Abstract
Forest fires have become a national issue yearly and elicited serious attention from the government and researchers in Indonesia. Copula-based joint distribution can construct a fire risk model to improve the early warning system of forest fires, especially in Kalimantan. This study models and analyzes the copula-based joint distribution between climate conditions and hotspots. Several climate conditions, such as total precipitation, dry spells, and El Nino–Southern Oscillation (ENSO), are used. The joint distributions are constructed using rotated a.k.a. reflected copula functions with reduced sample size according to ENSO conditions, and the copula regression model is employed to estimate the fire size. The marginal distribution is selected by inference of function for margin method using the Anderson–Darling hypothesis test, while root-mean-squared error (RMSE), Akaike’s information criterion, and the Cramer–von Mises hypothesis test are employed to select the fittest copula function. Results show that the probability of extreme hotspots during normal ENSO conditions is rare and almost near zero during La Nina. Moreover, extreme hotspot events (more severe than in 2019) during El Nino are more sensitive to total precipitation than dry spells according to the conditional survival function. The copula regression model used dry spells as a climate condition better than total precipitation, with the RMSE value of 1110 hotspots and the \({R}^{2}\) value of 73.02%. A total of 95% confidence interval of the expected hotspots can cover all actual hotspot data in this model.
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The raw data of climate conditions provided by NOAA CPC Morphing Technique (“CMORPH”) can be downloaded freely on https://ftp.cpc.ncep.noaa.gov/precip/PORT/SEMDP/CMORPH_CRT/DATA. Meanwhile, the raw data of hotspots provided by the Agency for Meteorology, Climatology, and Geophysics of Indonesia were processed from MODIS sensors of the Terra and Aqua satellites. The processed data (time series) of this article are available from the corresponding author on reasonable request.
References
Aflahah E, Hidayati R, Hidayat R (2019) Pendugaan hotspot sebagai indikator kebakaran hutan di Kalimantan berdasarkan faktor iklim. J Pengelolaan Sumberd Alam Dan Lingkung 9(2):405–418. https://doi.org/10.2924/jpsl.9.2.405-418
Afshar MH, Şorman AÜ, Tosunoğlu F, Bulut B, Yilmaz MT, Danandeh Mehr A (2020) Climate change impact assessment on mild and extreme drought events using copulas over Ankara. Turkey Theor Appl Climatol 141(3–4):1045–1055. https://doi.org/10.1007/s00704-020-03257-6
Anderson TW, Darling DA (1954) A test of goodness of fit. J Am Stat Assoc 49:765–769. https://doi.org/10.2307/2281537
Ardiansyah M, Boer R, Situmorang AP (2017) Typology of land and forest fire in South Sumatra, Indonesia Based on Assessment of MODIS Data. IOP Conf Ser Earth Environ Sci 54(1):012058. https://doi.org/10.1088/1755-1315/54/1/012058
Austin KG, Schwantes A, Gu Y, Kasibhatla PS (2019) What causes deforestation in Indonesia? Environ Res Lett 14(2):024007. https://doi.org/10.1088/1748-9326/aaf6db
Baran S, Szokol P, Szabó M (2021) Truncated generalized extreme value distribution-based ensemble model output statistics model for calibration of wind speed ensemble forecasts. Environmetrics. https://doi.org/10.1002/env.2678
Berg D (2009) Copula goodness-of-fit testing: an overview and power comparison. Eur J Financ 15(7–8):675–701. https://doi.org/10.1080/13518470802697428
