Abstract River ice monitoring is important for hydrological research and water resource management of the Tibetan Plateau but limited by the serious shortage of field observations, and remote sensing can be used as an effective supplementary means for monitoring river ice. However, remote sensing high-altitude river ice is scarce and a basin-scale understanding of river ice is lacking on the Tibetan Plateau. To ascertain the spatial and temporal distribution characteristics of high-altitude river ice at the basin scale, we selected the Babao River basin as the study area, which is a typical river basin located in the northeastern Tibetan Plateau. Utilizing 447 available Landsat images during the river ice period from 1999 to 2018 and the classical normalized difference snow index (NDSI) algorithm, we monitored the river ice in a long time series at the Babao River basin. The average Khat of accuracy validation reached 0.973. The average area of river ice in the river ice period of this basin showed a weak decreasing trend and was negatively correlated with air temperature. We also found that gentle slopes and high elevations are beneficial for the development of river ice. The melting of river ice supplements river discharge in spring. This study is the first to reveal the distribution characteristics and changing trend of river ice at the basin scale on the Tibetan Plateau, and the results provide a reference for river ice research in this region.

Abstract Gridded precipitation data are very important for hydrological and meteorological studies. However, gridded precipitation can exhibit significant statistical bias that needs to be corrected before application, especially in regions where high wind speeds, frequent snowfall, and sparse observation networks can induce significant uncertainties in the final gridded datasets. In this paper, we present a method for the production of gridded precipitation on the Tibetan Plateau (TP). This method reduces the statistical distribution error by correcting for wind-induced undercatch and optimizing the interpolation method. A gridded precipitation product constructed by this method was compared with previous products on the TP. The results show that undercatch correction is necessary for station data, which can reduce the distributional error by 30% at most. A thin-plate splines interpolation algorithm considering altitude as a covariate is helpful to reduce the statistical distributional error in general. Our method effectively inhibits the smoothing effect in gridded precipitation, and compared to previous products, results in a higher mean value, larger 98th percentile, and greater temporal variance. This study can help to improve the quality of gridded precipitation over the TP.

Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.