Abstract
Brazil’s Pantanal wetland is one of the most threatened Brazilian ecosystems from direct anthropogenic pressures and climate change. In this study, the overarching research question is to explore whether compound drought-heat events (CDHEs) have become more recurrent, intense, and widespread over Brazil’s Pantanal wetland in recent decades. For this, we purpose and tested two different approaches using validated long-term time series of monthly precipitation, temperature, and the satellite-based Vegetation Health Index (VHI) to characterize the spatiotemporal pattern of CDHEs over Pantanal. Firstly, we assessed global gridded precipitation and temperature data sets against ground measurements to choose an appropriate dataset for this study. Then, we calculated the Standardized Precipitation Index (SPI), Standardized Temperature Index (STI), and Standardized Precipitation Evapotranspiration Index (SPEI) from 1981 to 2021. The results showed that using both approaches (CDHE-M1 and CDHE-M2), the frequency of events is higher considering the moderate category, which is expected since the criteria are less restrictive. In addition, the highest frequency of CDHE events occurs between September and November (the end of the dry season). The results also indicated that CDHE events have been more recurrent and widespread since 2000 in Pantanal. Besides, considering all methods for identifying the CDHEs, the probability density function indicates a shift pattern to warmer and drier conditions in the last 40 years. The Mann–Kendall tests also confirmed the assumption that there is a significantly increasing trend in the compound drought-heat events in the Pantanal. Developing methodologies for monitoring compound climate events is crucial for assessing climate risks in a warming climate. Besides, it is expected that our results contribute to the convincing of the urgent need for environmental protection strategies and disaster risk reduction plans for the Pantanal.
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Observed daily meteorological data for Pantanal can be retrieved from https://portal.inmet.gov.br/dadoshistoricos.
References
Afroz M, Chen G, Anandhi A (2023) Drought- and heatwave-associated compound extremes: a review of hotspots, variables, parameters, drivers, impacts, and analysis frameworks. Front Earth Sci (lausanne) 10:2467. https://doi.org/10.3389/FEART.2022.914437
Anderson LO, Neto GR, Cunha AP et al (2018) Vulnerability of Amazonian forests to repeated droughts. Philos Trans R Soc B. https://doi.org/10.1098/rstb.2017.0411
Bandyopadhyay S, Kanji S, Wang L (2012) The impact of rainfall and temperature variation on diarrheal prevalence in Sub-Saharan Africa. Appl Geogr 33:63–72. https://doi.org/10.1016/j.apgeog.2011.07.017
Beck HE, Vergopolan N, Pan M et al (2017) Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol Earth Syst Sci 21:6201–6217. https://doi.org/10.5194/hess-21-6201-2017
Beguería S, Vicente-Serrano SM, Reig F, Latorre B (2014) Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int J Climatol 34:3001–3023. https://doi.org/10.1002/JOC.3887
Bezak N, Mikoš M (2020) Changes in the compound drought and extreme heat occurrence in the 1961–2018 period at the european scale. Water (switzerland). https://doi.org/10.3390/w12123543
Bhardwaj J, Kuleshov Y, Chua ZW et al (2021) Building capacity for a user-centred integrated early warning system for drought in Papua New Guinea. Remote Sens (basel). https://doi.org/10.3390/rs13163307
Bokusheva R, Kogan F, Vitkovskaya I et al (2016) Satellite-based vegetation health indices as a criteria for insuring against drought-related yield losses. Agric for Meteorol 220:200–206. https://doi.org/10.1016/j.agrformet.2015.12.066
Chen L, Chen X, Cheng L et al (2019) Compound hot droughts over China: identification, risk patterns and variations. Atmos Res 227:210–219. https://doi.org/10.1016/j.atmosres.2019.05.009
