The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area,primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational sit...The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area,primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational site was established to monitor climate, land surface dynamics, and hydrological variability in this region. Over a 10-year period(2010–19), an extensive observational dataset was compiled, now available to the scientific community. This dataset includes comprehensive details on site characteristics, instrumentation, and data processing methods, covering meteorological and radiative fluxes, energy exchanges, soil moisture dynamics, and heat transfer properties. The dataset is particularly valuable for researchers studying land surface processes, land–atmosphere interactions, and climate modeling, and may also benefit ecological, hydrological, and water resource studies. The report ends with a discussion on perspectives and challenges of continued observational monitoring in this region, focusing on issues such as cryosphere influences, complex topography,and ecological changes like the encroachment of weeds and scrubland.展开更多
The source region of the Yellow River(SRYR),with its semi-humid to semi-arid climate,is crucial for understanding water resource dynamics.Precipitation is key for replenishing surface water and balancing the ecosystem...The source region of the Yellow River(SRYR),with its semi-humid to semi-arid climate,is crucial for understanding water resource dynamics.Precipitation is key for replenishing surface water and balancing the ecosystem’s water cycle.However,the soil moisture response to precipitation across climate zones and soil layers remains poorly understood due to limited long-term data.This study examines the response of soil moisture to precipitation at multiple time scales in the SRYR,using data from Maqu,Mado,Ngoring Lake sites,and the Maqu monitoring network(MMN),along with CN05.1 precipitation and GLEAM v3.8a soil moisture data.Results show that the semi-humid area requires more precipitation to trigger soil moisture responses compared to the semi-arid area in the SRYR.Surface soil at Maqu,MMN,Ngoring Lake,and Mado sites require at least 8.6,8.4,5.2,and 2.84 mm of precipitation,respectively,for effective replenishment.Significant responses to precipitation events were observed in soil layers at 40 cm and above in the semi-humid area,while at 20 cm and above in the semi-arid area.Precipitation volume is the primary factor influencing soil moisture,affecting both the increment and time lag to maximum moisture.Precipitation intensity and pre-rain moisture have no direct effect.In the central SRYR,accumulated precipitation has a greater impact.Root-zone soil moisture has a weaker correlation with precipitation compared to surface soil moisture but persists longer,responding for up to 10 days,while surface soil moisture responds more immediately but only lasts about 5 days.展开更多
Thousands of lakes on the Tibetan Plateau(TP)play a critical role in the regional water cycle,weather,and climate.In recent years,the areas of TP lakes underwent drastic changes and have become a research hotspot.Howe...Thousands of lakes on the Tibetan Plateau(TP)play a critical role in the regional water cycle,weather,and climate.In recent years,the areas of TP lakes underwent drastic changes and have become a research hotspot.However,the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear,which inhibits model development and the accurate simulation of lake climate effects.The source region of the Yellow River(SRYR)has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP.Since 2011,three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces.This study presents an eight-year(2012–19),half-hourly,observation-based dataset related to lake–atmosphere interactions composed of three sites.The three sites represent the lake surface,the lakeside,and the land.The observations contain the basic meteorological elements,surface radiation,eddy covariance system,soil temperature,and moisture(for land).Information related to the sites and instruments,the continuity and completeness of data,and the differences among the observational results at different sites are described in this study.These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model.The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.展开更多
