An integration of gauge, satellite, and reanalysis precipitation datasets for the largest river basin of the Tibetan Plateau

As the largest river basin of the Tibetan Plateau, the upper Brahmaputra River basin (also called “Yarlung Zangbo” in Chinese) has profound impacts on the water security of local and downstream inhabitants. Precipitation in the basin is mainly controlled by the Indian summer monsoon and westerly and is the key to understanding the water resources available in the basin; however, due to sparse observational data constrained by a harsh environment and complex topography, there remains a lack of reliable information on basin-wide precipitation (there are only nine national meteorological stations with continuous observations). To improve the accuracy of basin-wide precipitation data, we integrate various gauge, satellite, and reanalysis precipitation datasets, including GLDAS, ITP-Forcing, MERRA2, TRMM, and CMA datasets, to develop a new precipitation product for the 1981–2016 period over the upper Brahmaputra River basin, at 3 h and 5 km resolution. The new product has been rigorously validated at different temporal scales (e.g., extreme events, daily to monthly variability, and long-term trends) and spatial scales (point and basin scale) with gauge precipitation observations, showing much improved accuracies compared to previous products. An improved hydrological simulation has been achieved (low relative bias: − 5.94 %; highest Nash–Sutcliffe coefficient of efficiency (NSE): 0.643) with the new precipitation inputs, showing reliability and potential for multidisciplinary studies. This new precipitation product is openly accessible at https://doi.org/10.5281/zenodo.3711155 (Wang et al., 2020) and additionally at the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn, last access: 10 July 2020, login required).


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In this study, because of the different spatial resolutions of different products, we 164 extracted the precipitation values from each product according to the locations of the 165 gauges to generate product-gauge data pairings for evaluation. Where there are at least 166 two gauges in the pixel of one product, we used the average value of the gauges to 167 evaluate the performance of the corresponding precipitation product data. 168 To ensure the consistency of different products, we interpolated all the products 169 into the same 5 km spatial resolution grid using the inverse distance weighted (IDW) where PB, PC, PG, PI, PM, PT represent the background precipitation and different 178 products, respectively, and ε denotes the corresponding precipitation anomalies. 179 Considering different weights for these anomalies, we combined the background 180 precipitation with these anomalies, where w represents the weight for each anomaly and P_int refers to the new integrated 183 precipitation at 5 km and 3-hourly resolution.

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After P_int was acquired, we corrected its probability distribution function (PDF) 185 based on the rain gauges, and undertook several validation steps for spatial 186 distribution and at different time scales (e.g. extreme events, seasonal to inter-annual 187 variability, and long-term trends). At the same time, we also analyzed the changing 188 trend over the 36 years, and the extremely high precipitation events during the warm 189 months in 2014 and 2016. In order to identify the extreme events, we first assumed 190 that daily precipitation conforms to a normal distribution. From this we calculated a 191 threshold, above which the probability of precipitation values occurring is less than  1. More information about this model can be found in many studies (Wang et al., 2009;207 Wang and Koike, 2009;Xue et al., 2013;Zhou et al., 2015a;Wang et al., 2016;Wang 208 et al., 2017a). Figure 2 shows the flowchart of this study and Figure 3 presents the 209 final spatial distribution of our integrated product.  (Table 1). Then we corrected the PDF of the newly integrated data based on the rain 256 gauge observations ( Figure 6).

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After P_int was derived, we first validated its performance against short time    Brahmaputra River Basin, and we are confident that our precipitation dataset will 343 greatly assist future research in this basin.

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With this in mind, we note some aspects of this study that deserve further studies can be carried out to explore these aspects in more detail.

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In the future, more studies are needed to validate the method and data in regions 367 with complex topography and climatic conditions, and to further improve the retrieval 368 algorithm. This will greatly benefit hydrological applications, especially in areas with 18 sparse and irregular observation networks. Furthermore, no products used in this 370 study accurately represent extreme precipitation events, thus, it is necessary to 371 improve the ability of all of these products to capture extreme events.

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The authors declare no conflicts of interest.
32 Table and figure captions  Table 1. The precipitation products used in this study.