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

Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer-CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA's land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.

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.

Distribution, Source Identification, and Output flux of Barium in Surface Waters in the Sanjiangyuan Region and Qilian Mountain Region of Tibetan Plateau

Water safety concerning Barium (Ba) has become a public issue worldwide. As the "Asian water tower", Tibetan Plateau is the birthplace of many rivers. However, the distribution, source, and output flux of Ba are largely unknown. In this study, surface water samples were collected from different catchments in the Sanjiangyuan Region (SJY) and the Qilian Mountain Region (QLM) in Tibetan Plateau. The concentration of Ba was determined by inductively coupled plasma optical emission spectroscopy, the source of Ba was discussed by a Gibbs diagram, and the output flux of Ba was estimated using the observation data from different hydrological stations. The results showed that the Ba concentrations were less than 160 µg/L, which is much lower than the guideline value of 700 µg/L for surface waters. The main sources of Ba were rock weathering and evaporation concentration. The total Ba output flux from SJY and QLM to downstream waters was 1,240 t/yr, which accounts for about 0.01% of the global freshwater Ba output flux to the ocean. The Ba production rate in Tibetan Plateau was comparable with that in the Arctic rivers. Under the scenario of global warming, water safety issues concerning Ba will be more serious since the output flux of Ba to downstream waters will be increased by intensified rock weathering, evaporation concentration, glacial retreat, and permafrost thawing. This study reveals the Ba flux and production rate in Tibetan Plateau, which will provide important information for evaluating the environmental impact of global warming on public health.

Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau

Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis-NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis-NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK₂O), and total phosphorus pentoxide (TP₂O₅)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis-NIR and TN, SOM, TK₂O, and TP₂O₅ data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30-40, 50-60, 30-35, and 25-35 for TK₂O, TN, TP₂O₅, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK₂O, TN, TP₂O₅, and SOM.

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.

Terrestrial water storage regime and its change in the endorheic Tibetan Plateau

Analogous to flow regime, this study proposed a new statistical framework to assess inter-annual and intra-annual terrestrial water storage (TWS) regime and its changes from the aspects of magnitude, variability, duration and components. The framework was applied to two endorheic basins, Inner Basin (IB) and Qaidam Basin (QB), in the Tibetan Plateau and their eight sub-regions. Our major findings are as follows: (1) TWS in the IB (2.09-2.35 mm/a, P < 0.05) and QB (0.05-0.52 mm/a, P > 0.1) increased in all seasons from 1989 to 2019 with regional climate warming and wetting. TWS showed high increase rates (>4.50 mm/a, P < 0.05) in northeastern IB but decrease rates (<-0.90 mm/a) in southern IB. Seasonal total storage in groundwater, lake, permafrost and glacier (GLPIA) also increased in both the IB (2.55-2.68 mm/a, P < 0.05) and QB (0.05-0.43 mm/a). Seasonal soil water storage (SWA) decreased in the IB (-0.39 to -0.26 mm/a) and slightly increased in the QB (0.002-0.08 mm/a); (2) Intra-annual TWS followed approximately a cosine curve. After mutation, monthly TWS showed a higher positive magnitude change (>50 mm), accompanied by a longer duration and higher variability in the IB and its northeastern sub-regions. There was a large reduction in low storage (-18.25 mm) combined with higher variability in southeastern IB; (3) SWA change dominated the storage surplus in summer (82%) and storage deficit in autumn (-78%) and winter (-51%) in the IB, while GLPIA change dominated the storage surplus in spring (57%). In the QB, TWS change was mainly contributed by SWA change in spring (94%) and by GLPIA change in summer (73%), autumn (-62%) and winter (-58%). Component contribution rates showed a significant change in spring and winter but not much change in summer and autumn, indicating that the TWS components were more sensitive to climate change in the cold season.

Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

Identifying vegetation restoration effectiveness and driving factors on different micro-topographic types of hilly Loess Plateau: From the perspective of ecological resilience

Vegetation restoration is an important way to improve the sustainability of the ecosystem in the hilly Loess Plateau. The variation of vegetation coverage, caused by the combined effects of meteorological factors and human activities, reflects the succession trend of regional ecosystems. Given the complexity and the diversity of landform in the hilly Loess Plateau, vegetation restoration is more affected by topographic factors. Nevertheless, few studies have considered the characteristics and trends of vegetation restoration under different micro-topographic types in the long-time series. From the perspective of ecological resilience based on the fractional vegetation cover (FVC), the trend, the hurst exponent, and the geographical spatial research were used to analyze the variation and future sustainability of vegetation restoration on different micro-topographic types for 20 years. Besides, the spatial autocorrelation, principal component analysis (PCA) and geographically weighted regression (GWR) were applied to identify the driving factors of vegetation restoration. The results showed: (1) the average of the overall regional vegetation coverage was 61.32%, and only 0.95% of the regional vegetation was degraded in the past 20 years. However, in the future, 69.87% of the area would be degraded from improvement, and 0.52% would be significantly decreased; (2) the vegetation coverage in descending order was as follows: ridge area with shady and steep slope, gully area with shady and steep slope, ridge area with sunny and steep slope, gully area with sunny and steep slope, gully area with shady and gentle slope, ridge area with shady and gentle slope, ridge area with sunny and gentle slope, gully area with sunny and gentle slope, valley area; (3) the difference of vegetation degradation among micro-topography was remarkable, and the valley area and gully area with sunny and steep slope have the greatest decrease; (4) the primary factors affecting vegetation restoration in the hilly Loess Plateau were temperature, moisture, soil quality, and social economical condition, and the dominant factors were various under different micro-topographic types and villages. Therefore, it is necessary to adjust ecological engineering measures by comprehensively considering the regional differences among dominant factors of vegetation restoration.

Trends in Satellite Earth Observation for Permafrost Related Analyses—A Review

Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.) and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet) will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.

Temporal and Spatial Variations in the Leaf Area Index and Its Response to Topography in the Three-River Source Region, China from 2000 to 2017

The Three-River Source Region (TRSR) is an important area for the ecological security of China. Vegetation growth has been affected by the climate change, topography, and human activities in this area. However, few studies have focused on analyzing time series tendencies of vegetation change in various terrain conditions. To address this issue in the TRSR, this study explored vegetation stability, tendency, and sustainability with multiple methods (e.g., coefficient of variation, Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index) based on the 2000–2017 Global LAnd Surface Satellite Leaf Area Index (GLASS LAI) product. The differentiation patterns of LAI variations and multiyear mean LAI value under different topographic factors were also investigated in combination with digital elevation model (DEM). The results showed that (1) the mean LAI value in the study area increased, with a linear tendency of 0.013·10 a−1; (2) LAI values decreased from southeast to northwest in terms of spatial distribution and the CV indicated LAI variations were relatively stable; (3) the trend analysis revealed that the improved area of LAI accounted for 62.72% which was larger than the degraded area (37.28%), and hurst index revealed a weak anti-sustaining effect of the current tendencies; and (4) the increasing trend was found in multiyear mean LAI value as relief amplitude and slope increased, while LAI stability improved with increasing slope. They exhibited a clear regular pattern. Moreover, significant improvement in LAI generally occurred in low-altitude and flat areas. Finally, the overall improvement and sustainability of LAI improved when moving from sunny aspects to shady aspects, but the LAI stability decreased. Note that vegetation degradation was observed in some high slope areas and was further aggravated. This study is beneficial for revealing the spatial and temporal changes of LAI and their changing rules as a function of different topographic factors in the TRSR. Meanwhile, the results of this study provide theoretical support for sustainable development of this area.

Estimation of the Qinghai-Tibetan Plateau runoff and its contribution to large Asian rivers

