HS4.4

The Science for Humanitarian Emergencies and Resilience (SHEAR) programme is an interdisciplinary, international research programme jointly funded for five years by the UK's Foreign, Commonwealth & Development Office (FCDO) and the Natural Environmental Research Council (NERC). It aims to support improved disaster resilience and humanitarian response by advancing monitoring, assessment and prediction of natural hazards and risks across sub-Saharan Africa and South Asia. SHEAR projects have been working with stakeholders to co-produce demand-led, people-centred science and solutions to improve risk assessment, preparedness, early action and resilience to natural hazards.

This session will share recently published challenges, learning and research outcomes from the SHEAR programme related to operational forecasting and early warning on: i) improvements in forecasting science, data, tools and decision making; ii) putting stakeholder needs at the centre; iii) interdisciplinary collaboration; iv) and lessons for future funding.

SHEAR projects have worked to advance the quality of the forecast information to support preparedness, by increasing the confidence, credibility and usability of forecasting science. The session will share advances made in developing new or improved forecast products for various natural hazards and their impacts in Asia and Africa.

SHEAR has also been working towards improvements in data; data plays a key role in preparing for and responding to disaster risks. With improved quality, availability, and accessibility of hazard-related data, disaster impacts can be better defined and anticipated.

The SHEAR projects have generated new knowledge through the development and use of new co-designed tools to support forecasting, early warning, and early action. The strong focus on participatory methods improved the effectiveness, the sustainability and the (policy) commitments to address risks and strengthen resilience in some of the most hazard-prone parts of the world. The co-designed, practical tools applied in SHEAR has enabled effective, appropriate and accessible transformation of knowledge into action.

The action people take based on forecasts is not always sufficient. SHEAR has worked with stakeholders at all levels and across sectors to improve anticipatory capacities and decision-making processes to enhance action in the face of future hazards. The session will show learning and examples from SHEAR demonstrating the requirement for a dedicated processes to support stakeholders in vulnerable areas to access, understand and subsequently plan for action that can strengthen their resilience in the face of potential upcoming disasters.

How to cite: Brown, S., Budimir, M., and Sneddon, A.: Learning from the Science for Humanitarian Emergencies and Resilience (SHEAR) programme: challenges, innovations and research outcomes on forecasting and early warning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5405, https://doi.org/10.5194/egusphere-egu22-5405, 2022.

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.

Significant progress has been made in the capability and accuracy of forecasting extreme rainfall events and their associated impacts. However, damages remain high and will continue to rise unless immediate actions are taken to support communities in decreasing the impacts of upcoming extreme weather-induced events. In this context, innovative technological tools can help to quickly disseminate relevant impact-based warning information and trigger appropriate self-protection actions based on the local vulnerability and exposure for effective disaster risk reduction.  For this purpose, a mobile app named “A4alerts” has been designed in this research.  

The tailor-based A4alerts app communicates impact-based warnings for vulnerable locations within high-risk areas (SSWs) generated by a community-based site-specific early warning system (SS-EWS). Based on a participatory approach with community stakeholders, the SS-EWS blends meteorological information coming from radar-based nowcasting, numerical weather prediction models and local risk information to trigger the SSWs disseminated via the A4alerts app. In addition to communicating the active warnings in the area, the app lists the available actions recommended to mitigate and reduce the potential local impacts for each warning level based on pre-approved self-protection plans. Furthermore, users can send geotagged photos and information through the A4alerts app to validate the events and their impacts.

The A4alerts app has been implemented and tested for selected vulnerable points in cities across Catalonia, Spain. Its capabilities and design have been improved following an iterative approach with end-users to incorporate their feedback and suggestions. Finally, the configuration of the A4alerts app allows it to be easily implemented and exported to new cities to help communities be prepared in times of climate emergency.

How to cite: Meléndez-Landaverde, E., Sempere-Torres, D., and Berenguer, M.: A4alerts: Design and implementation of a mobile device app for a community-based Site-Specific Early Warning System (SS-EWS) in Catalonia, Spain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-520, https://doi.org/10.5194/egusphere-egu22-520, 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.

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.

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.

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.

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.