Bringing together climate scientists and impact modellers to build knowledge to effectively deal with climate change


Bringing together climate scientists and impact modellers to build knowledge to effectively deal with climate change
Co-organized by HS12/NH1
Convener: Giorgia Fosser | Co-conveners: Hayley Fowler, Elizabeth Kendon, Andreas F. Prein
vPICO presentations
| Wed, 28 Apr, 15:30–17:00 (CEST)

vPICO presentations: Wed, 28 Apr

Chairpersons: Giorgia Fosser, Hayley Fowler
Anneli Guthke, Amin E. Bakhshipour, Felipe P.J. de Barros, Holger Class, James E. Daniell, Ulrich Dittmer, Markus Friedrich, Jannik Haas, Cordula Kropp, Bruno Merz, Sergey Oladyshkin, Andreas Schäfer, Michael Sinsbeck, Daniel Straub, Kristina Terheiden, Silke Wieprecht, and Wolfgang Nowak

Climate change impact and risk assessment is per definition a highly interdisciplinary task. Collaboration across disciplines is, unfortunately, often complicated by different perspectives, approaches, and terminology. To help building bridges, we propose a generalized mathematical framework for impact and risk assessment.

In an unprecedented community effort, we have derived a generally applicable risk equation for spatially-distributed and dynamic systems. We start off with a general framing and then refine individual parts of the equation as much as needed. We will show how the individual terms of our unified risk equation explicitly relate to concepts of frequency, intensity, duration, exposure, vulnerability and asset worth. The rigorous mathematical treatment allows investigating the importance of risk factors and serves as a basis for risk management and reduction. Yet, the actual quantification of risk is not our primary goal – rather, the proposed framework forces us to be very precise in definitions and terminology. Thereby, it effectively improves communication and collaboration across disciplines. Indeed, we even learn greatly in cases where we identify limitations that seem to spoil such a mathematically rigorous treatment.

We have successfully applied the framework to various disciplines of civil and environmental engineering, such as flood risk assessment, seismic risk assessment and reliability analysis of critical infrastructure. Users of the equation praise the structured common ground for discussion and highly recommend at least hypothetically applying this framework to gain a more unified understanding of the problem at hand. In this presentation, we discuss the potential of our proposed framework for risk assessment under climate change. Our transparent and rigorous approach is ideally suited to inform stakeholders and policymakers. Further, we are confident that our approach will serve as a catalyst for interdisciplinary advances toward effective adaptation and mitigation strategies.

How to cite: Guthke, A., Bakhshipour, A. E., de Barros, F. P. J., Class, H., Daniell, J. E., Dittmer, U., Friedrich, M., Haas, J., Kropp, C., Merz, B., Oladyshkin, S., Schäfer, A., Sinsbeck, M., Straub, D., Terheiden, K., Wieprecht, S., and Nowak, W.: Building bridges between disciplines: A generalized mathematical framework for climate change impact assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13386,, 2021.

Thorsten Wagener and Francesca Pianosi

Understanding the implications of climate change for our environment and subsequent services and disservices for nature and society is a key science challenge of our days. Simulation model chains that link the causality of climate-meteorology-hydrology-impact in some way or another are rapidly being developed and increasingly applied to understand the implications of future climate change projections. We discuss in our contribution the urgent need to simultaneously develop protocols to evaluate such models and their adequacy to ensure that scientific rigour is upheld in such analyses. We believe that such an evaluation protocol should consist of at least 3 evaluation stages to ensure a model is justified and its limitations are understood. These are: [1] Establishing an impact model as an adequate representation of our current understanding of the underlying system. [2] Establishing an impact model as an adequate model for the task at hand. [3] Establishing that dominant processes are adequately depicted to enable the assessment of intervention strategies. We argue that it is important to distinguish these stages because achieving stage 1 does not guarantee stage 2, while both stages 1 and 2 can potentially be achieved without ensuring stage 3. Different approaches to implement each of these stages exist and they range in rigour from simple (possibly simplistic) to complex (and therefore demanding). In our contribution we will use different impact modelling examples to discuss the current state of impact model evaluation, the limitations of current strategies and methods, and define additional development needs to obtain the scientific rigour we believe is needed for credible and robust impact assessment.

How to cite: Wagener, T. and Pianosi, F.: A protocol for the evaluation of climate change impact models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3413,, 2021.

Kevin Sieck, Bente Tiedje, Hendrik Feldmann, and Joaquim Pinto

Given the current developments in climate science it becomes more a more feasible to provide climate information at the kilometer-scale from convection-permitting climate simulations. This progress will enable many users to directly feed high-resolution climate information into their impact-models for climate impact studies at the local scale. Examples include urban heat stress at street level or the design of drainage systems for future precipitation extremes. Within the RegIKlim (Regional information for action on climate change) consortium, the NUKLEUS (Actionable local climate information for Germany) project will not only provide climate information at the local scale, but also to co-develop interfaces between climate and impact models, in order to fulfil the needs of the impact modelling community as good as possible. Within the RegIKlim consortium, the impact modelling community is organised in six “model regions” across Germany, which cover a wide range of geographical and socio-economic conditions.

