ITS2.4/NH13.7 | Transdisciplinary science for climate change solutions: bridging the gap between scientific research, impacts, policy and economics
EDI
Transdisciplinary science for climate change solutions: bridging the gap between scientific research, impacts, policy and economics
Convener: Haider Ali | Co-conveners: Colin Manning, Hayley Fowler, Conrad Wasko, Andreas F. Prein
Orals
| Mon, 15 Apr, 16:15–17:55 (CEST)
 
Room 1.34
Posters on site
| Attendance Mon, 15 Apr, 10:45–12:30 (CEST) | Display Mon, 15 Apr, 08:30–12:30
 
Hall X4
Posters virtual
| Attendance Mon, 15 Apr, 14:00–15:45 (CEST) | Display Mon, 15 Apr, 08:30–18:00
 
vHall X4
Orals |
Mon, 16:15
Posters on site |
Mon, 10:45
Posters virtual
Mon, 14:00
As highlighted by the UN development goals, climate change is a reality to which we need to adapt. However, the many disciplines required to effectively plan and adapt to climate change often work in isolation. For example, physical climate modelling, hydrology, and hazard impact and risk assessment are largely separate disciplines with difficulties interacting due to different terminologies and backgrounds. Moreover, until recently, climate modellers did not have the capability to generate long-term projections at a spatial and temporal resolution useful for impact studies.

With the advent of kilometre-scale atmospheric models, called 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 gap between disciplines, sharing knowledge and understanding. With all these tools at our disposal we have substantially improved the representation of sub-daily precipitation characteristics and have model output at a spatial resolution closer to what many impacts modellers, for example hydrologists, need. Now is the time to exploit these high-resolution, consistent datasets as input for impact studies and adaptation strategies; to foster interdisciplinary collaboration to build a common language and understand limitations and needs of the different fields; to learn together how to provide policymakers with information that can be used to design effective measures at to adapt to climate change as well as to inform mitigation decisions.

This interdisciplinary session invites contributions that address the linkages between high-resolution climate scientists, impact modellers, 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 observational data sets in characterising and quantifying climate change hazards.
- Examples of good practice, storylines and communication to both stakeholders and policymakers.

Orals: Mon, 15 Apr | Room 1.34

Chairpersons: Haider Ali, Hayley Fowler
16:15–16:35
|
EGU24-8794
|
ECS
|
solicited
|
Highlight
|
On-site presentation
Using high resolution climate data to help prepare future energy systems for weather-driven extremes. 
Hannah Bloomfield

Energy systems across the world are rapidly evolving to meet climate mitigation targets. This requires a rapid transition to electricity systems lower reliance on fossil fuels and greater weather-dependent renewable generation (such as wind power, solar power, and hydropower). This increased weather dependence adds a new set of challenges for balancing supply and demand due to the inherent variability of weather, increasing the need for investment in storage and flexible technologies. The impacts of climate variability and climate change on national energy systems is a topic of current academic interest. Both in terms of security of supply risks from system level challenges (e.g., energy shortfall events, where existing generation is insufficient to meet demand) or from smaller-scale infrastructure challenges (e.g., extreme weather impacting the operability of energy system components).

This talk will discuss a programme of work on energy sector impacts using the UK Climate projections data (UKCP18). This is a suite of state-of-the-art climate model projections available at 60km resolution globally, 12km spatial resolution over Europe, and 2.2km resolution over the UK. Electricity demand, wind power, and solar photovoltaic power timeseries are developed for the period 1980-2080 using the regional climate model outputs. Climate data of this high spatial and temporal resolution is critical for the accurate quantification of meteorological hazards of relevance to the energy sector. The UK energy sector will be used as a case study in this talk due to its large share of variable renewables and commitments to reach net-zero emissions by 2050 and decarbonising the electricity system by 2035.

This talk will highlight weather-driven risks to the energy sector in both a present and future climate, with a particular focus on compound events. At short timescales examples of these risks could be periods of high demand combined with low wind power generation, or weather patterns extending over a very large area of Europe (therefore creating a spatial compound event) or sequences of extreme weather (such as several storms happening in quick succession, which could damage energy infrastructure). At longer timescales these types of compound events could be years with low renewable energy production relative to demand, or as successive years with low production. Future work will use the years containing extreme events highlighted in this talk as inputs within high resolution power system modelling simulations.

 

How to cite: Bloomfield, H.: Using high resolution climate data to help prepare future energy systems for weather-driven extremes., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8794, https://doi.org/10.5194/egusphere-egu24-8794, 2024.

16:35–16:45
|
EGU24-17774
|
ECS
|
On-site presentation
Validation of CPM’s wind and precipitation field against observations and the highresolution reanalysis dataset SPHERA 
Luigi Cesarini and Giorgia Fosser

The correct representation of fine-scale atmospheric processes, like convection, is vital for predicting extreme weather events and CPMs have already shown to provide more reliable representation of extreme precipitation. However, in most cases their validation is limited to the precipitation field and based on sparse in-situ observations or coarser resolution observational gridded dataset. In this study, we first explore whether high-resolution (i.e., grid spacing 2.2km) reanalysis product SPHERA provides a realistic representation of the in-situ observations, thus offering  a comprehensive overview of the atmosphere at fine scale and functioning as a reliable reference dataset for CPMs evaluation. Then the sub-daily precipitation and wind fields of the CPMs ensemble from the CORDEX Flagship Pilot project on Convective Phenomena over Europe and the Mediterranean (FPS Convection) is validated against both in-situ observation and SPHERA. The validation focuses on extreme quantiles, spatial variability and event representation with a quantile based approach (i.e., the event starts when atmospheric variables are above a certain quantile, and ends when it goes below). Results show a general good agreement between in-situ observations and SPHERA, that is found to be a good reference dataset to evaluate the CPM models. When looking at the extreme quantiles, the CPMs well represent  both wind and precipitation fields, although they underestimate heavy precipitation in summer (i.e., June-July-August). Similarly, the spatial distribution of precipitation and wind is well represented for all the season, with a decrease in the spatial variability and spatial correlation for the heavy precipitation in the summer. Finally the CPMs underestimate the number of the events when precipitation and wind are treated singularly, while they substantially overestimate the number of compound events of rainfall and winds. The analysis shows the capability of CPMs to represent the precipitation and wind fields and highlights the possibility of using high-resolution reanalysis into the evaluation of convection-permitting models. Moving from point-based measurements to high-resolution gridded observational datasets opens the path to the use of SPHERA for advanced bias correction methods that could take into account the full 3D dimension of the atmosphere and the processes within it.

