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To improve streamflow predictions, researchers have implemented updating procedures that correct predictions from a simulation model using machine learning methods, in which simulated streamflow and meteorological data are used as predictors. Few studies however have included an extensive set of meteorological and hydrological state variables simulated by the simulation model. We developed and evaluated a Random Forests (RF)-based approach to correct predictions from a global hydrological model PCR-GLOBWB. From PCR-GLOBWB, meteorological input as well as its simulated hydrological state variables were used as predictors in the RF to estimate errors of PCR-GLOBWB streamflow predictions, which were then applied to correct simulated hydrograph. The RF was trained and applied separately at three streamflow gauging stations in the Rhine basin with different physiographic characteristics. Daily streamflow simulations from an uncalibrated PCR-GLOBWB run were improved by applying the RF-based error-correction model (KGE improved from 0.37 to 0.62 to 0.76–0.89, NSE from 0.19 to 0.39 to 0.64–0.80). A similar improvement was found in the simulations from a calibrated PCR-GLOBWB run (KGE 0.72–0.87 and NSE 0.60–0.78). The PCR-GLOBWB state variables that are informative to the improvement differed between catchments. Variables related to groundwater are informative in catchments dominated by the sedimentary basins characterizing large aquifers, while snow cover and surface water state variables are informative in a nival regime with large lakes. Here we quantified the improvement from combining a process-based and machine learning approach. • A random forest for correcting daily discharge predictions is proposed. • Meteorological and simulated state variables are involved in the random forest. • The most important variables vary with catchments with different characteristics. • The model framework has shown to address the temporal autocorrelation. [ABSTRACT FROM AUTHOR]

• The PCR-GLOBWB model was calibrated for 10-year rolling periods for 1901 to 2010. • The optimal parameter values changed notably through time in the Rhine-Meuse basin. • Changes in optimal parameter values are larger at the upstream parts of the basin. • Parameter changes are highly correlated with land use change and climate change. • Systemic change has occurred in the upstream parts of the Rhine-Meuse basin. In hydrological modelling, traditionally one calibration was performed over a certain calibration period before the model is used to study the hydrological system. This implies that a constant model structure and parameterization are assumed. However, if the catchment system is subject to changes that are not incorporated in the model, the parameter values found in a calibration period may not be optimal for other periods, which is called systemic change. The aim of this study was to identify systemic change and its possible causes with the PCR-GLOBWB hydrological model in the Rhine-Meuse basin, by performing a brute-force calibration for multiple periods for five calibration locations between 1901-2010. Systemic change was studied for the main model components, by selecting a key parameter from each component (minimum soil depth fraction, saturated hydraulic conductivity, groundwater recession coefficient, degree day factor, Manning's n). These parameters were calibrated for 10-year rolling periods between 1901-2010. The results showed that at the downstream locations, the changes in optimal parameter values were small, while at the upstream locations, the optimal values of most parameters changed considerably over the different rolling calibration periods, signifying systemic change. Especially the degree day factor showed large variations, varying over time between 0.5 and 2.5 times its default value at Basel and Maxau (upstream and middle part of the Rhine basin). Based on correlation analysis, it was found that climate change as well as changes in land use and river structure are possible causes of changes in optimal parameter values through time. [ABSTRACT FROM AUTHOR]

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: "Tipping points and thresholds in hydrology," "Intensification of the water cycle," and "Water services under pressure." Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement. [ABSTRACT FROM AUTHOR]