The Qinghai-Tibetan Plateau (QTP), named the Asian Water Towers, feeds more than 2.5 billion people in downstream regions. It is still unknown how much water outflows from this region owing to lack of observations. The main objective of this study is to clarify availability of water flowed out of this region and its contribution to large Asian rivers. The Global Land Data Assimilation System (GLDAS) products are evaluated with the help of observations of the QTP. In addition, a velocity-based routing method is embedded into the GLDAS model to route runoff products to the basin outlet in this study. The results show that the simulated dry season runoff in the GLDAS model is generally lower than the observed value, which is mainly because most hydrological models only consider the potential evapotranspiration (ET) when simulating ET, while ignoring the water constraint factor. Noah10_v2.0 has the highest precision at the QTP. For the monthly precipitation and runoff series, the relative error is within 5%, the correlation coefficient is greater than 0.90, and the Nash-Sutcliffe efficiencies are 0.95 and 0.76, respectively. Glacier melt runoff plays an important role in the QTP runoff, with a proportion of approximately 22%. It is relatively high in the Tarim River basin (83%), Syr Darya River and Amu Darya River basins (69%), and Indus River basin (60%). The contribution ratio also reaches 23% in the Yarlung Zangbo-Brahmaputra River and Ganges River basins, whereas it is the lowest in the Irrawaddy River basin (2%). According to the Noah10_v2.0 simulations, the mean annual runoff provided by the QTP exceeds 620 billion cubic metres, of which approximately 440 billion cubic metres flow out of the QTP and supply downstream regions of international rivers. The contribution ratio of the QTP runoff to the total runoff of its affected basins is approximately 16%.

Sensitivity of global major crop yields to climate variables: A non-parametric elasticity analysis

Climate variability controls crop yield variability with impacts on food security at the local, regional and global levels. This study uses non-parametric elasticity to investigate the sensitivity of crop yields of the top four global crops (wheat, rice, maize, and soybean) to three climate variables (precipitation (PRE), potential evapotranspiration (PET), and mean air temperature (TMP)). Trends and serial correlations exist in both climate variables and crop yields over the study period (1961 to 2014). To overcome this limitation, the Trend Free Pre-Whitening (TFPW) method was applied. Crop yields are most sensitive to TMP globally. But the exact sensitivity varies across continents. The highest sensitivity regions are located in parts of the Southeast Asia. Wheat yields are more sensitive to TMP in Western Europe and Northern America, whereas maize has higher sensitivity to TMP for regions located in South America and parts of Eastern and Western Africa. Soybean is more sensitive in North and South America. The elasticities of wheat and rice yields to TMP are negative in most of the regions (i.e. increased TMP decreases yield), whereas maize witnessed positive and soybean witnessed mixed positive and negative signals depending on the region. PRE has lower influence on crop yields. The non-parametric elasticity concept is a simple and an efficient approach that complements the existing linear models methods used to detect climate change impacts on crop yields and can be used to investigate the future consequences of climate change on local to global scale agricultural production.

Climate warming benefits alpine vegetation growth in Three-River Headwater Region, China

Environmental factors that drive vegetation change in the Three River Headwater Region (TRHR) on Qinghai-Tibetan Plateau are largely unknown. In particular, the response of alpine grasslands in the TRHR to changing climate and ecological compensations is still poorly understood. Here, we present data on vegetation trends of the TRHR from 1982 to 2015 by employing multiple high-resolution satellite data to determine the mean annual normalized difference vegetation index (NDVI). In addition, spatio-temporal changes in climate were monitored by long-term climate data collection and by using the distributed modeling system. It emerged that: 1) there was a weak increasing trend, albeit not significant, in overall TRHR NDVI, ranging between 0.23 and 0.27; whereas, grassland NDVI ranged between 0.43 and 0.50, and displayed a significant (r²_adj = 0.46; P = 0.004) linear increase with year; 2) annual average temperature was below 0 °C and increased linearly (r²_adj = 0.60; P = 0.01) at a rate of 0.06 °C/yr from 2000 to 2015, which was almost four times faster than the rate of global warming; and 3) average rainfall was 493 mm/yr, with no significant yearly trend. In conclusion, climate warming enhanced vegetation growth and recovery in the TRHR since 2000; whereas, rainfall did not show a trend. However, vegetation changes on the spatial scale demonstrated zoning and segmentation effects. Consequently, for restoration of degraded lands in the TRHR, effective one-to-one ecological conservation projects, which are particular to an eco-fragile area, should be implemented. In addition, these results are important for regional planning of livestock stocking rates and animal husbandry systems, which can have great impact on the livelihood of the people in the area.

