© 2015 Faculty of Geography UGM and The Indonesian Geographers Association Relationships between Sea Surface Temperature (SST) and rainfall distribution pattern in South-Central Java, Indonesia (original) (raw)
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International Journal of Climatology, 2003
The characteristics of climatic rainfall variability in Indonesia are investigated using a double correlation method. The results are compared with empirical orthogonal function (EOF) and rotated EOF methods. In addition, local and remote responses to sea-surface temperature (SST) are discussed. The results suggest three climatic regions in Indonesia with their distinct characteristics. Region A is located in southern Indonesia from south Sumatera to Timor island, southern Kalimantan, Sulawesi and part of Irian Jaya. Region B is located in northwest Indonesia from northern Sumatra to northwestern Kalimantan. Region C encompasses Maluku and northern Sulawesi. All three regions show both strong annual and, except Region A, semi-annual variability. Region C shows the strongest El Niño-southern oscillation (ENSO) influence, followed by Region A. In Region B, the ENSO-related signal is suppressed. Except for Region B, there are significant correlations between SST and the rainfall variabilities, indicating a strong possibility for seasonal climate predictions. March to May is the most difficult season to predict the rainfall variability. From June to November, there are significant responses of the rainfall pattern to ENSO in Regions A and C. A strong ENSO influence during this normally dry season (June to September) is hazardous in El Niño years, because the negative response means that higher SST in the NIÑO3 of the Pacific region will lower the rainfall amount over the Indonesian region. Analyses of Indonesian rainfall variability reveal some sensitivities to SST variabilities in adjacent parts of the Indian and Pacific Oceans.
IOP Conference Series: Earth and Environmental Science
Indications of climate change can be known from the changes in rainfall distribution pattern and its volume. Yogyakarta Special Region lately often experiences drought, and has an impact on decreasing agriculture productivity and crop failure in several districts. Some of the research that has been done has not reviewed specifically in the Special Region of Yogyakarta with several Global Climate Indices and has not been spatially displayed. The aims of this research is to determine the relationship between the global climate indices with rainfall patterns in the Special Region of Yogyakarta. Global Climate Indices data used are: Southern Oscillation Index (SOI), Sea Surface Temperature Nino 3.4, Sea Surface Temperature Nino West, and Indian Ocean Basin-wide Warming (IOBW), and rainfall data for 2009-2013. The analysis process uses Principal Component Analysis (PCA) method to determine the relationship between the Global Climate Index and rainfall patterns and then change the data to be spatial using GIS. Based on the results, overall rainfall is negatively correlated with SOI, the area with a quite strong negative correlation (>-0.462) in the northernmost areas namely Cangkringan, Pakem and Ngemplak Districts, and the correlation value continues to decline to the southern regions. The correlation of rainfall with SST Nino 3.4 is dominantly negatively correlated and the area with strong enough negative correlation (>-0.492) in the northern and southernmost regions, namely Pakem, Cangkringan, Ngemplak, Saptosari, Tanjungsari, and Girisubo Districts, and the correlation value continues to decrease to the middle area. Then the correlation of rainfall with Nino West overall has a positive value, the northern region has a strong correlation (>-0.455) in the Districts of Pakem, Cangkringan and Ngemplak, while other regions have a strong enough correlation (>-0.455). The last correlation is with IOBW, it is divided into 2 areas that have positive correlations in the north and south areas and the negative correlation of the middle part is almost entirely, areas with strong positive correlations (> 0.57), namely in Tanjungsari and Girisubo Districts and areas with strong negative correlations (-0,556) in Pundong District. Thus it can be concluded that the Global Climate Indices (SOI, SST Nino 3.4, Nino West, and IOBW) influences the pattern of rainfall distribution in the Special Region of Yogyakarta.
