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From vision to action: transdisciplinary approaches for providing climate change impact and adaptation information and capacity development

Climate impact and adaptation research has made considerable progress in various fields in the recent years. However, the concrete implementation on the ground needs to be improved.
Local decision makers are facing several challenges with regard to climate adaptation. At the center of this process lies the coupling of climate, impact and risk (incl. vulnerability) models in order to identify future climate risk levels. Finding and correctly using the necessary data in climate impacts and risks assessments and planning for climate action is not without challenges for specialists from other fields.
While climate modelling and technical integration of diverse model data are crucial, social science as well as interdisciplinary perspectives are essential to assess local adaptation capacities, the costs and benefits of adaptive measures and to ensure the usability and transferability of the climate services. Similarly important is capacity building and trainings on properly using, interpreting and communicating climate and impact information.
This session touches upon innovative ways to address theses challenges. It also supports exchange on experiences in impact and adaptation studies, using all kinds of climate data. Former participants from the C3S ULS and IS-ENES3 training events are particularly encouraged to join.
This session discusses approaches and challenges towards the support of climate change adaptation and disaster risk reduction. Central to the discussion is the question how such services can be developed in a stringent co-design process that integrates different natural and social science disciplines as well as users and practitioners. We are therefore seeking for contributions that discuss:
• Actionable services for regional decision-making in regional climate adaptation and disaster risk reduction and challenges in the interaction between researchers and decision makers
• New scientific insights into regional climate and impact modelling (data interfaces and harmonization)
• Assessing local climate adaptation capacities and measures in an integrated way
• New insights into transdisciplinary processes in climate change adaptation
• Data availability for climate impact studies and methods for dealing with limited data availability as well as the opposite, a large number of seemingly similar datasets.
• Experiences with existing tools or newly developed tools for data processing

Co-organized by NH10
Convener: Jörg Cortekar | Co-conveners: Judith Klostermann, Janette Bessembinder, Stefan Kienberger
| Thu, 26 May, 13:20–15:52 (CEST)
Room N1

Thu, 26 May, 13:20–14:50

Chairperson: Janette Bessembinder

Introduction to the session

Virtual presentation
Jan-Albrecht Harrs and Kevin Laranjeira

Climate risks and the appropriate climate change adaptation (CCA) strategies and solutions are highly localized, as they are dependent on the local climate signal, normative assessments on associated risks and the capacities and motivation of municipalities to plan and implement adaptive measures. Research projects trying to explore and pilot applied local solutions therefore need to co-develop recommendations with local practitioners and stakeholders.

Even though a diverse landscape of climate information (CI) is already available and many municipalities know which risk they may face, knowledge and skills on how to interpret, apply and integrate this information in adaptation action is regarded as necessary. Different, albeit non-representative surveys among municipalities in Germany show that more cities are engaging in developing concepts and strategies (Hasse & Willen, 2018; Hagelstange et al., 2021; Handschuh et al., 2020), but that more practice-oriented information on how to identify regional and local vulnerabilities, evaluate efficient adaptive measures, and identify and build up adaptive capacities is needed (Handschuh et al., 2020; Kahlenborn et al., 2021; BBSR, 2016).

Based on an extensive literature analysis of journal articles, research project reports and strategic policy document as well as the experience of accompanying six transdisciplinary research projects, the following categorization of challenges will be presented:

  • Governance
  • Adaptive capacities
  • Integrative assessment of adaptive measures
  • Climate model data and information
  • Transdisciplinary work in applied research projects

Drawing on insights on the challenges, a list of recommendations for increasing the use-value of climate information and knowledge for CCA in municipalities is outlined. Tackling these five challenges through co-creating and inserting CI and services into municipal procedures and systems can then address the “last mile problem” (Celliers et al., 2021) of CI and support the lagging implementation of CCA.

In order to conduct impactful transdisciplinary research projects, the specific governance context of municipalities needs to be explored. A survey shows that spatial planning not environmental departments implement most CCA measures (EEA, 2020), whereas planning often lacks climate awareness (Skelton, 2020), signifying the need for cross-departmental approaches. Likewise, the understanding and possible usages of CI needs to be conveyed through appropriate transdisciplinary methods.    

How to cite: Harrs, J.-A. and Laranjeira, K.: Challenges and approaches in transdisciplinary climate change adaptation projects, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7773, https://doi.org/10.5194/egusphere-egu22-7773, 2022.


Virtual presentation
Claudia Teutschbein et al.

For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensemble simulations provided by global (GCMs) and regional climate models (RCMs), which are then downscaled and bias-adjusted for each study site. For bias adjustment, different statistical methods that re-scale climate model outputs have been suggested in the scientific literature. They range from simple univariate methods that adjust each meteorological variable individually to more complex and statistically as well as computationally more demanding multivariate methods that take existing relationships between meteorological variables into consideration. While several attempts have been made over the past decade to evaluate such methods in various regions, there is no guidance for choosing an appropriate bias adjustment method in relation to the study question at hand. In particular, the question whether more complex multivariate methods are worth the effort by resulting in better adjustments of a wide range of univariate, multivariate and temporal features, remains unanswered. 
We here present an approach to systematically assess the performance of the most commonly used univariate and multivariate bias adjustment methods at different catchment scales in Sweden. Based on a multi-catchment and multi-model approach, we evaluated numerous univariate, multivariate and temporal features of precipitation, temperature and streamflow. Finally, we discuss potential benefits (skills and added value) and trade-offs (complexity and computational demand) of each method, in particular for hydrological climate-change impact studies in high latitudes.

