Copula-based joint distribution analysis of the ENSO effect on the drought indicators over Borneo fire-prone areas

Najib, Mohamad Khoirun; Nurdiati, Sri; Sopaheluwakan, Ardhasena

doi:10.1007/s40808-021-01267-5

Cited by 14 publications

(8 citation statements)

References 54 publications

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“…By dividing the three parts of each edge, each variable's dry, normal, and wet conditions can be determined. Coincidence probability is the probability of dry, normal, and wet conditions of two variables simultaneously, whether the conditions are the same or not (Najib et al, 2022a). It is called synchronous probability if the two conditions are the same (wet-wet, normal-normal, and dry-dry).…”

Section: Coincidence Probabilitymentioning

confidence: 99%

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

NURDIATI¹,

NAJIB²,

THALIB³

2022

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El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can affect the increase in rainfall intensity and the number of dry days, also known as dry spells that can cause drought and increase the potential for forest fires. This study examines the effect of ENSO and IOD conditions on the joint distribution of the number of dry days and total precipitation in a fire-prone area in southern Sumatra, Indonesia. The joint distribution is constructed using rotated copulas from several families, including Gaussian, student's t, Clayton, Gumbel, Frank, Joe, Galambos, BB1, BB6, BB7, and BB8. Fire-prone areas are defined using k-mean clustering, while the copula parameters are estimated using the inference of function for margins (IFM) method. Based on the peak of joint probability density functions (PDFs), ENSO and IOD conditions had a significant effect in the dry season but had no significant effect in the rainy season. The peak of joint PDFs is getting to the dry-dry conditions when the ENSO and IOD indexes increase in the dry season. However, based on coincidence probability, ENSO conditions still influence the joint distribution between the number of dry days and total precipitation during the rainy season but not with IOD conditions. The lower the ENSO index, the higher the probability of wet conditions co-occurring in the number of dry days and total precipitation. Meanwhile, ENSO and IOD conditions significantly affect the coincidence probability between the number of dry days and total precipitation. Moderate-Strong El Niño has the most considerable coincidence probability of 68.5%, followed by Positive IOD with 62.6%. The two conditions had similar effects on the joint distribution of the number of dry days and total precipitation. Moreover, the association between the number of dry days and the total precipitation was stronger in the dry season than in the rainy season.

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Section: Coincidence Probabilitymentioning

confidence: 99%

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

NURDIATI¹,

NAJIB²,

THALIB³

2022

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“…Untuk menentukan fungsi distribusi univariat yang paling cocok, digunakan Anderson Darling sebagai uji kelayakan model [18]. Uji ini digunakan untuk melihat tingkat kesamaan di antara data dan fungsi sebaran yang didasarkan pada selisih kuadrat.…”

Section: Distribusi Bersama Berbasisunclassified

Analisis Pembentukan Sebaran Bivariat Berbasis Copula Antara Luas Area Terbakar dan Curah Hujan di Sumatra Bagian Selatan

