HS4.5 | Reducing the impacts of natural hazards through forecast-based action: from early warning to early action
EDI PICO
Reducing the impacts of natural hazards through forecast-based action: from early warning to early action
Co-organized by NH1
Convener: Marc van den Homberg | Co-conveners: Gabriela Guimarães Nobre, Andrea Ficchì, Maurine Ambani, Annegien Tijssen
PICO
| Fri, 28 Apr, 16:15–18:00 (CEST)
 
PICO spot 4
Fri, 16:15
The Sendai Framework for Disaster Risk Reduction (SFDRR) and its seventh global target recognizes that increased efforts are required to develop risk-informed and impact-based multi-hazard early warning systems. Despite significant advances in disaster forecasting and warning technology, it remains challenging to produce useful forecasts and warnings that are understood and used to trigger early actions. Overcoming these challenges requires understanding of the reliability of forecast tools and implementation barriers in combination with the development of new risk-informed processes. It also requires a commitment to create and share risk and impact data and to co-produce impact-based forecasting models and services. To deal with the problem of coming into action in response to imperfect forecasts, novel science-based concepts have recently emerged. As an example, Forecast-based Financing and Impact-based Multi-Hazard Early Warning Systems are currently being implemented operationally by both governmental and non-governmental organisations in several countries as a result of increasing international effort by several organizations such as the WMO, World Bank, IFRC and UNDRR to reduce disaster losses and ensuring reaching the objectives of SFDRR. This session aims to showcase lessons learnt and best practices on impact-based multi-hazards early warning system from the perspective of both the knowledge producers and users. It presents novel methods to translate forecast of various climate-related and geohazards into an impact-based forecast. The session addresses the role of humanitarian agencies, scientists and communities at risk in creating standard operating procedures for economically feasible actions and reflects on the influence of forecast uncertainty across different time scales in decision-making. Moreover, it provides an overview of state-of-the-art methods, such as using Artificial Intelligence, big data and space applications, and presents innovative ways of addressing the difficulties in implementing forecast-based actions. We invite submissions on the development and use of operational impact-based forecast systems for early action; developing cost-efficient portfolios of early actions for climate/geo-related impact preparedness such as cash-transfer for droughts, weather-based insurance for floods; assessments on the types and costs of possible forecast-based disaster risk management actions; practical applications of impact forecasts.

PICO: Fri, 28 Apr | PICO spot 4

16:15–16:20
16:20–16:30
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PICO4.1
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EGU23-9345
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ECS
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solicited
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Virtual presentation
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Arielle Tozier, Eduardo Castro Jr., Hafizur Rahaman, Dorothy Heinrich, Yolanda Clatworthy, and Luis Mundorega

The Red Cross Red Cresent is among the organizations with the longest and most extensive experience with forecast-based action. We present the findings of recently-published research based on interviews with 139 stakeholders involved in Red Cross Red Crescent (RCRC) AA programs in 18 countries. We find that the organizaitonal benefits of forecast-based ation include capacity building, more proactive operations, and expedited humanitarian response. Forecast-based action can also help to overcome common challenges in climate services by providing a framework and decision-making and resources for early action. Despite these benefits, AA practitioners struggle with challenges common to climate services, development, and humanitarian aid, including local project ownership, capacity and infrastructure, integration with existing systems, data availability, forecast uncertainty, and monitoring and evaluation. We conclude that forecast-based action systems can only be sustainble if they address these perennial challenges and focus on building capacity and ownership. Furthermore, donors can play a major role in facilitating these shifts by providing funding designed to support long-term multi-stakeholder processes.

How to cite: Tozier, A., Castro Jr., E., Rahaman, H., Heinrich, D., Clatworthy, Y., and Mundorega, L.: Lessons from Red Cross Red Crescent Anticipatory Action, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9345, https://doi.org/10.5194/egusphere-egu23-9345, 2023.

Flood impact-based forecasting
16:30–16:32
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PICO4.2
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EGU23-3876
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On-site presentation
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fredrik huthoff, kris van den berg, and carolien wegman

In March 2022, the United Nations set as a five year target that every place on Earth should be served by Early Warning Systems (EWS) for natural hazards. Such an EWS provides emergency alerts when a natural disaster is imminent and can support local or international (aid) organizations to take effective action early on. Places most vulnerable to natural disasters are often those where little local data and capacity is available to locally develop and operate such a system. As local EWS are not yet available everywhere, robust and reliable global approaches and collaboration initiatives are needed as initial and possible fallback solution.

