Three-River Headwaters Region Research Articles

Effects of hillslope aspect on erosion rate of alpine meadows in the Three-River Headwater Region, China

Soil erosion causes serious grassland degradation in the Three-River Headwater Region (TRHR) of the Qinghai-Tibet Plateau. Identifying source areas of erosion is important, including understanding the influences of hillslope aspect on soil erosion. However, the effect of hillslope aspect on soil erosion is a much-debated process, wherein contrasting results were previously reported. We hypothesized that aspect-induced differences in environmental variables will be related to differences in erosion magnitude and spatial pattern, and that these differences will impact soil physiochemical characteristics as well. To test this hypothesis, soil samples were collected at conjoined south- and north-facing hillslopes along elevational profiles in a small watershed in Yushu County, with measurements of soil water content (SWC), soil organic carbon (SOC), soil organic matter (SOM), soil particle size distribution, soil total nitrogen (STN) and phosphorus (STP). Soil erosion rate was estimated using 137Cs technology. The results showed that soil erosion rates of the two hillslopes ranged from 0.073 to 10.83 t ha-1 yr-1, and were 3 times greater on the south-facing hillslope. Soil water content ranged from 7.6% to 76.8% on the two hillslopes with lower values on the south-facing hillslope due to the greater solar radiation. By Spearman correlation analysis and path analysis, we concluded that lower soil water content on the south-facing hillslope restricts vegetation growth, and hence resulting in greater soil erosion under the case where vegetation growth is dominantly controlled by water availability. Greater soil erosion on the south-facing hillslope caused greater surficial accumulation of coarser materials and greater loss of soil nutrients, which are expected to complicate the hydrological erosion process. Moreover, the downslope increased soil erosion rate should be fully considered in conservation practices. These results will improve our understanding of the interaction and feedback mechanisms between soil characteristics, vegetation function, and soil erosion in the TRHR.

Responses of annual streamflow variability to annual precipitation, extreme climate events and large-scale climate phenomena in the Qinghai-Tibet Plateau

Solar activity and large-scale climate phenomena have significant effects on extreme climate events and streamflow variability. As the roof of the world and the water tower of Asia, the Qinghai-Tibet Plateau (QTP) is highly sensitive to climate change. Therefore, it is of great significance to study the relationship between extreme hydrometeorological events of the QTP and climate change for global hydroclimate research. In this study, we analyzed the spatiotemporal variation and significant oscillation period of several hydrometeorological variables such as extreme precipitation indices (EPIs), extreme temperature indices (ETIs) and annual streamflow variability based on the observation data of hydrometeorological stations in the QTP during 1962–2019 using Sen’s slope estimator, Mann-Kendall test and continuous wavelet analysis (CWT). And the teleconnection patterns and the lead-lag relationship between annual precipitation, extreme climate events, multiple nonstationary climate mode and annual streamflow were evaluated using wavelet coherence (WTC). The results show that the climate of the QTP has been wetter and warmer in the past 58 years. The annual streamflow of several natural rivers in the QTP mainly show an increasing trend over the last 60 years. The annual streamflow of natural river (except Yellow River) increases significantly in three-rivers headwater region, and the rivers in the northeast and northwest also show a significant increase trend, but there is no significant change in the natural river streamflow in the south of QTP. In addition, the annual streamflow of natural rivers had significant periodicities in the bands of 2–4 years, 4–7 years and 7–11 years. The significant coherence of WTC spectra between annual precipitation and streamflow indicate that precipitation is the main reason for annual streamflow variation in the QTP. The variation of extreme climate events significantly regulates the regional annual streamflow. All EPIs except consecutive dry days show an in-phase correlation with annual streamflow of most headwater. Annual streamflow of Naijin River is positively and negatively correlated with extreme warm and extreme cold, respectively, and has a clearer lead-lag relationship than that with EPIs. Annual streamflow of headwater has higher correlations with EI Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) than with sunspot number (SSN). Solar activity may first affect ENSO and PDO, and then this effect is transferred sequentially to the hydrological cycle of the QTP by SSN-modulated PDO and PDO-SSN co-modulated ENSO. It is of great significance to investigate the response of streamflow variation to annual precipitation, extreme climate events and large-scale climate phenomena for water resources management, flood control, ecosystem restoration, and the well-being of surrounding residents and sustainable development in the QTP.