Ciais Ph, Reichstein M, Viovy N et al (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437:529–533. https://doi.org/10.1038/nature03972
Costa JC, Pereira G, Siqueira ME et al (2019) Validação dos dados de precipitação estimados pelo chirps para o brasil. Revista Brasileira De Climatologia. https://doi.org/10.5380/ABCLIMA.V24I0.60237
Cuartas LA, Cunha APMDA, Alves JA et al (2022) Recent hydrological droughts in Brazil and their impact on hydropower generation. Water (switzerland) 14:601. https://doi.org/10.3390/W14040601/S1
Cunha APMA, Tomasella J, Ribeiro-Neto GG et al (2018) Changes in the spatial–temporal patterns of droughts in the Brazilian Northeast. Atmos Sci Lett 19:e855. https://doi.org/10.1002/ASL.855
Cunha APMA, Zeri M, Leal KD et al (2019) Extreme drought events over Brazil from 2011 to 2019. Atmosphere (basel). https://doi.org/10.3390/atmos10110642
de Brito CS, da Silva RM, Santos CAG et al (2021) Monitoring meteorological drought in a semiarid region using two long-term satellite-estimated rainfall datasets: a case study of the Piranhas River basin, northeastern Brazil. Atmos Res 250:105380. https://doi.org/10.1016/J.ATMOSRES.2020.105380
de Morisson VM, de Fátima RD (2012) Topodata: Brazilian full coverage refinement of SRTM data. Appl Geogr 32:300–309. https://doi.org/10.1016/J.APGEOG.2011.05.004
Debortoli NS, Dubreuil V, Hirota M et al (2017) Detecting deforestation impacts in Southern Amazonia rainfall using rain gauges. Int J Climatol 37:2889–2900. https://doi.org/10.1002/JOC.4886
Diffenbaugh NS, Swain DL, Touma D, Lubchenco J (2015) Anthropogenic warming has increased drought risk in California. Proc Natl Acad Sci USA 112:3931–3936. https://doi.org/10.1073/pnas.1422385112
Feng S, Hao Z, Zhang X, Hao F (2019) Probabilistic evaluation of the impact of compound dry-hot events on global maize yields. Sci Total Environ 689:1228–1234. https://doi.org/10.1016/J.SCITOTENV.2019.06.373
Funk C, Peterson P, Landsfeld M et al (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2:1–21. https://doi.org/10.1038/sdata.2015.66
Funk C, Peterson P, Peterson S et al (2019) A high-resolution 1983–2016 TMAX climate data record based on infrared temperatures and stations by the climate hazard center. J Clim 32:5639–5658. https://doi.org/10.1175/JCLI-D-18-0698.1
Ganguli P (2022) (2022) Amplified risk of compound heat stress-dry spells in Urban India. Clim Dyn 60(3):1061–1078. https://doi.org/10.1007/S00382-022-06324-Y
Garcia LC, Szabo JK, de Oliveira RF et al (2021) Record-breaking wildfires in the world’s largest continuous tropical wetland: integrative fire management is urgently needed for both biodiversity and humans. J Environ Manage 293:112870. https://doi.org/10.1016/J.JENVMAN.2021.112870
Geirinhas JL, Russo A, Libonati R et al (2021) Recent increasing frequency of compound summer drought and heatwaves in Southeast Brazil. Environ Res Lett. https://doi.org/10.1088/1748-9326/abe0eb
Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4:135–143. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135)
Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol (Amst) 377:80–91. https://doi.org/10.1016/J.JHYDROL.2009.08.003
Hansen J, Sato M, Ruedy R (2012) Perception of climate change. Proc Natl Acad Sci USA 109:14726. https://doi.org/10.1073/PNAS.1205276109/-/DCSUPPLEMENTAL
Hao Z, Hao F, Singh VP, Zhang X (2018a) Changes in the severity of compound drought and hot extremes over global land areas. Environ Res Lett. https://doi.org/10.1088/1748-9326/aaee96
Hao Z, Singh VP, Hao F (2018b) Compound extremes in hydroclimatology: a review. Water (switzerland) 10:16–21. https://doi.org/10.3390/w10060718
Hao Y, Hao Z, Fu Y et al (2021) Probabilistic assessments of the impacts of compound dry and hot events on global vegetation during growing seasons. Environ Res Lett. https://doi.org/10.1088/1748-9326/ac1015
