Evolving Drought Dynamics in Barcelona: Leveraging a Bayesian Ensemble Algorithm for Insightful Analysis and a Bidirectional Long Short-Term Memory Network for Predictive Modeling

Evolving Drought Dynamics in Barcelona: Leveraging a Bayesian Ensemble Algorithm for Insightful Analysis and a Bidirectional Long Short-Term Memory Network for Predictive Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evolving Drought Dynamics in Barcelona: Leveraging a Bayesian Ensemble Algorithm for Insightful Analysis and a Bidirectional Long Short-Term Memory Network for Predictive Modeling Francesco Granata, Fabio Di Nunno This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5042426/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted 8 You are reading this latest preprint version Abstract In recent times, the growing influence of climate change has emphasized the significance of examining hydrological patterns for efficient planning and management of water resources. This study proposes an investigation of the Standard Precipitation Index (SPI) trends and abrupt changes, at time scales of 12 and 24 months, for the municipality of Barcelona, Spain. The overall trend of SPI was assessed based on the seasonal Mann-Kendall (MK) test. The severity and duration of drought events, considering the entire time series and twenty-year intervals from 1820–1840 to 2000–2020, were also evaluated. Then, the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm was employed to identify abrupt changes in trend along the SPI time series. The seasonal MK analysis reveals a rising trend, indicating a positive shift in precipitation patterns over time. On the other hand, the BEAST analysis presents a more intricate scenario, where recent decades demonstrate a simultaneous presence of short-term positive shifts alongside prolonged negative trends, indicating a shift toward drought conditions. Furthermore, the effectiveness of a Bi-LSTM-based model in forecasting the SPI with a temporal horizon of up to 6 months was evaluated. The forecasting model displayed a decline in performance as the forecasting horizon extended, with the most precise predictions achieved for a 1-month lead time, with R 2 up to 0.899 for SPI-24. Drought SPI Trend analysis BEAST Deep Learning Barcelona Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted Editorial decision: Revision requested 22 Nov, 2024 Reviews received at journal 26 Sep, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers invited by journal 09 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Submission checks completed at journal 06 Sep, 2024 First submitted to journal 06 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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