Bischiniotis K, Van Den Hurk B, Jongman B, Coughlan De Perez E, Veldkamp T, De Moel H, Aerts J (2018) The influence of antecedent conditions on flood risk in sub-Saharan Africa. Nat Hazards Earth Syst Sci 18(1):271–285. https://doi.org/10.5194/nhess-18-271-2018
Boubakar T, Lassina D, Belco T, Abdou F (2018) The shortest confidence interval for the mean of a normal distribution. Int J Stat Probab 7(2):33. https://doi.org/10.5539/ijsp.v7n2p33
Bouyé E, Durrleman V, Nikeghbali A, Riboulet G, Roncalli T (2000) Copulas for finance–a reading guide and some applications. https://doi.org/10.2139/ssrn.1032533
Brechmann EC, Schepsmeier U (2013) Modeling dependence with C- and D-vine copulas: the R package CDVine. J Stat Softw 52(3):1–27. https://doi.org/10.18637/jss.v052.i03
Brogan D, Nelson P, MacDonald L (2019) Spatial and temporal patterns of sediment storage and erosion following a wildfire and extreme flood. Spat Temporal Patterns Sediment Storage Eros Follow a Wildfire Extrem Flood 7(2):1–48. https://doi.org/10.5194/esurf-2018-98
Brunner M, Furrer R, Favre A-C (2019) Modeling the spatial dependence of floods using the Fisher copula. Hydrol Earth Syst Sci 23:107–124. https://doi.org/10.5194/hess-2018-159
Budiarti R, Wigena AH, Purnaba IGP, Achsani NA (2018) Modelling the dependence structure of financial assets: a bivariate extreme data study. IOP Conf Ser Earth Environ Sci 187(1):012003. https://doi.org/10.1088/1755-1315/187/1/012003
Cardil A, Rodrigues M, Ramirez J, de-MiguelSilvaMarianiAscoli SCAMD (2021) Coupled effects of climate teleconnections on drought, Santa Ana winds and wildfires in southern California. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142788
Chen L, Guo S (2019) Copulas and its application in hydrology and water resources. Springer, Singapore
Cooke RM, Joe H, Chang B (2020) Vine copula regression for observational studies. AStA Adv Stat Anal 104(2):141–167. https://doi.org/10.1007/s10182-019-00353-5
Daşdemir İ, Aydın F, Ertuğrul M (2021) Factors affecting the behavior of large forest fires in Turkey. Environ Manage 67(1):162–175. https://doi.org/10.1007/s00267-020-01389-z
De Michele C, Salvadori G (2003) A Generalized Pareto intensity-duration model of storm rainfall exploiting 2-Copulas. J Geophys Res Atmos. https://doi.org/10.1029/2002jd002534
De Leon AR, Wu B (2011) Copula-based regression models for a bivariate mixed discrete and continuous outcome. Stat Med 30(2):175–185. https://doi.org/10.1002/sim.4087
De Michele C, Salvadori G, Canossi M, Petaccia A, Rosso R (2005) Bivariate statistical approach to check adequacy of dam spillway. J Hydrol Eng 10(1):50–57. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:1(50)
Doane DP, Seward LE (2011) Measuring skewness: a forgotten statistic? J Stat Educ. https://doi.org/10.1080/10691898.2011.11889611
Fanin T, van der Werf G (2016) Precipitation-fire linkages in Indonesia (1997–2015). Biogeosciences Discuss. https://doi.org/10.5194/bg-2016-443
Fahimirad Z, Shahkarami N (2021) The impact of climate change on hydro-meteorological droughts using copula functions. Water Resour Manag 35(12):3969–3993. https://doi.org/10.1007/s11269-021-02918-z
Farooq M, Shafique M, Khattak MS (2018) Flood frequency analysis of river swat using Log Pearson type 3, generalized extreme value, normal, and gumbel max distribution methods. Arab J Geosci. https://doi.org/10.1007/s12517-018-3553-z
Field RD, Van Der Werf GR, Fanin T, Fetzer EJ, Fuller R, Jethva H, Levy R, Livesey NJ, Luo M, Torres O, Worden HM (2016) Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc Natl Acad Sci U S A 113(33):9204–9209. https://doi.org/10.1073/pnas.1524888113