Harris I, Osborn TJ, Jones P, Lister D (2020) Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7:109. https://doi.org/10.1038/s41597-020-0453-3
Hauser M, Orth R, Seneviratne SI (2016) Role of soil moisture versus recent climate change for the 2010 heat wave in western Russia. Geophys Res Lett 43:2819–2826. https://doi.org/10.1002/2016GL068036
Hunt JD, Stilpen D, de Freitas MAV (2018) A review of the causes, impacts and solutions for electricity supply crises in Brazil. Renew Sustain Energy Rev 88:208–222. https://doi.org/10.1016/j.rser.2018.02.030
IPCC (2021) Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
Junk W (2012) Effects of climate change on wetlands current state of knowledge regarding South America wetlands and their future under global climate change. https://doi.org/10.1007/s00027-012-0253-8
Kim W, Iizumi T, Nishimori M (2019) Global patterns of crop production losses associated with droughts from 1983 to 2009. J Appl Meteorol Climatol 58:1233–1244. https://doi.org/10.1175/JAMC-D-18-0174.1
Kling H, Fuchs M, Paulin M (2012) Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. J Hydrol (Amst) 424–425:264–277. https://doi.org/10.1016/J.JHYDROL.2012.01.011
Kogan FN (2001) Operational space technology for global vegetation assessment. Bull Am Meteorol Soc 82:1949–1964. https://doi.org/10.1175/1520-0477(2001)082%3c1949:OSTFGV%3e2.3.CO;2
Kogan F (2002) World droughts in the new millennium from avhrr-based vegetation health indices. Eos (washington DC). https://doi.org/10.1029/2002EO000382
Kogan F, Goldberg M, Schott T, Guo W (2015) Suomi NPP/VIIRS: improving drought watch, crop loss prediction, and food security. Int J Remote Sens 36:5373–5383. https://doi.org/10.1080/01431161.2015.1095370
Krause P, Boyle DP, Bäse F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97. https://doi.org/10.5194/ADGEO-5-89-2005
Lázaro WL, Oliveira-Júnior ES, da Silva CJ et al (2020) Climate change reflected in one of the largest wetlands in the world: an overview of the Northern Pantanal water regime. Acta Limnol Bras 32:1–8. https://doi.org/10.1590/S2179-975X7619
Leal Filho W, Azeiteiro UM, Salvia AL et al (2021) Fire in paradise: Why the Pantanal is burning. Environ Sci Policy 123:31–34. https://doi.org/10.1016/J.ENVSCI.2021.05.005
Li J, Wang Z, Wu X et al (2021) A standardized index for assessing sub-monthly compound dry and hot conditions with application in China. Hydrol Earth Syst Sci 25:1587–1601. https://doi.org/10.5194/hess-25-1587-2021
Libonati R, DaCamara CC, Peres LF et al (2020) Rescue Brazil’s burning Pantanal wetlands. Nature 588(7837):217–219. https://doi.org/10.1038/d41586-020-03464-1
Libonati R, Geirinhas JL, Silva PS et al (2022) Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal. Environ Res Lett 17:015005. https://doi.org/10.1088/1748-9326/AC462E
Lopes Lázaro W, Sobreira Oliveira-Júnior E, Joana da Silva C et al (2020) Thematic Section: Opinions about Aquatic Ecology in a Changing World Climate change reflected in one of the largest wetlands in the world: an overview of the Northern Pantanal water regime Mudança climática refletida em uma das maiores áreas úmidas do mundo: uma visão geral do regime das águas do Pantanal do Norte. Acta Limnol Bras 32:104. https://doi.org/10.1590/S2179-975X7619
Mabel Calim Costa, Marengo JA, Alves LAM, Cunha APMA (2023) Persistent extreme compound drought and heatwave events in the Brazilian Pantanal in 2020–2021. Theor Appl Climatol
Mapbiomas (2021a) Mapbiomas Brasil: Principais destaques do bioma Pantanal. https://mapbiomas.org/pantanal-perdeu-29-de-superficie-de-agua-entre-a-cheia-de-19881989-e-a-ultima-em-2018. Accessed 3 Jul 2023
MapBiomas (2021b) Projeto MapBiomas – Coleção v.5.0 da Série Anual de Mapas de Uso e Cobertura da Terra do Brasil. https://mapbiomas.org/. Accessed 13 Oct 2021b
Marengo JA, Ambrizzi T, Barreto N et al (2021a) The heat wave of October 2020 in central South America. Int J Climatol. https://doi.org/10.1002/JOC.7365