How to cite: Teutschbein, C., Tootoonchi, F., Todorovic, A., Räty, O., Haerter, J., and Grabs, T.: Bias adjustment of RCM simulations in high-latitude catchments: complexity versus skill in a changing climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10396, https://doi.org/10.5194/egusphere-egu22-10396, 2022.

On-site presentation
Georgia Lazoglou et al.

Climate model output is widely used as input to impact models. Such applications include hydrological, crop, energy modeling, and more.  However, due to model deficiencies and the stochastic nature of climate processes, some variables (e.g., daily precipitation) tend to present systematic biases and deviations from the observed conditions. This is particularly important when studying high-impact extreme events. The present study aims to develop a Copula-based method for bias-correcting modeled daily precipitation. Precipitation data are provided by two EURO-CORDEX regional climate models (KNMI-RACMO22E and CLMcom-CCLM4) and for two time periods (1981-2010 and 2031-2060). The demonstration area is the island of Cyprus, located in the eastern Mediterranean climate change hot-spot. Cyprus is characterized by a complex coastline and steep orography that drive the precipitation distribution. As a reference dataset, we used a high resolution (1x1km) gridded observational dataset, derived from a dense network of stations. For this application, we developed a copula-based structure scheme between the reference and the simulated data sets. This was for the historical period and each model grid cell. Then, assuming this relation remains unchanged, we corrected the biases for both study periods (historical and near future). Due to the stochastic nature of precipitation, the copula schemes were developed separately for each hydrological season (i.e., wet: November to March and dry: April to October). In addition, different copula schemes were developed for non-extreme and extreme events. The results showed that the proposed method could significantly improve the modeled precipitation for both models in 85% and 92% of grid cells, respectively. These improvements are evident throughout the year and for both extreme and non-extreme values. The climate change signal (precipitation decline near 7%) remains unchanged after applying the bias correction.

How to cite: Lazoglou, G., Zittis, G., and Bruggeman, A.: A novel, Copula-based approach for the bias correction of daily precipitation: a case study in the eastern Mediterranean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2518, https://doi.org/10.5194/egusphere-egu22-2518, 2022.

Alexandru Dumitrescu et al.

Four climate parameters (i.e., maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO-CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971-2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP45 and RCP85).

The best method for bias-correction was selected following a 2-fold cross-validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N-dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between the inter-variables correlation of the adjusted datasets and the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC climate scenario dataset.

Based on the MBCn adjusted dataset, a climate change analysis over Romania was performed at the seasonal and annual scales. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount.

The adjusted RCMs are provided without any restrictions via an open-access repository in netCDF CF-1.4-compliant file format (https://doi.org/10.5281/zenodo.4642463). The BC climate models are archived at the 0.1° spatial resolution, in the WGS-84 coordinate system, at a daily temporal resolution. Based on bias-corrected dataset, relevant information about climate change over Romania’s territory is provided by using an interactive dashboard, implemented in an open-source web application (RoCliB data explorer - http://suscap.meteoromania.ro/roclib).



How to cite: Dumitrescu, A., Vlad Amihăesei, V., and Cheval, S.: RoCliB - Bias corrected CORDEX RCM dataset over Romania, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12415, https://doi.org/10.5194/egusphere-egu22-12415, 2022.

Virtual presentation
Inna Khomenko and Roshanak Tootoonchi

Under the climate change, extreme precipitation responsible for flash floods, which can cause significant economic losses and human casualties, become more frequent and severe. These escalations are expected to become higher due to global warming which leads to increased water vapor in the atmosphere and thus, intensified precipitation events. Recent reports show that most flood events in Italy constitute flash floods, therefore it is projected for the region of Italy to be increasingly affected by flood events caused by heavy precipitation.

In this study, trends in extreme precipitation for present day and future projections up to 2100 under the worst-case scenario of warming, namely the Representative Concentration Pathway (RCP) 8.5 scenario are investigated using Copernicus and KNMI Climate Explorer databases.

On the basis of extremely easy-to-use KNMI Climate Explorer database anomalies of RX1day (1981-2010 reference period) for historical period and up to 2100 are retrieved for 7 Italian cities highly affected by flash floods (Venice, Rome, Naples, Genoa, Cagliari, Catanzaro, Palermo).For the mentioned regions the strong positive trends are calculated and the highest positive anomalies up to 50-80 mm/day are observed in the half of the XXI century.

The Copernicus toolbox editor was used to retrieve the RX1day index and 95th percentile from present day simulation (2011–2020) and future projection (2021–2100) of global precipitation from a total of 18 bias adjusted Global Climate Models from CMIP5 and precipitation time series for 7 Italian cities were extracted in order to obtain the trends. RX1day index doesn’t show significant increasing trend. Moreover, for the 95th percentile negative trends are obtained for most of the Italian cities in question.

