Within the research project RUINS we assess the risk of inland floods of the Krummhörn region at the German North Sea coast. One third of this area lies below mean sea level, which demands to drain inland water during low tides by sluicing or otherwise by pumping. If, at any point in time, the drainage demand exceeds the drainage capacity, the available storage in polders and canals will be filled. Once this storage capacity is exceeded inland floods will occur.

Previous risk assessment for such inland floods assumed a constant daily drainage capacity, which results from the installed pump capacity. We analysed process data provided by the operator of the drainage system (1. Entwässerungsverband Emden) at sub hourly resolution. The recorded water levels within the canal system showed that under current conditions the maximum areal drainage capacity is usually limited by the flow capacity within the canal network. The capacity of the pumps is dependend on the gradient from canal to North Sea water level.

Under increased tidal water levels in the North Sea (e.g. storm flood situations) the pumping capacity can drop below the canal flow capacity. In consequence the areal drainage capacity is variable and can become much smaller than the constant daily drainage capacity assumed in previous studies. Due to the predicted increase in mean sea level with climate change the area might face an increased risk of inland floods despite a situation of insignificant changes in predicted rainfall patterns.

Instead of costly infrastructural improvements, we propose a forecast system i) to optimise the drainage capacity in foresight of short term extreme situations and ii) to enable preparation for inland floodings. The proposed system includes the inherent uncertainty of the analysed processes and predicts the magnitude of upcoming inland floods. Currently, we use synthetic data as drivers, but these shall be exchanged by available weather and tide level predictions. The forecast system is realized as online accessible app, providing an easy usable and understandable access point for the operator and the interested public.

How to cite: Lenz, J., Jackisch, C., Burkhard, K., Schibalski, A., and Schröder-Esselbach, B.: Developing an operational forecast system as byproduct of scientific research - an example for inland floods at the German North Sea coast, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3185, https://doi.org/10.5194/egusphere-egu22-3185, 2022.

A flood forecasting system (FFS) is widely recognized as essential to protect people’s lives and prosperities. Developing an FFS with high accuracy, longer lead time, and high resolution is the ideal goal, but there are lots of obstacles to achieving this challenge. Here, we would like to introduce our progress in the development of 5-km resolution FFS system in Japan by Today’s Earth (TE) system (Ma et al., 2021). TE was developed by the collaboration between JAXA and The University of Tokyo and is routinely run at https://www.eorc.jaxa.jp/water/index.html. Among various events, we focus on a case study for forecasting Typhoon Hagibis by assessing its forecasting performance. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning. On average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. Further, we would like to present our current work for developing an FFS with much higher resolution (1 km), with a probabilistic approach by the ensemble method using NEXRA (NICAM-LETKF JAXA Research Analysis, Kotsuki et al. 2017, https://www.eorc.jaxa.jp/theme/NEXRA/) data, and other developing versions of Today’s Earth system of Global scale (https://www.eorc.jaxa.jp/water/).

Ma, W., Ishitsuka, Y., Takeshima, A. et al. (2021). Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019. Sci Rep 11, 10213. https://doi.org/10.1038/s41598-021-89522-8.

Kotsuki S, Miyoshi T, Terasaki K. Lien GY, Kalnay E (2017) Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), J. Geophys. Res. Atmos., 122, 631–650. doi: 10.1002/2016JD025355.

How to cite: Ma, W., Ishitsuka, Y., Takeshima, A., Hibino, K., Yamazaki, D., Oki, T., Chen, Y.-W., Satoh, M., Shunji, K., Miyoshi, T., Yamamoto, K., Kachi, M., Kubota, T., Oki, R., and Yoshimura, K.: Progress of developing flood forecasting system by Today’s Earth (TE), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6731, https://doi.org/10.5194/egusphere-egu22-6731, 2022.

How to cite: Gabellani, S., Libertino, A., Delogu, F., Ercolani, G., Darienzo, M., Sini, F., and Giordano, V.: A probabilistic hydrometeorological forecasting chain for operational warning procedures in Marche Region , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8144, https://doi.org/10.5194/egusphere-egu22-8144, 2022.

