Spatiotemporal patterns of agricultural and meteorological droughts using SPI and MODIS-based estimates over a Brazilian semiarid region: study case of Upper Paraíba River basin

References

• AESA. 2021. Agência Executiva e de Gestão das Águas do Estado da Paraíba. Plano Estadual de Recursos Hídricos do estado da Paraíba. [accessed 2021 Dec 16].
   Google Scholar
• Alamdarloo EH, Manesh MB, Khosravi H. 2018. Probability assessment of vegetation vulnerability to drought based on remote sensing data. Environ Monit Assess. 190(12):702.
   PubMed Web of Science ®Google Scholar
• Ali S, Xu ZT, Henchirli M, Wilson K, Zhang J. 2020. Studying of drought phenomena and vegetation trends over South Asia from 1990 to 2015 by using AVHRR and NASA's MERRA data. Environ Sci Pollut Res Int. 27(5):4756–4768.
   PubMed Web of Science ®Google Scholar
• Alves TLB, de Azevedo PV, Costa dos Santos CA. 2017. Influence of climate variability on land degradation (desertification) in the watershed of the upper Paraíba River. Theor Appl Climatol. 127(3–4):741–751.
   Web of Science ®Google Scholar
• Arabameri A, Pal CS, Santosh M, Chakrabortty R, Roy P, Moayedi H. 2021. Drought risk assessment: integrating meteorological, hydrological, agricultural and socio-economic factors using ensemble models and geospatial techniques. Geocarto Int. doi:10.1080/10106049.2021.1926558.
   Web of Science ®Google Scholar
• Bandyopadhyay N, Bhuiyan C, Saha AK. 2020. Drought mitigation: critical analysis and proposal for a new drought policy with special reference to Gujarat (India). Progr Disaster Sci. 5:100049.
   Google Scholar
• Bedran-Martins AM, Lemos MC. 2017. Politics of drought under Bolsa Família program in Northeast Brazil. World Dev Perspect. 7–8(1):15–21.
   Google Scholar
• Belal AA, El-Ramady HR, Mohamed ES, Ahmed MS. 2014. Drought risk assessment using remote sensing and GIS techniques. Arab J Geosci. 7(1):35–53.
   Web of Science ®Google Scholar
• Boori MS, Choudhary K, Kupriyanov A. 2020. Detecting vegetation drought dynamics in European Russia. Geocarto Int. doi:10.1080/10106049.2020.1750063.
   Web of Science ®Google Scholar
• Brasil Neto RM, Santos CAG, Silva J, Silva RM, Santos CAC, Mishra M. 2021a. Evaluation of the TRMM product for monitoring drought over Paraíba State, northeastern Brazil: a trend analysis. Sci Rep. 11:286–296. doi:10.1038/s41598-020-80026-5.
   PubMed Web of Science ®Google Scholar
• Brasil Neto RM, Santos CAG, Silva RM, Dos Santos CAC, Liu Z, Quinn NW. 2021b. Geospatial cluster analysis of the state, duration and severity of drought over Paraíba State, Northeastern Brazil. Sci Total Environ. 799:149492. doi:10.1016/j.scitotenv.2021.149492.
   PubMed Web of Science ®Google Scholar
• Brito CS, Silva RM, Santos CAG, Brasil Neto RM, Coelho VHR. 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. Atmosp. Res. 250:105380. doi:10.1016/j.atmosres.2020.105380.
   Web of Science ®Google Scholar
• Campos JNB. 2015. Paradigms and public policies on drought in Northeast Brazil: a historical perspective. Environ Manage. 55(5):1052–1063.
   PubMed Web of Science ®Google Scholar
• Carlson TN, Gillies RR, Perry EM. 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens Rev. 9(1–2):161–173.
   Google Scholar
• Cong D, Zhao S, Chen C, Duan Z. 2017. Characterization of droughts during 2001–2014 based on remote sensing: a case study of Northeast China. Ecol Inform. 39:56–67.
   Web of Science ®Google Scholar
• Costa LC, Cunha APMA, Anderson LO, Cunningham C. 2021. New approach for drought assessment: a case study in the northern region of Minas Gerais. Int J Disaster Risk Reduct. 53:102019.
   Web of Science ®Google Scholar
• Cunha APM, Alvalá RC, Nobre CA, Carvalho MA. 2015. Monitoring vegetative drought dynamics in the Brazilian semiarid region. Agric Forest Meteorol. 214–215:494–505.
