Designing a multi-objective framework for forecast-based action of extreme rains in Peru

2020 ◽  
Author(s):  
Jonathan Lala ◽  
Juan Bazo ◽  
Paul Block
Keyword(s):  
Disaster Preparedness ◽  
Disaster Response ◽  
Lead Time ◽  
Early Warning Systems ◽  
Red Cross ◽  
Warning Systems ◽  
Short Term ◽  
Early Action ◽  
Wide Range ◽  
Technological Constraints

<p>The last few years have seen a major innovation within disaster management and financing through the emergence of standardized forecast-based action protocols. Given sufficient forecasting skill and lead time, financial resources can be shifted from disaster response to disaster preparedness, potentially saving both lives and property. Short-term (hours to days) early warning systems are common worldwide; however, longer-term (months to seasons) early actions are still relatively under-studied. Seeking to address both, the Peruvian Red Cross has developed an Early Action Protocol (EAP) for El Niño-related extreme precipitation and floods. The EAP has well-defined risk metrics, forecast triggers, and early actions ranging from 5 days to 3 months before a forecasted disaster. Changes in climate regimes, forecast technology, or institutional and financial constraints, however, may significantly alter expected impacts of these early actions. A robust sensitivity analysis of situational and technological constraints is thus conducted to identify benefits and tradeoffs of various actions given various future scenarios, ensuring an adaptive and effective protocol that can be used for a wide range of changing circumstances.</p>

scholarly journals Communicating Complex Forecasts for Enhanced Early Warning in Nepal

2019 ◽  
Author(s):  
Mirianna Budimir ◽  
Amy Donovan ◽  
Sarah Brown ◽  
Puja Shakya ◽  
Dilip Gautam ◽  
...  
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Warning System ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Deterministic Models ◽  
Early Action ◽  
Probabilistic Forecasts ◽  
Barriers And Challenges

Abstract. Early warning systems have the potential to save lives and improve resilience. Simple early warning systems rely on real-time data and deterministic models to generate evacuation warnings; these simple deterministic models enable life-saving action, but provide limited lead time for resilience-building early action. More complex early warning systems supported by forecasts, including probabilistic forecasts, can provide additional lead time for preparation. However, barriers and challenges remain in disseminating and communicating these more complex warnings to community members and individuals at risk. Research was undertaken to analyse and understand the current early warning system in Nepal, considering available data and forecasts, information flows, early warning dissemination and decision making for early action. The research reviewed the availability and utilisation of complex forecasts in Nepal, their integration into dissemination (Department of Hydrology and Meteorology (DHM) bulletins and SMS warnings), and decision support tools (Common Alerting Protocols and Standard Operating Procedures), considering their impact on improving early action to increase the resilience of vulnerable communities to flooding.

scholarly journals The Legitimacy, Accountability, and Ownership of an Impact-Based Forecasting Model in Disaster Governance

2020 ◽  
Vol 8 (4) ◽  
pp. 445-455 ◽  
Author(s):  
Sterre Bierens ◽  
Kees Boersma ◽  
Marc J. C. Van den Homberg
Keyword(s):  
Disaster Response ◽  
Early Warning Systems ◽  
The Philippines ◽  
Data Driven ◽  
Red Cross ◽  
Forecasting Model ◽  
Mutual Understanding ◽  
Warning Systems ◽  
New Paradigm ◽  
Implementation Challenges

The global shift within disaster governance from disaster response to preparedness and risk reduction includes the emergency of novel Early Warning Systems such as impact based forecasting and forecast-based financing. In this new paradigm, funds usually reserved for response can be released before a disaster happens when an impact-based forecast—i.e., the expected humanitarian impact as a result of the forecasted weather—reaches a predefined danger level. The development of these impact-based forecasting models are promising, but they also come with significant implementation challenges. This article presents the data-driven impact-based forecasting model as developed by 510, an initiative of the Netherlands Red Cross. It elaborates on how questions on legitimacy, accountability and ownership influenced the implementation of the model within the Philippines with the Philippine Red Cross and the local government as the main stakeholders. The findings imply that the exchange of knowledge between the designer and manufacturer of impact-based models and the end users of those models fall short if novel Early Warnign Systems are seen as just a matter of technology transfer. Instead the development and implementation of impact based models should be based on mutual understanding of the users’ needs and the developers of such models.

