Increases in extreme rainfall intensities as a result of climate change pose a great risk due to the possible increases in pluvial flooding. But evidence is emerging that the observed increases in extreme rainfall are not resulting in universal increases in flooding. Here, we begin by presenting historical evidence for changes in extreme rainfalls and floods discussing the underlying mechanisms for the changes, before examining the implications of climate change projections on engineering design.

Extreme rainfall is intensifying universally across the globe with more extreme events experiencing larger degrees of intensification. Simultaneously, and somewhat paradoxically, the magnitude of frequent floods (those expected to occur on average once per year) are in general decreasing, particularly in the tropical and arid regions of the world. We suggest this is likely due to the dominance of drying antecedent soil moisture conditions and shorter storm durations at higher temperatures offsetting any increases in rainfall intensity. However, for rare magnitude floods (those expected, on average, to occur less than once every twenty years) the increase in rainfall appears to outweigh any decrease in soil moisture or change in the temporal pattern of the storm.

Climate model projections, downscaled through a continental scale water balance model and locally calibrated rainfall-runoff models, show that future projections of flood responses follow historical trends – with the rarer the flood, the more likely it is to be increasing. To deepen our understanding, we focus our analysis on event runoff coefficients as an indicator of future runoff changes. Across Australia we find runoff coefficients are projected to decrease, that is, reduced runoff resulting from the same amount of rainfall. These results indicate drier conditions and a compounding of the reduced average rainfall and drier conditions already being experiences in many arid parts of the world.

With these historical changes and projections in mind we conclude with some insights and implications on how best to incorporate the additional uncertainty due to climate change when estimating floods for planning and design purposes. As floods constitute a large portion of the inflows into reservoirs, we suggest that future water resources planning will need to account for reduced runoff yields. To assess the potential impacts of future climate change for planning and design purposes we need to consider how changes to rainfall intensity vary with both storm duration and storm rarity, as well as how antecedent conditions influence the proportion of rainfall that appears as runoff. There remains significant work in adapting our current flood guidance to reflect these historical and projected changes.

How to cite: Wasko, C., Ho, M., Nathan, R., Sharma, A., Dykman, C., and Vogel, E.: Implications for engineering design of shorter more extreme rainfalls and increased flood variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1224, https://doi.org/10.5194/egusphere-egu23-1224, 2023.

We present the results of an NHESS (Natural Hazards and Earth System Sciences) 20th anniversary survey, in which 350 natural hazard community members responded to two questions: (Q1) “what are the top three scientific challenges you believe are currently facing our understanding of natural hazards” and (Q2) “what three broad step changes should or could be done by the natural hazard community to address natural hazards in achieving the Sustainable Development Goals”? We have analysed the data quantitatively and qualitatively. According to the 350 respondents, the most significant challenges (Q1) are the following (within brackets % of 350 respondents who identified a given theme): (i) shortcomings in the knowledge of risk and risk components (64 %), (ii) deficiencies of hazard and risk reduction approaches (37 %), (iii) influence of global change, especially climate change (35 %), (iv) integration of social factors (18%), (v) inadequate translation of science to policy and practice (17 %), and (vi) lack of interdisciplinary approaches (6 %). In order for the natural hazard community to support the implementation of the Sustainable Development Goals (Q2), respondents called for (i) enhanced stakeholder engagement, communication and knowledge transfer (39 %), (ii) increased management and reduction of disaster risks (34 %), (iii) enhanced interdisciplinary research and its translation to policy and practice (29 %), (iv) a better understanding of natural hazards (23 %), (v) better data, enhanced access to data and data sharing (9 %), and (vi) increased attention to developing countries (6 %). We note that while the most common knowledge gaps are felt to be around components of knowledge about risk drivers, the step changes that the community felt were necessary related more to issues of wider stakeholder engagement, increased risk management and interdisciplinary working.

How to cite: Malamud, B. D., Šakić Trogrlić, R., and Donovan, A.: Three Hundred and Fifty Views on what the Natural Hazard Community should do to Support the Implementation of the SDGs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9379, https://doi.org/10.5194/egusphere-egu23-9379, 2023.

The understanding of climate change impacts on tropical storms (TS) activity is crucial for better planning and risk assessment. Despite the theory and modeling suggest an increase in the TS activity with warming, the change in TS characteristics remain uncertain due to the limitations in the global climate models and tracking algorithms (tracker). Here, we performed tracker-inter-comparison and model-evaluation to find out the reliability of trackers and models at simulating the TS characteristics. We found that both trackers produce qualitatively similar results but quantitative different results due to different specifications of the algorithms and model bias. Our results show a decline in the frequency but rise in the strength of TS in the future for the Ganges and the Mekong basin.