Warming-induced carbon loss via ecosystem respiration(R_(e))is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of...Warming-induced carbon loss via ecosystem respiration(R_(e))is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of R_(e)(Q_(10)).However,little is known about the patterns and controlling factors of Q_(10)on the plateau,impeding the comprehension of the intensity of terrestrial carbon-climate feedbacks for these sensitive and vulnerable ecosystems.Here,we synthesized and analyzed multiyear observations from 14 sites to systematically compare the spatiotemporal variations of Q_(10)values in diverse climate zones and ecosystems,and further explore the relationships between Q_(10)and environmental factors.Moreover,structural equation modeling was utilized to identify the direct and indirect factors predicting Q_(10)values during the annual,growing,and non-growing seasons.The results indicated that the estimated Q_(10)values were strongly dependent on temperature,generally,with the average Q_(10)during different time periods increasing with air temperature and soil temperature at different measurement depths(5 cm,10 cm,20 cm).The Q_(10)values differentiated among ecosystems and climatic zones,with warming-induced Q_(10)declines being stronger in colder regions than elsewhere based on spatial patterns.NDVI was the most cardinal factor in predicting annual Q_(10)values,significantly and positively correlated with Q_(10).Soil temperature(Ts)was identified as the other powerful predictor for Q_(10),and the negative Q_(10)-Ts relationship demonstrates a larger terrestrial carbon loss potentiality in colder than in warmer regions in response to global warming.Note that the interpretations of the effect of soil moisture on Q_(10)were complicated,reflected in a significant positive relationship between Q_(10)and soil moisture during the growing season and a strong quadratic correlation between the two during the annual and non-growing season.These findings are conducive to improving our understanding of alpine grassland ecosystem carbon-climate feedbacks under warming climates.展开更多
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars (Grant No.42325502)the 2nd Scientific Expedition to the Qinghai–Tibet Plateau (Grant No.2019QZKK0102)+3 种基金the West Light Foundation of the Chinese Academy of Sciences (Grant No.xbzg-zdsys-202215)the Science and Technology Research Plan of Gansu Province (Grant Nos.23JRRA654 and 20JR10RA070)the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant No.QCH2019004)iLEAPS (integrated Land Ecosystem–Atmosphere Processes Study)。
文摘The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area,primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational site was established to monitor climate, land surface dynamics, and hydrological variability in this region. Over a 10-year period(2010–19), an extensive observational dataset was compiled, now available to the scientific community. This dataset includes comprehensive details on site characteristics, instrumentation, and data processing methods, covering meteorological and radiative fluxes, energy exchanges, soil moisture dynamics, and heat transfer properties. The dataset is particularly valuable for researchers studying land surface processes, land–atmosphere interactions, and climate modeling, and may also benefit ecological, hydrological, and water resource studies. The report ends with a discussion on perspectives and challenges of continued observational monitoring in this region, focusing on issues such as cryosphere influences, complex topography,and ecological changes like the encroachment of weeds and scrubland.
基金supported by the National Natural Science Foundation of China(Grant No.42325502,and 42275045)the West Light Foundation of the Chi-nese Academy of Sciences(Grant No.xbzg-zdsys-202215)+1 种基金the Sci-ence and Technology Research Plan of Gansu Province(Grant Nos.23JRRA654 and 20JR10RA070)iLEAPs(Integrated Land Ecosystem-Atmosphere Processes Study).
文摘The source region of the Yellow River(SRYR),with its semi-humid to semi-arid climate,is crucial for understanding water resource dynamics.Precipitation is key for replenishing surface water and balancing the ecosystem’s water cycle.However,the soil moisture response to precipitation across climate zones and soil layers remains poorly understood due to limited long-term data.This study examines the response of soil moisture to precipitation at multiple time scales in the SRYR,using data from Maqu,Mado,Ngoring Lake sites,and the Maqu monitoring network(MMN),along with CN05.1 precipitation and GLEAM v3.8a soil moisture data.Results show that the semi-humid area requires more precipitation to trigger soil moisture responses compared to the semi-arid area in the SRYR.Surface soil at Maqu,MMN,Ngoring Lake,and Mado sites require at least 8.6,8.4,5.2,and 2.84 mm of precipitation,respectively,for effective replenishment.Significant responses to precipitation events were observed in soil layers at 40 cm and above in the semi-humid area,while at 20 cm and above in the semi-arid area.Precipitation volume is the primary factor influencing soil moisture,affecting both the increment and time lag to maximum moisture.Precipitation intensity and pre-rain moisture have no direct effect.In the central SRYR,accumulated precipitation has a greater impact.Root-zone soil moisture has a weaker correlation with precipitation compared to surface soil moisture but persists longer,responding for up to 10 days,while surface soil moisture responds more immediately but only lasts about 5 days.