We propose an innovative flood hazard mapping method based on globally available data that can spatially indicate oncoming floods and thereby inform on preparatory actions to take, such as required emergency stocks, needed shelter capacity, clearing of evacuation routes, and strategic protection of vulnerable people and assets. It instantaneously calculates forecasted flood extents based on global precipitation forecasts and the terrain’s natural drainage network. Its functioning is demonstrated for a selection of historical flood events and shows to good agreement with satellite-observed inundated areas, even where flood extents have gone beyond catchment boundaries. The method can easily be scaled-up to other areas around the world and can be expanded to issue automated warnings and provide impact estimates.

 

How to cite: huthoff, F., van den berg, K., and wegman, C.: Rapid global hazard forecasting to support early action in data poor regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3876, https://doi.org/10.5194/egusphere-egu23-3876, 2023.

16:32–16:34
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PICO4.3
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EGU23-9862
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ECS
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Virtual presentation
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Douglas Mulangwa, Andrea Ficchi, Philip Nyenje, Jotham Sempewo, Linda Speight, Hannah Cloke, Shaun Harrigan, Benon Zaake, and Liz Stephens

This study investigates the importance of assessing ensemble flood forecasts with action-relevant scores to support flood preparedness actions by analyzing the discrepancies between traditional general scores that focus on the overall accuracy with other more specific flood event-based scores. Popular general scores such as the Kling-Gupta Efficiency (KGE) or the Continuous Ranked Probability Score (CRPS) are widely used in hydrological modeling and forecasting, but they aggregate different aspects of model quality into a single overall score. On the other hand, flood event-based scores, such as Flood Timing Error (FTE), False Alarm Ratios (FAR) and Probability of Detection (POD), provide more specific verification measures of forecast quality that should be more informative to decision-makers. Both classes of overall accuracy and event-based scores include either deterministic or probabilistic scores, focusing on either the ensemble mean (or quantiles) or on probabilities. 

Results are presented for ten catchments in Uganda with different morphological and hydrological characteristics. An evaluation of extended-range re-forecasts from the Copernicus-Emergency Management Service Global Flood Awareness System (GloFAS) has been carried out against observed streamflow data, contrasting overall performance scores, including the KGE and the CRPS, and event-based scores, including the FTE, FAR and POD for forecasts at different lead times (< 45 days). The relative performance of two different versions of GloFAS (2.1 and 3.1) is assessed by this multi-criteria verification setting. Results show that the relative ranking of forecast performance across model versions and catchments may vary based on the scores considered, suggesting that a multi-criteria and event-based evaluation is needed to inform flood preparedness actions.

How to cite: Mulangwa, D., Ficchi, A., Nyenje, P., Sempewo, J., Speight, L., Cloke, H., Harrigan, S., Zaake, B., and Stephens, L.: Assessing ensemble flood forecasts with action-relevant scores to support flood preparedness actions: An application to the Global Flood Awareness System in Uganda., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9862, https://doi.org/10.5194/egusphere-egu23-9862, 2023.

16:34–16:36
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PICO4.4
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EGU23-16024
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ECS
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On-site presentation
Margherita Sarcinella, Brianna R. Pagán, Lisa Landuyt, Jeremy S. Pal, Arthur H. Essenfelder, and Jaroslav Mysiak

The global economic loss caused by weather-related extreme events amounts to over $260 billion in 2022. Storms and floods are among the deadliest disasters and are responsible for the highest toll. Despite committed research efforts in strengthening flood forecasting and making those predictions readily and openly available, much remains to be done to facilitate intervention when locally acting upon those forecasts. This research aims at building an automated tool to forecast flood direct damages with a high spatial resolution and timeliness. Thus, allowing prompt, informed and targeted early action on site before the disaster hits. Moreover, it can serve as a device to unravel criticalities within preparedness plans and guide the adoption of adaptation measures in the long term. The proposed research develops a tool to rapidly link GLOFAS discharge forecasts with the relative inundation map and direct damages caused. The method includes three modules: i) a factual component collecting satellite-derived flood maps of historical events; ii) a probabilistic component based on hydrological modelling and iii) the impact assessment. The past event database comprises 10-meter resolution inundation maps derived from Sentinel-1 SAR imagery with a single-scene automated classification method. The outcome of hydrological modelling is then integrated with the remote sensing database to improve its accuracy and spatial resolution. Lastly, the impact assessment module estimates affected people and the economic damage to buildings. The presented methodology is applied to two case studies: the flooding caused by Tropical Cyclone Idai that made landfall in March 2019 in Mozambique and the country-wide flood event that occurred in Pakistan in the summer of 2022.

