Impact based forecasting is identified as a major challenge for hydrometeorological services by WMO. Impact based flood forecasting aims at closing the gap from forecasting how high the streamflow might be in the coming days to forecasting what the high flows might do. The target audience for impact-based flood forecasts can be both the population at large and the authorities responsible for civil protection and emergency management at national, regional, and local levels.

Since 1989 The Norwegian Water Resources and Energy Directorate (NVE) has been running the national services for forecasting floods and issuing warnings for regions and municipalities. Precipitation-runoff models for almost 160 catchments distributed all over Norway are used to forecast streamflow. Flood warnings are then mainly issued based on predefined thresholds for streamflow (mean flood, 5- and 50 years floods). The warnings mention typical impacts of the expected floods but not specific impacts.

NVE aims to provide impact-based flood forecasts for Norway and has started a 4-year pilot for four selected catchments in Norway to assess the information, models and tools needed to achieve this aim. This presentation will present the approaches selected and discuss their challenges.

The model chain used to assess impacts needs to represent the relevant processes. In a first step we focus on riverine floodings. A first challenge is that the hydrological models need to provide forecasts where the flood might have an impact, which signifies that the hydrological models will be applied in ungauged locations. Subsequently, hydraulic models can be used to estimate water levels and depths in susceptible areas. We evaluate an approach where the models are configured to sub-reaches of the rivers were the up- and downstream boundary conditions are well defined and forecasted streamflow at selected points is used as input. The hydrologic model can be used to establish an archive of flood levels and or depths to reduce computing time. This approach is challenging when downstream boundary conditions are dynamic, e.g., close to sea with tidal influence or inland lakes where the water level peaks later than discharge in the upstream rivers. The water levels and depths are subsequently used to estimate impacts based on the buildings and infrastructure potentially affected by the flood. Here we can use the type of building (storage, residential, school etc) type of road (municipality, regional or national road) to assess impact. An important challenge here is how to weight and aggregate potential impacts.

How to cite: Engeland, K., Hegdahl, T. J. H., Jjunju, E., and Sandboe, K. S.: Towards operational impact based flood forecasting in Norway, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18395, https://doi.org/10.5194/egusphere-egu24-18395, 2024.

Estimating flood (inundation) extent for river systems is a key element for impact-based flood forecasting and warning. Hydrodynamic Inundation mapping is widely used for planning for local flood mitigation measures and climate adaptation strategies. An interesting but challenging option is to use these models in real-time to provide locally relevant and impact-based flood warning. The challenge is that these methods can be computationally demanding and therefore may not be able to provide timely forecasts and effective warnings for early action. One approach used in practical applications, is to simulate pre-defined flood scenarios that are calculated, ahead of time, and then used as a look-up table by flood forecasters. However, this approach may not necessarily capture the actual flood dynamics and requires time-consuming manual interpretation.

The Danish Meteorological Institute (DMI) has recently been appointed as the national authority for flood forecasting for Denmark, and is tasked with developing and implementing a flood forecasting and early warning. The initial focus is on informing decision-making for local and national emergency services. In this study, we explore approximate, but computationally efficient, flood mapping for flood early warning. As a starting point, we have formulated a static flood mapping approach, based on an extension of the deterministic 8-node approach, and established an approximate hydrodynamic model, using LISFLOOD-FP for the Vejle River in Denmark. Adopting these simpler approaches recognizes that for flood warning the most relevant information is the identification of the areas at risk during an extreme flood event rather than the precise extent and magnitude of flooding. The township of Vejle, located near the mouth of Vejle River in a deep lowland glacial valley, is subject to frequent flooding from the coast as well as fluvial flooding from heavy rainfall and cloudbursts. Severe flooding from extreme rainfall occurred in the Vejle River during both February and March 2019. To evaluate the impact of the approximations used, we have compared the resulting flood map with drone observations of flood extent, photographs and satellite data during the flood in March 2019. These evaluations will guide DMI in developing operational flood mapping for flood early warning and emergency actions across the whole of Denmark.

How to cite: Plum, C., Butts, M., Trolle, D., and Nielsen, A.: Real-time fluvial flood mapping for impact-based flood early warning in Denmark , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15089, https://doi.org/10.5194/egusphere-egu24-15089, 2024.

