The Current Landscape of Early Warning Systems and Traditional Approaches to Disaster Detection
DOI:
https://doi.org/10.62486/latia202577Keywords:
Community preparedness, Multi-hazard approach, Predictive modeling, Real-time monitoring, Risk assessment and Technological integrationAbstract
Early warning systems (EWS) are crucial for disaster risk reduction, providing timely and reliable information to communities and authorities for proactive mitigation. Traditional methods, such as weather stations, river gauges, and seismic networks, have limitations in spatial coverage, real-time data availability, and precursor signal detection. Recent technological advancements have enhanced EWS by integrating remote sensing data from satellites, airborne platforms, and ground-based sensors, enabling real-time monitoring of phenomena like wildfires, volcanic activity, and landslides. The Internet of Things (IoT) and crowdsourced data from social media, mobile apps, and citizen reports have further improved situational awareness and response times, complementing traditional systems. Increased computational power has enabled the development of sophisticated models, such as numerical weather prediction and seismic hazard models, which predict disaster impacts more accurately. Despite these advancements, challenges remain in data interoperability, resilient communication infrastructure, and delivering clear, actionable alerts to at-risk populations. Future EWS will likely become more data-driven and interconnected, leveraging artificial intelligence, big data analytics, and IoT. Collaboration among governments, academic institutions, and local communities is essential to building robust, inclusive EWS that save lives and reduce the economic impact of disasters.
References
Li Y, Sun X, Zhu X, Cao H. An early warning method of landscape ecological security in rapid urbanizing coastal areas and its application in Xiamen, China. Ecol Modell. 2010;221(19):2251–60. DOI: https://doi.org/10.1016/j.ecolmodel.2010.04.016
Nijp JJ, Temme AJAM, van Voorn GAK, Kooistra L, Hengeveld GM, Soons MB, et al. Spatial early warning signals for impending regime shifts: A practical framework for application in real‐world landscapes. Glob Chang Biol. 2019;25(6):1905–21. DOI: https://doi.org/10.1111/gcb.14591
Newnham E, Mitchell C, Balsari S, Leaning J. The Changing Landscape of Early Warning Systems Policy Brief Promoting Effective Decision Making and Action in Disasters. 2017;(April).
Anderson-Berry L, Achilles T, Panchuk S, Mackie B, Canterford S, Leck A, et al. Sending a message: How significant events have influenced the warnings landscape in Australia. Int J Disaster Risk Reduct. 2018;30(March):5–17. DOI: https://doi.org/10.1016/j.ijdrr.2018.03.005
Boer MM, Nolan RH, Resco De Dios V, Clarke H, Price OF, Bradstock RA. Changing Weather Extremes Call for Early Warning of Potential for Catastrophic Fire. Earth’s Futur. 2017;5(12):1196–202. DOI: https://doi.org/10.1002/2017EF000657
Naidu S, Sajinkumar KS, Oommen T, Anuja VJ, Samuel RA, Muraleedharan C. Early warning system for shallow landslides using rainfall threshold and slope stability analysis. Geosci Front [Internet]. 2018;9(6):1871–82. Available from: https://doi.org/10.1016/j.gsf.2017.10.008 DOI: https://doi.org/10.1016/j.gsf.2017.10.008
Newnham E, Mitchell C, Balsari S, Leaning J. The Changing Landscape o f Early Warning Systems. Promot Eff Decis Mak Action i n Disasters Policy briefing HARVARD TH CHAN Sch PUBLIC Heal. 2017;
Perera D, Agnihotri J, Seidou O, Djalante R. Identifying societal challenges in flood early warning systems. Int J Disaster Risk Reduct. 2020;51:101794. DOI: https://doi.org/10.1016/j.ijdrr.2020.101794
Shehabuddeen NTMH, Probert DR. Excavating the technology landscape: deploying technology intelligence to detect early warning signals. In: 2004 IEEE International Engineering Management Conference (IEEE Cat No 04CH37574). IEEE; 2004. p. 332–6. DOI: https://doi.org/10.1109/IEMC.2004.1407130
Funk C, Shukla S, Thiaw WM, Rowland J, Hoell A, McNally A, et al. Recognizing the famine early warning systems network: over 30 years of drought early warning science advances and partnerships promoting global food security. Bull Am Meteorol Soc. 2019;100(6):1011–27. DOI: https://doi.org/10.1175/BAMS-D-17-0233.1
Gorr WL, Lee Y. Early warning system for temporary crime hot spots. J Quant Criminol. 2015;31:25–47. DOI: https://doi.org/10.1007/s10940-014-9223-8
Dakos V, Carpenter SR, van Nes EH, Scheffer M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Philos Trans R Soc B Biol Sci. 2015;370(1659):20130263. DOI: https://doi.org/10.1098/rstb.2013.0263
Chapple K, Zuk M. Forewarned: The use of neighborhood early warning systems for gentrification and displacement. Cityscape. 2016;18(3):109–30.