How to cite: Sarcinella, M., Pagán, B. R., Landuyt, L., Pal, J. S., Essenfelder, A. H., and Mysiak, J.: From flood forecast to direct damage prediction: Supporting early action with an Impact-based Forecasting system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16024, https://doi.org/10.5194/egusphere-egu23-16024, 2023.

16:36–16:38
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PICO4.5
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EGU23-15732
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On-site presentation
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Jafet C.M. Andersson, Aishatu T. Ibrahim, Ahmed Lamine Soumahoro, Vakaba Fofana, Abdou Ali, and Berit Arheimer

Floods pose an increasing challenge for societies in West Africa; causing loss of lives, damaged infrastructure, and food insecurity. Improving flood management is hence paramount for the region, which several initiatives aim to contribute to. Hydrological forecasting systems can help, but only if they lead to appropriate action.

This presentation focusses on how a flood forecasting system has been used to save lives and property in West Africa within the FANFAR project (www.fanfar.eu). The system was co-designed and co-developed together with hydrological services, emergency management agencies, river basin organisations, and regional expert centres in 17 countries. The pilot system was launched early in the project, producing new forecasts every day. This enabled operational staff at national and regional agencies to utilize the system during the current rainy season, for every season since 2019.

During 2020, Nigeria experienced severe flooding. The Nigeria Hydrological Services Agency (NIHSA) hence decided to utilize FANFAR to warn the population of forthcoming flood risks, which resulted in 2 500 lives saved on one occasion, and minimisation of property damage on another. In the presentation we describe these events, and how NIHSA acted together with other institutions to entice action.

FANFAR was also used in Ivory Coast during the 2022 rainy season. Operational staff at SODEXAM – the meteorological services of Ivory Coast – utilized the system to inform two flood-prone communities of forthcoming flood risks. This resulted in on-the-fly construction of a drainage ditch, which reduced impacts on the nearby community. In the presentation we describe the event and also the approach SODEXAM took to build trust and communicate with the communities.

We also briefly describe the FANFAR system that employs a daily forecasting chain including meteorological reanalysis and forecasting based on HydroGFD, data assimilation of gauge observations, hydrological initialisation and forecasting with the HYPE model, flood severity assessment, and distribution through e.g. web visualisation. 

How to cite: Andersson, J. C. M., Ibrahim, A. T., Soumahoro, A. L., Fofana, V., Ali, A., and Arheimer, B.: How a flood forecasting system saved lives and property in West Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15732, https://doi.org/10.5194/egusphere-egu23-15732, 2023.

16:38–16:40
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PICO4.6
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EGU23-7070
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ECS
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On-site presentation
Adele Young, Biswa Bhattacharya, and Chris Zevenbergen

Ensemble prediction systems (EPS) have been proposed to quantify uncertainty in forecasts, but to what extent they are useful for supporting flood anticipatory actions in an urban data-scarce city has not been fully explored. This research uses a Bayesian decision theory framework to support sequential decisions for reducing flood impacts. The predictive information is derived from probability distributions of flood depth simulated from a coupled ensemble Weather Research and Forecasting (WRF) and hydrodynamic MIKE urban inundation model. A damage function is used to value user actions and expected damages. Posterior probabilities are computed using prior probabilities and expected damages to select an action which minimises the expected losses.

The analysis is done for the Egyptian coastal city of Alexandria, which experiences extreme rainfall and pluvial flooding from winter storms resulting in disruptions, damages and loss of lives. The decision framework supports anticipatory actions which can be taken 12-72 hours before an event. These include cleaning drains, dispatching pump trucks to critical flood locations before events, and proactive pumping to increase storage.

Results suggest the use of a probabilistic decision framework can help support mitigating actions and reduce the occurrence of false and missed alarms. However, it depends on the combination of event intensity and probability (e.g. high intensity, low probability) the specific action and the loss function used. This approach helps decision-makers understand the value of probabilistic forecasts and models to trigger actions for improved decision support.

How to cite: Young, A., Bhattacharya, B., and Zevenbergen, C.: A Bayesian decision framework to support flood anticipatory actions in the urban data scarce city of Alexandria, Egypt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7070, https://doi.org/10.5194/egusphere-egu23-7070, 2023.