How to cite: Cesarini, L. and Fosser, G.: Validation of CPM’s wind and precipitation field against observations and the highresolution reanalysis dataset SPHERA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17774, https://doi.org/10.5194/egusphere-egu24-17774, 2024.

16:45–16:55
|
EGU24-22471
|
On-site presentation
From National Climate Scenarios to National Climate Information 
Carol McSweeney, Jason Lowe, and Neha Mittal

National Climate Scenarios provide a common basis for national risk assessment and adaptation planning. Recent examples include the UK’s UKCP18, the Netherlands’ KNMI23 and the Australian ‘Climate Change in Australia’ (2015).

Advances in climate modelling approaches provide the potential for a step change in the quality, and type of national climate scenarios that will likely be produced over coming years. While these advances include improvements in the traditional approaches employed in the provision of future climate projections for adaptation planning (updated global model ensembles, various downscaling approaches including convective permitting regional projections, improvements in constraining model ensembles), developments in a wider range techniques are increasingly being used in the assessment of climate resilience. These include large initial-condition ensembles, event attribution, the exploration of ‘High Impact Low Likelihood’ (HILL) scenarios, as well as the potential to exploit enhanced skill in initialised seasonal and decadal forecasts.

Here we will share what we are learning through parallel activities which seek to (a) develop our understanding of the needs of the diverse user community in the UK through an extensive user consultation to enhance usefulness and usability and (b) scope the opportunities emerging from the climate science community may have to address the gaps in existing information, and their readiness to contribute to a National Climate Information package.

How to cite: McSweeney, C., Lowe, J., and Mittal, N.: From National Climate Scenarios to National Climate Information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22471, https://doi.org/10.5194/egusphere-egu24-22471, 2024.

16:55–17:05
|
EGU24-9297
|
ECS
|
Highlight
|
On-site presentation
From Stakeholder Engagement to Inclusivity: Advancing Participatory Modeling for Net-Zero Sustainable Development 
Victoria Herbig, Stephanie Briers, and Bianca Vienni-Baptista

Despite the significant advancements of Integrated Assessment Models [IAMs] in recent years, criticisms underscore their limitations in effectively responding to questions on climate change adaptation and mitigation (4). Such critiques highlight the need for IAMs to be not only technologically advanced but also transparently accessible to both the modeling community and stakeholders (1).

The Horizon Europe project “Delivering the next generation of Open Integrated Assessment Models for net-zero, sustainable development” [DIAMOND] seeks to bridge these gaps. By leveraging participatory and transdisciplinary approaches, DIAMOND aims to enhance, extend, and open up IAMs, aligning them more closely with climate action and sustainable development objectives through open and responsible stakeholder engagement.

Engaging a broad range of stakeholders and working collaboratively with them stands out as pivotal in bolstering the credibility and effectiveness of modeling results (5; 6). Acknowledging policymakers’ inputs further strengthens the potential integration of modeling results into policy-making processes (1). This paper presents co-created comprehensive good practice guidelines for inclusive stakeholder engagement, grounded in a case study of the DIAMOND project. The focus is on establishing an inclusive modeling environment that ensures representation and decision making embody diverse stakeholders’ perspectives, knowledge, and interests, including those of policymakers. Utilizing a transdisciplinary approach facilitates a move towards genuine inclusivity, ensuring all relevant parties, regardless of their background or expertise, are given the opportunity to participate, contribute, and have their voices heard in the decision-making process (2). Employing a mixed-methods approach that combines a literature review, stakeholder elicitation, an online survey, and semi-structured interviews, this study triangulates these methods to comprehensively assess collaborative dynamics, adaptive strategies, and the operational context, providing a nuanced understanding of the complex interactions at play.

This paper endeavors to guide modelers, irrespective of their modeling background, towards producing relevant and actionable results that are aligned with real-world implications and policy needs (3). Through assessing and integrating the dimension of “inclusivity” within participatory modeling processes and demonstrating its integration within a transdisciplinary framework, this study aspires to offer valuable insights to the broader modeling community. The insights derived can empower modelers across disciplines to provide policymakers with evidence-based approaches for designing effective climate change adaptation measures and informing mitigation decisions, paving the way for better-informed policies guiding society towards a sustainable and net-zero future.

References:

(1) Doukas, H., Nikas, A. (2019). European Journal of Operational Research, 280, 1-24. https://doi.org/10.1016/j.ejor.2019.01.017
(2) Ernst, A., Fischer-Hotzel, A., Schumann, D. (2017). Energy Research & Social Science, 29, 23-35. http://dx.doi.org/10.1016/j.erss.2017.04.006
(3) Jordan, R., Gray, S., Zellner, M., Glynn, P. D., Voinov, A., et al. (2018). Earth’s Future, 6, 1046–1057. https://doi.org/10.1029/2018EF000841
(4) Keppo, I., Butnar, I., Bauer, N., Caspani, M., Edelenbosch, O., et al. (2021). Environmental Research Letters, 16, 053006. https://doi.org/10.1088/1748-9326/abe5d8
(5) McGookin C., Gallachóir B., Byrne, E. (2021). Renewable and Sustainable Energy Review, 151, 111504. https://doi.org/10.1016/j.rser.2021.111504
(6) Pisano, U., Lange, L., Lepuschitz, K., Berger, G. (2015). European Sustainable Development Network. ESDN Quarterly Report, 39.

How to cite: Herbig, V., Briers, S., and Vienni-Baptista, B.: From Stakeholder Engagement to Inclusivity: Advancing Participatory Modeling for Net-Zero Sustainable Development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9297, https://doi.org/10.5194/egusphere-egu24-9297, 2024.