Soil water sources and their implications for vegetation restoration in the Three-Rivers Headwater Region during different ablation periods

Abstract. Amid global warming, the timely supplementation of soil water is crucial for the effective restoration and protection of the ecosystem. It is therefore of great importance to understand the temporal and spatial variations of soil water sources. The research collected 2451 samples of soil water, precipitation, river water, ground ice, supra-permafrost water, and glacier snow meltwater in June, August, and September 2020. The goal was to quantify the contribution of various water sources to soil water in the Three-Rivers Headwater Region (China) during different ablation periods. The findings revealed that precipitation, ground ice, and snow meltwater constituted approximately 72 %, 20 %, and 8 % of soil water during the early ablation period. The snow is fully liquefied during the latter part of the ablation period, with precipitation contributing approximately 90 % and 94 % of soil water, respectively. These recharges also varied markedly with altitude and vegetation type. The study identified several influencing factors on soil water sources, including temperature, precipitation, vegetation, evapotranspiration, and the freeze–thaw cycle. However, soil water loss will further exacerbate vegetation degradation and pose a significant threat to the ecological security of the “Chinese Water Tower”. It emphasizes the importance of monitoring soil water, addressing vegetation degradation related to soil water loss, and determining reasonable soil and water conservation and vegetation restoration models.

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.

Enhancing sustainable livelihoods in the Three Rivers Headwater Region: A geospatial and obstacles context

Applying novel approaches to assess the sustainable livelihood potentially contribute to learning and managing social-ecological systems. This research demonstrates the trajectory of sustainable livelihoods in the Three Rivers Headwater Region (TRHR) of Western China for a period of 2003 to 2017, utilizing a bespoke Sustainable Livelihood Index (SLI). Devised through a top-down data collation method, the SLI integrates comprehensive official statistics, encompassing socio-economic metrics alongside climatic and other environmental conditions. We employed spatial visualization and cold hotspot analysis to discern the spatio-temporal nuances of the SLI. Furthermore, the Obstacle Factor Analysis (OFA) was implemented to identify salient barriers impacting the SLI. Results indicated a steady rise in the SLI over the examined period, indicating progressive enhancements in the livelihood sustainability. However, marked disparities were observed across the TRHR counties, reflecting heterogeneity in socioeconomic progress and resource allocation. While Yushu county displayed commendable growth, Jiuzhi county stood out as a significant cold spot. Notably, natural and financial resources surfaced as paramount stresses to SLI progression, emphasizing the necessity for robust resource management and economic support. Key challenges, such as the grassland carrying pressure index, deteriorated grassland territories, and per capita disposable income were identified as critical obstacles to the sustainable livelihood of pastoral communities. These factors collectively influence economic viability, grassland resource stewardship, and human capital development, which are intertwined with local economic gains and future potentials. In nutshell, this study highlights the multifaceted challenges faced by the TRHR in charting sustainable livelihood pathways and champions recalibrated policies to protect natural assets, amplify economic support, reinforce human capital, and nurture regional synergies for sustainable evolution. The key insights suggest the pressing need for innovative, nature-based solutions that amplify regional-scale livelihood resilience, especially considering the challenges related to natural assets, financial support, and human capital development.

Trends and contribution of different grassland types in restoring the Three River Headwater Region, China, 1988–2012

Rapid greening of the Qinghai–Tibet Plateau had been confirmed, but the contributions to the overall change and its causes in various grassland types has been less studied. Previous research has focused on exogenous factors such as climate change and human activities, rather than on endogenous factors, such as grassland types. Using net primary productivity (NPP), precipitation and temperature data, we applied trend, contribution and pull contribution analysis to understand the spatiotemporal evolution and driving factors of six different grassland types at a pixel scale in the Three River Headwater Region (TRHR) of China from 1988 to 2012. The results showed that grassland NPP in the TRHR increased at an average growth amount of 3.46 gC m−2 yr−1 and an average growth rate of 2.26 %. The average growth amount of alpine desert and alpine steppe (0.42 gC m−2 yr−1, 1.74 gC m−2 yr−1, respectively) showed great potential improvement. The average growth rate (1.27 %, 1.87 %) of montane meadow and alpine meadow, respectively, presented a high potential to increase (P < 0.05). Alpine meadow, montane meadow and temperate steppe were positive pullers to the average growth amount. Alpine steppe and alpine desert were positive pullers to the average growth rate. In general, alpine meadow had the highest growth amount contribution (84.86 %), while alpine meadow and alpine steppe had the highest contribution to the growth rate (62.16 %, 34.24 %, respectively). The study implied that, in addition to external factors, differences in internal factors such as the community composition and structure of different grassland types could also affected the grassland recovery process. These results contribute to understanding the specific differences in the contribution of regional grassland restoration processes from vegetation composition. Assessing grasslands with the potential to increase productivity, we can provide scientific reference for implementing more precise and efficient measures in future grassland management restoration.