How to cite: Ali, H., Fowler, H., Roberts, M., and Vanniere, B.: Change in the Tropical Storms activity in the future over the Ganges basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13826, https://doi.org/10.5194/egusphere-egu23-13826, 2023.

In the past 5 years large efforts have been made to improve our understanding of scale-interactions in the Earth system, and to better resolve atmospheric and oceanic meso-scale processes and their response to greenhouse warming. Here, we provide an overview of the technical and scientific achievements of a new collaboration between the IBS Center for Climate Physics (South Korea) and the Alfred Wegener Institute for Polar and Marine Research (Germany) to simulate the climate system at km-scale resolution using the AWI Climate Model, version 3 (AWI-CM3). AWI-CM3 is based on the OpenIFS-FESOM2 coupled model and we conducted several control simulations and transient greenhouse warming runs in a medium-resolution (MR) configuration (31 km in the OpenIFS and 5~27 km in the FESOM2, ‘Tco319-DART’). These simulations will be used in future as initial conditions for shorter coupled storm-resolving (SR) simulations with target resolutions of 9 km and 4 km (Tco1279 and Tco2559). Our presentation focuses on the performance of the MR configuration (with a throughput of about 7 simulation years per day on 350 nodes) and its representation of the mean climate, climate variability such as the El Niño-Southern Oscillation, tropical cyclone statistics. We will also present preliminary estimates of the expected scaling behavior of the AWI-CM3 SR configuration on different multi-Petaflop supercomputing systems.

How to cite: Lee, S.-S., Timmermann, A., Jung, T., Semmler, T., Chu, J.-E., Streffing, J., and Raavi, P. H.: Towards global km-scale greenhouse warming simulations with the AWI-CM3, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10715, https://doi.org/10.5194/egusphere-egu23-10715, 2023.

Measuring carbon emissions at high temporal and spatial resolution covering all parts of the globe is key to understanding the sources and sinks of carbon. These measurements are critical for informing both climate modeling and policy decisions to mitigate climate change. Fragmented data sources and the requirement of significant programming knowledge to retrieve, clean, and analyze data from existing data sources pose a significant barrier for climate researchers. As understanding of climate science becomes crucial for fields beyond geophysical sciences, it is especially urgent to build tools that can enable researchers from diverse academic backgrounds to analyze carbon emission data from satellites. 

In this presentation, we will present a novel, user-friendly platform which has pre-built functions and analysis pipelines allowing scientists to perform common data analysis tasks without the need to write code. The underlying data lake combines NASA’s Orbiting Carbon Observatory (OCO-2 and OCO-3) data with other data sources (e.g. MODIS-based fire data) that facilitate a more accurate and complete understanding of the dynamics of the carbon cycle and the factors that influence it. 

We highlight how our approach integrating data discovery, access, and analysis of climate data can help democratize climate research and inform policymaking.

Potential research questions that can be addressed using this approach include: 

(i) studying the impacts of fires on the global carbon cycle with MODIS fire products providing information on the location, intensity, and types of fires, 

(ii) studying the photosynthetic activity of plants and the carbon cycle assimilating OCO-2 SIF data. OCO2-SIF data measures the fluorescence emitted by plants as a result of photosynthesis, which can be used as an indicator of plant health and productivity and 

(iii) AI-assisted audit of industrial emissions incorporating publicly available data on critical CO2 emitting sectors e.g. power plants, steel mills, cement plants, atmospheric “spillover” from agricultural and forest fires, traffic emissions, demographic and economic variables, etc

How to cite: Odhiambo, M., Jain, R., Gorana, Y., Kaushal, N., and Mishra, A.: Measuring Carbon: A Tool for Analysing Gridded, Continuous, Carbon Measurements at High Temporal and Spatial Resolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15643, https://doi.org/10.5194/egusphere-egu23-15643, 2023.

