Multi-Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins

• URL: http://arxiv.org/abs/2507.18423v1
• Date: Thu, 24 Jul 2025 14:00:18 GMT
• Title: Multi-Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins
• Authors: Mizuki Funato, Yohei Sawada,
• Abstract summary: Many regions lack sufficient river discharge observations, limiting the skill of rainfall-runoff analyses.<n>We develop a novel method, HYdrological Prediction with multi-model ensemble and reservoir computing.<n>We evaluate HYPER using data from 87 river basins in Japan.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the critical need for accurate flood prediction and water management, many regions lack sufficient river discharge observations, limiting the skill of rainfall-runoff analyses. Although numerous physically based and machine learning models exist, achieving high accuracy, interpretability, and computational efficiency under data-scarce conditions remains a major challenge. We address this challenge with a novel method, HYdrological Prediction with multi-model Ensemble and Reservoir computing (HYPER) that leverages multi-model ensemble and reservoir computing (RC). Our approach first applies Bayesian model averaging (BMA) to 43 "uncalibrated" catchment-based conceptual hydrological models. An RC model is then trained via linear regression to correct errors in the BMA output, a non-iterative process that ensures high computational efficiency. For ungauged basins, we infer the required BMA and RC weights by linking them to catchment attributes from gauged basins, creating a generalizable framework. We evaluated HYPER using data from 87 river basins in Japan. In a data-rich scenario, HYPER (median Kling-Gupta Efficiency, KGE, of 0.56) performed comparably to a benchmark LSTM (KGE 0.55) but required only 5% of its computational time. In a data-scarce scenario (23% of basins gauged), HYPER maintained robust performance (KGE 0.55) and lower uncertainty, whereas the LSTM's performance degraded significantly (KGE -0.04). These results reveal that individual conceptual hydrological models do not necessarily need to be calibrated when an effectively large ensemble is assembled and combined with machine-learning-based bias correction. HYPER provides a robust, efficient, and generalizable solution for discharge prediction, particularly in ungauged basins, making it applicable to a wide range of regions.

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