In Switzerland, the Federal Office for the Environment issues hydrological forecasts and general flood warnings for the main river network. However, recent global flood events underscore that gaps in the communication channels from warning services to target groups inhibit effective mitigation efforts. One approach addressing this issue is impact-based warning.

Aligned with Switzerland's existing flood forecasting system, we introduce a library-based surrogate flood model approach aimed at advancing current technologies towards robust impact-based warning systems. We evaluate the model based on the main river network of Northern Switzerland by comparing the impacts to buildings, persons and workplaces with hazard classification, estimated with transient simulations for nine extreme precipitation scenarios.

Across 78 analyzed model regions, our surrogate approach yields a Flood Area Index between 0.74 and 0.90 for each scenario (overall 0.84) compared to the transient, computationally expensive flood modelling approach. Furthermore, the Critical Success Index, computed based on exposed persons, ranges between 0.77 and 0.93 (overall 0.89).

Our prototype of a library-based flood surrogate model demonstrates the ability of accurately replicate highly resolved transient models This capability bears the potential of nationwide real-time flood impact prediction and potential integration into probabilistic forecasting. Leveraging an API, this library-based approach could enhance the existing forecasting system, offering a pathway toward impact-based flood warnings.

How to cite: Mosimann, M., Kauzlaric, M., Schick, S., Martius, O., and Zischg, A. P.: Enhancing National Flood Forecasting: Leveraging Library-based Surrogate Models for Impact-based Warnings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17635, https://doi.org/10.5194/egusphere-egu24-17635, 2024.

According to the latest IPCC report, climate change directly impacts the intensity and frequency of floods in urban areas. As a result, Flash floods, characterized by a rapid increase in flood peak during a short duration, are becoming more common. Managing these flash floods is a crucial yet challenging task for water authorities. One important tool supporting the management of flash floods are 2-D hydrodynamic models.

In this study, we use the 2-D module of the openTELEMAC-MASCARET software to investigate the effect of the building representation on an urban flash flood. For this, we isolate a sinuous-shaped building group within an artificial study area. The building group itself and the topography in the model are derived from the Moabit district in Berlin. The buildings are cut out from the model as a hole, and the impact on the model results from the vertex distribution around these holes is assessed. We compare different algorithms to resample the vertices of the building. First, we design an algorithm that resamples the vertices of the building edges at an even distance while conserving the overall shape of the building. The resampling distance is set globally but slightly varies for every building edge. This algorithm is then compared with built-in resampling tools from other software: one tool also conserves the shape of the building yet cannot resample the vertices at an even distance (from QGIS), and another tool resamples at an even distance, yet does not conserve the building shape (from SMS). A change in the distribution of vertices causes a change in the mesh distribution around the buildings. Hence, the flow pattern around the buildings also changes. Additionally, we study the numerical stability of the different distributions. With this study, we aim to deepen the understanding of building representation in urban flood modeling and set the path for further investigations within this topic.

How to cite: Mahajan, K. and De Vos, L. F.: Assessment of different building representations in numerical urban flood modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15865, https://doi.org/10.5194/egusphere-egu24-15865, 2024.

In recent years, Landslide Early Warning Systems (LEWS) have garnered increasing attention, both from the scientific community and from professionals engaged in prevention, monitoring, and forecasting activities.

However, the extensive scientific literature on the subject primarily focuses on the development of algorithms, methods, and experiments. This literature often falls short in bridging the significant gap between the theoretical design of an early warning system and its actual operational deployment (though exceptions exist, as indicated by Guzzetti et al. 2020). This disparity poses a pivotal challenge in the sector. An effective system transcends mere theoretical algorithmic creation; it necessitates, among other factors, a pragmatic understanding of end-user requirements, a seamless and continuous operational framework, and efficient communication tools.

The Institute for Geo-Hydrological Protection Research of the National Research Council (IRPI CNR) has pioneered the domain of geographical LEWS in Italy. By managing five regional and national-scale geographical LEWS in collaboration with the National Civil Protection and the national railway network operator, IRPI CNR has highlighted the practical significance of these systems. The institute has developed solutions tailored to provide decision-support tools aligning with diverse stakeholders' needs.