Cervest, a climate intelligence startup, addresses the need for effective adaptation strategies by bridging the gap between disciplines. We use cutting-edge science techniques such as high-resolution convection-permitting models, remote sensing, hydrology, bayesian statistical modeling, machine learning, and data science to provide accurate, localized physical risk assessments for assets. Our climate intelligence product also accounts for assets vulnerability and multi-hazard, multi-risks. It can be used to assess not only the direct impacts of extreme events but also their indirect effects on supply chains and economic production networks. In this session, we will present our vision for the future of climate intelligence and share our novel probabilistic approach to assessing the impacts of climate change.

How to cite: Morris Boyne, E. P., Prodhomme, C., Pain, A. J., and Laken, B.: Bridging the Gap: Cervest's Climate Intelligence Approach for Effective Adaptation Strategies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17497, https://doi.org/10.5194/egusphere-egu23-17497, 2023.

Flood-related damage has increased dramatically in recent decades with direct and indirect economic impacts accounting for a large share of gross national products. Therefore, there is an urgent need to acquire more quantitative knowledge about flood damage to mitigate economic losses and reduce exposure to flood risk.

Firms are especially affected in case of flood. Still, flood damage assessment to businesses is hindered by the paucity of available data to characterize the enterprises, the lack of high-quality damage data to derive new models or validate existing ones, and the high variability of activity types which hampers generalization. This study contributes at improving knowledge about types and extent of damage of flood events on economic activities through the analysis of empirical data, focusing on direct damage and with specific reference to the Italian context.

In detail, the investigated dataset is composed by around a thousand of observed damage records collected after four flood events in Italy, along with additional information on the dimension (i.e., surface and number of employees) and the typology of the affected firms (i.e., NACE category) as well as on local water depth levels. Damage data are further classified in damage to the building structure, the stock, and the equipment.

Several econometric models have been implemented to better understand the links among the damage, the characteristics of the economic activities and the water depth. Since the heterogeneity of the affected firms is very high, in terms of surface, water depth levels, and number of employees and this might have had influence on the firm’s damage reporting, data has been analyzed with Heckman's selection bias model.

Obtained results show the absence of a constant return scale relationship, therefore, the total damage increases less than proportionally to the firm’s surface; the water depth plays an important role to explain the damage to the stock that results the more vulnerable asset.  Information on the NACE category made it possible to quantify the differences in damage by economic sector. The results reveal as the most vulnerable sectors for building structure, stock and equipment, respectively, human health, commercial, and manufacture. The accuracy of the prediction models represented by adjusted R² varies between 0.25, 0.36 depending on the damage component.

Despite characterized by significant uncertainty, obtained results supply a first model for the prediction of flood damage to firms for the Italian context, in the support of more effective risk mitigation actions. In fact, the model identifies the more vulnerable elements within the business sectors orienting modelers and decision-makers choices.

Acknowledgements:

Authors acknowledge with gratitude: Francesca Carisi, Alessio Domeneghetti and Armando Brath (from University of Bologna), Giovanni Menduni, Giulia Pesaro and Guido Minucci (from Politecnico di Milano), Simone Sterlacchini and Marco Zazzeri (from the Italian National Research Council) for their collaboration in collecting the observed damage records analysed in the research. A special thanks to Marta Galliani (from Politecnico di Milano) for providing the refined dataset used in this study.

How to cite: Ballocci, M., Molinari, D., Ballio, F., and Marin, G.: Econometric modelling for the  estimation of direct flood damage to enterprises: a local-scale approach from post-event records in Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11320, https://doi.org/10.5194/egusphere-egu23-11320, 2023.

The aviation-induced non-CO₂ climate effects, being responsible for two-thirds of aviation radiative forcing [1], have a direct dependency on atmospheric location and time of emissions. This implies that their associated impacts can be mitigated by planning climate-aware trajectories to avoid areas of airspace with large climate effects [2]. However, for the efficiency of such a mitigation strategy, one needs to consider various sources of uncertainty. In fact, if not accounted for within flight planning a priori, the rather immature scientific understanding of aviation-induced climate effects and uncertainty associated with emissions calculation and meteorological conditions can lead to inefficient aircraft trajectories. In addition, the mitigation potential achieved by the climate-optimal routing option increases the operating costs as the aircraft flies longer by re-routing climate-sensitive areas. In this respect, there is a need to plan robust climate-optimal aircraft trajectories having a minimum cost increase compared to the Business-as-usual (BAU) scenario.

