Monthly prediction of drought classes using log-linear models under the influence of NAO for early-warning of drought and water management

• Published in: Water
• Main Authors: Moreira, Elsa, Russo, Ana, Trigo, Ricardo M.
• Other Authors: CMA - Centro de Matemática e Aplicações
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2018
• Subjects: Drought classes; Log-linear modeling; North Atlantic Oscillation (NAO); Persistence; Standardized Precipitation and Evapotranspiration Index (SPEI); Standardized Precipitation Index (SPI); Biochemistry; Geography; Planning and Development; Aquatic Science; Water Science and Technology; SDG 13 - Climate Action; North Atlantic; North Atlantic oscillation
• Online Access: http://www.scopus.com/inward/record.url?scp=85040779526&partnerID=8YFLogxK; https://doi.org/10.3390/w10010065

SFRH/BPD/99757/2014 This work was partially supported by the projects IMDROFLOOD-Improving Drought and Flood Early Warning, Forecasting and Mitigation using real-time hydroclimatic indicators (WaterJPI/0004/2014) and project UID/MAT/00297/2013 (Centro de Matematica e Aplicacoes), both funded by funded by Fundacao para a Ciencia e a Tecnologia, Portugal (FCT). Ana Russo thanks also FCT by the Post-Doc research grant SFRH/BPD/99757/2014. Drought class transitions over a sector of Eastern Europe were modeled using log-linear models. These drought class transitions were computed from time series of two widely used multiscale drought indices, the Standardized Preipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI), with temporal scales of 6 and 12 months for 15 points selected from a grid over the Prut basin in Romania over a period of 112 years (1902-2014). The modeling also took into account the impact of North Atlantic Oscillation (NAO), exploring the potential influence of this large-scale atmospheric driver on the climate of the Prut region. To assess the probability of transition among different drought classes we computed their odds and the corresponding confidence intervals. To evaluate the predictive capabilities of the modeling, skill scores were computed and used for comparison against benchmark models, namely using persistence forecasts or modeling without the influence of the NAO index. The main results indicate that the log-linear modeling performs consistently better than the persistence forecast, and the highest improvements obtained in the skill scores with the introduction of the NAO predictor in the modeling are obtained when modeling the extended winter months of the SPEI6 and SPI12. The improvements are however not impressive, ranging between 4.7 and 6.8 for the SPEI6 and between 4.1 and 10.1 for the SPI12, in terms of the Heidke skill score. publishersversion published