BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios
Abstract Traditional downscaling techniques often fail to accurately represent critical extremes necessary for effective adaptation planning. This paper introduces the first application of Bidirectional Long Short-Term Memory (BiLSTM) networks with an adaptive Kalman filter for multi-scenario, high-...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-08264-z |
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| author | Melika Jahangiri Mahdi Asghari Mohammad Hossein Niksokhan Mohammad Reza Nikoo |
| author_facet | Melika Jahangiri Mahdi Asghari Mohammad Hossein Niksokhan Mohammad Reza Nikoo |
| author_sort | Melika Jahangiri |
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| description | Abstract Traditional downscaling techniques often fail to accurately represent critical extremes necessary for effective adaptation planning. This paper introduces the first application of Bidirectional Long Short-Term Memory (BiLSTM) networks with an adaptive Kalman filter for multi-scenario, high-resolution precipitation downscaling. We applied our methodology to Tehran, Iran, and systematically compared and ranked the performance of different CMIP6 projections, with the best performing model being MIROC (NSE: 0.902, R2: 0.91, RMSE: 7.76). The optimized BiLSTM network alone demonstrated strong performance (R2: 0.638, KGE: 0.684), with the adaptive Kalman filter dynamically adjusting its parameters according to precipitation intensity. Our novel contributions are a symmetric dependence loss for predicting extremes and graduated correction using percentiles. Examination of the Shared Socioeconomic Pathways (SSPs) 1 to 5 revealed surprising findings: the SSP1-2.6 (more sustainable) pathway predicted the highest extremes, with a 24.3% increase in 99th percentile intensity over the past. SSP2-4.5, SSP3-7.0, and SSP5-8.5 had increases of 17.8%, 16.5%, and 21.1%, respectively. Generated Intensity–Duration–Frequency curves indicated dramatic changes for short-duration events (10–30 min) under SSP5-8.5 with essential implications for infrastructure planning. Extreme precipitation events (> 95th percentile) revealed a frequency increase from 2.1 to 3.5% for SSP1-2.6 for events exceeding 20 mm/day. The integrated framework effectively translates coarse climate model outputs into practical engineering tools, providing the required quantitative information for planning climate-resilient infrastructure. |
| format | Article |
| id | doaj-art-4fac268c876a48e1a7f61cd77dcd22b8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-4fac268c876a48e1a7f61cd77dcd22b82025-08-20T03:42:28ZengNature PortfolioScientific Reports2045-23222025-07-0115112310.1038/s41598-025-08264-zBiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenariosMelika Jahangiri0Mahdi Asghari1Mohammad Hossein Niksokhan2Mohammad Reza Nikoo3Faculty of Environment, University of TehranFaculty of Environment, University of TehranFaculty of Environment, University of TehranDepartment of Civil and Architectural Engineering, Sultan Qaboos UniversityAbstract Traditional downscaling techniques often fail to accurately represent critical extremes necessary for effective adaptation planning. This paper introduces the first application of Bidirectional Long Short-Term Memory (BiLSTM) networks with an adaptive Kalman filter for multi-scenario, high-resolution precipitation downscaling. We applied our methodology to Tehran, Iran, and systematically compared and ranked the performance of different CMIP6 projections, with the best performing model being MIROC (NSE: 0.902, R2: 0.91, RMSE: 7.76). The optimized BiLSTM network alone demonstrated strong performance (R2: 0.638, KGE: 0.684), with the adaptive Kalman filter dynamically adjusting its parameters according to precipitation intensity. Our novel contributions are a symmetric dependence loss for predicting extremes and graduated correction using percentiles. Examination of the Shared Socioeconomic Pathways (SSPs) 1 to 5 revealed surprising findings: the SSP1-2.6 (more sustainable) pathway predicted the highest extremes, with a 24.3% increase in 99th percentile intensity over the past. SSP2-4.5, SSP3-7.0, and SSP5-8.5 had increases of 17.8%, 16.5%, and 21.1%, respectively. Generated Intensity–Duration–Frequency curves indicated dramatic changes for short-duration events (10–30 min) under SSP5-8.5 with essential implications for infrastructure planning. Extreme precipitation events (> 95th percentile) revealed a frequency increase from 2.1 to 3.5% for SSP1-2.6 for events exceeding 20 mm/day. The integrated framework effectively translates coarse climate model outputs into practical engineering tools, providing the required quantitative information for planning climate-resilient infrastructure.https://doi.org/10.1038/s41598-025-08264-zPrecipitation downscalingBiLSTM neural networksAdaptive Kalman filteringExtreme event forecastingClimate change scenariosDeep learning |
| spellingShingle | Melika Jahangiri Mahdi Asghari Mohammad Hossein Niksokhan Mohammad Reza Nikoo BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios Scientific Reports Precipitation downscaling BiLSTM neural networks Adaptive Kalman filtering Extreme event forecasting Climate change scenarios Deep learning |
| title | BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios |
| title_full | BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios |
| title_fullStr | BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios |
| title_full_unstemmed | BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios |
| title_short | BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios |
| title_sort | bilstm kalman framework for precipitation downscaling under multiple climate change scenarios |
| topic | Precipitation downscaling BiLSTM neural networks Adaptive Kalman filtering Extreme event forecasting Climate change scenarios Deep learning |
| url | https://doi.org/10.1038/s41598-025-08264-z |
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