Barlindhaug S, Holm‐Olsen IM, Tømmervik H. Monitoring archaeological sites in a changing landscape–using multitemporal satellite remote sensing as an ‘early warning’method for detecting regrowth processes. Archaeol Prospect. 2007;14(4):231–44. DOI: https://doi.org/10.1002/arp.307
Bury TM, Sujith RI, Pavithran I, Scheffer M, Lenton TM, Anand M, et al. Deep learning for early warning signals of tipping points. Proc Natl Acad Sci. 2021;118(39):e2106140118. DOI: https://doi.org/10.1073/pnas.2106140118
Verdin J, Funk C, Senay G, Choularton R. Climate science and famine early warning. Philos Trans R Soc B Biol Sci. 2005;360(1463):2155–68. DOI: https://doi.org/10.1098/rstb.2005.1754
Berg A, Borensztein E, Pattillo C. Assessing early warning systems: how have they worked in practice? IMF Staff Pap. 2005;52(3):462–502. DOI: https://doi.org/10.2307/30035972
Strauss JA, Allen RM. Benefits and costs of earthquake early warning. Seismol Res Lett. 2016;87(3):765–72. DOI: https://doi.org/10.1785/0220150149
Lenton TM, Livina VN, Dakos V, Van Nes EH, Scheffer M. Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness. Philos Trans R Soc A Math Phys Eng Sci. 2012;370(1962):1185–204. DOI: https://doi.org/10.1098/rsta.2011.0304
Hartley D, Nelson N, Walters R, Arthur R, Yangarber R, Madoff L, et al. The landscape of international event-based biosurveillance. Emerg Health Threats J. 2010;3(1):7096. DOI: https://doi.org/10.3402/ehtj.v3i0.7096
Pasqualetti MJ. Landscape permanence and nuclear warnings. Geogr Rev. 1997;87(1):73–91. DOI: https://doi.org/10.2307/215659
Al Kharusi S, BenZvi SY, Bobowski JS, Bonivento W, Brdar V, Brunner T, et al. SNEWS 2.0: a next-generation supernova early warning system for multi-messenger astronomy. New J Phys. 2021;23(3):31201. DOI: https://doi.org/10.1088/1367-2630/abde33
Ludwig JA, Tongway DJ. Viewing rangelands as landscape systems. Rangel Desertif. 2000;39–52. DOI: https://doi.org/10.1007/978-94-015-9602-2_4
Ceccato P, Connor SJ, Jeanne I, Thomson MC. Application of geographical information systems and remote sensing technologies for assessing and monitoring malaria risk. Parassitologia. 2005;47(1):81–96.