16:40–16:42
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PICO4.7
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EGU23-1569
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ECS
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On-site presentation
Akshay Singhal, Nibedita Samal, Sanjeev Jha, Louise Crochemore, and Isabelle Ruin

Occurrences of short-duration extreme rainfall have significantly increased over India, leading to frequent flash floods. Growing incidences of urban floods pose a challenge to rainfall forecasting agencies and disaster mitigation authorities. Advancement in the numerical weather prediction (NWP) models has resulted in improved skills of rainfall forecast for longer lead times. However, in recent years, there is a growing emphasis on developing an impact-based approach to communicate the probable impacts of the forecast and reduce the socio-economic losses. In this study, we aim to generate Impact-Based Forecasts (IBFs) in response to the growing incidences of urban flash floods in metropolitan cities of India such as Mumbai. IBFs will provide warnings about the potential impacts as well as communicate protective responses based on the category of impact, i.e., high, moderate, and low. To this end, an inventory of several urban floods over the city of Mumbai during the past decades is prepared, and the relationship between past extreme hazards and related impacts is investigated. Various available Quantitative Precipitation Forecasts (QPFs) from the European Centre for Medium-range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), UK Met Office (UKMO), and National Centre for Medium-Range Weather Forecasting (NCMRWF) will be used in the study. Moreover, several observation datasets, such as from the Indian Meteorological Department (IMD), and from Integrated Multi-satellitE Retrievals for GPM (IMERG), will be used to validate the forecast information. The raw precipitation forecasts will be post-processed using a Bayesian joint probability (BJP) model-based rainfall post-processing approach to improve reliability and accuracy. With this study, decision-makers are expected to gain crucial insights regarding the probable impacts arising due to multiple realistic flash floods in Mumbai scenarios. The analysis is underway, and the results will be presented at the conference.

How to cite: Singhal, A., Samal, N., Jha, S., Crochemore, L., and Ruin, I.: Improved flood-related decision-making in case of urban flash floods in a metropolitan city of India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1569, https://doi.org/10.5194/egusphere-egu23-1569, 2023.

16:42–16:44
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PICO4.8
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EGU23-8724
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ECS
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On-site presentation
Heather J. Murdock, Antje Otto, Anna Heidenreich, and Annegret H. Thieken

Floods in Europe regularly cause damage and disruption to communities and infrastructure. The extreme flood of July 2021 which affected Germany, Belgium, Luxemburg and the Netherlands provides an example of a flood event with a rapid onset time with corresponding short warning times and high uncertainty. This was a flood event with high velocities and volumes of debris. In addition to casualties there was extensive damage and disruption to infrastructure including roads, rail, water supply, and power transmission. Some negative impacts can be mitigated through the use of flood early warning systems (FEWS) and spatial planning using hazard maps. For such risk reducing measures, it is important to understand what challenges remain towards implementation. For example, challenges may differ between actors with different mandates and capacities.   

Infrastructure operators have an important role in flood risk management as the functioning of critical infrastructure (CI) is of high importance for society. CI in this context includes infrastructure, such as dams and railroad which we focus on, whose failure or impairment results in lasting disruptions to the overall system. Is it therefore possible that the prevention of damage and disruption to CI can reduce risk for society as a whole? Flood early warning information can support early action including moving mobile assets to higher ground, preventative closures, or protecting critical parts of a network with mobile flood barriers. Little empirical data exists, however, to address this question. It is therefore unclear to what extent flood risk management measures have become integrated into CI management by infrastructure operators.   

In this study we conduct expert interviews with CI operators in Germany and Belgium to investigate: (1) what FEWS information CI operators use, (2) how has it been applied during past flood events, particularly in 2021, (3) what information is shared with other stakeholders in an emergency context, (4) what flood hazard maps do operators currently use, and (5) how are flood hazard maps integrated into infrastructure planning. Our focus on dam and railway operators is due to the important role they play in water management and regional transportation, respectively. The interviews are transcribed and coded using MaxQDA to address the five points mentioned above. The empirical basis of this research can help to shed light on the effectiveness of available information to reduce risk in an emergency management context as well as for infrastructure planning. 

How to cite: Murdock, H. J., Otto, A., Heidenreich, A., and Thieken, A. H.: Flood Early Warning and Hazard Mapping for Railway and Dam Management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8724, https://doi.org/10.5194/egusphere-egu23-8724, 2023.