The project “South-East European Multi-Hazard Early Warning Advisory System” (SEE-MHEWS-A) is a collaborative effort to strengthen the existing early warning capacity in south-eastern Europe. The project was initiated in 2016 by the World Meteorological Organization (WMO), and has been supported by the U.S. Agency for International Development (USAID), World Bank and the European Commission and has now developed from a concept into implementation of a pilot for a multi-hazard forecasting system. The pilot consists of four limited area numerical weather prediction models which are used as forcing to three hydrological models. In the implementation phase the hydrological models are setup over small catchments, but the plan is to increase the coverage when the project moves to the operationalization phase. The pilot also consists of a nowcasting system and the output are visualized on a web-based common information platform. The project has led the countries in the region to increase sharing of observational data, knowledge and resources to create a common information platform that can potentially deliver a tailored decision support system for hydrometeorological hazards to agencies and authorities.

How to cite: Wetterhall, F., Modigliani, U., Dacic, M., and Lappi, S.: Lessons learned from developing a multi-model hydrometeorological forecasting system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12766, https://doi.org/10.5194/egusphere-egu22-12766, 2022.

Impact based Forecasting (IbF) represents a shift away from traditional hazard focussed hydrometeorological forecasts and warnings (e.g. wind gusts exceeding 80mph at a location and time), towards those that communicate the risk, as a function of probability of the hazard occurring and its consequence(s) or impact on society. To achieve this shift, there is recognition that the exposure and vulnerability of society to the hazard, need to be considered in addition to hazard forecasts. The methods by which these additional variables are integrated to provide IbF outputs varies, but there has been limited research to understand why this is the case and what implications this has for operational IbF services.

To understand the variation in perceptions around IbF and the possible consequences these perceptions may have for operational implementation, this work invited practitioners, forecasters and researchers, working within the NERC and FCDO Science for Humanitarian Emergencies and Resilience (SHEAR) Programme, to provide their perspectives on a range of IbF related topics. Semi structured interviews were conducted with individuals that were selected by the project team based on their experience and expertise regarding IbF. A total of 11 interviews were held with stakeholders from the UK, South Africa, Uganda, Kenya, India, and Nepal, with representation from international institutions and NGOs, research institutes and hydrometeorological agencies.  

Our research aimed to answer the following questions: (1) Is there a shared understanding of what IbF is and means across individuals involved in its development? (2) Is there a shared perception of the challenges, barriers and opportunities associated with implementing IbF operationally? In this session, we illustrate areas of consensus and clarity, as well as areas of divergence, and knowledge gaps that could impede effective collaboration and implementation. We review the relevance of our findings for researchers and practitioners and explore how this might inform IbF activities in the future. 

How to cite: Robbins, J., Bee, E., Sneddon, A., Amuron, I., Stephens, E., and Brown, S.: Insights into stakeholder perceptions of Impact -based Forecasting (IbF) and implications for operational implementation in hydrometerology , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12160, https://doi.org/10.5194/egusphere-egu22-12160, 2022.

The COMET program has been supporting impact-based forecast and warning services (IBFWS) capacity development.  An IBFWS system has been implemented at the Barbados Meteorological Service (BMS) as part of the US National Weather Service (NWS) Weather Ready Nations (WRNs) initiative.  COMET collaborated with local partners and stakeholders including BMS, Barbados Department of Emergency Management, (DEM), and the Caribbean Institute of Meteorology and Hydrology (CIMH) in the implementation of the IBFWS framework.  The IBFWS system was implemented in six phases that included 1) identifying the hazards, impacts, risks through stakeholder workshops; 2) developing new standard operating procedures; 3) adapting software tools that integrates the IBFWS framework; 4) training of stakeholders; 5) testing and evaluation of system; and 6) the development of documentation for public outreach.  Recently, IBFWS training resources have been developed following the guidance of WMO-No. 1150: WMO Guidelines on Multi-hazard Impact-based Forecast and Warning services.  The IBFWS course includes topics on the Process for Implementing Impact-based Forecast and Warning Services, Identifying Hazards and Constructing Impacts Tables, Using Multi-hazard, Impacts-based Forecast and Warning Services, and Communicating Risk.  These online training modules include engagement simulations related to the types of decisions that need to be made in developing impact-based forecasting programs. Future work is planned to develop a full curriculum related to impact-based forecasting. The presentation will provide an overview of the IBFWS system implementation in Barbados and the associated training resources that have been developed.

