ITS1.2/AS5.14 | Bringing together climate scientists, impact modellers, and economists to build knowledge to effectively deal with climate change

ITS1.2/AS5.14

EDI
Bringing together climate scientists, impact modellers, and economists to build knowledge to effectively deal with climate change
Convener: Conrad Wasko | Co-conveners: Giorgia Fosser, Haider Ali, Alessandro Caiani, Francesco Dottori, Hayley Fowler
Orals
| Mon, 24 Apr, 08:30–10:15 (CEST)
 
Room 0.94/95
Posters on site
| Attendance Mon, 24 Apr, 10:45–12:30 (CEST)
 
Hall X5
Posters virtual
| Attendance Mon, 24 Apr, 10:45–12:30 (CEST)
 
vHall AS
Orals |
Mon, 08:30
Mon, 10:45
Mon, 10:45
The UN development goals highlight we need to adapt to the reality of climate change. However, climate modelling, hydrology, hazard impact, risk and economic assessment – the disciplines needed for adaptation – all largely work in isolation with different terminologies and backgrounds. Moreover, until only recently, climate modellers were not able to generate long-term projections at the spatial and temporal resolution required for impact studies.

With the advent of kilometre-scale convection-permitting models CPMs, high resolution remote sensed data sets, and global sub-daily rainfall observations, we are now in a position to bridge the gaps between disciplines. We have substantially improved the representation of sub-daily precipitation characteristics and have model output at a spatial resolution closer to what impacts modellers, for example hydrologists, need.

Unfortunately, impact studies at regional or sub-regional scale, which are crucial for effective adaptation strategies, are often limited to the direct economic impact of specific extreme events occurred in the past, like hurricane Katrina. As a result, impact studies rarely consider the indirect socio-economic effect or/and apply a probabilistic methodology to assess the potential direct and indirect impacts of extreme events in the future in specific regions.

This interdisciplinary session invites contributions that address the linkages between high-resolution climate scientists, impact as well as macro-economic and economic network models and end users with a special focus on:
- Recent advances in climate modelling for impact studies, particularly using high resolution convection- permitting models.
- Bias correction techniques to overcome bias in climate models affecting impact models.
- Analysis of the uncertainty propagation from climate into impact models.
- Improved understanding of processes that will alter hazards resulting from climate change.
- Novel use of new and existing data sets in characterising and quantifying climate change hazards and their socio-economic impacts.
- Examples of good practice, storylines and communication to both stakeholders and policymakers.
- Novel probabilistic approaches to assess not only the direct impacts generated by extremes, but also their indirect effects due to their propagation along supply chains and economic production and financial networks.

Orals: Mon, 24 Apr | Room 0.94/95

Chairpersons: Giorgia Fosser, Hayley Fowler, Alessandro Caiani
08:30–08:40
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EGU23-10210
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ITS1.2/AS5.14
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ECS
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On-site presentation
Caleb Dykman, Ashish Sharma, Conrad Wasko, and Rory Nathan

Can total annual streamflow in any given year be largely characterised by a relatively small number of high flow events? A comprehensive assessment of this is of high value as there is evidence to suggest that as flood events increase in rarity a more consistent response between streamflow extremes and temperature increases can be established — providing greater reliability in projections of rare events. We propose here a novel methodology to characterise streamflow regimes in the context of total annual streamflow for water supply. Using the Australian Bureau of Meteorology’s Hydrologic Reference Station database, we developed annual event flow distributions that standardise the relationship between total annual streamflow and event flows. It was found that the annual event flow distributions are primarily a function of local climate and catchment size and were largely insensitive to interannual variability represented by the El Nino Southern Oscillation Index, mean annual temperature, or total annual rainfall volume. Statistically significant trends were found in the timeseries of annual event flow distribution values, signalling a move to a less even distribution in the southern latitudes and a more even distribution in the northern latitudes. Our results show that total annual streamflows can be characterised by a small number of high flow events. This suggests that for Australia’s most critical surface drinking water supply catchments the streamflow yields can be represented by changes in a few, high flow events, independent of interannual variability. As these relationships are non-stationary, they may provide a basis for understanding changes in water supply into the future.

