Study on the possibility of predicting the onset and rainfall of wet season in Yogyakarta Special Province, Indonesia
Dewi Galuh Condro Kirono(1*), Budi Salmon(2)
(1) Faculty of Geography Universitas Gadjah Mada
(2) Faculty of Geography Universitas Gadjah Mada
(*) Corresponding Author
Abstract
Indonesian region frequently experiences a prolonged drought and/or flood hazard One of the key factors that often triggers these Awards is the occurrence of seasonal rainfall anomaly. To minimize the possible impact of such extreme event, it is necessary to develop a model that can be applied to predict the wet season onset and wet season rainfall. This paper is a preliminary effort on this mailer. As a pilot study, Yogyakarta Special Province (DIY) has been selected for this purpose. In particular; the analysis is emphasized on the Adisucipto airport station, Yogyakarta, as it is one of the first-class climatological station in DIY which has a very good and long data required for such intention.
Detail objectives of this study are to address the following three questions: (1) is it possible to predict wet season onset in Adisucipto airport station using local and regional atmospheric indicators? (2) if it is possible and the model(s) have been developed, can the model(s) be applied for predicting the onset of wet season in other parts of DIY and its surround? (3) does an early or late onset of wet season provide any indication to subsequent rainfall during the wet season?
To achieve these objectives, the study requires several types of data including daily rainfall data, monthly air pressure data, Southern Oscillation Index (SO!) and Sea Surface Temperature data. Most of the data cover the period of 1976 to 2001. Methods that have been applied to meet the goals are statistical descriptive and simple liner regression analysis.
The results suggest that: (I) wet season onset time in Yogyakarta can be predicted using both local and regional atmospheric factors. namely August and September SOI, and air pressure index at Adisucipto airport station in June. July and August; (2) models that have been developed for Adisucipto airport station are modest enough to be applied for predicting the onset of wet season at other location; (3) the onset of wet season cannot be used as an indicator to estimate rainfall in wet season itself.
Detail objectives of this study are to address the following three questions: (1) is it possible to predict wet season onset in Adisucipto airport station using local and regional atmospheric indicators? (2) if it is possible and the model(s) have been developed, can the model(s) be applied for predicting the onset of wet season in other parts of DIY and its surround? (3) does an early or late onset of wet season provide any indication to subsequent rainfall during the wet season?
To achieve these objectives, the study requires several types of data including daily rainfall data, monthly air pressure data, Southern Oscillation Index (SO!) and Sea Surface Temperature data. Most of the data cover the period of 1976 to 2001. Methods that have been applied to meet the goals are statistical descriptive and simple liner regression analysis.
The results suggest that: (I) wet season onset time in Yogyakarta can be predicted using both local and regional atmospheric factors. namely August and September SOI, and air pressure index at Adisucipto airport station in June. July and August; (2) models that have been developed for Adisucipto airport station are modest enough to be applied for predicting the onset of wet season at other location; (3) the onset of wet season cannot be used as an indicator to estimate rainfall in wet season itself.
Keywords
prediction; wet season onset; Southern Oscillation Index (SOI)
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PDFDOI: https://doi.org/10.22146/ijg.57272
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Copyright (c) 2003 Dewi Galuh Condro Kirono, Budi Salmon
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)
ISSN 2354-9114 (online), ISSN 0024-9521 (print)
IJG STATISTIC