Multivariate fire risk models using copula regression in Kalimantan, Indonesia

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

doi:10.1007/s11069-022-05346-3

Cited by 6 publications

(4 citation statements)

References 87 publications

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“…In recent years, the introduction of copula in drought research has advanced the field of probabilistic drought modeling [21][22][23][24][25][26][27][28][29][30][31][32]. For instance, Yang et al [21] developed a Bayesian copula method using multiple GCMs to generate reliable ensemble projections of drought risk.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

Wang,

Li,

Huang

et al. 2024

NSO

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In the context of global climate warming, the propagation of meteorological drought (MD) may aggravate the devastating impact of hydrological drought (HD) on water security and sustainable development. There are challenges in accurately predicting the propagation of drought and effectively quantifying the effects of uncertainty, especially in datadeficient regions. In this study, a novel method called RFCFA is developed through integrating random forest (RF), copula, and factorial analysis (FA) into a general framework as well as applied to the Aral Sea Basin (a typical arid and data-scarce basin in Central Asia) under considering the impact of climate change. Several findings can be summarized: (1) the projected future drought propagation probability of ASB is 39.2%, which is about 8% higher than historical level; (2) drought propagation is mainly affected by mean climate condition, catchment characteristics (i.e., elevation, LUCC, and slope), and human activities (i.e., irrigation and reservoir operation); (3) the lower propagation probability in spring is expected under SSP1-2.6 due to increased snow meltwater, and the drought propagation probability in autumn is the highest (reaching 45.4%) under the influence of reservoir operation; (4) the combined effects of meteorological conditions and agricultural irrigation can lead to a higher probability of future propagation in the upper river basin in summer. Findings are valuable for predicting drought propagation risk, revealing main factors and inherent uncertainties, as well as providing support for drought management and disaster prevention.

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Section: Literature Reviewmentioning

confidence: 99%

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

Wang,

Li,

Huang

et al. 2024

NSO

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“…There are 11 copulas considered to investigate the dependency structure between the two variables, including Gaussian, Student t, Clayton, Gumbel, Frank, Joe, Galambos, BB1, BB6, BB7, and BB8. The fittest copula function was selected and tested using the Cramer-von Mises hypothesis test (Najib et al, 2022b). Moreover, the first and second steps of the IFM method aim to find the parameter values by maximizing the log of the likelihood of each function.…”

Section: Parameters Estimationmentioning

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

Self Cite

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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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“…Oleh karena itu, beberapa fungsi copula digunakan pada penelitian ini seperti yang dapat dilihat pada Tabel 2. Untuk menentukan fungsi copula yang paling cocok, digunakan uji hipotesis Cramervon Mises [19]. Genest dan Rmillard [12] dalam [3] mengusulkan suatu pendekatan untuk menghitung statistik uji Cramer-von Mises untuk Copula berdasarkan fungsi copula empirik dan fungsi copula parametrik.…”

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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Multivariate fire risk models using copula regression in Kalimantan, Indonesia

Cited by 6 publications

References 87 publications

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

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

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