How to cite: Kucera, P. and Page, E.: Impact-based Forecast and Warning Services Capacity Development, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9056, https://doi.org/10.5194/egusphere-egu22-9056, 2022.

How to cite: Libertino, A., Masoero, A., Poletti, M. L., Filimone, I., Darienzo, M., Pignone, F., Fagugli, G., Romano, L., Vilanculos, A., Rossi, L., and Gabellani, S.: Operational impact-based flood forecasting in data-scarce environments: the Early Warning System of the Buzi watershed in Mozambique , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8184, https://doi.org/10.5194/egusphere-egu22-8184, 2022.

Implementing hydrological models in data-scarce watersheds involves several critical issues, especially in relation to the availability and reliability of input data. This becomes particularly challenging when dealing with real-time hydrological applications for EW purposes (e.g., flood forecasting chains) where input data should be up-to-date and reliable, to provide timely warnings and drive trustworthy early actions.

When local data are available, those are often collected with inadequate frequency and continuity and cannot be used for proper calibration, configuration and subsequent operational use of the hydrological model underpinning a flood forecast chain. Furthermore, the lack of information reduces knowledge and awareness of risk and increases the vulnerability of these data-scarce areas to water-related disasters. It is therefore of utmost importance to build reliable EWS for these watersheds, making the best of what (little) is available.

The combined use of satellite observations and innovative hydrometeorological data processing can be a practical solution to integrate and enhance local observations, improving the hydrological model performance in poorly gauged watersheds.

This approach has been applied to the upstream portion of the Beni River in Bolivia (Alto Beni, closing at Rurrenabaque, 70’000 km²), an Amazon River tributary originating from the Andes. The Flood-PROOFS forecasting chain, based on the CONTINUUM hydrological model (Silvestro, 2013) has been implemented on the Alto Beni together with SENAMHI (Hydrometeorological Service) and VIDECI (Civil Defence).

Despite the large size of the watershed and its socio-economic importance (hosting several riverine communities and representing a main connection route between Bolivian Altiplano and Amazon plain) few water-level and weather stations are available and in operation in Alto Beni. This scarcity of information, particularly to feed the Flood-PROOFS chain, can be mitigated by using satellite data and by complementing available local data with additional analyses.

To test the approach and select the best available data source, the hydrological model reconstruction of the 2014 event, the highest on records, has been performed comparing different remote-sensed rainfall inputs: GSMaP, IMERG, MSWEP, PERSIANN, GHE. Performance of each input in reproducing the 2013-2014 rainy season hydrograph at Rurrenabaque has been evaluated. GSMaP and MSWEP performed the best, yet with a non-negligible underestimation of discharge values. Moreover, none of the rainfall inputs was able to reconstruct the double peak shape of the 2014 event. The uncertainty in the rating curve, lacking regular updates and high flow records, should be also considered.

To address these issues two innovative data processing approaches have been undertaken: firstly, the level-discharge relation at Rurrenabaque has been revised, using an innovative approach (BayDERS, Darienzo 2021) to review the rating curve and update the discharge timeseries. Then, the satellite rainfall inputs have been integrated with the available ground weather station records, using an innovative conditional merging technique (GRISO, Bruno 2021).

After having performed these two local data enhancement techniques, the combination of GSMaP and ground stations demonstrated to perform the best in reproducing the 2014 event. Moreover, GSMaP, given its near-real-time availability, is a solid data source to feed the operational flood forecasting and EWS for the Alto Beni.

How to cite: Masoero, A., Libertino, A., Darienzo, M., Gabellani, S., and Rossi, L.: Making the best of little information: operational forecasting and early warning systems in a data-scarce environment, the Beni River watershed in Bolivia., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7162, https://doi.org/10.5194/egusphere-egu22-7162, 2022.