Guzzetti F, Gariano SL, Peruccacci S, Brunetti MT, Marchesini I, Rossi M, et al. Geographical landslide early warning systems. Earth-Science Rev. 2020;200:102973. DOI: https://doi.org/10.1016/j.earscirev.2019.102973
Zakaria A, Alhassan SI, Kuwornu JKM, Azumah SB, Derkyi MAA. Factors Influencing the Adoption of Climate-Smart Agricultural Technologies Among Rice Farmers in Northern Ghana. Earth Syst Environ [Internet]. 2020;4(1):257–71. Available from: https://doi.org/10.1007/s41748-020-00146-w DOI: https://doi.org/10.1007/s41748-020-00146-w
Fritz S, See L, Bayas JCL, Waldner F, Jacques D, Becker-Reshef I, et al. A comparison of global agricultural monitoring systems and current gaps. Agric Syst. 2019;168:258–72. DOI: https://doi.org/10.1016/j.agsy.2018.05.010
Merz B, Kuhlicke C, Kunz M, Pittore M, Babeyko A, Bresch DN, et al. Impact forecasting to support emergency management of natural hazards. Rev Geophys. 2020;58(4):e2020RG000704. DOI: https://doi.org/10.1029/2020RG000704
Pendergrass AG, Meehl GA, Pulwarty R, Hobbins M, Hoell A, AghaKouchak A, et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat Clim Chang. 2020;10(3):191–9. DOI: https://doi.org/10.1038/s41558-020-0709-0
Segoni S, Piciullo L, Gariano SL. A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides. 2018;15(8):1483–501. DOI: https://doi.org/10.1007/s10346-018-0966-4
Otkin JA, Zhong Y, Hunt ED, Christian JI, Basara JB, Nguyen H, et al. Development of a flash drought intensity index. Atmosphere (Basel). 2021;12(6):741. DOI: https://doi.org/10.3390/atmos12060741
Hoell A, Parker B-A, Downey M, Umphlett N, Jencso K, Akyuz FA, et al. Lessons learned from the 2017 flash drought across the US Northern Great Plains and Canadian Prairies. Bull Am Meteorol Soc. 2020;101(12):E2171–85. DOI: https://doi.org/10.1175/BAMS-D-19-0272.1
Huang Z, Zhao T, Xu W, Cai H, Wang J, Zhang Y, et al. A seven-parameter Bernoulli-Gamma-Gaussian model to calibrate subseasonal to seasonal precipitation forecasts. J Hydrol. 2022;610:127896. DOI: https://doi.org/10.1016/j.jhydrol.2022.127896
Noguera I, Vicente‐Serrano SM, Domínguez‐Castro F. The rise of atmospheric evaporative demand is increasing flash droughts in Spain during the warm season. Geophys Res Lett. 2022;49(11):e2021GL097703. DOI: https://doi.org/10.1029/2021GL097703
Hoylman ZH, Bocinsky RK, Jencso KG. Drought assessment has been outpaced by climate change: empirical arguments for a paradigm shift. Nat Commun. 2022;13(1):2715. DOI: https://doi.org/10.1038/s41467-022-30316-5
Tijdeman E, Blauhut V, Stoelzle M, Menzel L, Stahl K. Different drought types and the spatial variability in their hazard, impact, and propagation characteristics. Nat Hazards Earth Syst Sci. 2022;22(6):2099–116. DOI: https://doi.org/10.5194/nhess-22-2099-2022
Abunyewah M, Erdiaw-Kwasie MO, Acheampong AO, Arhin P, Okyere SA, Zanders K, et al. Understanding climate change adaptation in Ghana: The role of climate change anxiety, experience, and knowledge. Environ Sci Policy. 2023;150:103594. DOI: https://doi.org/10.1016/j.envsci.2023.103594