How to cite: Dykman, C., Sharma, A., Wasko, C., and Nathan, R.: Can annual streamflow volumes be characterised by flood events alone?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10210, https://doi.org/10.5194/egusphere-egu23-10210, 2023.

08:40–08:45
08:45–08:55
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EGU23-9338
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ITS1.2/AS5.14
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ECS
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On-site presentation
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Eleonora Dallan, Giorgia Fosser, Christoph Schaer, Bardia Roghani, Antonio Canale, Marco Marani, Marco Borga, and Francesco Marra

Sub-daily extreme precipitation can generate fast hydro-geomorphic hazards such as flash floods and debris flows, which cause fatalities and damages especially in mountainous regions. Reliable projections of extreme future precipitation is fundamental for risk management and adaptation strategies. Convection-permitting climate models (CPMs) esplicitely resolve large convective systems and represent local processes, especially sub-daily extreme precipitation, more realistically than coarser resolution models, thus leading to higher confidence in their projections. Given their high computation cost, however, the available CPM simulations cover relatively short time periods (10–20 years), too short for deriving precipitation frequency analyses with conventional extreme value methods based on annual maxima or threshold exceedances.

In this work, we evaluate the potential of a non-asymptotic approach based on “ordinary” events, the so-called Simplified Metastatistical Extreme Value (SMEV), to provide information on the future change of short-duration precipitation extremes. We focus on a complex-orography region in the Eastern Italian Alps, where significant changes in sub-daily annual maxima have been already observed. The study is based on COSMO-crCLIM model simulations at 2.2 km resolution under the RCP8.5 scenario and uses three 10-year time periods: historical 1996-2005 (the control period), near-future 2041-2050 and far future 2090-2099. We estimate extreme precipitation for durations ranging from 1 h to 24 h and assess the projected changes with respect to the control period. Specifically, we analyze annual maxima, return levels up to 50 years, and the parameters of the statistical model. A bootstrap procedure is used to evaluate the uncertainty of the estimates, and a permutation test is applied to assess the statistical significance of the projected changes. We compare our results with a modified Generalized Extreme Value (GEV) approach, recently applied for the study of extremes in CPM future time periods.

We found that annual maxima and higher return levels exhibit a general increase in the future especially for the far future and the shorter event durations. On average, the magnitude of the far future change decreases with the precipitation temporal scale. The changes show an interesting spatial organization that can be associated with the orography of the region: significant future increases are mostly located at high elevations, while lowlands and coastal zones show no clear pattern.

This work shows that SMEV reduces the uncertainty in the estimates of higher return levels compared to GEV and can thus provide improved estimates of their future changes from short CPM runs. These findings advance our knowledge about the projected changes in extreme precipitation and their spatial distribution at the different time scales. They can thus help improving risk management and adaptation strategies.

How to cite: Dallan, E., Fosser, G., Schaer, C., Roghani, B., Canale, A., Marani, M., Borga, M., and Marra, F.: Future changes in sub-daily extreme precipitation over a complex-orography area from a convection-permitting climate model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9338, https://doi.org/10.5194/egusphere-egu23-9338, 2023.

08:55–09:00
09:00–09:10
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EGU23-16492
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ITS1.2/AS5.14
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On-site presentation
Compounding effects of drought and heat stress on macroeconomy
(withdrawn)
Andrej Ceglar, Martin Bruns, and Catalina Martinez Hernandez
09:10–09:15
09:15–09:25
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EGU23-11432
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ITS1.2/AS5.14
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ECS
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On-site presentation
Patrese Anderson, Frank Davenport, Kathy Baylis, and Shraddhanand Shukla

In this paper we combine traditional econometric time series techniques and machine learning algorithms to construct skillful monthly maize price prediction models for four southern African countries – namely, Malawi, Mozambique, Zambia, and Zimbabwe. Theoretical models of price transmission commonly assume that shocks are transmitted from an external market (typically modeled as the world market) to the largest domestic city or port within a country and then, depending on the degree of market integration within the country, these shocks are transmitted to local markets. However recent evidence suggests that internal shocks have a larger impact on prices than external shocks. In an analysis of 554 local commodity markets across 51 countries during the period between 2008-2012, Brown and Kshirsagar (2015) find that 20% of local market prices were affected by domestic weather disturbances in the short-run in comparison to 9% by international price changes. This finding has prompted more recent literature to relax assumptions about international price transmission to investigate how shocks are transmitted through local and regional markets. 