FWI (2020) Within 75 Years of Independence, Indonesia has lost more than 75 times the size of Yogyakarta Province of its forest. https://fwi.or.id/en/within-75-years-of-independence-indonesia-has-lost-more-than-75-times-the-size-of-yogyakarta-province-of-its-forest/. Accessed 29 Aug 2021
Greene AM, Seager R (2016) Categorical representation of North American precipitation projections. Sci Rep. https://doi.org/10.1038/srep23888
Gringorten II (1963) A plotting rule for extreme probability paper. J Geophys Res 68(3):813–814
Gudendorf G, Segers J (2010) Extreme-Value Copulas. Copula theory and its applications. Springer, Berlin, Heidelberg, pp 127–145
Hao Z, AghaKouchak A (2013) Multivariate standardized drought Index: a parametric multi-index model. Adv Water Resour 57:12–18. https://doi.org/10.1016/j.advwatres.2013.03.009
Hilbe JM (2011) Negative binomial regression. Cambridge University Press
Inouye DI, Yang E, Allen GI, Ravikumar P (2017) A review of multivariate distributions for count data derived from the Poisson distribution. Wiley Interdiscip Rev Comput Stat. https://doi.org/10.1002/wics.1398
Jha BK, Danjuma YJ (2020) Unsteady dean flow formation in an annulus with partial slippage: a riemann-sum approximation approach. Results Eng. https://doi.org/10.1016/j.rineng.2019.100078
Joe H (1997) Multivariate models and multivariate dependence concepts. CRC Press, London
Joe H, Li H, Nikoloulopoulos AK (2010) Tail dependence functions and vine copulas. J Multivar Anal 101(1):252–270. https://doi.org/10.1016/j.jmva.2009.08.002
Kang KI, Kang K, Kim C (2021) Risk factors influencing cyberbullying perpetration among middle school students in korea: analysis using the zero-inflated negative binomial regression model. Int J Environ Res Public Health 18(5):1–13. https://doi.org/10.3390/ijerph18052224
KLHK (2020) Hutan dan Deforestasi Indonesia Tahun 2019. https://ppid.menlhk.go.id/siaran_pers/browse/2435. Accessed 30 Apr 2021
Kolev N, Paiva D (2009) Copula-based regression models: a survey. J Stat Plan Inference 139(11):3847–3856. https://doi.org/10.1016/j.jspi.2009.05.023
Kosmidis I, Karlis D (2016) Model-based clustering using copulas with applications. Stat Comput 26(5):1079–1099. https://doi.org/10.1007/s11222-015-9590-5
Krebs MA, Reeves MC, Baggett LS (2019) Predicting understory vegetation structure in selected western forests of the United States using FIA inventory data. For Ecol Manage 448:509–527. https://doi.org/10.1016/j.foreco.2019.06.024
LAPAN (2016) Informasi Titik Panas (Hotspot) Kebakaran Hutan/Lahan. http://pusfatja.lapan.go.id/files_uploads_ebook/publikasi/Panduan_hotspot_2016 versi draft 1_LAPAN.pdf. Accessed 21 Feb 2021
Larsen A, Hanigan I, Reich BJ, Qin Y, Cope M, Morgan G, Rappold AG (2021) A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication. J Expo Sci Environ Epidemiol 31(1):170–176. https://doi.org/10.1038/s41370-020-0246-y
Laux P, Vogl S, Qiu W, Knoche HR, Kunstmann H (2011) Copula-based statistical refinement of precipitation in RCM simulations over complex terrain. Hydrol Earth Syst Sci 15(7):2401–2419. https://doi.org/10.5194/hess-15-2401-2011
Li Z, Shao Q, Tian Q, Zhang L (2020) Copula-based drought severity-area-frequency curve and its uncertainty, a case study of Heihe River basin. China Hydrol Res 51(5):867–881. https://doi.org/10.2166/nh.2020.173
Link R, Wild TB, Snyder AC, Hejazi MI, Vernon CR (2020) 100 years of data is not enough to establish reliable drought thresholds. J Hydrol X. https://doi.org/10.1016/j.hydroa.2020.100052
