Meteorological Services for Forecast Based Early Actions in Indonesia

  • PerdinanEmail author
  • Enggar Yustisi Arini
  • Ryco Farysca Adi
  • Raja Siregar
  • Yolanda Clatworthy
  • Nurhayati
  • Ni Wayan Srimani Puspa Dewi
Part of the Climate Change Management book series (CCM)


The increasing frequency of climate related hazards leading to disasters stipulates the government of Indonesia to pay serious attention to mitigate the adverse impacts of the disasters. An initiative is the utilization of climate and/or weather forecasts to support the installment of impact-based forecasts and risk-based warnings. This study investigated the feasibility of supporting Forecast Based Early Actions (FbA) implementation in Indonesia based on document reviews, identification of supporting tools, and stakeholders’ consultations with the key informants. Understanding the available resources for supporting early actions, the study recommends focusing on two major climate related hazards, i.e., floods and drought, as the two most impacted hazards on human lives and assets with refer to the available datasets from 1972 to 2018. The implementation of FbA for the two hazards also sounds promising with regards to available and accessible forecasted rainfall occurrences and amount (e.g., one day, 3-day and 10-day prediction, and seasonal forecasts) across the country provided by Meteorological, Climatological, and Geophysical Agency named in bahasa Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). Nevertheless, the forecast accuracies should still be improved and automatically connected with hazard-based models (e.g., flood or drought) nationally. This demand urges that further efforts are needed to endorse the implementation of FbA in Indonesia.


Weather services Risk-based warnings Early actions Disasters Indonesia Climate change 



This manuscript is based on work supported by IFRC-RCS, research study #CLMX013580 for conducting the study, and APN grant #CBA2018-09SY-Perdinan and WCU program of Indonesian Ministry of Research, Technology and Higher Education managed by Institute Technology Bandung for finalizing the report version for publication. The authors also send gratitude to the key stakeholders from climate center and respondents for their invaluable insights during the interview processes.