Health Aspects of Disaster Preparedness and Response Panel Session 2: Seismic Risks including Tsunamis

2006 ◽  
Vol 21 (S3) ◽  
pp. s82-s86 ◽  
Author(s):  
Keyword(s):  
Disaster Preparedness ◽  
Health Workers ◽  
Early Warning Systems ◽  
Legal Framework ◽  
Disaster Medicine ◽  
Warning Systems ◽  
Panel Session ◽  
The Many ◽  
Disaster Preparedness And Response ◽  
And Control

AbstractThis Panel Session consisted of five country reports (India, Indonesia, Maldives, Thailand, andNepal) and the common issues identified during the Panel discussions relative to seismic events in the Southeast Asia Region. Important issues identified included the needs for: (1) a legal framework upon which to base preparedness and response; (2) coordination between the many organizations involved; (3) early warning systems within and between countries; (4) command and control; (5) access to resources including logistics; (6) strengthening the health infrastructure; (7) professionalizing the field of disaster medicine and management; (8) management of communications and information; (9) management of dead bodies; and (10) mental health of the survivors and health workers.

scholarly journals Forecasting Vegetation Condition with a Bayesian Auto-regressive Distributed Lags (BARDL) Model

2021 ◽  
Author(s):  
Edward E. Salakpi ◽  
Peter D. Hurley ◽  
James M. Muthoka ◽  
Adam B. Barrett ◽  
Andrew Bowell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Lead Time ◽  
Expert Knowledge ◽  
Research Work ◽  
Early Warning Systems ◽  
Ar Model ◽  
Lead Times ◽  
Vegetation Condition ◽  
Early Action ◽  
Auto Regressive

Abstract. Droughts form a large part of climate/weather-related disasters reported globally. In Africa, pastoralists living in the Arid and Semi-Arid Lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish Early Warning Systems (EWS) to save lives and livelihoods. Existing EWS use a combination of Satellite Earth Observation (EO) based biophysical indicators like the Vegetation Condition Index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWS rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like Auto-Regression, Gaussian Processes and Artificial Neural Networks can provide very skilled models for forecasting vegetation condition at short to medium range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. The objective of this research work is to develop models that forecast vegetation conditions at longer lead times on the premise that vegetation condition is controlled by factors like precipitation and soil moisture. To achieve this, we used a Bayesian Auto-Regressive Distributed Lag (BARDL) modelling approach which enabled us to factor in lagged information from Precipitation and Soil moisture levels into our VCI forecast model. The results showed a ∼2-week gain in the forecast range compared to the univariate AR model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85 and 0.74, compared to the AR model's R2 of 0.88, 0.77 and 0.65 for 6, 8 and 10 weeks lead time respectively.

scholarly journals Communication Structures and Decision Making Cues and Criteria to Support Effective Drought Warning in Central Malawi

2020 ◽  
Vol 2 ◽  
Author(s):  
Alexia Calvel ◽  
Micha Werner ◽  
Marc van den Homberg ◽  
Andrés Cabrera Flamini ◽  
Ileen Streefkerk ◽  
...  
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Warning System ◽  
Agricultural Practices ◽  
Early Warning Systems ◽  
Sudden Onset ◽  
Small Scale ◽  
Warning Systems ◽  
Structured Interviews ◽  
Early Action