Regional resilience has been identified as a key strategic priority for the Waikato Regional Council in New Zealand. Weather extremes are going to impact more our communities and what is important is how the regions can anticipate and respond to the impact of climate change. Flooding is the Waikato region’s most frequent and widespread natural hazard. The council’s priority is to prevent risks to people and property by providing flood protection and flood warnings. The local government works close to emergency services and civil defence to help people at risk. In addition to flood defences, flood impact prediction can help our communities be more resilient. This research is part of the TAIAO project (taiao.ai) that aims to develop new machine learning (ML) methods to provide a robust and fit-for-purpose tool to help New Zealand solve critical environmental problems. Over the past decade, increased research has aimed to develop new hydrological models for flood forecasting using machine learning. A data-driven approach provides the ability to deliver reliable results, especially for short-term forecasts, without the complete and complex knowledge of the physical processes usually required by a physically-based approach. Our research focuses on developing a regional real-time flood forecasting tool for emergency management that can run with low computational effort and a small number of parameters. Our target is to provide a better flood prediction with available information from the observation network. For our pilot study, we focus on the Coromandel Peninsula, a popular destination for the holidays, and where the weather is often challenging to forecast, like in New Zealand in general. We have used and compared the capability of various ML models to provide accurate results with low timing errors. To solve the problem of lagged prediction, we have developed a more holistic approach that combines hydrological state parameters and Long Short-Term Memory networks (LSTM). From these preliminary results, we demonstrate the real challenge to embed our LSTM-based model into operational procedures to predict with a lead time from 1 hour to 6 hours the severity of the impacts of heavy rainfall. The predictions are presented in a helpful way that facilitates decision-making and improves the regional flood response management.

How to cite: Mourot, P., Lim, N., Pfahringer, B., and Bifet, A.: A regional flood impact prediction tool using machine learning to manage flood risk in real-time. A case study in New Zealand., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12455, https://doi.org/10.5194/egusphere-egu22-12455, 2022.

The European Coastal Flood Awareness System - ECFAS (EU H2020 GA 101004211) - project aims to deliver a proof of concept for a coastal flood awareness system as an improvement of the Copernicus Emergency Management Service. One of the project’s keystones is the generation of a flood map catalogue for European flood-prone coastlines. To obtain this product, the work started with the identification of 28 historical test cases representing the wide variety of oceanographic and morphological conditions observed along European coastlines. The inundations generated by these events were numerically reproduced to calibrate and validate the LISFLOOD-FP model that will be used to generate the catalogue. For this step, observed flood maps derived from Very High Resolution satellite images and in situ observations were used as references. In parallel, validated hindcasts of oceanographic conditions in shallow water were produced using the ANYEU-SSL model. An Extreme Value Analysis was performed on the hindcast along the European coastlines to provide: (i) local storm conditions for a set of return periods (1, 2, 5, 10 and 20 years), (ii) local total water level thresholds for triggering the awareness system. Finally, 100 km long coastal sectors were identified along the European coastline for which a 100 m resolution LISFLOOD-FP numerical model will be generated. The catalogue will collect the maps generated with the storm conditions identified from the hindcast for each flood-prone coastal sector. These flood maps will represent a set of reference flooding scenarios in case of forecasted over-threshold coastal oceanographic events triggering the awareness system. 

How to cite: Le Gal, M., Fernández-Montblanc, T., Montes Perez, J., Souto Ceccon, P. E., Duo, E., Gastal, V., Delbour, S., and Ciavola, P.: A flood map catalogue for integration into aEuropean flood awareness system (ECFAS), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9832, https://doi.org/10.5194/egusphere-egu22-9832, 2022.

The present study aims to analyze the high-resolution model-simulated meteorological conditions during the Chamoli disaster, Uttarakhand, India (30.37°N, 79.73°E), which occurred on 7^th February 2021. The Weather Research and Forecasting (WRF) model is used to simulate the spatiotemporal distribution of meteorological variables pre and post-event. The numerical simulations are carried out over two fine resolution nested model domains covering the Uttarakhand region over a period of 2 weeks (2^nd February to 14^th February 2021). The model simulated meteorological variables, e.g., air temperature, surface skin temperature, turbulent heat flux, radiative fluxes, heat and momentum transfer coefficients, specific humidity, and upper wind patterns are found to show significant departure from their usual pattern starting from 72 h until a few hours prior to the Chamoli rock-ice avalanche event. The average 2-m air and skin temperatures near the rock-ice avalanche site 48 h prior to the event are found to be much lower than the average temperatures post-event. The total turbulent heat flux mostly remained downward (negative) throughout 72 h prior to the event and was found to have an exceptionally large negative value just a few hours before the rock-ice avalanche event. Model simulated rainfall and Global Precipitation Measurement Mission (GPM, IMERG) derived rainfall suggest that the part of the Himalayan region falling in the simulation domain received a significant amount of rainfall on 4^th February, ~ 48 h prior to the event, while the rest of days prior and post-event mostly remained dry. 

