Science for Humanitarian Emergencies and Resilience (SHEAR) scoping study: Annex 3 - Early warning system and risk assessment case studies

Brown, Sarah K.; Cornforth, Rosalind; Boyd, Emily; Standley, Sarah; Allen, Mary; Clement, Kalonga; Gonzalo, Alcade; Erwin, Garzona; Michelle, Slaney; Haseeb, Irfanullah

doi:10.12774/eod_cr.june2014.brown

Cited by 11 publications

(22 citation statements)

References 13 publications

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“…This means that advance warning systems and continuous monitoring helps in developing strategic planning in the entire HSC network by minimizing uncertainties and also aids in better coordination for deliveries in crisis-affected areas. The findings are supported by the findings of previous studies such as those by Yang et al (2011); Zhou et al (2011) and Brown et al (2014).…”

Section: Research Methods and Analysis Overviewsupporting

confidence: 91%

Towards understanding key enablers to green humanitarian supply chain management practices

Bag

Luthra²,

Venkatesh³

et al. 2020

MEQ

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PurposeHumanitarian supply chains (HSCs) by their very nature require urgent reaction to unforeseeable needs, making it difficult to properly plan for the support of actual demands. As such, integrating sustainability into traditional HSC practices continues to present a challenge to governments, nongovernmental organizations (NGOs) and other humanitarian-related agencies. This study focuses on identifying and categorizing the leading enablers to green humanitarian supply chains (GHSCs) and proposes a model for improving the responsiveness based upon a fuzzy total interpretive structural modelling approach.Design/methodology/approachTotal interpretive structural modelling (TISM) uses group decision-making to identify contextual relationships among each pair of enablers and elucidates the nature of each underlying relationship. The fuzzy TISM shows the level of strength (very high influence, high influence, low influence and very low influence) of each enabler in relation to other enablers, which can help to inform management decision-making.FindingsGHSC management requires strategic planning of inventory and logistics management. The importance of collaborative relationship building with HSC partners for developing capability and the effective use of available resources are keys to success. These improved relationships also help to promote postponement and similar speculation-based logistics strategies, as well as advanced purchasing and pre-positioning strategies. Finally, the speed and quality of response is found to be the top enabler in GHSC management.Research limitations/implicationsOne noted shortcoming of the chosen research method is its reliance on subjective expert judgement. However, collecting judgements is at the basis of many research methods, and the research team took utmost care throughout the research process to allay biases. Future empirical research can further examine the relationships suggested herein. Managers can use the model developed in this research to consider impactful ways to design and execute sustainable HSCs.Originality/valueTo the best of the authors' knowledge, this is a novel attempt to identify enablers to GHSC management. Secondly, the research team has used an advanced methodology (fuzzy TISM) to develop the contextual inter-relationships among the enablers which has not been used earlier in this direction before and thus advances the GHSC literature.

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Section: Research Methods and Analysis Overviewsupporting

confidence: 91%

Towards understanding key enablers to green humanitarian supply chain management practices

Bag

Luthra²,

Venkatesh³

et al. 2020

MEQ

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“…Meteorological forecasts are also compromised by a low spatial resolution and high uncertainty, particularly at longer lead‐times. Furthermore, the scarcity of national hydrological monitoring programs in Sub‐Saharan Africa prevents national agencies from augmenting these forecasts with local data (Brown, 2014; Stephens et al., 2015). Therefore, combining low resolution indicators of physical drought intensity with these forecasts results in high uncertainty about current and future drought exposure at the local scale (Boluwade, 2020; Dinku et al., 2018; Kumar et al., 2016; Pappenberger et al., 2005).…”

Section: Introductionmentioning

confidence: 99%

“…As a result of these limitations, drought managers, including national agencies and international NGOs, depend on national socioeconomic data to spatially monitor drought exposure through evidence of drought impacts (FAO, 2018; FSNAU, 2021). These data are the primary source of information used to plan and finance early interventions with prioritized communities (Brown, 2014; Buchanan‐Smith & McCelvey, 2018; FSNAU, 2021; Stephens et al., 2015). In Somalia and Somaliland, this information is made available in real‐time by the United Nations Food and Agriculture Organization's Food Security and Nutrition Analysis Unit's (UN FAO FSNAU) Early Warning Early Action dashboard (FSNAU, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Requiring signals from socioeconomic drought impacts for an early warning to be triggered means that opportunities for early, water shortage‐mitigating interventions have already been missed. A further limitation is the coarse spatial and temporal resolution of the socioeconomic indicators, with monthly data aggregated by FAO FSNAU in Somalia for each administrative region (Brown, 2014; FSNAU, 2021). This has the effect of smoothing out localized drought impacts, to the extent that DEWSs have little information on relative exposure at the community level.…”

Section: Introductionmentioning

confidence: 99%

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Localizing Hydrological Drought Early Warning Using In Situ Groundwater Sensors

Veness

Butler

Ochoa‐Tocachi³

et al. 2022

Water Resources Research

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Drought-prone regions host many of the world's least developed countries, which are experiencing increasing drought risk as a result of population pressures and increasing drought frequencies (UNDRR, 2015). This is evident in Somalia, where estimated fatalities from the 2011 drought exceeded 250,000 and a further drought in 2017 required humanitarian assistance for 6 million people (FAO, 2018(FAO, , 2019. Drought early warning systems (DEWSs) aim to monitor and forecast drought risk, enabling early interventions that mitigate the most severe socioeconomic impacts. To formulate this risk, DEWSs require evidence of drought intensity and community exposure collected by monitoring systems (Brown, 2014; UNDRR, 2015). Current Drought Monitoring Methods for Early WarningA range of regional-scale, satellite-based indicators are used to monitor physical drought development. The Standardized Precipitation Index (SPI) is a popular indicator for monitoring meteorological drought (McKee et al., 1993;Van Loon, 2015). The SPI indicates rainfall anomaly compared to the long-term average for that chosen time period (Sheffield et al., 2014). For agricultural drought (Van Loon, 2015), satellite-based indicators are used such as soil moisture and the Normalized Difference Vegetation Index (NDVI; Liang et al., 1994;NASA Worldview, 2022;Tarpley et al., 1984). These indicate available soil moisture for vegetation growth, which in turn indicates crop failure in areas where agriculture is nonirrigated (Brown, 2014; FEWS NET, 2021; FSNAU, 2021;Sheffield et al., 2014). However, in most arid zones during late dry seasons, water availability for human consumption depends on groundwater supply. As such, these surface indicators are not ideal for assessing hydrological droughts-the most intense form of drought that causes water shortages (

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“…A CBEWS is an early warning system (EWS) where communities are active participants in the design, monitoring and management of the EWS, not just passive recipients of warnings. Community-based flood early warning systems are in place in a wide range of countries, for example in Malawi with support from Christian Aid (Brown, 2014), in Indonesia (e.g. in the Binajaan basin Practical Action and Mercy Corps, 2012) and in Cambodia (Kandal Province) with support from the national societies Red Cross and Red Crescent (IFRC, 2010).…”

Section: Introductionmentioning

confidence: 99%

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

Smith

Brown

Dugar

2017

Nat. Hazards Earth Syst. Sci.

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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 databased 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 floodprone 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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Science for Humanitarian Emergencies and Resilience (SHEAR) scoping study: Annex 3 - Early warning system and risk assessment case studies

Cited by 11 publications

References 13 publications

Towards understanding key enablers to green humanitarian supply chain management practices

Towards understanding key enablers to green humanitarian supply chain management practices

Localizing Hydrological Drought Early Warning Using In Situ Groundwater Sensors

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

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