Adaptation of ecosystems'root zones to climate change critically affects drought resilience and vegetation productivity.However,a global quantitative assessment of this mechanism is missing.In this study,we analyz...Adaptation of ecosystems'root zones to climate change critically affects drought resilience and vegetation productivity.However,a global quantitative assessment of this mechanism is missing.In this study,we analyzed high-quality observation-based data to find that the global average root zone water storage capacity(S_(R))increased by 11%,from 182 to 202 mm in 1982-2020.The total increase of Sr equals to 1652 billion m^(3) over the past four decades.S_(R) increased in 9 out of 12 land cover types,while three relatively dry types experienced decreasing trends,potentially suggesting the crossing of ecosystems'tipping points.Our results underscore the importance of accounting for root zone dynamics under climate changetoassessdroughtimpacts.展开更多
This paper introduces the method of designation of water storage capacity for each grid cell within a catchment, which considers topography, vegetation and soil synthetically. For the purpose of hydrological process s...This paper introduces the method of designation of water storage capacity for each grid cell within a catchment, which considers topography, vegetation and soil synthetically. For the purpose of hydrological process simulation in semi-arid regions, a spatially varying storage capacity （VSC） model was developed based on the spatial distribution of water storage capacity and the vertical hybrid runoff mechanism. To verify the applicability of the VSC model, both the VSC model and a hybrid runoff model were used to simulate daily runoff processes in the catchment upstream of the Dianzi hydrological station from 1973 to 1979. The results showed that the annual average Nash-Sutcliffe coefficient was 0.80 for the VSC model, and only 0.67 for the hybrid runoff model. The higher annual average Nash-Sutcliffe coefficient of the VSC model means that this hydrological model can better simulate daily runoff processes in semi-arid regions. Furthermore, as a distributed hydrological model, the VSC model can be applied in regional water resource management.展开更多
In this article, the names of 3 varieties of Monarda didyma L., which are considered to be introduced species, some indicators of the water regime in the climatic conditions of Uzbekistan: the amount of water in the l...In this article, the names of 3 varieties of Monarda didyma L., which are considered to be introduced species, some indicators of the water regime in the climatic conditions of Uzbekistan: the amount of water in the leaves, water deficit, water storage capacity were studied in spring and summer, and seasonal changes were determined. According to these indicators of the water regime, the studied varieties belong to the labile water regime, high green mass (centner), seed yield (how many grams), resistance to diseases and pests have been determined, which shows that it is promising for introduction in the conditions of our republic. Therefore, it is recommended to breed these varieties in the foothills and hilly regions of Uzbekistan, where the amount of precipitation is more than 400 - 500 mm.展开更多
Root zone maximum water deficit(S_(Rmax))refers to the maximum water consumption of the root zone during drought,which directly influences the partitioning of precipitation between infiltration and runoff.It is a key ...Root zone maximum water deficit(S_(Rmax))refers to the maximum water consumption of the root zone during drought,which directly influences the partitioning of precipitation between infiltration and runoff.It is a key parameter in land surface hydrological modeling.Since the implementation of the Grain-for-Green Project(GFG)on the Loess Plateau(LP),vegetation restoration has achieved significant success,resulting in the“greening”of LP while simultaneously reducing surface runoff.However,the lack of consideration for the root zone,a key link between terrestrial ecological and hydrological processes,has hindered understanding of ecohydrological mechanisms and limited comprehensive assessments of regional water resource management and ecological engineering outcomes.This study analyzes the spatiotemporal dynamic of S_(Rmax)on the LP from 1982 to 2018 using multi-source datasets and the Mass Curve Technique.Additionally,we employ a hybrid machine learningstatistical attribution model to quantify the contributions of land use and climate change to the S_(Rmax) dynamic.The results indicate an average S_(Rmax)of 85.3 mm across the LP,with significant variations among land use types:natural forest(116.3 mm)>planted forest(104.6 mm)>grassland(87.0 mm)>cropland(78.8 mm).Following the implementation of GFG,S_(Rmax)increased by 37.7%,with an upward trend observed across all land use types,particularly in changed land type,which experienced the largest increases.The attribution model achieved a coefficient of determination(R^(2))of 0.92.The key factors driving S_(Rmax) variation varied by land use type:in unchanged land type,climate change accounted for 53.8%of the S_(Rmax)increase,whereas land use change explained 71.3%of the increase in changed land type,with GFG contributing 52.1%.These findings provide a scientific basis for enhancing drought resilience and implementing the“Water-for-Greening”strategy on the LP and similar regions under changing environmental conditions.展开更多
Lakes are an important component of the terrestrial hydrosphere,and have a strong influence on the regional hydrological cycle[1].Due to the distinctive geographic location and climatic characteristics of the Tibetan ...Lakes are an important component of the terrestrial hydrosphere,and have a strong influence on the regional hydrological cycle[1].Due to the distinctive geographic location and climatic characteristics of the Tibetan Plateau(TP),the water level,surface area,and water storage of lakes across this region are extremely sensitive to climate change[2-4].Rapid lake expansion has become one of the most significant environmental changes across the TP[5],motivating the need for continuous monitoring of lake dynamics[4].展开更多
基金supported by the National Key Research and Development Program of China(2024YFF0809304)National Natural Science Foundation of China(42071081)+2 种基金the European Research Council(ERC-2016-ADG-743080,Horizon Europe 101081661)Formas(2022-02089 and 2019-01220)the IKEA Foundation.