基金supported by the National Natural Science Foundations of China(Grant Nos.41930759,41822501,42075089,41975014)the 2nd Scientific Expedition to the Qinghai-Tibet Plateau(2019QZKK0102)+3 种基金The Science and Technology Research Plan of Gansu Province(20JR10RA070)the Chinese Academy of Youth Innovation and Promotion,CAS(Y201874)the Youth Innovation Promotion Association CAS(QCH2019004)iLEAPs(Integrated Land Ecosystem-Atmosphere Processes Study-iLEAPS)。
文摘Thousands of lakes on the Tibetan Plateau(TP)play a critical role in the regional water cycle,weather,and climate.In recent years,the areas of TP lakes underwent drastic changes and have become a research hotspot.However,the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear,which inhibits model development and the accurate simulation of lake climate effects.The source region of the Yellow River(SRYR)has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP.Since 2011,three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces.This study presents an eight-year(2012–19),half-hourly,observation-based dataset related to lake–atmosphere interactions composed of three sites.The three sites represent the lake surface,the lakeside,and the land.The observations contain the basic meteorological elements,surface radiation,eddy covariance system,soil temperature,and moisture(for land).Information related to the sites and instruments,the continuity and completeness of data,and the differences among the observational results at different sites are described in this study.These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model.The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.
基金supported by the National Science Foundation of China(Grant No.41930759)the Gansu Provincial Science and Technology Program(Grant No.22ZD6FA005)+4 种基金the National Science Foundation of China(Grant Nos.41875018 and 41875016)the Science and Technology Research Plan of Gansu Province(Grant Nos.20JR10RA070 and 22JR5RA048)the Chinese Academy of Sciences(CAS)“Light of West China”Program(Grant No.E2290302)the Gansu Provincial Science and Technology Program(Grant No.23JRRA609)the integrated Land Ecosystem-Atmosphere Processes Study(iLEAPS).
文摘Warming-induced carbon loss via ecosystem respiration(R_(e))is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of R_(e)(Q_(10)).However,little is known about the patterns and controlling factors of Q_(10)on the plateau,impeding the comprehension of the intensity of terrestrial carbon-climate feedbacks for these sensitive and vulnerable ecosystems.Here,we synthesized and analyzed multiyear observations from 14 sites to systematically compare the spatiotemporal variations of Q_(10)values in diverse climate zones and ecosystems,and further explore the relationships between Q_(10)and environmental factors.Moreover,structural equation modeling was utilized to identify the direct and indirect factors predicting Q_(10)values during the annual,growing,and non-growing seasons.The results indicated that the estimated Q_(10)values were strongly dependent on temperature,generally,with the average Q_(10)during different time periods increasing with air temperature and soil temperature at different measurement depths(5 cm,10 cm,20 cm).The Q_(10)values differentiated among ecosystems and climatic zones,with warming-induced Q_(10)declines being stronger in colder regions than elsewhere based on spatial patterns.NDVI was the most cardinal factor in predicting annual Q_(10)values,significantly and positively correlated with Q_(10).Soil temperature(Ts)was identified as the other powerful predictor for Q_(10),and the negative Q_(10)-Ts relationship demonstrates a larger terrestrial carbon loss potentiality in colder than in warmer regions in response to global warming.Note that the interpretations of the effect of soil moisture on Q_(10)were complicated,reflected in a significant positive relationship between Q_(10)and soil moisture during the growing season and a strong quadratic correlation between the two during the annual and non-growing season.These findings are conducive to improving our understanding of alpine grassland ecosystem carbon-climate feedbacks under warming climates.