Early warning systems trigger early action and enable better disaster preparedness. People-centered dissemination and communication are pivotal for the effective uptake of early warnings. Current research predominantly focuses on sudden-onset hazards, such as floods, ignoring considerable differences with slow-onset hazards, such as droughts. We identify the essential factors contributing to effective drought dissemination and communication using the people-centered approach advocated in the WMOs Multi-Hazard Early Warning System Framework (MHEWS). We use semi-structured interviews with key stakeholders and focus group discussions with small-scale farmers in the Mangochi and Salima Districts of Malawi. We show that the timely release of seasonal forecast, the tailoring of the drought warning content (and its timing) to agricultural decision making, and the provision of several dissemination channels enhance trust and improve uptake of drought warning information by farmers. Our analysis demonstrates that farmers seek, prepare, and respond to drought warning information when it is provided as advice on agricultural practices, rather than as weather-related information. The information was found to be useful where it offers advice on the criteria and environmental cues that farmers can use to inform their decisions in a timely manner. Based on our findings, we propose that by focusing on enhancing trust, improving information uptake and financial sustainability as key metrics, the MHEWS can be adapted for use in monitoring the effectiveness of early warning systems.

scholarly journals Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Vol 21 (9) ◽  
pp. 2753-2772
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach ◽  
Landslide Early Warning

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

Risk-based modeling of early warning systems for pollution accidents

2002 ◽  
Vol 46 (3) ◽  
pp. 41-49 ◽  
Author(s):  
W.M. Grayman ◽  
R.M. Males
Keyword(s):  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Ohio River ◽  
Warning Systems ◽  
Simulation Techniques ◽  
Wide Range ◽  
Contamination Event ◽  
Probabilistic Nature ◽  
The Impact

An early warning system is a mechanism for detecting, characterizing and providing notification of a source water contamination event (spill event) in order to mitigate the impact of contamination. Spill events are highly probabilistic occurrences with major spills, which can have very significant impacts on raw water sources of drinking water, being relatively rare. A systematic method for designing and operating early warning systems that considers the highly variable, probabilistic nature of many aspects of the system is described. The methodology accounts for the probability of spills, behavior of monitoring equipment, variable hydrology, and the probability of obtaining information about spills independent of a monitoring system. Spill Risk, a risk-based model using Monte Carlo simulation techniques has been developed and its utility has been demonstrated as part of an AWWA Research Foundation sponsored project. The model has been applied to several hypothetical river situations and to an actual section of the Ohio River. Additionally, the model has been systematically applied to a wide range of conditions in order to develop general guidance on design of early warning systems.

Using Ground Microtremor Data in Advanced Rockfall Early Warning Systems and Predicting Spatiotemporal Characteristics of Rockfall Hazards

2021 ◽  
Author(s):  
Yi-Rong Yang ◽  
Tzu-Tung Lee ◽  
Tai-Tien Wang
Keyword(s):  
Slope Stability ◽  
Early Warning ◽  
Local Governments ◽  
Lead Time ◽  
Early Warning Systems ◽  
Slope Instability ◽  
Warning Systems ◽  
Spatiotemporal Characteristics ◽  
Stability Of Slopes ◽  
Rockfall Hazards

Abstract Identifying cliffs that are prone to fall and providing a sufficient lead time for rockfall warning are crucial steps in disaster risk reduction and preventive maintenance work, especially that led by local governments. However, existing rockfall warning systems provide uncertain rockfall location forecasting and short warning times because the deformation and cracking of unstable slopes are not sufficiently detected by sensors before the rock collapses. Here, we introduce ground microtremor signals for early rockfall forecasting and demonstrate that microtremor characteristics can be used to detect unstable rock wedges on slopes, quantitatively describe the stability of slopes and lengthen the lead time for rockfall warning. We show that the change in the energy of ground microtremors can be an early precursor of rockfall and that the signal frequency decreases with slope instability. This finding indicates that ground microtremor signals are remarkably sensitive to slope stability. We conclude that microtremor characteristics can be used as an appropriate slope stability index for early rockfall warning systems and predicting the spatiotemporal characteristics of rockfall hazards. This early warning method has the advantages of providing a long lead time and on-demand monitoring, while increasing slope stability accessibility and prefailure location detectability.

