakeholder organizations, including Zeeuwse Land- en Tuinbouworganisatie (ZLTO) and Gebiedsoverleg Zuidwestelijke Delta, represent the agricultural sector and regional governance, respectively. They use ArcGIS StoryMaps, an interactive web platform based on simple narratives, visuals, and maps to communicate their findings. Our study demonstrates that their approaches effectively facilitate collaboration across sectors and support the development of climate adaptation strategies that acknowledge and navigate priorities. However, future research should broaden stakeholder engagement by prioritizing key disciplines and stakeholders and increasing the frequency of interactions through collaborative digital tools for more efficient communication. This paper provides insights and lessons that could be applied in other delta regions facing similar challenges and in similar transition processes to a long-term strategic delta planning approach.
Keywords: Climate Adaptation, Sea Level Rise, Transdisciplinary Approach, Stakeholder Engagement, Climate Resilience Strategies, Delta Wealth Project
How to cite: Nirwana, A. R., Maljers, Y., Piedelobo, L., and Terpstra, T.: Transdisciplinary Approaches for Climate-Resilient Adaptation: Insights from the Delta Wealth Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18666, https://doi.org/10.5194/egusphere-egu25-18666, 2025.
The EU’s CBAM is the first Carbon Border Adjustment Mechanism introduced in the world. By putting a fair price on the carbon emitted during the production of carbon-intensive goods that are entering the EU from non-EU countries, the CBAM aims to prevent EU producers from being put at a competitive disadvantage to imports from countries where carbon is not priced and, eventually, to encourage cleaner industrial production in non-EU countries. Therefore, we may say that the ultimate goal of CBAM is to promote corporations’ efforts in carbon reduction. Currently, six industries are subject to carbon border adjustment.While the potentials of CBAM receive great attention from governments, practitioners, and scholars, there are also many criticisms and skepticisms about the effectiveness of CBAM. One major skepticism is whether CBAM can actually promote significant carbon reduction. However, since CBAM will not be applied in its definite regime until 2026, there are few empirical studies that evaluate its effectiveness and impact. Therefore, the objective of this study is to empirically test the effectiveness of CBAM and the financial impacts of CABM on the affected firms.
The research design is based on the assumption that, since it takes significant time for corporations to effectively reduce their carbon footprint, corporations will invest efforts in carbon reduction and gradually exhibit lower carbon emissions well before 2026 if CBAM is going to be an effective mechanism or policy. We used the panel data from 2019 to 2022 to empirically analyze 144 firms that belong to those six industries that are subject to carbon border adjustment; 73 of them have been exporting to the EU (i.e., CBAM-affected) and 71 have not (non CBAM-affected).
In terms of policy effectiveness, we hypothesize that CBAM is effective. Empirically, if CBAM is an effective policy, the degree of carbon reduction of the CBAM-affected corporations after the announcement of CBAM in 2021 will be higher than that of non-affected corporations. In terms of the CBAM’s impacts on firms’ financial performance, based on the increasing trend in green consumerism, we hypothesized that the increased sales of the CBAM-affected firms due to green production will outweigh the cost of carbon reduction, yielding better financial performance. Empirically, if the hypothesis is true, the financial performance improvement of the CBAM-affected corporations after the announcement of CBAM in 2021 will be higher than that of non-affected corporations.
The empirical results show that, while both CBAM-affected and non-affected firms exhibit “similar” level of carbon reduction before 2021, the year of announcing CBAM, the CBAM-affected firms exhibit “higher degree” of carbon reduction than the non-affected firms after the announcement of CBAM. Therefore, we conclude that the data supports that CBAM is an effective policy in terms of reducing the carbon emissions of the CBAM-affected firms. The results also show that, while both CBAM-affected and non-affected firms exhibit “similar” level of financial performance before 2021, the CBAM-affected firms exhibit “higher degree” of financial performance improvement than the non-affected firms after the announcement of CBAM.
How to cite: Ho, S. P., Wang, C.-S., and Lai, W. Z. H.: The Effectiveness of EU’s Carbon Border Adjustment Mechanism (CBAM) and the Financial Impacts of CBAM on the Affected Firms: An Empirical Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19681, https://doi.org/10.5194/egusphere-egu25-19681, 2025.
