Keywords: Disaster Management, Flood Hazard, Mononobe, WRF Model, Rainfall, Cloud Cover


Indonesia's climate classification is divided into three rainfall patterns. The three patterns are Seasonal Pattern, Equatorial Pattern, and Local Pattern (Anti Seasonal). Flood Disaster Management based on extreme rainfall is very much needed, as the analysis was taken as a case study on January 22, 2019, a flood disaster occurred in South Sulawesi. The flood event indicated that there was heavy rain that flushed the South Sulawesi region for several days, which is classified as monsoonal rainfall. This study aims to analyze the characteristics of heavy rain with atmospheric anomalies during these events by calculating rainfall intensity to determine future flooding patterns and using the WRF model to analyze cloud distribution patterns and rainfall distribution. The method used in this research is Mononobe and Weather Research and Forecasting (WRF) using the Fabric Fritsch cumulus parameterization scheme. The analysis showed that the intensity and duration of rainfall of 2, 5 10, 25, and 50 years were obtained from the Mononobe model, as well as from the atmospheric dynamics data, there was rain for 3 consecutive days caused by cumulonimbus type rain clouds. Based on the WRF model, it can be seen that the CAPE value before the onset of rain is quite significant, thus supporting the growth of rain clouds as an important variable in flood disaster management in the South Sulawesi region in particular and the tropical zone in. general.

Motivation/Background: Indonesia is included in a tropical climate where extreme rainfall is important to analyze. The majority of flood disasters in the tropics occur in decades of extreme atmosphere, this is an important reason in this study.

Method: The Mononobe method can be used to calculate the distribution pattern of rainfall intensity throughout 2, 5, 10, 25, and 50 years, as a prediction of future rainfall intensity patterns. The WRF model is used to calculate the cloud distribution pattern and the spatial distribution of rainfall.

Results: The results of this study obtained patterns of rainfall intensity and duration of 2, 5, 10, 25, and 50 years from the Mononobe model, as well as from the atmospheric dynamics data, there was rain for 3 consecutive days caused by cumulonimbus rain clouds. The pattern of cloud distribution and rainfall at the time of the incident at the WRF model research location.

Conclusions: Analysis of the distribution pattern of rainfall intensity for the periods of 2, 5, 10, 25, and 50 years, as well as the distribution pattern of clouds and rainfall, is very necessary for disaster identification, especially hydrometeorology. This is very important as a variable in flood disaster management, especially in the tropics


Download data is not yet available.


Hidayat R, Juniarti MD, Ma'rufah U. Impact of La Niña and La Niña Modoki on Indonesia rainfall variability. IOP Conf. Ser.: Earth Environ. Sci. 2018; 149 012046. DOI:

Siswanto Geert JVOB, Gerald VDS, Lende RK, Bart VDH. Trends in High-Daily Precipitation Events in Jakarta and Flooding of January 2014, Special Supplement to the Bulletin of the American Meteorological Society. 2015; 96(12). DOI:

Hou T, Lei H, Yang J, Hu Z, Feng Q. Investigation of riming within mixed-phase stratiform clouds using Weather Research and Forecasting (WRF) model. Atmospherics Research Journal. 2016; 178-179: 291-303. DOI:

Abidin HZ, Andreas H, Gumilar I, Wibowo IR. On the correlation between urban development, land subsidence, and flooding phenomena in Jakarta. Proc. IAHS. 2015; 370: 15–20. DOI:

Mori S, Jun-Ichi H, YudiIman T, Yamanaka MD, Okamoto N, Murata F, Sakurai N, Hashiguchi H, Sribimawati T. Diurnal Land-Sea Rainfall Peak Migration over Sumatra Island, Indonesia Maritime Continent, Observed by TRMM Satellite and Intensive Rawinsonde soundings, American Meteorological Society. 2004; 2021-2039. DOI:<2021:DLRPMO>2.0.CO;2

Sakurai N, Murata F, Yamanaka MD, Mori S, Hamada JI, Hasiguchi H, Tauhid YI, Sribimawati T, Suhardi B. Diurnal Cycle of Cloud System Migration over Sumatera Island. Journal of the Meteorological. 2005;83(5). DOI:

Wooyoung Na and Chulsang Yoo. Evaluation of Rainfall Temporal Distribution Models with Annual Maximum Rainfall Events in Seoul, Korea, Water 2018, 10, 1468; doi:10.3390/w10101468. DOI:

Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, and Powers JG. A Description of the Advanced Research WRF Version 2, Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research Colorado USA. 2005.