How to cite: Srivastava, P., Namdev, P., and Singh, P. K.: 7th February 2021 Chamoli (Uttarakhand, India) Rock-ice Avalanche: Numerical Model Simulated Prevailing Meteorological Conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2476, https://doi.org/10.5194/egusphere-egu22-2476, 2022.

Droughts have directly affected at least 1.5 billion people in the last century, generating economic losses up to $124 billion. They are a recurrent, creeping meteorological hazard that may endanger the water and food security of large regions. The frequency and severity of droughts are expected to increase with climate change, especially in Africa, Central America, and also in Europe where annual losses may multiply by 7 and represent up to 2 times the size of the European economy in the medium-long term.

Drought Early Warning Systems (DEWS) are key pillars of a risk-based, proactive management strategy. The increasing number of sources of EO data has remarkedly improved the monitoring capabilities of DEWS. Despite there are good examples of global and regional drought monitoring systems, these tools still lack of seasonal forecasting capabilities able to provide enough accurate and specific predictions of drought impacts at the subregion level (e.g. basin, district). These deficiencies constitute a challenge for the scientific community and provide an opportunity to improve the current services.

To address this gap in the DEWS landscape, the InfoSequia DEWS is developed to integrate the strengths of spatial, satellite-derived data with machine learning techniques for seasonal forecasting. InfoSequia consists of two modules:

• InfoSequia-MONITOR provides more than 50 drought predictors including meteorological (SPI, SPEI), vegetative (VCI / TCI / VHI), and hydrological (water level in reservoirs, groundwater storage status) drought indices, as well as atmospheric oscillation indices, all of them retrieved from satellite (e.g. MODIS, Sentinel-2, Sentinel-3, GRACE), hybrid (eg CHIRPS), or reanalysis and modeling (ERA5-Land) products. All indices are obtained from dekad values ​​ which are timescale aggregated at 1, 3, 6 and 12 months. The spatial resolution of the indices ranges from 5km (SPI, SPIE) to 250m (VH indices).
• Acknowledging the limitations of physically-based modelling on the seasonal time scale, the InfoSequia-4CAST module rests on the Fast and Frugal Tree (FFT) algorithm, a machine learning technique in which binary decision trees are trained and generated at the subregional level with the historical and spatially-aggregated predictors of drought. Final outputs are delivered in the form of monthly warnings of risk of failure up to 6 month lead times.

All InfoSequia algorithms run on a cloud platform, with cloud geoprocessing functionalities.

With support of the European Space Agency (ESA), InfoSequia is being developed and piloted to provide operational seasonal forecasts of: a) crop yield failures at the district level in Mozambique, and b) water supply failures in the Segura river basin in SE Spain.

Seasonal outlooks of drought impact support improvement of the water and food security of a region by allowing the early exploitation of groundwater reserves or unconventional water resources (desalination, reuse), the optimal water allocation of limited resources among users, or the implementation of ex-ante cash transfers or food vouchers. This research introduces the general workflow which underpins InfoSequia, how limitations due to technical barriers and data gaps are addressed, and the key performance indicators generated for both pilot cases.

How to cite: Contreras, S., G. Nobre, G., Fernández-Rodríguez, A., Khanal, S., Nolet, C., and Simons, G.: InfoSequia: Towards an operational satellite-based Drought Early Warning and Forecasting System for quantifying risks of crop and water supply by using machine learning and remote sensing , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8016, https://doi.org/10.5194/egusphere-egu22-8016, 2022.

Droughts are complex and a major threat globally as they can cause substantial damage to society, especially in regions that depend on rain-fed agriculture. It is understood that acting early based on alerts provided by early warning systems (EWS) can potentially provide substantial mitigation, reducing the financial and human cost of such hazards. Several satellite-based indicators such as the Vegetation Condition Index (VCI) or the Vegetation Health Index (VHI) are included in these EWS to monitor the agricultural and ecological droughts. In this presentation, we first present a suite a machine-learning techniques that we developed to forecast up to 12 weeks ahead these indicators at the second administrative boundaries across Kenya. Our approaches (Gaussian Process, auto-regressive distributed lag model, Hierarchical Bayesian Model) all provided skilful forecasts at various lead times. Finally, we show our Africa-wide forecasts of VCI and VHI using Gaussian Processes where we analyse whether the performance of the forecasts is influenced by season, land cover, or agro-ecological zone. Providing highly skilful forecast on vegetation condition will allow disaster risk managers act early to support vulnerable communities and limit the impact of a drought hazard.