Liu J, Sirikanchanarak D, Sriboonchitta S, **e J (2018) Analysis of household consumption behavior and indebted self-selection effects: case study of Thailand. Math Probl Eng. https://doi.org/10.1155/2018/5486185
Madadgar S, Moradkhani H (2014) Spatio-temporal drought forecasting within Bayesian networks. J Hydrol 512:134–146. https://doi.org/10.1016/j.jhydrol.2014.02.039
Madadgar S, Sadegh M, Chiang F, Ragno E, AghaKouchak A (2020) Quantifying increased fire risk in California in response to different levels of warming and drying. Stoch Environ Res Risk Assess 34(12):2023–2031. https://doi.org/10.1007/s00477-020-01885-y
Malá I, Sládek V, Bílková D (2021) Power comparisons of normality tests based on l-moments and classical tests. Math Stat 9(6):994–1003. https://doi.org/10.13189/ms.2021.090615
Marinović I, Cindrić Kalin K, Güttler I, Pasarić Z (2021) Dry spells in Croatia: Observed climate change and climate projections. Atmosphere (basel). https://doi.org/10.3390/atmos12050652
Masarotto G, Varin C (2017) Gaussian copula regression in R. J Stat Softw. https://doi.org/10.18637/jss.v077.i08
Miettinen J, Shi C, Liew SC (2011) Deforestation rates in insular Southeast Asia between 2000 and 2010. Glob Chang Biol 17(7):2261–2270. https://doi.org/10.1111/j.1365-2486.2011.02398.x
Najib MK, Nurdiati S, Sopaheluwakan A (2021a) Copula in wildfire analysis: a systematic literature review. Inpr Indones J Pure Appl Math 3(2):101–111. https://doi.org/10.15408/inprime.v3i2.22131
Najib MK, Nurdiati S, Sopaheluwakan A (2021b) Quantifying the joint distribution of drought indicators in Borneo fire-prone area. IOP Conf Ser Earth Environ Sci 880(1):012002. https://doi.org/10.1088/1755-1315/880/1/012002
Najib MK, Nurdiati S, Sopaheluwakan A (2021c) Copula based joint distribution analysis of the ENSO effect on the drought indicators over Borneo fire-prone areas. Model Earth Syst Environ. https://doi.org/10.1007/s40808-021-01267-5
Najib MK, Nurdiati S (2021) Koreksi bias statistik pada data prediksi suhu permukaan air laut di wilayah indian ocean dipole barat dan timur. Jambura Geosci Rev 3(1):9–17. https://doi.org/10.34312/jgeosrev.v3i1.8259
Nikonovas T, Spessa A, Doerr SH, Clay GD, Mezbahuddin S (2022) ProbFire: a probabilistic fire early warning system for Indonesia. Nat Hazards Earth Syst Sci 22(2):303–322. https://doi.org/10.5194/nhess-22-303-2022
Nikonovas T, Spessa A, Doerr SH, Clay GD, Mezbahuddin S (2020) Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun Earth Environ. https://doi.org/10.1038/s43247-020-00069-4
Noh H, El Ghouch A, Bouezmarni T (2013) Copula-based regression estimation and inference. J Am Stat Assoc 108(502):676–688. https://doi.org/10.1080/01621459.2013.783842
Nurdiati S, Bukhari F, Julianto MT, Najib MK, Nazria N (2021a) Heterogeneous correlation map between estimated ENSO and IOD From ERA5 and hotspot In Indonesia. Jambura Geosci Rev 3(2):65–72. https://doi.org/10.34312/jgeosrev.v3i2.10443
Nurdiati S, Khatizah E, Najib MK, Fatmawati LL (2021b) El nino index prediction model using quantile map** approach on sea surface temperature data. Desimal J Mat 4(1):79–92. https://doi.org/10.24042/djm.v4i1.7595
Nurdiati S, Sopaheluwakan A, Septiawan P (2021c) Spatial and temporal analysis of el niño impact on land and forest fire in kalimantan and sumatra. Agromet 35(1):1–10. https://doi.org/10.29244/j.agromet.35.1.1-10
Onken A, Panzeri S (2016) Mixed vine copulas as joint models of spike counts and local field potentials. Adv Neural Inf Process Syst :1333–1341