   Web of Science ®Google Scholar
• Currit N, St Clair SB. 2010. Assessing the impact of extreme climatic events on aspen defoliation using MODIS imagery. Geocarto Int. 25(2):133–147.
   Web of Science ®Google Scholar
• Dantas JC, Silva RM, Santos CAG. 2020. Drought impacts, social organization, and public policies in northeastern Brazil: a case study of the upper Paraíba River basin. Environ Monit Assess. 192(5):317.
   PubMed Web of Science ®Google Scholar
• de Medeiros IC, da Costa Silva JFCB, Silva RM, Santos CAG. 2019. Run-off-erosion modelling and water balance in the Epitácio Pessoa Dam river basin, Paraíba State in Brazil. Int J Environ Sci Technol. 16(7):3035–3048. doi:10.1007/s13762-018-1940-3.
   Web of Science ®Google Scholar
• Diaz V, Perez GAC, Van Lanen HAJ, Solomatine D, Varouchakis EA. 2020. An approach to characterize spatiotemporal drought dynamics. Adv Water Resour. 137:103512.
   Web of Science ®Google Scholar
• Dobrovolski SG. 2015. World droughts and their time evolution: agricultural, meteorological, and hydrological aspects. Water Resour. 42(2):147–158.
   Web of Science ®Google Scholar
• Ferreira da Silva GJ, de Oliveira NM, Santos CAG, da Silva RM. 2020. Spatiotemporal variability of vegetation due to drought dynamics (2012-2017): a case study of the Upper Paraíba River basin. Nat Hazards. 102(3):939–964. doi:10.1007/s11069-020-03940-x.
   Web of Science ®Google Scholar
• Gomes ACC, Bernardo N, Alcântara E. 2017. Accessing the southeastern Brazil 2014 drought severity on the vegetation health by satellite image. Nat Hazards. 89(3):1401–1420.
   Web of Science ®Google Scholar
• Gou F, Liang W, Sun S, Jin Z, Zhang W, Yan J. 2019. Analysis of the desertification dynamics of sandy lands in Northern China over the period 2000–2017. Geocarto Int. 36:1938–1959.
   Web of Science ®Google Scholar
• Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF. 2007. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeor. 8(1):38–55.
   Web of Science ®Google Scholar
• IPCC – Intergovernmental Panel on Climate Change. 2007. Climate change 2007: synthesis report: summary for policymakers, contribution of working groups I-III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
   Google Scholar
• Kogan FN. 1995. Droughts of the late 1980s in the United States as derived from NOAA polar orbiting satellite data. Bull Am Meteorol Soc. 76(5):655–668.
   Web of Science ®Google Scholar
• Kogan FN. 1997. Global drought watch from space. Bull Am Meteorol Soc. 78(4):621–636.
   Web of Science ®Google Scholar
• Kukunuri ANJ, Murugan D, Singh D. 2020. Variance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data. Geocarto Int. doi:10.1080/10106049.2020.1837256.
   Web of Science ®Google Scholar
• Labudová L, Labuda M, Takáč J. 2017. Comparison of SP I and SP EI applicability for drought impact assessment on crop production in the Danubian Lowland and the East Slovakian Lowland. Theor Appl Climatol. 128:491–506.
   Google Scholar
• Lottering S, Mafongoya P, Lottering R. 2020. Detecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa. Geocarto Int. doi:10.1080/10106049.2020.1783580.
   Web of Science ®Google Scholar
• Marengo JA, Cunha AP MA, Nobre CA, Ribeiro Neto GG, Magalhães AR, Torres, RR, Sampaio G, Alexandre F, Alves LM, Cuartas LA, et al. 2020. Assessing drought in the drylands of northeast Brazil under regional warming exceeding 4 °C. Natural Hazards. 103:2589–2611.
   Google Scholar
• Marengo JA, Torres RR, Alves LM. 2017. Drought in Northeast Brazil—past, present, and future. Theor Appl Climatol. 129(3–4):1189–1200.
   Web of Science ®Google Scholar
• McKee T, Doesken N, Kleist J. 1993. The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society.
   Google Scholar
• Nascimento SS, Lima ERV, Lima PPS. 2014. Uso do NDVI na análise temporal da degradação da caatinga na bacia do Alto Rio Paraíba. OKARA: Geog debate. 8(1):72–93.
   Google Scholar
• Pereira G, Silva MES, Moraes EC, Cardozo FS. 2013. Avaliação dos dados de precipitação estimados pelo satélite TRMM para o Brasil. Rev Bras Rec Hídr. 18(3):139–148.