16:44–16:46
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PICO4.9
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EGU23-16824
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ECS
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On-site presentation
Brett Snider, Robin Bourke, and Mathew Godsoe

In Canada, floods are the most common and most costly natural disaster. Floods threaten lives, properties, and the environment and these risks are only expected to increase alongside expected population increase and impacts from climate change. Flood early warning systems (FEWs) can help mitigate the impact of floods by helping inform the public when and where a flood may occur, identifying infrastructure that may be impacted, and disseminating evacuation routes that avoid flooded roads. FEWs have been shown to save lives and mitigate flood impacts. However, many existing FEWs are limited in terms of their forecast horizon and geographical coverage, and also require precise hydraulic models and substantial computing.

This paper develops a flood preparedness application for all of Canada to help prepare Canadians for future and imminent floods. This Canadian flood preparedness application addresses limitations associated with many of the developed FEWS in Canada by matching predicted river flows to predetermined return periods for developed global (or country-wide) flood inundation maps. By matching predicted river flow to return periods of predetermined inundation maps, complex computation is avoided reducing response time, and improving geographical coverage (by using a Canada-wide model). Lastly, using the static map approach, the public and emergency personal can help prepare for floods well in advance, identifying their own flood risk and as well as evacuation and muster locations strategies by identifying roads that would likely be flooded under various flood return periods. Overall the Canada-wide flood preparedness application will help protect and better prepare Canadians as flood risks continue to rise by increasing forecast horizon and geographical coverage and minimizing computation. The new approach of using global (or country-wide) static flood inundation maps to inform FEWS may be applicable in other countries where detailed hydraulic models are unavailable or too time consuming to calculate on a continuous or as needed basis.

How to cite: Snider, B., Bourke, R., and Godsoe, M.: Flood Preparedness Application Using Pre-determined Global Flood Inundation Maps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16824, https://doi.org/10.5194/egusphere-egu23-16824, 2023.

Tropical cyclone and compound hazard impact-based forecasting
16:46–16:48
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PICO4.10
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EGU23-15188
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ECS
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On-site presentation
Andrea Ficchì, Guido Ascenso, Matteo Giuliani, Enrico Scoccimarro, Linus Magnusson, Rebecca Emerton, Elisabeth Stephens, and Andrea Castelletti

Tropical Cyclones (TCs) have the potential to cause extreme rainfall and storm surge, which in turn can lead to riverine and coastal flooding with huge damage to property and loss of lives.

The use of precipitation forecasts in the context of decision-making and anticipatory action is currently hampered by the limited skill of numerical weather prediction models in forecasting the characteristics of such extreme rainfall events (especially their severity and location) with a sufficiently long lead time.

In this study, we present a post-processing scheme for precipitation forecasts based on a popular deep-learning algorithm (U-Net). We design our Machine Learning (ML) model to reduce the local biases of precipitation forecasts from TCs and adjust the spatial distribution of extreme rainfall. For this, we use a composite loss function to train the model, based on the combination of the Mean Absolute Error (MAE) and the Fractions Skill Score (FSS). We first demonstrate the potential of our ML-based approach working on ERA5 reanalysis data and subsequently apply it to the ensemble mean of ECMWF sub-seasonal forecasts with a lead time up to 10-days. As for the ensemble spread, we investigate possible post-processing adjustments based on the improvement of the spread-error relationship and of action-relevant scores of interest for humanitarian agencies, namely False Alarm Ratios (FAR) and Hit Rates (HR). We train and validate the model on a historical dataset of global TC precipitation events, using ECMWF re-forecasts over 20 years and a multi-source observational dataset (MSWEP) as reference. The results are evaluated with a multi-criteria approach including MAE, FSS, FAR, and HR, to assess the capacity of improving the predicted severity and spatial patterns of TC precipitation, as well as their potential for triggering anticipatory actions. Finally, we discuss how the outputs of our model can be used and further improved to support humanitarian actions aimed at saving lives in vulnerable communities in Mozambique.

How to cite: Ficchì, A., Ascenso, G., Giuliani, M., Scoccimarro, E., Magnusson, L., Emerton, R., Stephens, E., and Castelletti, A.: Machine-learning enhanced forecast of tropical cyclone rainfall for anticipatory humanitarian action, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15188, https://doi.org/10.5194/egusphere-egu23-15188, 2023.

