Impacts of climate warming on the frozen ground and eco-hydrology in the Yellow River source region, China

Primary driving factors of ecological environment system change based on directed weighted network illustrating with the Three-River Headwaters Region

The primary driving factors of ecological environment change have received significant attention. However, previous research methods for identifying the main drivers of ecological environment change have primarily relied on correlation analysis and regression analysis. While these methods can reveal co-occurrences, associations, and correlations among elemental characteristics, they often struggle to uncover the deep-seated interactions among elements within complex, unstable, nonlinear, and high-dimensional systems. To address this, we used the Three-River Headwaters Region as a case study and introduced a complex network model from the perspective of the ecological environment system to investigate the main driving factors of ecological environment change. In our analysis, we considered 12 factors related to the atmosphere, hydrology, vegetation, and soil, including evaporation, long-wave radiation, short-wave radiation, specific humidity, soil temperature, precipitation rate, soil water content, air temperature, air pressure, vegetation normalization index, wind speed, and natural surface runoff. Watersheds were selected as the fundamental units for constructing ecological environment datasets. We applied the Ensemble Empirical Mode Decomposition (EEMD) method and Hilbert-Huang Transform (HHT) to analyze causal relationships between time series pairs and constructed two directed weighted network models based on sub-catchments. The results showed that both network models yielded consistent conclusions, with the sparse network exhibiting higher efficiency. Radiation and temperature were identified as the primary driving factors of ecosystem change, and the water cycle was determined to be the ultimate manifestation of ecological system change throughout the Three-River Headwaters Region. Furthermore, based on node out-strength, we generated a vegetation protection priority map.

The role of root zone soil moisture return on vegetation green-up: A comparison of two degrading permafrost sites

The effects of permafrost degradation on ecological changes have attracted more and more attention, but the underlying interactive mechanisms are still not well understood. From a unique angle of view, this study focuses on the linkage between the freezing-induced root zone soil moisture return (SMR) and vegetation green-up along with climate warming and permafrost degradation. Using 7-year soil-weather monitoring data in the high-altitude and high-latitude permafrost regions, we investigated the changes of root zone SMR in two degrading permafrost sites. Results demonstrate that, as one important water source for vegetation green-up, the ratio of root zone SMR to the soil moisture storage after final thawed, Rsmr, could reach 22.8 % and 10.5 % at the two sites, respectively. In contrast to the negligible change at the high-latitude permafrost site, a rapid increase rate of 7.7 % per decade in Rsmr was found at the high-altitude permafrost site over the monitoring period of 2012-2018. Generally, vegetation green-up definitely benefited from the enrichment of soil moisture storage, but their sensitivities to climate warming and permafrost degradation were different at the two sites. The increase of Rsmr effectively buffered the shrinking trend of soil water availability of the root zone both over the pre-freezing and the green-up period at the high-altitude permafrost site. The changing role of root zone SMR might be threatened during the vegetation green-up in the longer future. Whereas, there would be no water shortage risk for the foreseeable future at the high-latitude permafrost site. Overall, this study emphasized the importance of SMR in enhancing soil water availability for vegetation green-up under the background of quick climate warming and permafrost degradation.

Projected changes in soil freeze depth and their eco-hydrological impacts over the Tibetan Plateau during the 21st century

In the context of global warming, the soil freeze depth (SFD) over the Tibetan Plateau (TP) has undergone significant changes, with a series of profound impacts on the hydrological cycle and ecosystem. The complex terrains and high elevations of the TP pose great challenges in data acquisition, presenting difficulties for studying SFD in this region. This study employs Stefan's solution and downscaled datasets from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the future SFDs over the TP. The changing trends of the projected SFDs under different Shared Socio-economic Pathways (SSP) scenarios are investigated, and; the responses of SFDs to potential climatic factors, such as temperature and precipitation, are analyzed. The potential impacts of SFD changes on eco-hydrological processes are analyzed based on the relationships between SFDs, the distribution of frozen ground, soil moisture, and the Normalized Difference Vegetation Index (NDVI). Results show that the projected SFDs of the TP are estimated to decrease at rates of 0.100 cm/yr under the SSP126, 0.330 cm/yr under the SSP245, 0.565 cm/yr under the SSP370, and 0.750 cm/yr under the SSP585. Additionally, the SFD decreased at a rate of 0.160 cm/yr during the historical period from 1950 to 2014, which was between the decreasing rates of the SSP126 and SSP245 scenarios. The projected SFDs are negatively correlated with air temperature and precipitation, more significant under the higher emissions scenario. The projected decrease in SFDs will significantly impact eco-hydrological processes. A rapid decrease in SFD may lead to a decline in soil moisture content and have adverse impacts on vegetation growth. This research provides valuable insights into the future changes in SFD on the TP and their impacts on eco-hydrological processes.

Dynamic Response and Adaptation of Grassland Ecosystems in the Three-River Headwaters Region under Changing Environment: A Review

The Three-River Headwaters Region (TRHR) is crucial to the sustainable development of China and Southeast Asia. The sustainability of grassland ecosystems in the region has been seriously challenged in recent years. This paper reviewed the changes in the grasslands of the TRHR and their responses to climate change and human activities. The review showed that accurate monitoring of grassland ecological information is the basis for effective management. Although alpine grassland coverage and the above-ground biomass of the alpine grassland have generally increased in the region over the past 30 years, the degradation has not been fundamentally curbed. Grassland degradation substantially reduced topsoil nutrients and affected their distribution, deteriorated soil moisture conditions, and aggravated soil erosion. Grassland degradation led to loss of productivity and species diversity, and this is already harming the well-being of pastoralists. The "warm and wet" trend of the climate promoted the restoration of alpine grasslands, but widespread overgrazing is considered as one of the main reasons for grassland degradation, and related differences still exist. Since 2000, the grassland restoration policy has achieved fruitful results, but the formulation of the policy still needs to integrate market logic effectively and strengthen the understanding of the relationship between ecological protection and cultural protection. In addition, appropriate human intervention mechanisms are urgently needed due to the uncertainty of future climate change. For mildly and moderately degraded grassland, traditional methods are applicable. However, the severely degraded "black soil beach" needs to be restored by artificial seeding, and the stability of the plant-soil system needs to be emphasized to establish a relatively stable community to prevent secondary degradation.

Spatiotemporal differences and influencing factors of high-quality utilization of land resources in the Yellow River Basin of China

High-quality utilization of land resources (HULR) is critical to the security of land ecosystem and sustainable socioeconomic development. To promote HULR, we explored the spatiotemporal differences and influencing factors of HULR in the Yellow River Basin (an important ecological barrier in China) by using entropy method, spatial panel regression model, and geographically and temporally weighted regression model. We found that the HULR values were 0.22 to 0.28 from 2008 to 2019, showing an increasing trend with obvious spatiotemporal differences. The spatial connectivity, technological innovation, industrialization, industrial upgrading, and marketization are important factors influencing HULR, and different factors have different spatial effects in different regions. Therefore, the important principle of HULR is to pursue the sustainable land utilization within the ecological environment carrying capacity, taking into account the unique ecological and socioeconomic conditions of each region. We hope that our study can provide references for HULR around the world.

Inverse Trend in Runoff in the Source Regions of the Yangtze and Yellow Rivers under Changing Environments

The source regions of the Yangtze River (SRYZ) and the Yellow River (SRYR) are sensitive areas of global climate change. Hence, determining the variation characteristics of the runoff and the main influencing factors in this region would be of great significance. In this study, different methods were used to quantify the contributions of climate change and other environmental factors to the runoff variation in the two regions, and the similarities and differences in the driving mechanisms of runoff change in the two regions were explored further. First, the change characteristics of precipitation, potential evapotranspiration, and runoff were analyzed through the observational data of the basin. Then, considering the non-linearity and non-stationarity of the runoff series, a heuristic segmentation algorithm method was used to divide the entire study period into natural and impacted periods. Finally, the effects of climate change and other environmental factors on runoff variation in two regions were evaluated comprehensively using three methods, including the improved double mass curve (IDMC), the slope change ratio of cumulative quantity (SCRCQ), and the Budyko-based elasticity (BBE). Results indicated that the annual precipitation and potential evapotranspiration increased during the study period in the two regions. However, the runoff increased in the SRYZ and decreased in the SRYR. The intra-annual distribution of the runoff in the SRYZ was unimodal during the natural period and bimodal in the SRYR. The mutation test indicated that the change points of annual runoff series in the SRYZ and SRYR occurred in 2004 and 1989, respectively. The attribution analysis methods yielded similar results that climate change had the greatest effect on the runoff variation in the SRYZ, with a contribution of 59.6%~104.6%, and precipitation contributed 65.3%~109.6% of the increase in runoff. In contrast, the runoff variation in the SRYR was mainly controlled by other environmental factors such as permafrost degradation, land desertification, and human water consumption, which contributed 83.7%~96.5% of the decrease in the runoff. The results are meaningful for improving the efficiency of water resources utilization in the SRYZ and SRYR.

Spatiotemporal Reconstruction of MODIS Normalized Difference Snow Index Products Using U-Net with Partial Convolutions

Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product is one of the prevailing datasets for global snow monitoring, but cloud obscuration leads to the discontinuity of ground coverage information in spatial and temporal. To solve this problem, a novel spatial-temporal missing information reconstruction model based on U-Net with partial convolutions (PU-Net) is proposed to recover the cloud gaps in the MODIS Normalized Difference Snow Index (NDSI) products. Taking the Yellow River Source Region as a study case, in which the snow cover is characterized by shallow, fast-changing and complex heterogeneity, the MODIS NDSI product in the 2018–2019 snow season is reconstructed, and the reconstruction accuracy is validated with simulated cloud mask and in situ snow depth (SD) observations. The results show that under the simulated cloud mask scenario, the mean absolute error (MAE) of the reconstructed missing pixels is from 4.22% to 18.81% under different scenarios of the mean NDSI of the patch and the mask ratio of the applied mask, and the coefficient of determination (R2) ranges from 0.76 to 0.94. The validation based on in situ SD observations at 10 sites shows good consistency, the overall accuracy is increased by 25.66% to 49.25% compared with the Aqua-Terra combined MODIS NDSI product, and its value exceeds 90% at 60% of observation stations.

Community Assembly and Co-Occurrence Patterns of Microeukaryotes in Thermokarst Lakes of the Yellow River Source Area

Thermokarst lakes are important aquatic ecosystems in cold regions, experiencing several changes due to global warming. However, the fundamental assembly mechanisms of microeukaryotic communities in thermokarst lakes are unknown. In this study, we examined the assembly processes and co-occurrence networks of microeukaryotic communities in sediment and water of thermokarst lakes in the Yellow River Source Area. Sediment microeukaryotic communities had a significantly lower α-diversity but higher β-diversity than water microeukaryotic communities. pH, sediment organic carbon, and total phosphorus significantly affected taxonomic and phylogenetic diversity of sediment communities, while conductivity was a significant driver for water communities. Both sediment and water microeukaryotic communities were strongly governed by dispersal limitation. However, deterministic processes, especially homogenous selection, were more relevant in structuring microeukaryotic communities in water than those in sediment. Changes in total nitrogen and phosphorus in sediment could contribute to shift its microeukaryotic communities from homogeneous selection to stochastic processes. Co-occurrence networks showed that water microeukaryotic communities are more complex and interconnected but have lower modularity than sediment microeukaryotic communities. The water microeukaryotic network had more modules than the sediment microeukaryotic network. These modules were dominated by different taxonomic groups and associated to different environmental variables.

Retrieving dynamics of the surface water extent in the upper reach of Yellow River

Multi-time scale surface water extent (SWE) dynamics are very important to understand climate change impacts on water resources. With Landsat 5/7/8 images and Google Earth Engine (GEE), an improved threshold-based water extraction algorithm and a novel surface water gaps (SWGs) interpolation method based on historical water frequency were applied to build surface water area (SWA, namely SWE without ice) and water body area (WBA, namely SWE with ice) monthly (January 2001-December 2019) and annual (1986-2019) time series in the upper reaches of the Yellow River (UYR). The Mann-Kendall test was used to analyse SWE trends, and the ridge regression was performed to figure out the relative contributions of meteorological factors to SWE dynamics. The pixels with modified normalized difference water index (MNDWI) higher than normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) were identified as SWE. The mean relative error (MRE) of the SWGs interpolation results was below 10%. At the annual scale, the average SWA and number of lakes over 1 ha showed significant upward trends of 4.4 km² yr^-1 and 7.53 yr^-1, respectively. The monthly WBA increased in summer and autumn while decreased in spring and winter. The maximum freezing and thawing ratios were 53.74% in December and 37.32% in May, respectively. Attribution analysis showed that precipitation and wind speed were the foremost factors dominating the dynamics of annual SWA and monthly WBA, respectively. Our findings confirmed that climatic changes have altered the dynamics of water bodies in the UYR.

Changes in climate and snow cover and their synergistic influence on spring runoff in the source region of the Yellow River

As an important runoff-producing area, the runoff variation in the source region of the Yellow River (SRYR) has critical importance for the whole basin in broad aspects. In recent decades, the climate in the SRYR has undergone drastic changes, which affected runoff across different time scales. Many studies have focused on runoff in the SRYR with a long-time series, and presented a discordant relationship between precipitation and runoff. However, differences in this relationship over different time scales are ignored. Here, by using multi-source observation data and correlation analysis, climate elasticity, and principal component analysis methods, we document the changes in climate and snow cover and their synergistic influence on spring runoff. When the 20-year period was innovatively adopted, the runoff and precipitation coincided well during last three periods (1960-2019). The yearly precipitation presented a bimodal pattern, with the most significant increase in late spring and early summer. A bimodal pattern also appeared in annual runoff, and the rate of increase was much greater than that of precipitation (2.51%/year vs 1.01%/year). The runoff during main increase period (particularly in April) showed a high correlation with the remote sensing snow depth from November to March, but a poor correlation with snow depth from meteorological stations. Climate warming in the SRYR was much more reflected in minimum surface temperature (0.235 °C/year) than in air temperature minimum (0.081 °C/year) in last 20 years. However, the principal component analysis shows that the effect of temperature on spring runoff was obviously less than that of snow cover. A 1% variation in snow depth in the SRYR from November to March caused a 0.43% variation in runoff in April, and a 1% variation in snow days caused a 0.82% variation in runoff. This study will bring to light for understanding the evolution mechanism of spring runoff in the SRYR.

Perspectives on emerging pressures and their integrated impact on large river systems: An insight from the Yellow River basin

The Yellow River, with a developmental and historical significance to China, is now facing several emerging pressures, which are degrading the river status and creating challenges for high-quality development in the basin. Numerous studies on such emerging pressures, present scattered outcomes, and trigger uncertainties and deficient assumptions on the river's problems. This review integrated such scattered information and investigated the emerging pressures, their drivers and integrated impacts at the basin level. The study intended to prioritize those pressures needing expeditious consideration, and carried a discussion on the alternative pathways to the solution. To determine the critical emerging pressures, a literature review was conducted and experts' opinion was sought. The outcome further led to a comprehensive review, data collection, and analysis of three groups of emerging pressures. The review recognized 'Water Stress' in the lower reach, primarily caused by an abated flow, as the most distressing emerging pressure inflicting social, ecological, and economic consequences. Such decline in flow was mostly induced by a recent increase in 'Anthropogenic activities', such as intensive water withdrawal for irrigation (≥27 BCM), and construction of check dams in the Loess Plateau region (trapping~5 BCM water). The increasing 'Pollution' in the river, besides threatening public health and ecology, also contributed to the water stress by rendering certain stretches of the river biologically dead and unsuitable for any use. The 'Climate Change', with its key negative effect on precipitation in the middle sub-basin, overall contributed small (8-11 %) to the observed reduction in river flow. With increasing challenges for the adopted engineering solutions tackling the water stress, the study suggested the use of a demand management approach, employing adaptive policy measures, as an alternative or supplementary solution to the current approach. In addition, the study highlights that regular reviewing and reforming the key decisions based on evidence and updated information, and taking a participatory approach, may offer a sustainable pathway to the environment as well as socio-economic goals.

Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features

BACKGROUND

Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products.

METHODS

This study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated.

RESULTS

(1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm).

CONCLUSION

This study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland.

Spatial and Temporal Shifts in Historic and Future Temperature and Precipitation Patterns Related to Snow Accumulation and Melt Regimes in Alberta, Canada

Shifts in winter temperature and precipitation patterns can profoundly affect snow accumulation and melt regimes. These shifts have varying impacts on local to large-scale hydro-ecological systems and freshwater distribution, especially in cold regions with high hydroclimatic heterogeneity. We evaluate winter climate changes in the six ecozones (Mountains, Foothills, Prairie, Parkland, Boreal, and Taiga) in Alberta, Canada, and identify regions of elevated susceptibility to change. Evaluation of historic trends and future changes in winter climate use high-resolution (~10 km) gridded data for 1950–2017 and projections for the 2050s (2041–2070) and 2080s (2071–2100) under medium (RCP 4.5) and high (RCP 8.5) emissions scenarios. Results indicate continued declines in winter duration and earlier onset of spring above-freezing temperatures from historic through future periods, with greater changes in Prairie and Mountain ecozones, and extremely short or nonexistent winter durations in future climatologies. Decreases in November–April precipitation and a shift from snow to rain dominate the historic period. Future scenarios suggest winter precipitation increases are expected to predominantly fall as rain. Additionally, shifts in precipitation distributions are likely to lead to historically-rare, high-precipitation extreme events becoming more common. This study increases our understanding of historic trends and projected future change effects on winter snowpack-related climate and can be used inform adaptive water resource management strategies.

Analyzing Changes in Frozen Soil in the Source Region of the Yellow River Using the MODIS Land Surface Temperature Products

The degradation of the frozen soil in the Qinghai–Tibetan Plateau (QTP) caused by climate warming has attracted extensive worldwide attention due to its significant effects on the ecosystem and hydrological processes. In this study, we propose an effective approach to estimate the spatial distribution and changes in the frozen soil using the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products as inputs. A comparison with in-situ observations suggests that this method can accurately estimate the mean daily land surface temperature, the spatial distribution of the permafrost, and the maximum thickness of the seasonally-frozen ground in the source region of the Yellow River, located in the northeastern area of the QTP. The results of The Temperature at the Top of the Permafrost model indicates that the area of permafrost in the source region of the Yellow River decreased by 4.82% in the period from 2003 to 2019, with an increase in the areal mean air temperature of 0.35 °C/10 years. A high spatial heterogeneity in the frozen soil changes was revealed. The basin-averaged active layer thickness of the permafrost increased at a rate of 5.46 cm/10 years, and the basin-averaged maximum thickness of the seasonally-frozen ground decreased at a rate of 3.66 cm/10 years. The uncertainties in calculating the mean daily land surface temperature and the soil’s thermal conductivity were likely to influence the accuracy of the estimation of the spatial distribution of the permafrost and the maximum thickness of the seasonally-frozen ground, which highlight the importance of the better integration of field observations and multi-source remote sensing data in order to improve the modelling of frozen soil in the future. Overall, the approach proposed in this study may contribute to the improvement of the application of the MODIS land surface temperature data in the study of frozen soil changes in large catchments with limited in-situ observations in the QTP.

Terrestrial Water Storage Change Retrieved by GRACE and Its Implication in the Tibetan Plateau: Estimating Areal Precipitation in Ungauged Region

The Tibetan Plateau (TP) is referred to as the water tower of Asia, where water storage and precipitation have huge impacts on most major Asian rivers. Based on gravity recovery and climate experiment data, this study analyzed the terrestrial water storage (TWS) changes and estimated areal precipitation based on the water balance equation in four different basins, namely, the upper Yellow River (UYE), the upper Yangtze River (UYA), the Yarlung Zangbo River (YZ), and the Qiangtang Plateau (QT). The results show that the TWS change exhibits different patterns in the four basins and varies from −13 to 2 mm/year from 2003 to 2017. The estimated mean annual precipitation was 260 ± 19 mm/year (QT), 697 ± 26 mm/year (UYA), 541 ± 36 mm/year (UYE), and 1160 ± 39 mm/year (YZ) which performed better than other precipitation products in the TP. It indicates a potential method for estimating basin-scale precipitation through integrating basin average precipitation from the water balance equation in the poorly gauged and ungauged regions.

A Modified ABCD Model with Temperature-Dependent Parameters for Cold Regions: Application to Reconstruct the Changing Runoff in the Headwater Catchment of the Golmud River, China

The runoff changes due to global warming in hydrological basins in the Qinghai–Tibetan Plateau (QTP) have received worldwide attention. The headwater catchment of the Golmud River, located in the northern QTP, is the main source of water resources for the Golmud city in an arid region but has been poorly known for the hydroclimatological behaviors. In this study, a widely-used hydrological model, the ABCD model (Thomas, H.A., Washington, DC, USA), is modified by incorporating temperature-dependent hydrological processes and groundwater evapotranspiration in cold regions with a few additional parameters. The new model is used to reconstruct the monthly runoff in the past decades for the headwater catchment of the Golmud River and performs better than other comparable models. As indicated, the annual snowmelt runoff increased with the increasing air temperature and became more concentrated in April than in May. The frozen soil degradation could increase the hydraulic conductivity of soils and lead to a rise in cold season runoff. The groundwater level in the Golmud city was positively correlated to the annual runoff in the headwater catchment of the Golmud River, indicating that an increase of the groundwater level could be triggered by the rising trend in the streamflow of the Golmud River. This study suggests a useful hydrological model for the groundwater management in the Golmud city.

Spatiotemporal Variation of NDVI in the Vegetation Growing Season in the Source Region of the Yellow River, China

Research on vegetation variation is an important aspect of global warming studies. The quantification of the relationship between vegetation change and climate change has become a central topic and challenge in current global change studies. The source region of the Yellow River (SRYR) is an appropriate area to study global change because of its unique natural conditions and vulnerable terrestrial ecosystem. Therefore, we chose the SRYR for a case study to determine the driving forces behind vegetation variation under global warming. Using the Normalized Difference Vegetation Index (NDVI) and climate data, we investigated the NDVI variation in the growing season in the region from 1998 to 2016 and its response to climate change based on trend analysis, the Mann–Kendall trend test and partial correlation analysis. Finally, an NDVI–climate mathematical model was built to predict the NDVI trends from 2020 to 2038. The results indicated the following: (1) over the past 19 years, the NDVI showed an increasing trend, with a growth rate of 0.00204/a. There was an upward trend in NDVI over 71.40% of the region. (2) Both the precipitation and temperature in the growing season showed upward trends over the last 19 years. NDVI was positively correlated with precipitation and temperature. The areas with significant relationships with precipitation covered 31.01% of the region, while those with significant relationships with temperature covered 56.40%. The sensitivity of the NDVI to temperature was higher than that to precipitation. Over half (56.58%) of the areas were found to exhibit negative impacts of human activities on the NDVI. (3) According to the simulation, the NDVI will increase slightly over the next 19 years, with a linear tendency of 0.00096/a. From the perspective of spatiotemporal changes, we combined the past and future variations in vegetation, which could adequately reflect the long-term vegetation trends. The results provide a theoretical basis and reference for the sustainable development of the natural environment and a response to vegetation change under the background of climate change in the study area.

Quantified hydrological responses to permafrost degradation in the headwaters of the Yellow River (HWYR) in High Asia

The impact on the hydrologic cycle of permafrost degradation under the influence of climate change has caused an inestimable threat to sustainable regulation of the ecosystem. This study quantified the responses of main hydrological elements, including soil moisture, groundwater, runoff components and discharge to totally degraded permafrost in eastern High Asia by establishing cases with and without thermodynamics using a cold region model combining hydrological processes and thermodynamics. The results showed that the model successfully simulated discharge in cold region basins. Totally degraded permafrost decreased soil moisture in the vadose zone (SMV) and increased the absolute depth to ground water (ADGW). In the daily scale, total permafrost degradation decreased the direct flow in autumn, slightly increased direct flow in spring and decreased interflow in summer. Total permafrost degradation also increased daily baseflow all year round and by >50% in spring, decreased daily discharge during autumn and increased daily discharge during spring. In the annual scale, total permafrost degradation increased direct flow, baseflow, and discharge, and decreased interflow. The magnitudes of these changes were positively related to the ratios of permafrost to the subbasin area. The responses of daily runoff components and discharge to totally degraded permafrost were significantly larger than the annual value. The groundwater level, direct flow and baseflow were far more sensitive to permafrost degradation than SMV, interflow and discharge. The responses of annual individual hydrological elements were more obvious than the annual discharge. These quantified results can be extensively used in lumped hydrology simulations, water resource assessments and eco-system management for partial permafrost degradation.

Climate Change Impacts on Cold Season Runoff in the Headwaters of the Yellow River Considering Frozen Ground Degradation

Climate change has effects on hydrological change in multiple aspects, particularly in the headwaters of the Yellow River (HWYR), which is widely covered by climate-sensitive frozen ground. In this study, the annual runoff was partitioned into four runoff compositions: winter baseflow, snowmelt runoff, rainy season runoff, and recession flow. In addition, the effects of global warming, precipitation change, and frozen ground degradation were considered in long-term variation analyses of the runoff compositions. The moving t-test was employed to detect change points of the hydrometeorological data series from 1961 to 2013, and flow duration curves were used to analyze daily runoff regime change in different periods. It was found that the abrupt change points of cold season runoff, such as recession flow, winter baseflow, and snowmelt runoff, are different from that of the rainy season runoff. The increase in winter baseflow and decrease in snowmelt runoff at the end of 1990s was closely related to global warming. In the 21st century, winter baseflow presented a larger relative increase compared to rainy season runoff. The correlation analyses indicate that winter baseflow and snowmelt runoff are mainly controlled by water-resource-related factors, such as rainy season runoff and the accumulated precipitation in cold season. To analyze the global warming impacts, two runoff coefficients—winter baseflow discharge rate (Rw) and direct snowmelt runoff coefficients (Rs)—were proposed, and their correlation with freezing–thawing indices were analyzed. The increase of Rw is related to the increase in the air temperature thawing index (DDT), but Rs is mainly controlled by the air temperature freezing index (DDF). Meanwhile, the direct snowmelt runoff coefficient (Rs) is significantly and positively correlated to DDF and has decreased at a rate of 0.0011/year since 1980. Under global warming, the direct snowmelt runoff (runoff increment between March to May) of the HWYR could decrease continuously in the future due to the decrease of accumulative snow in cold season and frozen ground degradation. This study provides a better understanding of the long-term runoff characteristic changes in the HWYR.

Three-dimensional distribution of permafrost and responses to increasing air temperatures in the head waters of the Yellow River in High Asia

Fine-scale three-dimensional (3D) permafrost distributions at the basin scale are currently lacking. They are needed to monitor climate and ecosystem change and for the maintenance of infrastructure in cold regions. This paper determined the horizontal and vertical distributions of permafrost and its quantitative responses to climate warming in the High Asia region by constructing a quasi-3D model that couples heat transfer and water movement and is forced by spatially-interpolated air temperatures using an elevation-dependent regression method. Four air temperature scenarios were considered: the present state and air temperature increases of 1, 2 and 3 °C. A fine-scale permafrost map was constructed. The map considered taliks and local factors including elevation, slope and aspect, and agreed well with field observations. Permafrost will experience severe degradation with climate warming, with decreases in area of 36% per degree increase in air temperature, increases in the depth-to-permafrost table of 2.67 m per degree increase in air temperature, and increases in 15 m-depth ground temperatures of 1.25 °C per degree increase in air temperature. Permafrost is more vulnerable in and beside river valleys than in high mountains, and on sunny rather than shady slopes. These results provide an effective reference for permafrost prediction and infrastructure and ecosystem management in cold regions affected by global warming.

River health assessment of the Yellow River source region, Qinghai-Tibetan Plateau, China, based on tolerance values of macroinvertebrates

For decades, the river health of the Yellow River source region (YRSR) on the Qinghai-Tibetan Plateau has been a focal issue owing to its unique geographic location and ecological functions. This study investigated the ecological status of the headwater streams, the main stem, and the tributaries of the Yellow River in the YRSR using the tolerance values of macroinvertebrates and those related to biotic indices. The macroinvertebrate assemblages of the headwater streams were characterized by lower biodiversity than the tributaries downstream, based on comparisons of taxonomical composition, functional feeding group composition, and the pollution-tolerant capacity of taxa. The headwater streams had a lower ratio (16%) of pollution-sensitive macroinvertebrate taxa than that of the tributaries downstream (30%). The biotic indices (family- and genus-level biotic indices) indicated that the ecological health of the headwater streams was comparably poorer than that of the downstream tributaries. The combined effect of vulnerable natural conditions and increasing human disturbance is likely the main cause of eco-environmental degradation in the Yellow River headwater streams.

Land Use and Land Cover Change in the Kailash Sacred Landscape of China

Land use and land cover change (LUCC) is an important driver of ecosystem function and services. Thus, LUCC analysis may lay foundation for landscape planning, conservation and management. It is especially true for alpine landscapes, which are more susceptible to climate changes and human activities. However, the information on LUCC in sacred landscape is limited, which will hinder the landscape conservation and development. We chose Kailash Sacred Landscape in China (KSL-China) to investigate the patterns and dynamics of LUCC and the driving forces using remote sensing data and meteorological data from 1990 to 2008. A supervised classification of land use and land cover was established based on field survey. Rangelands presented marked fluctuations due to climatic warming and its induced drought, for example, dramatic decreases were found in high- and medium-cover rangelands over the period 2000–2008. And recession of most glaciers was also observed in the study period. Instead, an increase of anthropogenic activities accelerated intensive alteration of land use, such as conversion of cropland to built-up land. We found that the change of vegetation cover was positively correlated with growing season precipitation (GSP). In addition, vegetation cover was substantially reduced along the pilgrimage routes particularly within 5 km of the routes. The findings of the study suggest that climatic warming and human disturbance are interacted to cause remarkable LUCC. Tourism development was responsible land use change in urban and pilgrimage routes. This study has important implications for landscape conservation and ecosystem management. The reduction of rangeland cover may decrease the rangeland quality and pose pressure for the carrying capacity of rangelands in the KSL-China. With the increasing risk of climate warming, rangeland conservation is imperative. The future development should shift from livestock-focus animal husbandry to service-based ecotourism in the sacred landscape.

Data-driven mapping of the spatial distribution and potential changes of frozen ground over the Tibetan Plateau

Frozen ground degradation profoundly impacts the hydrology, ecology and human society on the Tibetan Plateau (TP) and the downstream regions. The spatial distribution and potential changes of permafrost and maximum thickness of seasonally frozen ground (MTSFG) on the TP is of great importance and needs more in-depth studies. This study maps the permafrost and MTSFG distribution in the baseline period (2003-2010) and in the future (2040s and 2090s) with 1-km resolution. Logistic regression (LR), support vector machine (SVM) and random forest (RF) are validated using 106 borehole observations and proved to be applicable in estimating permafrost distribution. According to the majority voting results of the three algorithms, 45.9% area of the TP is underlain by permafrost in the baseline period, and respectively 25.9% and 43.9% of the current permafrost will disappear by the 2040s and the 2090s projected by mean of the projections from the five General Circulation Models under the Representative Concentration Pathway 4.5 scenario. SVM performs better in spatial generalization than RF based on the results of nested cross validation. According to the MTSFG results derived from SVM, the most dramatic decrease in MTSFG will occur in the southwestern TP, which is projected to exceed 50 cm in the 2090s compared with the baseline period. This study introduces the statistics and machine learning algorithms to frozen ground estimation on the TP, and the high resolution permafrost and MTSFG maps produced by this study can provide useful information for future studies on the third pole region.

Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000-2016 on the Tibetan Plateau

Climate warming on the Tibetan Plateau tends to induce an uphill shift of temperature isolines. Observations and process-based models have both shown that climate warming has resulted in an increase in vegetation greenness on the Tibetan Plateau in recent decades. However, it is unclear whether the uphill shift of temperature isolines has caused greenness isolines to shift upward and whether the two shifts match each other. Our analysis of satellite observed vegetation greenness during the growing season (May-Sep) and gridded climate data for 2000-2016 documented a substantial mismatch between the elevational shifts of greenness and temperature isolines. This mismatch is probably associated with a lagging response of greenness to temperature change and with the elevational gradient of greenness. The lagging response of greenness may be associated with water limitation, resources availability, and acclimation. This lag may weaken carbon sequestration by Tibetan ecosystems, given that greenness is closely related to primary carbon uptake and ecosystem respiration increases exponentially with temperature. We also found that differences in terrain slope angle accounted for large spatial variations in the elevational gradient of greenness and thus the velocity of elevational shifts of greenness isolines and the sensitivity of elevational shifts of greenness isolines to temperature, highlighting the role of terrain effects on the elevational shifts of greenness isolines. The mismatches and the terrain effect found in this study suggest that there is potentially large micro-topographical difference in response and acclimation/adaptation of greenness to temperature changes in plants. More widespread in situ measurements and fine-resolution remote sensing observations and fine-gridded climate data are required to attribute the mismatch to specific environmental drivers and ecological processes such as vertical changes in community structure, plant physiology, and distribution of species.

Detection of hydrological variations and their impacts on vegetation from multiple satellite observations in the Three-River Source Region of the Tibetan Plateau

The Three-River Source Region (TRSR) of the Tibetan Plateau (TP) is regarded as the "Chinese water tower". Climate warming and the associated degradation of permafrost might change the water cycle and affect the alpine vegetation growth in the TRSR. However, the quantitative changes in the water budget and their impacts on the vegetation in the TRSR are poorly understood. In this study, the spatial-temporal changes in the hydrological variables and the normalized difference vegetation index (NDVI) during 2003-2014 were investigated using multiple satellite data and a remote sensing energy balance model. The results indicated that precipitation showed an increasing trend at a rate of 14.0 mm 10 a^-1, and evapotranspiration (ET) showed a slight decreasing trend. The GRACE-derived total water storage (TWS) change presented a significant increasing trend at a rate of 35.1 mm a^-1. The change in groundwater (GW) which showed an increasing trend at a rate of 18.5 mm a^-1, was estimated by water budget. The time lag of the GRACE-TWS that was influenced by precipitation was more obviously than was the GLDAS-SM (Soil Moisture) change. The vegetation in the TRSR was greening during the study period, and the accumulation of the NDVI increased rapidly after 2008. The effect of total TWS and GLDAS-SM on vegetation was considerably more than that the effects of other factors in this region. It was concluded that the hydrological cycle had obviously changed and that more soil water was transferred into the GW since the aquiclude changed due to climate warming. The increasing area and number of lakes and the thickening of the active layer in the permafrost area led to the greater infiltration of surface water into the groundwater, which resulted in increased water storage.

Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the Qinghai-Tibet Plateau

Multisource reanalysis datasets provide an effective way to help us understand hydrological processes in inland alpine regions with sparsely distributed weather stations. The accuracy and quality of two widely used datasets, the China Meteorological Assimilation Driving Datasets to force the SWAT model (CMADS), and the Climate Forecast System Reanalysis (CFSR) in the Qinghai-Tibet Plateau (TP), were evaluated in this paper. The accuracy of daily precipitation, max/min temperature, relative humidity and wind speed from CMADS and CFSR are firstly evaluated by comparing them with results obtained from 131 meteorological stations in the TP. Statistical results show that most elements of CMADS are superior to those of CFSR. The average correlation coefficient (R) between the maximum temperature and the minimum temperature of CMADS and CFSR ranged from 0.93 to 0.97. The root mean square error (RMSE) for CMADS and CFSR ranged from 3.16 to 3.18 °C, and ranged from 5.19 °C to 8.14 °C respectively. The average R of precipitation, relative humidity, and wind speed for CMADS are 0.46; 0.88 and 0.64 respectively, while they are 0.43, 0.52, and 0.37 for CFSR. Gridded observation data is obtained using the professional interpolation software, ANUSPLIN. Meteorological elements from three gridded data have a similar overall distribution but have a different partial distribution. The Soil and Water Assessment Tool (SWAT) is used to simulate hydrological processes in the Yellow River Source Basin of the TP. The Nash Sutcliffe coefficients (NSE) of CMADS+SWAT in calibration and validation period are 0.78 and 0.68 for the monthly scale respectively, which are better than those of CFSR+SWAT and OBS+SWAT in the Yellow River Source Basin. The relationship between snowmelt and other variables is measured by GeoDetector. Air temperature, soil moisture, and soil temperature at 1.038 m has a greater influence on snowmelt than others.