How to cite: Rowhani, P., Salakpi, E., Bowell, A., Tran, M., and Oliver, S.: Forecasting agricultural drought using VCI and VHI across Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8549, https://doi.org/10.5194/egusphere-egu22-8549, 2022.

The National Institute of Water and Atmospheric Research (NIWA) is mandated to research and develop tools to increase New Zealand’s resilience to environmental hazards, including floods. NIWA generates and delivers its bespoke past, present and future environmental information services via a platform called EcoConnect. Comprising forecast output from numerical models of meteorological, hydrological and hydrodynamical hazards and data from related observation platforms, EcoConnect specialises in the creation and delivery of information that increases awareness of a broad range of environmental conditions, and provides input for a variety of specialist decision-support tools, chief of which is a customisable user-interface called NIWA Forecast, that uses this information to mitigate environmental hazards and commercial risk.  EcoConnect operates 24 hours a day, 7 days a week and is fully supported by scientific and technical staff.

The EcoConnect workflow, which operates autonomously via the Cylc workflow meta-scheduler, begins with the data-assimilating New Zealand Limited Area Model (NZLAM) and New Zealand Convective-Scale Model (NZCSM) numerical weather prediction models.  These are based on the Met Office Unified Model, running with horizontal resolutions of 4.5km and 1.5km respectively over the full New Zealand, Tasman Sea and eastern Australia region (NZLAM) and just New Zealand and its coastal waters (NZCSM). These models provide input data for a hydrological river flow model, TopNet, based on the TopModel framework, that forecasts streamflow for just under 50,000 river reaches around New Zealand and a hierarchy of sea state and wave forecast models, based on the Wavewatch III model and locally called NZWAVE and NZTIDE. A coastal inundation model called RiCOM is also driven using data from the weather forecast models. Observation datasets provided within EcoConnect include satellite imagery, surface weather station data, river gauges and wave buoys. All of these data are created, collected, processed and archived by bespoke tasks in the EcoConnect workflow, all managed by Cylc. 

Almost all users of forecast products have bespoke needs, such as operational decision-making, and hence it is important to be able to cater to specific client requirements. Through EcoConnect, fit-for-purpose warnings can be configured, based on a user’s operational requirements, for any of the data sources in EcoConnect. For example, if the forecasted wave, or streamflow discharge, at a specified location were to exceed a specific threshold, a client can be warned via customisable alerts within EcoConnect and thus react appropriately. A collection of standard products is generated within EcoConnect and tools within the primary user-interface are provided to interrogate the data and define custom “workspaces” that provide at-a-glance monitoring capabilities. 

In this presentation, we will describe capabilities of the EcoConnect platform as they relate to hazard forecasting and warning. By means of a case study, we will show how EcoConnect was used to provide heads-up forecasting and decision-making support for an event that comprised weather, hydrological and wave hazards at the same time.  We will also highlight lessons learned and future development plans.

How to cite: Moore, S., Rautenbach, C., Cattoën-Gilbert, C., Carey-Smith, T., Turner, R., Miville, B., Sutherland, D., Andrews, P., Lane, E., Gorman, R., Reeve, G., Oliver, H., and Uddstrom, M.: EcoConnect - a specialist environmental multi-hazard forecasting and information service, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6832, https://doi.org/10.5194/egusphere-egu22-6832, 2022.

Coastal resources are productive drivers of the so-called blue economy, impacting rapidly growing industries, as the Seawater desalination. Yet, the efficiency of desalination operations is at stake as a result of an imminent operational threat at a global scale, i.e., the proliferation of microscopic algae in seawater. Algal blooms are associated with operational difficulties in the desalination industry, i.e., clogging and bio-fouling, which increase the costs of chemicals, energy and maintenance.