   Google Scholar
• Santos CAG, Brasil Neto RM, Silva RM, Dos Santos DC. 2019. Innovative approach for geospatial drought severity classification: a case study of Paraíba State, Brazil. Stoch Environ Res Risk Assess. 33(2):545–562.
   Web of Science ®Google Scholar
• Santos CAG, Silva RM, Silva AM, Brasil Neto RM. 2017. Estimation of evapotranspiration for different land covers in a Brazilian semi-arid region: a case study of the Brígida River basin, Brazil. J South Am Earth Sci. 74:54–66.
   Web of Science ®Google Scholar
• Schaab LL. 2018. Impact of effects El Niño and La Niña in Brazilian agricultural sector: an analysis of input-ouput. Rev FAE. 21(2):131–146.
   Google Scholar
• Sholihah RI, Trisasongko BH, Shiddiq D, Iman LOS, Kusdaryanto S, Manijo Panuju DR. 2016. Identification of agricultural drought extent based on vegetation health indices of Landsat data: case of Subang and Karawang, Indonesia. Proc Environ Sci. 33:14–20.
   Google Scholar
• Silva AM, Silva RM, Silva BB. 2015. Determinação de temperatura da superfície, saldo de radiação e evapotranspiração usando imagens Landsat e dados observados. Rev Bras Cartog. 67:1203–1216.
   Google Scholar
• Silva J, Silva RM, Santos CAG, Silva AM, Vianna PCG. 2021. Analysis of the response of the Epitácio Pessoa reservoir (Brazilian semiarid region) to potential future drought, water transfer and LULC scenarios. Nat Hazards. 107:458–478.
   Google Scholar
• Silva RM, Silva J, Santos CAG, Silva AM, Brasil Neto RM. 2020. Spatial distribution and estimation of rainfall trends and erosivity in the Epitácio Pessoa reservoir catchment, Paraíba, Brazil. Nat Hazards. 101:765–785.
   Google Scholar
• Soares ASD, Paz AR, Piccilli DGA. 2016. Avaliação das estimativas de chuva do satélite TRMM no Estado da Paraíba. Rev Bras Rec Hídr. 21(2):288–299.
   Google Scholar
• Son NT, Chen CF, Chen CR, Masferrer MGM, Recinos LEM. 2019. Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador. Geocarto Int. 34(12):1363–1383.
   Web of Science ®Google Scholar
• Son NT, Thanh BX. 2020. Remotely sensed drought evaluation over rice cultivated areas in Cambodia during 2000 to 2019. Geocarto Int. doi:10.1080/10106049.2020.1773546.
   Web of Science ®Google Scholar
• Sultana MS, Gazi MY, Mia MB. 2021. Multiple indices based agricultural drought assessment in the northwestern part of Bangladesh using geospatial techniques. Environ Chall. 4:100120.
   Google Scholar
• Tomasella J, Silva Pinto Vieira RM, Barbosa AA, Rodriguez DA, Oliveira Santana M d, Sestini MF. 2018. Desertification trends in the Northeast of Brazil over the period 2000–2016. Int J Appl Earth Obs Geoinf. 73(2):197–206.
   Google Scholar
• Viana JFS, Montenegro SMGL, da Silva BB, Silva RM, Srinivasan R, Santos CAG, Araujo DCS, Tavares CG. 2021. Evaluation of gridded meteorological datasets and their potential hydrological application to a humid area with scarce data for Pirapama River basin, northeastern Brazil. Theor Appl Climatol. 145(1–2):393–410.
   Web of Science ®Google Scholar
• Wu J, Zhou L, Mo X, Zhou H, Zhang J, Jia R. 2015. Drought monitoring and analysis in China based on the Integrated Surface Drought Index (ISDI). Int J Appl Earth Obs Geoinf. 41:23–33.
   Web of Science ®Google Scholar
• Zhang L, Jiao W, Zhang H, Huang C, Tong Q. 2017. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sens Environ. 190:96–106.
   Web of Science ®Google Scholar
• Zhang Q, Yu H, Sun P, Singh VP, Shi P. 2019. Multisource data based agricultural drought monitoring and agricultural loss in China. Glob Planet Change. 172:298–306.
   Web of Science ®Google Scholar
• Zhou L, Wu J, Zhang J, Leng S, Liu M, Zhang J, Zhao L, Zhang F, Shi Y. 2013. The integrated surface drought index (ISDI) as an indicator for agricultural drought monitoring: theory, validation, and application in Mid-Eastern China. IEEE J Sel Top Appl Earth Obs Remote Sens. 6(3):1254–1262.
   Web of Science ®Google Scholar