16:48–16:50
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PICO4.11
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EGU23-14435
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Virtual presentation
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Mersedeh Kooshki, Marc van den Homberg, Kyriaki Kalimeri, Andreas Kaltenbrunner, Yelena Mejova, Leonardo Milano, Pauline Ndirangu, Daniela Paolotti, Aklilu Teklesadik, and Monica Turner

Due to its geographical location, the Philippines is prone to tropical cyclones (TC) which produce strong winds, accompanied by heavy rains and flooding of large areas, resulting in heavy casualties to human life and destruction to livelihoods and properties. To reduce the humanitarian impact of TC, the Philippine Red Cross with the German Red Cross and 510, an initiative of The Netherlands Red Cross, designed and implemented a machine learning impact-based forecasting model based on XGBoost, which is used operationally to release funding and to trigger early action. The model predicts the percentage of houses that will be completely damaged due to a TC using predictive features for the hazard (wind speed, rainfall, storm surge and landslides), exposure (such as ruggedness and population density) and vulnerability (such as housing material and poverty) . However, this model is not easily transferable to other countries, due to its use of country specific data from the Philippines.

Here, we develop upon this line of research around the XGBoost model, in three ways. First, we evaluate multiple ML algorithms for classification and regression of impact data of tropical storms. Secondly, we perform a sensitivity analysis on the predictive features, replacing where possible those features for which only Philippines-specific data sources can be used with features for which data from global open data sources are available. Thirdly, the XGBoost model provides predictions at the aggregated geographical level of a municipality. Our research centres on transforming it to a grid based model with a resolution of 0.1 x 0.1 latitude-longitude degrees. For all experiments, due to the scarcity and skewness of the training data (algorithms are trained on only 40 historical typhoon events), specific attention is paid to data stratification, sampling and validation techniques. 

We find that XGBoost slightly outperforms random forest and that regression is more suitable to detect outliers than classification. Furthermore, we show that we can limit the predictive features from the original model to a subset of 20 features. The transformation to a grid-based model was possible by de-aggregating the impact data using OpenStreetMap housing data obtained from Humanitarian Data Exchange. Preliminary results show that the ML model performance worsens when going from municipality to grid-based level. This is likely caused by a larger error variance between the individual grid cells of a municipality which get averaged when aggregated. To conclude, relying on globally available data sources and working at grid level holds potential to render a machine learning based impact model generalisable and transferable to locations outside of the Philippines impacted by TCs. Future research will focus on validation with data for other countries. Ultimately, a transferable model will facilitate the scaling up of anticipatory action for tropical cyclones. 

How to cite: Kooshki, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., Ndirangu, P., Paolotti, D., Teklesadik, A., and Turner, M.: Towards a global machine learning based impact model for tropical cyclones, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14435, https://doi.org/10.5194/egusphere-egu23-14435, 2023.

16:50–16:52
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PICO4.12
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EGU23-11434
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ECS
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Virtual presentation
Sahara Sedhain, Marc van den Homberg, Aklilu Teklesadik, Maarten van Aalst, and Norman Kerle

The disaster risk community has notably shifted from a response-driven approach to making informed anticipatory action choices through impact-based forecasting (IBF). Algorithms are being developed and improved to increase impact prediction abilities, and to allow automatic triggers to reduce the reliance on human judgement. However, as complexities in modelling algorithms increase, it becomes more difficult for decision makers to interpret and explain the results. This reduces the accountability and transparency, and can lead to lower adoption of the models. Therefore, humanitarian decision-makers can benefit from a mechanism to evaluate different IBF approaches, which has not yet been developed. Through a case study of anticipatory action for tropical cyclones in the Philippines, we evaluated two very different approaches to IBF: (1) a statistical trigger model that uses a machine learning algorithm with several predictor variables, and (2) an elementary trigger model that combines damage curves and weighted overlay of vulnerability indicators, to predict the impact and prioritize areas for intervention. The models were evaluated based on their performance for damage prediction and their sensitivity to different risk indicators for Typhoon Kammuri (2019) in the Philippines. The study also proposed a way of characterising the explainability specific to an IBF model, and that gives clarity on which elements, and why, influence the results, done via a model card. To facilitate this process a prototype interactive decision portal was built, which shows decision makers the sensitivity of the results to variations in input parameters. The results show that in relative terms the elementary model performed better and would have allowed to maximise impact reduction through early action, suggesting that, for this particular case, complex was not necessarily a better choice. However, the uncertainty in both models due to limitations in the initial hazard forecast indicates that multiple models need to be evaluated for practical cases that cover different characteristics of the hazard and socio-vulnerable situations. For this, the evaluation framework we developed can be expanded across operational IBF projects.

How to cite: Sedhain, S., van den Homberg, M., Teklesadik, A., van Aalst, M., and Kerle, N.: Evaluating the explainability and performance of an elementary versus a statistical impact-based forecasting model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11434, https://doi.org/10.5194/egusphere-egu23-11434, 2023.

16:52–16:54
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PICO4.13
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EGU23-1894
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ECS
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On-site presentation
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Asha Barendregt, Irene Benito Lazaro, Sanne Muis, Marc van den Homberg, and Aklilu Teklesadik

The Philippines is one of the countries most at risk to natural disasters. Amongst these disasters, typhoons and its associated landslides, storm surges and floods have caused the largest impact. Due to increased typhoon intensity, the country’s high population density in coastal areas and rising mean sea levels, the coastal flood risk in the Philippines is only expected to increase. The 510 initiative of the Netherlands Red Cross uses an Impact Based Forecasting (IBF) model based on machine learning to anticipate the impact of an incoming typhoon to set early action into motion. The IBF model underperformed in regions that are susceptible to storm surges. Most notably, it showed a poor performance for Super-Typhoon Haiyan (2013), which caused storm surges to reach up to over five meters high. The goal of this research is to evaluate how the IBF model can be improved by applying a fast hydrodynamic modelling approach that can forecast storm surges and coastal flooding associated with typhoons. First, the accuracy of the Global Tide and Surge Model (GTSM) in simulating Haiyan’s coastal water levels was examined. GTSM was forced with two different meteorological datasets: a gridded climate reanalysis dataset, ERA5, and observed track data combined with Holland’s parametric windfield model. Second, GTSM’s water levels were used as input for a hydrodynamic inundation model to simulate the flood depth and extent in San Pedro Bay, which was subjected to a widespread coastal flood during Haiyan. This was explored both with and without the inclusion of wave setup. Our results show that Haiyan’s flood cannot adequately be indicated using the ERA5 reanalysis dataset as meteorological forcing, as it underestimated Haiyan’s extreme wind speeds with ~60 m/s. By applying the Holland parametric wind field model, more accurate flood predictions and storm surge simulations can be made. Additionally, GTSM’s temporal resolution influences the models performance substantially. By increasing the 1 hour resolution to a 30 minute resolution the prediction of the overall flood extent improved by 16%. In future research we recommend examining the applicability of the Global Tide and Surge Model when using a higher spatial resolution to help better represent local processes. Additionally, exploring the accuracy for other typhoons that struck the Philippines and the applicability in operational setting using forecasted track data can contribute to further improving forecast-based early action systems in anticipating coastal flood occurrences.

 

 

How to cite: Barendregt, A., Benito Lazaro, I., Muis, S., van den Homberg, M., and Teklesadik, A.: Using hydrodynamic flood modelling to support impact-based forecasting: a case study for Super-Typhoon Haiyan in the Philippines, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1894, https://doi.org/10.5194/egusphere-egu23-1894, 2023.

Drought impact-based forecasting
16:54–16:56
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PICO4.14
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EGU23-803
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ECS
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On-site presentation
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Tim Busker, Hans de Moel, Bart van den Hurk, and Jeroen C.J.H. Aerts

The Horn of Africa faces an ongoing multi-year drought due to five consecutive failed rainy seasons, a novel climatic event with unpreceded impacts. Over 50 million individuals in the region are expected to be highly food insecure by the end of 2022 and early 2023. The severity of these drought impacts call for the urgent upscaling and optimisation of early warning systems that trigger early actions. However, drought research focuses mainly on meteorological and hydrological forecasting, while early action is seldom addressed specifically. This leads to a gap between early warning and early action, which heavily reduces the effectiveness of these systems.

To address this gap, this study investigates the effectiveness of early action for droughts by using seasonal ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 system, predicting rainfall for the March-April-May (MAM) and October-November-December (OND) rainy seasons. We show that these seasonal rainfall forecasts reflect major on-the-ground impacts, which we identify from 9 years of monthly drought surveillance data from 21 counties in Kenya. Subsequently, we show that the SEAS5 drought forecasts with short lead times have substantial potential economic value (PEV) when used to trigger action before the OND season across the region (PEV max = 0.43). Increasing lead time to one or two months ahead of the season decreases PEV, but the benefits of early action still persist (PEV max = 0.2). Highest value for early action is found for the OND season in Kenya and Somalia, with excellent PEV max  of around 0.8 in Somalia. This indicates exceptional potential for early action to reduce impacts in this drought-prone country. The potential for early action is relatively low for the MAM season across the region, due to the season’s lower predictability. To illustrate the practical value of this research, we showcase how our methodology can be used by a pastoralist in the Kenyan drylands to effectively trigger livestock destocking ahead of a drought using SEAS5 forecasts.

These results are making headway to the development of concrete early action triggers for drought-prone regions, which are urgently needed to translate early warning to early action for droughts. It also emphasizes the need to expand historical datasets of drought impacts and early actions to support future research and policy development. Therefore, this work supports early decision-making and the development of early action protocols across the different countries in the Horn of Africa.

How to cite: Busker, T., de Moel, H., van den Hurk, B., and C.J.H. Aerts, J.: Impact-based seasonal rainfall forecasting to trigger early action for droughts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-803, https://doi.org/10.5194/egusphere-egu23-803, 2023.

16:56–16:58
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PICO4.15
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EGU23-10940
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ECS
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Virtual presentation
Nishadh Kalladath Abdul Rasheed, Viola Otieno, Herbert Misiani, Jully Ouma, Erick Otenyo, Jason Kinuya, and Ahmed Amdihun

The regions of east Africa are facing unprecedented drought impacts at present and it is expected to intensify with climate change. Impact based forecast can give critical information for disaster preparedness, adaptation, and anticipatory action thereby increasing communities’ resilience. Probabilistic forecasts with uncertainty metrics have in the past provided early warning information for early actions. However, the complexity of drought as a disaster, encompassing and effecting wide range of socio-economic activities with interlinked compounding and cascading effect often makes drought impact forecasting bound to be less effective and robust (Boult et al. 2022). Moreover, drought impacts which are subjected to the influence of other high-impact weather related events, increases the difficulty to ascertain the extent of the impact. Therefore, drought impact forecasting should be viewed as a dynamic process that involves multi-stakeholders to realize its full potential of triggering early action (de Brito 2021). In such a scenario, the availability of an open, and widely accessible information portal can be effective in ensuring early waning information is disseminated widely across all stakeholders to trigger timely action.   

This study demonstrates an automatic impact-based drought forecast system to be integrated with existing East Africa Drought Watch (EADW) web portal. For the last two-to-three years, EADW has proven to be single window portal for major hazard related information dissemination for disaster early warning and action. The proposed automatic impact-based drought forecast system is based on TMAST ALERT probabilistic soil moisture and Water Requirement Satisfaction Index (WRSI) forecast using their data Application Programming Interface (API). TAMSAT ALERT is region specific validated, calibrated data source and its effectiveness assessed in impact-based forecast for the region (Boult et al. 2020, Busker et. al 2022). CLIMADA, an open-source software for climate risk assessment was used for integrating the soil moisture hazard data with exposure, and vulnerability to forecast socio-economic impact of drought. The current version of the system, directed for agriculture drought IBF, uses Spatially-Disaggregated Crop Production Statistics Data in Africa and WRSI maize crop unimodal relationship as impact function. The probabilistic forecast of WRSI is used to generate the Impact Based Forecasting (IBF), impact versus probability matrix for region specific map generation.  Finally, implications for early warning and early action on agricultural practices in the Eastern Africa region are discussed.  

1. Boult, Victoria L., et al. "Towards drought impact-based forecasting in a multi-hazard context." Climate Risk Management 35 (2022): 100402. 

2. de Brito, Mariana Madruga. "Compound and cascading drought impacts do not happen by chance: A proposal to quantify their relationships." Science of the Total Environment 778 (2021): 146236.​ 

3. Boult, Victoria L., et al. "Evaluation and validation of TAMSAT‐ALERT soil moisture and WRSI for use in drought anticipatory action." Meteorological Applications 27.5 (2020): e1959. 

4. Busker, T., de Moel, H., van den Hurk, B., Asfaw, D., Boult, V., and Aerts, J.: Impact-based drought forecasting for agro-pastoralists in the Horn of Africa drylands, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-255, https://doi.org/10.5194/iahs2022-255, 2022. 

How to cite: Kalladath Abdul Rasheed, N., Otieno, V., Misiani, H., Ouma, J., Otenyo, E., Kinuya, J., and Amdihun, A.: Automatic generation of impact-based drought forecast, implications for early warning and action in East Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10940, https://doi.org/10.5194/egusphere-egu23-10940, 2023.

16:58–18:00