Analysis on impacts of hydro-climatic changes and human activities on available water changes in Central Asia

Water resources in Central Asia are very scarce due to natural and anthropogenic impacts. Water shortages have been a major factor hampering the socio-economic development of Central Asia. Exploring internal interactions among climate change, human activities and terrestrial hydrological cycles will help to improve the management of water resources in Central Asia. In this paper, hydro-climatic and anthropogenic data for the period 2003-2016 from the Gravity Recovery and Climate Experiment (GRACE), the Global Land Data Assimilation System (GLDAS), the Climatic Research Unit (CRU) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to analyze the influence of natural factors and human activities on changes of available water (AWC). The terrestrial water storage derived from GRACE and GLDAS remarkably declined in 2008, due to a serious drought, but increased thereafter. The AWC positively responded to the vegetation index, evapotranspiration, potential evapotranspiration and air temperature at a lag of 0-1 month, but to precipitation at a lag of 2-3 months. Results of correlation analysis with a spatial square moving window indicated that forests, grasses, croplands and water areas presented significantly positive correlations with AWC, while barren areas and urban areas were negatively correlated with AWC. According to the Boruta algorithm and the Random Forest model, natural factors, namely precipitation, evapotranspiration and potential evapotranspiration, were major factors for AWC in the whole Central Asia. Human activities had direct and indirect impacts on AWC. With the development of society and economy, croplands and urban areas gradually increased, resulting in a rising demand for water withdrawals for agriculture irrigation and industry. The unreasonable utilization and exploitation of water resources led to vegetation degradation and ecosystem deterioration, which would worsen the shortage of water resources in arid regions of Central Asia.

CDOM in the source regions of the Yangtze and Yellow Rivers, China: optical properties, possible sources, and their relationships with environmental variables

The source regions of the Yangtze and Yellow Rivers on the Qinghai-Tibet Plateau are extremely important water resources and ecological functional areas in China, and the ecological environment is fragile and sensitive to climate change. Chromophoric dissolved organic matter (CDOM) is an important component that plays a crucial role in the biogeochemical cycle in aquatic ecosystems. However, knowledge of the distribution characteristics of CDOM in this area is limited. In this study, the optical properties, possible sources of CDOM, and their relationships with environmental variables were investigated in the two regions. The results indicated that the CDOM absorption spectra of these two source regions had a high degree of consistency, and the absorption coefficient a_CDOM(355) was small, with a mean of 2.07 ± 1.10 m^-1. Two fluorescence components (C1 and C2) were identified and grouped into the humic-like component with parallel factor analysis (PARAFAC) of fluorescence excitation-emission matrices (EEMs), which exhibited highly similar (excitations/emission)max positions between each pair of components in the two regions. Comprehensive CDOM spectral absorption and fluorescence parameters suggested that CDOM was mainly derived from externally input humus, and the source region of the Yellow River showed stronger allochthonous sources. The dissolved organic carbon (DOC) gradients in the water affected the fluorescence intensity and indicated that the humic-like component was an important component of DOC. Water temperature (WT) and turbidity (Turb) positively affected the concentration of CDOM and the ability to absorb light in the aquatic ecosystems. Due to global warming, the rising temperature may lead to an increase in meltwater inflow in the source area and will also bring more external inputs through the runoff.

Discharge Estimates for Ungauged Rivers Flowing over Complex High-Mountainous Regions based Solely on Remote Sensing-Derived Datasets

Reliable information about river discharge plays a key role in sustainably managing water resources and better understanding of hydrological systems. Therefore, river discharge estimation using remote sensing techniques is an ongoing research goal, especially in small, headwater catchments which are mostly ungauged due to environmental or financial limitations. Here, a novel method for river discharge estimation based entirely on remote sensing-derived parameters is presented. The model inputs include average river width, estimated from Landsat imagery by using the modified normalized difference water index (MNDWI) approach; average depth and velocity, based on empirical equations with inputs from remote sensing; channel slope from a high resolution shuttle radar topography mission digital elevation model (SRTM DEM); and channel roughness coefficient via further analysis and classification of Landsat images with support of previously published values. The discharge of the Lhasa River was then estimated based on these derived parameters and by using either the Manning equation (Model 1) or Bjerklie equation (Model 2). In general, both of the two models tend to overestimate discharge at moderate and high flows, and underestimate discharge at low flows. The overall performances of both models at the Lhasa gauge were satisfactory: comparisons with the observations yielded Nash–Sutcliffe efficiency coefficient (NSE) and R2 values ≥ 0.886. Both models also performed well at the upper gauge (Tanggya) of the Lhasa River (NSE ≥ 0.950) indicating the transferability of the methodology to river cross-sections with different morphologies, thus demonstrating the potential to quantify streamflow entirely from remote sensing data in poorly-gauged or ungauged rivers on the Tibetan Plateau.

