This study investigated daily δ¹⁸O variations of water vapour (δ¹⁸O_v) and precipitation (δ¹⁸O_p) simultaneously at Nagqu on the central Tibetan Plateau for the first time. Data show that the δ¹⁸O tendencies of water vapour coincide strongly with those of associated precipitation. The δ¹⁸O values of precipitation affect those of water vapour not only on the same day, but also for the following several days. In comparison, the δ¹⁸O values of local water vapour may only partly contribute to those of precipitation. During the entire sampling period, the variations of δ¹⁸O_v and δ¹⁸O_p at Nagqu did not appear dependent on temperature, but did seem significantly dependent on the joint contributions of relative humidity, pressure, and precipitation amount. In addition, the δ¹⁸O changes in water vapour and precipitation can be used to diagnose different moisture sources, especially the influences of the Indian monsoon and convection. Moreover, intense activities of the Indian monsoon and convection may cause the relative enrichment of δ¹⁸O_p relative to δ¹⁸O_v at Nagqu (on the central Tibetan Plateau) to differ from that at other stations on the northern Tibetan Plateau. These results indicate that the effects of different moisture sources, including the Indian monsoon and convection currents, need be considered when attempting to interpret paleoclimatic records on the central Tibetan Plateau.

This study integrated a water isotope module into the hydrological model THREW (Tsinghua Representative Elementary Watershed) which has been successfully used in high and cold regions. Signatures of oxygen stable isotope (¹⁸O) of different water inputs and stores were simulated coupling with the simulations of runoff generation. Isotope measurements of precipitation water samples and assumed constant isotope signature of ice meltwater were used to force the isotope module. Isotope signatures of water stores such as snowpack and subsurface water were updated by an assumed completely mixing procedure. Fractionation effects of snowmelt and evapotranspiration were modeled in a Rayleigh fractionation approach. The isotope-aided model was subsequently applied for the quantification of runoff components and estimations of mean water travel time (MTT) and mean residence time (MRT) in the glacierized watershed of Karuxung river on the Tibetan Plateau. Model parameters were calibrated by three variants with different combinations of streamflow, snow cover area and isotopic composition of stream water. Modeled MTT and MRT were validated by estimates of a tracer-based sine-wave method. Results indicate that (1) the proposed model performs well on simultaneously reproducing the observations of streamflow, snow cover area and isotopic composition of stream water, despite the fact that only precipitation water samples were available for tracer input; (2) isotope data facilitate more robust estimations on contributions of runoff components (CRCs) to streamflow in the melting season, as well as on MTT and MRT; (3) involving isotope data for the model calibration obviously reduces uncertainties in the quantification of CRCs and estimations of MTT and MRT, through better constraining the competitions among different runoff processes induced by meltwater and rainfall. Our results inform scientists on the high value of water isotope data for improving process understanding in a glacierized basin on the Tibetan Plateau.

Issues related to large uncertainty and parameter equifinality have posed big challenges for hydrological modeling in cold regions where runoff generation processes are particularly complicated. Tracer-aided hydrological models that integrate the transportation and fractionation processes of water stable isotope are increasingly used to constrain parameter uncertainty and refine the parameterizations of specific hydrological processes in cold regions. However, the common unavailability of site sampling of spatially distributed precipitation isotopes hampers the practical applications of tracer-aided models in large-scale catchments. This study, taking the precipitation isotope data (isotopes-incorporated global spectral model – isoGSM) derived from the isotopic general circulation models (iGCMs) as an example, explored its utility in driving a tracer-aided hydrological model in the Yarlung Tsangpo River basin (YTR; around 2×10⁵ km², with a mean elevation of 4875 m) on the Tibetan Plateau (TP). The isoGSM product was firstly corrected based on the biases between gridded precipitation isotope estimates and the limited site sampling measurements. Model simulations driven by the corrected isoGSM data were then compared with those forced by spatially interpolated precipitation isotopes from site sampling measurements. Our results indicated that (1) spatial precipitation isotopes derived from the isoGSM data helped to reduce modeling uncertainty and improve parameter identifiability in a large mountainous catchment on the TP, compared to a calibration method using discharge and snow cover area fraction without any information on water isotopes; (2) model parameters estimated by the corrected isoGSM data presented higher transferability to nested subbasins and produced higher model performance in the validation period than that estimated by the interpolated precipitation isotope data from site sampling measurements; (3) model calibration forced by the corrected isoGSM data successfully rejected parameter sets that overestimated glacier melt contribution and gave more reliable contributions of runoff components, indicating the corrected isoGSM data served as a better choice to provide informative spatial precipitation isotope than the interpolated data from site sampling measurements at the macro scale. This work suggested plausible utility of combining isoGSM data with measurements, even from a sparse sampling network, in improving hydrological modeling in large high mountain basins.

We present radiometric ⁸¹Kr dating results for ice samples collected at the outlets of the Guliya ice cap in the western Kunlun Mountains of the Tibetan Plateau. This first application of ⁸¹Kr dating on midlatitude glacier ice was made possible by recent advances in Atom Trap Trace Analysis, particularly a reduction in the required sample size down to 1 μL STP of krypton. Eight ice blocks were sampled from the bottom of the glacier at three different sites along the southern edges. The ⁸¹Kr data yield upper age limits in the range of 15–74 ka (90% confidence level). This is an order of magnitude lower than the ages exceeding 500 ka which the previous ³⁶Cl data suggest for the bottom of the Guliya ice core. It is also significantly lower than the widely used chronology up to 110 ka established for the upper part of the core based on ice δ¹⁸O.

Quantifying the transpiration fraction of evapotranspiration (T/ET) is crucial for understanding plant functionality in ecosystem water cycles, land‐atmosphere interactions, and the global water budget. However, the controls and mechanisms underlying the temporal change of T/ET remain poorly understood in arid and semiarid areas, especially for remote regions with sparse observations such as the Tibetan Plateau (TP). In this study, we used combined high‐frequency laser spectroscopy and chamber methods to constrain estimates of T/ET for an alpine meadow ecosystem in the central TP. The three isotopic end members in ET (δ_ET), soil evaporation (δ_E), and plant transpiration (δ_T) were directly determined by three newly customized chambers. Results showed that the seasonal variations of δ_ET, δ_E, and δ_T were strongly affected by the precipitation isotope (R² = 0.53). The δ¹⁸O‐based T/ET agreed with that of δ²H. Isotope‐based T/ET ranged from 0.15 to 0.73 during the periods of observation, with an average of 0.43. This mean result was supported by T/ET derived from a two‐source model and eddy covariance observations. Our overarching finding is that at the seasonal timescale, surface soil water content (θ) dominated the change of T/ET, with leaf area index playing only a secondary role. Our study confirms the critical impact of soil water on the temporal change of T/ET in water‐limited regions such as the TP. This knowledge sheds light on diverse land‐surface processes, global hydrological cycles, and their modeling.

Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (T_max) and minimum (T_min) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %), which may affect the air temperature (T_air) estimation accuracy. This study comprehensively analyzes the effects of clouds on T_air estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the T_min estimation accuracy. However, for T_max estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between T_max and daytime LST. The errors of T_max estimation are obviously larger than those of T_min and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate T_min estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on T_max and T_min estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the T_air estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.