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<p>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, R<sup>2</sup>: 0.91, RMSE: 7.76). The optimized BiLSTM network alone demonstrated strong performance (R<sup>2</sup>: 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 (&gt; 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.</p>

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BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月8日

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  • Melika Jahangiri,
  • Mahdi Asghari,
  • Mohammad Hossein Niksokhan,
  • Mohammad Reza Nikoo

摘要

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.