17:05–17:15
|
EGU24-8945
|
On-site presentation
Bringing high-resolution climate data into action: Experiences from the transdisciplinary funding measure RegIKlim 
Kevin Sieck, Joaquim Pinto, Jan-Albrecht Harrs, Bente Tiedje, Astrid Ziemann, Elena Xoplaki, Beate Geyer, Hendrik Feldmann, Julia Mömken, Heiko Paeth, Katja Trachte, Christopher Kadow, and Laura Dalitz

In the RegIKlim funding measure (Regional Information for Action on Climate Change, https://www.fona.de/en/measures/funding-measures/regional-information-for-action-on-climate-change.php), the cross-sectional project NUKLEUS (Actionable Local Climate Information for Germany) is concerned with the provision of useful, actionable, and high-resolution climate information for Germany and the improvement of the interface between climate data and subsequent use, e.g. in impact models for adaptation to climate change, in six pilot regions distributed across Germany.   

Climate simulations on the convection-permitting scale were hardly available at the beginning of the project and their simulation areas generally did not cover all model regions or longer time periods. Based on the requirements of the users from the model regions, the prototype of an ensemble with simulations of three regional climate models was generated and thus the first multi-decadal multi-climate model ensemble on a convection-permitting scale (approx. 3 km horizontal resolution) for Germany. It can be shown that the model results are within the expected deviations compared to measured values and that the high-resolution data of the 3 km simulations on short time and spatial scales offer added value compared to the EURO-CORDEX simulations. 

In order to improve the interface between climate data and impact models for application, a data and analysis portal (Freva) was set up in NUKLEUS, which facilitates users from the model regions to find suitable data and generate customized data sets using small programs (plugins). The first user-driven plugins have been developed and their application will be presented.  

The improvement of the interface also includes information on the uncertainties of certain influencing variables in the impact modeling and the reduction of systematic deviations of the simulations from the observed climate by e.g. bias correction methods. An important result of the uncertainty analysis of the model chain is that the range of climate information is not always the most important variable. Insufficient or outdated land use information can also have a decisive influence on the climate signal. The testing of different bias correction methods shows that the bias correction in principle leads to a reduction in systematic errors, but that the availability of high-resolution observational data for the correction is a major challenge in s. With the statistical refinement approach, good results were achieved for precipitation at a very high resolution of 300-500 m, especially in geographically highly structured regions. 

To ensure the translation of the modeling-based information into practical application, the cross-sectional project WIRKsam (Scientific Coordination for the Development of a Regional Climate Register) has developed a set of best practices based on transdisciplinary working group discussions.  To specifically address public spatial planning, it is important to exemplify the utilization potential of the data in pilot application (e.g. development plans) and develop user-oriented capacity-building modules and interpretations guidelines. Through surveys and workshops, transdisciplinary research projects can identify crucial municipal administrative processes, develop information tools for decision support and learn how they could benefit from the new data. This might involve facilitating a cross-departmental understanding of roles and responsibilities.

How to cite: Sieck, K., Pinto, J., Harrs, J.-A., Tiedje, B., Ziemann, A., Xoplaki, E., Geyer, B., Feldmann, H., Mömken, J., Paeth, H., Trachte, K., Kadow, C., and Dalitz, L.: Bringing high-resolution climate data into action: Experiences from the transdisciplinary funding measure RegIKlim, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8945, https://doi.org/10.5194/egusphere-egu24-8945, 2024.

17:15–17:25
|
EGU24-19847
|
ECS
|
Highlight
|
On-site presentation
Challenges in quantifying physical risk to assets globally 
Joe Stables, Graham Reverly, James Brennan, Sally Woodhouse, Nicholas Leach, Laura Ramsamy, Patricia Sullivan, and Jonathan Davies

As the physical processes of our world change, the landscape of risk has changed with it. At Climate X, we provide high-quality data to the financial sector so that evolving risks to global portfolios can be quantified. A crucial element of this is the physical risk from events, including extreme weather events.

Traditionally risk assessments have been carried out at an asset level on small scales, with a dedicated team spending days on tens of assets. The high price and slow turnaround makes this unfeasible for large scale operations. We provide an alternative, leveraging open source datasets and research to estimate the physical risk to over half a billion buildings worldwide. This talk will highlight some challenges of working at this scale, and illustrate our approaches to resolving them.

How to cite: Stables, J., Reverly, G., Brennan, J., Woodhouse, S., Leach, N., Ramsamy, L., Sullivan, P., and Davies, J.: Challenges in quantifying physical risk to assets globally, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19847, https://doi.org/10.5194/egusphere-egu24-19847, 2024.

17:25–17:35
|
EGU24-20739
|
Highlight
|
On-site presentation
Incorporating Ethics into Climate Intervention Research, Experimentation, and Potential Deployment 
Mark Shimamoto, Janice Lachance, and Billy Williams

Climate change requires urgent action. Aggressive actions toward carbon emissions reduction must remain the primary strategy for reversing and addressing climate change. However, increasingly the world is considering technology-based climate intervention approaches—often called climate engineering. There are major practical and ethical questions about the significant risks and potential trade-offs some of these approaches would bring and how they would be measured against the risks of our warming world. Recognizing the need for guiding principles in this fast-moving, dynamic space and building on AGU’s longstanding history of advancing and advocating for strong scientific ethics, AGU is facilitating the development of a draft Ethical Framework for Climate Intervention Research, Experimentation, and Deployment. The ethical framework will be released in 2024 and will serve as a resource to help governments, researchers, NGOs, and the private sector make responsible decisions when engaging in climate intervention research or policy. In 2023, the draft framework completed a rigorous three-month public comment period and consultation process to include more holistic input from other scientists and ethicists, as well as community voices, youth advocates, and many more. This presentation will highlight the ethical principles and how the science community can incorporate and advocate for ethics in climate intervention research.

How to cite: Shimamoto, M., Lachance, J., and Williams, B.: Incorporating Ethics into Climate Intervention Research, Experimentation, and Potential Deployment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20739, https://doi.org/10.5194/egusphere-egu24-20739, 2024.

