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.

The relative effect of climate change and El Niño–Southern Oscillation (ENSO) is essential not only for understanding the hydrological mechanism over Jiangxi province in China but also for local water resources management as well as flood control. This study quantitatively researched in-depth information on climate change in Jiangxi using the up-to-date “ground truth” precipitation and temperature data, the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 1961–2015, 0.25°) data; analyzed the connections between ENSO and climate factors (including precipitation and temperature); and discussed the relationships between the ENSO and climate change. The main findings of this study were (1) during the period of 1961–2015, annual precipitation and temperature generally increased at a rate of 2.68 mm/year and 0.16 °C/10a, respectively; (2) the precipitation temporal trends have significant spatial differences. For example, the high precipitation increasing rates occurred in northern Jiangxi province in summer, while the large decreasing rates happened in most regions of Jiangxi province in spring; (3) an abrupt temperature change was detected around 1984, with general decreasing trends and increasing trends in 1961–1984 and 1984–2015, respectively; (4) ENSO had significant impacts on precipitation changes over Jiangxi province, for example; the El Niño events, beginning in April and May, were likely to enlarge the amounts of precipitation in the following summer, and the El Niño events beginning in October were likely to enlarge the precipitation amounts in the following spring and summer; and (5) the El Niño events, starting in the second half of the year, were likely to raise the temperature in the winter and the following spring. These findings would provide valuable information for better understanding the climate change issues over Jiangxi province.

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.

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.