Since heavy rainfalls are usually caused by convective precipitation, near surface convective precipitation trends for the period of 1991 to 2020 are derived from ERA5 monthly averaged reanalysis for the Mediterranean region and Italy, for the months in which the flash floods are often observed. The most significant increases in convective precipitation are obtained in July for Northern Italy, and in September for Southern Italy, and in November for the west coast zone.

It can therefore be said that for the historical data the positive trends in precipitation are dominated. However, for different projections and climate models from different database different results, sometimes even opposite results, are obtained.

How to cite: Khomenko, I. and Tootoonchi, R.: A Study on Heavy Rainfall and Flash Floods Using Different Climate Toolboxes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12534, https://doi.org/10.5194/egusphere-egu22-12534, 2022.


Virtual presentation
Huong Nguyen Thi et al.

Selection of a suitable Global Climate Model (GCM) in hydrological research for basin-scale of monsoon affected regions under future climate projection scenarios is a great necessity. This study comprehensively evaluated the suitability of 25 available GCMs issued of  Coupled Model Intercomparison Project 6 (CMIP6) to choose the best performing GCMs in precipitation simulating skill over the whole main River Basin System in South Korea for the historical period of 1973–2014. Bilinear interpolation method was used for mapping the grid resolution of the simulated GCMs precipitation and observed precipitation with a 0.1250 x 0.1250 resolution. Where, the observed monthly precipitation at 56 automated weather stations from 1973 to 2014 were derived from the Korea Meteorological Administration (KMA). Multi-Criteria Decision Making (MCDM) approach based on four spatial metrics, Cramer’s V, Goodman-Kruskal (GK) Lambda, Mapcurves and TheilU were proposed to compare the simulated GCMs precipitation with the observed precipitation. To calculate the overall ranking of the GCMs and identify the best performing GCMs, this study applied Jenks Natural Break classification based on the Compromise Programming index. The results indicated that: 1) The GCMs performance was different with different spatial indices with the most suitable of GCMs ranking for each watershed. 2) The best performing GCMs well simulated the annual mean precipitation with a bias of less than 15% for southwestern watersheds and higher biases (30-50%) for remaining watersheds. 3) Majority of CMIP6 GCMs could be capture trends and the spatial distribution of annual, seasonal precipitation over South Korea. However, the result was also found that most GCMs underestimated summer precipitation and overestimated spring precipitation. Therefore, the selected GCMs with corrected biases can be usefully employed for analyzing future changes of hydrological pattern associated with climate change projections.

Keywords: Global Climate Models (GCMs), CMIP6, Bilinear interpolation, Multi-Criteria Decision Making, Jenks Natural Break classification.

How to cite: Nguyen Thi, H., Kim, H.-J., Jung, M.-K., and Kwon, H.-H.: Selection of CMIP6 Global Climate Models for long-term hydrological projections , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11883, https://doi.org/10.5194/egusphere-egu22-11883, 2022.

Swen Metzger et al.

We share our experiences for impact and adaptation studies, by presenting results of a climate modelling study, which is based on ERA5 data at different horizontal resolutions, i.e., down from approximately 300 km to 25 km. The ERA5 data is used as a meteorological constraint (nudging) to perform a numerical model study on the influence of horizontal resolution on aerosol hygroscopic growth effects on meteorology in urban and remote atmospheric locations. For this sensitivity study we only switch on/off the associated aerosol water mass. Aerosol water is crucial form climate impact and adaptation studies as it links air pollution with weather and climate through direct and indirect radiative feedbacks. We try to separate urban from continental-scale effects using the EMAC atmospheric chemistry climate and Earth system model. EMAC is applied globally in various horizontal resolutions, in a set-up similar to our previous PMAp evaluation study (https://www.eumetsat.int/PMAp), i.e., resolving weather time-scales. We compare our EMAC results of the aerosol optical depth (AOD) against CAMS reference simulations (40 km), various satellite data (MODIS-Aqua/Terra, PMAp) and AERONET surface observations (~ 30km radius around the instrument). While CAMS REA includes AOD data assimilation (Modis/PMAp), EMAC calculates the AOD ab initio from size-resolved aerosol hygroscopic growth without any data assimilation, and with an option to include aerosol-cloud feedbacks. Our results show that the EMAC AOD results are within the range of CAMS and satellite AOD. Aerosol water effect on AOD is noticeable for nudged and free running EMAC versions at both, urban and remote locations. The aerosol water effect is larger for free running EMAC versions, and more pronounced for urban AERONET sites, e.g., Hamburg, Karlsruhe, Thessaloniki, Zaragoza. The moisture feedback with air pollution is resolution dependent (time and space). Generally, this becomes more relevant with increasing resolution due to finer moisture and air pollution gradients, which is an indication for the importance of horizontal resolution for impact and adaptation studies.

How to cite: Metzger, S., Feigel, G., Steil, B., Rémy, S., and Christen, A.: Influence of horizontal resolution on aerosol hygroscopic growth effects in urban andremote boundary layers in the context of climate impact and adaptation studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13495, https://doi.org/10.5194/egusphere-egu22-13495, 2022.

Dino Zardi et al.

Medium- and long-term energy planning at regional scale requires, among others, the estimate of the future energy demand driven by expected  heating and cooling needs of buildings, according to the local impact of changing climate. To support the development of the 2021-2030 Energy Plan of the Province of Trento in the Alps, temperature projections provided by EURO-CORDEX Regional Climate Models (RCMs) were downscaled at 11 weather stations, representative of altitudes between 0 and 700 m a.m.s.l., to estimate the future values of a set of parameters that are commonly used to model the energy demand of buildings, such as: Heating and Cooling Degree Days (HDDs and CDDs), Test Reference Years (TRYs) and Extreme Reference Years (ERYs). A dataset of temperature and solar radiation hourly measurements, taken at the stations starting from 1983, was quality-controlled and analyzed to estimate statistics and observed trends for both variables, as well as degree days, reference years and climate change indices from the ETCCDI set. A hybrid downscaling approach (combining statistical and dynamical techniques) is then applied to temperature projections, based on the application of the morphing method to the results of an ensemble of 16 RCMs, allowing the estimate of future TRYs, ERYs and degree days in 2030 and 2050 at the selected sites (notice that no significant variation associated with climate change was assumed for solar radiation). According to historical observations (1983-2019), the warming tendency for monthly mean temperatures is clear and falls around 0.06 °C year-1, slightly higher than reported at national level. The increase is more pronounced in spring and summer than in autumn and winter, with minima in December and especially May. No significant trend is observed for solar radiation trends. As for HDDs, stations at different altitudes show comparable reductions, of around -10 HDDs year-1, with an apparent tendency to accelerate in the most recent years. The increase of CDDs can be quantified in less than 5 CDDs year-1. The ensemble of temperature projections estimate temperature increases of 0.5 °C between 2016 and 2030 and 1.3 °C between 2016 and 2050 on average (0.03-0.04 °C year-1), implying further future reductions of HDDs (between -4 and -11% at 2030, between -10 and -21% at 2050) and increases of CDDs (between 12 and 36% at 2030, between 36 and 87% at 2050). Such changes will correspond to major modifications in the seasonal profile of the energy demand associated with the winter heating and summer cooling of buildings in the Alpine area.

How to cite: Zardi, D., Laiti, L., and Giovannini, L.: Local downscaling of temperature projections for energy planning purposes in an Alpine area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11372, https://doi.org/10.5194/egusphere-egu22-11372, 2022.

Virtual presentation
Paula Aschenbrenner et al.

West Africa is characterized by high variability in climate, has a fast growing population, and is home to a population strongly reliant on rainfed agriculture. The largely weather-dependent agricultural production is now further at risk under increasing climate change. To adequately address climate risks and avoid further pressure on food security, evidence-based information on climate impacts and guidance on the suitability of adaptation measures is required. Simulations of regional impacts of climate change on crop production are strongly influenced by the climate data used as input. The selection of climate forcing data is most influential in regions with high uncertainties in past climate data and where the agricultural production varies greatly under climate variability (Ruane et al., 2021). Both is the case in West Africa, calling for an improved understanding of past and future climate data for its use in agricultural modelling over the region. 

In this session we want to contribute to an increased understanding on the usability of different past and future climate data sets for agricultural impact models over West Africa. In a recent study, we compared ten CMIP6 (Coupled Model Inter-comparison Project Phase 6) models and their respective bias-adjusted ISIMIP3b (Inter-Sectoral Impact Model Intercomparison Project Phase 3b) versions against different observational and reanalysis data sets. Focusing on their use for agricultural impact assessments we centred the analysis on climate indicators highly influencing agricultural production and their representation in the different climate data sets.

Results show that the ten CMIP6 models contain regional and model dependent biases with similar systematic biases as have been observed in earlier CMIP versions. Although the bias-adjusted version of this data aligns overall well with observations, we could detect some regional strong deviations from observations in agroclimatic variables like length of dry spells and rainy season onset. The use of the multi-model ensemble mean has resulted in an improved agreement of CMIP6 and the bias-adjusted ISIMIP3b data with observations. Choosing a subensemble of bias-adjusted models could only improve the performance of the ensemble mean locally but not over the whole region. The results of this study can support agricultural impact modelling in quantifying climate risk hotspots as well as suggesting suitable adaptation measures to increase the resilience of the agricultural sector in West Africa.

How to cite: Aschenbrenner, P., Gleixner, S., and Gornott, C.: Dealing with climate data uncertainty for agricultural impact assessments in West Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9299, https://doi.org/10.5194/egusphere-egu22-9299, 2022.


Virtual presentation
Beate Zimmermann et al.

Adaptation to climate change is an inevitable challenge in many regions. In our study area, which is located in the state of Brandenburg in eastern Germany, land use is increasingly affected by long-lasting soil moisture deficits in the vegetation period. It is therefore important to implement measures for water retention at the landscape scale that postpone and mitigate the severity of these drought periods. Our objective is to identify cost-effective measures in a manner that maximizes expected ecological benefits for available budgets. For this purpose, we combine a scientific analysis of the determinants of land surface temperature with site-specific cost calculations.

The distribution of land surface temperature serves as a proxy for environmental conditions that favor water retention and, as a consequence, provide a certain cooling effect during hot and dry periods. Landsat thermal images from the vegetation seasons of 2013 to 2020 were rescaled (min-max normalization) and used as the response variable for a Bayesian multilevel model. Several parameters of the physical environment such as land cover, forest and crop type, soil water holding capacity, canopy cover and degree of soil sealing were used as explanatory variables. In addition, an antecedent moisture index and potential evapotranspiration at time of satellite overpass were incorporated into the model. First results highlight the importance of land use and canopy cover for land surface temperature distribution. In general, the analysis enables the identification of overheated landscapes. Moreover, model predictions after hypothetical implementation of adaptation measures provide an ecological benefit assessment based on the cooling capacities. We also determine the costs of the different measures in a spatially differentiated manner. An integrated modeling procedure combines the results from the ecological and economic assessments.

In this contribution, we will present the results of the Bayesian modeling and discuss a first example of the cost-effectiveness analysis in an agricultural landscape.

How to cite: Zimmermann, B., Hildmann, C., Kruber, S., Witt, J. C., Sturm, A., Hecker, L. P., and Wätzold, F.: Cost-effective measures for climate change adaptation in a drought-prone area in eastern Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4596, https://doi.org/10.5194/egusphere-egu22-4596, 2022.

Virtual presentation
Paula Aschenbrenner et al.

Agricultural production is highly weather-dependent in sub-Saharan Africa. Under climate change the risk of yield losses increases even further, posing a threat to farmers’ income and livelihood. Despite the availability of a wide range of adaptation strategies for the agricultural sector, information on their suitability at the local scale is limited.

In this session, we would like to discuss an example of a climate risk analysis that supports decision makers on a local scale in northern Ghana in adaptation planning. Using latest past and projected climate data as well as biophysical crop models, the study at first quantified climate impacts on agriculture.  Secondly, the suitability of different adaptation strategies was assessed under socio-economic and biophysical aspects using mixed methods including interviews, literature, a cost-benefit analysis and agricultural modelling. Differential vulnerabilities of farmers based on their identities were taken into account. Relevant stakeholders from Ghanaian local and national governmental institutions, civil society, academia, the private sector, practitioners and development partners were engaged throughout the study process in three workshops, selected the adaptation strategies and were consulted in various interviews.

Results show the dominant negative impacts of climate change on main staple crop yields in northern Ghana with differences according to region, crop and management possibilities of the farmer. The four analysed adaptation strategies (using improved seeds, cashew plantations alley cropped with legumes, Famer Managed Natural Regeneration and Irrigation) can all increase agricultural production and income while having differential positive co-benefits and negative side-effects. Unequal access to power, assets and land leads to differing opportunities in the uptake of suitable measures. Detailed recommendations for an implementation of the adaptation strategies ensuring an increased adaptive capacity of whole communities were developed and discussed with stakeholders. The information was prepared in policy briefs and short films.

How to cite: Aschenbrenner, P., Chemura, A., Habtemariam, L., Jarawura, F., and Gornott, C.: From science to action - climate risk analyses to support adaptation policies and planning at a local level in northern Ghana, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3125, https://doi.org/10.5194/egusphere-egu22-3125, 2022.

On-site presentation
Finn Laurien et al.

Communities around the world in natural hazard-prone regions are increasingly aware of the benefits of using spatio-temporal data to better understand their predicament. With the advent of new service technologies, such as web mapping, free and open satellite data and the proliferation of mobile technologies, the possibilities for both understanding and improving community resilience are on the rise. Resilience service technologies aim to provide risk-informed products in easy-to-use manner for enabling stakeholders to implement efficient and practical resilience activities in their communities.

This paper presents a service-oriented approach aiming to harnessing risks and resilience data in hazard-prone regions for raise awareness regarding early warning systems, safety conditions of minorities in community groups and plan for long-term resilience strategies. With our resilience dashboard platform, we utilize information of various risk and resilience services to identify and visualize susceptible hotspots for decision-makers. Our resilience dashboard also brings about the coordination between different web services to retrieve the features and impose the thresholds. We co-developed with local humanitarian and development teams the resilience dashboard which is designed to put geo-spatial flood resilience data into the hands of decision-makers. We identified three use cases which consider an added value of resilience service technologies by focusing on early warning systems, targeting minority groups and long-term resilience planning in Nicaragua, Nepal and Bangladesh. We will demonstrate the context-specific needs of resilience services technologies, how to target user needs and how it could potentially be scaled up and applied to similar regions around the world.

How to cite: Laurien, F., Mccallum, I., Velev, S., and Mechler, R.: Resilience service technologies for identifying climate change adaptation gaps , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1187, https://doi.org/10.5194/egusphere-egu22-1187, 2022.

On-site presentation
Jeroen Kluck et al.

In the Netherlands municipalities are searching for guidelines for a heat resilient design of the urban space. One of the guidelines which has recently been picked up is that each house should be within a 300 meter of an attractive cool spot outside. The reason is that houses might get too hot during a heat wave and therefor it is important that inhabitants have an alternative place to go. The distance of 300 m has been adopted because of practical reasons. This guideline has been proposed after a research of the University of Amsterdam of applied sciences and TAUW together with 15 municipalities.

To help municipalities to take cool spots into account in their urban design the national organization for disseminating climate data has developed a distance to coolness map for all Dutch built up areas. This map shows the cool spots with a minimum of 200 m2 based on a map of the PET for a hot summer day (2*2 m2 spatial resolution). Furthermore the map shows the walking distance for each house (via streets and foot paths) to the nearest cool spot.

This map helps as a starting point. Because not all cool spots are attractive cool spots. A research in 2021 showed what further basis and optional characteristics those cool spots should have: e.g. sufficiently large, combination of sun and shadow, benches, quiet, safe and clean. In fact those places should be attractive places to stay for most days of the year.

With the distance to attractive cool spots municipalities can easily see which areas lack attractive cool spots. The distance to cool spot maps is therefore a way to simplify complex climate data into an understandable and practical guideline. This is an improvement as compared to using thresholds for temperatures and thresholds for duration of exceedance of those temperatures in a guideline.: Municipalities like this practical approach that combines climate adaptation with improving the livability of a city throughout the year.

How to cite: Kluck, J., Kleerekoper, L., Solcerova, A., Erwin, S., Klok, L., de Groot, M., and Koekoek, A.: Distance to cool spots, a practical design guideline for heat resilient urban areas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10358, https://doi.org/10.5194/egusphere-egu22-10358, 2022.

Clemens Rendl et al.

Negative effects of climate change lead to diverse and extensive impacts. While some regions are more vulnerable than others to uncertain outlooks, reliable tools to assess climate risks, drive decisions and turn threats into opportunities are increasingly needed. Geospatial environmental data are globally available, covering populated as well as remote areas. The pool of data reaches back decades in time and grows day by day. Satellite data play a crucial role in improving the multi-dimensional description of the Earth system. This invaluable resource, when merged with socio-economic information and other open and free datasets, enables us to better understand dynamics of a globally changing climate and thus rapid and sound decision making.

ClimateLynx is a knowledge management system for climate related data and information. A knowledge base, also called “second brain”, is a tool that supports creating relationships between data and information to help think better. In our proposed service, the knowledge we want to gather, explore and exploit is data relevant for climate change induced decision making. Our vision is to create a constantly growing and evolving climate change knowledge graph supporting decision and policy makers to contribute to the sustainable development and helping us to move closer to achieving current and future climate pledges, and eventually a more sustainable future for all. ClimateLynx includes climate data and data from interdisciplinary domains alike, such as socio-economy (WB[1], ADB[2]) or health (WHO[3]). The scope is to fuse these data and thus generate location and time relevant insight. This way, a holistic approach to strengthen resilience is fostered. When the data pools are fused and put into context, it is possible to generate connections and correlations between indicators of different domains. The combination and linkage of inter-domain specific indicators could help to better understand interdisciplinary climate change induced global dynamics and tail effects. Moreover, non-obvious linkages between indicators or domains could be highlighted or even uncovered. With the help of such a tool, it could be possible to detect negative emerging climate trends based on the time series analysis of indicators earlier and react adequately.

ClimateLynx focuses on urban regions and is devoted to decision makers, urban planners and data experts. Urban planners can take advantage of ClimateLynx through comparing initiatives and developments with other cities of e.g., similar size, climatic conditions, or GDP. This enables for efficient planning and can support ideas and initiatives to create more liveable and climate resilient cities. Likewise, data experts might be interested to explore the various data sets and create new connections through linking indicators from natural and social science disciplines and thus discovering location relevant specificities.

ClimateLynx is built on top of the data access and processing capabilities of the ADAM[4] platform, to quickly access and process large volumes of data. Through ADAM, ClimateLynx is fed with climate indicators calculated from data from historic, currently operating, and future satellite missions. Global climate indicators are computed periodically, city-aggregated information is extracted off-line to offer optimal user experience.


How to cite: Rendl, C., Figuera, R. M., and Natali, S.: ClimateLynx. Generating global climatic linkages, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11838, https://doi.org/10.5194/egusphere-egu22-11838, 2022.

Thu, 26 May, 15:10–16:40

Chairperson: Judith Klostermann

Stephanie Gleixner et al.

Many countries recognise the importance of adaptation to climate change, but have limited access to reliable information on climate impacts and risks which should inform the selection of adaptation strategies. The AGRICA Climate Risk Profiles (CRPs) provide a condensed overview of present and future climate impacts and climate risks for different sectors. Based on projections from four climate models under two Greenhouse Gas emission scenarios, climate and climate impact data from the ISIMIP project is used to assess changes in climate, water resources, agriculture, infrastructure, ecosystems and human health. To date, CRPs have been published for 12 countries in sub-Saharan Africa and further CRPs are currently being developed both under the AGRICA project as well as in collaboration with external organisations. The CRPs are intended to inform decision makers from governments, international institutions, civil society, academia and the private sector regarding the risks of climate impacts in key sectors. The findings can feed into national and sub-national climate adaptation planning including NDC and NAP development, implementation and review, but also provide useful information and evidence at other strategic planning and implementation levels.

How to cite: Gleixner, S., Tomalka, J., Lange, S., and Gornott, C.: AGRICA - Climate Risk Profiles for Sub-Saharan Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11337, https://doi.org/10.5194/egusphere-egu22-11337, 2022.

Virtual presentation
Christos Tsompanidis et al.

APENA 3 ”Strengthening the capacity of regional and local administrations for implementation and enforcement of EU environmental and climate change legislation and development of infrastructure projects” is an EU-funded project, targeting to effectively raise Ukrainian public authorities capacities at local and regional level in designing and implementing key reforms. The main feature of Component 3 is the development of climate adaptation strategies followed by implementation plans for three Ukrainian Oblasts. Following an evaluation process, the Oblasts of Ivano-Frankivs’ka, L’vivs’ka and Mykolaivs’ka were determined to be the most appropriate in which to undertake the above activities. The first important step, was to identify the sectors of interest in relation to climate change, in Oblast but also National level utilizing the experiences and know-how from Europe and internationally, since the pilot Strategies and Implementation Plans, will be used as a guidance for other Oblasts in the future to elaborate regional climate adaptation planning. The selected sectors include agriculture, forests, biodiversity and ecosystems, water management, fisheries, coastal areas, tourism, critical infrastructure, energy, health, built environment and cultural heritage. The next step in the methodology is the vulnerability and risk assessment. The project team will identify the appropriate climate indices to evaluate vulnerability and risk based on specific climatic impact drivers for the respective sectors. Sensitivity and exposure analysis will follow in order to identify the degree of vulnerability of each sector and geographic area in the three pilot Oblasts. Based on the previous assessment, impacts will be identified and examined in terms of likelihood and severity, guiding the team to determine the risk. The various challenges (stakeholder engagement, sectoral issues identification, collection of climate data etc.) in the use of climate data will be identified and tackled in this stage. Following the preparation of the project’s scientific basis, the Experts team will determine sectoral adaptation thematic pillars, that will include horizontal and location specific measures and actions for the evaluated sectors.

How to cite: Tsompanidis, C., Krakovska, S., Lolos, T., Sakalis, A., Ieremiadi, E., Gittelson, A., Kysil, O., and Krasnozhon, A.: APENA3 – Methodology and steps for the preparation of three pilot climate adaptation strategies and implementation plans in Ukraine, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12037, https://doi.org/10.5194/egusphere-egu22-12037, 2022.

On-site presentation
Markela Zeneli et al.

As uncertainty around the impacts of climate change become more apparent, businesses and communities are relying on cutting-edge information to help them navigate their next steps. Climate X are a climate risk information provider that aims to help businesses and communities prepare for a rapidly changing environment, with an explainable and transparent method.

Our flagship product, Spectra, presents users with a multitude of potential hazards including flooding (fluvial, pluvial, and coastal), subsidence, landslides, and extreme heat. Each hazard risk is quantified at street level, and we project risks and impacts for low emissions (RCP2.6) and high emissions (RCP8.5) scenarios. This allows users to see the difference between the best-case and worst-case scenarios for assets across the UK.

This poster will cover our methods of finding data, interpolating, modelling, and predicting, as well as a tour of our easy-to-use UI.

How to cite: Zeneli, M., Burke, C., Ramsamy, L., Mitchell, H., Brennan, J., and Kluza, K.: Climate X: Meeting the demand for multi-hazard climate risk information tailored to financial services, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7810, https://doi.org/10.5194/egusphere-egu22-7810, 2022.


Virtual presentation
Katie J. Parsons et al.

We are the midst of a climate emergency requiring urgent climate action that is, as yet, unforthcoming both on the scale, and at the speed, commensurate with the associated hazard and risk. This paper presents work that considers this current state of inaction and explores how we might understand the underpinning processes of attitudinal and behavioural change needed through the emotional framework of loss.

This inaction is also explored through the additional lens of the year 2020, a year of tumultuous social change created by the COVID–19 pandemic. The article draws parallels with and looks to learn from the ways in which the collective loss experienced as a result of COVID–19 may offer a sense of hope in the fight to adequately address climate change but how meeting the Sustainable Development Goals will require climate injustices to also be addressed. We argue that appropriate leadership that guides widespread climate action from all is best sought from those groups already facing the loss of climate change and therefore already engaged in climate-related social action and activism, including youth and Indigenous peoples.

In this regard we present work from an ongoing project based within the Red River catchment (Vietnam), which is already experiencing enhanced hydrological extremes. Resultant floods, landslides and soil erosion in the upper region is having impacts in communities, whilst relative sea-level rises in the region are affecting the frequency and magnitude of flooding. Our research is working with young people and their communities, alongside social and environmental scientists in partnership, to identify imaginative ways to mitigate these climate change challenges and foster action. The paper will outline how this youth-led approach explores how local, traditional, and indigenous knowledges can develop understandings and strengthen local and societal resilience, incorporating peer-to-peer, intergenerational and cross-/inter-cultural forms of collaborative, and socially just, learning.

How to cite: Parsons, K. J., Jones, L., Halstead, F., Le, H., Thi Vo, T., Hackney, C. R., and Parsons, D. R.: 2020 Vision: Using transdisciplinary approaches in understanding climate (in)action through youth led participation in mitigating hydrological extremes., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3568, https://doi.org/10.5194/egusphere-egu22-3568, 2022.

Elke Keup-Thiel et al.


Climate change increasingly affects all parts of society. Different economic sectors such as the agricultural sector have to adapt to climate change. More and more climate services are being developed in order to support this adaptation to climate change with accurate and suitable products. Good practises for the design of climate services include transdisciplinary approaches and co-creation of climate service products. The development of usable and useful climate service products and effective adaptation measures requires constant interactions between climate service providers and users of the products. To assess the effectiveness of these co-creation endeavours, continuous evaluation is crucial. At present, output and outcome assessments are conducted occasionally in this research field. However, these summative evaluations that are preformed ex-post do not help to adjust the ongoing process of co-creation. Therefore, the focus of the presented work is on formative evaluation of the co-creative development of science-based climate service products. A formative evaluation is done during the run-time of a project with the intention to reflect and readjust it. For this purpose, we analysed in detail the process of co-creation of climate service products in the knowledge transfer project ADAPTER (ADAPT tERrestrial systems, https://adapter-projekt.org/) and combine this analysis with a systematic literature review. In ADAPTER, simulation-based climate service products are developed together with key partners and practitioners from the agricultural sector, with the aim of supporting decision making in the context of climate change adaptation.

As a first step, main characteristics of the product development process were identified empirically and six sub-processes of product development were determined. Secondly , questions for a formative evaluation were assigned to the different steps and sub-processes. Thirdly, a literature review including fields other than climate services delivered additional qualitative aspects. As a result, a scheme of quality criteria and related assessment questions for the different sub-processes in climate service development was created, based on both empirical and theoretical work. Subsequently, this scheme needs validation and testing. The resulting formative evaluation scheme will be particularly helpful to reflect on and to improve the co-creation processes in climate services and beyond.


How to cite: Keup-Thiel, E., Bathiany, S., Dressel, M., El Zohbi, J., Rechid, D., Schuck-Zöller, S., Suhari, M., and Timm, E.: Evaluation of co-creation processes in climate services  -  Development of a formative evaluation scheme, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4090, https://doi.org/10.5194/egusphere-egu22-4090, 2022.

Christian Bernhofer et al.

How do we succeed in supporting climate adaptation also outside of the large urban areas? Which measures for mitigating do we need to deal with the consequences of climate change? What support and which tools do smaller communities need in planning and implementing necessary measures? What is specific for low mountain ranges? To answer these questions a targeted process was initiated by researchers and practitioners from three German federal states (Saxony, Saxony-Anhalt and Thuringia), working together in the interdisciplinary project KlimaKonform within BMBF RegIKlim.

The model region covers three counties in the catchment area of the Weiße Elster. The low mountain region is typical for large parts of Germany and other Central European countries. Thus, the approach, experiences, methods and products are easily transferable to other low mountain ranges. Small and medium-sized municipalities have to deal often with limited budgets, as well as limited technical and administrative capacities. Community income is mainly generated by agriculture and forestry, small businesses and partly tourism. At the same time, the challenges posed by the increasing intensity and frequency of extreme events such as flash floods, water shortage, heat waves and storms are similar to large cities with much higher capacities in personnel and finances.

Unfortunately, adaptation to extreme weather and climate change often comes only after a damaging event, for example after extreme precipitation destroyed the municipal water infrastructure (paths, sewer network, and waste water treatment plants). KlimaKonform supports communities to become active before damage occurs and thus foster the move from event-related to preventive and strategic action. Therefore, KlimaKonform offers new concepts and customised tools to assess the impacts of climate change, determine their capacities for adaptation and derive appropriate measures. The tools will consider the needs in the model region and address the uncertainties related to future climate change and climate model output.

Examples are given for various foci of the project. One focus of KlimaKonform involves the interdisciplinary assessment of extreme events by coupled model chains ranging from climate change ensembles to third order impact models. Hazards as heavy rainfall and floods with their impacts are incorporated. The location in the low mountain range requires high-resolution climate input data for modelling due to corresponding high flow velocities. These data are not sufficiently available for regional climate impact modelling. In cooperation with the project NUKLEUS and hydro-impact modellers in RegIKlim, approaches like bias adjustment of climate model outputs are tested for applicability. The aim is to reduce uncertainties in model application while increasing the effectiveness of precautionary and adaptation measures. Another focus of KlimaKonform is the systematic identification of vulnerable infrastructure during heat waves. In this context, urban climate simulations are used to assess the potential of green infrastructure to reduce outdoor and indoor heat stress conditions. All results of KlimaKonform will be available free of charge and in a comprehensible form via a freely accessible internet platform. Here, the already existing and well-received Regional Climate Information System ReKIS will be expanded to provide guidance for smaller communities.

How to cite: Bernhofer, C., Heidenreich, M., Maleska, V., Schinke, R., and Wollschläger, N.: KlimaKonform – An interdisciplinary project to support smaller communities in climate change adaptation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8085, https://doi.org/10.5194/egusphere-egu22-8085, 2022.