Modelers often use “off the shelf” climate projections from downscaled Global Climate Models (GCMs) to simulate the effects of climate change on biophysical processes such as wildfire regimes. Many downscaled GCMs are available at the scales relevant for biophysical modeling (e.g., 4-km resolution). When it is too computationally intensive to run biophysical models using all GCMs, modelers may select a subset of GCMs to represent different climate futures. These models are often chosen to bookend a range of climate changes. This “model selection” process typically focuses on a limited number of future climate characteristics (e.g., temperature and precipitation trends) while ignoring others, such as the timing of drought. An equally important concern when simulating multiple study areas, is that model selection is conducted at the encompassing regional scale and then applied to smaller landscapes within the region. However, if time series characteristics vary among GCMs and/or spatially within regions, then the drivers of biophysical projections may be misattributed. To investigate the extent and effects of these concerns, we quantified how multiple time series characteristics vary among 20 downscaled GCM projections from the statistically downscaled Multivariate Adaptive Constructed Analog (MACA) dataset for four watersheds in the Sierra Nevada Ecoregion, and assessed how each GCM’s time series characteristics vary between watershed and regional scales. We then simulated how each of the 20 GCMs influenced fire regimes in one of the watersheds using the biophysical, fire regime model RHESSys-WMFire. Finally, investigated how different time series characteristics influenced fire size, number of fires, and the timing of fires.
We found that in some GCMs, periodic events occurred at the regional scale but not in all of the watersheds, whereas in others the inverse was true. When analyzing how different GCMs influenced fire regime projections, we found that even when two GCMs had similar temperature and precipitation trends, they could still produce very different fire regimes due to differences in other time series characteristics, such as precipitation variability. Our study demonstrates that it is essential for biophysical modelers to incorporate robust time series and spatial analyses into their GCM model selection approach in order to confidently interpret the mechanisms driving their climate change projections.
How to cite: Cale, A. and Hanan, E.: Reckoning with complexity: robust time series and spatial analyses are critical when selecting GCM models for biophysical modeling studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20648, https://doi.org/10.5194/egusphere-egu25-20648, 2025.
Climate change poses significant risks to economic systems and corporate financial performance, yet a critical gap remains in understanding how these risks evolve into economic disruptions and financial stability challenges. This study investigates the pathways through which physical risks, such as extreme weather events and rising global temperatures, and transition risks, including policy shifts, regulatory changes, and technological advancements, disrupt key economic elements like supply chains, resource availability, and market dynamics. It also examines how these disruptions propagate into financial risks increasingly reflected in corporate financial statements and disclosures. A central focus is the integration of standardized frameworks, particularly the Task Force on Climate-related Financial Disclosures (TCFD) and the International Financial Reporting Standards (IFRS) S2, to assess their role in addressing climate-related risks. The TCFD framework provides a structured approach for companies to disclose climate risks and opportunities, focusing on governance, strategy, risk management, and metrics. At the same time, IFRS S2 builds on these principles to establish a global baseline for sustainability-related financial disclosures, enhancing transparency and comparability across industries and regions. By mapping how climate risks impact economic and financial systems, the study evaluates the effectiveness of these frameworks in helping organizations identify vulnerabilities, improve corporate reporting consistency, and enhance resilience against disruptions. The findings provide actionable insights into how climate-related risks challenge economic stability and corporate performance while offering strategies for policymakers, businesses, and investors to mitigate risks, promote sustainability, and safeguard financial stability in an increasingly climate-vulnerable world.
How to cite: Lin, S. and Tung, C.: Bridging Climate Risks and Financial Stability: Analyzing Economic Disruptions and Corporate Resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7523, https://doi.org/10.5194/egusphere-egu25-7523, 2025.
Spatial resolution is a key factor in the modelling of convective rainfall extremes and their environmental impacts under current and future climate. Rapid developments in the field of high-performance computing have advanced dynamical downscaling of climate simulations to convection-permitting scale. Such high-resolution regional climate models hold great potential for improved modelling of convective processes through refined depiction of land surface properties and solving of the vertical momentum equation. However, these simulations currently operate on scales (~ 3km) still too coarse to serve as direct input for hydrological modelling of flash floods in fast responding catchments with diverse land use/ land cover (LULC). We investigate the added value of such uncorrected convection-permitting regional climate model (CPRCM) data for hydrological impact modelling in a catchment of medium topographic complexity in Germany and suggest an outline for an integrated modelling framework for very high-resolution simulation of hydrometeorological extremes.