17:35–17:45
|
EGU24-20838
|
ECS
|
On-site presentation
Towards an Ethics of Modeling and Data Use for Actionable Climate Science 
Monica Morrison

Considerable risk is involved in the use of climate models or their products (i.e., simulations and data) when there is a lack of adequacy or fitness for one’s purpose. Of specific concern is the risk of generating information in response to an actionable or applied question or aim that is irrelevant, misleading, inappropriate, inconsistent, or highly inaccurate, as this can lead to downstream harms such as maladaptation. This form of “misuse” is innocent or unintentional, and is largely a function of a user’s misunderstanding or misinterpretation of the intended purposes of a model and/or modeling exercise and the applicability of the model’s products. Ineffective communication and lack of transparency into the intended purposes, assumptions, representational features, adequacies, as well as inadequacies and limitations of a model, can lead to this form of inappropriate and unjustified repurposing. Currently, there is an increase in the demand for open and accessible data, and an increase in the use of climate data, especially data from high-resolution modeling efforts, for applied and actionable purposes (contexts in which derived products are used to inform decision-making). Given both conditions, the reduction and management of possible inappropriate repurposing, i.e., misuse, has become a highly salient consideration for any modeling effort. Producers of models and their products have a moral duty to implement mechanisms to aid users in the identification, understanding, and control of this risk. This can happen by way of the distribution of expert guidance, increase in intentional transparency, and instantiation of systematic norms for clearly and plainly communicating the fitness of purpose and inadequacies of models and their products. This would provide a large step forward toward the reduction of misuse of information in climate science that could lead to harmful consequences, and pave the way for the development of an ethics of scientific practice for the climate science community.

How to cite: Morrison, M.: Towards an Ethics of Modeling and Data Use for Actionable Climate Science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20838, https://doi.org/10.5194/egusphere-egu24-20838, 2024.

17:45–17:55
|
EGU24-16297
|
Highlight
|
Virtual presentation
|
Impacts and risks of “realistic” global warming projections for the 21st century 
Nicola Scafetta

The IPCC AR6 assessment of the impacts and risks associated with projected climate changes for the 21st century is both alarming and ambiguous. According to computer projections, global surface may warm from 1.3 to 8.0 °C by 2100, depending on the global climate model (GCM) and the shared socioeconomic pathway (SSP) scenario used for the simulations. Actual climate-change hazards are estimated to be high and very high if the global surface temperature rises, respectively, more than 2.0 °C and 3.0 °C above pre-industrial levels. Recent studies, however, showed that a substantial number of CMIP6 GCMs run “too hot” because they appear to be too sensitive to radiative forcing, and that the high/extreme emission scenarios SSP3-7.0 and SSP5-8.5 must be rejected because judged to be "unlikely" and "highly unlikely", respectively. Yet, the IPCC AR6 mostly focused on such alarmistic scenarios for risk assessments. This paper examines the impacts and risks of “realistic” climate change projections for the 21st century generated by assessing the theoretical models and integrating them with the existing empirical knowledge on global warming and the various natural cycles of climate change that have been recorded by a variety of scientists and historians. This is achieved by combining the "realistic" SSP2-4.5 scenario and empirically optimized climate modeling. The GCM macro-ensemble that best hindcast the global surface warming observed from 1980–1990 to 2012–2022 is found to be made up of models that are characterized by a low equilibrium climate sensitivity (ECS) (1.5<ECS<3.0 °C), in contrast to the IPCC AR6 likely and very likely ECS ranges of 2.5-4.0 °C and 2.0-5.0 °C, respectively. This GCM macro-ensemble projects a global surface temperature warming of 1.68-3.09 °C by 2080–2100 instead of 1.98-3.82 °C obtained with the 2.5-4.0 °C ECS GCMs. However, if the global surface temperature records are affected by significant non-climatic warm biases — as suggested by satellite-based lower troposphere temperature records and current studies on urban heat island effects — the same climate simulations should be scaled down by about 30%, resulting in a warming of about 1.18-2.16 °C by 2080–2100. Furthermore, similar moderate warming estimates (1.15-2.52 °C) are also projected by alternative empirically derived models that aim to recreate the decadal-to-millennial natural climatic oscillations, which the GCMs do not reproduce. The obtained climate projections show that the expected global surface warming for the 21st century will likely be mild, that is, no more than 2.5-3.0 °C and, on average, likely below the 2.0 °C threshold. This should allow for the mitigation and management of the most dangerous climate-change-related hazards through appropriate low-cost adaptation policies. In conclusion, enforcing expensive decarbonization and net-zero emission scenarios, such as SSP1-2.6, is not required because the Paris Agreement temperature target of keeping global warming below 2 °C throughout the 21st century should be compatible also with moderate and pragmatic shared socioeconomic pathways such as the SSP2-4.5.

Reference: Scafetta, N.: 2024. Impacts and risks of “realistic” global warming projections for the 21st century. Geoscience Frontiers 15(2), 101774. https://doi.org/10.1016/j.gsf.2023.101774

How to cite: Scafetta, N.: Impacts and risks of “realistic” global warming projections for the 21st century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16297, https://doi.org/10.5194/egusphere-egu24-16297, 2024.

Posters on site: Mon, 15 Apr, 10:45–12:30 | Hall X4

Display time: Mon, 15 Apr, 08:30–Mon, 15 Apr, 12:30
Chairpersons: Haider Ali, Hannah Bloomfield, Hayley Fowler
X4.25
|
EGU24-8328
|
ECS
Flood Impact on Urban Mobility: An Assessment of Subway Infrastructure Vulnerability in China
(withdrawn)
Xuyan Gao
X4.26
|
EGU24-9098
|
ECS
Climate risk analysis for adaptation planning in Zambia’s agricultural sector 
Rahel Laudien, Abel Chemura, Carla Cronauer, Tim Heckmann, Stephanie Gleixner, Christoph Gornott, Lisa Murken, and Julia Tomalka

Climate change and climate extremes increasingly threaten agricultural production and thereby pose a serious risk to agricultural livelihoods, particularly in the Global South. In support of adaptation planning, science-based information on projected climate impacts and sound information on the suitability of adaptation options is needed.