文摘Adaptation of ecosystems'root zones to climate change critically affects drought resilience and vegetation productivity.However,a global quantitative assessment of this mechanism is missing.In this study,we analyzed high-quality observation-based data to find that the global average root zone water storage capacity(S_(R))increased by 11%,from 182 to 202 mm in 1982-2020.The total increase of Sr equals to 1652 billion m^(3) over the past four decades.S_(R) increased in 9 out of 12 land cover types,while three relatively dry types experienced decreasing trends,potentially suggesting the crossing of ecosystems'tipping points.Our results underscore the importance of accounting for root zone dynamics under climate changetoassessdroughtimpacts.
基金supported by the National Key Basic Research Program of China (Grant No. 2006CB400502)the Special Basic Research Fund for Methodology in Hydrology (Grant No. 2007FY140900)the 111 Project (Grant No. B08048)
文摘This paper introduces the method of designation of water storage capacity for each grid cell within a catchment, which considers topography, vegetation and soil synthetically. For the purpose of hydrological process simulation in semi-arid regions, a spatially varying storage capacity （VSC） model was developed based on the spatial distribution of water storage capacity and the vertical hybrid runoff mechanism. To verify the applicability of the VSC model, both the VSC model and a hybrid runoff model were used to simulate daily runoff processes in the catchment upstream of the Dianzi hydrological station from 1973 to 1979. The results showed that the annual average Nash-Sutcliffe coefficient was 0.80 for the VSC model, and only 0.67 for the hybrid runoff model. The higher annual average Nash-Sutcliffe coefficient of the VSC model means that this hydrological model can better simulate daily runoff processes in semi-arid regions. Furthermore, as a distributed hydrological model, the VSC model can be applied in regional water resource management.
文摘In this article, the names of 3 varieties of Monarda didyma L., which are considered to be introduced species, some indicators of the water regime in the climatic conditions of Uzbekistan: the amount of water in the leaves, water deficit, water storage capacity were studied in spring and summer, and seasonal changes were determined. According to these indicators of the water regime, the studied varieties belong to the labile water regime, high green mass (centner), seed yield (how many grams), resistance to diseases and pests have been determined, which shows that it is promising for introduction in the conditions of our republic. Therefore, it is recommended to breed these varieties in the foothills and hilly regions of Uzbekistan, where the amount of precipitation is more than 400 - 500 mm.
基金supported by the National Key Research and Development Program of China(Grant Nos.2024YFE0113200,2024YFC3213700)the National Natural Science Foundation of China(Grant Nos.42122002,42471040,and 42071081)。
文摘Root zone maximum water deficit(S_(Rmax))refers to the maximum water consumption of the root zone during drought,which directly influences the partitioning of precipitation between infiltration and runoff.It is a key parameter in land surface hydrological modeling.Since the implementation of the Grain-for-Green Project(GFG)on the Loess Plateau(LP),vegetation restoration has achieved significant success,resulting in the“greening”of LP while simultaneously reducing surface runoff.However,the lack of consideration for the root zone,a key link between terrestrial ecological and hydrological processes,has hindered understanding of ecohydrological mechanisms and limited comprehensive assessments of regional water resource management and ecological engineering outcomes.This study analyzes the spatiotemporal dynamic of S_(Rmax)on the LP from 1982 to 2018 using multi-source datasets and the Mass Curve Technique.Additionally,we employ a hybrid machine learningstatistical attribution model to quantify the contributions of land use and climate change to the S_(Rmax) dynamic.The results indicate an average S_(Rmax)of 85.3 mm across the LP,with significant variations among land use types:natural forest(116.3 mm)>planted forest(104.6 mm)>grassland(87.0 mm)>cropland(78.8 mm).Following the implementation of GFG,S_(Rmax)increased by 37.7%,with an upward trend observed across all land use types,particularly in changed land type,which experienced the largest increases.The attribution model achieved a coefficient of determination(R^(2))of 0.92.The key factors driving S_(Rmax) variation varied by land use type:in unchanged land type,climate change accounted for 53.8%of the S_(Rmax)increase,whereas land use change explained 71.3%of the increase in changed land type,with GFG contributing 52.1%.These findings provide a scientific basis for enhancing drought resilience and implementing the“Water-for-Greening”strategy on the LP and similar regions under changing environmental conditions.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19070301)the National Natural Science Foundation of China(41771089 and 41988101)the Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK020604)。
文摘Lakes are an important component of the terrestrial hydrosphere,and have a strong influence on the regional hydrological cycle[1].Due to the distinctive geographic location and climatic characteristics of the Tibetan Plateau(TP),the water level,surface area,and water storage of lakes across this region are extremely sensitive to climate change[2-4].Rapid lake expansion has become one of the most significant environmental changes across the TP[5],motivating the need for continuous monitoring of lake dynamics[4].