The study compares reanalysis-driven hourly precipitation simulations from the non-hydrostatic model ICON-CLM 2.6.4 at 3 km resolution (ICON3km) and its nest model ICON-CLM 2.6.4 with parametrised convection at 11 km (ICON11km) to adjusted radar data upscaled to respective resolution over a study area of 13,210 km² embedded between the Leipzig Lowlands and the Elster/ Ore Mountains in East Central Germany. While ICON3km alleviated the drizzle bias, it strongly overestimated heavy precipitation both in intensity and frequency. As a result, discharge computed using the distributed, physically based hydrological model WaSiM for the enclosed small to medium-sized catchments (107 to 529 km²) of the Weiße Elster river basin showed a strong positive bias when simulated based on uncorrected ICON3km data. The results suggest a necessity of bias correction of the CPRCM data before use in flash flood modelling.
In fast responding catchments with diverse LULC, hydrological impact simulations require meteorological data on an even finer scale than provided by common CPRCM setups. We suggest an integrated modelling framework for rural catchments, combining statistically downscaled CPRCM data and fully distributed hydrological models. An adequate representation of cultivated steep catchment slopes is implemented by high-resolution parametrisation of surface, vegetation and soil properties, as gained from freely available remote sensing and cadastral data. Key hydrological processes, such as Hortonian overland flow and saturation, are accounted for through process-based representation in an open-source modelling environment. The framework is envisioned to be applied i.a. for local flood hazard assessment and for the study of drivers of runoff dynamics under current and future climatic conditions. Furthermore, it is to be employed for the assessment of the effectiveness of selected agricultural runoff countermeasures under different climate change scenarios.
How to cite: Wagner, O., Rechid, D., Conrad, O., Böhner, J., and Bouwer, L. M.: High-resolution fully distributed hydrological modelling of flash floods based on convection-permitting regional climate model data: An integrated modelling framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10140, https://doi.org/10.5194/egusphere-egu25-10140, 2025.
It is well known that urban parks cause a cooling effect on the urban climate and have a decisive role in the formation of the Park Cool Island (PCI) effect. Urban parks can help lower the Land Surface Temperature (LST), and consequently mitigate the effects of the Surface Urban Heat Island (SUHI).
Parks in Romanian cities vary in size, shape, vegetation density, and configuration, all of which influence their ability to produce a cooling effect. In the current study, various parks in Romania's major cities have been investigated to understand their capacity to locally alleviate/buffer the UHI effect and contribute to more comfortable thermal urban environments. In the present study, we also aimed to develop an algorithm to classify the cooling efficiency of parks. This algorithm incorporates various aspects, such as urban metrics (distance to the center of the urban heat island or UHI boundaries, distance to the center of densely built-up areas), urban built-up conditions (areas with extensive impervious surfaces, paved, asphalted, and concreted areas), or urban land cover (the percentage of the total area occupied by water bodies, wooded, grassed areas).
To be able to extract the percentage of the total area occupied by wooded, grassed, paved, asphalted, and concreted areas, a number of biophysical indices that aim to evaluate the amount of urban vegetation or the percentage occupied by different types of natural or artificial surfaces were used, such as the NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), LAI (Leaf Area Index), NDII (Normalized Difference Impervious Index), NDBI (Normalized Difference Building Index).
Overall, the analysis of multiannual Land Surface Temperature (LST) data extracted from Landsat 8-9 thermal bands in summer 2024 reveals that Romanian urban parks generally exhibit cooler and more stable thermal profiles compared to surrounding urban areas. The thermal difference between the different urban parks and the surrounding urban areas ranged between 1.5-3.5°C. This significant variation in the cooling effect depends strongly on the position of the parks within the urban landscape and the relation to the UHI boundaries (quasi-central, peripheral, or bordering), the compositional (ratio of green or artificial surfaces), and configurational (area, shape index) characteristics and tree density.
