Background Mara River Basin is an ecologically fragile area in East Africa,with a pattern of alternating wet and dry seasons shaped by periodic precipitation.Considering the regional biological traits and climatic cha...Background Mara River Basin is an ecologically fragile area in East Africa,with a pattern of alternating wet and dry seasons shaped by periodic precipitation.Considering the regional biological traits and climatic change,the vegetation’s response to seasonal variation is complicated and frequently characterized by time lags.This study analyzed the variation of the Normalized Difference Vegetation Index(NDVI)and investigated its time lag to precipitation at the monthly scale.NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006,2009,and 2017.The NDVI showed an increasing trend in 75%of areas of the basin,while showed a decreased significance in 3.5%of areas,mainly in savannas.As to the time lag,the 1-month lag effect dominated most months,and the spatiotemporal disparities were noticeable.Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days.Based on the time distribution of NDVI characteristic peaks,the average time lag was 35.5 days and increased with the range of seasons.Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020,while the trends were most obvious in the downstream related to human activities.The results could reflect the time lag of NDVI response to precipitation,and the 1-month lag effect dominated in most months with spatial heterogeneity.Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.展开更多
In order to alleviate the shortage of natural gas supply in winter,relevant policies have been issued to promote the construction of gas peak-shaving and storage facilities.Largescale gas storage can transfer the supp...In order to alleviate the shortage of natural gas supply in winter,relevant policies have been issued to promote the construction of gas peak-shaving and storage facilities.Largescale gas storage can transfer the supply-demand relationship of natural gas in time sequence,which has great potential in improving the economy and reliabillity of urban multi-energy flow systems.Addressing this issue,this paper proposes a mid-and long-term energy optimization method for urban multi-energy flow system that considers seasonal peak shaving of natural gas.First,the energy supply and demand features of different energy subsystems are analyzed.Then,a network model of the electricity-gas-heat multi-energy flow system is established.Considering the time-of-use electricity price mechanism and the seasonal fluctuations of the natural gas price,a mid-and long-term energy optimization model maximizing the annual economic revenue is established.The alternative direction multiplier method with Gaussian back substitution(ADMM-GBS)algorithm is used to solve the optimal dispatch model.Finally,the proposed method is verified by employing the actual data of the demonstration zone in Yangzhong City,China.The simulation results show that the proposed method is effective.展开更多
基金supported by the National Key R&D Program of China[Grant Number 2018YFE0105900].
文摘Background Mara River Basin is an ecologically fragile area in East Africa,with a pattern of alternating wet and dry seasons shaped by periodic precipitation.Considering the regional biological traits and climatic change,the vegetation’s response to seasonal variation is complicated and frequently characterized by time lags.This study analyzed the variation of the Normalized Difference Vegetation Index(NDVI)and investigated its time lag to precipitation at the monthly scale.NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006,2009,and 2017.The NDVI showed an increasing trend in 75%of areas of the basin,while showed a decreased significance in 3.5%of areas,mainly in savannas.As to the time lag,the 1-month lag effect dominated most months,and the spatiotemporal disparities were noticeable.Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days.Based on the time distribution of NDVI characteristic peaks,the average time lag was 35.5 days and increased with the range of seasons.Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020,while the trends were most obvious in the downstream related to human activities.The results could reflect the time lag of NDVI response to precipitation,and the 1-month lag effect dominated in most months with spatial heterogeneity.Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.
基金supported by the National Key R&D Program of China(2018YFB0905000)Science and Technology Project of State Grid Corporation of China(SGTJDK00DWJS1800232).
文摘In order to alleviate the shortage of natural gas supply in winter,relevant policies have been issued to promote the construction of gas peak-shaving and storage facilities.Largescale gas storage can transfer the supply-demand relationship of natural gas in time sequence,which has great potential in improving the economy and reliabillity of urban multi-energy flow systems.Addressing this issue,this paper proposes a mid-and long-term energy optimization method for urban multi-energy flow system that considers seasonal peak shaving of natural gas.First,the energy supply and demand features of different energy subsystems are analyzed.Then,a network model of the electricity-gas-heat multi-energy flow system is established.Considering the time-of-use electricity price mechanism and the seasonal fluctuations of the natural gas price,a mid-and long-term energy optimization model maximizing the annual economic revenue is established.The alternative direction multiplier method with Gaussian back substitution(ADMM-GBS)algorithm is used to solve the optimal dispatch model.Finally,the proposed method is verified by employing the actual data of the demonstration zone in Yangzhong City,China.The simulation results show that the proposed method is effective.