To alleviate the impact of algal blooms, desalination could be supported by innovative tools that foretell the onset and evolution of bloom events. However, the desalination sector lacks near-real time decision-support tools. This work aims to address this gap. To this end, an operational forecasting service was developed, deployed and tested in a seawater desalination plant,  located at the Saronic Gulf (Greece).

The operational forecasting service comprises three components: (a) a hydrodynamic component, (b) a water quality component, and (c) an early warning system for algal bloom events.

The hydrodynamic model predicts the hydrodynamic regime in the Gulf, including vertical mixing, circulation patterns, temperature and salinity profiles. The hydrodynamic model accounts for the heat exchange between the water body and the atmosphere, the salinity, wind and wave action. Both the hydrodynamic and the wave component have been calibrated and validated using satellite-derived and reanalysis data for the first and in situ data for the latter. Specifically, on the validation of the hydrodynamic component, comparisons with satellite-derived water temperatures proved the model’s ability to accurately predict water temperature profiles in the domain, with MAE=1.11oC and MAPE=4% at the validation period from 01/07/2018 to 30-11-2018. To further improve the predictive capacity of the forecast model, the service assimilates satellite-derived sea surface temperature (obtained by Landsat-8 imagery) using the Ensemble Kalman Filtering method.

The prediction of algal-related water quality attributes (i.e., chlorophyll-a) is based on a data-driven approach. An ensemble learning method (i.e., a random forest) was trained to map hydrodynamic data (temperature, mixed layer thickness), biogeochemical data (inorganic nutrients) and meteorological data (air temperature, wind speed, solar radiation) to chlorophyll-a concentrations at the area of interest. The random-forest-based model produced accurate predictions in hindcast (the mean absolute percentage error was 14% for the held-out data), allowing for its further deployment in an operational setting.

Ultimately, forecasted hydrodynamic and water quality attributes of the coastal zone are integrated into an early warning system that generates and disseminates readily interpretable warning information to enable operators threatened by a probable shift in the regime of the coastal environment to act promptly and appropriately to reduce the vulnerability of those due to be impacted.

In conclusion, this work delivers an operational platform that predicts accurately algal-related parameters in coastal waters. Following its deployment and testing in hindcast, the service line will be tested and validated in operational conditions, aiming to assess the limitations in its forecasting abilities.

Acknowledgements: This work is supported by a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism 2014-2021, within the framework of the Programme “Business Innovation Greece”. 

How to cite: Sklia, M., Kandris, K., Romas, E., and Tzimas, A.: Operational short-term hydro-ecological forecasting for algal-related threats in seawater desalination, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4028, https://doi.org/10.5194/egusphere-egu22-4028, 2022.

Seasonally occurring oxygen deficiency zones (ODZs) are a regular feature in the coastal zones of the Baltic and North Seas, and their frequency has increased over the last years. The development of ODZs is favoured in areas of high primary production supported by excess nutrient loads from land, sluggish ventilation and strong salinity and/or temperature gradients. For forecasting the risk of oxygen deficiency, we redefine the oxygen deficiency index (ODI) originally developed for the North Sea by Große et al. (2016) to account for the fundamental differences between Baltic and North Seas (e.g., haline vs. thermal stratification) and to obtain a common ODI, applicable to both seas and in an operational context. The InfoWas system is based on the results from the operational physical-biogeochemical model (HBM-ERGOM) at the BSH. The model system is further coupled with the Parallel Data Assimilation Framework (PDAF) developed at the Alfred Wegener Institute (AWI). Since ODZs in coastal zones can become hazardous to organisms and can cause ecological and economic consequences for the environment, the fisheries and the tourism-industries, combining the operational InfoWas system with the revised ODI offers intuitive, short-term forecasts of the risk of oxygen deficiency on a high spatio-temporal resolution for the entire coastal zone of the North and Baltic Seas. These easily interpretable forecasts will help to quickly inform environmental agencies of potentially upcoming harmful events and to act in advance in order to diminish environmental and economic consequences.

How to cite: Marki, A., Große, F., Jandt-Scheelke, S., Li, X., Schwichtenberg, F., van der Lee, E., Sathyanarayanan, A., Nerger, L., and Lorkowski, I.: InfoWas – Developing an Information System for Water Quality in the North and Baltic Seas – Forecasting Oxygen Deficiency Zones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12097, https://doi.org/10.5194/egusphere-egu22-12097, 2022.