This contribution aims to illustrate how scientific research outcomes can be leveraged, transforming them into operational tools in line with decision-makers' requirements. The presentation offers a detailed overview of real-world use cases of LEWS administered by IRPI in Italy. Emphasis is placed on disseminating information to end-users, specifically practical operators, and on the agreed-upon tools and approaches to distribute information and trigger alerts. More specifically, we describe 5 LEWS aimed at predicting the possible initiation of rainfall-induced landslides. Three of these systems operate at a regional scale (two administrative regions and a single railway segment in the Apennine region), while the other two cover the entire national territory, with the objective of assessing the potential initiation of rainfall-induced landslides and their potential impact on the national railway network. These systems differ in the type and quantity of data used in the forecasting chain, the extent of monitored areas, product resolutions, interfaces, and communication systems.

The intent is to share our experiences, challenges, and solutions, thereby fostering advancements and refinements in landslide early warning systems at both national and international levels.

How to cite: Marchesini, I., Rossi, M., Peruccacci, S., Brunetti, M. T., De Stefanis, P., Solimano, M., Esposito, R., Agostino, I., Loddo, S., Cinus, S., Valgimigli, G., and Corazza, A.: Operational Landslide Early-Warning Systems (LEWS) in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12402, https://doi.org/10.5194/egusphere-egu24-12402, 2024.

Vietnam faces heightened vulnerability to severe climate change impacts, notably sea level rise, flooding, and landslides. In recent years, the northwest mountainous regions have experienced recurrent and widespread landslides during the rainy season (May to October), resulting in significant economic losses. This study focuses on the Lao Cai province, employing various data mining techniques—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF)—to spatially predict landslide hazards. Initially, a comprehensive landslide inventory map was constructed from diverse sources, pinpointing past landslide occurrences. Subsequently, multiple factors influencing landslides were considered, including slope angle, slope aspect, profile curvature, wetness index, lithology, Normalized Difference Vegetation Index (NDVI), soil type, soil moisture, road density, house density, and rainfall. Utilizing these factors, landslide susceptibility indexes were computed through the respective models. Validation, using landslide locations not utilized in the training phase, revealed that models employing Random Forest (RF) exhibited the highest prediction capability. The trained model was then applied to generate real-time forecasts of landslide susceptibility maps for up to 16 days, using bias-corrected Global Forecast System (GFS) precipitation data. This WebGIS operational prediction system enhances preparedness and awareness, facilitating improved mitigation strategies to mitigate the impact of landslides.

How to cite: Bui Du, D., Nguyen Quoc, P., Du Le Thuy, T., Bui Khanh, L., Tran Thi Tra, G., Hoang Van, H., Vu Van, L., and Vu Minh, C.: AI-Empowered Near-realtime Operational Prediction System of Landslides in Lao Cai province, Vietnam, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7070, https://doi.org/10.5194/egusphere-egu24-7070, 2024.

We hereby introduce our latest Information Physical Quantum Technological Intelligence (IPQuTI), further empowering next-generation innovation and service workflows from sensing to computing, communications and security across multissectorial theatres of operation.

Methodologically, we build a novel synergistic dynamic interface among the novel augmented sensing technologies in our Quantum Aerospace Systems Intelligence (QuASI), the enhanced complex system dynamic analytics and model design methodologies in our latest Information Physical Artificial Intelligence (IPAI) and Earth System Dynamic Intelligence (ESDI), and the latest computational developments in our Synergistic Nonlinear Quantum Wave Intelligence Networks (SynQ-WIN).

With multi-hazard system dynamic complexity across diverse interacting geospheres and space in mind, we leverage and further build on our Meteoceanics QITES Constellation to tackle critical intelligence and security challenges to improve crucial awareness, understanding, preparedness and resilience in the face of pressing challenges facing our environment and society.

Operationally, our technologies are developed in-house and deployed across an infrastructural ecosystem on Earth and in Space. In doing so, we produce an integrated synergistic platform to support scientific, technical, management and security forces across challenging theaters of operation. From prediction and detection of early warning signs of hazards and multi-hazards, to processing and relaying complex sensitive information in a swift, secure manner across environmental and security value chains.