For the NUKLEUS project, the baseline will be the latest generation of EURO-CORDEX downscaled CMIP6 simulations, which will be further refined to roughly 3 km horizontal resolution and 30-year time-slices for Germany with convection-permitting climate models (ICON CLM, COSMO-CLM, REMO-NH) and statistical-dynamical downscaling approaches. A detailed analysis on the performance of the multi-model mini-ensemble is planned to assess the quality of the provided data. At the interface to the users, we will follow three different approaches to provide usable climate information at the kilometer-scale. One is to provide easy-access to data and post-processing opportunities using the FREVA system. FREVA offers various access-levels from shell to web-based, which serves different levels of user-expertise. In addition, it provides a transparent way of post-processing data by workflow sharing mechanisms. The second one is to develop appropriate additional downscaling methods for the “last mile” where needed. For this “last mile”, we will apply dynamical and statistical methods such as urban climate models and/or weather generators. With the third approach we explicitly aim at integrating a collected user-feedback into the regional modelling systems used within NUKLEUS. Specifically, we intend to identify and incorporate data processing that is best done during the simulation permanently into the models. Examples are wind speeds at rotor heights of windmills or high frequency precipitation sums. NUKLEUS is a contribution to the German research program RegIKlim funded by the Federal Ministry of Education and Research (BMBF).

How to cite: Sieck, K., Tiedje, B., Feldmann, H., and Pinto, J.: NUKLEUS - User-relevant and applicable kilometer-scale climate information for Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12137,, 2021.

Camilla Andersson and the BioDiv-Support research team

Biodiversity includes any type of living variation, from the ecosystem level to genetic variation within organisms. The greatest threats to biodiversity is climate change, destruction of habitats and other human activities. High-altitude mountain regions are pristine environments, with historically small impacts from air pollution, but at risk of being disproportionately impacted by climate change. We focus on three mountainous regions: the Scandinavian Mountains, the Guadarrama Mountains in Spain, and the Pyrenees in France, Andorra and Spain. We study the impact of drivers of change of biodiversity such as future climate change, increased incidences of wild fires, emissions from new shipping routes in the Arctic as ice sheets are melting, human impacts on land use and management practices (such as reindeer grazing) and air pollution.

We simulate future climate change using WRF and a convective permitting climate model, HARMONIE-Climate, with a spatial resolution of 3km. The high resolution strongly improves the representation of precipitation compared to coarser scale simulations (Lind et al., 2020). We use these simulations to develop future scenarios of air pollution load, using two well established chemistry transport models (MATCH and CHIMERE; Marécal et al., 2015). These climate and air pollution scenarios are subsequently used, together with management scenarios, to develop scenarios for biodiversity and ecosystem services. These scenarios are developed applying a process-based dynamic vegetation and biogeochemistry model, LPJ-GUESS (Smith et al., 2014). 

The scenarios, representing mid-21st century, will be made available through a web-based planning tool, where local stakeholders in each region can explore the project results to understand how scenarios of climate change, air pollution and policy development will affect these ecosystems. Local stakeholders are involved throughout the project, such as reindeer herder communities, regional county boards and national authorities, and in a time of changing climate and a global pandemic we have learned the necessity for flexibility in such interactions.



Lind et al. 2020., Climate Dynamics 55, 1893-1912.

Marécal et al., 2015. Geosci. Mod. Dev. 8, 2777-2813.

Smith et al. 2014 Biogeosciences 11, 2027-2054.

How to cite: Andersson, C. and the BioDiv-Support research team: BioDiv-Support: scenario-based decision support tool for policy planning and adaptation to future challenges in biodiversity and ecosystem services, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14412,, 2021.

Sarah Chapman, Cathryn Birch, Marcelo Galdos, Edward Pope, Jemma Davie, Catherine Bradshaw, Samuel Eze, and John Marsham

