Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions.

With the continuous emergence of remote sensing technologies and atmospheric models, multi-source precipitation products (MSPs) are increasingly applied in hydrometeorological research, especially in ungauged or data-scarce regions. This study comprehensively evaluates the reliability of MSPs and quantifies the uncertainty of sources in streamflow simulation. Firstly, the performance of seven state-of-the-art MSPs is assessed using rain gauges and the Block-wise use of the TOPMODEL (BTOP) hydrological model under two calibration schemes over Jialing River Basin, China. Then, a variance decomposition approach (Analysis of variance, ANOVA) is employed to quantify the uncertainty contribution of precipitation products, model parameters, and their interaction in streamflow simulation. The MSPs include five satellite-based (GSMaP, IMERG, PERCDR, CHIRPS, CMORPH), one reanalysis (ERA5L), and one ensembled product (PXGB2). The results of precipitation evaluation show that the MSPs have temporal and spatial variability and PXGB2 has the best performance. The hydrologic utility of MSPs is different under different calibration methods. When using gauge-based calibration parameters, the PXGB2-based simulation performs best, whereas CHIRPS, PERCDR, and ERA5L show relatively poor performance. In comparison, the model recalibrated by individual MSPs significantly improves the simulation accuracy of most MSPs, with GSMaP having the best performance. The ANOVA results reveal that the contribution of precipitation products to the streamflow uncertainty is larger than model parameters and their interaction. The impact of interaction suggests that a better simulation attributes to an optimal combination of precipitation products and model parameters rather than solely relying on the best MSPs. These new findings are valuable for improving the suitability of MSPs in hydrologic applications.

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.