Pleis JR (2018) Mixtures of discrete and continuous variables: Considerations for dimension reduction. Dissertation, University of Pittsburgh
Pobočíková I, Sedliačková Z, Michalková M (2017) Application of four probability distributions for wind speed modeling. Procedia Eng 192:713–718. https://doi.org/10.1016/j.proeng.2017.06.123
Ribeiro AFS, Russo A, Gouveia CM, Páscoa P (2019) Copula-based agricultural drought risk of rainfed crop** systems. Agric Water Manag. https://doi.org/10.1016/j.agwat.2019.105689
Rizani M, Fathurrahmani F (2018) Aplikasi Monitoring Hari Tanpa Hujan (HTH) berbasis web pada stasiun klimatologi kelas 1 Banjarbaru. J Sains Dan Inform 4(2):63–72. https://doi.org/10.34128/jsi.v4i2.137
Ryan RG, Silver JD, Schofield R (2021) Air quality and health impact of 2019–20 black summer mega-fires and COVID-19 lockdown in Melbourne and Sydney. Australia Environ Pollut. https://doi.org/10.1016/j.envpol.2021.116498
Sachdeva S, Bhatia T, Verma AK (2018) GIS-based evolutionary optimized gradient boosted decision trees for forest fire susceptibility map**. Nat Hazards 92(3):1399–1418. https://doi.org/10.1007/s11069-018-3256-5
Salvadori G, De Michele C (2007) On the Use of Copulas in hydrology: Theory and practice. J Hydrol Eng 12(4):369–380. https://doi.org/10.1061/(asce)1084-0699(2007)12:4(369)
Schölzel C, Friederichs P (2008) Multivariate non-normally distributed random variables in climate research–introduction to the copula approach. Nonlinear Process Geophys 15(5):761–772. https://doi.org/10.5194/npg-15-761-2008
Schurer AP, Ballinger AP, Friedman AR, Hegerl GC (2020) Human influence strengthens the contrast between tropical wet and dry regions. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab83ab
Serinaldi F, Kilsby CG (2017) A blueprint for full collective flood risk estimation: demonstration for european river flooding. Risk Anal 37(10):1958–1976. https://doi.org/10.1111/risa.12747
Silveira S, Kornbluh M, Withers MC, Grennan G, Ramanathan V, Mishra J (2021) Chronic mental health sequelae of climate change extremes: a case study of the deadliest Californian wildfire. Int J Environ Res Public Health 18(4):1–15. https://doi.org/10.3390/ijerph18041487
Sklar M (1959) Fonctions de répartition àn dimensions et leurs marges. Publ L’institut Stat L’université Paris 8:229–231
Soto M, González-Fernández Y, Ochoa A (2015) Modeling with copulas and vines in estimation of distribution algorithms. Rev Investig Operaciona 36(1):1–23
Sriboonchitta S, Liu J, Wiboonpongse A, Denoeux T (2017) A double-copula stochastic frontier model with dependent error components and correction for sample selection. Int J Approx Reason 80:174–184. https://doi.org/10.1016/j.ijar.2016.08.006
Stephens MA (1974) EDF Statistics for goodness of fit and some comparisons. J Am Stat Assoc 69(347):730–737. https://doi.org/10.1080/01621459.1974.10480196
Sulova A, Arsanjani JJ (2021) Exploratory analysis of driving force of wildfires in Australia: an application of machine learning within google earth engine. Remote Sens 13(1):1–23. https://doi.org/10.3390/rs13010010
Sun R, Yuan H, Liu X, Jiang X (2016) Evaluation of the latest satellite-gauge precipitation products and their hydrologic applications over the Huaihe River basin. J Hydrol 536:302–319. https://doi.org/10.1016/j.jhydrol.2016.02.054
Sylvi N, Ispriyanti D, Sugito S (2018) Penerapan regresi zero-inflated generalized poisson dan pengujian autokorelasi spasial pada kasus penyakit filariasis di jawa tengah. J Stat Univ Muhammadiyah Semarang 6(1):29–33
