With high resolution and wide coverage, satellite precipitation products like Global Precipitation Measurement (GPM) could support hydrological/ecological research in the Tianshan Mountains, where the spatial heterogeneity of precipitation is high, but where rain gauges are sparse and unevenly distributed. Based on observations from 46 stations from 2014–2015, we evaluated the accuracies of three satellite precipitation products: GPM, Tropical Rainfall Measurement Mission (TRMM) 3B42, and the Climate Prediction Center morphing technique (CMORPH), in the Tianshan Mountains. The satellite estimates significantly correlated with the observations. They showed a northwest–southeast precipitation gradient that reflected the effects of large-scale circulations and a characteristic seasonal precipitation gradient that matched the observed regional precipitation pattern. With the highest correlation (R = 0.51), the lowest error (RMSE = 0.85 mm/day), and the smallest bias (1.27%), GPM outperformed TRMM and CMORPH in estimating daily precipitation. It performed the best at both regional and sub-regional scales and in low and mid-elevations. GPM had relatively balanced performances across all seasons, while CMORPH had significant biases in summer (46.43%) and winter (−22.93%), and TRMM performed extremely poorly in spring (R = 0.31; RMSE = 1.15 mm/day; bias = −20.29%). GPM also performed the best in detecting precipitation events, especially light and moderate precipitation, possibly due to the newly added Ka-band and high-frequency microwave channels. It successfully detected 62.09% of the precipitation events that exceeded 0.5 mm/day. However, its ability to estimate severe rainfall has not been improved as expected. Like other satellite products, GPM had the highest RMSE and bias in summer, suggesting limitations in its way of representing small-scale precipitation systems and isolated deep convection. It also underestimated the precipitation in high-elevation regions by 16%, suggesting the difficulties of capturing the orographic enhancement of rainfall associated with cap clouds and feeder–seeder cloud interactions over ridges. These findings suggest that GPM may outperform its predecessors in the mid-/high-latitude dryland, but not the tropical mountainous areas. With the advantage of high resolution and improved accuracy, the GPM creates new opportunities for understanding the precipitation pattern across the complex terrains of the Tianshan Mountains, and it could improve hydrological/ecological research in the area.

Sinadoxa corydalifolia is a perennial grass with considerable academic value as a rare species owing to habitat destruction and a narrow distribution. However, its distribution remains unclear. In this study, we predicted the distribution of Sinadoxa corydalifolia in the three-river region (the source of the Yangtze River, Yellow River, and Lancang River) under the context of climate change using the maximum entropy (MaxEnt) model. Under the current climate scenario, the suitable distribution mainly occurred in Yushu County and Nangqian County. The suitable distribution area of Sinadoxa corydalifolia covered 3107 km², accounting for 0.57% of the three-river region. The mean diurnal air temperature range (Bio2), temperature seasonality (Bio4), and mean air temperature of the driest quarter (Bio9) contributed the most to the distribution model for Sinadoxa corydalifolia, with a cumulative contribution of 81.4%. The highest suitability occurred when air temperature seasonality (Bio4) ranged from 6500 to 6900. The highest suitable mean air temperature of the driest quarter ranged from −5 to 0 °C. The highest suitable mean diurnal temperature (Bio2) ranged from 8.9 to 9.7 °C. In future (2041–2060) scenarios, the suitable distribution areas of Sinadoxa corydalifolia from high to low are as follows: representative concentration pathway (RCP)26 (6171 km²) > RCP45 (6017 km²) > RCP80 (4238 km²) > RCP60 (2505 km²). In future (2061–2080) scenarios, the suitable distribution areas of Sinadoxa corydalifolia from high to low are as follows: RCP26 (18,299 km²) > RCP60 (11,977 km²) > RCP45 (10,354 km²) > RCP80 (7539 km²). In general, the suitable distribution will increase in the future. The distribution area of Sinadoxa corydalifolia will generally be larger under low CO₂ concentrations than under high CO₂ concentrations. This study will facilitate the development of appropriate conservation measures for Sinadoxa corydalifolia in the three-river region.