Nurdiati¹,

Najib²,

Bayu³

2022

jmi

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Kebakaran hutan dan lahan di Indonesia memiliki hubungan yang erat dengan kondisi iklim di sekitarnya, seperti curah hujan. Salah satu model yang dapat digunakan untuk menganalisis hubungan antar kedua variabel tersebut adalah copula. Akan tetapi, selain curah hujan, fenomena iklim global seperti El Nino-Southern Oscillation (ENSO) dan Indian Ocean Dipole (IOD) juga memiliki pengaruh terhadap kebakaran hutan dan lahan. Oleh karena itu, studi ini menganalisis dan memodelkan distribusi bersama berbasis copula antara curah hujan dan luas area terbakar dengan mempartisi data berdasarkan fenomena ENSO dan IOD. Metode yang digunakan adalah analisis distribusi bersama berbasis copula yang diestimasi menggunakan metode Inference of Function for Margins (IFM). Beberapa fungsi copula digunakan untuk membentuk distribusi bersama, seperti Gaussian, student's t, Clayton, Gumbel, Frank, Joe, Galambos, BB1 (Clayton-Gumbel), BB6 (Joe-Clayton), BB7 (Joe-Gumbel), dan BB8 (Joe-Frank). Hasil penelitian menunjukkan bahwa korelasi tertinggi terhadap luas area terbakar terjadi untuk data curah hujan kumulatif dua bulan berdasarkan korelasi Kendall-Tau. Setiap kondisi ENSO dan IOD memiliki karakteristik yang berbeda-beda, ditunjukkan oleh perbedaan fungsi sebaran univariat dan fungsi copula yang terpilih. Peluang luas area terbakar semakin tinggi pada saat curah hujan rendah. Selain itu, semakin tinggi indeks ENSO dan IOD, semakin tinggi pula peluang luas area terbakar pada saat curah hujan rendah. Berdasarkan peluang bersyarat yang terbentuk, kondisi IOD Positif relatif lebih memiliki pengaruh yang signifikan dibandingkan El Nino Moderat-Kuat. Selain El Nino Moderat-Kuat dan IOD Positif, kondisi lain yang memiliki peluang bersyarat untuk luas area terbakar di Sumatra bagian Selatan yang cukup tinggi adalah El Nino Lemah. Sementara itu, kondisi lainnya, seperti La Nina, ENSO normal, IOD negatif, dan IOD Netral, memiliki peluang bersyarat untuk luas area terbakar yang sangat kecil.

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“…The study area was taken in the Kalimantan fire-prone area, Indonesia (Najib et al 2021a). This fire-prone area is obtained using k-mean clustering by eliminating the cluster with minimum hotspots.…”

Section: Study Area and Datamentioning

confidence: 99%

“…We defined dry spells as the number of days with total precipitation less than 1 mm/day. Based on previous research, we use an average of 2 months of total precipitation (TP) and a total of 3 months of dry spells (DS) because it has the strongest association with hotspots in the Kalimantan fire-prone area (Najib et al 2021a). We only observe months with a significant number of hotspots, i.e., July-November.…”

Section: Study Area and Datamentioning

confidence: 99%

Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

Najib

Nurdiati

Sopaheluwakan

2021

Preprint

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Forest fires have become a national issue every year and get serious attention from the government and researchers, especially in Kalimantan. The copula-based joint distribution can construct a fire risk model to improve the early warning system of forest fires. This study aims to model and analyze the copula-based joint distribution between climate conditions and hotspots in Kalimantan. We constructed the bivariate joint distributions between climate conditions, either total precipitation or dry spells, and hotspots with sample size reduced by ENSO conditions, i.e., La Nina, normal, and El Nino. From the joint distribution, fire risk models are calculated using conditional probability and copula regression. The results show that the relationship between climate conditions and hotspots in La Nina and normal ENSO conditions have an upper tail dependence but no lower tail dependence. Meanwhile, the relationship has both upper and lower tail dependences during El Nino. There is an outlier in normal ENSO conditions with more hotspots than normally, i.e., in September 2019. The probability is very low during normal ENSO conditions, i.e., less than 2%. The only relatively high probability is during El Nino, i.e., more than 10%. Moreover, the copula regression models show that the model given specific dry spells is better than that given specific total precipitation as climate condition. The copula regression for hotspots given specific total precipitation and ENSO conditions has the RMSE value of 1339 hotspots and the R2 value of 60.70%. Meanwhile, the copula regression for hotspots given specific dry spells and ENSO conditions has the RMSE value of 1185 hotspots and the R2 value of 69.21%.

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Copula-based joint distribution analysis of the ENSO effect on the drought indicators over Borneo fire-prone areas

Cited by 14 publications

References 54 publications

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

Joint Distribution and Coincidence Probability of the Number of Dry Days and the Total Amount of Precipitation in Southern Sumatra Fire-Prone Area

Analisis Pembentukan Sebaran Bivariat Berbasis Copula Antara Luas Area Terbakar dan Curah Hujan di Sumatra Bagian Selatan

Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

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