scholarly journals After the extreme flood in 2002: changes in preparedness, response and recovery of flood-affected residents in Germany between 2005 and 2011

2014 ◽  
Vol 2 (10) ◽  
pp. 6397-6451 ◽  
Author(s):  
S. Kienzler ◽  
I. Pech ◽  
H. Kreibich ◽  
M. Müller ◽  
A. H. Thieken
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Lead Times ◽  
Warning Systems ◽  
Telephone Interviews ◽  
Private Households ◽  
Extreme Flood ◽  
Response And Recovery ◽  
Flood Experience ◽  
Flood Characteristics

Abstract. In the aftermath of the severe flood in August 2002, a number of changes in flood policies were launched in Germany and other European countries aiming at an improved risk management. The question arises, whether these changes have already an impact on the residents' capabilities of coping with floods and whether flood-affected private households are now better prepared than in 2002. Therefore, computer-aided telephone interviews with private households in Germany that suffered from property damage due to flooding in 2005, 2006, 2010 or 2011 were performed and analysed with respect to flood awareness, precaution, preparedness and recovery. The data were compared to a similar investigation after the flood in 2002. After the flood in 2002, the level of private precaution increased considerably. One contribution factor is that a larger part of people knew that they are at risk of flooding. Yet this knowledge did not necessarily result in building retrofitting or flood proofing measures. The best level of precaution was found before the flood events in 2006 and 2011. This might be explained by more flood experience and overall greater awareness of the residents. Still, costs and damage avoiding benefits of these measures have to be communicated in a better way. Early warning and emergency response were substantially influenced by flood characteristics. In contrast to flood-affected people in 2006 or 2011, people affected by flooding in 2005 or 2010 had to deal with shorter lead times, less time to take emergency measures; consequently they suffered from higher losses. Therefore, it is important to further improve early warning systems and communication channels, particularly in hilly areas with fast onset flooding.

scholarly journals After the extreme flood in 2002: changes in preparedness, response and recovery of flood-affected residents in Germany between 2005 and 2011

2015 ◽  
Vol 15 (3) ◽  
pp. 505-526 ◽  
Author(s):  
S. Kienzler ◽  
I. Pech ◽  
H. Kreibich ◽  
M. Müller ◽  
A. H. Thieken
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Rapid Onset ◽  
Lead Times ◽  
Telephone Interviews ◽  
Private Households ◽  
Extreme Flood ◽  
Response And Recovery ◽  
Flood Experience ◽  
Emergency Measures

Abstract. In the aftermath of the severe flooding in Central Europe in August 2002, a number of changes in flood policies were launched in Germany and other European countries, aiming at improved risk management. The question arises as to whether these changes have already had an impact on the residents' ability to cope with floods, and whether flood-affected private households are now better prepared than they were in 2002. Therefore, computer-aided telephone interviews with private households in Germany that suffered from property damage due to flooding in 2005, 2006, 2010 or 2011 were performed and analysed with respect to flood awareness, precaution, preparedness and recovery. The data were compared to a similar investigation conducted after the flood in 2002. After the flood in 2002, the level of private precautions taken increased considerably. One contributing factor is the fact that, in general, a larger proportion of people knew that they were at risk of flooding. The best level of precaution was found before the flood events in 2006 and 2011. The main reason for this might be that residents had more experience with flooding than residents affected in 2005 or 2010. Yet, overall, flood experience and knowledge did not necessarily result in building retrofitting or flood-proofing measures, which are considered as mitigating damages most effectively. Hence, investments still need to be stimulated in order to reduce future damage more efficiently. Early warning and emergency responses were substantially influenced by flood characteristics. In contrast to flood-affected people in 2006 or 2011, people affected by flooding in 2005 or 2010 had to deal with shorter lead times and therefore had less time to take emergency measures. Yet, the lower level of emergency measures taken also resulted from the people's lack of flood experience and insufficient knowledge of how to protect themselves. Overall, it was noticeable that these residents suffered from higher losses. Therefore, it is important to further improve early warning systems and communication channels, particularly in hilly areas with rapid-onset flooding.

scholarly journals New insights into flood warning and emergency response from the perspective of affected parties

2016 ◽  
Author(s):  
Heidi Kreibich ◽  
Ina Pech ◽  
Kai Schröter ◽  
Meike Müller ◽  
Annegret H. Thieken
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Emergency Plan ◽  
Flood Warning ◽  
Private Households ◽  
Last Mile ◽  
Flood Warning Systems ◽  
Flood Experience ◽  
Emergency Measures

