COMPARISON OF INTENSITY-DURATION-FREQUENCY CURVES FROM TRMM, GSMAP, AND GPM DATA WITH OBSERVATION DATA (Case Study: Bandung)

Intensity-Duration-Frequency (IDF) curve is a curve that combines the intensity, duration, and frequency of rainfall data. The IDF curve is a pretty good tool in predicting the nature of rain in projects related to water resource management, other infrastructure projects, and flood disasters. Rai...

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Main Author: Juliansyah, Bayu
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/42741
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:42741
spelling id-itb.:427412019-09-23T14:22:35ZCOMPARISON OF INTENSITY-DURATION-FREQUENCY CURVES FROM TRMM, GSMAP, AND GPM DATA WITH OBSERVATION DATA (Case Study: Bandung) Juliansyah, Bayu Indonesia Final Project Rainfall, IDF-Curve, TMPA, GSMaP, GPM, Bandung. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/42741 Intensity-Duration-Frequency (IDF) curve is a curve that combines the intensity, duration, and frequency of rainfall data. The IDF curve is a pretty good tool in predicting the nature of rain in projects related to water resource management, other infrastructure projects, and flood disasters. Rainfall data with wide spatial coverage as well as dense intervals and long time needed to build the IDF curve. But in Indonesia, especially in Bandung, the availability of that kind of data is very hard to find, almost none. Nowadays there is a lot of remote sensing based data available in high spatial and temporal resolution. Some examples of observation satellites with high spatial resolution and temporal resolution are TMPA (TRMM Multisatellite Precipitation Analysis), GSMaP (Global Satellite Mapping of Precipitation), and GPM (Global Precipitation Measurement). For ground observation data using observation data conducted by WCPL from November 2011 to October 2018 with data recording intervals every 5 minutes. The satellite data set is then compared to many rainfall events, the pattern of average monthly rainfall, extreme rain, peaks over threshold, as well as the IDF curve that is constructed using the Theoritical Extreme Value (EV) Distribution approach using the Gumbel type I distribution. TMPA satellite data are particularly good at capturing rainfall patterns and average monthly rainfall, while GSMaP and GPM data can be used as alternative data in the formation of IDF curves especially for rain with a duration of 6 hours, 12 hours and 24 hours. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Intensity-Duration-Frequency (IDF) curve is a curve that combines the intensity, duration, and frequency of rainfall data. The IDF curve is a pretty good tool in predicting the nature of rain in projects related to water resource management, other infrastructure projects, and flood disasters. Rainfall data with wide spatial coverage as well as dense intervals and long time needed to build the IDF curve. But in Indonesia, especially in Bandung, the availability of that kind of data is very hard to find, almost none. Nowadays there is a lot of remote sensing based data available in high spatial and temporal resolution. Some examples of observation satellites with high spatial resolution and temporal resolution are TMPA (TRMM Multisatellite Precipitation Analysis), GSMaP (Global Satellite Mapping of Precipitation), and GPM (Global Precipitation Measurement). For ground observation data using observation data conducted by WCPL from November 2011 to October 2018 with data recording intervals every 5 minutes. The satellite data set is then compared to many rainfall events, the pattern of average monthly rainfall, extreme rain, peaks over threshold, as well as the IDF curve that is constructed using the Theoritical Extreme Value (EV) Distribution approach using the Gumbel type I distribution. TMPA satellite data are particularly good at capturing rainfall patterns and average monthly rainfall, while GSMaP and GPM data can be used as alternative data in the formation of IDF curves especially for rain with a duration of 6 hours, 12 hours and 24 hours.
format Final Project
author Juliansyah, Bayu
spellingShingle Juliansyah, Bayu
COMPARISON OF INTENSITY-DURATION-FREQUENCY CURVES FROM TRMM, GSMAP, AND GPM DATA WITH OBSERVATION DATA (Case Study: Bandung)
author_facet Juliansyah, Bayu
author_sort Juliansyah, Bayu
title COMPARISON OF INTENSITY-DURATION-FREQUENCY CURVES FROM TRMM, GSMAP, AND GPM DATA WITH OBSERVATION DATA (Case Study: Bandung)
title_short COMPARISON OF INTENSITY-DURATION-FREQUENCY CURVES FROM TRMM, GSMAP, AND GPM DATA WITH OBSERVATION DATA (Case Study: Bandung)
title_full COMPARISON OF INTENSITY-DURATION-FREQUENCY CURVES FROM TRMM, GSMAP, AND GPM DATA WITH OBSERVATION DATA (Case Study: Bandung)
title_fullStr COMPARISON OF INTENSITY-DURATION-FREQUENCY CURVES FROM TRMM, GSMAP, AND GPM DATA WITH OBSERVATION DATA (Case Study: Bandung)
title_full_unstemmed COMPARISON OF INTENSITY-DURATION-FREQUENCY CURVES FROM TRMM, GSMAP, AND GPM DATA WITH OBSERVATION DATA (Case Study: Bandung)
title_sort comparison of intensity-duration-frequency curves from trmm, gsmap, and gpm data with observation data (case study: bandung)
url https://digilib.itb.ac.id/gdl/view/42741
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