This study provides a comprehensive analysis of current and future climate-related risks in Zambia – a country that is highly vulnerable to climate change due to its geographic location and the strong socio-economic dependency on agriculture. Using data from ten Global Climate Models (GCMs) under two climate change scenarios (SSP1-RCP2.6 and SSP3-RCP7.0), we analyze future trends in climatic conditions and model their impacts on agricultural yields and crop suitability. Moreover, the study evaluates two adaptation options to promote climate-resilience in the agricultural system i.e. 1) conservation agriculture and 2) a climate and agricultural extension service called PICSA (Participatory Integrated Climate Services for Agriculture). The evaluation includes biophysical, economic, financial and gender aspects to provide comprehensive and usable information that can inform adaptation policies on the ground. The study was co-designed together with stakeholders from Zambian governmental institutions, civil society, academia, the private sector, practitioners and development partners.

Results show the strongest negative impacts of climate change in South Western Zambia where the strongest increases in temperature and dry conditions are projected. The projected impacts underline the need for strong adaptation efforts: 1) Conservation agriculture can buffer climate impacts in the near term and even increase sorghum yields by 25 to 31% in drought-prone areas in Zambia. It can play a vital role in adapting to increasingly extreme and dry climatic conditions. 2) The PICSA approach proved to be a highly economically beneficial adaptation option with each USD invested generating between 3.6 and 3.8 USD in benefits.

In addition, the study reflects on lessons learned from interdisciplinary and stakeholder-driven research – focusing not only on the Zambian context, but also on climate risk analyses that were conducted in Burkina Faso, Cameroon, Ethiopia, Ghana, Madagascar, Niger and Uganda.

How to cite: Laudien, R., Chemura, A., Cronauer, C., Heckmann, T., Gleixner, S., Gornott, C., Murken, L., and Tomalka, J.: Climate risk analysis for adaptation planning in Zambia’s agricultural sector, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9098, https://doi.org/10.5194/egusphere-egu24-9098, 2024.

X4.27
|
EGU24-10592
|
ECS
Effective Science Communication for Climate Change Adaptation on Regional Scale – Regional Climate Information Platforms: A Case Study in Oberland (Upper Bavaria – Germany) 
Gamze Koc, Christof Lorenz, David Feldmann, and Brian Böker

Climate change poses a significant threat to communities on regional scale as well as worldwide, and the urgency for adaptation is particularly crucial for small- and medium-sized communities and cities. However, a pervasive knowledge gap exists in these regions, hindering their ability to adapt effectively. The lack of accessible and tailored climate information and services exacerbates the vulnerability of these communities. Therefore, this study focuses on addressing this knowledge gap and developing effective science communication strategies, emphasizing the regional scale through the implementation of Regional Climate Information Platforms.

The chosen case study location, Oberland (Upper Bavaria, Germany), is characterized by complex terrain, encompassing Alpine and Pre-Alpine regions, with three distinct climate zones in close proximity. The diverse topography of Oberland presents unique challenges, as climate change impacts may manifest differently across the region, particularly for hydro-meteorological extremes. Moreover, the region heavily depends on tourism, making it economically susceptible to changing climate conditions and increasing extreme events, such as extreme precipitation, flooding, summer heatwaves and decreasing snowfall affecting tourism activities (e.g. skiing, hiking, climbing, etc.).

Thus, the study aims to follow a comprehensive workflow, starting with the collection of climate data, followed by bias correction and regionalization for Oberland. High-resolution rainfall statistics will be developed and integrated into hydrodynamic simulations and cluster analyses of flood triggering mechanisms. The outcome will be the creation of risk maps for hydro-meteorological extremes, providing crucial information for stakeholders and decision-makers. Finally, these risk maps will be then incorporated into the digital decision support system, Platform Oberland within the KARE (Klimawandelanpassung auf regionaler Ebene) Project.

In addition to the scientific aspects, the study emphasizes the importance of stakeholder interaction and co-design in the development of Platform Oberland. The collaboration between scientists and stakeholders ensures that the information generated is relevant and usable for decision-making. With this study, it is also aimed to identify "best-practice" approaches for transferring scientific workflows and results into actionable climate-related measures for small- and medium-sized communities.

This case study in Oberland could serve as a regional model for effective science communication and adaptation strategies at the regional level for hydro-meteorological extremes, offering insights into the development of climate indicators and the integration of scientific findings into practical, community-centered climate adaptation.

How to cite: Koc, G., Lorenz, C., Feldmann, D., and Böker, B.: Effective Science Communication for Climate Change Adaptation on Regional Scale – Regional Climate Information Platforms: A Case Study in Oberland (Upper Bavaria – Germany), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10592, https://doi.org/10.5194/egusphere-egu24-10592, 2024.

X4.28
|
EGU24-10602
|
ECS
|
Bias correction of SMILEs: A bulk approach to preserve internal variability 
Jorge Sebastian Moraga, Sabine Undorf, Peter Uhe, Natalie Lord, and Nans Addor

Single Model Initial-condition Large Ensembles (SMILEs) represent a pivotal progress in climate modeling, offering multiple simulations from a single model to address the inherent uncertainties in climate projections (Maher et al., 2021). However, biases intrinsic to climate models can distort SMILEs' outputs, potentially misrepresenting climate risks and uncertainties.

In climate impact studies, bias correction of Earth System Models (ESMs) typically aligns model outputs with observed historical data, using statistical methods to adjust climatic variables. While essential, this correction may suppress the range of climatic conditions, particularly when applied individually to each ensemble member, thus diminishing the ensemble's diversity and its ability to represent varied climate futures. Instead, we explore whether a bulk approach to bias correction is more appropriate for SMILEs. This method involves applying a consistent correction across the entire ensemble, thereby maintaining the relative differences and natural variability among the ensemble members and preserving the unique capacity of SMILEs to represent a broad spectrum of climatic conditions, in particular under current and near-future climate.

Our analysis used the 100-member dataset from the Community Earth System Model Large Ensemble Project Phase 2 (CESM-LENS2, Rodgers et al., 2021), covering historical and future climate simulations. We adjusted key climate variables—precipitation, temperature, relative humidity, and surface pressure within the CONUS domain—using the ISIMIP3basd algorithm (Lange, 2019), with MSWX reanalysis data as the historical reference (Beck et al., 2022). Our experiment involved a twofold comparison: We first evaluated the results after adjusting the entire ensemble at once using (the bulk approach) and, secondly, after adjusting each individual ensemble member separately (member-by-member approach). This comparative analysis allowed us to discern the effects of these two different bias correction methodologies on the ensemble's ability to represent climate variability and extremes.