scholarly journals Effective real-time forecasting of inundation maps for early warning systems during typhoons

2018 ◽  
Vol 147 ◽  
pp. 03014
Author(s):  
Jhih-Huang Wang ◽  
Gwo-Fong Lin ◽  
Bing-Chen Jhong
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Reference Points ◽  
Support Vector ◽  
Spatial Expansion ◽  
Warning Systems ◽  
Inundation Maps ◽  
Inundation Depth ◽  
Proposed Model

Accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation during typhoons. In this paper, an effective forecasting model is proposed to yield 1- to 6-h lead-time inundation maps for early warning systems during typhoons. The proposed model based on Support Vector Machine (SVM) is composed of two modules, point forecasting and spatial expansion. In the first module, the rainfall intensity, inundation depth, cumulative rainfall and forecasted inundation depths are considered as model input for point forecasting. In the second module, the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps for spatial expansion. The results show that the proposed model is able to provide accurate point forecasts at each inundation point. Moreover, the spatial expansion module is capable of producing accurate spatial inundation forecasts. Obviously, the proposed model provides reasonable spatial inundation forecasts, and is able to deal with the nonlinear relationships between inputs and desired output. In conclusion, the proposed model is suitable and useful for inundation forecasting.

scholarly journals Exploring the impact, response and preparedness to water-related natural disasters in the Barisal division of Bangladesh: a mixed methods study

BMJ Open ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. e026459 ◽  
Author(s):  
Jagnoor Jagnoor ◽  
Aminur Rahman ◽  
Patricia Cullen ◽  
Fazlul Kader Chowdhury ◽  
Caroline Lukaszyk ◽  
...  
Keyword(s):  
Natural Disasters ◽  
Disaster Preparedness ◽  
Risk Perceptions ◽  
Early Warning Systems ◽  
Significant Loss ◽  
Primary Concern ◽  
Disaster Resilience ◽  
Warning Systems ◽  
Cross Sectional ◽  
The Impact

ObjectivesTo investigate the impact of natural disasters on communities in the Barisal division of Bangladesh, exploring community approaches to disaster preparedness and mitigation.SettingCommunities in all districts of the Barisal division of Bangladesh.ParticipantsQuantitative data were collected through a cross-sectional household survey (n=9263 households; n=38 981 individuals). Qualitative data were collected through in-depth interviews (n=7) and focus group discussions (n=23) with key informants.Outcome measuresQuantitative research recorded features of natural disaster events from the previous 5 years, documenting risk factors that increase vulnerability to disaster, use of disaster warning systems and evacuation processes. Qualitative research investigated disaster risk perceptions, experiences during and following disaster, and disaster preparedness practices.ResultsThe survey response rate was 94.7%. Exposure to disaster in the last 5 years was high (82%) with flooding and cyclones considered the greatest threats. Awareness of evacuation processes was low; and only 19% of respondents evacuated their homes at the time of disaster. Drowning during disaster was the primary concern (87%), followed by debt, livestock and crop loss (78%). The qualitative findings indicated prevailing fatalistic perceptions towards natural disasters among community. The consequences of disasters included significant loss of livelihoods and exposure to infections due to poor sanitation. There was also insufficient support for the most vulnerable, particularly women, children and the elderly. Although several community preparedness and practices existed, there was a lack of response to early warning systems. Barriers to disaster response and resilience included financial insecurities, loss of livelihoods and cultural concerns regarding women’s privacy.ConclusionsCritical to achieving disaster resilience is increased government investment in infrastructure and systems-level responses that empower communities. Further research can support this by addressing community challenges to promoting disaster resilience and how to leverage existing community strengths to implement locally owned solutions.

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