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Satellite‐based precipitation products (SBPs) have been widely used in hydrological applications in recent decades but may contain large uncertainties in mountainous regions. Atmospheric simulation‐based datasets (ABDs) have been greatly improved in recent years, but their applications in mountainous hydrology are rare and need further validation. This study compares the performance of three SBPs and two ABDs in the mountainous Minjiang River basin, focusing on their potential for closing the basin‐wide water budget. The three SBPs include the China Merged Precipitation Analysis (CMPA), the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Global Satellite Mapping of Precipitation (GSMaP); the two ABDs are the fifth generation Reanalysis product of the European Centre for Medium Range Weather Forecasts (ERA5) and the High Asia Refined Analysis Version 2 (HAR V2). The five products are validated with rain gauge data and results show that all the five products except IMERG can generally reproduce the spatial pattern and elevation dependence of observed precipitation. Particularly, precipitation amount in the two ABDs is close to gauge observations in the low elevations, but much more than the gauge observations in areas with a sharp rise in elevations. Assessment of water budget shows that all SBPs yield severe negative water imbalance (greater than 50.0% of corresponding runoff) at most sub‐basins, while the ABDs can better close the water budget with water imbalance values between ±30.0% in most sub‐basins, respectively. Further analyses show that large relative differences between SBPs and ABDs mainly occur in areas with large topographical relief and in winter, which is likely because the ABDs outperform the SBPs in capturing orographic precipitation in complex terrain and solid precipitation in winter. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), the 'Third Pole' of the world, has experienced significant warming over the past several decades. Several studies have investigated the temperature change in this region, but data scarcity and the uneven distribution of meteorological stations have resulted in uncertainty concerning the warming trend. Here, we calculated a new average temperature indicator (area-weighted average temperature, T ) to quantify the warming of the TP during the period 1961-2015 and compared it with the traditionally used arithmetic average temperature ( T ). The result shows that T is less sensitive to the irregular distribution and number of stations than T , indicating that it can produce more reliable information on temperature change. Based on annual mean T , the TP showed a warming rate of 0.35 °C/decade in the recent 55 years, which is higher than the corresponding rate calculated using T (0.30 °C/decade). Seasonal warming rates of T over the TP were also analyzed. Winter had the highest warming rate (0.44 °C/decade), followed by autumn, spring, and summer (0.38, 0.30, and 0.30 °C/decade, respectively). For comparison, the seasonal warming rates of T gave different trends (0.43, 0. 30, 0.25, and 0.25 °C/decade for winter, autumn, summer, and spring, respectively). The use of T indicated stronger warming trends in the spring, summer, and fall seasons (but not in winter), which is important for the impact of the climate warming on vegetation growth in this region. Both T and T showed more prominent warming at higher elevations during 1961-2015, indicating an elevation dependence of the warming trend over the TP. Since 2001, the warming rates calculated with T were lower than those for the previous four decades across all elevation zones, suggesting a continuing but decelerating warming tendency since the turn of the twenty-first century. This tendency was not shown in calculations using T , which suggested faster warming since 2001. The T , which is less sensitive to the number and spatial distribution of meteorological stations, provides an improved understanding of temperature changes on the TP. [ABSTRACT FROM AUTHOR]

This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission. [ABSTRACT FROM PUBLISHER]

It is widely acknowledged that the complicated underlying surface is one of the prominent reasons leading to serious uncertainty in satellite precipitation data sets over mountainous regions. However, no analysis has been conducted to quantitatively investigate the correlation between the errors in satellite precipitation data sets and the underlying surface. Using 133 monthly rain gauge observations over the Tibetan Plateau, the Bias and the residuals of ordinary least regression (the latter called ‘Ɛ’) in Climate Prediction Center morphing (CMORPH) data were calculated and were fitted with underlying surface factors using the geographically weighted regression (GWR) method, aiming at quantitatively understanding the dependence of the uncertainty in the CMORPH data set on the underlying surface over the Tibetan Plateau. We found that the 39.4% and 50.5% of the variance of the Bias andƐ, respectively, could be explained by the digital elevation model, the normalized difference vegetation index and land surface temperature. Furthermore, the explained variance of the Bias andƐcould be increased to 53.6% and 75.1%, respectively, by adding the CMORPH to the explanatory variables. Subsequently, the errors in the CMORPH were estimated by the GWR model, which was selected by comparing the explanation strengths of these models, and then the simulated errors were used to correct the precipitation estimated by CMORPH over areas without gauges. Independent validation indicated that the corrected CMORPH showed obviously better performance compared with the uncorrected CMORPH as well as two widely used precipitation estimates – the Universal Kriging interpolation model and the Tropical Rainfall Measuring Mission 3B43. These results reveal the significant influence of the underlying surface on the uncertainty in satellite precipitation data sets over mountainous areas and provide a promising approach to improve the precipitation estimates derived from satellite observations using underlying surface information. [ABSTRACT FROM PUBLISHER]