Here we investigate the effects of domestic weather disturbances on regional maize price transmission. We then use these results of to build skillful price prediction models that use limited price data, weather disturbances, and other readily accessible free secondary data to predict monthly grain prices three, six, and nine months ahead in four Southern African countries. The collection of subnational price data in developing countries is costly and often difficult to obtain. We limit the amount of price data used by first determining if monthly price series in each country co-move and how these co-movements are influenced by domestic climate disturbances. We then use bivariate error correction models to both assess whether price movements in each country follow well-defined paths and identify influencing and influenced markets.

From this analysis we classify markets that act as price anchors in each country. Because local climate conditions have been found to affect and accurately predict agricultural prices, price dispersion, and yields in developing countries we use climate conditions at both the market location and anchor market locations as predictors. We show that during periods classified by drought, price prediction models using anchor market prices and high-resolution climate data have high degrees of predictive accuracy. We hope the results presented in this paper will assist policymakers, government stakeholders, and researchers in systematically constructing subnational price forecasts with minimal price data to be used in early warning and food security monitoring models.

 

How to cite: Anderson, P., Davenport, F., Baylis, K., and Shukla, S.: Using domestic weather disturbances and price transmission for maize price predictions in Southern Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11432, https://doi.org/10.5194/egusphere-egu23-11432, 2023.

09:25–09:30
09:30–09:40
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EGU23-7110
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ITS1.2/AS5.14
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ECS
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On-site presentation
Chantal Hari, Inne Vanderkelen, Markus Fischer, and Édouard Davin

Biodiversity loss, land degradation, and climate change are acknowledged environmental challenges faced by humanity. Human activities including land-use changes are key stressors for biodiversity, thus, future projections of biodiversity impacts need to include both climate change and land-use change. While a lot of studies focused on mapping and projecting the vulnerability of multiple species based on different climate mitigation scenarios or warming levels, land-use trajectories are often not included in these projections. Recent work made first steps to address these deficiencies. For example, Hof et al. (2018) evaluated potential future impacts of climate and land-use changes on global species richness of terrestrial vertebrates under a low and high emission scenario. However, they used the same land-use change assumptions for both emission scenarios. In this study, we aim to fill the described research gap by combining future climate scenarios and a matrix of land-use projections derived from integrated assessment modeling (IAM) to estimate the fractional land-use patterns, underlying land-use transitions, and key agricultural management information, to assess the impact of climate change on biodiversity and quantify the additional impact of land-use change.

 

To this end, we use the global simulations with a species distribution model from the Hof et al. (2018) study forced by four GCMs and both RCP2.6 and RCP6.0 climate scenarios following the ISIMIP2b simulation protocol and apply a land-use filter on the species occurrence probabilities to determine the implications for the world’s amphibians, mammals and reptiles at a 0.5° resolution. The land use data used to include future projections of land-use change is the Land Use Harmonization dataset v2 (LUH2). LUH2 reconstructs and projects changes in land use among 12 categories. To match the species’ habitat preferences, data from IUCN Habitat and Classification Scheme for each species is mapped onto the 12 land-use types represented in the LUH2 dataset according to the conversion table from Carlson et al. (2022). The land-use data is then used to refine the climatic envelope and filter out regions where species cannot persist.

 

This approach allows to quantify the change of the proportion of affected species distributions between different climate and land-use scenarios and combinations of both. In addition, it provides quantitative information on the impact of future climate change on biodiversity accounting for the combination of land-use change projections and climate-driven species distribution models.

 

Key Reference:

Hof, C., Voskamp, A., Biber, M. F., Böhning-Gaese, K., Engelhardt, E. K., Niamir, A., Willis, S. G., & Hickler, T. 2018: Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity. Proceedings of the National Academy of Sciences of the United States of America, 115(52), 13294–13299.

How to cite: Hari, C., Vanderkelen, I., Fischer, M., and Davin, É.: Combining future projections of land-use and climate change to assess their impact on biodiversity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7110, https://doi.org/10.5194/egusphere-egu23-7110, 2023.