Substantial Reduction in Vegetation Photosynthesis Capacity during Compound Droughts in the Three-River Headwaters Region, China

Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially within mountain grassland ecosystems. In this study, we used three types of high-spatial-resolution SIF datasets (0.05°), coupled with meteorological and soil moisture datasets, to investigate the characteristics of atmospheric, soil, and compound drought types. We centered this investigation on the years spanning 2001–2020 in the Three-River Headwaters Region (TRHR). Our findings indicate that the TRHR experienced a combination of atmospheric drying and soil wetting due to increases in the standardized saturation vapor pressure deficit index (SVPDI) and standardized soil moisture index (SSMI). In the growing season, atmospheric drought was mainly distributed in the southern and eastern parts of the TRHR (reaching 1.7 months/year), while soil drought mainly occurred in the eastern parts of the TRHR (reaching 2 months/year). Compound drought tended to occur in the southern and eastern parts of the TRHR and trended upward during 2001–2020. All three SIF datasets consistently revealed robust photosynthetic activity in the southern and eastern parts of the TRHR, with SIF values generally exceeding 0.2 mW· m−2·nm−1·sr−1. Overall, the rise in SIF between 2001 and 2020 corresponds to enhanced greening of TRHR vegetation. Vegetation photosynthesis was found to be limited in July, attributable to a high vapor pressure deficit and low soil moisture. In the response of CSIF data to a drought event, compound drought (SVPDI ≥ 1 and SSMI ≤ −1) caused a decline of up to 14.52% in SIF across the source region of the Yellow River (eastern TRHR), while individual atmospheric drought and soil drought events caused decreases of only 5.06% and 8.88%, respectively. The additional effect of SIF produced by compound drought outweighed that of atmospheric drought as opposed to soil drought, suggesting that soil moisture predominantly governs vegetation growth in the TRHR. The reduction in vegetation photosynthesis capacity commonly occurring in July, characterized by a simultaneously high vapor pressure deficit and low soil moisture, was more pronounced in Yellow River’s source region as well. Compound drought conditions more significantly reduce SIF compared to singular drought events. Soil drought evidently played a greater role in vegetation growth stress than atmospheric drought in the TRHR via the additional effects of compound drought.

Short-term restoration effects of ecological projects detected using the turning point method in the Three River Headwater Region, China.

The Three River Headwater Region (TRHR) is an important river source area providing important ecological functions. Decades ago, climate change and human activities severely degraded the ecosystem in the TRHR. To restore vegetation, a series of ecological projects have been implemented since 1989. Using net primary productivity (NPP) data from 1988 to 2012, a sequential Mann-Kendall trend test (SQ-MK) method was applied to identify the turning point of vegetation NPP. This approach was able to represent the critical response time of the vegetation to important disturbances. A 3-year time window was set after the implementation of one ecological project to detect and analyze its short-term effects. The ecological projects included the Yangtze River Basin Shelterbelt System Construction Project (YRCP), the TRHR Nature Reserve Construction Project (TNR), the Returning Grazing Land to Grassland Project (RGLGP), and the first phase of the Ecological Conservation and Restoration Project of the TRHR (ECRP). Our results showed that the vegetation in the TRHR responded positively to restoration: 89% of pixels showed an increasing trend and 54% of pixels underwent an abrupt change. The accelerated growth type accounted for the highest proportion among all types of detected turning points. In the ECRP's window, the positive turns rose rapidly, from 41% in 2005 to 86% in 2008, and it showed the most balanced restoration effects across grasslands. The alpine meadow and montane meadow restoration was largely influenced by the ECRP and the RGLGP (both >40%). The alpine steppe restoration was mainly attributed to the ECRP (68%). On the county scale, the positive turns in Yushu at the source of the Yangtze River mainly benefited from the RGLGP (56%), while the positive turns in Maduo at the source of the Yellow River benefited from the ECRP (77%). Nangqian, Tanggula and Zaduo County were still in need of intervention for restoration (< 3%). The results of the study can enhance our understanding of the spatio-temporal distribution of the short-term ecological benefits of different ecological projects, thus provide a scientific and timely reference for future planning and adjustment of the conservation and restoration projects.

