Accurate estimation of flow discharge is crucial for hydrological modelling, water resources planning, and flood prediction. This study examines seven common runoff schemes within the widely used Noah-Multi-parameterisation (Noah-MP) land surface model (LSM) and evaluates their performance using ERA5-Land runoff data as a benchmark for assessing runoff and in situ streamflow observations for evaluating discharge across the globe. Then, to assess the sensitivity of global river discharge to runoff, we simulate the discharge using the Catchment-based Macro-scale Floodplain (CaMa-Flood) model across various climatic regions. The results indicate significant variability in the accuracy of the runoff schemes, with model experiments that use TOPMODEL-based runoff schemes, which are based on topography, underestimating runoff across many regions, particularly in the Northern Hemisphere, while experiments using the other runoff schemes, including default Schaake free-drainage scheme from Noah, BATS (Biosphere–Atmosphere Transfer Scheme), Variable Infiltration Capacity (VIC) scheme, and Xinanjiang scheme (XAJ), showed improved performance. Dynamic VIC consistently overestimated runoff globally. Seasonal analysis reveals substantial regional and seasonal variability. ERA5-Land and several Noah-MP schemes successfully replicated general discharge patterns of in situ observations, with ERA5-Land and Noah-MP Schaake scheme simulations closely aligning with observed data. The Noah-MP simulations demonstrated robust versatility across various land covers, soil types, basin sizes, and topographies, indicating its broad applicability. Despite overall good performance, significant biases in high-flow extremes highlight the need for continued model improvement or calibration. These findings are critical for improving global hydrological models, which are essential for developing more reliable water resources management strategies and adapting to the growing challenges posed by climate change, such as shifts in water availability and extreme flood events.

Machine learning (ML)-based data-driven modelling is an efficient approach for good estimates of flow and maximum discharge at certain points within a basin. This paper is mainly aimed at evaluating the predictive capability of ML algorithms for daily streamflow modelling in the Besós River Basin (Spain), based on open source flow discharge and rainfall historical time series. In this sense, two modelling scenarios, without and with consideration of the antecedent hydrologic conditions, were evaluated, and three ML algorithms—support vector machines, random forest (RF) and gradient boosting (GB)—were compared to multiple linear regression (MLR), and were implemented. The prediction results revealed that the SVR model outperformed the other suggested models. Additionally, it was deduced that taking into account preceding hydrologic conditions clearly improves prediction results.

River discharge into the Mediterranean Sea is a vital component of the regional water cycle, influencing ecological and climatic dynamics. Although some regional coupled models that include a river routing component exist for the Mediterranean region, their performance in reproducing river discharge is poor. This study compares the hydrological routing models CaMa-Flood and WRF-Hydro for discharge simulations into the Mediterranean Sea. Evaluating their performance across key basins, this study highlights CaMa-Flood's computational efficiency but underperformance in flow variability and high-flow extremes, contrasted by WRF-Hydro's superior timing and bias reduction, especially after calibration. In fact, results indicate that the calibration improved WRF-Hydro’s metrics, including Kling-Gupta Efficiency (KGE) and lag times, underscoring its potential for precise discharge predictions at higher computational costs. These findings offer critical insights for advancing regional coupled Earth system models, enhancing hydrological forecasting, and addressing basin-specific hydrological challenges.

River discharge into the Mediterranean Sea is a vital component of the regional water cycle, influencing ecological and climatic dynamics. Although some regional coupled models that include a river routing component exist for the Mediterranean region, their performance in reproducing river discharge is poor. This study compares the hydrological routing models CaMa-Flood and WRF-Hydro for discharge simulations into the Mediterranean Sea. Evaluating their performance across key basins, this study highlights CaMa-Flood's computational efficiency but underperformance in flow variability and high-flow extremes, contrasted by WRF-Hydro's superior timing and bias reduction, especially after calibration. In fact, results indicate that the calibration improved WRF-Hydro’s metrics, including Kling-Gupta Efficiency (KGE) and lag times, underscoring its potential for precise discharge predictions at higher computational costs. These findings offer critical insights for advancing regional coupled Earth system models, enhancing hydrological forecasting, and addressing basin-specific hydrological challenges.

River discharge into the Mediterranean Sea is a vital component of the regional water cycle, influencing ecological and climatic dynamics. Although some regional coupled models that include a river routing component exist for the Mediterranean region, their performance in reproducing river discharge is poor. This study compares the hydrological routing models CaMa-Flood and WRF-Hydro for discharge simulations into the Mediterranean Sea. Evaluating their performance across key basins, this study highlights CaMa-Flood's computational efficiency but underperformance in flow variability and high-flow extremes, contrasted by WRF-Hydro's superior timing and bias reduction, especially after calibration. In fact, results indicate that the calibration improved WRF-Hydro’s metrics, including Kling-Gupta Efficiency (KGE) and lag times, underscoring its potential for precise discharge predictions at higher computational costs. These findings offer critical insights for advancing regional coupled Earth system models, enhancing hydrological forecasting, and addressing basin-specific hydrological challenges.