Greening Implication Inferred from Vegetation Dynamics Interacted with Climate Change and Human Activities over the Southeast Qinghai–Tibet Plateau

Vegetation dynamics are sensitive to climate change and human activities, as vegetation interacts with the hydrosphere, atmosphere, and biosphere. The Yarlung Zangbo River (YZR) basin, with the vulnerable ecological environment, has experienced a series of natural disasters since the new millennium. Therefore, in this study, the vegetation dynamic variations and their associated responses to environmental changes in the YZR basin were investigated based on Normalized Difference Vegetation Index (NDVI) and Global Land Data Assimilation System (GLDAS) data from 2000 to 2016. Results showed that (1) the YZR basin showed an obvious vegetation greening process with a significant increase of the growing season NDVI (Zc = 2.31, p < 0.05), which was mainly attributed to the wide greening tendency of the downstream region that accounted for over 50% area of the YZR basin. (2) Regions with significant greening accounted for 25.4% of the basin and were mainly concentrated in the Nyang River and Parlung Tsangpo River sub-basins. On the contrary, the browning regions accounted for <25% of the basin and were mostly distributed in the urbanized cities of the midstream, implying a significant influence of human activities on vegetation greening. (3) The elevation dependency of the vegetation in the YZR basin was significant, showing that the vegetation of the low-altitude regions was better than that of the high-altitude regions. The greening rate exhibited a significantly more complicated relationship with the elevation, which increased with elevated altitude (above 3500 m) and decreased with elevated altitude (below 3500 m). (4) Significantly positive correlations between the growing season NDVI and surface air temperature were detected, which were mainly distributed in the snow-dominated sub-basins, indicating that glaciers and snow melting processes induced by global warming play an important role in vegetation growth. Although basin-wide non-significant negative correlations were found between precipitation and growing season NDVI, positive influences of precipitation on vegetation greening occurred in the arid and semi-arid upstream region. These findings could provide important information for ecological environment protection in the YZR basin and other high mountain regions.

Long-Term Spatiotemporal Dynamics of Terrestrial Biophysical Variables in the Three-River Headwaters Region of China from Satellite and Meteorological Datasets

Terrestrial biophysical variables play an essential role in quantifying the amount of energy budget, water cycle, and carbon sink over the Three-River Headwaters Region of China (TRHR). However, direct field observations are missing in this region, and few studies have focused on the long-term spatiotemporal variations of terrestrial biophysical variables. In this study, we evaluated the spatiotemporal dynamics of biophysical variables including meteorological variables, vegetation, and evapotranspiration (ET) over the TRHR, and analyzed the response of vegetation and ET to climate change in the period from 1982 to 2015. The main input gridded datasets included meteorological reanalysis data, a satellite-based vegetation index dataset, and the ET product developed by a process-based Priestley–Taylor algorithm. Our results illustrate that: (1) The air temperature and precipitation over the TRHR increased by 0.597 °C and 41.1 mm per decade, respectively, while the relative humidity and surface downward shortwave radiation declined at a rate of 0.9% and 1.8 W/m2 per decade during the period 1982–2015, respectively. We also found that a ‘dryer warming’ tendency and a ‘wetter warming’ tendency existed in different areas of the TRHR. (2) Due to the predominant ‘wetter warming’ tendency characterized by the increasing temperature and precipitation, more than 56.8% of areas in the TRHR presented a significant increment in vegetation (0.0051/decade, p < 0.05), particularly in the northern and western meadow areas. When energy was the limiting factor for vegetation growth, temperature was a considerably more important driving factor than precipitation. (3) The annual ET of the TRHR increased by 3.34 mm/decade (p < 0.05) with an annual mean of 230.23 mm/year. More importantly, our analysis noted that ET was governed by terrestrial water supply, e.g., soil moisture and precipitation in the arid region of the western TRHR. By contrast, atmospheric evaporative demand derived by temperature and relative humidity was the primary controlling factor over the humid region of the southeastern TRHR. It was noted that land management activities, e.g., irrigation, also had a nonnegligible impact on the temporal and spatial variation of ET.

Spatiotemporal Variation of Drought and Associated Multi-Scale Response to Climate Change over the Yarlung Zangbo River Basin of Qinghai–Tibet Plateau, China

Drought is one of the most widespread and threatening natural disasters in the world, which has terrible impacts on agricultural irrigation and production, ecological environment, and socioeconomic development. As a critical ecologically fragile area located in southwest China, the Yarlung Zangbo River (YZR) basin is sensitive and vulnerable to climate change and human activities. Hence, this study focused on the YZR basin and attempted to investigate the spatiotemporal variations of drought and associated multi-scale response to climate change based on the scPDSI (self-calibrating Palmer drought severity index) and CRU (climate research unit) data. Results showed that: (1) The YZR basin has experienced an overall wetting process from 1956 to 2015, while a distinct transition period in the mid 1990s (from wet to dry) was detected by multiple statistical methods. (2) Considering the spatial variation of the scPDSI, areas showing the significantly wetting process with increasing scPDSI values were mostly located in the arid upstream and midstream regions, which accounted for over 48% area of the YZR basin, while areas exhibiting the drying tendency with decreasing scPDSI values were mainly concentrated in the humid southern part of the YZR basin, dominating the transition period from wet to dry, to which more attention should be paid. (3) By using the EEMD (ensemble empirical mode decomposition) method, the scPDSI over the YZR basin showed quasi-3-year and quasi-9-year cycles at the inter-annual scale, while quasi-15-year and quasi-56-year cycles were detected at the inter-decadal scale. The reconstructed inter-annual scale showed a better capability to represent the abrupt change characteristic of drought, which was also more influential to the original time series with a variance contribution of 55.3%, while the inter-decadal scale could be used to portray the long-term drought variation process with a relative lower variance contribution of 29.1%. (4) The multi-scale response of drought to climate change indicated that changes of precipitation (PRE) and diurnal temperature range (DTR) were the major driving factors in the drought variation at different time scales. Compared with potential evapotranspiration (PET), DTR was a much more important climate factor associated with drought variations by altering the energy balance, which is more obvious over the YZR basin distributed with extensive snow cover and glaciers. These findings could provide important implications for ecological environment protection and sustainable socioeconomic development in the YZR basin and other high mountain regions.

Hydro-climatic changes and their impacts on vegetation in Xinjiang, Central Asia

Central Asia is one of the most arid regions in the world. Xinjiang is the core area of the arid region in Central Asia. Climate warming and hydrological changes might affect the vegetation dynamics in the region; however there has been no systematic evaluation of the hydro-climatic changes and their impacts on vegetation in Xinjiang. In this study, the vegetation growth and its response to hydro-climatic changes from 2003 to 2013 were analyzed based on multiple satellite observations. It was found that precipitation increased, with fluctuations, at a rate of 12.07 mm/decade, and evapotranspiration decreased, also with fluctuations, at a rate of -14.79 mm/decade. The change in total water storage, derived from the Gravity Recovery and Climate Experiment satellite, displayed an increasing trend, with a rate of increase of 112.91 mm/decade. The changes in the Global Land Data Assimilation System-derived soil moisture and groundwater estimated by the water budget presented a slight increasing trend from 2003 to 2013. The total water storage, soil moisture, and groundwater all significantly increased after 2008, and the increases in soil moisture and groundwater had positive effects on the increasing total water storage in Xinjiang. There were more obvious time lags in the response of changes in total water storage to precipitation than for the changes in soil moisture. The changes in the normalized difference vegetation index from 2003 to 2013 indicated a slight greening, and the accumulated normalized difference vegetation index anomalies also increased sharply after 2008. There were significant increases in the Tianshan Mountains, Altay Mountains, and around the Tarim Basin, especially along the Tarim River. The results suggested that the changes in total water storage and soil moisture were regarded as better indicators of the vegetation dynamics than other hydro-climatic variables in Xinjiang. Climate warming has led to accelerated glacier shrinkage and snow melt, and the increased runoff is likely to lead to more infiltration of surface water into the soil and ground, resulting in increased total water storage.