Parks with a quasi-central position in the urban landscape, with an area of more than 30 ha, a percentage of green areas of more than 70%, a rounded or slightly rectangular shape, and a high tree density generated the most substantial cooling effects, with temperature differences of up to 3.5-4 °C. The analyzed urban parks also generate a temperature gradient effect, whereby temperatures gradually rise as one moves away from the park into the surrounding urban environment. As a key finding, we outline that in Romanian cities, the cooling effect on air temperature decreases by approximately 1.3-1,6°C per 10 meters from the park's edge.
In conclusion, this research demonstrates the vital role of urban parks in mitigating UHI effects in Romania's main cities, emphasizing the need for strategic urban planning that maximizes their cooling potential.
How to cite: Corocăescu, A.-C., Sfîcă, L., Ichim, P., Grozavu, A., Miron, R., and Baltag, M.-A.: Assessment of the thermal capacity of urban parks to mitigate the urban heat island in the main cities in Romania, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12428, https://doi.org/10.5194/egusphere-egu25-12428, 2025.
The Vanuatu Klaemet Infomesen blong Redy, Adapt mo Protekt (Van-KIRAP) project demonstrated the transformative role of tailored climate information services in building resilience to climate variability and change. Focused on key sectors such as agriculture, water, fisheries, tourism, and infrastructure, the project integrated advanced tools and methods to empower decision-makers, communities, and individuals. Under Van-KIRAP I, the project aimed to enhance decision-making capacities by developing the OSCAR system, an agro-meteorological information platform, alongside tools like the Crop-Climate Diary (CCD) application. These tools leveraged experimental trials, model calibration for crops like taro and cassava, and APCC’s seasonal climate forecasts to deliver actionable insights. The results enhanced farmers’ ability to optimize crop yields and adapt to climate-related challenges. Based on the success of OSCAR, efforts are underway in collaboration with the Vanuatu government and SPREP to develop OSCAR-II, with a focus on strengthening community engagement and expanding to include cash crops, under Van-KIRAP II through the One Pacific Programme funded by the Green Climate Fund. This planned initiative aims to further improve localized decision-support systems, farmer engagement, and the integration of crop-climate insights into broader resilience strategies. The success of Van-KIRAP emphasized the importance of multi-stakeholder collaboration, sustained capacity building, and scaling of proven methods to other vulnerable regions in the Pacific. Recommendations include strengthening regional partnerships, investing in localized climate infrastructure, and refining user-centric tools to address community-specific needs. These efforts highlighted how climate information services can drive sustainable development and enhance resilience in the face of a changing climate.
How to cite: Chun, J. A., Park, S., Jung, I., Lee, S., Kim, J. H., Leo, P., Matou, M., and Seuseu, S.: Enhancing Agricultural Resilience in Vanuatu through Climate Information Services: Insights from the Van-KIRAP Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14266, https://doi.org/10.5194/egusphere-egu25-14266, 2025.
Extreme sub-daily precipitation can trigger natural disasters such as flash floods, urban floods, and debris flows, causing significant damage to infrastructure, homes, and livelihoods. With rising global temperatures, the atmosphere’s increased moisture-holding capacity enhances the potential for more intense and frequent extreme precipitation events. Sub-daily precipitation extremes are already increasing in magnitude, and the associated recurrence intervals are decreasing. A key component of climate change adaptation and resilience is quantifying the likelihood that future sub-daily extreme precipitation will exceed historical levels under different climate scenarios. Convection-permitting models (CPMs) are capable of resolving the physical processes driving precipitation extremes at high spatial and temporal resolutions. However, CPM simulations are computationally expensive and are available for a limited number of future scenarios. A recently proposed stochastic framework (TENAX) leverages temperature-precipitation scaling relationships and projected changes in daily temperature during wet days to estimate changes in extreme sub-daily precipitation. Can such a stochastic approach based on climate model simulations of temperature during wet days deliver projections of sub-daily extreme precipitation comparable to explicit simulations from CPMs?