East Africa has high rates of soil erosion which negatively impact agricultural yields. Climate projections suggest that rainfall intensity will increase in East Africa, which may increase soil erosion. Soil erosion estimates require information on rainfall erosivity, which is calculated using sub-daily storm characteristics that are known to be biased in traditional parameterized convection climate models. Convection-permitting climate models, which are run at higher resolution to negate the need for convection parameterisation, generally better represent rainfall intensity and frequency. We use a novel convection-permitting pan-Africa regional climate model (CP4A) to estimate rainfall erosivity in Tanzania and Malawi, and compare it to its parameterized counterpart (P25), to determine if there is a benefit to using convection permitting climate models to look at rainfall erosivity. We use 8-year historical and end-of-century RCP8.5 simulations to examine the impact of climate change on rainfall erosivity. We then apply the Revised Universal Soil Loss Equation (RUSLE), using the rainfall erosivity estimates from CP4A and P25, to calculate soil erosion in Tanzania and Malawi. The distribution of rainfall intensity and duration was closer to the TRMM rainfall observations in the convection permitting model than in the parameterized model before and after bias-correction. We found that rainfall erosivity was lower in the parameterized convection model than in CP4A due to differences in storm characteristics, even after bias-correction. These results suggest that parameterized convection regional and global climate models might under-estimate rainfall erosivity, and the associated soil erosion. We found high values of present day erosion associated with mountainous regions in Tanzania and Malawi in CP4A. Under climate change, areas at high risk of soil erosion expanded due to increases in rainfall intensity in CP4A. The levels of soil erosion were high enough to negatively impact on agricultural yields.  Soil management was less effective in the future at reducing soil erosion risk than in the present day, and more extensive soil management may be required in the future to manage soil erosion and reduce the negative impacts of soil erosion on agriculture.

How to cite: Chapman, S., Birch, C., Galdos, M., Pope, E., Davie, J., Bradshaw, C., Eze, S., and Marsham, J.: AFRICAP - The impact of climate change on soil erosion in Tanzania and Malawi in a convection-permitting model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1032,, 2021.

Laura Massano, Giorgia Fosser, and Marco Gaetani

In Italy the wine industry is an economic asset representing the 8% of the annual turnover of the Food & Beverage sector, according to Unicredit Industry Book 2019. Viticulture is strongly influenced by weather and climate, and winegrowers in Europe have already experienced the impact of climate change in terms of more frequent drought periods, warmer and longer growing seasons and an increased frequency of weather extremes. These changes impact on both yield production and wine quality.

Our study aims to understand the impact of climate change on wine production, to estimate the risks associated with climate factors and to suggest appropriate adaptation measurement. The weather variables that most influence grape growth are: temperature, precipitation and evapotranspiration. Starting for these variables we calculate a range of bioclimatic indices, selected following the International Organisation of Vine and Wine Guidelines (OIV), and correlate these with wine productivity data. According to the values of different indices it is possible to determine the more suitable areas for wine production, where we expect higher productivity, although the climate is not the only factor influencing yield.

Using the convection-permitting models (CPMs – 2.2 horizontal resolution) we investigate how the bioclimatic indices changed in the last 20 years, and the impact of this change on grapes productivity. We look at possible climate trends and at the variation in the frequency distribution of extreme weather events. The CPMs are likely the best available option for this kind of impact studies since they allow a better representation of surface and orography field, explicitly resolve deep convection and show an improved representation of extremes events. In our study, we compare CPMs with regional climate models (RCMs – 12 km horizontal resolution) to evaluate the possible added value of high resolution models for impact studies. To compare models' output to observation the same analysis it carried out using E-OBS dataset.

Through our impact study, we aim to provide a tool that winegrower and stakeholders involved in the wine business can use to make their activities more sustainable and more resilient to climate change.

How to cite: Massano, L., Fosser, G., and Gaetani, M.: Climate change impacts on viticulture in Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3112,, 2021.

Katrin Ziegler, Felix Pohl, Felix Pollinger, and Heiko Paeth

Adapting the impacts of climate change is a great challenge. To facilitate forest adaptation long-term and forward-looking decisions must be made today since they have to be valid for several decades. Therefore, fundamental knowledge of the future climate and of tree species which are more resilient to the future climate than trees growing in the forests today is necessary.

To give local foresters a basis for their decisions, we use the so-called analogue region method. With this method we aim to find regions in Europe which currently have the same climate as it is projected in a specific reference region for different future scenarios. For the projections, the model runs of the regional climate model REMO are used. As an example of finding analogue regions, we selected the forest region Steigerwald in North Bavaria. We use different climatic and forest specific indices and data preparation methods to test the influence of varying indices and methods on the resulting regions. After identifying the respective analogue regions, we analyze which tree species are growing currently in these regions by using the EU-Forest data set.

How to cite: Ziegler, K., Pohl, F., Pollinger, F., and Paeth, H.: Different approaches of finding European climate analogue regions for the Steigerwald forest (Germany) in the future, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-542,, 2021.

