{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:30:39Z","timestamp":1777505439667,"version":"3.51.4"},"reference-count":58,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2016,1,30]],"date-time":"2016-01-30T00:00:00Z","timestamp":1454112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>This study aims at predicting the Standard Precipitation Index (SPI) drought class transitions in Portugal, considering the influence of the North Atlantic Oscillation (NAO) as one of the main large-scale atmospheric drivers of precipitation and drought fields across the Western European and Mediterranean areas. Log-linear modeling of the drought class transition probabilities on three temporal steps (dimensions) was used in an SPI time series of six- and 12-month time scales (SPI6 and SPI12) obtained from Global Precipitation Climatology Centre (GPCC) precipitation datasets with 1.0 degree of spatial resolution for 10 grid points over Portugal and a length of 112 years (1902\u20132014). The aim was to model two monthly transitions of SPI drought classes under the influence of the NAO index in its negative and positive phase in order to obtain improvements in the predictions relative to the modeling not including the NAO index. The ratios (odds ratio) between transitional probabilities and their confidence intervals were computed in order to estimate the probability of one drought class transition over another. The prediction results produced by the model with the forcing of NAO were compared with the results produced by the same model without that forcing, using skill scores computed for the entire time series length. Overall results have shown good prediction performance, ranging from 73% to 76% in the percentage of corrects (PC) and 56%\u201362% in the Heidke skill score (HSS) regarding the SPI6 application and ranging from 82% to 85% in the PC and 72%\u201376% in the HSS for the SPI12 application. The model with the NAO forcing led to improvements in predictions of about 1%\u20136% (PC) and 1%\u20138% (HSS), when applied to SPI6, but regarding SPI12 only seven of the locations presented slight improvements of about 0.4%\u20131.8% (PC) and 0.7%\u20133% (HSS).<\/jats:p>","DOI":"10.3390\/w8020043","type":"journal-article","created":{"date-parts":[[2016,2,1]],"date-time":"2016-02-01T10:03:26Z","timestamp":1454321006000},"page":"43","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["SPI Drought Class Predictions Driven by the North Atlantic Oscillation Index Using Log-Linear Modeling"],"prefix":"10.3390","volume":"8","author":[{"given":"Elsa","family":"Moreira","sequence":"first","affiliation":[{"name":"Center of Mathematics and Applications (CMA), Faculty of Sciences and Technology, New University of Lisbon, Caparica 2829-516, Portugal"}]},{"given":"Carlos","family":"Pires","sequence":"additional","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculty of Sciences, University of Lisbon, Lisboa 1749-016, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4425-3408","authenticated-orcid":false,"given":"Lu\u00eds","family":"Pereira","sequence":"additional","affiliation":[{"name":"Research Center for Landscape, Environment, Agriculture and Food (LEAF), Institute of Agronomy, University of Lisbon, Lisbon 1349-017, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,30]]},"reference":[{"key":"ref_1","unstructured":"Pereira, L.S., Cordery, I., and Iacovides, I. (2009). Addressing the Challenges, Springer."},{"key":"ref_2","unstructured":"Wilhite, D.A., Sivakumar, M.V.K., and Wood, D.A. (2000, January 5\u20137). Early Warning Systems for Drought Preparedness and Drought Management. Proceedings of an Expert Group Meeting, Lisbon, Portugal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1175\/BAMS-D-11-00176.1","article-title":"Toward global drought early warning capability: Expanding international cooperation for the development of a framework for monitoring and forecasting","volume":"94","author":"Pozzi","year":"2013","journal-title":"Am. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.wace.2014.03.005","article-title":"Information systems in a changing climate: Early warnings and drought risk management","volume":"3","author":"Pulwarty","year":"2014","journal-title":"Weather Clim. Extremes"},{"key":"ref_5","unstructured":"McKee, T.B., Doesken, N.J., and Kleist, J. (1993, January 17\u201322). The relationship of drought frequency and duration to time scales. Proceedings of 8th Conference on Applied Climatology, California, CA, USA."},{"key":"ref_6","unstructured":"Palmer, W.C. (1965). Meteorological Drought."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.5194\/nhess-12-1481-2012","article-title":"Climate trends and behavior of drought indices based on precipitation and evapotranspiration in Portugal","volume":"12","author":"Paulo","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_8","unstructured":"World Meteorological Organization (2012). Standardized Precipitation Index User Guide, WMO. Available online: http:\/\/www.wamis.org\/agm\/pubs\/SPI\/WMO_1090_EN.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1175\/1520-0493(1990)118<2056:MWROTN>2.0.CO;2","article-title":"Multiple weather regimes over the North Atlantic: Analysis of precursors and successors","volume":"118","author":"Vautard","year":"1990","journal-title":"Mon. Weather Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1175\/1520-0469(1995)052<1237:WRRAQS>2.0.CO;2","article-title":"Weather regimes: Recurrence and quasi stationarity","volume":"52","author":"Michelangeli","year":"1995","journal-title":"J. Atmos. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1175\/1520-0442(2004)017<1055:NAWCRS>2.0.CO;2","article-title":"North Atlantic climate regimes: Spatial asymmetry, stationarity with time, and oceanic forcing","volume":"17","author":"Cassou","year":"2004","journal-title":"J. Clim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1002\/joc.1101","article-title":"Weather regimes and their connection to the winter rainfall in Portugal","volume":"25","author":"Santos","year":"2005","journal-title":"Int. J. Climatol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1126\/science.269.5224.676","article-title":"Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation","volume":"269","author":"Hurrell","year":"1995","journal-title":"Science"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1175\/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2","article-title":"Classification, seasonality and persistence of low-frequency atmospheric circulation patterns","volume":"115","author":"Barnston","year":"1987","journal-title":"Mon. Weather Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1002\/(SICI)1097-0088(19971115)17:13<1433::AID-JOC203>3.0.CO;2-P","article-title":"Extension to the North Atlantic oscillation using early instrumental pressure observations from Gibraltar and south-west Iceland","volume":"17","author":"Jones","year":"1997","journal-title":"Int. J. Climatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1175\/MWR3407.1","article-title":"Non-Gaussianity and asymmetry of the winter monthly precipitation estimation from the NAO","volume":"135","author":"Pires","year":"2007","journal-title":"Mon. Weather Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1002\/joc.1048","article-title":"North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula","volume":"24","author":"Trigo","year":"2004","journal-title":"Int. J. Climatol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1175\/2007JCLI1739.1","article-title":"Positive and negative phases of the wintertime North Atlantic Oscillation and drought occurrence over Europe: A multitemporal-scale approach","volume":"21","year":"2008","journal-title":"J. Clim."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1175\/JCLI-D-11-00296.1","article-title":"On the Increased Frequency of Mediterranean Drought","volume":"25","author":"Hoerling","year":"2012","journal-title":"J. Clim."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.5194\/hess-18-2669-2014","article-title":"Global meteorological drought\u2014Part 2: Seasonal forecasts","volume":"18","author":"Dutra","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1175\/1520-0442(1994)007<1513:LSSTCP>2.0.CO;2","article-title":"Linear statistical short-term climate predictive skill in the Northern Hemisphere","volume":"7","author":"Barnston","year":"1994","journal-title":"J. Clim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3704","DOI":"10.1175\/JCLI3801.1","article-title":"Towards an integrated seasonal forecasting system for South America","volume":"19","author":"Coelho","year":"2006","journal-title":"J. Clim."},{"key":"ref_23","unstructured":"Wilks, D.S. (2011). Statistical Methods in the Atmospheric Sciences, Academic Press. [3rd ed.]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5524","DOI":"10.1175\/JCLI-D-11-00386.1","article-title":"Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging","volume":"25","author":"Wang","year":"2012","journal-title":"J. Clim."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"253","DOI":"10.3402\/tellusa.v57i3.14664","article-title":"Forecast Assimilation: A Unified Framework for the Combination of Multi-Model Weather and Climate Predictions","volume":"57A","author":"Stephenson","year":"2005","journal-title":"Tellus"},{"key":"ref_26","first-page":"1701","article-title":"Seasonal drought predictability in Portugal using statistical-dynamical techniques","volume":"17","author":"Ribeiro","year":"2015","journal-title":"J. Phys. Chem. Earth"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.5194\/nhess-12-1493-2012","article-title":"Spatial and temporal variability of precipitation and drought in Portugal","volume":"12","author":"Martins","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s00477-012-0575-z","article-title":"Assessing homogeneous regions relative to drought class transitions using an ANOVA-like inference. Application to Alentejo, Portugal","volume":"27","author":"Moreira","year":"2012","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3011","DOI":"10.5194\/hess-16-3011-2012","article-title":"Are drought occurrence and severity aggravating? A study on SPI drought class transitions using log-linear models and ANOVA-like inference","volume":"16","author":"Moreira","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"W03503","DOI":"10.1029\/2009WR008071","article-title":"Spatial and temporal variability of droughts in Portugal","volume":"46","author":"Santos","year":"2010","journal-title":"Water Resour. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.jhydrol.2008.03.002","article-title":"SPI-based drought category prediction using log-linear models","volume":"354","author":"Moreira","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1007\/s11269-007-9225-5","article-title":"Stochastic prediction of drought class transitions","volume":"22","author":"Paulo","year":"2008","journal-title":"Water Resour. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3483","DOI":"10.1080\/01431160010006430","article-title":"ENSO drought onset prediction in northeast Brazil using NDVI","volume":"22","author":"Liu","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1016\/j.mcm.2009.10.031","article-title":"Drought forecasting based on the remote sensing data using ARIMA models","volume":"51","author":"Han","year":"2010","journal-title":"Math. Comput. Model."