This study evaluates the performance of TENAX in comparison to an ensemble of CPM simulations from the CORDEX-FPS Convection project over north-eastern Italy. Using historical (1996–2005) and far-future (2090–2099) CPM simulations under the RCP8.5 scenario and in-situ measurements of precipitation and temperature, we compare the return levels estimated using TENAX with the ones estimated with an extreme value method (SMEV) from the CPM ensemble. We assess two approaches for the application of TENAX: first, we train the model using CPM hourly precipitation and temperature for the historical period; then we train it using in-situ observations of the same quantities. In both cases, we project future return levels based on the changes in mean and variance of the daily temperature during the wet days as projected by the CPMs.
This analysis examines the potential of TENAX as a computationally efficient alternative to CPMs, as one of its key advantages is the ability to project sub-daily precipitation extremes even in the absence of CPM simulations, expanding its applicability to regions or scenarios where CPMs are not yet available.
How to cite: Vohnicky, P., Akbary, R., Dallan, E., Peleg, N., Marra, F., Fosser, G., and Borga, M.: Future sub-daily extreme precipitation: can a stochastic method based on temperature shifts agree with explicit simulations from an ensemble of convection-permitting models?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16274, https://doi.org/10.5194/egusphere-egu25-16274, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 2
EGU25-19957 | ECS | Posters virtual | VPS30
Leveraging Integrated Assessment Models to Assess Socioeconomic Impacts of Potential Stratospheric Aerosol InjectionFri, 02 May, 14:00–15:45 (CEST) | vP2.12
Stratospheric aerosol injection (SAI) is a proposed climate intervention that involves injecting aerosols (or aerosol precursors) into the stratosphere to reduce global warming and associated devastating impacts. In this study, I estimate the socioeconomic effects of future SAI using model results from the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS-SAI) and the Assessing Responses and Impacts of Solar Climate Intervention on the Earth System (ARISE-SAI) as inputs to the Climate Framework for Uncertainty, Negotiation, and Distribution Integrated Assessment model (FUND). GLENS-SAI and ARISE-SAI are an ensemble of SAI simulations between 2020 and 2100 (GLENS) and 2035-2064 (ARISE-SAI-1.5) using the Community Earth System Model, wherein SAI is simulated to offset the warming produced by a high-emission scenario (RCP 8.5) and a middle of the road (SSP2-4.5). FUND's components include agriculture, forestry, heating, cooling, water resources, tropical and extratropical storms, biodiversity, cardiovascular and respiratory mortality, vector-borne diseases, diarrhea, migration, morbidity, and rising sea levels. These aggregate impacts culminate in net damages, calculated as a percentage of gross domestic product (GDP). In both emission scenarios, global damages take a more linear trend in time, with up to 1% of global GDP loss under SSP2 - 4.5, as opposed to 6% under RCP8.5 (Figure 1). Under GLENS and ARISE SAI, damages follow a beneficial pathway, resulting in up to 0.6% and 1% savings of global GDP, respectively (Figure 1). Significant aspects of net damages include cooling and heating demand, agriculture, and water resources. Whereas cooling costs rise under both warming scenarios, savings accrue from avoided heating costs. However, SAI elicits the opposite effect. Additionally, the Dynamic Integrated Climate-Economy model, a neoclassical IAM, was tailored similarly to give further insight into damages. A nonlinear regression approach was then applied to climate and economic data to validate the results from the integrated assessment models. Finally, a cost-benefit analysis was performed on the GLENS and ARISE scenarios using operational and deployment cost estimates from Wagner and Smith (2018). SAI benefits (savings) are more than sufficient to cover the costs of operation and deployment. Even in the extreme case (GLENS-SAI), cost peaks at around 0.03% of global GDP (Figure 2). This analysis will be pivotal in advising policymakers on the economic outcomes and feasibility of SAI.
Figure 1 ( Damages as a percentage of global GDP. Left: SSP2-4.5 and ARISE-SAI. Right: RCP8.5 and GLENS-SAI)
Figure 2 (SAI costs as a percentage of Global GDP. Blue: ARISE-SAI, Yellow: GLENS-SAI)
References
Smith, W., & Wagner, G. (2018). Stratospheric aerosol injection tactics and costs in the first 15 years of deployment. Environmental Research Letters, 13(12), 124001.
How to cite: Ansah, P.: Leveraging Integrated Assessment Models to Assess Socioeconomic Impacts of Potential Stratospheric Aerosol Injection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19957, https://doi.org/10.5194/egusphere-egu25-19957, 2025.