Steven Chan, Elizabeth Kendon, Benjamin Youngman, Giorgia Fosser, Christopher Short, Hayley Fowler, Simon Tucker, and Murray Dale

The UK Climate Projections (UKCP) provide the latest information on future climate change expected in the UK. The latest UKCP products include the first UK national climate scenarios at a resolution consistent with weather forecasting. In particular, they include projections from a 12-member 2.2km convection permitting climate model (CPM) ensemble, called UKCP Local (2.2km), released by the UK Met Office in September 2019. A key added value of CPMs is their improved representation of precipitation extremes, and as such the UKCP Local ensemble is particularly useful for water management stakeholders (water utilities and flood risk management professionals) for future adaptation in waste water and flood risk management. A key metric of interest is future increases (“uplifts”) of precipitation return levels. However, diagnosing precipitation return levels for such high-resolution model simulations is difficult due to their spatial-temporal variability and correlation. Here, we adapt an Exeter University-developed spatial extreme statistical model which incorporates the spatial-temporal variability and correlation of precipitation extreme, and apply it to daily and hourly precipitation data from the UKCP Local Ensemble for both the present-day and future RCP8.5 simulations. This allows us to provide robust estimates of uplifts for high return levels across all of the UK for months and seasons of interest.

How to cite: Chan, S., Kendon, E., Youngman, B., Fosser, G., Short, C., Fowler, H., Tucker, S., and Dale, M.: Providing future UK heavy precipitation guidance for water management stakeholders using a convection-permitting climate model ensemble and a spatial extreme statistical model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-726,, 2021.

Trine Jahr Hegdahl, Kolbjørn Engeland, Malte Müller, and Jana Sillman

Atmospheric rivers (AR) are responsible for the most extreme precipitation events causing devastating landslides and floods in western Norway. In this study an event-based storyline approach is used to compare the flood impact of extreme AR events in a warmer climate to those of the current climate.  The four most extreme precipitation events were selected from 30 years of present and future climate simulations from the high-resolution global climate model, the EC-Earth model. For each of the four events, EC-Earth was rerun creating 10 perturbed realizations. A regional convective permitting weather prediction model, AROME-MetCoOp, was used to further downscale the events, and thereafter the operational Norwegian flood-forecasting model was used to estimate the flood levels for 37 catchments in western Norway. The magnitude and the spatial impact were analyzed, and different hydrological initial conditions, which affect the total flooding, were analyzed.

The results show that more catchments were affected with larger floods in the future climate events compared to the current climate events. In addition, the combination of multiple realizations of meteorological forcing and different hydrological initial conditions, for example soil saturation and snow storage, were important for the estimation of the maximum flood level. The meteorological forcing had the highest overall effect on flood magnitude; however, varying and depending on event and catchment. Finally, operational flood warning levels were used to visualize the difference between future and current climate flood events. Applying a setup similar to the one used operationally and relating the future events to known current events associated with ARs, enables a common reference and ease communication with end-users and decision makers.

How to cite: Hegdahl, T. J., Engeland, K., Müller, M., and Sillman, J.: Applying a storyline approach to explore the impact of future Atmospheric River induced floods in western Norway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8309,, 2021.

Sarah Johnson, Robert Wilby, Dapeng Yu, and Tom Matthews

Flooding is a major global hazard that accounts for one-third of all reported natural disasters and over 500,000 fatalities since 1980. Globally, vulnerable populations (very young, elderly, medical special needs individuals, etc.) are disproportionately affected by flooding and predominantly encompass the majority of flood associated injuries and fatalities. This is caused by their low self-reliance, weak political voice and insufficient inclusion into climate adaptation and emergency response plans.

Vulnerable individuals are largely reliant on Ambulance and Fire & Rescue Services due to flood induced injuries, exacerbated medical conditions, and requiring evacuative assistance. These services are primary emergency responders to flooding that provide rescue and relief efforts. However, during flood events, the demand for Ambulance and Fire & Rescue Service often exceeds the potential capacity and limits service provision, whilst flooded road networks and short emergency responder-timeframes decrease accessibility, service area and population coverage.

Therefore, an important step towards resolving these social inequalities and emergency responder strains from flooding is to understand the geographic, spatial, temporal, and demographic distributions of vulnerability. This will be undertaken by identifying vulnerability ‘hotspots’ of global populations in terms of emergency service provision during times of flooding of various magnitude under climate change.

The research will use Big Geographical and Climate Data and a ‘hotspot’ approach to investigate how the global extent and distribution of flood hazards and vulnerable population hotspots vary spatially and temporally, based on differing global fluvial and coastal flooding (at 10-year and 100-year return periods), and present and future flood conditions (present-day and 2050, under RCP 4.5 and RCP8.5 climate scenarios). Network Analysis modelling will be used to investigate the impact of this on Ambulance and Fire & Rescue accessibility from service stations to vulnerable populations based on restrictions of road network inundation and emergency response-times (8-, 15-, and 60- minutes). Finally, comparisons will be made to highlight how vulnerability and emergency service accessibility compares demographically between different vulnerable population groups.