Marengo JA, Cunha AP, Cuartas LA et al (2021b) Extreme Drought in the Brazilian Pantanal in 2019–2020: characterization, causes, and impacts. Front Water. https://doi.org/10.3389/FRWA.2021.639204
Mazdiyasni O, AghaKouchak A (2015) Substantial increase in concurrent droughts and heatwaves in the United States. Proc Natl Acad Sci USA 112:11484–11489. https://doi.org/10.1073/pnas.1422945112
McKee TB, Nolan J, Kleist J (1993) The relationship of drought frequency and duration to time scales. Preprints, Eighth Conf on Applied Climatology, Amer Meteor Soc
MK (1975) Rank correlation measures. Charles Griffin, London
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol (Amst) 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Nova RAV, Gonçalves RM, FerreiraLima LAAFVMS (2021) The influence of the remotely sensed rainfall products’ spatial resolution to unmask extreme events in northeast Brazil. Boletim De Ciências Geodésicas 27:2021023. https://doi.org/10.1590/S1982-21702021000300023
Ramos AM, Russo A, DaCamara CC et al (2023) The compound event that triggered the destructive fires of October 2017 in Portugal. iScience. https://doi.org/10.1016/J.ISCI.2023.106141
Ribeiro AFS, Brando PM, Santos L et al (2022) A compound event-oriented framework to tropical fire risk assessment in a changing climate. Environ Res Lett 17:065015. https://doi.org/10.1088/1748-9326/AC7342
Rozante JR, Ramirez Gutierrez E, De A et al (2020) Performance of precipitation products obtained from combinations of satellite and surface observations. Braz Int J Remote Sens 41:7585–7604. https://doi.org/10.1080/01431161.2020.1763504
Salles L, Satgé F, Roig H et al (2019) Seasonal effect on spatial and temporal consistency of the new GPM-based IMERG-v5 and GSMaP-v7 satellite precipitation estimates in Brazil’s Central Plateau Region. Water 11:668. https://doi.org/10.3390/W11040668
Sandi SG, Rodriguez JF, Saintilan N et al (2020) Resilience to drought of dryland wetlands threatened by climate change. Sci Rep 10:1–15. https://doi.org/10.1038/s41598-020-70087-x
Schamm K, Ziese M, Becker A et al (2014) Global gridded precipitation over land: a description of the new GPCC First Guess Daily product. Earth Syst Sci Data 6:49–60. https://doi.org/10.5194/essd-6-49-2014
Silva ERM, Barbosa ICC, Silva HJF et al (2020) Análise do Desempenho da Estimativa de Precipitação do Produto CHIRPS para Sub-Bacia do Rio Apeú, Castanhal-PA Evaluating the Performance of Precipitation Estimate from CHIRPS Product for the Apeú River Basin, Castanhal-PA Revista Brasileira de Geografia Física, pp 1094–1105
Sutanto SJ, Vitolo C, Di Napoli C et al (2020) Heatwaves, droughts, and fires: exploring compound and cascading dry hazards at the pan-European scale. Environ Int 134:105276. https://doi.org/10.1016/j.envint.2019.105276
Svoboda M, Lecomte D, Hayes M et al (2002) The drought monitor. Bull Am Meteor Soc 83(3):1181–1190
Thielen D, Schuchmann KL, Ramoni-Perazzi P et al (2020a) Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. PLoS ONE 15:e0227437. https://doi.org/10.1371/JOURNAL.PONE.0227437
Thielen D, Schuchmann KL, Ramoni-Perazzi P et al (2020b) Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. PLoS ONE 15:e0227437. https://doi.org/10.1371/JOURNAL.PONE.0227437
Tian Q, Lu J, Chen X (2022) A novel comprehensive agricultural drought index reflecting time lag of soil moisture to meteorology: a case study in the Yangtze River basin. China. Catena (Amst) 209:105804. https://doi.org/10.1016/J.CATENA.2021.105804
Tomasella J, Cunha APMA, Simões PA, Zeri M (2022) Assessment of trends, variability and impacts of droughts across Brazil over the period 1980–2019. Nat Hazards. https://doi.org/10.1007/S11069-022-05759-0/FIGURES/5
UNDRR (2020) Hazard definition & classification review: technical report. Hazard definition & classification reviewazard definition & classification review 1–88
