STATISTICAL UNCERTAINTY IN DROUGHT FORECASTING USING MARKOV CHAINS AND THE STANDARD PRECIPITATION INDEX (SPI)
Resumo
Droughts affect basic human activities, and food and industry production. An adequate drought forecasting is crucial to guarantee the survival of population and promote societal development. The Standard Precipitation Index (SPI) is recommended by the World Meteorological Organization (WMO) to monitor meteorological drought. Using drought classification based on SPI to build Markov chains is a common tool for drought forecasting. However, Markov chains building process produce uncertainties inherent to the transition probabilities estimation. These uncertainties are often ignored by practitioners. In this study the statistical uncertainties of using Markov chains for drought annual forecasting are assessed. As a case study, the dry region of the State of Ceará (Northeastern Brazil) is analyzed, considering the precipitation records from 1911 to 2019. In addition to 100-year database (1911-2011) for Markov chain modeling and 8-year data (2012-2019) for forecasting validation, four fictional database extensions were considered in order to assess the effect of database size in the uncertainty. A likelihood ratio is used to assess model performance. The uncertainties assessment showed that an apparent performant Markov chain model for drought class forecasting may not be more informative than the historic proportion of drought class. Considering these uncertainties is crucial for an adequate forecasting with Markov chains.Downloads
Referências
ALLAN, J. A. Water in the environment/ socio-economic development discourse: Sustainability, changing management paradigms and policy responses in a global system. Government and Opposition, [S. l.], v. 40, n. 2, p. 181–199, 2005. DOI: 10.1111/j.1477-7053.2005.00149.x.
BISWAS, K. R.; DENNIS, A. S. Formation of a Rain Shower by Salt Seeding. Journal of Applied Meteorology, 1971.
BLITZSTEIN, J. K.; HWANG, J. Introduction to Probability. 2nd. ed. [s.l.] : Chapman & Hall/CRC Texts in Statistical Science, 2019.
BRENT, R. Algorithms for Minimization without Derivatives. NJ: Prentice-Hall: Englewood Cliffs, 1973.
CANCELLIERE, A.; MAURO, G. Di; BONACCORSO, B.; ROSSI, G. Drought forecasting using the standardized precipitation index. Water Resources Management, [S. l.], v. 21, n. 5, p. 801–819, 2007. DOI: 10.1007/s11269-006-9062-y.
DELIGNETTE-MULLER, Marie Laure; DUTANG, Christophe. fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, [S. l.], v. 64, n. 4, p. 1–34, 2015.
DOS SANTOS, Sergio Rodrigo Quadros; CUNHA, Ana Paula Martins do Amaral; RIBEIRO-NETO, Germano Gondim. Avaliação De Dados De Precipitação Para O Monitoramento Do Padrão Espaço-Temporal Da Seca No Nordeste Do Brasil. Revista Brasileira de Climatologia, [S. l.], v. 25, p. 80–100, 2019. DOI: 10.5380/abclima.v25i0.62018.
FLÖRKE, Martina; KYNAST, Ellen; BÄRLUND, Ilona; EISNER, Stephanie; WIMMER, Florian; ALCAMO, Joseph. Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study. Global Environmental Change, [S. l.], v. 23, n. 1, p. 144–156, 2013. DOI: 10.1016/j.gloenvcha.2012.10.018.
FREITAS, M. A. S.; BILLIB, M. H. A. Drought prediction and characteristic analysis in semiarid Ceará, northeast Brazil. In: SUSTAINABILITY OF WATER RESOURCES UNDER INCREASING UNCERTAINTY 1997, Rabat. Anais [...]. Rabat: IAHS, 1997. p. 105–112.
FRISCHKORN, H.; SANTIAGO, M. F.; DE ARAUJO, J. C. Water Resources of Ceará and Piauí. In: T. GAISER, M. KROL, H. FRISCHKORN, J. C. De Araújo (org.). Global Change and Regional Impacts. Berlin: Springer-V, 2003. p. 87–94.
FUNG, K. F.; HUANG, Y. F.; KOO, C. H.; SOH, Y. W. Drought forecasting: A review of modelling approaches 2007–2017. Journal of Water and Climate Change, [S. l.], v. 11, n. 3, p. 771–799, 2020. DOI: 10.2166/wcc.2019.236.
GABRIEL, KR. The Israeli artificial rainfall stimulation experiment. Statistical evaluation for the period 1961-1965. Proceedings of the Fifth Berkeley Symposium, [S. l.], 1967.
GHIL, M. et al. Extreme events: Dynamics, statistics and prediction. Nonlinear Processes in Geophysics, [S. l.], v. 18, n. 3, p. 295–350, 2011. DOI: 10.5194/npg-18-295-2011.
GOIS, Givanildo De; OLIVEIRA-JÚNIOR, José Francisco De; PAIVA, Roberta Fernanda da Paz de Souza; FREITAS, Welington Kiffer; TERASSI, Paulo Miguel de Bodas; SOBRAL, Bruno Serafini. Variabilidade pluviométrica, indicadores de seca e a aplicação do índice SPI para a região do médio Vale Paraíba do Sul no Estado do Rio de Janeiro. Revista Brasileira de Climatologia, [S. l.], v. 27, p. 122–157, 2020.
GÜNTNER, A.; BRONSTERT, A. Representation of landscape variability and lateral redistribution processes for large-scale hydrological modelling in semi-arid areas. Journal of Hydrology, [S. l.], v. 297, n. 1–4, p. 136–161, 2004. DOI: 10.1016/j.jhydrol.2004.04.008.
HEILIG, Morton L. United States Patent Office 1994.
HEIM R R, Jr. A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society, [S. l.], n. August, 2002.
KAMPE, H. J.; WEICKMANN, H. K. The effectiveness of natural and artificial aerosols as freezing nuclei. Journal of Meteorology, [S. l.], v. 8, p. 283–288, 1951.
KEYANTASH, John A.; DRACUP, John A. An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resources Research, [S. l.], v. 40, n. 9, p. 1–14, 2004. DOI: 10.1029/2003WR002610.
KIM, Tae Woong; JEHANZAIB, Muhammad. Drought risk analysis, forecasting and assessment under climate change. Water (Switzerland), [S. l.], v. 12, n. 7, p. 1–7, 2020. DOI: 10.3390/W12071862.
KWON, Hyun Han; DE ASSIS DE SOUZA FILHO, Francisco; BLOCK, Paul; SUN, Liqiang; LALL, Upmanu; REIS, Dirceu S. Uncertainty assessment of hydrologic and climate forecast models in Northeastern Brazil. Hydrological Processes, [S. l.], v. 26, n. 25, p. 3875–3885, 2012. DOI: 10.1002/hyp.8433.
MACEDO, Maria José Herculano; GUEDES, Roni Valter de Souza; SOUSA, Francisco de Assis Salviano. Monitoramento e intensidade das secas e chuvas na cidade de Campina Grande/PB. Revista Brasileira de Climatologia, [S. l.], v. 8, p. 105–117, 2011.
MATHBOUT, Shifa; LOPEZ-BUSTINS, Joan A.; MARTIN-VIDE, Javier; BECH, Joan; RODRIGO, Fernando S. Spatial and temporal analysis of drought variability at several time scales in Syria during 1961–2012. Atmospheric Research, [S. l.], v. 200, n. May 2017, p. 153–168, 2018. DOI: 10.1016/j.atmosres.2017.09.016.
MCKEE, Thomas B.; DOESKEN, Nolan J.; KLEIST, John. The relationship of drought frequency and duration to time scales. In: EIGHTH CONFERENCE ON APPLIED CLIMATOLOGY 1993, Anaheim, California. Anais [...]. Anaheim, California p. 17–22.
MERABTI, Abdelaaziz; MARTINS, Diogo S.; MEDDI, Mohamed; PEREIRA, Luis S. Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales. Water Resources Management, [S. l.], v. 32, n. 3, p. 1087–1100, 2018. DOI: 10.1007/s11269-017-1856-6.
MINISTÉRIO DA FAZENDA. Manual de Preenchimento da Declaração do Impoto sobre a Propriedade Territorial Rural. Receita Fedral. [s.l: s.n.] 2010.
MISHRA, A. K.; SINGH, V. P.; DESAI, V. R. Drought characterization: A probabilistic approach. Stochastic Environmental Research and Risk Assessment, [S. l.], v. 23, n. 1, p. 41–55, 2009. DOI: 10.1007/s00477-007-0194-2.
MISHRA, Ashok K.; SINGH, Vijay P. A review of drought concepts. Journal of Hydrology, [S. l.], v. 391, n. 1–2, p. 202–216, 2010. DOI: 10.1016/j.jhydrol.2010.07.012.
PALMER, W. C. Meteorological drought. [s.l.] : U.S. Dept. of Commerce Weather Bureau Research Paper 45, 1965.
PIMENTEL, Felipe Viana; COSTA, Alexandre Araújo; ARAGÃO, Dias Tyhago; RIOS, Francisco Franklin Sousa; LIMA, Vanessa Araújo. A influência das paleotemperaturas da superfície do mar na precipitação sobre o Nordeste brasileiro durante o Holoceno. In: XIII ABEQUA CONGRESS - THE SOUTH AMERICAN QUATERNARY: CHALLENGES AND PERSPECTIVES 2011, Anais [...]. [s.l: s.n.] p. 1–3.
PONTES FILHO, João Dehon; PORTELA, Maria Manuela; STUDART, Ticiana Marinho de Carvalho; SOUZA FILHO, Francisco de Assis. A Continuous drought probability monitoring system, CDPMS, based on copulas. Water (Switzerland), [S. l.], v. 11, n. 9, p. 1–18, 2019. DOI: 10.3390/w11091925.
RAHMAT, Siti Nazahiyah; JAYASURIYA, Niranjali; BHUIYAN, Muhammed A. Short-term droughts forecast using Markov chain model in Victoria, Australia. Theoretical and Applied Climatology, [S. l.], v. 129, n. 1–2, p. 445–457, 2017. DOI: 10.1007/s00704-016-1785-y.
RAZIEI, T.; ARASTEH, Daneshkar; AKHTARI, R.; SAGHAFIAN, B. Investigation of Meteorological Droughts in the Sistan and Balouchestan Province, Using the Standardized Precipitation Index and Markov Chain Model. Iran-Water Resources Research, [S. l.], v. 3, n. 1, p. 86–76, 2007.
REZAEIANZADEH, Mehdi; STEIN, Alfred; COX, Jonathan Peter. Drought Forecasting using Markov Chain Model and Artificial Neural Networks. Water Resources Management, [S. l.], v. 30, n. 7, p. 2245–2259, 2016. DOI: 10.1007/s11269-016-1283-0.
SANTOS, Esdras Adriano Barbosa Dos; STOSIC, Tatijana; BARRETO, Ikaro Daniel de Carvalho; CAMPOS, Laélia; SILVA, Antonio Samuel Alves Da. Application of Markov chains to Standardized Precipitation Index (SPI) in São Francisco River Basin. Ambiente e Agua - An Interdisciplinary Journal of Applied Science, [S. l.], v. 14, n. 3, p. 1, 2019. DOI: 10.4136/ambi-agua.2311.
SHAFER, B. A.; DEZMAN, L. E. Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas ( Colorado) .Proceedings: Eastern Snow Conference, 39th annual meeting, 1982.
SIASAR, Hadi; SHOJAEI, Saeed. Drought forecast using standardized precipitation index and Markov chain in Iran. [S. l.], n. December, p. 1–10, 2018. DOI: 10.20944/preprints201812.0276.v1.
SIQUEIRA, Beatriz; NERY, Jonas Teixeira. Aplicação E Análise Do Índice Padronizado De Precipitação No Circuito Das Águas Paulista. Revista Brasileira de Climatologia, [S. l.], v. 16, p. 93–107, 2015. DOI: 10.5380/abclima.v16i0.40331.
SOUZA FILHO, F. A. de. Projeto Ceará 2050: Diagnóstico dos Recursos Hídricos no Ceará. Fortaleza: [s.n], 2018.
SOUZA FILHO, F. A.; LALL, U. Seasonal to interannual ensemble streamflow forecasts for Ceara , Brazil : Applications of a multivariate , semiparametric algorithm. [S. l.], v. 39, n. 11, p. 1–13, 2003. DOI: 10.1029/2002WR001373.
SUN, LIQIANG; MONCUNILL, DAVID FERRAN; LI, HUILAN; MOURA, ANTONIO DIVINO; SOUZA FILHO, FRANCISCO DE ASSIS. Climate Downscaling over Nordeste , Brazil , Using the NCEP RSM97. Journal of Climate, [S. l.], v. 18, p. 551–567, 2005.
WARD, M. Neil; FOLLAND, Chris K. Prediction of seasonal rainfall in the north nordeste of Brazil using eigenvectors of sea‐surface temperature. International Journal of Climatology, [S. l.], v. 11, n. 7, p. 711–743, 1991. DOI: 10.1002/joc.3370110703.
WMO. Standardized precipitation index user guide. Geneva. 2012 Disponível em: http://www.wamis.org/agm/pubs/SPI/WMO_1090_EN.pdf.
ZHOU, Qing; DENG, Xiangzheng; WU, Feng. Impacts of water scarcity on socio-economic development: A case study of Gaotai County, China. Physics and Chemistry of the Earth, [S. l.], v. 101, p. 204–213, 2017. DOI: 10.1016/j.pce.2017.03.009.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
A aprovação dos artigos implica a aceitação imediata e sem ônus de que a Revista Brasileira de Climatologia terá exclusividade na primeira publicação do artigo. Os autores continuarão, não obstante, a deter os direitos autorais. Os autores autorizam também que seus artigos sejam disponibilizados em todos os indexadores aos quais a revista está vinculada.
Os autores mantém seus direitos de publicação sem restrições
A Comissão Editorial não se responsabiliza pelos conceitos ou afirmações expressos nos trabalhos publicados, que são de inteira responsabilidade dos autores.
A Revista Brasileira de Climatologia oferece acesso livre imediato ao seu conteúdo, seguindo o entendimento de que disponibilizar gratuitamente o conhecimento científico ao público proporciona maior democratização do conhecimento e tende a produzir maior impacto dos artigos publicados. Os artigos publicados na revista são disponibilizados segundo a Licença Creative Commons CC-BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/). Segundo essa licença é permitido acessar, distribuir e reutilizar os artigos para fins não comerciais desde que citados os autores e a fonte. Ao submeter artigos à Revista Brasileira de Climatologia, os autores concordam em tornar seus textos legalmente disponíveis segundo essa licença