It is expected that there will be significant geographical and temporal differences in social vulnerability and emergency service provision between countries and regions globally. Although to what extent is currently unknown. Ultimately, the framework of this research may provide real-world applications for informing strategic planning of emergency response operations and resolving social inequalities to flood hazards. These applications could include the production of more detailed flood hazard and evacuation maps that highlight vulnerability hotspots, the prioritisation of vulnerable population groups in emergency response plans to minimise geographic and population disparities of flood injuries and fatalities, and the allocation of emergency service hubs in regions of high-vulnerability but low-emergency response provision.

How to cite: Johnson, S., Wilby, R., Yu, D., and Matthews, T.: Global assessment of flood impact on emergency service provision to vulnerable populations under climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1566,, 2021.

Evaluation of the impacts of climate change and land-use dynamics on water resources: The case of the Lobo River watershed: Central-Western Côte d'Ivoire
Berenger Koffi, Zilé Alex Kouadio, Affoué Berthe Yao, Kouakou Hervé Kouassi, Martin Sanchez Angulo, and Kouakou Lazare Kouassi
Albrecht Weerts, Frederiek Spera Weiland, and Marjanne Zander

Rainfall-runoff modelling of volcanic islands for future risk assessment (and beyond)

Volcanic islands are often densely populated and attract large numbers of tourists each year. Climate change may alter weather related risks like floods on these islands. They have often complex orography that influences the precipitation patterns. For impact assessments therefore, this complexity calls for detailed modelling. As part as part of the H2020 European Climate Prediction System project ( we aim investigating the usage of the new generation convection-permitting regional climate models (Ban et al., accepted for publication 2021) for future flood risk and water security assessments.

Here we focus on the Lesser Antilles and La Reunion that are part of EU’s outermost regions. We have setup distributed hydrological wflow_sbm models at ~1km2 resolution for each island following the approaches of Imhoff et al (2020) and Eilander et al. (2021). Validation of these models is difficult because of the poor quality of available precipitation data sources and limited discharge observations records if available at all. Still, for La Reunion, we show that wflow_sbm performs well once driven with high resolution gridded rainfall (provided by MeteoFrance). CHIRPS rainfall (Funk et al., 2014) shows potential in some seasons but leads to significant underestimation of flow for other seasons. Similar behavior is obtained for rivers on Guadeloupe and Martinique islands in the Lesser Antilles (where high-quality gridded rainfall data is lacking). To further validate the approach/models for the Lesser Antilles, we also setup a wflow_sbm model for the whole of the Dominican Republic (including Haiti) and compared the wflow_sbm model against available discharge observations using both ERA5, CHIRPS and MSWEP2.0 as rainfall sources.

Next step in the project will be to force the wflow_sbm model for La Reunion with future climate projections obtained with AROME. For the Lesser Antilles, we will force the wflow_sbm models using pseudo global warming scenarios.

Besides the intended use for flood risk (incl. operational forecasting) and water resources management, these high-resolution hydrological models and climate scenarios may be helpful in exploring future changes to river water salinization, inflows of sediment and nutrient/pollutants to nearby coastal waters and coral reefs.


Ban, N., E. Brisson, C. Caillaud, E. Coppola, E. Pichelli, S. Sobolowski, …, M.J. Zander (2021): “The first multi-model ensemble of regional climate simulations at the kilometer-scale resolution, Part I: Evaluation of precipitation”, manuscript accepted for publication in Climate Dynamics.

Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J.: A hydrography upscaling method for scale invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci. Discuss. [preprint],, in review, 2020

Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p.

Imhoff, R.O., W. van Verseveld, B. van Osnabrugge, A.H. Weerts, 2020. “Scaling point-scale pedotransfer functions parameter estimates for seamless large-domain high-resolution distributed hydrological modelling: An example for the Rhine river.” Water Resources Research, 56. Doi: 10.1029/2019WR026807


How to cite: Weerts, A., Spera Weiland, F., and Zander, M.: Rainfall-runoff modelling of volcanic islands for future risk assessment (and beyond), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12992,, 2021.

Matilde García-Valdecasas Ojeda, Erika Coppola, Fabio Di Sante, Francesca Raffaele, Rita Nogherotto, and Filippo Giorgi

A common way to study the impact of climate change on water resources is through hydrological models fed by precipitation from global or regional climate models (GCMs and RCMs, respectively). However, precipitation from climate models is usually affected by systematical biases that may produce inadequate streamflow estimations. For this reason, users find it necessary to apply some bias-corrected technique to reduce errors in precipitation before its use in hydrological simulations. Among the different methods, quantile mapping (QM) is a widely used method as it has shown satisfactory results for historical conditions.

In recent years, several studies have investigated the QM method, with a focus on mean precipitations. However, it remains quite uncertain how bias-corrected precipitation modifies river discharges, particularly the extreme discharges on a sub-daily timescale. In this framework, this study aims to quantify differences between simulated river discharges using corrected and uncorrected precipitation to feed a hydrological model in the context of flood hazard assessment in Italy.

