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To evaluate the performance and hydrological utility of merged precipitation products at the current technical level of integration, a newly developed merged precipitation product, Multi-Source Weighted-Ensemble Precipitation (MSWEP) Version 2.1 was evaluated in this study based on rain gauge observations and the Variable Infiltration Capacity (VIC) model for the upper Huaihe River Basin, China. For comparison, three satellite-based precipitation products (SPPs), including Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) Version 2.0, Climate Prediction Center MORPHing technique (CMORPH) bias-corrected product Version 1.0, and Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Version 7, were evaluated. The error analysis against rain gauge observations reveals that the merged precipitation MSWEP performs best, followed by TMPA and CMORPH, which in turn outperform CHIRPS. Generally, the contribution of the random error in all four quantitative precipitation estimates (QPEs) is larger than the systematic error. Additionally, QPEs show large uncertainty in the mountainous regions, with larger systematic errors, and tend to underestimate the precipitation. Under two parameterization scenarios, the MSWEP provides the best streamflow simulation results and TMPA forced simulation ranks second. Unfortunately, the CHIRPS and CMORPH forced simulations produce unsatisfactory results. The relative error (RE) of QPEs is the main factor affecting the RE of simulated streamflow, especially for the results of Scenario I (model parameters calibrated by rain gauge observations). However, its influence on the simulated streamflow can be greatly reduced by recalibration of the parameters using the corresponding QPEs (Scenario II). All QPEs forced simulations underestimate the streamflow with exceedance probabilities below 5.0%, while they overestimate the streamflow with exceedance probabilities above 30.0%. The results of the soil moisture simulation indicate that the influence of the precipitation input on the RE of the simulated soil moisture is insignificant. However, the dynamic variation of soil moisture, simulated by precipitation with higher precision, is more consistent with the measured results. The simulation results at a depth of 0-10 cm are more sensitive to the accuracy of precipitation estimates than that for depths of 0-40 cm. In summary, there are notable advantages of MSWEP and TMPA with respect to hydrological applicability compared with CHIRPS and CMORPH. The MSWEP has a greater potential for basin-scale hydrological modeling than TMPA.

Abstract. Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here. Accordingly, this study produces a long-term (1979–2020) high-resolution (1/30∘, daily) precipitation dataset (TPHiPr) for the TP by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the climatologically aided interpolation and random forest methods. Validation shows that TPHiPr is generally unbiased and has a root mean square error of 5.0 mm d−1, a correlation of 0.76 and a critical success index of 0.61 with respect to 197 independent rain gauges in the TP, demonstrating that this dataset is remarkably better than the widely used datasets, including the latest generation of reanalysis (ERA5-Land), the state-of-the-art satellite-based dataset (IMERG) and the multi-source merging datasets (MSWEP v2 and AERA5-Asia). Moreover, TPHiPr can better detect precipitation extremes compared with these widely used datasets. Overall, this study provides a new precipitation dataset with high accuracy for the TP, which may have broad applications in meteorological, hydrological and ecological studies. The produced dataset can be accessed via https://doi.org/10.11888/Atmos.tpdc.272763 (Yang and Jiang, 2022).

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Field capacity is one of the most important soil hydraulic properties in water cycle, agricultural irrigation, and drought monitoring. It is difficult to obtain the distribution of field capacity on a large scale using manual measurements that are both time-consuming and labor-intensive. In this study, the field capacity ensemble members were established using existing pedotransfer functions (PTFs) and multiple linear regression (MLR) based on three soil datasets and 2388 in situ field capacity measurements in China. After evaluating the accuracy of each ensemble member, an integration approach was proposed for estimating the field capacity distribution and development of a 250 m gridded field capacity dataset in China. The spatial correlation coefficient (R) and root mean square error (RMSE) between the in situ field capacity and ensemble field capacity were 0.73 and 0.048 m 3 ·m −3 in region scale, respectively. The ensemble field capacity shows great consistency with practical distribution of field capacity, and the deviation is revised when compared with field capacity datasets provided by previous researchers. It is a potential product for estimating field capacity in hydrological and agricultural practices on both large and fine scales, especially in ungauged regions.