RUMI (Ratio of Uncertainty to Mutial Information): Uncertainty consideration in rainfall-runoff models calibration

The ratio of uncertainty to mutual information (RUMI) is proposed as a new and novel objective function for rainfall-runoff model calibration. Uncertainty is quantified by means of BLUECAT (likelihood-free approach), whereas mutual information through entropy-based concepts. The deterministic GR4J rainfall-runoff model is considered to illustrate RUMI’s calibration capabilities over around 100 catchments in Chile. Those catchments have a pseudo-natural hydrological regime and are located in different macroclimatic zones. Calibration with the Kling-Gupta Efficiency (KGE) was also performed. Additionally, several hydrological signatures were used to assess RUMI’s performance and comparison with KGE-based results was carried out. Key findings showed that RUMI-based simulations had improved performance and reduced variability (in comparison with KGE-based simulations). This study highlights RUMI’s capabilities for hydrological model calibration by considering uncertainty quantification as a key computation step and, therefore, contributing to more accurate and reliable hydrological predictions. This work was supported by The National Research and Development Agency of the Chilean Ministry of Science, Technology, Knowledge and Innovation (ANID), grant no. FONDECYT Iniciación 11240171; the RETURN Extended Partnership which received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005); and, the Italian Science Fund through the project "Stochastic amplification of climate change into floods and droughts change (CO$_2$2Water)", grant number J53C23003860001.