To adequately estimate flood events, high spatiotemporal resolution data are required. Therefore, sub-daily precipitation outputs from the ICTP RegCM Regional Climate Model driven by the HadGEM2-ES model at 12 km were contemplated in this study. Precipitation outputs for the period 1976-2100 were bias-corrected concerning the observations from GRIPHO, which is a high-resolution observational product. Then, bias-corrected and uncorrected precipitations were used to feed the CETEMPS Hydrological Model (CHyM) completing thus, a set of hydrological simulations covering the entire Italian Territory, in both present-day and future conditions. Analyses focused on the comparison between simulated and observed discharges for present-day conditions, but also on the comparison between corrected and uncorrected values ​​in the future.

The results of this study could provide valuable information on whether the use of the QM method is appropriate for studying extreme discharges on a sub-daily scale, an essential issue for assessing the impacts of climate change on extreme hydrological events.

Keywords: flood hazard assessment, quantile delta mapping, CHyM model, RegCM model, Italy

Acknowledgments: The research reported in this work was supported by OGS and CINECA under HPC-TRES program award number 2020-02.

How to cite: García-Valdecasas Ojeda, M., Coppola, E., Di Sante, F., Raffaele, F., Nogherotto, R., and Giorgi, F.: The impact of using bias-corrected precipitation in estimating extreme discharge: A study over the Italian Territory, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13201,, 2021.

Soohyun Yang, Olaf Büttner, Rohini Kumar, Stefano Basso, and Dietrich Borchardt

Climate change impacts on natural environments and human-built landscapes have been extensively studied from the meteorological, hydrological, agricultural, and urban point of views. Embracing the inevitability of climate change, there is a need for investigating and establishing adaptation strategies to changing climate conditions in order to protect essential resources for the survival of humans and ecosystems. Especially for surface water resources, water quality in rivers is a sensitive aspect which might be affected by the impact of climate change on hydrological regimes along river networks.

In fact, with a grand target of achieving Good-Ecological-Status for all European surface water bodies, the implementation of the EU Water Framework Directive since year 2000 has facilitated remarkable reductions of point-source nutrient loads discharged from municipal wastewater treatment plants (WWTPs) into rivers. Nevertheless, satisfying the environmental regulations at the emission-pipe-end of individual WWTPs has not guaranteed a perfect resolution of river water quality problems (e.g., eutrophication) at the scale of entire river basins. This likely occurred because decisions concerning WWTPs size and location were mainly influenced by the scale and location of residential areas and driven by efficiency purposes. That is, the hydrological, biogeochemical, and ecological characteristics of river water bodies receiving the WWTPs emissions were less likely to be considered. Climate-change-driven shifts of hydrological regimes in rivers could exacerbate the current situation and accelerate the water quality degradation caused by the urban emissions.

To tackle this issue, this study aims to decipher the interplays between WWTPs discharges and hydrological regimes of the receiving river water bodies, and to assess water quality risks due to WWTPs emissions under climate-change-induced alteration of hydrologic regimes, by using systematic and general tools at the scale of entire river networks (e.g., combined dimensions of stream-orders and WWTP-sizes). To this end, we synthesize the EU-scale reliable dataset for river networks and WWTPs and the simulation results of the mesoscale hydrologic model under a climate change scenario. We focus on nutrient concentrations (NH4-N, total P) and urban discharge fraction from WWTPs (i.e., the fraction of treated wastewater in river flows), performing the risk assessments for three large European river basins. Our diagnostic results at the river-network-scale could assist river basin managers and stakeholders to select WWTPs to be preferentially managed for minimizing water quality risks in the future under climate change. The presented concept here for the specific components is generally applicable to assess environmental risks and guide strategic management options for other pollutants in urban emissions (e.g., microplastics and pharmaceuticals).

How to cite: Yang, S., Büttner, O., Kumar, R., Basso, S., and Borchardt, D.: Risk Assessment of Climate Change Impacts on Urban Discharge Fraction and Eutrophication in Large European River Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5568,, 2021.

Maria Sunyer, Louise Parry, Oliver Pritchard, Harriet Obrien, Astrid Kagan, Laura Frost, and Ben Smith

Climate resilient infrastructure is essential for the safety, wellbeing, sustainability and economic prosperity of cities. An understanding of current and future climate risks is an essential consideration for the planning, design, delivery and management of new and existing resilient infrastructure systems. While there is a growing number of tools which focus on assessing specific components of climate risk there is a need for tools which help bridge the gap between climate science, resilience practitioners, infrastructure owners and policy makers.

