Simulation and reconstruction of global monthly runoff based on hydrological models and machine learning models

Hydrological models and machine learning models are widely used in streamflow simulation and data reconstruction. However, a global assessment of these models is still lacking and no synthesized catchment-scale streamflow product derived from multiple models is available over the globe. In this study, we comprehensively evaluated four conceptual hydrological models (GR2M, XAJ, SAC, Alpine) and four machine learning models (RF, GBDT, DNN, CNN) based on the selected 16,218 gauging stations worldwide, and then applied multi-model weighting ensemble (MWE) method to merge streamflow simulated from these models. Generally, the average performance of the machine learning model for all stations is better than that of the hydrological model, and with more stations having a quantified simulation accuracy (KGE>0.2); However, the hydrological model achieves a higher percentage of stations with a good simulation accuracy (KGE>0.6). Specifically, for the average accuracy during the validation period, there are 67% (27%) and 74% (21%) of stations showed a “quantified” (“good”) level for the hydrological models and machine learning models, respectively. The XAJ is the best-performing model of the four hydrological models, particularly in tropical and temperate zones. Among the machine learning models, the GBDT model shows better performance on the global scale. The MWE can effectively improve the simulation accuracy and perform much better than the traditional multi-model arithmetic ensemble (MAE), especially for the constrained least squares prediction combination method (CLS) with 82% (28%) of the stations having a “qualified” (“good”) accuracy. Furthermore, by exploring the influencing factors of the streamflow simulation, we found that both machine-learning models and hydrological models perform better in wetter areas.