In the current study, we present robust climate optimal aircraft trajectory planning, considering meteorological uncertainties. The airspace is assumed to be fully free routing. The information on the spatio-temporal dependency of aviation-induced climate effects is based on the latest version of the prototype algorithmic climate change functions (aCCF V1.1) [3]. An ensemble prediction weather forecast is used to characterize meteorological uncertainty. The flight planning objective is to find an efficient balance between the increased operating costs and the mitigated climate effects with acceptable ranges of uncertainty. The general approach for decision-making between conflicting objectives relies on building a Pareto-frontier by running the optimization many times, each corresponding to a weighting parameter in the objective function (see e.g., [4]). In this study, by proposing a more efficient modeling scheme in the definition of the aircraft trajectory optimization within the context of optimal control theory, we provide an ability to determine the highest possible mitigation potential with a user-specified limit on the increased operating cost and vice versa only in two iterations. In this approach, we define the “Lagrangian” term of the performance index (used to represents climate effects in the objective function) as an additional state variable, enabling to impose path and boundary constraints on the climate effect and its dispersion.

The effectiveness of the proposed approach is illustrated by considering the optimization of 10 flights on June 13, 2018, 0000UTC. Due to the strong variability among different members of relative humidity within the EPS weather forecast, the climate impact of contrails is highly uncertain. This in turn leads to high uncertainty in quantifying the net climate effects due to the dominant impact of contrails compared to the remaining species. For the considered case studies, it is shown that by employing the proposed trajectory optimizer, it is possible to minimize the climate effects while respecting the specified available extra operating cost in US dollars. In addition, the uncertainty on the quantified climate effects lies within the user-defined range, implying that the sensitivity of climate impact to the uncertainty in the forecasted weather conditions can be controlled.

How to cite: Simorgh, A. and Soler, M.: Cost-Effective Climate-Friendly Aircraft Flight Planning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17504, https://doi.org/10.5194/egusphere-egu23-17504, 2023.

How to cite: Baneshi, F. and Soler, M.: Network Assessment of the Aviation Climate Impact Considering the European Structured Airspace, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17517, https://doi.org/10.5194/egusphere-egu23-17517, 2023.

Floods have triggered about 166 million displacements globally since 2008, according to the Internal Displacement Monitoring Center (IDMC). Since 2008, most of the displacements triggered by floods have been localized in Asia and the Pacific and with an overall estimate of 129 million displacements. Small Island Developing States (SIDS) states bear the greatest displacement risk relative to their population size. Climate change combined with vulnerability of exposed infrastructure, and housing poses an existential threat for some Pacific islands that could see their populations move not only internally but also across borders. These magnitudes of forced movement highlight the importance of the phenomenon. In this context, we present a first attempt to estimate present and future riverine flood displacement risk at the national and sub-national level for two countries in the Pacific Ocean: Fiji and Vanuatu.

This work proposes a new methodology that provides a more comprehensive assessment of vulnerability in the context of disaster displacement risk and recognizes that people’s vulnerability depends on several physical and social factors. Such elements, however, are not yet included in standard risk models because difficultly quantifiable. While quantitative approaches to disaster displacement risk assessment generally consider the likelihood of housing rendered unhabitable as a proxy for displacement, this new methodology expands this concept by taking into account different elements that may trigger displacements or may increase the susceptibility to forced movement: 1) impact on houses; 2) impact on livelihoods; 3) impact on critical facilities and services.

A probabilistic risk assessment was performed at building scale in present and future climate conditions: under current climate conditions (1979-2016); under medium-term projected climate conditions (2016 - 2060); under long-term projected climate conditions (2061 – 2100). As results, displacement risk information - expressed in annual average displacement (AAD) and probable maximum displacement (PMD) - were calculated at national and subnational (NUTS2) scales, allowing for a geographic and quantitative comparison both within and between countries. The computation performed at building scale also allowed for result aggregation by sectors.

The outputs of the probabilistic model  show an important role of climate change in determining future likelihood to displacement due to riverine floods in the area. Flood displacement risk is likely to double by 2060 in both countries, and under the pessimistic long-term scenarios AAD is expected to triple in Fiji and quadruple in Vanuatu. These analyses are an important step in risk awareness processes and key to pushing for risk reduction, adaptation, and management mechanisms to be put in place.

How to cite: Trasforini, E., Rossi, L., Ponserre, S., Campo, L., Libertino, A., Ottonelli, D., and Rudari, R.: From vulnerability to vulnerabilities for a probabilistic flood displacement risk model: the case study of Fiji and Vanuatu., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5563, https://doi.org/10.5194/egusphere-egu23-5563, 2023.