The Climate Risk Infrastructure Assessment Tool developed within the Climate Science for Service Partnership China (CSSP China) aims to help planners and policy-makers understand how climate change may impact a city’s infrastructure systems. CSSP China seeks to bring together climate practitioners in China and the UK, and to forge links between climate scientists and industry practitioners to develop practical tools that translate the science into valuable insights for policymaking, planning and design. The development of this tools builds on earlier work carried out with the Shanghai Met Service and the British Embassy in Beijing to develop a qualitative tool to guide the assessment of climate risks for infrastructure.

The tool guides the user through a semi-quantitative climate risk assessment for a section of an infrastructure system. At present it uses ensemble data from global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to estimate and visualise future climate change projections helping cities understand the current and future likelihood of weather events. The tool then enables cities to assess the overall impact of severe weather on infrastructure by determining its vulnerability and criticality. Risk is estimated as a combination of event likelihood and impact. For key risks, guidance on implementing appropriate adaptation measures is provided to support planners and policy-makers to consider what action is needed.

How to cite: Sunyer, M., Parry, L., Pritchard, O., Obrien, H., Kagan, A., Frost, L., and Smith, B.: Understanding climate-related risks to infrastructure in Chinese cities, Climate Risk Assessment of Infrastructure Tool, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13365,, 2021.

Yuliya Rudakova, Igor Shkolnik, Elena Khlebnikova, and Vladimir Kattsov

The prospects of using the probabilistic regional climate projection technique for adaptation to climate change in the territory of Russia are considered. The analysis focuses on future changes in the climatic indicators of the thermal regime and humidification which play a significant role in the evaluation of the reliability of the functioning of construction and technical systems as well as transport and energy infrastructure.

The analysis is based on the output of the 50-member ensemble of high-resolution climate projections using an RCM developed at the Main Geophysical Observatory (MGO). The RCM grid has a horizontal resolution of 25 km across Russia. Modeling projections have been recently used to assess the impacts of regional climate change on hydropower facilities (Shkolnik et al., 2018).

Numerical experiments are carried out from different (random) initial conditions for the baseline 1990-1999 and future periods 2050-2059 and 2090-2099 using the IPCC RCP8.5 scenario (Kattsov et al., 2020). The boundary conditions on the ocean surface are derived from the output of the five CMIP5 models. For each ocean state trajectory, ten experiments from the different initial conditions are conducted. Lateral boundary conditions for the RCM ensemble are provided by MGO AGCM under an identical experimental setup.

To study the future impacts of the thermal regime, several universal indicators are used, particularly, the annual and seasonal extremes of temperature for a given averaging period as well as the characteristics of intra-annual periods with the temperature above/below the thresholds. The thresholds ​​are selected to meet the needs of construction, land transport, and the energy sector. Besides, the indicators of the precipitation regime are considered (seasonal maxima of daily amounts and characteristics of dry/wet periods).

Along with obtaining median ensemble estimates of changes in mean values, an analysis of future changes in the indicators in the probabilistic aspect is conducted. Using the temperature of the hottest 30-day period and the maximum duration of the dry period, the regional features of their projected changes are demonstrated accounting for the contribution of internal climatic variability. In agreement with observations, significant differences in the changes between the European part of Russia and certain regions of its Asian part are revealed.

The study is supported by the Russian Science Foundation (grant 16-17-00063).


Kattsov V., E. Khlebnikova, I. Shkolnik, and Yu. Rudakova: Probabilistic Regional Climate Projecting as a Basis for the Development of Adaptation Programs for the Economy of the Russian Federation. Russian Meteorology and Hydrology, 2020, Vol. 45, No. 5, pp. 330–338. Allerton Press, Inc., 2020.

Shkolnik, I., Pavlova, T., Efimov, S. et al. Future changes in peak river flows across northern Eurasia as inferred from an ensemble of regional climate projections under the IPCC RCP8.5 scenario. Clim Dyn 50215–230 (2018).

How to cite: Rudakova, Y., Shkolnik, I., Khlebnikova, E., and Kattsov, V.: Probabilistic projection of the regional climate as the basis for the development of adaptation programs in the economy of Russia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2030,, 2021.

