The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets.

Ensemble precipitation forecasts (EPFs) derived from single numerical weather predictions (NWPs) often miss extreme events, and individual hydrological models (HMs) often fail to accurately capture all types of flows, including flood peaks. To address these shortcomings, this study introduced four “EPF + HM” schemes for ensemble flood forecasting (EFF) by combining two EPFs and two HMs. A generator-based post-processing (GPP) method was applied to correct biases and under-dispersion within the raw EPF data. The effectiveness of these schemes in delivering high-quality flood forecasts was assessed using both deterministic and probabilistic metrics. The results indicate that, once post-processed by GPP, all proposed schemes show improvements in both deterministic and probabilistic performances, with skillful flood forecasts for 1–7 lead days. The deterioration in forecast performance with extended lead times is also lessened. Notably, the results indicate that uncertainty within hydrological models has a more pronounced impact on capturing flood peaks than uncertainty in precipitation inputs. This study recommends combining individual EPF with multiple hydrological models for reliable flood forecasting. In conclusion, effective flood forecasting necessitates employing post-processing techniques to correct EPFs and accounting for the uncertainty inherent in hydrological models, rather than relying solely on the uncertainty of the input data.

The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in EPF and ESP over a mountain river basin in China. This evaluation was carried out based on both deterministic and probabilistic metrics at a daily temporal scale. The effectiveness of two postprocessing methods, the Generator-based Postprocessing (GPP) method, and the Bayesian Model Averaging (BMA) method were also investigated for EPF and ESP. Results showed that: (1) The ECMWF shows better performances than NCEP in both EPF and ESP in terms of evaluation indexes and representation of the hydrograph. (2) The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances. (3) Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, there are desirable EPF performances for both ECMWF and NCEP at all 7 lead days, as well as highly skillful ECMWF ESP for 1~5 lead days and average moderate skillful NCEP ESP for all 7 lead days. The results of this study can provide a reference for the applications of TIGGE over mountain river basins.