Spatiotemporal Changes in Water Storage and Its Driving Factors in the Three-River Headwaters Region, Qinghai–Tibet Plateau

Water storage (WS) is a crucial terrestrial ecosystems service function. In cold alpine regions (CAR), the cryosphere elements are important solid water resources, but the existing methods for quantitatively assessing WS usually ignore cryosphere elements. In this study, a revised Seasonal Water Yield model (SWY) in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), which considers the effects of frozen ground (FG) and snow cover (SC) on WS, was employed to estimate the spatiotemporal distribution and changes in WS in the Three-Rivers Headwaters region (TRHR) from 1981 to 2020. Sensitivity analyses were conducted to understand the overall effects of multiple factors on WS, as well as the dominant driving factors of WS change at the grid scale in the TRHR. The results show that (1) the WS in the TRHR generally increased from 1981 to 2020 (0.56 mm/year), but the spatial distribution of WS change varied greatly, with a significant increasing trend in the northwest part and a significant decreasing trend in the southeast part. (2) In the last 40 years, increased precipitation (Pre) positively affected WS, while increased potential evapotranspiration (ET0) reduced it. Increased permeability caused by degradation of frozen ground increased WS, while snow cover and LULC changes reduced it. (3) In the TRHR, Pre primarily affected the WS with the largest area ratio (32.62%), followed by land use/land cover (LULC) (19.69%) and ET0 (18.49%), with FG being fourth (17.05%) and SC being the least (6.64%). (4) The highly important and extremely important zones generally showed a decreasing trend in WS and should be treated as key and priority conservation regions. It is expected that this research could provide a scientific reference for water management in the TRHR.

Characteristics of Solar-Induced Chlorophyll Fluorescence in the Three River Headwaters Region, Qinghai-Tibetan Plateau during 2001 to 2020

The ecology of the Three River Headwaters Region (TRHR) is related to the long-term sustainable development of Qinghai Province and the whole of China. The change in chlorophyll fluorescence is an important index to measure the ecological environment. Therefore, it is of great significance to study the spatial and temporal distributions of Solar-induced chlorophyll fluorescence (SIF)and the related influencing factors in the TRHR. In this study, a high-resolution SIF dataset (2001–2020) was selected to be averaged on a time scale of years and months to investigate the annual and seasonal SIF characteristics, and the influencing climate factors were analyzed in combination with meteorological data by statistical method. The results showed that the SIF values ranged from 0.05 to 0.073 during 2001–2020, with a peak value of 0.073 in 2005 and 2009 and a minimum value of 0.05 in 2002. The averages of SIF values were higher in the source regions of the Yellow River source region (YR) and Langcang (Mekong) River source region (LCR) than in the source region of the Yangtze River source region (YZR). The SIF values of the TRHR in July, August and September were significantly higher than those in other months. The maximum value occurred in August at 0.11, and the minimum value was 0.008 in December. The precipitation had greater effect on the inter-annual variations in SIF. The monthly variation of SIF is influenced by precipitation, temperature and relative humidity. In addition, the influence of human activities and altitude on SIF should not be ignored. The results have certain reference value for protecting vegetation in the TRHR, and provide a reference for other regions to analyze the spatiotemporal changes and influencing factors by using SIF data.

Effective Improvement of the Accuracy of Snow Cover Discrimination Using a Random Forests Algorithm Considering Multiple Factors: A Case Study of the Three-Rivers Headwater Region, Tibet Plateau

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover’ extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001–2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD &gt; 4 cm) and 98.54% for shallow snow cover (SD ≤ 4 cm), significantly (p &lt; 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability.

Spatial and Temporal Evolution Characteristics of Water Conservation in the Three-Rivers Headwater Region and the Driving Factors over the Past 30 Years

The Three-Rivers Headwater Region (TRHR), located in the hinterland of the Tibetan Plateau, serves as the “Water Tower of China”, providing vital water conservation (WC) services. Understanding the variations in WC is crucial for locally tailored efforts to adapt to climate change. This study improves the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) water yield model by integrating long-term time series of vegetation data, emphasizing the role of interannual vegetation variation. This study also analyzes the influences of various factors on WC variations. The results show a significant increase in WC from 1991 to 2020 (1.4 mm/yr, p &lt; 0.05), with 78.17% of the TRHR showing improvement. Precipitation is the primary factor driving the interannual variations in WC. Moreover, distinct interactions play dominant roles in WC across different eco-geographical regions. In the north-central and western areas, the interaction between annual precipitation and potential evapotranspiration has the highest influence. Conversely, the interaction between annual precipitation and vegetation has the greatest impact in the eastern and central-southern areas. This study provides valuable insights into the complex interactions between the land and atmosphere of the TRHR, which are crucial for enhancing the stability of the ecosystem.