Abstract. Early warning is essential for protecting people and mitigating damage in case of flood events. However, early warning is only helpful if the parties at risk are reached by the warning, if they believe the warning and if they know how to react appropriately. Finding suitable methods for communicating helpful warnings to the "last mile" remains a challenge. To gain more knowledge, surveys were undertaken after the August 2002 and the June 2013 floods in Germany, asking affected private households and companies about warnings they received and emergency measures they undertook. Results show that in 2002 early warning did not work well: in many areas warnings came late or were imprecise. Many people (27 %) and companies (45 %) stated that they had not received any flood warning. Additionally, preparedness of private households and companies was low before 2002, mainly due to a lack of flood experience. After the 2002 flood, many initiatives were launched and investments undertaken to improve flood risk management including the flood warning systems in Germany. In 2013 only a small share of the affected people (7 %) and companies (7 %) were not reached by any warning. Additionally, also private households and companies were better prepared. For instance, the share of companies which have an emergency plan in place has increased from 10 % in 2002 to 26 % in 2013. However, there is still room for improvement. Therefore, integrated early warning systems from monitoring through to the reaction of the affected parties as well as effective risk and emergency communication need continuous further improvement.

scholarly journals Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 183
Author(s):  
Paul Muñoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Jan Feyen ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning Systems ◽  
Data Representation ◽  
Machine Learning Techniques ◽  
Lead Times ◽  
Warning Systems ◽  
Tropical Andes ◽  
K Nearest Neighbors ◽  
Learning Techniques

Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.

scholarly journals Flood Early Warning Systems using Machine Learning Techniques. Case the Tomebamba Catchment at the Southern Andes of Ecuador

2021 ◽  
Author(s):  
Paul Muñoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Jan Feyen ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning Systems ◽  
Data Representation ◽  
Machine Learning Techniques ◽  
Lead Times ◽  
Warning Systems ◽  
Tropical Andes ◽  
K Nearest Neighbors ◽  
Learning Techniques

Flood Early Warning Systems (FEWSs) using Machine Learning (ML) has gained worldwide popularity. However, determining the most efficient ML technique is still a bottleneck. We assessed FEWSs with three river states, No-alert, Pre-alert, and Alert for flooding, for lead times between 1 to 12 hours using the most common ML techniques, such as Multi-Layer Perceptron (MLP), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1- and 12-hour cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of the society for floods.

scholarly journals Community-based early warning systems for flood risk mitigation in Nepal

2017 ◽  
Vol 17 (3) ◽  
pp. 423-437 ◽  
Author(s):  
Paul J. Smith ◽  
Sarah Brown ◽  
Sumit Dugar
Keyword(s):  
Early Warning ◽  
Risk Mitigation ◽  
Early Warning Systems ◽  
Appropriate Technology ◽  
Data Availability ◽  
Current Status ◽  
Lead Times ◽  
Warning Systems ◽  
Community Based ◽  
Probabilistic Forecasts

Abstract. This paper focuses on the use of community-based early warning systems for flood resilience in Nepal. The first part of the work outlines the evolution and current status of these community-based systems, highlighting the limited lead times currently available for early warning. The second part of the paper focuses on the development of a robust operational flood forecasting methodology for use by the Nepal Department of Hydrology and Meteorology (DHM) to enhance early warning lead times. The methodology uses data-based physically interpretable time series models and data assimilation to generate probabilistic forecasts, which are presented in a simple visual tool. The approach is designed to work in situations of limited data availability with an emphasis on sustainability and appropriate technology. The successful application of the forecast methodology to the flood-prone Karnali River basin in western Nepal is outlined, increasing lead times from 2–3 to 7–8 h. The challenges faced in communicating probabilistic forecasts to the last mile of the existing community-based early warning systems across Nepal is discussed. The paper concludes with an assessment of the applicability of this approach in basins and countries beyond Karnali and Nepal and an overview of key lessons learnt from this initiative.

Forecast of temperature-attributable mortality at lead times of up to 15 days for a very large ensemble of European regions

2021 ◽  
Author(s):  
Marcos Quijal-Zamorano ◽  
Desislava Petrova ◽  
Xavier Rodó ◽  
Èrica Martinez-Solanas ◽  
Joan Ballester
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Attributable Mortality ◽  
Lead Times ◽  
Warning Systems ◽  
Epidemiological Models ◽  
European Regions ◽  
Large Ensemble ◽  
Weather Forecasts

