Climate warming has intensified permafrost degradation, which could have a variety of implications on the hydrological regime in permafrost regions. In this study, we analyzed the effects of permafrost degradation on the hydrological regime via four hydrological variables for 10 unregulated catchments in the source regions of the Yangtze and Yellow rivers. The results demonstrate that catchments with high permafrost coverage are expected to have an increased winter discharge ratio (proportion of winter discharge contribution to total annual flow), a decreased recession coefficient and a decreased ratio of Q_max/Q_min due to permafrost degradation. However, the great storage effects of lakes and wetlands, which could contribute to more groundwater instead of direct surface discharge, may affect the hydrological effects of permafrost degradation and result in the abnormal performance at catchment scale. The correlation analysis between summer precipitation (July–September) and the following winter discharge (December–February) indicates that permafrost degradation may affect the redistribution of summer precipitation towards the following winter discharge via increasing the soil storage capacity and delaying the release of water into streams in permafrost regions. However, unlike the Arctic and sub-Arctic regions, no significant changes for the hydrological regime (four hydrological variables) are detected over the individual periods of records for each catchment. Decreased precipitation in summer seems to reduce the water infiltration to supply the groundwater, which weakens the effects of permafrost degradation on the hydrological regime. This study implies that the storage effects of lakes and wetlands and the changes of summer precipitation patterns should be considered in future permafrost hydrological simulations, which have suggested that a large increase in groundwater discharge to streams will likely occur in response to permafrost degradation due to the warming climate in the ideal scenario.

Remote sensing techniques provide data on the spatial–temporal distribution of environmental parameters over regions with sparse ground observations. However, the resolution of satellite precipitation data is too coarse to be applied to hydrological and meteorological research at basin scales. Downscaling research using coarse remote sensing data to obtain high-resolution precipitation data is significant for the development of basin-scale research. Here, we propose improvements to a spatial–temporal method for downscaling satellite precipitation. The improved method uses a nonlinear regression model and introduces longitude and latitude based on processed normalized difference vegetation index (NDVI) and a digital elevation model (DEM) to stimulate precipitation in the Qilian Mountains during 2006–2015. The final downscaled annual precipitation (FDAP) results are corrected by observed data to obtain corrected final downscaled annual precipitation (CFDAP) datasets. For temporal downscaling, monthly downscaled data are the corrected monthly ratio multiplied by the corresponding downscaled annual datasets. The results indicated that processed NDVI (PNDVI) reflected spatial precipitation patterns more accurately than the original NDVI. The accuracy was significantly improved when the final downscaled annual precipitation data were corrected by observed data. The average annual root mean square error (RMSE) from 2006 to 2015 of CFDAP was 66.48 and 83.07 mm less than that of FDAP and original Tropical Rainfall Measuring Mission (TRMM) data, respectively. Compared with previous methods, which use NDVI and/or DEM to downscale TRMM, the accuracy of FDAP and CFDAP from the improved method was higher, and the RMSE decreased on average by 13.63 and 80.11 mm. The RMSE of monthly data from corrected monthly ratio (CMR) decreased on average by 4.93 mm over monthly data from previous monthly ratio (PMR). In addition, the accuracy of the original satellite data affected the initial downscaling results but had no significant effects on the corrected downscaling results.

Under climate change, soil temperature change remarkably influences regional landscapes, ecosystems, hydrological processes, and other parameters. Based on daily soil temperature data from 56 stations from 1965 to 2014, the spatiotemporal variations in shallow soil temperature (0, 5, 10, 15, and 20 cm) on the Qinghai–Tibetan Plateau (QTP) were investigated. The mean shallow soil temperature decreased obviously with altitude both annually and seasonally, except in winter. Primarily dependent on station latitude, the spatial distribution of winter soil temperature was significantly affected by the snow cover conditions. As the soil depth increased, the soil temperature showed a cooling trend in spring and summer but a warming trend in autumn and winter. In the past 50 years, the shallow soil temperature increased considerably on the QTP both annually and seasonally. The warming rate of soil temperature was greatly dependent on station altitude in winter and dependent on latitude in summer and longitude in autumn. Unlike the increasing air temperature that was contributed mainly by winter, the warming rate of soil temperature in winter at depths of 5, 10, 15, and 20 cm was obviously lower than that in other seasons and air temperature in winter. This occurrence may be caused by relatively less snow cover in winter, which produces a weak insulation effect and further makes the soils more vulnerable to cooling from cold air. For the entire region, the soil temperature variability was strongly correlated with the change in air temperature but weakly correlated with precipitation. Although the warming rate in shallow soil tended to be low (high) at stations with a high amount of precipitation (snow cover), the influence of precipitation (including snow) on shallow soil temperature was limited on the QTP in the past few decades.

Changes in soil temperature will exert an important influence on the management and utilization of vegetation and agricultural production, whereas the spatiotemporal variations in soil temperature under different vegetation regions across China remain largely unknown. The shallow soil temperatures (0–20 cm) during the growing season (May–September) from 1965 to 2014 across China were analysed, and the findings showed that the spatial distribution of shallow soil temperature was determined primarily by latitude in eastern China (east of 110°E), and by altitude in western China (west of 110°E) with some high (low) temperature centres. The vegetation regions with relatively low soil temperatures are mainly concentrated in high altitude and high latitude areas, which have relatively large differences in soil temperature at different depths and relatively high soil warming rates. Over the past 50 years, shallow soil temperatures increased greatly during the growing season at the site, vegetation region and nation scales, but the warming rate and its relationship with climate change varied greatly. The soil temperature variables were affected substantially by air temperature and precipitation in the subtropical forest and tropical forest regions but were affected primarily by air temperature in the other vegetation regions. This difference may be due to the spatial difference in precipitation amounts across China, which affected the soil temperature via soil moisture feedback. Further study indicated that the warming rate in shallow soil was higher in stations (vegetation regions) with less precipitation than in those with more precipitation.