  1. BAPPENAS (2014) Rencana aksi nasional adaptasi perubahan iklim (RAN-API). BAPPENAS, IndonesiaGoogle Scholar
  2. Baros AN, Bowden GJ (2008) Toward long-lead operational forecasts of drought: an experimental study in the Murray-Darling River Basin. J Hydrol 357:349–367CrossRefGoogle Scholar
  3. Belayneh AM, Khalil B, Adamowski J (2015) Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustain Water Resourse Manag. Scholar
  4. BNPB (2017) Data Informasi Bencana Indonesia (DIBI). Retrieved 1 Aug 2018, from
  5. Bocchiola D, Rosso R (2006) Real time flood forecasting at dams: a case study in Italy. Int J Hydropower Dams 13(1):92–99Google Scholar
  6. Calvello M (2017) Early warning strategies to cope with landslide risk. Rivista Italiana di Geotecnica 51(2):63–91Google Scholar
  7. Calvello M, Piciullo L (2015) Assessing the performance of regional landslide early warning models: the EduMap method. Nat Hazards Earth System Science 16:103–122. Scholar
  8. Carrão H, Naumann G, Dutra E, Lavaysse C, Barbosa P (2018) Seasonal drought forecasting for Latin America using the ECMWF S4 forecast system. Climate 6(48):1–26Google Scholar
  9. Climate Centre (2017) Criteria for identification and design of Forecast-based Financing interventions. IFRCGoogle Scholar
  10. Coughlan de Perez E, van den Hurk MK, van Aalst B, Jongman T Klose, Suarez P (2015) Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts. Nat Hazards Earth Syst Sci 15:10. Scholar
  11. Dugar S, Smith P, Parajuli B, Khanal S, Brown S, Gautam D, Bhandari D, Gurung G, Shakya P, Kharbuja R, Uprety M (2017) Enhancing community based early warning systems in Nepal with flood forecasting using local and global models, EGU General Assembly. Geophysical Research Abstracts, vol 19, EGU2017-8995-1Google Scholar
  12. Durdu OF (2010) Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey. Stoch Environ Res Risk Assess 24:1145–1162CrossRefGoogle Scholar
  13. Dutra E, Di Giuseppe F, Wetterhall F, Pappenberger F (2013) Seasonal forecasts of droughts in African basins using the standardized precipitation index. Hydrol Earth Syst Sci 17:2359–2373. Scholar
  14. ECOSOC (2018) Innovation—financing, technology, knowledge and action. ECOSOC Humanitarian Affairs Segment, Palais des Nations, GenevaGoogle Scholar
  15. FAO (2017) From early warning to early action in Mongolia—bracing for the cold to protect livestock and livelihoods (Press release)Google Scholar
  16. Golding BW (2009) Review long lead time flood warnings: reality or fantasy? Meteorogical Appl 16:3–12. Scholar
  17. Government of India (2012) Flood early warning system—a warning mechanism for mitigating disaster during flood. Department of administrative reform and public grievances, Ministry of Personnel, Public Grievances & Pensions, p 44Google Scholar
  18. GRC (2018) REPORT Dialogue Platform on FbF Issue No. 02/2018 (Press release)Google Scholar
  19. ISRO (2012) Flood early warning system and damage mitigation. Indian Space Research Organization, IndiaGoogle Scholar
  20. Kalra A, Ahmad S (2012) Estimating annual precipitation for the Colorado River Basin using ocean ice atmospheric oscillations. Water Resour Res 48.
  21. Komma J, Blöschl G, Reszler C (2008) Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting. J Hydrol 357(3–4):228–242CrossRefGoogle Scholar
  22. Lyon B, Bell MA, Tippett MK (2012) Baseline probabilities for the seasonal prediction of meteorological drought. J Appl Meteorol Climatol 51(7):1222–1237CrossRefGoogle Scholar
  23. Matthew C, Freebairn C (2018) New fund could be a “game-changer” for humanitarian action. Retrieved 1 Aug 2018, from
  24. McEvoy DJ, Huntington JL, Mejia JF, Hobbins MT (2016) Improved seasonal drought forecasts using reference evapotranspiration anomalies. Geophys Res Lett 43(1):377–385CrossRefGoogle Scholar
  25. Mehr AD, Kahya E, Ozger M (2014) A gene–wavelet model for long lead time drought forecasting. J Hydrol 517:691–699. Scholar
  26. Mishra AK, Desai V (2005) Drought forecasting using stochastic models. Environ Res Risk Assess 19:326–339. Scholar
  27. Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural network and time series of drought indices. Int J Climatol 27:2103–2111. Scholar
  28. Ozger M (2011) Long lead time drought forecasting using a wavelet and fuzzy logic combination model: a case study in texas. J Hydrometeorol 13:284–297. Scholar
  29. Perdinan, Adi RF, Sutoro T (2017) Perkembangan Studi Kerentanan, Resiko, Dampak dan Adaptasi Perubahan Iklim: Tantangan dan Peluang. Direktorat Jenderal Perubahan Iklim – Kementerian Lingkungan Hidup dan Kehutanan, Jakarta. ISBN: 978-60-2740-119-8Google Scholar
  30. Philipp A, Kerl F, Büttner U, Metzkes C, Singer T, Wagner M, Schütze N (2016) Small-scale (flash) flood early warning in the light of operational requirements: opportunities and limits with regard to user demands, driving data, and hydrologic modeling techniques. IAHS-AISH Proc Rep 373(1):201–208CrossRefGoogle Scholar
  31. Rhee J, Im J, Park S (2016) Drought forecasting based on machine learning of remote sensing and long-range forecast data. International Archives of the Photogrammetry. Remote Sens Spat Inf Sci-ISPRS Arch 41(July):157–158CrossRefGoogle Scholar
  32. Sassa K, Nagai O, Solidum R, Yamazaki Y, Ohta H (2010) An integrated model simulating the initiation and motion of earthquake and rain induced rapid landslide and its application to the 2006 Leyte landslide. Scholar
  33. Schroter K, Gocht M, Ostrowski M, Nachtnebel HP (2008) EWASE—early warning systems efficiency: evaluation of flood forecast reliability. Paper presented at the Flood Risk Management: Research and Practice, LondonCrossRefGoogle Scholar
  34. Segoni S, Battistini A, Rossi G, Rosi A, Lagomarsino D, Catani F, Moretti S, Casagli N (2015) Technical note: an operational landslide early warning system at regional scale based on space-time-variable rainfall thresholds. Nat Hazards Earth Syst Sci 15(4):853–861CrossRefGoogle Scholar
  35. Timmermann A, Oberhuber J, Bacher A, Esch M, Latif M, Roeckner E (1999) Increased El Nino frequency in a climate model forced by future greenhouse warming. Nature 398(6729):694–697CrossRefGoogle Scholar
  36. Trambauer P, Werner M, Winsemius HC, Maskey S, Dutra E, Uhlenbrook S (2015) Hydrological drought forecasting and skill assessment for the Limpopo River basin, southern Africa. Hydrol Earth Syst Sci 19(4):1695–1711CrossRefGoogle Scholar
  37. Turco M, Ceglar A, Prodhomme C, Soret A, Toreti A, Francisco JD-R (2017) Summer drought predictability over Europe: empirical versus dynamical forecasts. Environ Res Lett 12. Scholar
  38. Wilkinson E, Weingartner L, Choularton R, Bailey M, Todd M, Kniveton D, Venton CC (2018) Forecasting hazards, averting disasters: implementing forecast-based early action at scale. Overseas Development Institute, LondonGoogle Scholar
  39. WMO (2011) Manual on flood forecasting and warning, vol 1. WMO, SwitzerlandGoogle Scholar
  40. Worldbank (2018) Improving lead time for tropical cyclone forecasting: review of operational practices and implications for Bangladesh. Worldbank, BangladeshCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Perdinan
    • 1
    Email author
  • Enggar Yustisi Arini
    • 2
  • Ryco Farysca Adi
    • 2
  • Raja Siregar
    • 3
  • Yolanda Clatworthy
    • 3
  • Nurhayati
    • 4
  • Ni Wayan Srimani Puspa Dewi
    • 2
  1. 1.Department of Geophysics and MeteorologyBogor Agricultural UniversityBogorIndonesia
  2. 2.PIAREA, Environmental and Technology Services,
  3. 3.Climate Centre, International Federation of Red Cross and Red Crescent Societies (IFRC)GenevaSwitzerland
  4. 4.Meteorological, Climatological, and Geophysical Agency (BMKG)JakartaIndonesia

Personalised recommendations