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s00477-005-0238-4","article-title":"Drought forecasting using stochastic models","volume":"19","author":"Mishra","year":"2005","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ecolmodel.2006.04.017","article-title":"Drought forecasting using feed-forward recursive neural network","volume":"198","author":"Mishra","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1002\/joc.1498","article-title":"Drought forecasting using artificial neural networks and time series of drought indices","volume":"27","author":"Morid","year":"2007","journal-title":"Int. J. Climatol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1111\/j.1752-1688.1997.tb03560.x","article-title":"An early warning system for drought management using the Palmer Drought Index","volume":"33","author":"Lohani","year":"1997","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"21","DOI":"10.2166\/nh.1998.0002","article-title":"Long-term analysis and short-term forecasting of dry spells by Palmer Drought Severity Index","volume":"29","author":"Lohani","year":"1998","journal-title":"Hydrol. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1007\/s11269-006-9129-9","article-title":"Prediction of SPI drought class transitions using Markov chains","volume":"21","author":"Paulo","year":"2007","journal-title":"Water Resour. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Moreira, E.E. (2015). SPI drought class prediction using log-linear models applied to wet and dry seasons. Phys. Chem. Earth, in press.","DOI":"10.1016\/j.pce.2015.10.019"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s11269-006-9062-y","article-title":"Drought forecasting using the Standardized Precipitation Index","volume":"21","author":"Cancelliere","year":"2007","journal-title":"Water Resour. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jhydrol.2015.01.070","article-title":"Probabilistic forecasting of drought class transitions in Sicily (Italy) using Standardized Precipitation Index and North Atlantic Oscillation","volume":"526","author":"Bonaccorso","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1061\/(ASCE)1084-0699(2003)8:6(319)","article-title":"Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks","volume":"8","author":"Kim","year":"2003","journal-title":"J. Hydrol. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1061\/(ASCE)1084-0699(2007)12:6(626)","article-title":"Drought forecasting using a hybrid stochastic and neural network model","volume":"12","author":"Mishra","year":"2007","journal-title":"J. Hydrol. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1175\/JHM-D-10-05007.1","article-title":"Long lead time drought forecasting using a wavelet and fuzzy logic combination model: A case study in Texas","volume":"13","author":"Ozger","year":"2012","journal-title":"J. Hydrometeorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1007\/s00477-008-0288-5","article-title":"Adaptive neuro-fuzzy inference system for drought forecasting","volume":"23","author":"Bacanli","year":"2009","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00704-010-0317-4","article-title":"Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques","volume":"104","author":"Farokhnia","year":"2011","journal-title":"Theor. Appl. Climatol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jhydrol.2011.03.049","article-title":"Drought modeling\u2014A review","volume":"403","author":"Mishra","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_50","unstructured":"Agresti, A. (1990). Categorical Data Analysis, J. Wiley & Sons."},{"key":"ref_51","unstructured":"Schneider, U., Becker, A., Meyer-Christoffer, A., Ziese, M., and Rudolf, B. (2010). Global Precipitation Analysis Products of the GPCC, Global Precipitation Climatology Centre (GPCC)."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/nclimate2067","article-title":"Global warming and changes in drought","volume":"4","author":"Trenberth","year":"2014","journal-title":"Nat. Climate Chang."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11269-014-0690-3","article-title":"SPI modes of drought spatial and temporal variability in Portugal: Comparing observations, PT02 and GPCC gridded datasets","volume":"29","author":"Raziei","year":"2015","journal-title":"Water Resour. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/02508060608691913","article-title":"Drought concepts and characterization: Comparing drought indices applied at local and regional Scales","volume":"31","author":"Paulo","year":"2006","journal-title":"Water Int."},{"key":"ref_55","unstructured":"McKee, T.B., Doesken, N.J., and Kleist, J. (1995, January 15\u201320). Drought monitoring with multiple time scales. Proceedings of 9th Conference on Applied Climatology, Dallas, TX, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.jhydrol.2006.05.022","article-title":"Analysis of SPI drought class transitions using log-linear models","volume":"331","author":"Moreira","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_57","unstructured":"Jolliffe, I.T., and Stephenson, D.B. (2003). Forecast Verification: A Practitioner\u2019s Guide in Atmospheric Science, Wiley."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"323","DOI":"10.2307\/2347125","article-title":"Log-linear models for contingency tables: A generalization of classical least squares","volume":"23","author":"Nelder","year":"1974","journal-title":"Appl. Stat."}],"container-title":["Water"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4441\/8\/2\/43\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:18:34Z","timestamp":1760210314000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4441\/8\/2\/43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,30]]},"references-count":58,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2016,2]]}},"alternative-id":["w8020043"],"URL":"https:\/\/doi.org\/10.3390\/w8020043","relation":{},"ISSN":["2073-4441"],"issn-type":[{"value":"2073-4441","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,30]]}}}