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.

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.

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.

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.

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.

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.

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.

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.

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.