09:40–09:45
09:45–09:55
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EGU23-7522
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ITS1.2/AS5.14
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ECS
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On-site presentation
Marcello Arosio, Alessandro Caiani, Giorgia Fosser, and Jlenia Di Noia

Climate change is causing increased risks linked to extreme weather events. In order to develop effective adaptation strategies and policies, there is an urgent need for methodologies able to assess how the socio-economic risks associated with extreme climate-related events will change in the coming decades especially at local scale. The development of these methodologies require the expertise from many different scientific disciplines, including: modelling of global and local climatic phenomena, assessment of the intensity and probability of extreme events, representation of their impacts on the society and quantification of the associated risk.

In this work we propose a methodological chain linking the risk of extreme events in a changing climate with both direct and indirect impacts on the socio-economic sector from regional to local scale. The proposed chain integrates the knowledge of three scientific fields: climatologists, engineers and macro-economist. Here, we present agreements and differences between communities (e.g., aim, terminology, methodology, etc.), and evaluate advantages and constrains of the combined used of high-resolution regional climate models, engineering risk assessment models and economic input-output models compared to the state of the art in this field.

To illustrate the advantages of the proposed methodology and its practical feasibility, we present preliminary results from an applied pilot study in the Italian context.

How to cite: Arosio, M., Caiani, A., Fosser, G., and Di Noia, J.: A transdisciplinary chain to assess the risk of direct and indirect impacts linked to extreme climate events from regional to local scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7522, https://doi.org/10.5194/egusphere-egu23-7522, 2023.

09:55–10:00
10:00–10:10
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EGU23-7346
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ITS1.2/AS5.14
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Highlight
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Virtual presentation
Anne Jones, Andrew Taylor, Junaid Butt, Blair Edwards, Jorge Luis Guevara Diaz, and Priscilla Barreria Avegliano

Climate change is driving increased urgency for better quantification of climate hazards and their impacts for stakeholders across multiple economic sectors. Flooding has been highlighted as one of the most significant climates risk to UK economic infrastructure, with costs expected to increase with climate-driven changes to rainfall, such as increased intensity of summer storms. To accelerate climate change adaptation and enable economic resilience to climate change impacts, close collaboration is needed between climate scientists, impact modellers, and stakeholders, and technology advances can support this by enabling and streamlining the process of developing and deploying climate impact modelling workflows to translate complex datasets and scientific models into actionable information.

In this presentation, we describe the application of such a technology for the case of pluvial flooding, undertaken as part of the IBM Research and Science and Technology Facilities Research Council partnership, the Hartree National Centre for Digital Innovation (HNCDI), a 5-year programme established to develop and apply new technology to key economic challenges in the UK. Here, we model pluvial flood hazard for a case study region in northeastern England, using a 2-d physical simulation model of flood inundation, driven by open-access geospatial and climate datasets. Flood hazard maps are translated to impact using open asset location data and damage functions.

We consider the sensitivity and scalability (in terms of computational cost) of the hazard and impact predictions to multiple factors, including (1) DEM/DSM representation of land surface (2) soil and land use parameterisation, and (3) model spatial resolution. We also contrast the use of drivers in the form of extreme rainfall scenarios created using a traditional design storm approach, and ensembles of synthetic storms from a stochastic weather generator, both derived from hourly 1km gridded rainfall observations. Finally, we reflect on key gaps to be addressed in the models, data and technology to meaningfully inform climate adaptation across industry sectors.

How to cite: Jones, A., Taylor, A., Butt, J., Edwards, B., Diaz, J. L. G., and Avegliano, P. B.: End-to-end modelling of flood risk and impact for climate change resilience, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7346, https://doi.org/10.5194/egusphere-egu23-7346, 2023.

10:10–10:15

Posters on site: Mon, 24 Apr, 10:45–12:30 | Hall X5

Chairpersons: Conrad Wasko, Giorgia Fosser, Haider Ali
X5.108
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EGU23-1224
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ITS1.2/AS5.14
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ECS
Conrad Wasko, Michelle Ho, Rory Nathan, Ashish Sharma, Caleb Dykman, and Elisabeth Vogel

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.