Vara Prasad PV, Nakashima K, Lata C et al (2017) Crop production under drought and heat stress: plant responses and management options. Front Plant Sci 8:1147. https://doi.org/10.3389/fpls.2017.01147
Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Vogel MM, Hauser M, Seneviratne SI (2020) Projected changes in hot, dry and wet extreme events’ clusters in CMIP6 multi-model ensemble. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab90a7
WMO (2020) State of the Climate in Latin America and the Caribbean 2020. World Meteorological Organization, Geneva
Wu Y, Miao C, Sun Y et al (2021) Global Observations and CMIP6 simulations of compound extremes of monthly temperature and precipitation. Geohealth 5:e2021GH000390. https://doi.org/10.1029/2021GH000390
Xu L, Chen N, Moradkhani H et al (2020) Improving global monthly and daily precipitation estimation by fusing gauge observations, remote sensing, and reanalysis data sets. Water Resour Res 56:e2019WR026444. https://doi.org/10.1029/2019WR026444
Ye L, Shi K, Xin Z et al (2019) Compound droughts and heat waves in China. Sustainability 11:3270. https://doi.org/10.3390/SU11123270
Yu R, Zhai P (2020) More frequent and widespread persistent compound drought and heat event observed in China. Sci Reports 10:14576. https://doi.org/10.1038/s41598-020-71312-3
Zampieri M, Ceglar A, Dentener F et al (2019) When will current climate extremes affecting maize production become the norm? Earths Future 7:113–122. https://doi.org/10.1029/2018EF000995
Zhou S, Zhang Y, Williams AP, Gentine P (2019) Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events. Sci Adv. https://doi.org/10.1126/SCIADV.AAU5740/SUPPL_FILE/AAU5740_SM.PDF
Zscheischler J, Seneviratne SI (2017) Dependence of drivers affects risks associated with compound events. Sci Adv 3:1–10. https://doi.org/10.1126/sciadv.1700263
Zscheischler J, Michalak AM, Schwalm C et al (2014) Impact of large-scale climate extremes on biospheric carbon fluxes: an intercomparison based on MsTMIP data. Global Biogeochem Cycles 28:585–600. https://doi.org/10.1002/2014GB004826
Zscheischler J, Westra S, van den Hurk BJJM et al (2018) Future climate risk from compound events. Nat Clim Change 8(6):469–477. https://doi.org/10.1038/s41558-018-0156-3
Zscheischler J, Martius O, Westra S et al (2020) A typology of compound weather and climate events. Nat Rev Earth Environ 1(7):333–347. https://doi.org/10.1038/s43017-020-0060-z
Zuhro A, Tambunan MP, Marko K (2020) Application of vegetation health index (VHI) to identify distribution of agricultural drought in Indramayu Regency, West Java Province. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/500/1/012047Kogan_2019.pdf
Acknowledgements
The authors wish to thank the DAAD, Augsburg University, CNPq and FAPESP for funding the research that supported this work.
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This research was funded by Deutscher Akademischer Austauschdienst (DAAD), grant number 91835581. This work also was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014-1, FAPESP Grants 2014/50848-9, and CNPq Grant 406667/2022-5.
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All authors contributed this paper: Conceptualization, APMAC; methodology, APMAC; validation, APMAC; formal analysis, APMAC and WB; investigation, APMAC, WB, JAM; resources, APMAC; writing—original draft preparation, APMAC, WB and JAM; writing—review and editing, APMAC, WB and JAM visualization, APMAC; supervision, APMAC; project administration, APMAC; funding acquisition, APMAC and WB.
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Cunha, A.P.M.A., Buermann, W. & Marengo, J.A. Changes in compound drought-heat events over Brazil’s Pantanal wetland: an assessment using remote sensing data and multiple drought indicators. Clim Dyn 62, 739–757 (2024). https://doi.org/10.1007/s00382-023-06937-x
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DOI: https://doi.org/10.1007/s00382-023-06937-x