Sungmin O, Park SK. Flash drought drives rapid vegetation stress in arid regions in Europe. Environ Res Lett. 2023;18(1):14028. DOI: https://doi.org/10.1088/1748-9326/acae3a
Yang P, Zhai X, Huang H, Zhang Y, Zhu Y, Shi X, et al. Association and driving factors of meteorological drought and agricultural drought in Ningxia, Northwest China. Atmos Res. 2023;289:106753. DOI: https://doi.org/10.1016/j.atmosres.2023.106753
Poonia V, Goyal MK, Jha S, Dubey S. Terrestrial ecosystem response to flash droughts over India. J Hydrol. 2022;605:127402. DOI: https://doi.org/10.1016/j.jhydrol.2021.127402
Fu Z, Ciais P, Makowski D, Bastos A, Stoy PC, Ibrom A, et al. Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems. Glob Chang Biol. 2022;28(6):2111–23. DOI: https://doi.org/10.1111/gcb.16050
Ndayiragije JM, Li F. Effectiveness of drought indices in the assessment of different types of droughts, managing and mitigating their effects. Climate. 2022;10(9):125. DOI: https://doi.org/10.3390/cli10090125
Alfieri L, Salamon P, Pappenberger F, Wetterhall F, Thielen J. Operational early warning systems for water-related hazards in Europe. Environ Sci Policy. 2012;21:35–49. DOI: https://doi.org/10.1016/j.envsci.2012.01.008
Chuvieco E. Fundamentals of satellite remote sensing: An environmental approach. CRC press; 2020. DOI: https://doi.org/10.1201/9780429506482
Karamitrou A, Sturt F, Bogiatzis P, Beresford-Jones D. Towards the use of artificial intelligence deep learning networks for detection of archaeological sites. Surf Topogr Metrol Prop. 2022;10(4):44001. DOI: https://doi.org/10.1088/2051-672X/ac9492
Ganeshkumar C, Jena SK, Sivakumar A, Nambirajan T. Artificial intelligence in agricultural value chain: review and future directions. J Agribus Dev Emerg Econ. 2023;13(3):379–98. DOI: https://doi.org/10.1108/JADEE-07-2020-0140
Grasso VF. The state of early warning systems. In: Reducing disaster: Early warning systems for climate change. Springer; 2014. p. 109–25. DOI: https://doi.org/10.1007/978-94-017-8598-3_6
Grasso VF, Singh A. Early warning systems: State-of-art analysis and future directions. Draft report, UNEP. 2011;1:7.
Golnaraghi M. An Overview: Building a global knowledge base of lessons learned from good practices in multi-hazard early warning systems. Institutional Partnerships Multi-Hazard Early Warn Syst A Compil Seven Natl Good Pract Guid Princ. 2012;1–8. DOI: https://doi.org/10.1007/978-3-642-25373-7_1
Basher R. Global early warning systems for natural hazards: systematic and people-centred. Philos Trans R Soc a Math Phys Eng Sci. 2006;364(1845):2167–82. DOI: https://doi.org/10.1098/rsta.2006.1819
Kelman I, Glantz MH. Early warning systems defined. Reducing disaster Early Warn Syst Clim Chang. 2014;89–108. DOI: https://doi.org/10.1007/978-94-017-8598-3_5
Zschau J, Küppers AN. Early warning systems for natural disaster reduction. Springer Science & Business Media; 2013.