Integrated Assessments of Land Degradation in the Three-Rivers Headwater Region of China from 2000 to 2020

Integrated Assessments of Land Degradation in the Three-Rivers Headwater Region of China from 2000 to 2020

Artificial grassland mapping using artificial grassland detection index of vegetation growth in the Three-River Headwaters region

Mapping the spatial distribution of artificial grassland for ecological restoration is of great significance for evaluating its secondary degradation and negative consequences, such as nonpoint source pollution of water bodies in the Three-River Headwaters (TRH) region. Because of the numerous challenges faced in obtaining ground training samples caused by adverse natural conditions, inclement cloudy weather and spectral similarity between natural and artificial grassland, commonly used classification or temporal-profile extraction methods have proven ineffective in identifying artificial grasslands. To overcome these challenges, we present a novel artificial grassland detection index for mapping their distribution using optical images with a resolution of 10 ∼ 30 m, along with their corresponding quality control data based on the Google Earth Engine cloud computing platform. The index is calculated using the ratio of the normalized difference vegetation index during the sowing and emergence period and the growth peak period of artificial grassland. A case study was conducted in Maqin County in the TRH region covering an area of 1.35 × 104 km2 from 2017 to 2021. Our proposed method demonstrated high accuracy and achieved a favorable balance between commission errors and omission errors. Over the study period, the average overall accuracy and Cohen's kappa were 96.2% and 0.91, respectively; with average precision, recall, and F1-score of artificial grassland being 89.6%, 99.2%, and 94.0%, respectively. The proposed method exhibited excellent robustness for the critical threshold used, with the average overall accuracy, F1-score, precision, and recall of artificial grassland between 2017 and 2021 consistently exceeding 90% for threshold values ranging from 1.5 to 2.0 throughout the study period. These findings suggest that our proposed method is capable of efficiently and accurately obtaining the detailed spatiotemporal distribution of artificial grassland in the TRH region. Moreover, the method also meets the pressing requirement for the rapid acquisition of detailed spatiotemporal distribution of artificial grassland across the Qinghai-Tibet Plateau.

A New Tool for Mapping Water Yield in Cold Alpine Regions

Watershed management requires reliable information about hydrologic ecosystem services (HESs) to support decision-making. In cold alpine regions, the hydrology regime is largely affected by frozen ground and snow cover. However, existing special models of ecosystem services usually ignore cryosphere elements (such as frozen ground and snow cover) when mapping water yield, which limits their application and promotion in cold alpine regions. By considering the effects of frozen ground and snow cover on water yield, a new version of the Seasonal Water Yield model (SWY) in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) was presented and applied in the Three-River Headwaters Region (TRHR) in southeastern Qinghai-Tibetan Plateau (QTP). Our study found that incorporating the effects of frozen ground and snow cover improved model performance. Frozen ground acts as a low permeable layer, reducing water infiltration, while snow cover affects water yield through processes of melting and sublimation. Both of these factors can significantly impact the distribution of spatial and temporal quickflow and baseflow. The annual average baseflow and water yield of the TRHR would be overestimated by 13 mm (47.58 × 108 m3/yr) and 14 mm (51.24 × 108 m3/yr), respectively, if the effect of snow cover on them is not considered. Furthermore, if the effect of frozen ground on water yield were not accounted for, there would be an average of 6 mm of quickflow misestimated as baseflow each year. Our study emphasizes that the effects of frozen ground and snow cover on water yield cannot be ignored, particularly over extended temporal horizons and in the context of climate change. It is crucial to consider their impacts on water resources in cold alpine regions when making water-related decisions. Our study widens the application of the SWY and contributes to water-related decision-making in cold alpine regions.