X5.109
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EGU23-9379
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ITS1.2/AS5.14
Bruce D. Malamud, Robert Šakić Trogrlić, and Amy Donovan

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.

X5.110
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EGU23-13826
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ITS1.2/AS5.14
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ECS
Haider Ali, Hayley Fowler, Malcolm Roberts, and Benoit Vanniere

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.

X5.111
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EGU23-9246
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ITS1.2/AS5.14
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ECS
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Jin Maruhashi, Mariano Mertens, Volker Grewe, and Irene Dedoussi

Aviation’s contribution to anthropogenic global warming is estimated to be between 3 – 5% [1]. This assessment comprises two contributions: the well understood atmospheric impact of carbon dioxide (CO2) and the more uncertain non-CO2 effects. The latter pertain to persistent contrails and pollutants like nitrogen oxides (NOx), water vapor (H2O), sulfur oxides (SOx) and soot particles. NOx emissions are involved in non-linear processes that result in the short-term production of ozone (O3) and longer-term destruction of methane (CH4), stratospheric water vapor (SWV), and primary mode ozone (PMO). The aviation-attributable impacts arising from this short-term increase in O3 can vary by more than a factor of 1.5 depending on the selected modelling approach. This O3 increase is associated with the second largest warming effect across aviation’s main climate forcers [1]. We therefore quantify this figure using three modelling approaches (an Eulerian and a Lagrangian tagging scheme as well as a perturbation approach) at three potential aircraft cruise altitudes (200, 250 and 300 hPa) at which NOx pulse emissions are introduced in the Americas, Africa, Eurasia and Australasia. In general, the tagging method computes the contribution by an emission source to the concentration of a chemical species while a perturbation approach consists in calculating the total impact of an emission to the concentration of a species by means of subtracting two simulations: one with all emissions and a second without the specific source’s emissions. We compare results from Eulerian and Lagrangian simulations using the same climate-chemistry code: the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model. With the Eulerian setup, we are able to capture non-linear processes and feedback effects, but not track the transport of emitted species in detail. The Lagrangian setup [2], on the other hand, allows for the accompaniment of thousands of air parcel trajectories, but at the cost of assuming a simplified linear chemistry mechanism. We find that the Lagrangian tagging approach provides the largest estimates for O3 production and radiative forcing (RF), followed by the Eulerian tagging scheme and lastly by the perturbation method. We therefore investigate the appropriateness of each of these in quantifying aviation’s total and marginal climate effects by addressing the following research questions: 1) By how much are the estimates for the short-term NOx-induced O3 perturbation and consequent RF varying across the three modelling approaches and why? 2) How does this RF vary with emission altitude within the upper Troposphere/lower Stratosphere (UTLS)?

[1] Lee, D.S., Fahey, D.W., Skowron, A., Allen, M.R., Burkhardt, U., Chen, Q., Doherty, S.J., Freeman, S., Forster, P.M., Fuglestvedt, J., Gettelman, A., De León, R.R., Lim, L.L., Lund, M.T., Millar, R.J., Owen, B., Penner, J.E., Pitari, G., Prather, M.J., Sausen, R., and Wilcox, L.J.: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018, Atmos. Environ., 244, 117834, https://doi.org/10.1016/j.atmosenv.2020.117834, 2021.

[2] Maruhashi, J., Grewe, V., Frömming, C., Jöckel, P., and Dedoussi, I. C.: Transport patterns of global aviation NOx and their short-term O3 radiative forcing – a machine learning approach, Atmos. Chem. Phys., 22, 14253–14282, https://doi.org/10.5194/acp-22-14253-2022, 2022.

How to cite: Maruhashi, J., Mertens, M., Grewe, V., and Dedoussi, I.: Assessing the appropriateness of different climate modelling approaches for the estimation of aviation NOx climate effects, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9246, https://doi.org/10.5194/egusphere-egu23-9246, 2023.

X5.112
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EGU23-10715
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ITS1.2/AS5.14
Sun-Seon Lee, Axel Timmermann, Thomas Jung, Tido Semmler, Jung-Eun Chu, Jan Streffing, and Pavan Harika Raavi

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.

