For oil pipeline in mountain areas,high hydrostatic pressure in the pipeline may cause error-opening of pressure relief valves,and oil is discharged from the pipeline to the pressure relief tanks,bringing spilling-ove...For oil pipeline in mountain areas,high hydrostatic pressure in the pipeline may cause error-opening of pressure relief valves,and oil is discharged from the pipeline to the pressure relief tanks,bringing spilling-over risk of the pressure relief tanks.Therefore,simulating the error-opening situations of the pressure relief valves and investigating the oil discharge process are necessary for checking the possibility of the spilling-over accident and then proposing measures to improve the pressure relief system.This research focuses on a continuous undulating oil pipeline with large elevation difference and a station along this pipeline,which is named B station in this paper,is studied.By OLGA software,simulation model of the pressure relief system of B station is established,and the accuracy of the model is verified by reconstructing a real accident and making a comparison with the actual accident data.The maximum discharge rate reached 8284 m3/h when the pressure relief valve was opened by mistake in the inlet and outlet of the station.The accumulated filling time of the two pressure relief tanks is 200 s,which is in good agreement with the accident data.On this basis,for error-opening of the pressure relief valves at the inlet and outlet of B station,simulation is performed to investigate variations of the discharge velocity,discharge flow rate,accumulated discharge volume and ventilation volume of the vent valve.The discharge velocity is found to be over the maximum velocity allowed for safety consideration.According to the accumulated discharge volume,it is inferred that spilling over of the pressure relief tanks will be caused once error-opening of the pressure relief valve occurs.Also it is judged that the existing breathing valve can not satisfy the ventilation requirement in case of failure of the pressure relief valves.From these simulation results,it is proposed that increasing the number of vent valves,replacing the manual valves with electrically operated valves,and employing security control interlock protection program are improvement measures to guarantee safe,efficient and reliable operation of the pressure relief system at B station.展开更多
Climate change in High Mountain Asia(HMA)is characterized by elevation dependence,which results in vertical zoning of vegetation distribution.However,few studies have been conducted on the distribution patterns of veg...Climate change in High Mountain Asia(HMA)is characterized by elevation dependence,which results in vertical zoning of vegetation distribution.However,few studies have been conducted on the distribution patterns of vegetation,the response of vegetation to climate change,and the key climatic control factors of vegetation along the elevation gradient in this region.In this study,based on the Normalized Difference Vegetation index(NDVI),we investigated the evolution pattern of vegetation in HMA during 2001-2020 using linear trend and Bayesian Estimator of Abrupt change,Seasonality,and Trend(BEAST)methods.Pearson correlation analysis and partial correlation analysis were used to explore the response relationship between vegetation and climatic factors along the elevation gradient.Path analysis was employed to quantitatively reveal the dominant climatic factors affecting vegetation distribution along the elevation gradient.The results showed that NDVI in HMA increased at a rate of 0.011/10a from 2001 to 2020,and the rate of increase abruptly slowed down after 2017.NDVI showed a fluctuating increase at elevation zones 1-2(<2500 m)and then decreased at elevation zones 3-9(2500-6000 m)with the increase of elevation.NDVI was most sensitive to precipitation and temperature at a 1-month lag.With the increase of elevation,the positive response relationship of NDVI with precipitation gradually weakened,while that of NDVI with temperature was the opposite.The total effect coefficient of precipitation(0.95)on vegetation was larger than that of temperature(0.87),indicating that precipitation is the dominant control factor affecting vegetation growth.Spacially,vegetation growth is jointly influenced by precipitation and temperature,but the influence of precipitation on vegetation growth is dominant at each elevation zone.The results of this study contribute to understanding how the elevation gradient effect influences the response of vegetation to climate change in alpine ecosystems.展开更多
The lofty and extensive Tibetan Plateau has significant mass elevation effect(MEE). In recent years, a great effort has been made to quantify MEE, with the recognition of intra-mountain basal elevation(MBE) as the mai...The lofty and extensive Tibetan Plateau has significant mass elevation effect(MEE). In recent years, a great effort has been made to quantify MEE, with the recognition of intra-mountain basal elevation(MBE) as the main determinant of MEE. In this study, we improved the method of estimating MEE with MODIS and NECP data, by refining temperature laps rate, and dividing MBE plots, and then analyzed the spatio-temporal variation of MEE in the Plateau. The main conclusions include: 1) the highest average annual MEE of the plateau is as high as 11.5488°C in the southwest of the plateau, where exists a high-MEE core and MEE takes on a trend of decreasing from the core to the surrounding areas; 2) in the interior of the plateau, the maximum monthly MEE is 14.1108°C in the highest MBE plot(4934 m) in August; while the minimum monthly MEE appeared primarily in January and February; 3) in the peripheral areas of the plateau, annual mean MEE is relatively low, mostly between 3.0068°C–5.1972°C, where monthly MEE is high in January and December and low in June and July, completely different from the MEE time-series variation in the internal parts of the plateau.展开更多