Increased southerly and easterly water vapor transport contributed to the dry-to-wet transition of summer precipitation over the Three-River Headwaters in the Tibetan Plateau

The Three-River Headwaters (TRH) region in the Tibetan Plateau is vulnerable to climate change; changes in summer (June–August) precipitation have a significant impact on water security and sustainability in both local and downstream areas. However, the changes in summer precipitation of different intensities over the TRH region, along with their influencing factors, remain unclear. In this study, we used observational and ERA5 reanalysis data and employed a precipitation categorization and water vapor budget analysis to quantify the categorized precipitation variations and investigate their possible linkages with the water vapor budget. Our results showed an increasing trend in summer precipitation at a rate of 0.9 mm per year (p < 0.1) during 1979–2020, with a significant dry-to-wet transition in 2002. The category ‘very heavy precipitation’ (≥10 mm d−1) contributed 65.1% of the increased summer precipitation, which occurred frequently in the northern TRH region. The dry-to-wet transition was caused by the effects of varied atmospheric circulations in each subregion. Southwesterly water vapor transport through the southern boundary was responsible for the increased net water vapor flux in the western TRH region (158.2%), while southeasterly water vapor transport through the eastern boundary was responsible for the increased net water vapor flux in the central TRH (155.2%) and eastern TRH (229.2%) regions. Therefore, we inferred that the dry-to-wet transition of summer precipitation and the increased ‘very heavy precipitation’ over the TRH was caused by increased easterly and southerly water vapor transport.

Moisture source anomalies connected to flood‐drought changes over the three‐rivers headwater region of Tibetan Plateau

AbstractThe Flood‐Drought condition over the Three‐River Headwater region (TRHR) of the Tibetan Plateau (TP) exerts vital impacts on regional climate and downstream livelihoods. However, the reasons behind precipitation variability over this region remain elusive. In this study, the multi‐year moisture sources of water vapour reaching the TRHR were identified to reveal their connection to the interannual variability of summer precipitation by utilizing a Lagrangian diagnosis analysis. The results show that the moisture sources could be tracked backward covering vast areas, with the peak values located mostly at the northern Indian continent and adjacent regions of TRH itself. The variability of integrated moisture sources' contribution agrees well with the summer precipitation on the interannual scale; However, the role of different regions varies in line with their geographical location. More specifically, summer precipitation is positively correlated with the moisture source over the adjacent areas of TRH and the remote terrestrial‐oceanic regions, in contrast to a negative correlation to the water vapour departing from the Northeastern Asian and western regions to the TP. The Indian summer monsoon circulation and local recycling process play a dominant role in modulating the summer precipitation on the interannual scale, whereas the role of the westerlies is almost negligible. The abnormal atmosphere circulations triggered by mid‐high latitude wave trains and anomalies in SST in the central and eastern Pacific Ocean and Indian Ocean could serve as possible drivers for the precipitation variability over TRHR.

Spatiotemporal variations of precipitation during the rainy season over the three-rivers headwater region of tibetan plateau from 1990 to 2020

Spatiotemporal variations of precipitation during the rainy season over the three-rivers headwater region of tibetan plateau from 1990 to 2020

The Radial Growth of Juniperus squamata Showed Sharp Increase in Response to Climate Warming on the Three-River Headwaters Region of Tibetan Plateau since the Early 21st Century

In order to explore the impact of climate change on the ecosystem at high altitudes, dendroclimatology was used to study the response of radial growth of Juniperus squamata Buch.-Ham. ex D.Don to the rapid warming in Nangqian County over the past 60 years, and a tree-ring width chronology for 115 years was established. (1) Meteorological data showed that the temperature in Nangqian County of the Tibetan Plateau has increased continuously during the past 60 years, and the minimum temperature has had the most significant change (0.63 °C/10a), especially between 2000–2019 (0.12 °C/a). Over the same time period precipitation has not changed significantly (0.94 mm/a, p &gt; 0.10). The standard chronology was used to reconstruct the mean temperature series from July to September in Nangqian meteorological station during the past 115 years (1905–2019). The explained variance of the reconstructed equation was 42.8% (40.8%, after adjusting for degrees of freedom). The reconstructed temperature series can be roughly divided into two stages: from 1905 to 1999, the temperature fluctuated around the average value, 12.10 °C, and from 2000 to 2019, the temperature showed a significant upward trend. (2) The analysis of the climate-tree growth relationship indicated that the response of radial growth of Juniperus squamata to temperature was significantly stronger than the response to precipitation; especially in the last 20 years, when the radial growth of Juniperus squamata was positively correlated with temperature (p &lt; 0.01). Compared to the maximum temperature and mean temperature, the correlation between radial growth of Juniperus squamata and minimum temperature was more significant. (3) Under the background of climate warming, the radial growth trend of Juniperus squamata in Nangqian county was consistent with temperature changes. Particularly in the past 20 years, the radial growth of Juniperus squamata showed a significantly increased trend and entered a rapid growth period.

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the flexible spatiotemporal data fusion (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR.