The of the Yellow River between its source and Hekou Town in Inner Mongolia is known as the Upper Yellow River Basin. It is the main source area of water resources in the Yellow River Basin, providing reliable water resources for 120 million people. Studying the hydrometeorological changes in the Upper Yellow River Basin is crucial for the development of human society. However, in the past, there has been limited research on hydrometeorological changes in the Upper Yellow River Basin. In order to clarify the four-dimensional spatiotemporal variation characteristics of hydrometeorological elements in the Upper Yellow River Basin, satellite and reanalysis hydrometeorological elements products need to be used. Unfortunately, there is currently a lack of precise evaluation studies on satellite and reanalysis hydrometeorological elements products in the Upper Yellow River Basin, and the geomorphic characteristics of this area have raised doubts about the accuracy of satellite and reanalysis hydrometeorological elements products. Thus, the evaluation study in the Upper Yellow River Basin is an important prerequisite for studying the four-dimensional spatiotemporal changes of hydrometeorological elements. When conducting evaluation study, we found that previous evaluation studies had a very confusing understanding of the spatiotemporal characteristics of datasets. Some papers even treated the spatiotemporal characteristics of evaluation metrics as the spatiotemporal characteristics of datasets. Therefore, we introduced a four-dimensional spacetime of both datasets and evaluation metrics to rectify the chaotic spatiotemporal view in the past. Our research results show that satellite and reanalysis hydrometeorological elements products have different abilities in describing the temporal and spatial distribution and change characteristics of hydrometeorological elements. The difference in the ability of satellite and reanalysis hydrometeorological elements products to describe temporal and spatial distribution and change characteristics requires us to select data at different temporal and spatial scales according to research needs when conducting hydrometeorological research, in order to ensure the credibility of the research results.

Study region: Upper Yellow River Basin (UYRB), China. Study focus: We provide a comprehensive overview of the changes in the natural social binary water cycle system in the UYRB from the perspectives of the atmosphere, hydrosphere, cryosphere, biosphere, and human society by summarizing previous research results. New hydrological insight for the region: Since the 1980s, the continuous temperature rise led to permafrost thawing, resulting in a decrease in runoff and an increase in groundwater in the UYRB. The ecological protection and high-quality development of human society continuously increase the demand for water resources. Especially the runoff of the river in the human gathering area has significantly decreased and there has been an overexploitation of groundwater, resulting in a serious shortage of water resources. The future water supply and demand situation in the UYRB will be more severe. However, the current understanding of the natural social binary water cycle in the Upper Yellow River Basin is still insufficient, which seriously limits the high-quality development of human society in the UYRB. Among them, some erroneous conclusions can even provide misleading information for policy-making and cause serious manpower and resources loss. Natural social binary water cycle is still in initial stage in the UYRB, that is reflected in a lot of contradictions and shortcomings in past research. We propose four feasible research directions to comprehensively promote hydrometeorological research, providing effective guidance for the formulation of high-quality development policies in the UYRB.

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high -precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for the production of highprecision AGC products and the formulation of ecological conservation policies. In this study, a transferable stacking deep learning (Stacking -DL) model is proposed based on a CNN, a DNN, and a GRU for AGC time series simulation. The applicability of deep learning models for AGC simulation is evaluated based on long time series of measured data, MODIS data, and environmental factors. Finally, the AGC spatiotemporal changes and controlling environmental factors in the alpine region were analyzed based on Sen 's slope and structural equation modeling (SEM). The results showed that feature selection and parameter optimization improved the applicability of the deep learning models in AGC simulations, and the DNN (R 2 = 0.899, RMSE = 0.078) model performed best among the base deep learning models. The Stacking -DL model combines the advantages of multiple models and achieves high transfer accuracy. In the YRSR, the AGC increase area (20.34 %) is greater than the AGC decrease area (3.34 %), the increase area is mainly located in the northeast, and the decrease area is mainly located in the southwest. AGC changes in the YRSR are mainly controlled by permafrost and climate. This study provides a high -precision and transferable vegetation monitoring model for alpine mountain regions based on advanced deep learning models and clarifies the response mechanism of AGC under climate change.

