Keywords
Semi-arid zone
Droughts
Climate change
How to Cite
Abstract
Brazil has the most populous and biodiverse semi-arid region in the world (Brazilian Semi-arid - SAB). However, in recent decades, clusters of desertification have emerged, a problem that could intensify from climate change. The objective of this study was to elaborate on the spatial distribution of areas susceptible to climatic desertification in the SAB, considering future climate change scenarios. Understanding this dynamic is essential for SAB's agri-environmental management. Aridity indices and proposition of climate classes for current condition (1970-2000) and future scenarios (2061-2080) of the Intergovernmental Panel on Climate Change (IPCC) were prepared, considering scenarios from Shared Socioeconomic Pathways: Optimistic (SSP 126) and pessimists (SSP 585). The results indicate that by the end of the century, the climate in the SAB should become significantly drier (Kruskal-Wallis = p-value < 0.05), with an intensification of the aridity index in SSP 585. In the scenarios, the expansion of more arid areas over humid climates could reach 56,500 km² (10%) in SSP 126 and 140,400 km² (24%) in SSP 585. Consequently, areas with high (622,400 km² to 706,300 km²) and very high (622,400 km² to 706,300 km²) are expected to expand. 4,400 to 21,700 km²) susceptibility to climate desertification in the SAB, respectively in scenarios SSPs 126 and 585. Confirming these projections would imply socioeconomic and ecological risks in the SAB.
References
AB'SÁBER, A. N. Os domínios de natureza no Brasil: potencialidades paisagísticas. São Paulo: Ateliê Editorial, 2003.
ADAMO, S. B.; CREWS-MEYER, K. A. Aridity and desertification: exploring environmental hazards in Jáchal, Argentina. Applied Geography, v. 26, n. 1, p. 61–85, 2006. https://doi.org/10.1016/j.apgeog.2005.09.001.
ALTHOFF, D.; DIAS, S. H. B.; FILGUEIRAS, R.; RODRIGUES, L. N. ETo‐Brazil: a daily gridded reference evapotranspiration data set for Brazil (2000–2018). Water Resources Research, v. 56, n. 7, p. e2020WR027562., 2020. https://doi.org/10.1029/2020WR027562.
BEZERRA, F. G. S.; AGUIAR, A. P. D. D.; ALVALÁ, R. C. D. S.; GIAROLLA, A., BEZERRA, K. R. A.; LIMA, P. V. P. S.; ARAI, E. Analysis of areas undergoing desertification, using EVI2 multi-temporal data based on MODIS imagery as indicator. Ecological Indicators, v. 117, p. 106579, 2020. https://doi.org/10.1016/j.ecolind.2020.106579.
BRUNSON, J. C. Alluvial Plots in ggplot2. 2020.
BURRELL, A. L.; EVANS, J. P.; DE KAUWE, M. G. Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nature Communications, v. 11, n. 1, p. 3853, dez. 2020. https://doi.org/10.1038/s41467-020-17710-7.
CASTRO OLIVEIRA, G.; ARRUDA, D. M.; FERNANDES FILHO, E. I.; VELOSO, G. V.; FRANCELINO, M. R.; SCHAEFER, C. E. G. R. Soil predictors are crucial for modelling vegetation distribution and its responses to climate change. Science of The Total Environment, v. 780, p. 14668, 2021. https://doi.org/10.1016/j.scitotenv.2021.146680.
DENISSEN, J. M.; TEULING, A. J.; PITMAN, A. J.; KOIRALA, S., MIGLIAVACCA, M.; LI, W.; ORTH, R. Widespread shift from ecosystem energy to water limitation with climate change. Nature Climate Change, v. 12, n. 7, p. 677–684, 2022. https://doi.org/10.1038/s41558-022-01403-8.
DIAS, S. H. B.; FILGUEIRAS, R.; FERNANDES FILHO, E. I.; ARCANJO, G. S.; SILVA, G. H. D.; MANTOVANI, E. C.; CUNHA, F. F. D. Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing. Plos one, v. 16, n. 2, p. e0245834, 2021. https://doi.org/10.1371/journal.pone.0245834.
FAY, P. A.; GUNTENSPERGEN, G. R.; OLKER, J. H.; JOHNSON, W. C. Climate change impacts on freshwater wetland hydrology and vegetation cover cycling along a regional aridity gradient. Ecosphere, v. 7, n. 10, p. e01504, 2016. https://doi.org/10.1002/ecs2.1504.
FENG, K., WANG, T.; LIU, S.; KANG, W.; CHEN, X.; GUO, Z.; ZHI, Y. Monitoring desertification using machine-learning techniques with multiple indicators derived from MODIS images in Mu Us Sandy Land, China. Remote Sensing, v. 14, n. 11, p. 2663, 2022. https://doi.org/10.3390/rs14112663.
FERNANDEZ, J. P.; FRANCHITO, S. H.; RAO, V. B. Future changes in the aridity of South America from regional climate model projections. Pure and Applied Geophysics, v. 176, n. 6, p. 2719–2728, 2019. https://doi.org/10.1007/s00024-019-02108-4.
FERNANDEZ, J. P.; FRANCHITO, S. H.; RAO, V. B.; LLOPART, M. Changes in Koppen–Trewartha climate classification over South America from RegCM4 projections. Atmospheric Science Letters, v. 18, n. 11, p. 427–434, 2017. https://doi.org/10.1002/asl.785.
FERREIRA, G. H. C.; OLIVEIRA, B. F.; LAURENTINO, C. M. M. A territorialização camponesa e do agronegócio no Norte de Minas Gerais. Confins. Revue franco-brésilienne de géographie/Revista franco-brasilera de geografia, n. 49, 2021. https://doi.org/10.4000/confins.35073.
FICK, STEPHEN E.; HIJMANS, ROBERT J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International journal of climatology, v. 37, n. 12, p. 4302-4315, 2017. https://doi.org/10.1002/joc.5086.
HAUSFATHER, Z.; MARVEL, K.; SCHMIDT, G. A.; NIELSEN-GAMMON, J. W.; ZELINKA, M. Climate simulations: Recognize the ‘hot model’problem. Nature, v.605, n.7908, p. 26 – 29, 2022. https://doi.org/10.1038/d41586-022-01192-2.
HUANG, J.; JI, M.; XIE, Y.; WANG, S.; HE, Y.; RAN, J. Global semi-arid climate change over last 60 years. Climate Dynamics, v. 46, n. 3, p. 1131–1150, 2016. https://doi.org/10.1007/s00382-015-2636-8.
HUANG, J.; ZHANG, G.; ZHANG, Y.; GUAN, X.; WEI, Y.; GUO, R. Global desertification vulnerability to climate change and human activities. Land Degradation & Development, v. 31, n. 11, p. 1380–1391, 2020. https://doi.org/10.1002/ldr.3556.
IBGE. Censo Demográfico. Rio de Janeiro, Brazil: Fundação Instituto Brasileiro de Geografia e Estatística. 2010. Disponível em: https://censo2010.ibge.gov.br/resultados.html Acesso em: 10 abr.. 2022.
KUHN, M.; QUINLAN, R. Cubist: Rule-and instance-based regression modeling. R package version 0.2. 2. 2018.
LIAW, A.; WIENER, M. Classification and regression by randomForest. R news, v. 2, n. 3, p. 18–22, 2002.
LIU, Y.; YAO, X.; WANG, Q.; YU, J.; JIANG, Q.; JIANG, W.; LI, L. Differences in reference evapotranspiration variation and climate-driven patterns in different altitudes of the Qinghai–Tibet plateau (1961–2017). Water, v. 13, n. 13, p. 1749, 2021. https://doi.org/10.3390/w13131749.
MARQUES DA SILVA, R.; SANTOS, C. A.; ARAÚJO MARANHÃO, K. U.; MEDEIROS SILVA, A.; PORTO DE LIMA, V. R. Geospatial assessment of eco-environmental changes in desertification area of the Brazilian semi-arid region. Earth Sciences Research Journal, v. 22, n. 3, p. 175–186, 2018. https://doi.org/10.15446/esrj.v22n3.69904.
MILBORROW, S.; TIBSHIRANI, R. Package ‘earth’: Multivariate Adaptive Regression Splines. 2019.
MMA, Ministério do Meio Ambiente (MMA). Programa de Ação Nacional de Combate à Desertificação e Mitigação dos Efeitos da Seca: PANBRASIL. Edição Comemorativa dos 10 anos da Convenção das Nações Unidades de Combate à Desertificação e Mitigação dos Efeitos da Seca – CCD. Brasília: MMA, 2004. 225p.
MUTTI, P. R.; ABREU, L. P.; MB ANDRADE, L.; SPYRIDES, M. H. C.; LIMA, K. C.; DE OLIVEIRA, C. P.; BEZERRA, B. G. A detailed framework for the characterization of rainfall climatology in semi-arid watersheds. Theoretical and Applied Climatology, v. 139, n. 1–2, p. 109–125, 2020. https://doi.org/10.1111/tgis.12926.
PEREZ-MARIN, A. M.; CAVALCANTE, A. D. M. B.; MEDEIROS, S. S.; TINÔCO, L. D. M.; SALCEDO, I. H. Núcleos de desertificação no semiárido brasileiro: ocorrência natural ou antrópica. Parcerias Estratégicas, v. 17, n. 34, p. 87-106, 2012.
POZO, A. D.; BRUNEL-SALDIAS, N.; ENGLER, A.; ORTEGA-FARIAS, S.; ACEVEDO-OPAZO, C.; LOBOS, G. A.; MOLINA-MONTENEGRO, M. A. Climate change impacts and adaptation strategies of agriculture in Mediterranean-climate regions (MCRs). Sustainability, v. 11, n. 10, p. 2769, 2019. https://doi.org/10.3390/su11102769.
RODRIGUEZ, P. P.; GIANOLA, D. BRNN: Bayesian regularization for feed-forward neural networks. R package version 0.6, 2016.
SANTOS, N. O.; MACHADO, R. A. S.; GONZÁLEZ, R. C. L. Identification of levels of anthropization and its implications in the process of desertification in the Caatinga biome (Jeremoabo, Bahia-Brazil). Cuadernos de Investigación Geográfica, v. 48, n. 1, p. 41-57, 2022. https://doi.org/10.1002/joc.4124.
SANZHEEV, E. D.; MIKHEEVA, A. S.; OSODOEV, P. V.; BATOMUNKUEV, V. S.; TULOKHONOV, A. K. Theoretical approaches and practical assessment of socio-economic effects of desertification in Mongolia. International Journal of Environmental Research and Public Health, v. 17, n. 11, p. 4068, 2020. https://doi.org/10.3390/ijerph17114068.
SILVA, L. A. P.; SOUZA, C. M. P.; SILVA, C. R.; FILGUEIRAS, R.; SENA-SOUZA, J. P.; FERNANDES-FILHO, E. I.; LEITE, M. E. Mapping the effects of climate change on reference evapotranspiration in future scenarios in the Brazilian Semi-arid Region - South America. Revista Brasileira de Geografia Física, v. 16, p. 1001-1012, 2023. https://doi.org/10.26848/rbgf.v16.2.p1001-1012
SOUZA, C. M. P. D.; THOMAZINI, A.; SCHAEFER, C. E. G. R.; VELOSO, G. V.; MOREIRA, G. M.; FERNANDES FILHO, E. I. Multivariate analysis and machine learning in properties of Ultisols (Argissolos) of Brazilian Amazon. Revista Brasileira de Ciência do Solo, v. 42, 2018. https://doi.org/10.1590/18069657rbcs20170419.
