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
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
The increasing demand due to development and advancement in every field of life has caused the depletion of fossil fuels.This depleting fossil fuel reserve throughout the world has enforced to get energy from alternat...The increasing demand due to development and advancement in every field of life has caused the depletion of fossil fuels.This depleting fossil fuel reserve throughout the world has enforced to get energy from alternative/renewable sources.One of the economicalways to get energy is through the utilization of solar ponds.In this study,a mathematical model of a salt gradient solar pond under the Islamabad climatic conditions has been analyzed for the first time.The model uses a one-dimensional finite difference explicit method for optimization of different zone thicknesses.The model depicts that NCZ(Non-Convective Zone)thickness has a significant effect on LCZ(Lower Convective Zone)temperature and should be kept less than 1.7mfor the optimal temperature.It is also observed that for long-termoperation of a solar pond,heat should be extracted by keeping the mass flowrate of 17.3 kg/m^(2)/day.Themodel also suggests that when the bottom reflectivity is about 0.3,then only 24%of the radiation is absorbed in the pond.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 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.
文摘The increasing demand due to development and advancement in every field of life has caused the depletion of fossil fuels.This depleting fossil fuel reserve throughout the world has enforced to get energy from alternative/renewable sources.One of the economicalways to get energy is through the utilization of solar ponds.In this study,a mathematical model of a salt gradient solar pond under the Islamabad climatic conditions has been analyzed for the first time.The model uses a one-dimensional finite difference explicit method for optimization of different zone thicknesses.The model depicts that NCZ(Non-Convective Zone)thickness has a significant effect on LCZ(Lower Convective Zone)temperature and should be kept less than 1.7mfor the optimal temperature.It is also observed that for long-termoperation of a solar pond,heat should be extracted by keeping the mass flowrate of 17.3 kg/m^(2)/day.Themodel also suggests that when the bottom reflectivity is about 0.3,then only 24%of the radiation is absorbed in the pond.