<p>Implementing adequate health preventing measures is essential for public health decision making, particularly in the current context of rising temperatures. Most of the early warning systems are only based on climate data, and in very few cases they truly model the actual impact of the climate phenomena.</p><p>Here we establish, for the first-time, the theoretical basis for the development of operational heat-health early warning systems that combine climate and health data. We studied the predictability of Temperature Attributable Mortality (TAM) at lead times of up to 15 days for a very large ensemble of European regions. To achieve this goal, we analysed daily counts of all-cause mortality for the period 1998-2012 in 147 NUTS2 regions in 16 European countries, representing more than 400 million people, and daily high-resolution weather forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). We applied epidemiological models for the fitting of the temperature-mortality relationship in each of the regions, accounting for the different vulnerabilities and socio-demographic characteristics existing in Europe. We compared the predictive skill of the temperature and health forecasts on seasons and days with higher mortality risk. </p><p>We conclude that the predictability of temperature can be used to issue skilful forecasts of TAM. In general, the predictability limit of temperature is similar to the one of TAM, which implies that the use of epidemiological models to transform the climate variables into health information does not reduce the lead time limit with significant forecast skill. Nonetheless, the spatial heterogeneity of the predictability lead time for TAM is higher than for temperature, especially in summer, where the complex shape of the temperature-mortality association amplifies the forecast errors. Overall, we find  a nearly-linear relationship between the predictability of temperature and TAM for different seasons and regions, suggesting that future improvements in the predictability of temperature could automatically lead to improvements in the predictability of TAM.</p>

scholarly journals Flood Early Warning Systems using Machine Learning Techniques. Application to a Catchment located in the Tropical Andes of Ecuador.

2021 ◽  
Author(s):  
Paul Muñoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Research Question ◽  
Early Warning Systems ◽  
Flood Forecasting ◽  
Data Representation ◽  
Machine Learning Techniques ◽  
Lead Times ◽  
Warning Systems ◽  
Tropical Andes

Abstract Short-rain floods, especially flash-floods, produce devastating impacts on society, the economy, and ecosystems. A key countermeasure is to develop Flood Early Warning Systems (FEWSs) aimed at forecasting flood warnings with sufficient lead time for decision making. Although Machine Learning (ML) techniques have gained popularity among hydrologists, the research question poorly answered is what is the best ML technique for flood forecasting? To answer this, we compare the efficiencies of FEWSs developed with the five most common ML techniques for flood forecasting, and for lead times between 1 to 12 hours. We use the Tomebamba catchment in the Ecuadorean Andes as a case study, with three warning classes to forecast No-alert, Pre-alert, and Alert of floods. For all lead times, the Multi-Layer Perceptron (MLP) technique achieves the highest model performances (f1-macro score) followed by Logistic Regression (LR), from 0.82 (1-hour) to 0.46 (12-hour). This ranking was confirmed by the log-loss scores, ranging from 0.09 (1-hour) to 0.20 (12-hour) for the above mentioned methods. Model performances decreased for the remaining ML techniques (K-Nearest Neighbors, Naive Bayes and Random Forest) but their ranking was highly variable and not conclusive. Moreover, according to the g-mean, LR models depict greater stability for correctly classifying all flood classes, whereas MLP models are specialized in the minority (Pre-alert and Alert) classes. To improve the performance and the applicability of FEWSs, we recommend future efforts to enhance input data representation and to develop communication applications between FEWSs and the public as tools to boost the preparedness of the society against floods.

scholarly journals Community Based Early Warning Systems for flood risk mitigation in Nepal

2016 ◽  
Author(s):  
Paul J. Smith ◽  
Sarah Brown ◽  
Sumit Dugar
Keyword(s):  
Early Warning ◽  
Flood Risk ◽  
Risk Mitigation ◽  
Early Warning Systems ◽  
Current Status ◽  
Lead Times ◽  
Time Series Models ◽  
Warning Systems ◽  
Community Based ◽  
Probabilistic Forecasts

Abstract. This paper focuses on the use of Community Based Early Warning Systems for flood risk mitigation in Nepal. The first part of the work outlines the evolution and current status of these community based systems. A significant ongoing challenge faced by Community Based Early Warning Systems in Nepal is the short lead times available for early warning. The second part of the paper therefore focuses on the development of a robust operational flood forecasting methodology for use by the Department for Hydrology and Meteorology (DHM), Government of Nepal to compliment the community based systems. The resulting methodology uses data based physically interpretable time series models and data assimilation to generate probabilistic forecasts. The paper concludes with an example application to a flood prone catchment (Karnali Basin) in western Nepal.

The CARE Telematics Network for the Surveillance of Influenza in Europe

1995 ◽  
Vol 34 (05) ◽  
pp. 518-522 ◽  
Author(s):  
M. Bensadon ◽  
A. Strauss ◽  
R. Snacken
Keyword(s):  
Public Health ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Surveillance Networks ◽  
Spanish Flu ◽  
The 1950S ◽  
National Networks ◽  
First Time

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.

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