No abstract

In this paper, we investigate the spatiotemporal characteristics and trends of snow cover fraction (SCF) in the Tibetan Plateau (TP) and seven upstream river basins (Yellow, Yangtze, Mekong, Salween, Brahmaputra, Indus and Yarkant) by employing the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2001-2014. The possible linkage between the SCF, and temperature and precipitation changes over the TP and individual basins is also investigated. Results suggest that the distribution of snow cover over the TP exhibit a large spatiotemporal heterogeneity with high SCFs mainly concentrating on the southern and western edges which is strongly linked to the moist air supplies. The distribution of snow cover is highly dependent on the elevations, with a higher SCF and a later onset of snow melt at the higher elevation zones than at the lowers. There is an elevation threshold existing for separating two distinct snow cover regimes, which are 4000 m for the western basins and 5000 m for the southeastern basins. The snow cover over the TP has slightly decreased by about 1.1% during 2001-2014, with dramatic reductions mostly lying in the heavy snowy regions and some light increases occurring in the areas with annual mean SCFs mostly less than 10%. The reduction rates of snow cover increase with the rising of altitudes for the TP average, and the basins of Indus, Yarkant, Salween and Brahmaputra. At the same time, the Yellow and Yangtze basins exhibit larger increasing rates of snow cover at the higher elevations zones. The SCF variations are linked to the temperature and precipitation changes. Precipitation tends to be the major factor impacting the snow cover changes in the TP during 2001-2014.

The spatial‐temporal changes in terrestrial water storage (TWS) over the Tibetan Plateau (TP) and six selected basins during 2003–2014 were analyzed by applying the Gravity Recovery and Climate Experiment data and the extended Variable Infiltration Capacity‐glacier model, including the upstream of Yangtze (UYA), Yellow (UYE), Brahmaputra (UB), and Indus river basins and the Inner TP and the Qaidam Basin. The possible causes of TWS changes were investigated from the perspective of surface water balance and TWS components through multisource data and the Variable Infiltration Capacity‐glacier model. There was a strong spatial heterogeneity in changes of Gravity Recovery and Climate Experiment TWS in the TP—with apparent mass accumulation in central and northern TP and a sharp decreasing trend in southern and northwestern TP. The TWS changes in the TP were mostly attributed to variations in precipitation and evapotranspiration from the perspective of land‐surface water balance. Precipitation played a dominant role on the TWS accumulation in the UYA and UYE, while evapotranspiration had a more important role than precipitation in TWS depletion in the UB. From the perspective of TWS components, the TWS increase in the UYA and UYE was mainly caused by an increase in soil moisture, whereas the decrease in TWS in the UB was mostly due to glacier mass loss. TWS was accumulating from March through August in southeastern TP while from November to April/May in northwestern TP. The seasonal variations of TWS are highly modulated by the large‐scale climate system, atmospheric moisture flux, and precipitation regime over the TP.

Precipitation is one of the most important input to hydrological models, although obtaining sufficient precipitation observations and accurate precipitation estimates in High Mountain Asia (HMA) is challenging. ERA5 precipitation is the latest generation of reanalysis dataset that is attracting huge attention from various fields but it has not been evaluated in hydrological simulations in HMA. To remedy this gap, we first statistically evaluated ERA5 precipitation with observations from 584 gauges in HMA, and then investigated its potential in hydrological simulation in 11 HMA basins using the Variable Infiltration Capacity (VIC) hydrological model. The ERA5 precipitation generally captures the seasonal variations of gauge observations, and the broad spatial distributions of precipitation in both magnitude and trends in HMA. The ERA5 exhibits a reasonable flow simulation (RB of 5%–10%) at the Besham hydrological station of the UI basin when the contribution from glacier runoff is added to the simulated total runoff. But it overestimates the observations in other HMA basins by 33%–106% without considering glacier runoff, mostly due to the overestimates in the ERA5 precipitation inputs. Therefore, a bias correction is definitely needed before ERA5 precipitation is used for hydrological simulations in HMA basins.