The Qinghai-Tibet Plateau (QTP) is known as the Third Polar of the earth and the Water Tower of Asia, with more than 70% of the area on the QTP is covered by permafrost possibly. An accurate permafrost distribution map based on valid and available methods is indispensable for the local environment evaluation and engineering constructions planning. Most of the previous permafrost maps have employed traditional mapping method based on field surveys and borehole investigation data. However their accuracy is limited because it is extremely difficulties in obtaining mass data in the high-altitude and cold regions as the QTP; moreover, the mapping method, which would effectively integrate many factors, is still facing great challenges. With the rapid development of remote sensing technology in permafrost mapping, spatial data derived from the satellite sensors can recognize the permafrost environment features and quantitatively estimate permafrost distribution. Until now there is no map indicated permafrost presence/absence on the QTP that has been generated only by remote sensing data as yet. Therefore, this paper used permafrost-influencing factors and examined distribution features of each factor in permafrost regions and seasonally frozen ground regions. Then, using the Decision Tree method with the environmental factors, the 1 km resolution permafrost map over the QTP was obtained. The result shows higher accuracy compared to the previous published map of permafrost on the QTP and the map of the glaciers, frozen ground and deserts in China, which also demonstrates that making comprehensive use of remote sensing technology in permafrost mapping research is fast, macro and feasible. Furthermore, this result provides a simple and valid method for further permafrost research.

An accurate estimation of evapotranspiration (ET) is critical to understanding the water cycle in watersheds and promoting the sustainable utilization of water resources. Although there are various ET products in the Yellow River Basin, various ET products have many uncertainties due to input data, parameterization schemes, and scale conversion, resulting in significant uncertainties in regional ET data products. To reduce the uncertainty of a single product and obtain more accurate ET data, more accurate ET data can be obtained by fusing different ET data. Addressing this challenge, by calculating the uncertainty of three ET data products, namely global land surface satellite (GLASS) ET, Penman–Monteith–Leuning (PML)-V2 ET, and reliability-affordable averaging (REA) ET, the weight of each product is obtained to drive the Bayesian three-cornered Hat (BTCH) algorithm to obtain higher quality fused ET data, which are then validated at the site and basin scales, and the accuracy has significantly improved compared to a single input product. On a daily scale, the fused data’s root mean square error (RMSE) is 0.78 mm/day and 1.14 mm/day. The mean absolute error (MAE) is 0.53 mm/day and 0.84 mm/day, respectively, which has a lower RMSE and MAE than the model input data; the correlation coefficients (R) are 0.9 and 0.83, respectively. At the basin scale, the RMSE and MAE of the annual average ET of the fused data are 11.77 mm/year and 14.95 mm/year, respectively, and the correlation coefficient is 0.84. The results show that the BTCH ET fusion data are better than single-input product data. An analysis of the fused ET data on a spatiotemporal scale shows that from 2001 to 2017, the ET increased in 85.64% of the area of the Yellow River Basin. Fluctuations in ET were greater in the middle reaches of the Yellow River than in the upstream and downstream regions. The BTCH algorithm has indispensable reference value for regional ET estimation research, and the ET data after BTCH algorithm fusion have higher data quality than the original input data. The fused ET data can inform the development of management strategies for water resources in the YRB and provide a deeper understanding of the regional water supply and demand balance mechanism.

Sanjiangyuan National Park (SNP), the first national park established in China, plays a pivotal role in conserving biodiversity on the Qinghai-Tibet Plateau. This study investigates the habitat suitability and projected future distribution of four key species: the Tibetan antelope (Pantholops hodgsonii), kiang (Equus kiang), Tibetan gazelle (Procapra picticaudata), and wild yak (Bos mutus). Utilizing 358 occurrence records and the MaxEnt model, we predicted current and future suitable habitats under different climate scenarios for the years 2050 and 2070. Our findings indicate that suitable habitats are significantly influenced by factors such as elevation, temperature, and vegetation health. Among the species studied, the Tibetan gazelle exhibits the most extensive suitable habitat, followed by the kiang, Tibetan antelope, and wild yak. Specifically, the Tibetan antelope’s habitat is anticipated to shift northward, while the kiang and wild yak may experience further habitat fragmentation, thereby increasing risks to their populations. Additionally, the results highlight the importance of integrating climate considerations into future conservation planning to ensure the long-term survival of these species. By identifying regions with optimal environmental conditions, conservation strategies can be more effectively directed to maintain and improve these habitats, thereby preserving biodiversity within the Sanjiangyuan region.

Introduction: This study aimed to distinguish drought accumulation timescale from response timing in stable vegetation in the Weihe River Basin. Methods: We analyzed MODIS enhanced vegetation index (EVI) and ERA5-Drought standardized precipitation evapotranspiration index (SPEI) data for 2003–2024 on the native 0.25° drought grid. Persistent land-cover classification required at least 18 valid annual classifications and a modal-class fraction of at least 80%. Four accumulation timescales (1, 3, 6, and 12 months) and 13 window-end lags (0–12 months) formed 52 response–exposure combinations. We controlled for growing-season month effects and a linear trend. We applied maximum-statistic tests with synchronized 2-year moving-block resampling and controlled the false discovery rate across cells using the Benjamini–Hochberg procedure. Results: The area-weighted mean of grid-cell partial correlations peaked at SPEI-12, lag 0 (r = 0.3216), with a mean slope of 0.00971 EVI units per SPEI unit. Of 190 eligible cells, 126 showed a corrected positive association, covering 69.68% of eligible stable-vegetation area. Grasslands and high-elevation areas had stronger descriptive responses. Relaxing valid-area coverage from 0.70 to 0.50 retained the basin-wide peak. Across detected cells, the median bootstrap reselection frequency of the observed best timescale–lag combination was 53.1%. Across 2,252 SPEI-3 grid-cell drought events, the mean EVI residual reached −0.00909 one month after onset (95% onset-year block-bootstrap interval, −0.01448 to −0.00439). Among 2,105 uncensored events, meteorological drought termination was confirmed a median of 3 months after the last dry month (interquartile range, 2–3 months). Discussion: The dominant association links vegetation condition to the 12-month water balance ending in the response month. These findings support monitoring that combines long-term water balance, early vegetation responses, and continued vegetation observations after meteorological drought termination.