Our results show the effect of both bias correction approaches on the variability of crucial climate extreme statistics and the correlation between ENSO and climate variables. Additionally, we discuss how the choice of bias adjustment method can influence the magnitude of projected changes under future climate scenarios, a key consideration in climate impact studies.

References:

  • Beck, H. E., Van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., & Miralles, D. G. (2022). MSWX: Global 3-hourly 0.1 bias-corrected meteorological data including near-real-time updates and forecast ensembles. Bulletin of the American Meteorological Society, 103(3), E710-E732.
  • Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). Geoscientific Model Development, 12(7), 3055-3070.
  • Maher, N., Milinski, S., & Ludwig, R. (2021). Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble. Earth System Dynamics, 12(2), 401-418.
  • Rodgers, K. B., Lee, S. S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., ... & Yeager, S. G. (2021). Ubiquity of human-induced changes in climate variability. Earth System Dynamics, 12(4), 1393-1411.



How to cite: Moraga, J. S., Undorf, S., Uhe, P., Lord, N., and Addor, N.: Bias correction of SMILEs: A bulk approach to preserve internal variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10602, https://doi.org/10.5194/egusphere-egu24-10602, 2024.

X4.29
|
EGU24-12154
|
Assessing and explaining future changes on sub-daily precipitation extremes using an ensemble of convection-permitting models 
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, and Marco Borga

Anticipating and understanding the future evolution of intense precipitation events is crucial for improved risk management, especially in regions with mountainous terrain and urban areas vulnerable to natural disasters from extreme weather. Convection-permitting climate models (CPMs) operating at kilometer scales realistically depict convective precipitation mechanisms and complex terrain, enhancing the description of sub-daily extreme precipitation. However, their computational demands restrict simulations to short time periods (10-20 years), and limit the availability of ensemble members, hindering the evaluation of extreme event change and associated uncertainty.

This study employs an innovative non-asymptotic extreme value approach, proven effective in estimating rare return levels with reduced stochastic uncertainty even from short datasets, and which can help in providing insights on the changing processes. We apply the Simplified Metastatistical Extreme Value distribution (SMEV) to estimate the projected changes in future extreme sub-daily precipitation in a region characterized by complex terrain—specifically, the North Italy area encompassing both lowlands and the Italian Alps. Our analysis focuses on an ensemble of 9 CPMs from the CORDEX-FPS project, with a spatial resolution of 3 kilometers. We investigate three time periods: historical (1996-2005), near future (2041-2050), and far future (2090-2099) under the RCP8.5 emission scenario. We estimate return levels up to a 1% yearly exceedance probability (100-year return time) for precipitation durations from 1 to 24 hours. Their future change is evaluated at each grid point, conducting a permutation test to assess the statistical significance of the changes.

Results indicate a general increase in extreme precipitation across the domain and all durations, with spatial patterns of significant changes varying with durations, time period, and location. A pronounced increase occurs in some of the mountainous areas: at short durations in Eastern Alps, and across all durations in the northern Apennines. The western Alps and surroundings show moderate and not-significant change. Leveraging SMEV's ability to separate precipitation intensity distribution from event occurrence, we also examine the change in distribution parameters to interpret the shift in return levels in term of changes in thermodynamics (linked to temperature and water vapor content) and atmospheric dynamics controls. Interestingly, thermodynamics seems to be driving significant changes at short durations, while small-scale local dynamics contribute across all durations. Differences emerge between the Eastern Alps and Northern Apennines, with the latter showing a stronger intensification of intense versus moderate extreme events.

These findings provide valuable insights towards quantifying and understanding the future changes in precipitation extremes, benefiting stakeholders involved in risk management and design of adaptation measures.

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).

How to cite: Dallan, E., Marra, F., Fosser, G., Marani, M., and Borga, M.: Assessing and explaining future changes on sub-daily precipitation extremes using an ensemble of convection-permitting models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12154, https://doi.org/10.5194/egusphere-egu24-12154, 2024.

X4.30
|
EGU24-12378
|
ECS
Comparing extreme sub-daily rainfall projections from temperature-scaling and convection-permitting climate models across an Alpine gradient 
Rashid Akbary, Marco Marani, Eleonora Dallan, and Marco Borga

Understanding projected changes in sub-daily extreme rainfall in mountainous basins can help increase our capability to adapt to and mitigate against flash floods and debris flows. Here we compare the changes in extreme rainfall projections from apparent Clausius-Clapeyron (CC) temperature scaling against those obtained from convection-permitting climate model simulations. Temperature and precipitation projections are obtained from an ensemble of convection-permitting climate models (CPM), which are suitable to the task given their ability to explicitly represent deep convection and to resolve the mountainous topography. The CPM data provided by the CORDEX-FPS Convection project at 1-hour temporal and remapped to 3 km spatial resolution, cover historical and far-future (2090-2099) time periods under the extreme climate change scenario (RCP8.5). Due to the computational demands however, CPM simulations are still too short (typically 10-20 years) for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (the Metastatistical Extreme Value, MEVD, Marani and Ignaccolo, 2015) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We use hourly precipitation and temperature data from 174 stations in an orographically complex area in northeastern Italy as a benchmark.

Results from our analysis reveal that the apparent CC temperature scaling method demonstrates effective performance when applied to 1-hour extreme rainfall projections and for high return periods. However, its accuracy decreases as the precipitation duration increases, highlighting potential limitations in accurately predicting changes in longer-duration extreme rainfall. Variations in performance are also noted when considering different return periods, as we find CPM changes depending on them, contradicting traditional CC-scaling. Furthermore, we show that elevation is a key factor influencing temperature variations, with higher elevation locations experiencing more pronounced temperature increases with respect to lowland areas. This affects more the results for 1 hr extreme rainfall projections, whereas it is less relevant for 24-h duration. These findings identify some serious limitations of traditional CC scaling and emphasize the need for a nuanced understanding of the scaling method's applicability under various conditions.

How to cite: Akbary, R., Marani, M., Dallan, E., and Borga, M.: Comparing extreme sub-daily rainfall projections from temperature-scaling and convection-permitting climate models across an Alpine gradient, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12378, https://doi.org/10.5194/egusphere-egu24-12378, 2024.

