{"authors":[{"id":"orcid_______::976b225e86e0abf08bbcb492f04f6d48","fullName":"Andrea Ficchì","name":"Andrea","surname":"Ficchì","rank":1,"pid":{"id":{"scheme":"orcid","value":"0000-0001-5630-7069"},"provenance":null}},{"id":null,"fullName":"Davide Bavera","name":"Davide","surname":"Bavera","rank":2,"pid":null},{"id":null,"fullName":"Stefania Grimaldi","name":"Stefania","surname":"Grimaldi","rank":3,"pid":null},{"id":"orcid_______::d6222142ff01d090b9b7ae9598a16d3a","fullName":"Francesca Moschini","name":"Francesca","surname":"Moschini","rank":4,"pid":{"id":{"scheme":"orcid","value":"0000-0001-5068-8497"},"provenance":null}},{"id":null,"fullName":"Alberto Pistocchi","name":"Alberto","surname":"Pistocchi","rank":5,"pid":null},{"id":"orcid_______::a200415ded863331ee5528b3d6b0dbfc","fullName":"Carlo Russo","name":"Carlo","surname":"Russo","rank":6,"pid":{"id":{"scheme":"orcid","value":"0000-0001-8296-4345"},"provenance":null}},{"id":"orcid_______::490516c1b36f18f9899fc0652b6812ee","fullName":"Peter Salamon","name":"Peter","surname":"Salamon","rank":7,"pid":{"id":{"scheme":"orcid","value":"0000-0002-5419-5398"},"provenance":null}},{"id":"orcid_______::4130c010eb515dcb30ef03dbe19e9fe6","fullName":"Andrea Toreti","name":"Andrea","surname":"Toreti","rank":8,"pid":{"id":{"scheme":"orcid","value":"0000-0002-1983-2523"},"provenance":null}}],"openAccessColor":"hybrid","publiclyFunded":false,"eoscIfGuidelines":null,"type":"publication","language":{"code":"und","label":"Undetermined"},"countries":null,"subjects":null,"mainTitle":"Improving low and high flow simulations at once: An enhanced metric for hydrological model calibration","subTitle":null,"descriptions":["<jats:p>Abstract. The choice of an objective function for hydrological model calibration is a critical step that directly influences model performance and suitability for the intended use cases. While calibration functions should ideally be tailored to specific modeling objectives, such as flood forecasting or drought monitoring, general-purpose metrics are typically used in practice. The two most widely adopted objective functions are the Nash–Sutcliffe Efficiency (NSE) and the Kling–Gupta Efficiency (KGE). While the NSE is a simple normalization of the mean square error, the KGE overcomes some of the NSE limitations and is often preferred due to its decomposable structure, capturing bias, relative variability, and correlation. However, KGE still suffers from limitations, including sensitivity to outliers and assumptions of linearity and normality in the error distribution, which particularly limit performance under low-flow conditions. Although several alternatives to NSE and KGE have been proposed, none has clearly outperformed these standard metrics across the full flow duration curve (FDC), especially for improving low flows without degrading performance elsewhere. To address these limitations, we propose a new metric, the Joint Divergence Kling-Gupta Efficiency (JDKGE), that enhances the KGE by incorporating an additional component based on the Jensen–Shannon Divergence (JSD). We evaluate the JDKGE metric using two hydrological process-based models (GR6J and OS-LISFLOOD), applied to two large and diverse samples of catchments spanning a broad range of hydroclimatic conditions. Calibrated using a suite of objective functions, both models are then evaluated with multiple performance metrics, including KGE, JSD, quantile ratios, and FDC-based signatures. Results show that calibrations using JDKGE significantly improve low-flow simulations compared to KGE, NSE and other competitors, while maintaining comparable or improved performance in other regimes, including high flows. Multi-objective calibration experiments further reveal that substantial gains in distributional similarity (i.e., reductions in JSD) can be achieved with only marginal changes in overall performance (KGE). Moreover, the JDKGE objective function leads to a balanced compromise between KGE and JSD and a reduction in model equifinality. This study highlights the importance of carefully selecting the objective function for hydrological model calibration and proposes JDKGE as an effective solution for improving low-flow performance while retaining general-purpose applicability for floods and water management.</jats:p>"],"publicationDate":"2026-02-13","publisher":"Copernicus 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