X5.113
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EGU23-14667
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ITS1.2/AS5.14
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ECS
Analysis of extreme precipitation and wind at convection-permitting resolution
(withdrawn)
Luigi Cesarini, Giorgia Fosser, Eleonora Dallan, Francesco Marra, Mario Martina, and Marco Marani
X5.114
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EGU23-15643
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ITS1.2/AS5.14
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ECS
Mitchell Odhiambo, Raunaq Jain, Yash Gorana, Nikita Kaushal, and Abhilash Mishra

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.

X5.115
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EGU23-17497
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ITS1.2/AS5.14
Edward Peter Morris Boyne, Chloé Prodhomme, Adam Jay Pain, and Benjamin Laken

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.

X5.116
|
EGU23-11320
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ITS1.2/AS5.14
Marta Ballocci, Daniela Molinari, Francesco Ballio, and Giovanni Marin

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.

X5.117
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EGU23-17504
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ITS1.2/AS5.14
Abolfazl Simorgh and Manuel Soler

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.

X5.118
|
EGU23-17517
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ITS1.2/AS5.14
Fateme Baneshi and Manuel Soler
The aviation industry contributes to global warming by releasing CO2 and non-CO2 species into the atmosphere. The climate impacts of non-CO2 emissions have been claimed to be two times higher than the effects of CO2 alone [1]. Unlike CO2 emission, the climate impacts of non-CO2 species highly depend on geographical location, altitude, and time of the emissions. Thus, performing more efficient maneuvers to avoid climate hotspots can potentially mitigate their associated climate effects. So far, several studies have been conducted on micro-scale climate optimal aircraft trajectory planning (i.e., trajectory level) [2]. However, generating a climatically optimal flight plan for each aircraft is not the ultimate solution to this problem when it comes to global traffic scenarios.  
 
  Besides increasing the operating costs as the aircraft fly longer routes ( mainly due to the tendency to avoid climate-sensitive regions), the climate-optimal trajectories also alter the traffic pattern by increasing the congestion around climate hotspots, which may have negative implications, including, but not limited to, high traffic density, increased workload, complexity, and conflicts. Therefore, the evolution toward an environmentally friendly trajectory planning framework required a holistic perspective on the consequences of adopting climate-optimal routes at network scale. Nonetheless, in the literature, the problem of aircraft trajectory planning for the benefits of climate at a network scale is explored only in a free-routing airspace, considering a regional scenario (i.e., only Spain airspace), and constant altitude for trajectory optimization [3].
 
 
  In this study, we aim to explore this problem considering a real large-scale scenario including ≈ 6000 flights on December 20th, 2018, from 12:00 to 16:00 over European airspace. The flight information, including the time and altitude of the first crossed waypoint within the considered time interval, has been extracted from the DDR2 dataset. For flights that start or land outside ECAC airports, we model only the segment of the flight that takes place within ECAC airspace. The algorithmic climate change functions proposed by [4] are employed to quantify the climate impact of each species, including contrails, and emissions of nitrogen oxides, CO2, and water vapor, in terms of average temperature response over the next 20 years. Our recently developed tool for climate-optimal aircraft trajectory planning, ROOST, is then used to optimize each trajectory 1 within the current structured airspace [5]. The effects of adopting climate optimized trajectories are assessed in terms of complexity, demand, and the number of conflicts. A performance map associated with each indicator is generated to spatially analyze the overall behavior of optimized trajectories and detect congested areas.  
  
  For the considered scenario, the results indicate that by adopting trajectories with less climate impact, the complexity, demand, and conflicts are increased around climate hotspots. This trend is mainly due to the tendency to avoid climate-sensitive regions. In order to mitigate such changes in traffic patterns, an efficient resolution strategy is needed to find the optimal mechanisms to manage the ATM system from a climatic perspective.  

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.

Posters virtual: Mon, 24 Apr, 10:45–12:30 | vHall AS

vAS.10
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EGU23-5418
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ITS1.2/AS5.14
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ECS
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Daria Ottonelli, Sylvain Ponserre, Lauro Rossi, Roberto Rudari, and Eva Trasforini

Disaster risk determines the potential loss of life, injury, or destroyed or damaged assets which could occur to a system, society or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability and capacity. This paper focuses on the exposure elements, that expresses people, infrastructure, housing, production capacities and other tangible human assets located in hazard-prone areas (UNDRR, 2017).  In performing risk analyses, an accurate exposure model should be constructed and specified according to the purpose and spatial scale of the assessment.