X4.31
|
EGU24-12806
|
ECS
The effect of terrestrial water storage anomalies on regional economic growth 
Anna Reckwitz, Maximilian Kotz, Christian Voigt, and Leonie Wenz

Terrestrial water storage (TWS) is an essential resource for agriculture, urban development, and energy production, as well as ecosystem health and climate change mitigation. Through satellite gravimetry methods, GRACE and GRACE-FO measurements enable the assessment of TWS anomalies globally, revealing significant alterations over the past two decades due to natural variability, climate change impacts, and direct human influence. Existing studies focus on the impacts of TWS changes on the production of specific crops or agricultural output in specific countries, yet the effects on agro-economic output on a more global scale are not yet well understood. 

To address this gap in our understanding of the macroeconomic impacts of TWS changes, we combine GRACE measurements with data on economic growth from more than 1600 subnational regions worldwide over the last 60 years. We then empirically assess the impact of TWS anomalies on regional economic growth, employing a long-difference model and fixed-effects panel regression, following recent work on temperature and precipitation impacts. We find that negative groundwater anomalies are associated with reductions in economic growth in a majority of regions. This highlights the critical role of freshwater availability, in particular in low-income regions. Furthermore, we observe that the relationship between TWS and economic growth depends on both meteorological and socioeconomic factors. These heterogeneous relations reflect the complex interplay between water resources and economic development, and indicate potential endogeneity therein. We therefore further discuss instrumental variable approaches for isolating the meteorological drivers of water storage and their causal impact on economic output. These findings contribute valuable insights to the ongoing discourse on sustainable water management and its implications for economic prosperity.

How to cite: Reckwitz, A., Kotz, M., Voigt, C., and Wenz, L.: The effect of terrestrial water storage anomalies on regional economic growth, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12806, https://doi.org/10.5194/egusphere-egu24-12806, 2024.

X4.32
|
EGU24-13127
|
ECS
|
Highlight
Climate adaptive urban extreme weather risk assessment and management 
Lingyan Kang, Jiang Wu, and Fengting Li

Climate change, an escalating global predicament, is intricately linked with the uncertainties surrounding urban development, a process that is intricately tied to economic growth and social progress. This interconnectedness gives rise to new interconnected risks that present significant social and economic challenges, threatening the sustainability of our urban centers. This study takes into account climate change risk mitigation and adaptation strategies, focuses on urban climate risk identification and establishment of climate adaptive city risk assessment index system. Through the sorting of historical data, the improvement of disaster statistics and other interconnection to clarify regional risks in different fields, and discusses methods to achieve efficient risk management and governance of climate-resilient cities under the dual background of urbanization and climate change. By adopting a perspective centered on climate risk management, this research provides forward-thinking guidance for long-term perspectives on urban planning and construction.

How to cite: Kang, L., Wu, J., and Li, F.: Climate adaptive urban extreme weather risk assessment and management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13127, https://doi.org/10.5194/egusphere-egu24-13127, 2024.

X4.33
|
EGU24-17775
Assessment of convection-permitting sub-daily extreme precipitation simulations over Italy 
Marco Borga, Paola Mazzoglio, Marco Lompi, Francesco Marra, Eleonora Dallan, Roberto Deidda, Pieluigi Claps, Salvatore Manfreda, Leonardo Noto, Alberto Viglione, Mario Raffa, and Enrica Caporali

Convection-permitting climate models have the potential to capture crucial processes in the climate system, presenting an opportunity to significantly enhance climate projections by providing more accurate representations of precipitation extremes. In this work, we conduct an evaluation of the accuracy of sub-daily precipitation extremes obtained from VHR-PRO_IT (Very High-Resolution PROjections for Italy, Raffa et al., 2023) over the Italian peninsula,. VHR-PRO_IT is generated through dynamic downscaling of the Italy 8km-CM climate projection at approximately 2.2 km resolution under the IPCC RCP4.5 and RCP8.5 scenarios, employing the Regional Climate Model COSMO-CLM.

Gauged locations are used to assess the accuracy of VHR-PRO_IT in reproducing observed extremes. More specifically, the observed dataset used as ground truth for the comparison is I2-RED (Improved Italian – Rainfall Extreme Dataset; Mazzoglio et al., 2020). For this work, 742 rain gauges covering the entire country with a minimum of 30 years of short-duration (1, 3, 6, 12, 24 h) annual maximum rainfall depths recorded from 1980 to 2022 are used. Conversely, the dataset derived from the VHR-PRO_IT climate projections includes annual maxima from a 30-year time series, connecting the historical period (1981-2005) with 5 years of the RCP8.5 scenario (2006-2010) of the CPM. Return levels are obtained for both dataset by means of a GEV distribution and inform the assessment of the CPM simulations. 

Preliminary results outline the quality of the CPM simulations, especially at 24 hours duration, and show the impacts of return period, seasonality, elevation, latitude and proximity to the sea on the CPM model deviations. The results from this work are expected to have implications for both water resources management and adaptation measures.

References

Mazzoglio P., Butera I., Claps P. (2020). I2-RED: a massive update and quality control of the Italian annual extreme rainfall dataset. Water, 12, 3308.

Raffa M., Adinolfi M., Reder A., Marras G.F., Mancini M., Scipione G., Santini M., Mercogliano P.  (2023). Very High Resolution Projections over Italy under different CMIP5 IPCC scenarios. Scientific Data, 10, 238.

How to cite: Borga, M., Mazzoglio, P., Lompi, M., Marra, F., Dallan, E., Deidda, R., Claps, P., Manfreda, S., Noto, L., Viglione, A., Raffa, M., and Caporali, E.: Assessment of convection-permitting sub-daily extreme precipitation simulations over Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17775, https://doi.org/10.5194/egusphere-egu24-17775, 2024.