Mass elevation effect （MEE） refers to the thermal effect of huge mountains or plateaus, which causes the tendency for tem- perature-related montane landscape limits to occur at higher elevations in the inner massifs...Mass elevation effect （MEE） refers to the thermal effect of huge mountains or plateaus, which causes the tendency for tem- perature-related montane landscape limits to occur at higher elevations in the inner massifs than on their outer margins. MEE has been widely identified in all large mountains, but how it could be measured and what its main forming-factors are still remain open. This paper, supposing that the local mountain base elevation （MBE） is the main factor of MEE, takes the Qinghai-Tibet Plateau （QTP） as the study area, defines MEE as the temperature difference （AT） between the inner and outer parts of mountain massifs, identifies the main forming factors, and analyzes their contributions to MEE. A total of 73 mountain bases were identified, ranging from 708 m to 5081 m and increasing from the edges to the central parts of the plateau. Climate data （1981-2010） from 134 meteorological stations were used to acquire ATby comparing near-surface air temperature on the main plateau with the free-air temperature at the same altitude and simi- lar latitude outside of the plateau. The AT for the warmest month is averagely 6.15 ~C, over 12~C at Lhatse and Baxoi. A multivariate linear regression model was developed to simulate MEE based on three variables （latitude, annual mean precipitation and MBE）, which are all significantly correlated to AT. The model could explain 67.3% of MEE variation, and the contribution rates of three independent variables to MEE are 35.29%, 22.69% and 42.02%, respectively. This confirms that MBE is the main factor of MEE. The intensive MEE of the QTP pushes the 10~C isotherm of the warmest month mean temperature 1300-2000 m higher in the main plateau than in the outer regions, leading the occurrence of the highest timberline （4900 m） and the highest snowline （6200 m） of the Northern Hemisphere in the southeast and southwest of the plateau, respectively.展开更多
Based on the survey data of forest assessment in Shandong province, mathematics statistic method was used to analyze the significance tests within the elevation distance of 100 m and the overall performance, concludin...Based on the survey data of forest assessment in Shandong province, mathematics statistic method was used to analyze the significance tests within the elevation distance of 100 m and the overall performance, concluding that elevation had a significant effect on the growth of Platycladus orientalis. The methods, pro- cess, and analysis on survey results were introduced, and the direction of its appli- cation was pointed out, as well as the limitations of the study.展开更多
Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be...Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be seen as an index for regular data support.As a type of change of scale,data refinement is useful for many scenarios where spatial scales of existing data,desired analyses,or specific applications need to be made commensurate and refined.As spatial data are related to certain data support,they can be conceived of as support-specific realizations of random fields,suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finerresolution ones.In this paper,geostatistical methods for downscaling are described,and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China,with the latter’s majority grid cells used as surrogate reference data.It was found that proper structural modeling is important for achieving increased accuracy in data refinement;here,structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter.It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means.展开更多
文摘For oil pipeline in mountain areas,high hydrostatic pressure in the pipeline may cause error-opening of pressure relief valves,and oil is discharged from the pipeline to the pressure relief tanks,bringing spilling-over risk of the pressure relief tanks.Therefore,simulating the error-opening situations of the pressure relief valves and investigating the oil discharge process are necessary for checking the possibility of the spilling-over accident and then proposing measures to improve the pressure relief system.This research focuses on a continuous undulating oil pipeline with large elevation difference and a station along this pipeline,which is named B station in this paper,is studied.By OLGA software,simulation model of the pressure relief system of B station is established,and the accuracy of the model is verified by reconstructing a real accident and making a comparison with the actual accident data.The maximum discharge rate reached 8284 m3/h when the pressure relief valve was opened by mistake in the inlet and outlet of the station.The accumulated filling time of the two pressure relief tanks is 200 s,which is in good agreement with the accident data.On this basis,for error-opening of the pressure relief valves at the inlet and outlet of B station,simulation is performed to investigate variations of the discharge velocity,discharge flow rate,accumulated discharge volume and ventilation volume of the vent valve.The discharge velocity is found to be over the maximum velocity allowed for safety consideration.According to the accumulated discharge volume,it is inferred that spilling over of the pressure relief tanks will be caused once error-opening of the pressure relief valve occurs.Also it is judged that the existing breathing valve can not satisfy the ventilation requirement in case of failure of the pressure relief valves.From these simulation results,it is proposed that increasing the number of vent valves,replacing the manual valves with electrically operated valves,and employing security control interlock protection program are improvement measures to guarantee safe,efficient and reliable operation of the pressure relief system at B station.
基金supported by the Xinjiang Uygur Autonomous Region Major Scientific and Technological Special Project Research and Demonstration on the Development Model of Ecological Agriculture and Efficient Utilization of Soil and Water Resources in Modern Irrigation Areas(2023A02002-1).