The operational and societal relevance of the overall methodological and technological advances are illustrated through the simulation of individual, compound and coevolutionary disaster occurrences across a sample of synthetically generated and real-world practical examples, thereby reporting concrete outputs of this platform. Some are representative of recurrent occurrences in line with the latest state-of-the-art abilities of dynamic modelling, machine learning and artificial intelligence, whereas others leap beyond the state-of-the-art with the new capabilities brought up by our latest advances, harnessing and simulating unprecedented non-recurrent emerging features and synergies elusive to prior data records and model designs.

These simulations further guide the mathematically robust, physically consistent deployment of system dynamic intelligence to address non-recurrent and other emerging phenomena. This is of special relevance in the face of structural-functional critical transitions and emergent multi-hazard behaviours associated to the synergistic coevolution between humans and nature e.g. pertaining a changing climate and land use, along with emerging transitions, criticalities and extremes, including black swan events, i.e. those non-recurrent high-impact phenomena elusive to traditional recurrence-based system dynamic modelling and information technologies.

Our novel IPQuTI brings added synergistic integrated value from sensing to computing and decision support, further enhancing the methodological and operational capabilities of current platforms along with ongoing projects on multi-hazard risk intelligence and disaster resilience such as the platforms being developed in the scope of Horizon Europe project C2IMPRESS.

Acknowledgement: This contribution is developed in the scope of the Meteoceanics Flagship on Quantum Information Technologies in the Earth Sciences (QITES), and of the C2IMPRESS project supported by the Εuropean Union under the Horizon Europe grant 101074004.

How to cite: Perdigão, R. A. P. and Hall, J.: Information Physical Quantum Technological Intelligence (IPQuTI) for Global High-Resolution Anticipatory Multi-Hazard Sensing, Modelling and Decision Support, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5308, https://doi.org/10.5194/egusphere-egu24-5308, 2024.

As a non-engineering measure for flood control, real-time forecasting provides valuable information like the magnitude and occurrence time of flood peak, which is essential for decision-making. In China, many reservoirs are built and operated in major river including ChangJiang River Basin. Operations of reservoirs pose new challenges for real-time forecasting. For example, (1) it’s difficult to calibrate model parameters due to human-impaired streamflow series, (2) the leading time of real-time forecasting is much shorter.

To address these challenges, we propose a distributed real-time streamflow forecasting framework using the Xin’anjiang (XAJ) hydrological model. We evaluate different scale of computational units of the XAJ model to better characterize the runoff processes, land surface characteristics, and meteorology factors. We then develop a set of models to calculate model parameters from land surface characteristics, which reduce the calibration requirement. We also develop an algorithm to correct the bias of precipitation forecasts, which is coupled with real-time forecasting framework. This helps to extend the leading time of real-time forecasting.

Our proposed framework is tested and validated at Upper Changjiang River Basin and get promising feedbacks.

How to cite: Fang, Y.-H., Zhang, X., Qian, R., Zhang, T., and Qin, P.: Developing a Distributed Real-time Streamflow Forecasting Framework for Use in Highly Regulated Basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3702, https://doi.org/10.5194/egusphere-egu24-3702, 2024.

Flooding poses a significant threat to human life, property, and the environment, especially in semi-arid river basins where the occurrence of intense rainfall events can lead to flash floods. Early warning systems are crucial for mitigating the impact of floods, and accurate streamflow prediction is a key component of these systems. This research focuses on developing an ensemble approach for streamflow prediction to enhance the effectiveness of early warning systems in Indian river basin. Traditional deterministic models may struggle to capture the complex hydrological processes and uncertainties associated with these regions. In response to this challenge, ensemble methods, which combine multiple models or data sources, have gained popularity for improving the accuracy and reliability of predictions. The Indian River Basin faces unique challenges in water resource management due to its diverse hydrological characteristics and the impact of climate variability. This research presents an innovative application of an ensemble approach for streamflow forecasting tailored specifically for the complex dynamics of the Indian River Basin. The research employs a combination of hydrological models, meteorological data, and machine learning techniques to develop an ensemble streamflow prediction system. A Hydrological model such as the HEC-HMS is integrated into the ensemble to leverage their strengths and compensate for individual weaknesses. Additionally, machine learning was applied for post processing of the ensemble data. These are incorporated to capture non-linear relationships and improve the overall predictive performance. The study area selected for this research is a semi-arid Sabarmati river basin with a history of past flood. Historical streamflow data, meteorological observations and remote sensing data are utilized to calibrate and validate the ensemble prediction system. TIGGE Ensemble data from ECMWF, NCEP, IMD and NCMRWF were used. Research covers machine learning approaches post processing methods such as BMA, cNLR, HXLR, OLR, logreg, hlogreg, etc were applied. The probabilistic forecasts were validated using the Brier Score (BS), Area Under Curve (AUC) of Receiver Operator Characteristics (ROC) plots and reliability plots. The cNLR and BMA strategies for postprocessing performed exceptionally well with Brier score value 0.10 and RPS value 0.11 at all grid points for both methods.  The ROC-AUC values for the cNLR and BMA methods were found to be 91.87% and 91.82% respectively. Furthermore, the research focuses on developing an effective flood early warning system based on the ensemble predictions. The results of the ensemble streamflow prediction system are evaluated against traditional deterministic models and individual hydrological models. Performance metrics such as accuracy, precision, and lead time are analysed to assess the effectiveness of the ensemble approach in comparison to single-model predictions. The findings demonstrate the superiority of the ensemble method in capturing the variability of streamflow by improving the lead time for flood warnings. In conclusion, this research contributes to the advancement of flood prediction methods in Indian river basin by introducing an ensemble approach that combines hydrological models and machine learning techniques. The findings have implications for water resource management, disaster preparedness, and the sustainable development of semi-arid regions.

How to cite: Patel, A., Yadav, S., Panchal, A., and Yadav, R.: Application of Ensemble approach for Stream flow forecasting for Indian River basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18944, https://doi.org/10.5194/egusphere-egu24-18944, 2024.

Based on WRF model, this paper constructs a mesoscale numerical weather forecast model covering Chinese Mainland, with a spatial resolution of 5km. Driven by ERA5 reanalysis data, long-term simulation experiments are carried out. We selected observation data from the National Meteorological Station of China to test and analyze the core forecast elements such as precipitation, temperature, and wind speed of the WRF model, and further verified the simulation ability of the model for typhoons, extreme precipitation, sustained drought, and total water resources in different water resource zones, obtaining relatively objective evaluation results. On this basis, based on WRF-Hydro, a regional distributed hydrological model in Chinese Mainland is constructed. The WRF-Hydro model is driven by the long-term simulation results output from the WRF model, and the runoff simulation data corresponding to major rivers in China are obtained. By comparing with the measured data, the validity of the WRF model built in this paper in hydrological simulation is verified, which provides a reference for the next step of operational application and improvement of the model.

How to cite: Yang, M.: Construction and Test of WRF Model in Chinese Mainland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14058, https://doi.org/10.5194/egusphere-egu24-14058, 2024.

How to cite: Bitsch, M., Grooss, J., Sørensen, O. R., and Engsig-Karup, A.: Fast flood finite volume model for the shallow water equations using high-resolution bathymetry data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19236, https://doi.org/10.5194/egusphere-egu24-19236, 2024.

Forecasting systems focusing on upcoming flood and drought events are essential to support various aspects such as disaster risk reduction, climate change mitigation, or long-term policy and planning. In particular, multiple model-based early warning systems have been developed to allow the simulation of future floods and droughts at different temporal-spatial scales. However, despite the successful development of many innovative and state-of-the-art modeling systems in the academic fields, their transition into an operational system is challenging, and it may take several years to set up appropriate technical requirements, especially into a new IT infrastructure. In this talk, we hence outline these challenges for the example of the ongoing project OUTLAST (operational, multi-sectoral global drought hazard forecasting system), where the main goal is to develop a modeling system that is ready for operational use. OUTLAST will provide model-based near real-time monitoring using recent updated ERA5 climate data and seasonal forecasting of drought globally across different sectors (water supply, riverine and non-agricultural land ecosystems, rainfed and irrigated agriculture). The system consists of a model chain of three models: (1) bias correction of global seasonal forecasting products SEAS5, (2) the global hydrological model WaterGAP, and (3) the global crop water model GCWM. The drought status in both monitoring and forecasting phase from OUTLAST will be provided globally for the next six months and be freely accessible via the HydroSOS portal, a Hydrological Status and Outlook System hosted by the World Meteorological Organization (WMO).

Highlights of OUTLAST are the ability to run the whole system within a cloud-ready automated workflow to ensure seamless integration into the HydroSOS framework. This includes the so-called “trigger” to automatically download the newly released climate data (ERA5 and SEAS5) from the source (ECWMF). To achieve this goal, each model and its dependencies in the model chain in OUTLAST are encapsulated in a "container" by the core developer in the research institution before being transferred to run in an IT infrastructure at an external government institution. The containers will then be orchestrated to enable the upscaling of the system based on computational requirements and the availability of hardware resources. This approach aims to (i) enable a seamless transition of OUTLAST into operation, (ii) avoid any conflict with the host operating system, and (iii) ensure a fast boot system in case one of the servers fails. We hope that the proposed infrastructure design can serve as a blueprint for other efforts to transfer scientific workflows into an operational environment. 

How to cite: Vu, T., Reinecke, R., Abbasi, N., Trautmann, T., Weber, J., Dietrich, S., Kneier, F., Lorenz, C., Weller, M., Koethe, H., Kunstmann, H., Döll, P., and Siebert, S.: Conceptualization and implementation of a global drought monitoring and forecasting system within the HydroSOS framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8599, https://doi.org/10.5194/egusphere-egu24-8599, 2024.

This study explores co-generation as a key strategy for the advancement and consequent uptake of hydro-climate servicesfor decision-making within the drinking water supply sector, focusing on the SMHI Aqua service as a case study for Sweden. The co-generation process investigated here is based on a four-pillar structure (co-design, co-development, co-delivery and co-evaluation), and it involves engagement and collaborative efforts among three main actors: service purveyors, data providers and users. The case studies were carried out in different regions of Sweden, with mainparticipation from Region Gotland and three other Swedish water-related users (Nodra, Karlskrona Municipality and Metsä Board). SMHI Aqua hydro-climate service is the result of this collective undertaking and it integrates data assimilation, forecast production and a web-based decision support system.

Addressing primarily the needs of drinking water producers and providers, freshwater availability was identified as the most descriptive indicator for supporting decisions for water management and drinking water supply. Two hydrological models were customized for the local conditions to simulate hydrological dynamics in surface and groundwater reservoirs. These models produce short- (up 10 days ahead) and long-range (up to 6 months ahead) forecasts which are updatedtwice a day, incorporating real-time hydro-meteorological measurements to update and initialize the model. Additionally, the service simulates various future freshwater availability scenarios by implementing different yearly water extraction strategies provided by the users. A user-friendly web-based platform displays the real-time (measured and modelled) and the future (forecasted) hydro-meteorological situation in the area of interest.

Outcomes of this study highlight the significance of knowledge co-evolution in facilitating the successful uptake of hydro-climate services. Effective communication of hydro-meteorological information, including its propagated uncertainty, proves to be crucial for water managers to takeinformed decisions. Beyond benefiting water-related users, co-generated hydro-climate services contribute to broader impacts reaching policy makers and the wider public by ensuring freshwater access, and improving awareness and preparedness for extreme conditions. User feedback emphasizes the substantial improvement in operational routines for drinking water management consequent to the implementation of SMHI Aqua. The active engagement and close collaboration of stakeholders throughout co-generation has a pivotal role leading to the successful uptake of the service when taking short- and long-term decisions. Overall, the co-generated SMHI Aqua hydro-climate service stands as a proof to the efficacy of co-generation in achieving informed decision-making, sustainable water resource utilization, and improved resilience especially under extreme conditions.

How to cite: Cantone, C., Ivars Grape, H., El Habash, S., and Pechlivanidis, I.: Overcoming challenges in the uptake of co-generated hydro-climate services for drinking water management: the inspiring case of SMHI Aqua, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18249, https://doi.org/10.5194/egusphere-egu24-18249, 2024.