X4.34
|
EGU24-22469
Dynamic agricultural weather indicators for extreme weather prediction in agriculture 
Timm Waldau, Pedro Batista, Peter Baumann, Thorsten Behrens, Peter Fiener, Jens Foeller, Markus Moeller, Ingrid Noehles, Karsten Schmidt, and Burkhard Golla

The project “DynAWI – dynamische Agararwetterindikatoren” (dynamic agriculture weather indices) aims to develop a process chain for data integration and real-time analysis for extreme weather. Extreme weather events have a major impact on agriculture and horticulture and cause significant economic costs. The damage depends not only on the type of extreme weather event (e.g. heat wave, drought stress or heavy precipitation), but also on the ontogenetic development of the crops. Previously, farmers calculated their risk with fixed weather indicators and because of the multi-dimensionality of the source data and it was difficult to calculate using traditional relational databases in an acceptable time.

We have developed a web application for real-time calculation of dynamic weather indicators by linking a back-end infrastructure of Datacube servers and a Vue front-end infrastructure with a machine learning model in an R environment. The web application can perform real-time analyses based on multi-dimensional spatio-temporal data. Future plans include enriching the web application with additional agricultural weather indicators and linking it to weather forecasts to provide an in-season risk assessment for crop losses.

How to cite: Waldau, T., Batista, P., Baumann, P., Behrens, T., Fiener, P., Foeller, J., Moeller, M., Noehles, I., Schmidt, K., and Golla, B.: Dynamic agricultural weather indicators for extreme weather prediction in agriculture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22469, https://doi.org/10.5194/egusphere-egu24-22469, 2024.

Posters virtual: Mon, 15 Apr, 14:00–15:45 | vHall X4

Display time: Mon, 15 Apr, 08:30–Mon, 15 Apr, 18:00
Chairpersons: Haider Ali, Andreas F. Prein
vX4.4
|
EGU24-270
|
ECS
Domino effects of Climate Change on Financial Capital of India under CMIP6 Projections 
Vivek Ganesh, Santonu Goswami, and Harini Nagendra

Climate change is a major driver of increased flood risk, which is causing economic meltdown in many parts of the world. Globally, economic losses incurred by floods are estimated at around 453 billion USD. In the Asian region, India experienced the third highest economic loss of 4.2 billion USD due to flooding. Mumbai, India’s financial capital, faces climate change threats due to rising sea level, increased rainfall, and intense cyclones, posing risks to infrastructures, economy, and population, especially in low-lying areas. The Mithi river which overflows during monsoon season, plays a crucial role in carrying storm water to the sea in Mumbai. As it flows through an international airport, major industrial complexes and densely populated residentials, these areas became more vulnerable to flooding. This study demonstrates the domino effects of climate change on Mithi River watershed by utilising CMIP6 13 GCM ensembled daily mean precipitation model data for the near future 2030 under shared socio-economic pathways (SSP) 245 and 585 scenarios. Using the Hydrodynamic model GeoHECRAS, the flood inundation depth and extent were estimated. Under both projections, July 25-26, 2030, observed maximum rainfall and exhibited maximum streamflow with a peak discharge of 51.2 m3/sec (SSP245) and 38.5 m3/sec (SSP585). A quantitative risk assessment conducted based on the domino effects triggered by flooding to determine the projected impacted population and economic losses. The annual projected impacted population under both scenarios is observed as SSP245: Very High (0.24M), High (0.74M), Moderate (0.80M), Low (2.90M), and SSP585: Very High (0.68M), High (0.70M), Moderate (0.86M), Low (2.45M). The annual expected amount of urban property damaged due to this effect will range from $157 billion to $535 billion, with a projected affected GDP of more than $84 billion. This cascading effect is likely to disrupt Mumbai's million-dollar trade, affecting global financial flows. This study will be useful to understand the domino effect and raising the flood risk awareness for the development of sustainable policies.

Keywords: Domino effects, CMIP6, economic loss, hydrodynamic, flood depth and extent

How to cite: Ganesh, V., Goswami, S., and Nagendra, H.: Domino effects of Climate Change on Financial Capital of India under CMIP6 Projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-270, https://doi.org/10.5194/egusphere-egu24-270, 2024.

vX4.5
|
EGU24-18267
|
ECS
Comprehensive Assessment of Climate-Induced Disaster Losses in Uttarakhand: A Time-Series Analysis and Vulnerability Mapping Approach  
Sakshi Goyal and Mahua Mukherjee

In the contemporary landscape, the aftermath of each weather-related disaster triggers swift estimations of economic losses, often accompanied by attributions of increased frequency or intensity of such events. The prompt assignment of blame for weather-related disaster losses is a complex endeavor, as discerning the precise role of climate change proves challenging due to the intricacy arising from intertwining climate alterations with societal transformations, contributing to the evolving dynamics of disaster impacts. In parallel, assessing disaster loss and damage is crucial, especially in vulnerable areas prone to natural disasters, such as the Himalayan region, as it is highly susceptible to climate-induced events and potentially severe consequences for the environment and human settlements. The study focuses on the state of Uttarakhand in India, aiming to comprehensively understand the interplays between climate change, societal shifts, and economic repercussions following weather-related calamities. The primary objective is to develop a detailed loss inventory for Uttarakhand, specifically focusing on past events, types of losses, and their spatial distribution. The methodology thoroughly examines secondary sources, data from the Em-Dat database, government reports, and relevant research articles. This comprehensive approach enables understanding of weather-related disaster losses, considering the impacts of climate change and societal changes in the region. The study also employs a robust time-series analysis methodology to unravel the temporal and spatial distribution of disasters due to extreme events, recognizing their significance in shaping disaster dynamics. The analysis aims to identify vulnerable rural and urban clusters within Uttarakhand, provide valuable insights into the spatial patterns of specific loss types, and map high-risk areas within Uttarakhand, contributing to proactive disaster mitigation strategies. This information is crucial for adapting disaster response and recovery strategies, allowing for the effective allocation of resources based on the unique needs of affected regions by integrating loss inventory creation, time-series analysis, and vulnerability mapping. The findings are expected to not only deepen our understanding of the complex interplays between climate change, societal shifts, and disaster losses but also provide actionable insights for mitigating the impact of future weather-related calamities in the Himalayan region, particularly in the state of Uttarakhand.

How to cite: Goyal, S. and Mukherjee, M.: Comprehensive Assessment of Climate-Induced Disaster Losses in Uttarakhand: A Time-Series Analysis and Vulnerability Mapping Approach , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18267, https://doi.org/10.5194/egusphere-egu24-18267, 2024.