The source region of the Yellow River (SRYR) in the northeastern Tibetan Plateau is critical for supplying water resources to downstream areas. However, streamflow in the SRYR declined despite a slight increase in precip-itation during the past few decades. The SRYR experienced significant frozen ground degradation with climate warming, but how frozen ground degradation influences runoff remains unclear. This study investigated the changes of the precipitation-runoff relationship using the double-mass curve method and examined the impact of long-term spatiotemporal changes in frozen ground on the water balance components using the geomorphology -based eco-hydrological model (GBEHM). The results showed that the precipitation-runoff relationship changed significantly since 1989 in the SRYR from 1960 to 2019. In the same period, the areal mean value of the maximum thickness of seasonally frozen ground (MTSFG) decreased by 0.10 m/10a and the areal mean active layer thickness (ALT) of permafrost increased by 0.06 m/10a. Besides, 21.0 % of the entire SRYR has degraded from permafrost to seasonally frozen ground (SFG). Runoff decreased mainly in the region with elevation below 4200 m, where the evapotranspiration increase exceeded the precipitation increase. Frozen ground degradation significantly altered the hydrological processes, which is reflected by the increased subsurface runoff and the decreased surface runoff. The total water storage increased by 2.9 mm/a in the permafrost region due to the increase in active layer thickness and by 5.7 mm/a in the degradation region where permafrost completely thawed during 1960-2019. The runoff seasonality was also altered, being indicated as an increase in winter runoff. These findings help provide a better understanding of the runoff change under climate warming in permafrost-affected regions and provide insights into future water resources management in the Yellow River basin under the climate warming.

Assessing the characteristics of runoff changes and quantifying the contribution of influencing factors to runoff changes are crucial for water resources management and sustainable development in the source region of the Yellow River (SRYR). The intra-annual distribution of runoff depicted a double-peak effect. The first runoff peak in July was primarily influenced by precipitation, which did not completely flow after falling to the ground. However, some water was stored in the active layer of permafrost and released in September resulting in the second runoff peak. The contributions of precipitation and temperature to the runoff changes were 74.2% and 25.8%, respectively. The runoff peaks advanced by 15 and 6 days for the first and second peaks, respectively, owing to the influence of the cryosphere change. Principal component analysis revealed that the contributions of climate change and human activity to runoff fluctuations were 72.9% and 27.1%, respectively, during 1961-2018, indicating that hydrological processes were mainly influenced by climate change in the SRYR. The combined effect of climate change created a warm and dry trend after 1990, indicating a spatial distribution of wetness in the northwest and aridity in the southeast of the SRYR.

As an important water source and ecological barrier in the Yellow River Basin, the source region of the Yellow River (above the Huangheyan Hydrologic Station) presents a remarkable permafrost degradation trend due to climate change. Therefore, scientific understanding the effects of permafrost degradation on runoff variations is of great significance for the water resource and ecological protection in the Yellow River Basin. In this paper, we studied the mechanism and extent of the effect of degrading permafrost on surface flow in the source region of the Yellow River based on the monitoring data of temperature and moisture content of permafrost in 2013-2019 and the runoff data in 1960-2019. The following results have been found. From 2013 to 2019, the geotemperature of the monitoring sections at depths of 0-2.4 m increased by 0.16 degrees C/a on average. With an increase in the thawing depth of the permafrost, the underground water storage space also increased, and the depth of water level above the frozen layer at the monitoring points decreased from above 1.2 m to 1.2-2 m. 64.7% of the average multiyear groundwater was recharged by runoff, in which meltwater from the permafrost accounted for 10.3%. Compared to 1960-1965, the runoff depth in the surface thawing period (from May to October) and the freezing period (from November to April) decreased by 1.5 mm and 1.2 mm, respectively during 1992-1997, accounting for 4.2% and 3.4% of the average annual runoff depth, respectively. Most specifically, the decrease in the runoff depth was primarily reflected in the decreased runoff from August to December. The permafrost degradation affects the runoff within a year by changing the runoff generation, concentration characteristics and the melt water quantity from permafrost, decreasing the runoff at the later stage of the permafrost thawing. However, the permafrost degradation has limited impacts on annual runoff and does not dominate the runoff changes in the source region of the Yellow River in the longterm.

Since the 20th century, due to global warming, permafrost areas have undergone significant changes. The degradation of permafrost has complicated water cycle processes. Taking the upper Yellow River basin (UYRB) as a demonstration, this study discusses the long-term (1960-2019) changes in frozen ground and their hydrological effects with a cryosphere-hydrology model, in particular a permafrost version of the water and energy budget-based distributed hydrological model. The permafrost at the UYRB, with thickening active layer and lengthening thawing duration, has degraded by 10.8%. The seasonally frozen ground has a more pronounced intra-annual regulation that replenishes surface runoff in the warm season, while the degradation of permafrost leads to a runoff increase. The occurrence of extreme events at the UYRB has gradually decreased with the degradation of frozen ground, but spring droughts and autumn floods become more serious. The results may help better understand the hydrological impacts of permafrost degradation in the Tibetan Plateau.