Human appropriation of net primary productivity (HANPP) is an important indicator for assessing ecological sustainability. However, the spatiotemporal dynamics of HANPP in the Qinghai grasslands remain unclear. In this study, we used the spatially explicit Biome-BGCMuSo model to quantify and map HANPP in the Qinghai grasslands from 1979 to 2018. Generally, the actual net primary productivity (NPP_act) was slightly lower than the potential net primary productivity (NPP_pot), and the difference between the NPP_pot and NPP_act increased slightly over time. From 1979 to 2001, the NPP_pot and NPP_act were relatively stable; however, from 2001 to 2018, both showed significant fluctuating upward trends. From 1979 to 2018, HANPP showed a fluctuating upward trend from 6.36 to 31.85 gC/m²/yr, with an average increase of 2.14 gC/m²/yr. The average HANPP was 16.90 gC/m²/yr, which represented 18.80% of the NPP_pot of Qinghai grasslands. High HANPP mainly occurred in eastern Qinghai, whereas it was low in central and western Qinghai. Conversely, from 1979 to 2018, the HANPP efficiency decreased in a fluctuating way from 98.28% to 72.05%, with an average annual decrease of 0.66%. The interannual variations in the HANPP efficiency and harvest were negatively correlated, with a correlation coefficient of −0.46 (p < 0.01). The average HANPP efficiency was 85.33%, and the values in most grids were between 80% and 100%, being relatively low in southern and eastern Qinghai. In rare cases, the HANPP efficiency was greater than 1. This study clarifies the details of spatiotemporal dynamics of HANPP in the Qinghai grasslands and indicates the need to optimize local management of grassland resources to ensure future ecological sustainability.

Understanding the impacts of environmental factors on spatial–temporal and large-scale rodent distribution is important for rodent damage prevention. Investigating rat hole density (RHD) is one of the most effective methods to obtain the intensity of rodent damage. However, most of the previous field surveys or UAV-based remote sensing methods can only evaluate small-scale RHD and its influencing factors. However, these studies did not consider large-scale temporal and spatial heterogeneity. Therefore, we collected small-scale and in situ measurement records of RHD on the northern slope of the Tien Shan Mountains in Xinjiang (NTXJ), China, from 1982 to 2015, and then used correlation analysis and Bayesian network (BN) to analyze the environmental impacts on large-scale RHD with satellite remote sensing data such as the GIMMS NDVI product. The results show that the built BN can better quantify causality in the environmental mechanism modeling of RHD. The NDVI and LAI data from satellite remote sensing are important to the spatial–temporal RHD distribution and the mapping in the future. In regions with an elevation higher than 600 m (UPR) and lower than 600 m (LWR) of NTXJ, there are significant differences in the driving mechanism patterns of RHD, which are dependent on the elevation variation. In LWR, vegetation conditions have a weaker impact on RHD than UPR. It is possibly due to the Artemisia eaten by the dominant species Lagurus luteus (LL) in UPR being more sensitive to precipitation and temperature if compared with the Haloxylon ammodendron eaten by the Rhombomys opimus (RO) in LWR. In LWR, grazing intensity is more strongly and positively correlated to RHD than UPR, possibly due to both winter grazing and RO dependency on vegetation distribution; moreover, in UPR, sheep do not feed Artemisia as the main food, and the total vegetation is sufficient for sheep and LL to coexist. Under the different conditions of water availability of LWR and UPR, grazing may affect the ratio of aboveground and underground biomass by photosynthate allocation, thereby affecting the distribution of RHD. In extremely dry years, the RHD of LWR and UPR may have an indirect interactive relation due to changes in grazing systems.

Accurate quantification of ecosystem carbon storage dynamics is very important in regional ecological management. However, the dynamics of grassland carbon storage in Qinghai, China, are still unexplored. We investigated the temporal and spatial dynamics of carbon storage in the Qinghai grasslands from 1979 to 2018, using the spatially explicit Biome-BGCMuSo model. The average annual value of vegetation carbon density (VCD) was 52.71 gC·m⁻². After 2000, VCD showed an overall increasing trend, with an average rate of 2.14 gC·m⁻². The VCD was relatively high in the eastern and southeastern regions of Qinghai compared with that in the western and central areas. The increasing trend in VCD was mainly observed in the eastern and southeastern regions, while a decreasing trend was evident in western and central Qinghai. Annual soil organic carbon density (SOCD) in Qinghai grasslands generally increased from 1979 to 2018. After 2001, the SOCD increased by an average rate of 7.07 gC·m⁻². The SOCD was relatively high in eastern and southeastern Qinghai compared with that in western and central Qinghai. The pronounced increasing trend of SOCD was mainly distributed in the southeast and northeast parts of Qinghai, while the decreasing trend was mainly distributed in the area between southeast and northeast Qinghai, and in the central and western regions. This study deepened our understanding of carbon dynamics in the Qinghai grasslands and provided data for guiding the ecological restoration and carbon management of local grasslands.