Tahroudi MN, Ramezani Y, De Michele C, Mirabbasi R (2020) A new method for joint frequency analysis of modified precipitation anomaly percentage and streamflow drought index based on the conditional density of copula functions. Water Resour Manag 34(13):4217–4231. https://doi.org/10.1007/s11269-020-02666-6
Thoithi W, Blamey RC, Reason CJC (2021) Dry spells, wet days, and their trends across southern africa during the summer rainy season. Geophys Res Lett. https://doi.org/10.1029/2020GL091041
Tilloy A, Malamud B, Winter H, Joly-Laugel A (2020) Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios. Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-2020-28
Tootoonchi F, Sadegh M, Haerter JO, Räty O, Grabs T, Teutschbein C (2022) Copulas for hydroclimatic analysis: a practice-oriented overview. Wiley Interdiscip Rev Water. https://doi.org/10.1002/wat2.1579
Viana-Soto A, Aguado I, Salas J, García M (2020) Identifying post-fire recovery trajectories and driving factors using Landsat time series in fire-prone Mediterranean pine forests. Remote Sens. https://doi.org/10.3390/RS12091499
Wang L, Yu H, Yang M, Yang R, Gao R, Wang Y (2019) A drought index: the standardized precipitation evapotranspiration runoff index. J Hydrol 571:651–668. https://doi.org/10.1016/j.jhydrol.2019.02.023
Wei X, Zhang H, Singh VP, Dang C, Shao S, Wu Y (2020) Coincidence probability of streamflow in water resources area, water receiving area and impacted area: implications for water supply risk and potential impact of water transfer. Hydrol Res 51(5):1120–1135. https://doi.org/10.2166/nh.2020.106
Wiboonpongse A, Liu J, Sriboonchitta S, Denoeux T (2015) Modeling dependence between error components of the stochastic frontier model using copula: application to intercrop coffee production in Northern Thailand. Int J Approx Reason 65:34–44. https://doi.org/10.1016/j.ijar.2015.04.001
** DDZ, Dean CB, Taylor SW (2020) Modeling the duration and size of extended attack wildfires as dependent outcomes. Environmetrics. https://doi.org/10.1002/env.2619
Xu Z, Liu D, Yan L (2021) Temperature-based fire frequency analysis using machine learning: a case of Changsha. China Clim Risk Manag. https://doi.org/10.1016/j.crm.2021.100276
Zhang X, Yi N (2020) Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data. Bioinformatics 36(8):2345–2351. https://doi.org/10.1093/bioinformatics/btz973
Zhao S, Xu Y (2021) Exploring the dynamic Spatio-temporal correlations between pm2.5 emissions from different sources and urban expansion in Bei**g-Tian**-Hebei region. Int J Environ Res Public Health 18(2):1–19. https://doi.org/10.3390/ijerph18020608
Zscheischler J, Fischer EM (2020) The record-breaking compound hot and dry 2018 growing season in Germany. Weather Clim Extrem. https://doi.org/10.1016/j.wace.2020.100270
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We would like to thank the Department of Mathematics, IPB University, and the Agency for Meteorology, Climatology, and Geophysics, Indonesia, for the assistance and support provided, the Directorate of Scientific Publications and Strategic Information, IPB University for facilitating language checking services, and three anonymous reviewers who have provided constructive comments and suggestions on this article.
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Najib, M.K., Nurdiati, S. & Sopaheluwakan, A. Multivariate fire risk models using copula regression in Kalimantan, Indonesia. Nat Hazards 113, 1263–1283 (2022). https://doi.org/10.1007/s11069-022-05346-3
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DOI: https://doi.org/10.1007/s11069-022-05346-3