Snowmelt is a major fresh water resource, and quantifying snowmelt and its variability under climate change is necessary for the planning and management of water resources. Spatiotemporal changes in snow properties in China have drawn wide attention in recent decades; however, country-wide assessments of snowmelt are lacking. Using precipitation and temperature data with a high spatial resolution (0.5^′; approximately 1 km), this study calculated the monthly snowmelt in China for the 1951–2017 period, using a simple temperature index model, and the model outputs were validated using snowfall, snow depth, snow cover extent and snow water equivalent. Precipitation and temperature scenarios developed from five CMIP5 models were used to predict future snowmelt in China under three different representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5 and RCP8.5). The results show that the mean annual snowmelt in China from 1951 to 2017 is 2.41×10¹¹ m³ yr⁻¹. The mean annual snowmelt values in Northern Xinjiang, Northeast China and the Tibetan Plateau – China's three main stable snow cover regions – are 0.18×10¹¹, 0.42×10¹¹ and 1.15×10¹¹ m³ yr⁻¹, respectively. From 1951 to 2017, the snowmelt increased significantly in the Tibetan Plateau and decreased significantly in northern, central and southeastern China. In the whole of China, there was a decreasing trend in snowmelt, but this was not statistically significant. The mean annual snowmelt runoff ratios are generally more than 10 % in almost all third-level basins in West China, more than 5 % in third-level basins in North and Northeast China and less than 2 % in third-level basins in South China. From 1951 to 2017, the annual snowmelt runoff ratios decreased in most third-level basins in China. Under RCP2.6, RCP4.5 and RCP8.5, the projected snowmelt in China in the near future (2011–2040; mid-future –2041–2070; far future – 2071–2099) may decrease by 10.4 % (15.8 %; 13.9 %), 12.0 % (17.9 %; 21.1 %) and 11.7 % (24.8 %; 36.5 %) compared to the reference period (1981–2010), respectively. Most of the projected mean annual snowmelt runoff ratios in third-level basins in different future periods are lower than those in the reference period. Low temperature regions can tolerate more warming, and the snowmelt change in these regions is mainly influenced by precipitation; however, the snowmelt change in warm regions is more sensitive to temperature increases. The spatial variability in snowmelt changes may lead to regional differences in the impact of snowmelt on water supply.

An experimental field study of wind-induced bias in precipitation measurements was conducted from September 2010 to April 2015 at a grassland site (99°52.9´ E, 38°16.1´ N; 2980 m) in the Hulu watershed in the Qilian Mountains, on the north-eastern Tibetan Plateau, in China. The experiment included (1) an unshielded Chinese standard precipitation gauge (CSPG_UN; orifice diameter = 20 cm, height = 70 cm), (2) a single Alter shield around a CSPG (CSPG_SA), (3) a CSPG in a pit (CSPG_PIT) and (4) a Double-Fence International Reference (DFIR) with a Tretyakov-shielded CSPG (CSPG_DFIR). The catch ratio (CR) used the CSPG_DFIR as a reference (CR = CSPG_X/CSPG_DFIR, %; X denotes UN, SA or PIT). The results show that the CSPG_SA, CSPG_PIT and CSPG_DIFR caught 0.9, 4.5 and 3.4 % more rainfall; 7.7, 15.6 and 14.2 % more mixed precipitation (snow with rain, rain with snow); 11.1, 16.0 and 20.6 % more snowfall and 2.0, 6.0 and 5.3 % more precipitation (of all types), respectively, than the CSPG_UN from September 2012 to April 2015. The CSPG_PIT and CSPG_DFIR caught 3.6 and 2.5 % more rainfall; 7.3 and 6.0 % more mixed precipitation; 4.4 and 8.5 % more snowfall; and 3.9 and 3.2 % more total precipitation, respectively, than the CSPG_SA. However, the CSPG_DFIR caught 1.0 % less rainfall; 1.2 % less mixed precipitation; 3.9 % more snowfall and 0.6 % less total precipitation than the CSPG_PIT. From most to least precipitation measured, the instruments ranked as follows: for rain and mixed precipitation, CSPG_PIT > CSPG_DFIR > CSPG_SA > CSPG_UN; for snowfall, CSPG_DFIR > CSPG_PIT > CSPG_SA > CSPG_UN. The CR vs. 10 m wind speed for the period of precipitation indicated that with increasing wind speed from 0 to 8.0 m s⁻¹, the CR_UN/DFIR and CR_SA/DFIR for rainfall decreased slightly. For mixed precipitation, the wind speed showed no significant effect on CR_UN/DFIR and CR_SA/DFIR below 3.5 m s⁻¹. For snowfall, the CR_UN/DFIR and CR_SA/DFIR vs. wind speed showed that CR decreased with increasing wind speed. The precipitation measured by the shielded gauges increased linearly relative to that of the unshielded gauges. However, the increase in the ratio of the linear correlation should depend on specific environmental conditions. A comparison of the wind-induced bias indicates that the CSPG_PIT could be used as a reference gauge for rain, mixed and snow precipitation events at the experimental site. As both the PIT and DFIR effectively prevented wind from influencing the catch of the precipitation gauge, the CR_PIT/DFIR had no relationship with wind speed. Cubic polynomials and exponential functions were used to quantify the relationship between catch ratio and wind speed. For snow, for both event and daily scales, the CR_UN/DFIR and CR_SA/DFIR were significantly related to wind speed; while for rain and mixed precipitation, only the event scale showed a significant relationship.

Understanding the spatio-temporal variations in the frost-free period (FFP) and the number of frost days (FD) is beneficial to reduce the harmful effects of climate change on agricultural production and enhancing agricultural adaptation. However, the spatio-temporal variations in FFP and FD and their response to climate change remain unclear across China. To investigate the impact of climate change on FFP and FD, the trends and variations in FFP and FD across China from 1950 to 2020 were quantified using ERA5-Land, a reanalysis dataset with high spatial and temporal resolution. The results showed that ERA5-Land has good applicability in quantifying the trends and variations in FFP and FD across China under climate change. The spatial distribution of multi-year average FFP and FD across China showed significant latitudinal zonality and altitude dependence, i.e., FFP decreased with increasing latitude and altitude, while FD increased with increasing latitude and altitude. As a result of climate warming across China, the FFP showed an increasing trend with an increase rate of 1.25 d/10a and the maximum increasing rate of FFP in the individual region was 6.2 d/10a, while the FD showed a decreasing trend with a decrease rate of 1.41 d/10a and the maximum decreasing rate of FD in the individual region was −6.7 d/10a. Among the five major climate zones in China, the subtropical monsoon climate zone (SUMZ) with the greatest increasing rate of 1.73 d/10a in FFP, while the temperate monsoon climate zone (TEMZ) with the greatest decreasing rate of −1.72 d/10a in FD. In addition, the coefficient of variation (C_v) of FFP showed greater variability at higher altitudes, while the C_v of FD showed greater variability at lower latitudes in southern China. Without considering the adaptation to temperature of crops, a general increase in FFP and a general decrease in FD were both beneficial to agricultural production in terms of FFP and FD promoting a longer growing period and reducing frost damage on crops. This study provides a comprehensive understanding of the trends and variations in FFP and FD under climate change, which is of great scientific significance for the adjustment of the agricultural production layout to adapt to climate change in China.

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.