Status: this preprint is currently under review for the journal HESS.
Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration
Alonso Pizarro,Demetris Koutsoyiannis,and Alberto Montanari
Abstract. A novel metric for rainfall-runoff model calibration and performance assessment is proposed. By integrating entropy and mutual information concepts as well as uncertainty quantification through BLUECAT (likelihood-free approach), RUMI (Ratio of Uncertainty to Mutual Information) offers a robust framework for quantifying the shared information between observed and simulated stream flows. RUMI’s capabilities to calibrate rainfall-runoff models is demonstrated using the GR4J rainfall-runoff model over 99 catchments from various macroclimatic zones, ensuring a comprehensive evaluation. Four additional performance metrics and 50 hydrological signatures were also used for performance assessment. Key findings indicate that RUMI-based simulations provide more consistent and reliable results compared to the traditional Kling-Gupta Efficiency (KGE), with improved performance across multiple metrics and reduced variability. Additionally, RUMI includes uncertainty quantification as a core computation step, offering a more holistic view of model performance. This study highlights the potential of RUMI to enhance hydrological modelling through better performance metrics and uncertainty assessment, contributing to more accurate and reliable hydrological predictions.
Received: 04 Dec 2024 – Discussion started: 21 Jan 2025
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This is a really nice paper but needs a few issues of clarification in both the introduction and in the presentation of the methodology before publication in line with the comments in the MSS.
Alonso Pizarro,Demetris Koutsoyiannis,and Alberto Montanari
Data sets
Supplement to: Alvarez-Garreton, C et al. (2018): The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies - Chile dataset. Hydrology and Earth System Sciences, 22(11), 5817-5846, https://doi.org/10.5194/hess-22-5817-2018Camila Alvarez-Garreton et al. https://doi.org/10.1594/PANGAEA.894885
Model code and software
Codes for “Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration.”Alonso Pizarro, Demetris Koutsoyiannis, and Alberto Montanari https://doi.org/10.17605/OSF.IO/93N4R
Alonso Pizarro,Demetris Koutsoyiannis,and Alberto Montanari
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We introduce RUMI, a new metric to improve rainfall-runoff simulations. RUMI better captures the link between observed and simulated stream flows by considering uncertainty at a core computation step. Tested on 99 catchments and with the GR4J model, it outperforms traditional metrics by providing more reliable and consistent results. RUMI paves the way for more accurate hydrological predictions.
We introduce RUMI, a new metric to improve rainfall-runoff simulations. RUMI better captures the...