SPINONI, J.; VOGT, J.; NAUMANN, G.; CARRAO, H.; BARBOSA, P. Towards identifying areas at climatological risk of desertification using the Köppen–Geiger classification and FAO aridity index. International Journal of Climatology, v. 35, n. 9, p. 2210-2222, 2015. https://doi.org/10.1002/joc.4124
TAVARES, V. C.; DE ARRUDA, Í. R. P.; DA SILVA, D. G. Desertificação, mudanças climáticas e secas no semiárido brasileiro: uma revisão bibliográfica. Geosul, v. 34, n. 70, p. 385–405, 2019. https://doi.org/10.5007/2177-5230.2019v34n70p385.
TEAM, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Dispoinível em: http://www.R-project.org/. Acesso em: 05 jan. 2022.
THORNTHWAITE, C. W. An approach toward a rational classification of climate. Geographical review, v. 38, n. 1, p. 55–94, 1948. https://doi.org/10.2307/210739.
UNEP. United Nations Environmental Programme, World Atlas of Desertification, London: Ed. Edward Arnold Publishers, 1992.
VERGARA, W. L. GALLARDO LOMELI, M. FRANCO CHUAIRE, S. Initiative 20X20: A landscape restoration movement rises in Latin America restoration enhance regeneration of seasonal deciduous and the Caribbean. World Resources Institute, 2015. Disponível em: https://www.wri.org/insights/initiative-20x20-landscape-restoration-movement-rises-latin-america-and-caribbean. Acesso em: 05 jan. 2022.
VIEIRA, R. M. D. S. P.; SESTINI, M. F.; TOMASELLA, J.; MARCHEZINI, V.; PEREIRA, G. R.; BARBOSA, A. A.; OMETTO, J. P. H. B. Characterizing spatio-temporal patterns of social vulnerability to droughts, degradation and desertification in the Brazilian northeast. Environmental and Sustainability Indicators, v. 5, p. 100016, 2020. https://doi.org/10.1016/j.indic.2019.100016.
VIEIRA, R. M. D.S.P; TOMASELLA, J.; BARBOSA, A. A.; MARTINS, M. A.; RODRIGUEZ, D. A.; REZENDE, F. S.; SANTANA, M. D. Desertification risk assessment in Northeast Brazil: Current trends and future scenarios. Land Degradation & Development, v. 32, n. 1, p. 224–240, 2021. https://doi.org/10.1002/ldr.3681.
Este é um artigo de acesso aberto distribuído nos termos da Licença de Atribuição Creative Commons, que permite o uso irrestrito, distribuição e reprodução em qualquer meio, desde que o trabalho original seja devidamente citado.
WU, Y.; ZHANG, X.; LI, C.; XU, Y.; HAO, F.; YIN, G. Ecosystem service trade-offs and synergies under influence of climate and land cover change in an afforested semi-arid basin, China. Ecological Engineering, v. 159, p. 106083, 2021. https://doi.org/10.1016/j.ecoleng.2020.106083.
ZARCH, M. A. A.; SIVAKUMAR, B.; MALEKINEZHAD, H.; SHARMA, A. Future aridity under conditions of global climate change. Journal of Hydrology, v. 554, p. 451-469, 2017. https://doi.org/10.1016/j.jhydrol.2017.08.043.
ZHOU, S.; WILLIAMS, A. P.; LINTNER, B. R.; BERG, A. M.; ZHANG, Y.; KEENAN, T. F.; GENTINE, P. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nature Climate Change, v. 11, n. 1, p. 38–44, 2021. https://doi.org/10.1038/s41558-020-00945-z.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2022 Lucas Augusto Pereira da Silva, Claudionor Ribeiro da Silva, Cristiano Marcelo Pereira de Souza, Édson Luís Bolfe, João Paulo Sena Souza, Marcos Esdras Leite