文摘Climate change in High Mountain Asia(HMA)is characterized by elevation dependence,which results in vertical zoning of vegetation distribution.However,few studies have been conducted on the distribution patterns of vegetation,the response of vegetation to climate change,and the key climatic control factors of vegetation along the elevation gradient in this region.In this study,based on the Normalized Difference Vegetation index(NDVI),we investigated the evolution pattern of vegetation in HMA during 2001-2020 using linear trend and Bayesian Estimator of Abrupt change,Seasonality,and Trend(BEAST)methods.Pearson correlation analysis and partial correlation analysis were used to explore the response relationship between vegetation and climatic factors along the elevation gradient.Path analysis was employed to quantitatively reveal the dominant climatic factors affecting vegetation distribution along the elevation gradient.The results showed that NDVI in HMA increased at a rate of 0.011/10a from 2001 to 2020,and the rate of increase abruptly slowed down after 2017.NDVI showed a fluctuating increase at elevation zones 1-2(<2500 m)and then decreased at elevation zones 3-9(2500-6000 m)with the increase of elevation.NDVI was most sensitive to precipitation and temperature at a 1-month lag.With the increase of elevation,the positive response relationship of NDVI with precipitation gradually weakened,while that of NDVI with temperature was the opposite.The total effect coefficient of precipitation(0.95)on vegetation was larger than that of temperature(0.87),indicating that precipitation is the dominant control factor affecting vegetation growth.Spacially,vegetation growth is jointly influenced by precipitation and temperature,but the influence of precipitation on vegetation growth is dominant at each elevation zone.The results of this study contribute to understanding how the elevation gradient effect influences the response of vegetation to climate change in alpine ecosystems.
基金supported by the Natural Science Foundation of China (Grant Nos.41401111 and 41601091)
文摘The lofty and extensive Tibetan Plateau has significant mass elevation effect(MEE). In recent years, a great effort has been made to quantify MEE, with the recognition of intra-mountain basal elevation(MBE) as the main determinant of MEE. In this study, we improved the method of estimating MEE with MODIS and NECP data, by refining temperature laps rate, and dividing MBE plots, and then analyzed the spatio-temporal variation of MEE in the Plateau. The main conclusions include: 1) the highest average annual MEE of the plateau is as high as 11.5488°C in the southwest of the plateau, where exists a high-MEE core and MEE takes on a trend of decreasing from the core to the surrounding areas; 2) in the interior of the plateau, the maximum monthly MEE is 14.1108°C in the highest MBE plot(4934 m) in August; while the minimum monthly MEE appeared primarily in January and February; 3) in the peripheral areas of the plateau, annual mean MEE is relatively low, mostly between 3.0068°C–5.1972°C, where monthly MEE is high in January and December and low in June and July, completely different from the MEE time-series variation in the internal parts of the plateau.
基金National Natural Science Foundation of China(No.41571099,41030528)
文摘Mass elevation effect （MEE） refers to the thermal effect of huge mountains or plateaus, which causes the tendency for tem- perature-related montane landscape limits to occur at higher elevations in the inner massifs than on their outer margins. MEE has been widely identified in all large mountains, but how it could be measured and what its main forming-factors are still remain open. This paper, supposing that the local mountain base elevation （MBE） is the main factor of MEE, takes the Qinghai-Tibet Plateau （QTP） as the study area, defines MEE as the temperature difference （AT） between the inner and outer parts of mountain massifs, identifies the main forming factors, and analyzes their contributions to MEE. A total of 73 mountain bases were identified, ranging from 708 m to 5081 m and increasing from the edges to the central parts of the plateau. Climate data （1981-2010） from 134 meteorological stations were used to acquire ATby comparing near-surface air temperature on the main plateau with the free-air temperature at the same altitude and simi- lar latitude outside of the plateau. The AT for the warmest month is averagely 6.15 ~C, over 12~C at Lhatse and Baxoi. A multivariate linear regression model was developed to simulate MEE based on three variables （latitude, annual mean precipitation and MBE）, which are all significantly correlated to AT. The model could explain 67.3% of MEE variation, and the contribution rates of three independent variables to MEE are 35.29%, 22.69% and 42.02%, respectively. This confirms that MBE is the main factor of MEE. The intensive MEE of the QTP pushes the 10~C isotherm of the warmest month mean temperature 1300-2000 m higher in the main plateau than in the outer regions, leading the occurrence of the highest timberline （4900 m） and the highest snowline （6200 m） of the Northern Hemisphere in the southeast and southwest of the plateau, respectively.
文摘Based on the survey data of forest assessment in Shandong province, mathematics statistic method was used to analyze the significance tests within the elevation distance of 100 m and the overall performance, concluding that elevation had a significant effect on the growth of Platycladus orientalis. The methods, pro- cess, and analysis on survey results were introduced, and the direction of its appli- cation was pointed out, as well as the limitations of the study.
基金Research reported in this paper is supported by the National Natural Science Foundation of China(grant numbers 41171346,41471375).
文摘Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be seen as an index for regular data support.As a type of change of scale,data refinement is useful for many scenarios where spatial scales of existing data,desired analyses,or specific applications need to be made commensurate and refined.As spatial data are related to certain data support,they can be conceived of as support-specific realizations of random fields,suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finerresolution ones.In this paper,geostatistical methods for downscaling are described,and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China,with the latter’s majority grid cells used as surrogate reference data.It was found that proper structural modeling is important for achieving increased accuracy in data refinement;here,structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter.It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means.