Garcia C, Fearnley CJ. Evaluating critical links in early warning systems for natural hazards. Nat Hazards Disaster Risk Reduct. 2016;53–67. DOI: https://doi.org/10.4324/9781315540146
Travis WR. Design of a severe climate change early warning system. Weather Clim Extrem. 2013;2:31–8. DOI: https://doi.org/10.1016/j.wace.2013.10.006
Hess JJ, Ebi KL. Iterative management of heat early warning systems in a changing climate. Ann N Y Acad Sci. 2016;1382(1):21–30. DOI: https://doi.org/10.1111/nyas.13258
Pulwarty RS, Sivakumar MVK. Information systems in a changing climate: Early warnings and drought risk management. Weather Clim Extrem. 2014;3:14–21. DOI: https://doi.org/10.1016/j.wace.2014.03.005
Alessa L, Kliskey A, Gamble J, Fidel M, Beaujean G, Gosz J. The role of Indigenous science and local knowledge in integrated observing systems: moving toward adaptive capacity indices and early warning systems. Sustain Sci. 2016;11:91–102. DOI: https://doi.org/10.1007/s11625-015-0295-7
Cools J, Innocenti D, O’Brien S. Lessons from flood early warning systems. Environ Sci Policy. 2016;58:117–22. DOI: https://doi.org/10.1016/j.envsci.2016.01.006
Mukhtar R. Review of national multi-hazard early warning system plan of Pakistan in context with sendai framework for disaster risk reduction. Procedia Eng. 2018;212:206–13. DOI: https://doi.org/10.1016/j.proeng.2018.01.027
Allen RM, Melgar D. Earthquake early warning: Advances, scientific challenges, and societal needs. Annu Rev Earth Planet Sci. 2019;47(1):361–88. DOI: https://doi.org/10.1146/annurev-earth-053018-060457
Zommers Z, Lumbroso D, Cowell R, Sitati A, Vogel E. Early warning systems for disaster risk reduction including climate change adaptation. In: The Routledge handbook of disaster risk reduction including climate change adaptation. Routledge; 2017. p. 429–44. DOI: https://doi.org/10.4324/9781315684260-40
Alcántara-Ayala I, Oliver-Smith A. The necessity of early warning articulated systems (EWASs): critical issues beyond response. Identifying Emerg Issues Disaster Risk Reduction, Migr Clim Chang Sustain Dev Shap Debates Policies. 2017;101–24. DOI: https://doi.org/10.1007/978-3-319-33880-4_7
de León JCV, Bogardi J, Dannenmann S, Basher R. Early warning systems in the context of disaster risk management. Entwicklung Ländlicher Raum. 2006;2(1):23–8.
Rogers D, Tsirkunov V. Costs and benefits of early warning systems. Glob Assess rep. 2011;
Marchezini V, Trajber R, Olivato D, Munoz VA, de Oliveira Pereira F, Oliveira Luz AE. Participatory early warning systems: Youth, citizen science, and intergenerational dialogues on disaster risk reduction in Brazil. Int J Disaster Risk Sci. 2017;8(4):390–401. DOI: https://doi.org/10.1007/s13753-017-0150-9
Šakić Trogrlić R, van den Homberg M, Budimir M, McQuistan C, Sneddon A, Golding B. Early warning systems and their role in disaster risk reduction. In: Towards the “perfect” weather warning: bridging disciplinary gaps through partnership and communication. Springer International Publishing Cham; 2022. p. 11–46. DOI: https://doi.org/10.1007/978-3-030-98989-7_2
Ebi KL, Schmier JK. A stitch in time: improving public health early warning systems for extreme weather events. Epidemiol Rev. 2005;27(1):115–21. DOI: https://doi.org/10.1093/epirev/mxi006
Samuel KLH, Cornforth RJ. Disaster risk reduction, early warning systems and global health: critiquing the current systems-based approach. 2019; DOI: https://doi.org/10.1017/9781108564540.021
Lowe D, Ebi KL, Forsberg B. Heatwave early warning systems and adaptation advice to reduce human health consequences of heatwaves. Int J Environ Res Public Health. 2011;8(12):4623–48. DOI: https://doi.org/10.3390/ijerph8124623
Liang Y, Quan D, Wang F, Jia X, Li M, Li T. Financial big data analysis and early warning platform: a case study. IEEE Access. 2020;8:36515–26. DOI: https://doi.org/10.1109/ACCESS.2020.2969039
Zhang W. Geological disaster monitoring and early warning system based on big data analysis. Arab J Geosci. 2020;13(18):946. DOI: https://doi.org/10.1007/s12517-020-05951-1
Li W, Hsu C-Y. GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography. ISPRS Int J Geo-Information. 2022;11(7):385. DOI: https://doi.org/10.3390/ijgi11070385
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Petros Chavula , Fredrick Kayusi , Gilbert Lungu , Agnes Uwimbabazi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
