Climate change can affect wind erosion power and hence induce changes in wind erosion rates.In this study,the wind erosion climate factor(C-factor),proposed by the Food and Agriculture Organization of the United Natio...Climate change can affect wind erosion power and hence induce changes in wind erosion rates.In this study,the wind erosion climate factor(C-factor),proposed by the Food and Agriculture Organization of the United Nations,was used to assess the impact of changes in climate on wind erosion climatic erosivity.The Mann-Kendall test was employed to detect trends in the C-factor during the period of 1961–2017 in the farming-pastoral zone of northern China.Sensitivity analysis was used to determine the sensitivity of the C-factor to changes in key climate factors.Furthermore,a comparison of the contributions of different climate factors was carried out to understand their impact on changes in the C-factor.The results indicated that most of the surveyed region exhibited decreasing trends in wind speed at a confidence level of 90%,while maximum and minimum temperatures showed increasing trends throughout the study area.As a consequence of decreasing wind speed,the annual C-factor exhibited significant decreasing trends,with a mean slope of–0.58/yr.Seasonal analysis revealed that in most regions,the changes in the C-factor had significant decreasing trends in spring,winter,and autumn,while in more than two-thirds of the study area,no significant change trends in the C-factor were detected in summer at a confidence level of 90%.Sensitivity analysis showed that the C-factor was most sensitive to wind speed,and that the sensitivity coefficients from July to September were much higher than those in other months.Contribution analysis revealed that,for most stations,wind speed(with greater values of sensitivity coefficients)was the dominant factor in the change of C-factor,while for some stations,the minimum temperature made the most contribution to the C-factor’s change due to its dramatic changes during the study period.Although the minimum temperature sensitivity coefficient was the lowest of all the sensitivity coefficients,it is urgent to evaluate the expected impact of minimum temperature due to its possible changes in the future.展开更多
The dry and windy climate and low ground cover in spring in the black soil region of Northeast China make the soil strongly affected by wind erosion,which seriously threatens the food security and ecological security ...The dry and windy climate and low ground cover in spring in the black soil region of Northeast China make the soil strongly affected by wind erosion,which seriously threatens the food security and ecological security of this region.In this paper,based on the daily observation data of 124 meteorological stations in study area from 1961 to 2020,seasonal and monthly wind erosion climate factor(C)in spring(March to May)were calculated by using the method proposed by the Food and Agriculture Organization of the United Nations(FAO),the wind erosion characterization in spring were systematically analyzed based on C by various statistical analysis methods.The results showed that in the past 60 years,spring wind erosion climate factor(CSp)and monthly C of the whole region and each province(region)all showed highly significant decreasing trend,but they began to show rebounded trend in the middle or late 2000s.CSp of the study area showed a significant upward trend since 2008 with an increase of 4.59(10a)^(-1).The main contributors to this upward trend are the changes of C in March and in April.For the four provinces(regions),CSp in Heilongjiang,Jilin,Liaoning and eastern Inner Mongolia all showed rebounded since 2008,2011,2008 and 2009,respectively.The rebounded trend of CSp in eastern Inner Mongolia was the most obvious with a tendency rate of 11.27(10a)^(-1),and its mutation occurred after 1984.The rebound trend of CSp in Heilongjiang Province takes the second place,with a trend rate of 4.72(10a)^(-1),but there’s no obvious time mutation characteristics.The spatial characteristics of CSpand monthly C are similar,showing decreasing characteristics centered on the typical black soil belt of Northeast China.Compared with 1961-1990,in the period from 1991 to 2020,the proportion of high value areas(CSp>35,monthly C>10)has decreased to varying degrees,while the proportion of low value areas(CSp≤10,monthly C≤4)has increased.The trends of seasonal and monthly C in 82.2%~87.7%of the stations show significant decreases at 95%confidence level.CSp is closely related to wind speed at 2m height,temperature difference,minimum temperature and precipitation in the same period,of which the correlation between CSp and wind speed is the strongest,indicating that the main control factor for CSp in the study area is wind speed,but the impact of the change of temperature and precipitation on CSp cannot be ignored.展开更多
China＇s dryland region has serious wind erosion problem and is sensitive to climate change due to its fragile ecological condition. Wind erosion climatic erosivity is a measure of climatic factors influencing wind er...China＇s dryland region has serious wind erosion problem and is sensitive to climate change due to its fragile ecological condition. Wind erosion climatic erosivity is a measure of climatic factors influencing wind erosion, therefore, evaluation of its intensity and response to recent climate changes can contribute to the understanding of climate change effect on wind erosion risk. Using the FAO equation, GIS and statistical analysis tools, this study quantified the climatic erosivity, analyzed its spatiotemporal variations, and detected the trend and sen- sitivity to climate factors during 1961-2012. The results indicate that mean annual climatic erosivity was 2-166 at 292 stations and 237-471 at 6 stations, with the spatial distribution highly in accordance with wind speed （R^2 = 0.94）. The climatic erosivity varied greatly over time with the annual variation （CV） of 14.7%-108.9% and monthly variation （concentration degree） of 0.10-0.71 in the region. Meanwhile, annual erosivity showed a significant down- ward trend at an annual decreasing rate mostly above 1.0%. This significantly decreasing trend was mainly attributed to the obvious decline of wind speed during the period. The results suggest that the recent climate changes were highly possible to induce a decrease of wind erosion risk in China＇s dryland region.展开更多