Chaudhry FH, Filho AGA, Calheiros RV. Statistics on Tropical Convective Storms Observed By Radar, Atmospheric Research. 1996; 42: 217-227. DOI:

Roxana C, Wajsowicz. Forecasting extreme events in the tropical Indian ocean sector climate. Journal dynamics of atmospheres, and ocean. 2005; 1-15. DOI:

Meteorology Climatology and Geophysics Council (BMKG), Climate Analysis. 2020.

National Disaster Management Authority (BNPB), Indonesian Disaster Information Data 2000-2020,

Gernowo R, Adi K, Yulianto T. Convective Cloud model for Analyzing of Heavy rainfall of Weather Extreme at Semarang Indonesia. Advanced Science Letter. 2017; 23(7): 6593-6597. DOI:

Thompson G, Tewari M, Ikeda K, Tessendorf S, Weeks C, Orkin J, Kong F. Explicitly-coupled cloud physics and radiation parameterizations and subsequent evaluation in WRF high-resolution convective forecasts. Atmospherics Research Journal. 2016;168: 92-104 DOI:

Gernowo R, Adi K, Yulianto T, Seniyatis S, Yatunnisa AA. Hazard Mitigation with Cloud Model-based rainfall and Convective data. Journal of Physics: Conference Series. 2018; 1025(1): 012023. DOI:

Castellano NE, Avila EE, Sounders CPR.Theoretical Model of the Bergeron-Fiendeisen Mechanism of Ice Crystal Growth in Clouds, Atmospheric Environment. 2004; 38: 6751-6761. DOI:

Hong S-Y, H-L Pan. Nonlocal boundary layer vertical diffusion in a medium-range forecast model, Mon. Wean. Rev. 1996; 124: 2322-2339.

Masouleh ZP, Walker DJ, Crowther JM. A Long-Term Study of Sea-Breeze Characteristics: A Case Study of the Coastal City of Adelaide. J. Appl. Meteor. Climatol. 2019; 58(2): 385–400.

Cooley D, D Nychka, PNaveau. Bayesian spatial modeling of extreme precipitation return levels. J. Amer. Stat. Assoc. 2007; 102: 824–840.

Diaz HF, MPHoerling, JKEischeid. ENSO variability, teleconnections, and climate change. Int., J. Climatol. 2001; 21: 1845-1862. DOI:

Roy I, Tedeschi RG, Collins M. ENSO teleconnections to the Indian summer monsoon under climate changing. International Journal of Climatology. 2019; 39(6): 3031-3042. DOI:

Prasetyo Y, Yuwono BD, Ramadhanis Z. Spatial Analysis of Land Subsidence and Flood Pattern BasedonDInSAR Method in Sentinel Sar Imagery and Weighting Method in Geo-Hazard Parameters Combination in NorthJakarta Region. IOP Conf. Series: Earth and Environmental Science. 2018; 123 012009. DOI:

Hashiguci H, S Fukao, T Tsuda, MD Yamanaka, SWB Harijono, H Wiryosumarto. An Overview of The Planetary Boundary Layer Observation Over Equatorial Indonesia with an L-Band Clear Air Doppler Radar, Beitr. Phy. Atmos. 1996;69: 13-25.

How to Cite
Gernowo, R., & Nurwidyanto, M. I. (2021). The FLOOD DISASTER MANAGEMENT BASED ON EXTREME TROPICAL RAINFALL IN DECADES OF CLIMATE CHANGE IN INDONESIA. International Journal of Engineering Science Technologies, 5(2), 124-130.