In the past years, the research and operation communities on climate, weather and hydrology have put efforts into developing on-demand services for the monitoring, forecasting or emergency response and recovery phases of extreme hydrometeorological events. This is the case for the ‘Copernicus EMS On Demand Mapping’ for natural disasters, including flood inundation, as well as the ‘Destination Earth’s on-demand extremes digital twin’ flagship initiative of the European Commission. These efforts often require new, configurable on-demand prediction capabilities to run Earth system models at very high resolution on global scales. From the hydrological sciences and services perspective, it raises questions about how the diversity of operational hydrological prediction systems that support local modelling and decision-making can integrate this new paradigm, without losing efficiency and predictive accuracy in the process.

In this study, we investigate existing (or soon-to-be) operational flood impact modelling simulation capabilities in nine countries: Bulgaria, Czech Republic, Denmark, Finland, France, Iceland, Ireland, Slovakia and Sweden. We developed technical model workflows for each country to illustrate the diversity of approaches encountered in national flood forecasting systems. Each workflow is a visual diagram that identifies nodes represented by start/end points, and tasks and processes that affect the outcomes (i.e., the flood forecasts). Workflow developers were guided to reflect on aspects such as offline setups (domain discretization, model calibration), input data (acquisition, type, source), data pre-processing steps, models and associated routines (data assimilation, post-processing), and outputs (web-based interfaces, visualization). Guidance for inter-comparable workflows were discussed, which allowed us to reflect on a generic workflow to depict the way data and models interact in the context of flood forecasting and warning. 

Altogether, the hydrological/flood forecasting technical workflows highlight the needs of each configured system to locally pre-process meteorological data before using them as input to the hydrological models. This may include different actions: file reading, data formatting, data interpolation, computation of sub-catchment areal precipitation, etc. As the workflows rely on continuous hydrological modelling (as opposed to event-based models), the role of model initialization to capture the catchment initial conditions at the time a forecast is issued (e.g., the amount of water stored or flowing in the catchment before a flood event) is also highlighted. These are important aspects to be considered when interfacing national flood forecasting systems with continental or global on-demand services. The workflows offer a comprehensive and diverse view of the many components that can facilitate or hinder reproducibility, transferability, and (event- or user-driven) triggering of on-demand services, contributing to inform the setup of new approaches that aim at more interactive and configurable access to data for flood risk assessment at different scales.

This work is funded by the EU under agreement DE_330_MF between ECMWF and Météo-France. The on-demand capability proposed by the Météo-France led international partnership is a key component of the weather-induced extremes digital twin, which ECMWF will deliver in the first phase of Destination Earth, launched by the EC.

How to cite: Ramos, M.-H. and McKnight, U. and the DE_330-MF Hydrology Team: How the diversity of locally driven operational hydrological prediction systems can support globally configured on-demand high-resolution services, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5399, https://doi.org/10.5194/egusphere-egu23-5399, 2023.

The goal of Google’s Flood Forecasting Initiative is to provide timely and actionable flood warnings to everyone, globally. Until recently, Google provided operational flood warnings only for specific partner countries, namely India, Bangladesh, Sri Lanka, Colombia, and Brazil. In 2021 our flood alerting system sent out around 115 million flood notifications, reaching over 23 million people in the affected local areas. In all of the regions mentioned above, our operational model relies on partnerships with local governments to provide real-time measurements of observed discharge or water level (Nevo et al. 2021). However, relying on real-time measurement data makes it harder to scale to new regions as a) this data does not exist everywhere, and b) even if it exists, it requires significant per-country time and resource investment.

Building on research results from the last few years (e.g., Kratzert et al. 2019a, Kratzert et al. 2019b, Klotz et al. 2021), we built a global rainfall-runoff model that does not rely on real-time measurements in the operational context but only uses globally available forcing data and globally available catchment attributes. It can therefore be deployed everywhere, including in ungauged basins. Following Kratzert et al. (2019a), our rainfall-runoff model is based on the Long Short-Term Memory network (LSTM) and is trained on thousands of hydrologically diverse basins from all around the world. To forecast river discharge for any given river on Earth, the model uses time series data from various meteorological forcing products (IMERG, CPC, ERA5-Land, ECMWF’s IFS), as well as static catchment characteristics.