The scope of the present work is the flood displacement risk assessment for two small island developing states in the Pacific Ocean, Fiji and Vanuatu, where a new methodology is proposed, that considers different but intrinsically linked components in assessing the contribution of disasters to displacement. In this assessment, three main elements are supposed to trigger (or at least contribute to cause) flood displacements: the loss of housing, the loss of livelihoods or the loss of access to basic services. This implies that, besides the classical vulnerability characterization of a asset based on occupancy (residential, commercial, industrial, etc.) and structural elements (number of stories, basement, etc.), the exposure model must also consider a spatial representation of the population relying on the specific function of that asset: residential population in case of residential building; population working in that building in case of commercial, industrial, or service buildings; population working in crop or grazing areas in case of agricultural field; number of students in case of school.

In this context, a procedure for avoiding potential double counting was also implemented. It means that, to evaluate the ratio of population that could suffer impacts due to floods on both livelihoods and housing, each worker must be associated to his/her home with his/her workplace.

Regarding the spatial scale, the small size of the countries allows for the definition of a high-resolution exposure model, that entails a characterization at building Level.

The construction of the exposure model is articulated in three main steps: 1) analysis and integration of different sources of employment and residential data (from global to local information); 2) physical characterization of assets at building scale, using building footprints from the Open Street Map layer and attributes from existing exposure models, such as Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) project that lasted from 2012 to 2017 and Global Earthquake Model (GEM) within the project Global Exposure Map (v2018.1); 3) the procedure to avoid double counting, which associates each worker to his/her home with his/her workplace, following the criterion of minimum geometric distance between workplace and residence.

The exposure model is then used in a probabilistic risk assessment, where different flood scenarios and related damage scenarios are computed at building scale. Physical damage above a certain threshold is considered to cause the unavailability of asset function (residence, workplace), thus triggering the displacement of people relying on that function.

How to cite: Ottonelli, D., Ponserre, S., Rossi, L., Rudari, R., and Trasforini, E.: High resolution exposure model for a flood displacement risk assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5418, https://doi.org/10.5194/egusphere-egu23-5418, 2023.

vAS.11
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EGU23-5563
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ITS1.2/AS5.14
Eva Trasforini, Lauro Rossi, Sylvain Ponserre, Lorenzo Campo, Andrea Libertino, Daria Ottonelli, and Roberto Rudari

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.

vAS.12
|
EGU23-5564
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ITS1.2/AS5.14
|
ECS
|
Mohd. Usman Saeed Khan, Maaz Abdullah, and Arisha Aslam Khan

Natural disasters are one of the main causes of worry for the majority of nations because they severely harm the global economy. One of the natural disasters that occur on a global scale that seriously damages infrastructure and claims thousands of lives is flooding. Due to its geographical location, India is one of the high-risk nations that is negatively impacted by floods every year. It ranks in the top 20% of countries worldwide for the number of flood-related fatalities. A natural tragedy cannot be prevented. However, if some preventative actions were taken in advance, a sizable portion of the potential damage may be prevented. Professionals and authorities need accurate figures regarding flood depth, amount of flow, scale, and distinct datasets in order to reduce and manage the effects of such catastrophes. The management of flood risk is heavily dependent on flood modelling. One of the many software tools that assists in computing the discharge, depth, magnitude, and statistics of rivers located in high-risk flood zones is HEC-RAS (Hydrologic Engineering Centre-River Analysis System).This study employed the Purna River's 1D hydrodynamic floods modelling (50 and 100 years out) on HEC-RAS and it has been found that the great portion of populated area will be affected in future. The goal of this study is to assess the prediction power and carry out a sensitivity analysis to identify the sensitive zones. This research project would enable different flood modelling and risk zone delineation for diverse flood-affected areas in India and around the world. In places affected by flooding, these technologies can also be used to create emergency response protocols.

How to cite: Khan, M. U. S., Abdullah, M., and Khan, A. A.: Flood Modelling and Simulation using HEC-RAS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5564, https://doi.org/10.5194/egusphere-egu23-5564, 2023.