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All issues Volume 249 (2021) E3S Web Conf., 249 (2021) 03013 References
Open Access
Issue
E3S Web Conf.
Volume 249, 2021
4th International Conference on Sustainability Science (CSS2020)
Article Number 03013
Number of page(s) 6
Section Conservation, Resilience, Environmental Vulnerability and Hazard
DOI https://doi.org/10.1051/e3sconf/202124903013
Published online 07 April 2021
  1. S. N. Jonkman, “Global Perspectives on Loss of Human Life Caused by Floods,” Nat. Hazards, vol. 34, no. 2, pp. 151–175 (2005) [Google Scholar]
  2. D. (IFRC) Fisher, K. (IFRC) Hagon, C. Lattimer, S. O’Callaghan, S. Swithern, and L. Walmsley, World Disasters Report 2018. Leaving No One Behind: The International Humanitarian Sector Must Do More to Respond to the Needs of the World’s Most Vulnerable People. Geneva: International Federation of Red Cross and Red Crescent Societies (2018) [Google Scholar]
  3. M. D. Yamanaka, “Physical climatology of Indonesian maritime continent: An outline to comprehend observational studies,” Atmos. Res., vol. 178–179, pp. 231–259 (2016) [Google Scholar]
  4. Badan Nasional Penanggulangan Bencana (BNPB), “Infografis Update Data BencanaTgl. 26 Maret 2020 Pkl. 10.00 WIB” (2020) [Online]. Available: https://bnpb.go.id/infografis/infografisupdate-data-bencana-tgl-26-maret-2020-pkl-1000-wib. [Accessed: 15-Apr-2020]. [Google Scholar]
  5. BNPB, “Bencana Alam Di Indonesia Tahun 2010 s/d 2020” (2020) [Online]. Available: http://bnpb.cloud/dibi/tabel1a. [Accessed: 20-Apr2020]. [Google Scholar]
  6. Y. Hirabayashi et al., “Global flood risk under climate change,” Nat. Clim. Chang., vol. 3, no. 9, pp. 816–821 (2013) [Google Scholar]
  7. Y. Budiyono, J. Aerts, J. J. Brinkman, M. A. Marfai, and P. Ward, “Flood risk assessment for delta mega-cities: a case study of Jakarta,” Nat. Hazards, vol. 75, no. 1, pp. 389–413 (2015) [Google Scholar]
  8. R. S. van Voorst, “Get ready for the flood! Riskhandling styles in Jakarta, Indonesia,” University of Amsterdam (2014) [Google Scholar]
  9. T. Kadri and E. Kurniyaningrum, “Impact Of Land Use On Frequency Of Floods In Upper Bekasi Watershed, Indonesia,” Int. J. Sci. Technol. Res., vol. 8, no. 12, pp. 3328–3334 (2019) [Google Scholar]
  10. A. Rojali and P. Elsari, “Pemodelan Banjir di Perumahan Pondok Gede Permai Bekasi,” Rekayasa Sipil, vol. 9, no. 1, p. 6 (2020) [Google Scholar]
  11. BNPB, “Geoportal Kebencanaan Indonesia,” (2020) [Online]. Available: https://gis.bnpb.go.id/. [Accessed: 01-Oct-2020]. [Google Scholar]
  12. UNISDR, “Developing Early Warning Systems : A Checklist,” Third Int. Conf. Early Warn., no. March, pp. 1–13 (2006) [Google Scholar]
  13. World Meteorological Organization, Guidelines on Early Warning Systems and Application of Nowcasting and Warning Operations. Geneva: WMO (2010) [Google Scholar]
  14. World Meteorological Organization (WMO), “Flood Forecasting and Early Warning,” Integr. Flood Manag. Tools Ser., no. 19, p. 59 (2013) [Google Scholar]
  15. J. G. Powers et al., “The Weather Research And Forecasting Model Overview, System Efforts, and Future Directions,” Bull. Am. Meteorol. Soc., no. August, pp. 1717–1738 (2017) [Google Scholar]
  16. J. Li, Y. Chen, H. Wang, J. Qin, J. Li, and S. Chiao, “Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model,” Hydrol. Earth Syst. Sci., vol. 21, no. 2, pp. 1279–1294 (2017) [Google Scholar]
  17. A. Senatore, G. Mendicino, D. J. Gochis, W. Yu, D. N. Yates, and H. Kunstmann, “Fully coupled atmosphere-hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scales,” J. Adv. Model. Earth Syst., vol. 7, no. 4, pp. 1693–1715 (2015) [Google Scholar]
  18. M. Sun et al., “Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios,” Water, vol. 12, no. 3, p. 23 (2020) [Google Scholar]
  19. Badan Nasional Penanggulangan Bencana, “Pedoman Sistem Peringatan Dini Berbasis Masyarakat” (2012) [Google Scholar]
  20. D. Gochis, W. Yu, and D. Yates, The NCAR WRFHydro Technical Description and User ’ s Guide Version 1.0 (2013) [Google Scholar]
  21. A. Sullivan and D. D. White, “An Assessment of Public Perceptions of Climate Change Risk in Three Western U.S. Cities,” Weather. Clim. Soc., vol. 11, no. 2, pp. 449–463 (2019) [Google Scholar]
  22. W. F. Krajewski et al., “Real-time flood forecasting and information system for the state of Iowa,” Bull. Am. Meteorol. Soc., vol. 98, no. 3, pp. 539–554 (2017) [Google Scholar]
  23. D. J. Gochis, W. Yu, and D. N. Yates, “The WRF-Hydro model technical description and user’s guide, version 3.0,” NCAR Tech. Doc., no. May, p. 120 (2015) [Google Scholar]
  24. E. Naabil, B. L. Lamptey, J. Arnault, H. Kunstmann, and A. Olufayo, “Water resources management using the WRF-Hydro modelling system: Case-study of the Tono dam in West Africa,” J. Hydrol. Reg. Stud., vol. 12, no. June, pp. 196–209 (2017) [Google Scholar]
  25. Y. Wehbe et al., “Analysis of an Extreme Weather Event in a Hyper Arid Region Using WRF-Hydro Coupling, Station, and Satellite data,” Nat. Hazards Earth Syst. Sci. Discuss., no. September, pp. 1–34 (2018) [Google Scholar]
  26. WMO, WMO Guidelines on Multi-hazard Impactbased Forecast and Warning Services, no. 1150. Geneva: World Meteorological Organization (2015) [Google Scholar]
  27. V. Tarchiani et al., “Community and impact based early warning system for flood risk preparedness: The experience of the Sirba river in Niger,” Sustain., vol. 12, no. 5, p. 24 (2020) [Google Scholar]
  28. E. Aldrian and R. D. Susanto, “Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface,” Int. J. Climatol., vol. 1452, pp. 1435–1452 (2003) [Google Scholar]
  29. X. Zhang, Q. Zhong, R. Zhang, and M. Zhang, “People-centered early warning systems in China: A bibliometric analysis of policy documents,” Int. J. Disaster Risk Reduct., vol. 51, no. June, p. 101877 (2020) [PubMed] [Google Scholar]
  30. P. Otto, A. Mehta, and B. Liu, “Mind The Gap: Towards and Beyond Impact Messaging to Enhance Tropical Cyclone Risk Communication,” Trop. Cyclone Res. Rev., vol. 7, no. 2, pp. 140–151 (2018) [Google Scholar]

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