Irene Brox Nilsen, Inger Hanssen-Bauer, Ole Einar Tveito, and Wai Kwok Wong

This presentation describes projected changes in the number of days with zero-crossings (DZCs) for Norway, that is, a day where the maximum temperature exceeds 0 °C and the minimum temperature drops below 0 °C, as an example of how the Norwegian Centre for Climate Services disseminates climate information to various user groups. Changes in DZCs have been requested by several user groups in Norway, for instance by agriculture and the transport sector. 
A cold bias was detected in the regional climate model ensemble for Norway (here: EURO-CORDEX), which highlighted the need for bias-adjusting temperature fields before analyses. This is important for any index that is dependent on a fixed temperature threshold, not only the given index DZCs.
Gridded projections of changes in DZCs were produced for the period 2071–2100 relative to 1971–2000 under RCP4.5 and RCP8.5, at a 1 × 1 km resolution. The projections have been made publicly available at the Norwegian Centre for Climate Services' website Results show that in regions and seasons that are mild, the number of DZCs is thus projected to decrease. This decrease was found for lowland regions in spring and coastal regions in winter. In regions and seasons that are cold, the number of DZCs is projected to give more frequent crossings of the 0 °C threshold. This increase was found for inland regions in winter and the northernmost county, Finnmark, in spring. Thus, more frequent icing of the snowpack is expected in Finnmark. This information can be used by the transport sector (e.g. winter road maintenance) and agriculture (e.g. reindeer herders) in the relevant regions. The Norwegian Centre for Climate Services disseminates information through fact-sheets, web-based maps and downloadable files.

How to cite: Nilsen, I. B., Hanssen-Bauer, I., Tveito, O. E., and Wong, W. K.: Projected changes in days with zero-crossings for Norway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1237,, 2021.

Huan Zhang and Merja Tölle

Convection-permitting regional climate model simulations may serve as driving data for crop and dynamic vegetation models. It is thus possible to generate physically consistent scenarios for the future-concerning effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM16 (CCLM) from 1980 to 2015 with a spin-up starting at 1979. The model was driven with hourly ERA5 data, which is the latest climate reanalysis product by ECMWF and directly downscaled to 3 km horizontal resolution over central Europe. The land-use classes are described by ECOCLIMAP, and the soil type and depth by HWSD. The evaluation is carried out in terms of temperature, precipitation, and extreme weather indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm/cold and dry/wet summer/winter bias found in its parent model, it reproduces the main features of the present climate of the study domain, including the distribution, the seasonal mean climate patterns, and probability density distributions. The bias for precipitation ranges between ±20 % and the bias for temperature between ±1 °C compared to the observations over most of the regions. This is in the range of the bias between observational data. Furthermore, the model catches extreme weather events related to droughts, floods, heat/cold waves, and agriculture-specific events. The results highlight the possibility to directly downscale ERA5 data with regional climate models avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate simulations of future changes in agricultural extreme events.

How to cite: Zhang, H. and Tölle, M.: Evaluation of agricultural-related extreme events in hindcast COSMO-CLM simulations over Central Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9507,, 2021.

Venkatraman Prasanna, Sandeep Sahany, Aurel F. Moise, Xin Rong Chua, Gerald Lim, Muhammad E. Hassim, and Chen Chen

Long-term convection-permitting dynamical downscaling has been carried out over the western Maritime Continent, using the Singapore Variable Resolution Regional Climate Model (SINGV-RCM) at 8km and 2km spatial resolutions. The SINGV-RCM is forced with ERA-5 reanalyses data for a 36-year period (1979-2014) at 8km resolution over Southeast Asia (79E-160E;16S-24N) with regular update of the sea surface temperature at 6-hr interval; further, this 8km domain simulation is used for forcing a smaller domain over the western Maritime continent at a resolution of 2km (93E-110E;7.2S-9.9N) for a 20-year period (1995-2014). Rainfall characteristics including the diurnal cycle and extremes from the two simulations evaluated against satellite retrievals, and the added value from dynamical downscaling will be presented.

How to cite: Prasanna, V., Sahany, S., Moise, A. F., Chua, X. R., Lim, G., Hassim, M. E., and Chen, C.: Rainfall Characteristics from Convection-Permitting Downscaling over the Western Maritime Continent, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6886,, 2021.

Kalamkas Yessimkhanova and Mátyás Gede

The majority of studies are dedicated to the analysis of climate change and climate models with no regard for data visualization part. Therefore, this research is aimed at highlighting challenges, with an emphasis on spatial referencing that can occur while visualizing CORDEX data. CORDEX data are stored in NetCDF file format, and sometimes georeferencing may be misconceived in QGIS software. For this reason, two techniques of georeferencing data are examined in this work. The first way of data georeferencing is re-projecting coordinates from original projection to an interpolated latitude/longitude grid. The second way is re-encrypting initial data file so that QGIS is able to interpret projection information. Preference of using QGIS explained by two reasons: it is open source GIS application and it has expanded visualization toolkit.

In addition, there are a great deal of climate models based on CORDEX data for some regions whereas there is a lack of climate projections for particular areas. In this regard, carrying out analysis for the region of Kazakhstan is beneficial. Outcomes of this research may stimulate spreading local climate models for Kazakhstan territory. Results are represented in the form of maps of Kazakhstan illustrating temperature change over 21st century time period.

How to cite: Yessimkhanova, K. and Gede, M.: Visual representation of CORDEX climate data by using QGIS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14599,, 2021.