Sea Surface Temperature Anomaly Characteristics Affecting Rainfall in Western Java, Indonesia
Agromet
Western Java is densely populated with high socio-economic activity. Climate-related disasters can be mitigated with the support of an understanding of systems that produce reliable climate predictions. One of the climate variables included in hydrometeorological disasters is rainfall. The characteristics of rainfall in Western Java cannot be separated from the sea surface temperature (SST) around the area. This study compares the relationship between SST and rainfall with singular value decomposition (SVD) and compares it with Pearson's correlation. SVD Model performance was evaluated using square covariance fraction (SCF) and Pearson correlation. The results showed that rainfall has a higher correlation with SST Anomaly (SSTA) by using SVD, with a correlation of about 0.63 in 6 to 9 months without lag time. Rainfall in western Java was closely related to the positive SSTA anomaly in southern Indonesia, especially the waters south of Java Island, and negative anomalies in other...
The Impact of El Nino on Rainfall Variability in Buleleng Regency (Case Study: Period 1995-2004)
Tunas Geografi
The climate in Indonesia usually runs yearly; there are times when a decrease in rainfall results in drought, and at other times, the rainfall increases resulting in flooding. One of the causes of changes in precipitation in Indonesia, including in most parts of the world, is ENSO (El Nino-Southern Oscillation), often called El Nino. This study aimed to determine the relationship between ENSO index data (SST Nino 3.4 anomaly) and monthly rainfall data in Buleleng Regency. This study uses secondary data, namely monthly rainfall data at 16 rain posts in Buleleng Regency and ENSO Index data from BMKG Region III Denpasar. Data were collected through observation, document recording and analyzed using statistical correlation methods. Furthermore, the results are processed spatially, namely by the Isohyet method. The research results show that the impact of El Nino on rainfall in Buleleng varies spatially and depends on the intensity of El Nino. In June-July-August (JJA/dry season) and Sep...
Acta Oceanologica Sinica, 2019
The Impact of the Indian Ocean Dipole (IOD) and the El Niño Southern Oscillation (ENSO) event for Indonesian rainfall has been investigated for the period from 1950 to 2011. Inter-annual change of IOD and ENSO indices are used to investigate their relationship with Indonesian rainfall. By using the wavelet transform method, we found a positive significant correlation between IOD and Indonesian rainfall on the time scale of nearly 2.5-4 years. Furthermore, the positive significant correlation between ENSO (sea surface temperature anomaly at Niño3.4 area indices) and Indonesian rainfall exists for shorter than 2 years and between 5.5 to 6.5-year time scales.
2019
The vast territory of Indonesia and its position flanked by two oceans and two continents make the Indonesia's climate very dynamic and complex. On the other hand, climate variability and its change continue to occur and the impacts vary signifikan in agriculture sector. The pattern and tendency of climate variability can be detected through global indices such as Nino 3.4 sea surface temperature (SST) and its relationship to rainfall anomalies. Rainfall is one of the climate parameters that most plays a role in agriculture. The most obvious impact of climate change on the agricultural sector is the reduction in planting area and the decrease in production. Therefore, the Agriculture Sectorneeds to make adaptation efforts to reduce the risk due to climate change. This paper presents the results of the analysis of the relationship between Nino 3.4 SST anomalies with rainfall anomalies to determine the key regions of Indonesia's climate diversity.Key Areas were identified based on the correlation more than 0.5 and less than-0.5 significance less than 0.1 of the Niño 3.4 SST index with rainfall anomalies. The results of the analysis show that every condition in both El-Niño and La-Niñahas a different lag and global index where the correlation is strong and significant. Key areas have been identified in El-Niño and La-Niñacondition.These regions are areas that are significantly affected by climate variability and have an impact on rainfall anomalies which will ultimately affect the agricultural sector.
