Historical simulation performance evaluation and monthly flow duration curve quantile-mapping (MFDC-QM) of the GEOGLOWS ECMWF streamflow hydrologic model

Global hydrological models are essential for managing water resources and predicting hydrological events. However, the local-scale usability of global models challenges big-data management, communication, adoption, and validation. Validation is the biggest challenge bercause of the need for large-scale data management and model calibration, which requires extensive and often inaccessible observed data. This study assesses the GEOGLOWS-ECMWF Global Hydrologic Model, revealing systematic biases that impact its accuracy. We propose a bias-correction methodology using flow duration curves to align non-exceedance probabilities of simulated and observed streamflow, significantly improving the GEOGLOWS model. Unfortunately, this approach does not inherently improve simulations in ungauged locations. The methodology not only enhances the GEOGLOWS model's accuracy but also stands as a versatile solution applicable across various hydrological models. This bias correction approach provides a tool for improving hydrological predictions and gives users the confidence to use global models for local water resource management and decision-making processes. • Global hydrological models are crucial for managing water resources, forecasting floods, and assessing climate impacts. • The GEOGLOWS Hydrologic Model has biases that limit its accuracy. • A bias correction method, Monthly Flow Duration Curve Quantile-Mapping (MFDC-QM), is proposed to correct systematic biases. • MFDC-QM improves the accuracy of the GEOGLOWS retrospective simulation on a global scale. • The MFDC-QM method enhances hydrological models, providing key insights for water resources management.