Understanding hydrological responses to rising levels of greenhouse gases are essential for climate and impact research.It is,however,often limited by a lack of long record of observational data to provide a basis for...Understanding hydrological responses to rising levels of greenhouse gases are essential for climate and impact research.It is,however,often limited by a lack of long record of observational data to provide a basis for understanding the longterm behavior of the climate system.Integrating reconstructed data and(global climate and hydrological)model simulations will help us to better understand the variability of climate and hydrology over timescales ranging from decades to centuries.In this study,we proposed an integrated approach to study flood variability in the upper reach of the Yangtze River over the last millennium to the end of the 21st century.To accomplish this,we first drove hydrological models using the precipitation and temperature from four Global Climate Models(GCM),BCC-CSM1.1,MIROC,MRI-CGCM3,and CCSM4,to simulate daily discharge for the upper reach of the Yangtze River during the period of the last millennium(850–1849),historical period(1850–2005),and a future period(2006–2099).Then,we evaluated whether the modeled precipitation,temperature,and extreme discharge had statistical properties similar to those shown in the documented dry-wet periods,temperature anomalies,and paleoflood records.Finally,we explored the extreme discharge variability using model simulations.The results indicate that:(1)The MIROC-ESM model,differing from the other three GCM models,revealed positive temperature changes from the warm period(Medieval Climate Anomaly;MCA)to the cold period(Little Ice Age;LIA),while the temperature variability of the other models was similar to the records.(2)The BCC-CSM1.1 model performed better than the others regarding correlations between modeled precipitation and documented dry-wet periods.(3)Over most of the subbasins in the upper Yangtze River,the magnitude of extreme discharge in the BCC-CSM1.1 model results showed that there was a decrease from the MCA to the LIA period and an increase in the historical period relative to the cold period,while a future increase was projected by the four GCMs under the influence of climate change.展开更多
Lake wetlands play a crucial role as global carbon sinks,significantly contributing to carbon storage and ecological balance.This study estimates the quarterly carbon storage in the Dongting Lake wetland for the years...Lake wetlands play a crucial role as global carbon sinks,significantly contributing to carbon storage and ecological balance.This study estimates the quarterly carbon storage in the Dongting Lake wetland for the years 2010,2015,and 2020,using MODIS remote sensing imagery and the InVEST model.A Structural Equation Model(SEM)was then employed to analyze the driving factors behind changes in carbon storage.Results show that intra-annual carbon storage increases and then decreases,with maximum level in the third quarter(average of 34.242 Tg)and a minimum one in the first quarter(average of 21.435 Tg).From 2010 to 2020,inter-annual carbon storage variations initially exhibited an increasing trend before decreasing,with the peak annual average carbon storage reaching 32.230 Tg in 2015.Notably,the coefficient of variation for intra-annual carbon storage increased from 8.5%in 2010 to 25.8%in 2020.Key driving factors that influence carbon storage changes include surface solar radiation,temperature,and water level,with carbon storage positively correlated with surface solar radiation and temperature,and negatively correlated with water level.These findings reveal the spatiotemporal evolution characteristics of carbon storage in the Dongting Lake wetland,offering scientific guidance for wetland conservation and regional climate adaptation policies.展开更多
基金supported by the National Key Research and Development Program(Grant No.2017YFA0603702)the Research Council of Norway(FRINATEK Project 274310)。
文摘Understanding hydrological responses to rising levels of greenhouse gases are essential for climate and impact research.It is,however,often limited by a lack of long record of observational data to provide a basis for understanding the longterm behavior of the climate system.Integrating reconstructed data and(global climate and hydrological)model simulations will help us to better understand the variability of climate and hydrology over timescales ranging from decades to centuries.In this study,we proposed an integrated approach to study flood variability in the upper reach of the Yangtze River over the last millennium to the end of the 21st century.To accomplish this,we first drove hydrological models using the precipitation and temperature from four Global Climate Models(GCM),BCC-CSM1.1,MIROC,MRI-CGCM3,and CCSM4,to simulate daily discharge for the upper reach of the Yangtze River during the period of the last millennium(850–1849),historical period(1850–2005),and a future period(2006–2099).Then,we evaluated whether the modeled precipitation,temperature,and extreme discharge had statistical properties similar to those shown in the documented dry-wet periods,temperature anomalies,and paleoflood records.Finally,we explored the extreme discharge variability using model simulations.The results indicate that:(1)The MIROC-ESM model,differing from the other three GCM models,revealed positive temperature changes from the warm period(Medieval Climate Anomaly;MCA)to the cold period(Little Ice Age;LIA),while the temperature variability of the other models was similar to the records.(2)The BCC-CSM1.1 model performed better than the others regarding correlations between modeled precipitation and documented dry-wet periods.(3)Over most of the subbasins in the upper Yangtze River,the magnitude of extreme discharge in the BCC-CSM1.1 model results showed that there was a decrease from the MCA to the LIA period and an increase in the historical period relative to the cold period,while a future increase was projected by the four GCMs under the influence of climate change.
基金supported by National Natural Science Foundation of China(No.42272291,No.42077176)the Strategic Research Program of the National Natural Science Foundation of China(No.42242202).
文摘Lake wetlands play a crucial role as global carbon sinks,significantly contributing to carbon storage and ecological balance.This study estimates the quarterly carbon storage in the Dongting Lake wetland for the years 2010,2015,and 2020,using MODIS remote sensing imagery and the InVEST model.A Structural Equation Model(SEM)was then employed to analyze the driving factors behind changes in carbon storage.Results show that intra-annual carbon storage increases and then decreases,with maximum level in the third quarter(average of 34.242 Tg)and a minimum one in the first quarter(average of 21.435 Tg).From 2010 to 2020,inter-annual carbon storage variations initially exhibited an increasing trend before decreasing,with the peak annual average carbon storage reaching 32.230 Tg in 2015.Notably,the coefficient of variation for intra-annual carbon storage increased from 8.5%in 2010 to 25.8%in 2020.Key driving factors that influence carbon storage changes include surface solar radiation,temperature,and water level,with carbon storage positively correlated with surface solar radiation and temperature,and negatively correlated with water level.These findings reveal the spatiotemporal evolution characteristics of carbon storage in the Dongting Lake wetland,offering scientific guidance for wetland conservation and regional climate adaptation policies.