This new model allows us to scale to new regions more quickly. As of January 2023, we now provide operational flood warnings to hundreds of sites across 48 countries worldwide, with hundreds of more sites being rolled out in the coming months. Besides our previous channels of communicating flood warnings (e.g. Google Search, Google Maps, Google Alerts, and direct communications with NGOs and governments), we also released FloodHub (g.co/floodhub), a new interactive portal that allows for easy access to all operational forecasts.

Here, we present more information about the modeling methodology shifts, the challenges we faced and finally showcase the latest advancements made.

References:

Klotz, D., et al. (2022). Uncertainty estimation with deep learning for rainfall–runoff modeling. Hydrology and Earth System Sciences, 26(6), 1673-1693.

Kratzert, F., et al. (2019a). Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences, 23(12), 5089-5110.

Kratzert, F., et al. (2019b). Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55(12), 11344-11354.

Nevo, S., et al., (2021). Flood forecasting with machine learning models in an operational framework. Hydrology and Earth System Sciences Discussions, pp.1-31.

How to cite: Kratzert, F., Gauch, M., Klotz, D., Metzger, A., Nearing, G., Shalev, G., Shenzis, S., Tekalign, T., Weitzner, D., and Gilon, O.: Towards flood warnings everywhere - data-driven rainfall-runoff modeling at global scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5326, https://doi.org/10.5194/egusphere-egu23-5326, 2023.

Flash floods often lead to significant damages and human suffering. To mitigate this, hydrological forecasting models provide extended warning time and allow for better preparedness in the affected areas.

Among other data, hydrological modeling highly depends on reliable precipitation input. Typically, for the use case of precipitation based hydrological flood forecasting three data product types appear useful: (i) observed data for the near past until now, (ii) nowcast data for the next few hours, and (iii) forecast data for precipitation amounts in the near future. The German Weather Service (DWD) provides a multitude of different products for all three types covering Germany.

Producing a coherent time series containing data of these three types can be challenging because of different file formats, different temporal and spatial resolutions, and even varying spatial representations (e.g. regular grid versus icosahedron). To facilitate hydrological forecast modeling, we present our open source Python package weatherDataHarmonizer. It overcomes the temporal and spatial differences between the data types and provides a harmonized time series of spatially distributed precipitation.

First, the package contains modules for low-level access of the DWD original binary data for quantitative radar composites. This comprises measured radar data, e.g. RADOLAN RW, and nowcasting products like RADVOR RQ and RADOLAN RV. The modules are generic enough to support other products in this binary format. Beyond RADOLAN binaries, the package provides low-level access to data used at DWD for the regional weather forecast modeling, e.g. Icon-D2 and the ensemble forecast model Icon-D2-EPS in grib2 format. Both low-level access modules offer specific data and metadata classes and include functions to give the correct spatial coordinates.

Second, we added high-level support for the following DWD products: RADOLAN RW, RADVOR RQ, RADOLAN RV, Icon-D2, and Icon-D2-EPS. All these classes comprise methods for reading files, regridding via IDW method, cropping, and exporting to netcdf with data and metadata.

Third, the package involves a weather data class that collects all supported data, harmonizes the temporal resolution, and invokes regridding for the same spatial distribution. It results in a coherent time series of precipitation data from the near past to the maximum forecast time. Users can directly use the harmonized data within Python or rely on the export to netcdf functionality.

For quality assurance and reproducibility purposes, the weatherDataHarmonizer is highly modular and extendable for other products. It further includes unittests and standardized docstrings, which describe packages, classes, methods, and functions.

The weatherDataHarmonizer is developed and used within the project HoWa-PRO to generate ensemble precipation timeseries for flood early warning in small catchments.

How to cite: Wagner, M. and Grundmann, J.: Precipitation Data Harmonizer: Harmonizing radar, nowcast, and forecast precipitation data for hydrological applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8978, https://doi.org/10.5194/egusphere-egu23-8978, 2023.

The World Meteorological Organization’s (WMO) Global Data-processing and Forecasting System (GDPFS) is an international network of global, regional, and national centres that make meteorological analysis and forecast products operationally available. GDPFS has strengthened the capabilities of countries to meet the needs of users by sharing Numerical Weather Prediction (NWP) products and services related to operational meteorology, climate, and application fields in all timescales.

Advances in supercomputing and science over the last decade have furthered environmental predictions and probabilistic forecasts. Larger ensembles of NWP with increased horizontal and vertical resolutions are generated across all time scales. These developments clearly indicate the potential to evolve GDPFS to the WMO Integrated Processing and Prediction System (WIPPS) from its current form to a seamless platform, covering predictions for all time scales from minutes to centuries and encapsulating everything known about the Earth system which involves the atmosphere, ocean, hydrosphere, and cryosphere, along with all the interconnections and feedbacks among them.

As part of the Earth system approach, a few activities relevant to marine meteorology and oceanography have been already developed.  Further expanding the area of WIPPS, following hydrological activities are being established:

• Sub-seasonal to seasonal (S2S) hydrological predictions
• Snow cover predictions
• Flash flood forecasting

The authors will present the draft idea of the concept and the expected benefits of bridging the gap between operational meteorological and hydrology as part of an integrated processing and prediction system and show the ways on how experts can contribute to the evolution of WIPPS under the framework of WMO. Hydrological and meteorological communities can benefit significantly from the improved coupling between the NWP and hydrological models as for example the predicted precipitation reflecting realistic soil moisture contributes to predicting more accurate soil moisture and discharge. This is especially the case for short time scales (flash floods), S2S times scales as well as for snow cover considering nonstationary boundary conditions through a changing climate. Through its thematic focus on weather, water and climate and the strengths of centres of WMO Members to run operational prediction system, WIPPS offers the possibility to bridge the gap of operational services/systems between meteorology, hydrology and climatology and provides the opportunity for researchers to collaborate with operational practitioners and National Meteorological and Hydrology Services (NMHS). Questions like, what are the needs of scientists and practitioners, what are the bottlenecks, what are the best ways of collaboration and how can WMO develop the framework for a transdisciplinary effort to improve operational Earth system prediction systems for meteorology, climatology, and hydrology, can be discussed during the session to accelerate future cooperation.

How to cite: Schwab, M. P., Ba, R., Kim, H., Lim, E., and Honda, Y.: Bridging the gap between operational meteorology and hydrology: Including hydrological forecasts into the WMO Integrated Processing and Prediction System (WIPPS/GDPFS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7818, https://doi.org/10.5194/egusphere-egu23-7818, 2023.

The exploitation of rivers and hydropower reservoirs involves daily monitoring of the water resources, the meteorological conditions, the status of the coast, the flood areas, etc. Providing with timely and easy to consume information, analytics and early warnings for current and upcoming statuses or events helps water resources managers and high level officials to adequately observe and plan operations for sustainable development of river areas. We present an intelligent web-based workflow that combines different methods of AI, e.g. linked data, deep learning and resoning, to provide an integrated information system that ensures interoperability between spatial information of GIS systems, remote sensing information, symbolic and numerical data like meteorological data and proprietary measurements and creates an actionable knowledge value chain for the needs of rivers and hydropower reservoirs exploitation with embedded early warning capability. We show how hydrodynamic modelling using Telemac with forecasted water economic data, produced from earth observation and in-situ measurements applied to a series of neural network architectures, derive predictive river models, that are integrated into the work-flow and made available for querying, reviewing, projecting the changes in the navigational conditions of navigable rivers, geo-spatial visualization on GIS. The intelligent work-flow further provides with functional features like forecasts generation for river discharge, turbidity, water level, alerting and querying of a variety of correlations and synchronized visualizations in tables, graphs and GIS maps. It helps improve the
operational efficiency by providing ability to interact with and view all water resources management information at ones, ensures accuracy and decision making ability by correlating historic and forecast data with satellite imagery and data, gives automated forecasting of water economic data using satellite meteorological data, reduce risk through automated alerts. We demonstrate on the example of Danube the advantages of the presented intelligent web-based work-flow for the monitoring of rivers and their environment for sustainable development and planning.

Acknowledgement
This work has been carried out within ESA Contract No 4000133836/21/NL/SC

How to cite: Damova, M., Stoyanov, E., Stankov, S., and Pessek, H.: Early Warning Embedded in Intelligent Web-based Workflow for River Monitoring through Earth Observation and AI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17149, https://doi.org/10.5194/egusphere-egu23-17149, 2023.

Hazard warning systems are being increasingly employed globally, though these fail to account for surface water flooding or flooding from ordinary water courses. Thus, Previsico currently delivers to asset owners a warning and forecast service for surface water flooding at 25m resolution using its proprietary live hydrodynamic modelling software. Flood forecasts are generated every three hours and are produced using the latest rainfall nowcasts (6-hour outlook) and forecasts (48-hour outlook). The service issues property-level alerts so assets can be moved to safety, and organisations can improve their flood response capabilities. However, whilst warnings are important for mitigating physical impacts and losses, they – in isolation – are insufficient for coordinating the responses of insurers, re-insurers, and the wider finance sector. Of particular note, the accuracy of catastrophe claim reserves that depends on correct and timely loss estimates can directly affect the solvency and stability of a company. Loss estimation tools combining flood nowcasting and forecasting for perils that are rarely accounted for (e.g. surface water and ordinary water course flooding) are urgently needed to help insurers and reinsurers make reserving decisions with confidence.

We have therefore developed a loss estimation algorithm parameterised using Previsico’s world-leading forecast and nowcast derived flood extent and depth data and asset exposure and vulnerability data to produce near-present views of financial risk. Loss estimates will in turn be delivered to customers via Previsico’s flood dashboard and email alerts and alongside asset alerts and flood tiles, will support improved flood response capabilities for both the financial sector and associated stakeholders, including property owners and managers.     

How to cite: Tsaknias, D., Pledger, A., Baruch, A., and Yu, D.: Surface water flood forecast-based loss estimation for resilient finance, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17458, https://doi.org/10.5194/egusphere-egu23-17458, 2023.

Global freshwater availability is extensively governed by streamflow availability. However, in the recent past, the streamflow in many world rivers is showing a greater variability as a response to extremes, such as floods, which in turn is challenging to water managers due to lack of sufficient information. Reliable flood forecasting system, exploiting also satellite information, can help decision-makers to take actions for addressing both disaster risk and water resources management.

In this study, a framework for a comprehensive rainfall-runoff database was developed to study the catchment response to a variety of rainfall events. The core of the framework is the hydrological model, MISDc (Modello Idrologico Semi-Distribuito in continuo), forced with satellite Global Precipitation Measurement (GPM) precipitation data and soil moisture Advanced SCATterometer (ASCAT) backscatter observations. The resulting rainfall-runoff database stores pre-simulated events classified on the basis of the rainfall amount, initial wetness conditions, and initial discharge. The system was developed and tested at several gauged river sections along the upper Tiber river (central Italy) and the Po river (North Italy), and it demonstrated to be an effective tool to assess possible streamflow scenarios assuming different soil moisture conditions and rainfall volumes for the following days. This activity is part of the European Space Agency Digital Thin Earth Hydrology project aimed to develop what-if scenarios for flood risk assessment.

How to cite: Sahoo, S., Camici, S., Pandolfo, C., Burnelli, A., Ciabatta, L., and Brocca, L.: Rainfall-Runoff Database for Facilitating Improved Adaptation Strategies to Climate Extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16118, https://doi.org/10.5194/egusphere-egu23-16118, 2023.

A well-configured, verified hydrological operational forecasting system is an invaluable tool for hydrological forecasting and warning services. Target users of such a service can be water managers, power generation planning, navigation, civil protection, and the public, whose priority is to obtain the best possible forecast for their area of interest. This was one of the reasons why SHMU proceeded to a more complex assessment of hydrological forecasts.

The main objective of the assessment was to analyse the uncertainties that significantly affect the quality of the forecast itself. The evaluation was conducted for 47 selected water-gauging profiles. It showed that in Slovak physical-geographical conditions, the precipitation data (measured and predicted) and the configuration of the hydrological model are the most significant sources of uncertainties. Forcing data for hydrological forecasts come from the deterministic ALADIN/SHMU model with 4.5 km resolution, generated 4 times per day with hourly time-step and lead time 69 hours. The HBV model efficiency was tested on a total of 138 forecast profiles during the period 08/2016 – 12/2020. The input data used was precipitation from a combined radar product (qPrec) with 1 km resolution, which also enters the models in operation. Model performance was expressed by NSE and KGE statistics as well as visual inspection of the hydrographs. It showed very good model simulation results for most of the catchments. A weak point was the simulation and forecast of peak flows, which the model underestimated in many cases. It was therefore necessary to proceed to a more detailed analysis of the precipitation input, both measured and predicted, in relation to the predicted flows. Monthly precipitation totals and for selected catchments also daily ones were analysed and feedback was sent to the precipitation data providers for hydrological models. Monthly precipitation totals were compared with totals obtained from spatial interpolation of 568 rain gauge stations in a GIS environment. From these comparisons, systematic errors are visible as well as their temporal evolution for the specific catchments. Such analyses are not a routine part of hydrological forecasting systems.

The work also includes a quantification of the uncertainty of the meteorological forecast and hydrological model separately expressed for different forecast lead times for a specific forecast profile. In the future, we would like to apply the methodology used for other profiles in order to detect possible systematic errors affecting the quality of the hydrological forecast.

This work was supported by the Slovak Research and Development Agency under the Contract no. APVV-19-0340.

How to cite: Hlaváčiková, H., Hrušková, K., Kopáčiková, E., Mikuličková, M., Zvolenský, M., Shenga, Z., and Lešková, D.: Different aspects of hydrological forecast assessment in Slovakia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7049, https://doi.org/10.5194/egusphere-egu23-7049, 2023.

Ensuring that early warning information is effectively translated into anticipatory/ early actions is a pressing challenge that requires partnership and coordination of multiple actors at different territorial levels. This issue has been investigated in the framework of the IPA Floods and Fires program (https://www.ipaff.eu/) for the Western Balkans and Türkiye in 7 pilot studies. An operational approach has been developed to guide key institutions in planning anticipatory actions in the event of a flood based on early warnings, considering EWS in its full length of value cycle.

The approach is grounded on the concept that an Early Warning System (EWS) should be an “integrated system” comprising hazard monitoring, forecasting and prediction, disaster risk assessment, communication and preparedness activities, systems and processes” (WMO, 2016, UNDRR, 2009). A system approach enables to intersect and interlink all the elements and actors of EWS at different territorial levels, including local administrations, which are typically the first responder in case of a flood due to their proximity to at-risk communities.

The approach consisted in few key steps part of gradual capacity development process. The first important step was the context analysis at Country level carried out through questionnaires, scoping tools on EWS, and interactive workshops, which informed a comprehensive stakeholder mapping, guided the constitution of multi-territorial and multi-sectorial working groups (from National Hydrometeorological Services, to River basin and water agencies and civil protection authorities at all the levels). The second step was the design and implementation of a Command Post Exercise (CPX) project to test coordination and communication among all the EWS actors, as well as the activation of the emergency plans and procedures in response to warnings from the national to the local level. This step was instrumental to strengthen inter-agency familiarity and functional capacities of the system, to identify barriers for effective operations, and raise awareness on strategies for EW-EA linkage. The third and final step of the proposed approach consisted in a lessons learned analysis, recognizing gaps and capacities to be strengthened

The implementation of this approach in 7 pilot cases in Western Balkans and Türkiye has highlighted several benefits and challenges, including the effort to achieve a broader and regional perspective by transcending country-specific results. Specifically, the lesson learnt analysis outlined the base of a set of criteria, built on the regional experience, for a general and co-designed path to move towards the integration of early warnings into emergency response planning and civil protection actions. Key learnings and discussions among the involved parties in the approach supported the identification of preliminary recommendations and effective practices. The implementation of pilot cases highlighted that engaging local administrations and establishing cross-institutional partnerships are essential for effective preparedness and the overall strength of the system, confirming that an EWS can be completely hampered by its weakest component.

How to cite: Giambelli, M., Meninno, S., Deda, M., Paniccia, C., Vio, R., Renzulli, S., Morando, M., Ponte, E., and Massabò, M.: Linking early warnings to early actions through system approach: learnings from pilots in Western Balkans and Türkiye, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14368, https://doi.org/10.5194/egusphere-egu23-14368, 2023.

Floods are often caused by small-scale weather and hydrological phenomena that require very high-resolution models to adequate resolve and simulate. Unfortunately, high-resolution models are expensive to run continuously in real-time, which makes the case for only running these specialized systems “on-demand” when it is deemed necessary. The aim of this study is to investigate a high-resolution digital twin designed for on-demand use and test it on the case of Vejle, Denmark. The city of Vejle is of special interest because it is prone to frequent floods from long-term winter precipitation, convective cloudburst events in summer as well as storm surges from the sea. On top of this, several fast and slow responding rivers meet inside the city

Here, we present results from a hydrological forecasting setup based around the conceptual HYPE model, which is developed by the Swedish Meteorological and Hydrological Institute. The model is developed with high-resolution soil and land use data, forced with high-resolution meteorological observations, and its predictions are evaluated at several gauges along the Vejle and Grejs rivers. The final aim of the research is to assess the benefits of utilizing subkilometer-scale HARMONIE weather predictions, which especially is expected to improve the resolution of local rainfall fields. The full forecasting chain will be put into operation in the coming year.

How to cite: Plum, C., Martinsen, G., Dybro Thomassen, E., Wied Pedersen, J., and Brian Butts, M.: On-demand flood predictions and warnings with high-resolution weather and hydrological models: the case of Vejle, Denmark, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15949, https://doi.org/10.5194/egusphere-egu23-15949, 2023.

Floods impact recurrently vulnerable populations in the Andean region. Anticipatory action approaches and mechanisms propose ways to reduce these impacts by acting ahead based on forecasting and monitoring systems. However, how much ahead in time can we take action with enough confidence? This is not a trivial question given the challenges of historical hydrometeorological records in the whole andean region. In this context, technological tools and approaches based on free and open source electronics have allowed new possibilities for hydrological monitoring instrumentation. By increasing the coverage in the Vilcanota river in Cusco, Peru, we can make empirical explorations and analytical estimates of riverine flood lead-times as well as historical flood maps. As a result, developments of early action mechanisms and early warning systems can be appropriate to what is feasible in such a context and similar ones.

How to cite: Arestegui, M., Lavado, W., Cisneros, A., Madueño, G., Almeida, C., Millán, C., Bazo, J., and Anicama, J.: Exploration of flood lead-times through river level monitoring: A case study from the Vilcanota river in Cusco, Peru, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17560, https://doi.org/10.5194/egusphere-egu23-17560, 2023.

Floods cause over $40 Billion of damage worldwide every year. In Aotearoa New Zealand, it is the most frequent natural disaster, with an average annual cost of NZ$100 million for residential properties. Effectively forecasting and communicating flood hazards at national or continental scales is critical to reducing the impacts of flooding. However, developing national-scale river flow forecasting systems remains a challenge due to the predominance of ungauged catchments in often complex and steep terrain. We will present the model development, communication, and evaluation of New Zealand’s first national flood awareness system prototype, the Aotearoa Flood Awareness System, AFAS (Cattoën et al., 2022). To produce river forecasts, a high-resolution convective-scale atmospheric model drives an uncalibrated and semi-distributed hydrological model. The system includes statistical perturbations in rainfall, soil moisture and baseflow to generate a 50-member ensemble. We implemented a relative flow and flood exceedance threshold framework to evaluate hourly forecasts across six categories from below normal to extremely high. We assessed forecast performance categorically against observations, for a 2.5-year reforecast period, at 272 flow sites nationwide, up to 48 hours ahead. AFAS produces skilful streamflow forecasts in catchments with complex topography, even with operational delays ingesting observations. We explored a novel approach to river forecast communication using daily videos and will present feedback gathered from stakeholder workshops and semi-structured interviews. Finally, we will share our experience providing real-time AFAS forecast information during flood responses on the West Coast in 2021 and 2022. AFAS appears to be the first river forecasting system to produce public-friendly videos to communicate streamflow forecasts in their topographical context. Further development of AFAS would benefit from a federated approach across national and regional agencies, including sharing real time weather observations, forecasting tools and expertise.

Cattoën, C., Conway, J., Fedaeff, N., Lagrava, D., Blackett, P., Montgomery, K., Shankar, U., Carey-Smith, T., Moore, S., Mari, A., Steinmetz, T., & Dean, S. (2022). A national flood awareness system for ungauged catchments in complex topography: The case of development, communication and evaluation in New Zealand. Journal of Flood Risk Management, e12864. https://doi.org/10.1111/jfr3.12864

How to cite: Cattoen, C., Conway, J., Fedaeff, N., Blackett, P., Shankar, U., Steinmetz, T., Carey-Smith, T., Moore, S., and Measures, R.: A national flood awareness system for ungauged catchments in complex topography for Aoetearoa New Zealand., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4653, https://doi.org/10.5194/egusphere-egu23-4653, 2023.