Despite the importance of the Yellow River to China, climate change for the middle reaches of the Yellow River Basin (YRB) has been investigated far less than for other regions. This work focuses on future changes in mean and extreme climate of the YRB for the near-term (2021-2040), mid-term (2041-2060), and far-term (2081-2100) future, and assesses these with respect to the reference period (1986-2005) using the latest REgional MOdel (REMO) simulations, driven by three global climate models (GCMs) and assuming historical and future [Representative Concentration Pathway (RCP) 2.6 and 8.5] forcing scenarios, over the CORDEX East Asia domain at 0.22 degrees horizontal resolution. The results show that REMO reproduces the historical mean climate state and selected extreme climate indices reasonably well, although some cold and wet biases exist. Increases in mean temperature are strongest for the far-term in winter, with an average increase of 5.6 degrees C under RCP 8.5. As expected, the future temperatures of the warmest day (TXx) and coldest night (TNn) increase and the number of frost days (FD) declines considerably. Changes to mean temperature and FD depend on elevation, which could be explained by the snow-albedo feedback. A substantial increase in precipitation (34%) occurs in winter under RCP 8.5 for the far-term. Interannual variability in precipitation is projected to increase, indicating a future climate with more extreme events compared to that of today. Future daily precipitation intensity and maximum 5-day precipitation would increase and the number of consecutive dry days would decline under RCP 8.5. The results highlight that pronounced warming at high altitudes and more intense rainfall could cause increased future flood risk in the YRB, if a high GHG emission pathway is realized.

Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe'yan Hydrological Station, with a catchment area of 21,000 km(2) and an areal extent of alpine permafrost at similar to 86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998-2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe'yan is reconstructed for the period of 1955-2019 using the double mass curve method, and then the streamflow at Huagnhe'yan is forecasted for the next 20 years (2020-2040) using the Elman neural network time-series method. The dam construction (1998-2000) has caused a reduction of annual streamflow by 53.5-68.4%, and a more substantial reduction of 71.8-94.4% in the drier years (2003-2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123-210% (2018-2019). Post-correction trends of annual maximum (Q(Max)) and minimum (Q(Min)) streamflow rates and the ratio of the Q(Max)/Q(Min) of the Yellow River in the HAYR (0.18 and 0.03 m(3).(-)s(-1).yr(-1) and -0.04 yr(-1), respectively), in comparison with those of precorrection values (-0.11 and -0.004 m(3).s(-1).yr(-1) and 0.001 yr(-1), respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m(3).s(-1).yr(-1). Rising rates of spring (0.57 m(3).s(-1).yr(-1)) and autumn (0.18 m(3).s(-1).yr(-1)) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins.

Permafrost is mostly warm and thermally unstable on the Tibetan Plateau (TP), particularly in some marginal areas, thereby being susceptible to degrade or even disappear under climate warming. The degradation of permafrost consequently leads to changes in hydrological cycles associated with seasonal freeze-thaw processes. In this study, we investigated seasonal hydrothermal processes of near-surface permafrost layers and their responses to rain events at two warm permafrost sites in the Headwater Area of the Yellow River, northeastern TP. Results demonstrated that water content in shallow active layers changed with infiltration of rainwater, whereas kept stable in the perennially frozen layer, which serves as an aquitard due to low hydraulic conductivity or even imperviousness. Accordingly, the supra-permafrost water acts as a seasonal aquifer in the thawing period and as a seasonal aquitard in the freezing period. Seasonal freeze-thaw processes in association with rain events correlate well with the recharge and discharge of the supra-permafrost water. Super-heavy precipitation (44 mm occurred on 2 July 2015) caused a sharp increase in soil water content and dramatic rises in soil temperatures by 0.3-0.5 degrees C at shallow depths and advancement thawing of the active layer by half a month. However, more summer precipitation amount tends to reduce the seasonal amplitude of soil temperatures, decrease mean annual soil temperatures and thawing indices and thin active layers. High salinity results in the long remaining of a large amount of unfrozen water around the bottom of the active layer. We conclude that extremely warm permafrost with T-ZAR (the temperature at the depth of zero annual amplitude) > 0.5 degrees C is likely percolated under heavy and super-heavy precipitation events, while hydrothermal processes around the permafrost table likely present three stages concerning TZAR of 0 degrees C.