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

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.

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Community Based Early Warning Systems for flood risk mitigation in Nepal

10.5194/nhess-2016-122 ◽

2016 ◽

Cited By ~ 1

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.

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Quantifying the effectiveness of early warning systems for natural hazards

Natural Hazards and Earth System Science ◽

10.5194/nhess-16-149-2016 ◽

2016 ◽

Vol 16 (1) ◽

pp. 149-166 ◽

Cited By ~ 16

Author(s):

M. Sättele ◽

M. Bründl ◽

D. Straub

Keyword(s):

Natural Hazards ◽

Early Warning ◽

Risk Mitigation ◽

Early Warning Systems ◽

Mitigation Measures ◽

Management Approach ◽

Warning Systems ◽

Integrated Risk ◽

Framework Approach

Abstract. Early warning systems (EWSs) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWSs is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWSs and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWSs is provided, which focuses on the degree of automations encountered in varying EWSs. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

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

Hydrology ◽

10.3390/hydrology8040183 ◽

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.

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Earthquake Early Warning Systems: Current Status and Perspectives

Early Warning Systems for Natural Disaster Reduction ◽

10.1007/978-3-642-55903-7_53 ◽

2003 ◽

pp. 409-423 ◽

Cited By ~ 9

Author(s):

Willy H. K. Lee ◽

Juan Manuel Espinosa-Aranda

Keyword(s):

Early Warning ◽

Early Warning Systems ◽

Earthquake Early Warning ◽

Current Status ◽

Warning Systems ◽

Earthquake Early Warning Systems

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Quantifying the effectiveness of early warning systems for natural hazards

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-3-4479-2015 ◽

2015 ◽

Vol 3 (7) ◽

pp. 4479-4526 ◽

Cited By ~ 1

Author(s):

M. Sättele ◽

M. Bründl ◽

D. Straub

Keyword(s):

Natural Hazards ◽

Early Warning ◽

Risk Mitigation ◽

Early Warning Systems ◽

Mitigation Measures ◽

Management Approach ◽

Warning Systems ◽

Integrated Risk ◽

Framework Approach

Abstract. Early warning systems (EWS) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWS is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWS and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWS is provided, which focuses on the degree of automations encountered in varying EWS. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

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

10.20944/preprints202111.0510.v1 ◽

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.

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Communicating Complex Forecasts for Enhanced Early Warning in Nepal

10.5194/gc-2019-3 ◽

2019 ◽

Cited By ~ 1

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.

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Science for Humanitarian Emergencies and Resilience (SHEAR) scoping study: Annex 2 - The current status of early warning systems and risk assessments in Africa, the Caribbean and South Asia - A literature review

10.12774/eod_cr.june2014.lumbrosorance ◽

2014 ◽

Cited By ~ 2

Author(s):

D Lumbroso ◽

J Rance ◽

G Pearce ◽

E Brown ◽

S Wade

Keyword(s):

Literature Review ◽

South Asia ◽

Early Warning ◽

Early Warning Systems ◽

Risk Assessments ◽

Current Status ◽

Warning Systems ◽

Scoping Study ◽

Humanitarian Emergencies ◽

The Caribbean

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An early warning system for rainfall-triggered shallow slides and debris flows. Application in Catalonia, Spain and Canton of Bern, Switzerland

10.5194/egusphere-egu2020-479 ◽

2020 ◽

Author(s):

Rosa M Palau ◽

Marc Berenguer ◽

Marcel Hürlimann ◽

Daniel Sempere-Torres ◽

Catherine Berger ◽

...

Keyword(s):

Early Warning ◽

Debris Flows ◽

Risk Mitigation ◽

Regional Scale ◽

Slope Angle ◽

Early Warning Systems ◽

Warning Systems ◽

Radar Rainfall ◽

Susceptibility Maps ◽

Landslide Event

Risk mitigation for rainfall-triggered shallow slides and debris flows at regional scale is challenging. Early warning systems are a helpful tool to depict the location and time of future landslide events so that emergency managers can act in advance. Recently, some of the regions that are recurrently affected by rainfall triggered landslides have developed operational landslide early warning systems (LEWS). However, there are still many territories where this phenomenon also represents an important hazard and lack this kind of risk mitigation strategy.The main objective of this work is to study the feasibility to apply a regional scale LEWS, which was originally designed to work over Catalonia (Spain), to run in other regions. To do so we have set up the LEWS to Canton of Bern (Switzerland).The LEWS combines susceptibility maps to determine landslide prone areas and in real time high-resolution radar rainfall observations and forecasts. The output is a qualitative warning level map with a resolution of 30 m.Susceptibility maps have been derived using a simple fuzzy logic methodology that combines the terrain slope angle, and land use and land cover (LULC) information. The required input parameters have been obtained from regional, pan-European and global datasets.Rainfall inputs have been retrieved from both regional weather radar networks, and the OPERA pan-European radar composite. A set of global rainfall intensity-duration data has been used to asses if a rainfall event has the potential of triggering a landslide event.The LEWS has been run in the region of Catalonia and Canton of Bern for specific rainfall events that triggered important landslides. Finally, the LEWS performance in Catalonia has been assessed.Results in Catalonia show that the LEWS performance strongly depends on the quality of both the susceptibility maps and rainfall data. However, in both regions the LEWS is generally able to issue warnings for most of the analysed landslide events.

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