Abstract
<p>Droughts and floods due to extreme climate events has caused yield loss in various regions of Indonesia, including the Provinces of Aceh and North Sumatra. An early detection model needs to be developed to anticipate the negative impacts of extreme climate event. The model may describe the association of surplus and rainfall deficits with paddy damage due to drought and flood. We used Standardized Precipitation Index (SPI) to explore drought and flood characteristics in period 1989-2016. The study aimed: (i) to analyze the relationship between SPI and paddy damage due to drought and flood events, (ii) to analyze the critical value of the duration and intensity of SPI which causes paddy damage, and (iii) to determine which districts were prone to drought and flood in the Provinces of Aceh and North Sumatra. The results concluded that SPI-3 and -6 months can better describe the frequency of drought and rice flooding. In addition, drought on paddy occured mostly if the SPI was smaller than -1 which took place within 4-5 months, whereas flood occured if the SPI was greater than 1. Short duration drought (2-3 months) were observed in five districts in Aceh (2) and North Sumatra (3). On other hand, more flood districts were identified (9 districts).</p>
Highlights
Droughts and floods due to extreme climate events has caused yield loss in various regions of Indonesia, including the Provinces of Aceh and North Sumatra
An early detection model needs to be developed to anticipate the negative impacts of extreme climate event
The model may describe the association of surplus and rainfall deficits with paddy damage
Summary
Data Penelitian Data penelitian ini antara lain: (i) data curah hujan bulanan periode tahun 1989-2016 pada 34 stasiun di 21 kabupaten di Provinsi Aceh dan 48 stasiun di 28 kabupaten di Provinsi Sumatera Utara, (ii) Data observasi curah hujan bulanan dari Global Precipitation Climatology Centre (GPCC) yang dapat diakses melalui http://www.esrl.noaa.gov/psd/data/gridded/data.gpcc. html, dan (iii) data luas wilayah sawah tanaman padi yang terkena akibat kekeringan dan banjir bulanan. Untuk menghitung SPI, curah hujan bulanan dicocokkan dalam peluang distribusi menggunakan distribusi Gamma. Data ini di-fit ke suatu fungsi distribusi probabilitas, yang kemudian ditransformasikan ke distribusi normal, sehingga rata-rata SPI di lokasi tersebut sama dengan nol. Distribusi curah hujan paling cocok didekati dengan distribusi gamma, yang disajikan pada Persamaan (1). Parameter alpha dan beta dari fitting kemudian digunakan untuk menghitung probabilitas kumulatif curah hujan di setiap bulan pengamatan berdasarkan skala waktu yang dipilih, dengan Persamaan (2). Untuk x=0 (tidak ada presipitasi) probabilitas kumulatif didekati dengan q, yaitu perbandingan antara jumlah kejadian presipitasi nol dengan jumlah total data. Probabilitas kumulatif H(x) kemudian ditrans-formasi ke variabel random berdistribusi normal dengan mean nol dan variansi 1, yang didefinisikan sebagai nilai SPI (Gambar 1) atau secara numerik diestimasi dengan Persamaan (5-8). Dimana: Gambar 1 Transformasi peluang kumulatif curah hujan ke variabel acak berdistribusi normal (SPI)
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