The Investigation of 1997 and 2015 El Nino Events in West Sumatera, Indonesia
International Journal on Advanced Science, Engineering and Information Technology, 2017
The 1997, 2010, and 2015 El Nino events have been recognized worldwide as a primary factor of decreasing biomass productivity. Its effects occur at farm as well as regional scale. Yet some still think that the effect is only perceived by farmers directly. We proposed a simple method to describe that its effect at catchment scale is not negligible. For this purpose, we analysed an upstream catchment in West Sumatera that normally receives high rainfall up to 5000 mm per year. This catchment is in pristine condition due to its status as a national park. We used satellite and ground monitoring systems i.e. rainfall and stream water level. Satellite data such as DEM (Digital Elevation Model) used to trace river networks is acquired from ASTER GDEM in 30 m resolution. To monitor vegetation health, we used NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) on board MODIS (MODerate resolution Imaging Spectroradiometer) Terra (daytime) MOD13Q1 (250 m resolution). Catchment delineation was performed using local land use map. Time series of EVI and NDVI was processed for the year 2015, comprising 23 datasets. Rainfall data from the year 1980 to 2012 from 9 stations and water level at the main river were analysed. We found the trace of 1997 El Nino very clearly through rainfall anomalies at all stations. As for the 2010 event, the pattern was not consistent across all rainfall stations. The 2015 event was not imminent until late October 2015. Throughout the year, NDVI remains above 0.8. In late October, maximum NDVI dropped below 0.4. This coincides with the very low water level in the stream. The same pattern was also found in two neighbouring catchments with similar land use, Kuranji and Air Dingin, and Tampo catchment. This proves that the 2015 El Nino did threatens not only farmers but also other aspects that depend on vegetation health and stream flow. Both methods are proven to be robust and may be used as an alternative way to monitor vegetation health and the impact of global climate change for ecology and water management such as domestic water use, irrigation, and flood control.
Impacts of the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events on the Indonesian rainfall were studied using observational data and the NCEP/NCAR reanalyzing global data. ENSO and IOD variability in the time-periods are detected by using wavelet analysis, and we attempt composite analysis of the rainfall amounts over Indonesia during ENSO and IOD events. The correlation between strong El Niño intensities and several regions in Indonesia with rainfall below normal (< 85%) are high, but when the intensities are weak the correlation becomes low. In this case other phenomena such as IOD can contribute to drought in Indonesia. Our analysis also indicates that during El Niño and positive IOD events, the southeast monsoon (Australian monsoon) over Indonesia is intensified, causing the dry season longer than rainy season.
2015
This study is mainly concerned an application of SST NiA±o 3.4, IOD and Monsoon index in determining the upcoming of the extreme rainfall over the Indonesian Maritime Continent (IMC). As one of the most important region located along the belt equator, the meteorological surface parameter over the IMC suspected is effecting mostly by the Monsoon system. This is a unique country, since located between two great continent (Asia and Australia) and two great ocean (Indian and Pacific). It indicates that the Sea Surface Temperature (SST) should become one of the most important parameter. Although, this region is affected by the Monsoon system, but another event called as the Indian Ocean Dipole (IOD) and El NiA±o suspected has a great effects also in determining the rainfall anomalies, especially for the extreme conditions. By this reason, we investigated the IOD and El-NiA±o index signal, especially the SST NiA±o 3.4 index. By assuming the drought and wet extreme condition is mostly affe...
Journal of the Meteorological Society of Japan, 2008
Pentad rainfall data from 46 stations in West Sumatera Province, Indonesia in 1992 reveal temporal and spatial variations of rainfall in coastal and inland regions. Distinct contrasts in rainfall amounts and variations are evident between the coastal and inland mountainous regions. Locality index is defined by pentad rainfall time series in coastal and inland regions to classify characteristic rainfall distribution patterns into four types: coastal-, inland-, active-, and inactive-type. In 1992 the coastal-or active-type rainfall patterns tend to appear throughout the year, whereas the inland-type rainfall pattern tends to appear during the Southern Hemisphere summer. The inactive-type tends to appear during the Southern Hemisphere winter.