A method was introduced to assess the sustainability of energy production over the lifetime (~20 y) of wind turbines. Community Earth System Model simulations were downscaled for the tourist seasons (mid-May to mid-Se...A method was introduced to assess the sustainability of energy production over the lifetime (~20 y) of wind turbines. Community Earth System Model simulations were downscaled for the tourist seasons (mid-May to mid-September) of 2006 to 2012 (CESM-P1) and 2026 to 2032 (CESM-P2) to obtain a reference and projected wind-speed climatology, respectively. The wind speeds served to calculate the potential power output and capacity factors of seven turbine types. CESM-P1 wind-speed climatology, power output, and capacity factors were compared to those derived from wind speeds obtained by numerical weather forecasts for reference to known standard to wind-farm managers. Juneau, Alaska served as a virtual testbed as this region is known to experience changes in wind speeds in response to the Pacific Decadal Oscillation. CESM-P2 suggested about 2% decrease for wind speeds between the speeds at cut-in and rated power, and about 8% - 10% decrease in potential wind-power output. This means that in regions of decadal climate variations, the sustainability of wind-energy production should be part of the decision-making process. The study demonstrated that using mean values of wind-speeds can provide qualitative knowledge about decreases/increases in potential energy production, but not about the magnitude. Using the total individual wind-speed data of all seasons provided the same amount of total power output than summing up the power outputs of individual seasons. The main advantage of calculating individual seasonal wind-power outputs, however, is that it theoretically permits assessment of interannual variability in power output and capacity factors. Comparison to a known standard may help stakeholders in understanding of uncertainty and interpretation of projected changes.展开更多
The ocean wave climate has a variety of applications in Naval defence.However,a long-term and reliable wave climate for the Indian Seas(The Arabian Sea and The Bay of Bengal)over a desired grid resolution could not be...The ocean wave climate has a variety of applications in Naval defence.However,a long-term and reliable wave climate for the Indian Seas(The Arabian Sea and The Bay of Bengal)over a desired grid resolution could not be established so far due to several constraints.In this study,an attempt was made for the simulation of wave climate for the Indian Seas using the third-generation wave model(3g-WAM)developed by WAMDI group.The 3g-WAM as such was implemented at NPOL for research applications.The specific importance of this investigation was that,the model utilized a“mean climatic year of winds”estimated using historical wind measurements following statistical and probabilistic approaches as the winds which were considered for this purpose were widely scattered in space and time.Model computations were carried out only for the deep waters with current refraction.The gridded outputs of various wave parameters were stored at each grid point and the spectral outputs were stored at selected locations.Monthly,seasonal and annual distributions of significant wave parameters were obtained by post-processing some of the model outputs.A qualitative validation of simulated wave height and period parameters were also carried out by comparing with the observed data.The study revealed that the results of the wave climate simulation were quite promising and they can be utilized for various operational and ocean engineering applications.Therefore,this study will be a useful reference/demonstration for conducting such experiments in the areas where wind as well as wave measurements are insufficient.展开更多
Global climate change may have serious impact on human activities in coastal and other areas.Climate change may affect the degree of storminess and,hence,change the wind-driven ocean wave climate.This may affect the r...Global climate change may have serious impact on human activities in coastal and other areas.Climate change may affect the degree of storminess and,hence,change the wind-driven ocean wave climate.This may affect the risks associated with maritime activities such as shipping and offshore oil and gas.So,there is a recognized need to understand better how climate change will affect such processes.Typically,such understanding comes from future projections of the wind and wave climate from numerical climate models and from the stochastic modelling of such projections.This work investigates the applicability of a recently proposed nonstationary fuzzy modelling to wind and wave climatic simulations.According to this,fuzzy inference models(FIS)are coupled with nonstationary time series modelling,providing us with less biased climatic estimates.Two long-term datasets for an area in the North Atlantic Ocean are used in the present study,namely NORA10(57 years)and ExWaCli(30 years in the present and 30 years in the future).Two distinct experiments have been performed to simulate future values of the time series in a climatic scale.The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41901355)National Key R&D Program of China(No.2021YFD1500702)。
文摘Climate change can affect wind erosion power and hence induce changes in wind erosion rates.In this study,the wind erosion climate factor(C-factor),proposed by the Food and Agriculture Organization of the United Nations,was used to assess the impact of changes in climate on wind erosion climatic erosivity.The Mann-Kendall test was employed to detect trends in the C-factor during the period of 1961–2017 in the farming-pastoral zone of northern China.Sensitivity analysis was used to determine the sensitivity of the C-factor to changes in key climate factors.Furthermore,a comparison of the contributions of different climate factors was carried out to understand their impact on changes in the C-factor.The results indicated that most of the surveyed region exhibited decreasing trends in wind speed at a confidence level of 90%,while maximum and minimum temperatures showed increasing trends throughout the study area.As a consequence of decreasing wind speed,the annual C-factor exhibited significant decreasing trends,with a mean slope of–0.58/yr.Seasonal analysis revealed that in most regions,the changes in the C-factor had significant decreasing trends in spring,winter,and autumn,while in more than two-thirds of the study area,no significant change trends in the C-factor were detected in summer at a confidence level of 90%.Sensitivity analysis showed that the C-factor was most sensitive to wind speed,and that the sensitivity coefficients from July to September were much higher than those in other months.Contribution analysis revealed that,for most stations,wind speed(with greater values of sensitivity coefficients)was the dominant factor in the change of C-factor,while for some stations,the minimum temperature made the most contribution to the C-factor’s change due to its dramatic changes during the study period.Although the minimum temperature sensitivity coefficient was the lowest of all the sensitivity coefficients,it is urgent to evaluate the expected impact of minimum temperature due to its possible changes in the future.
基金supported by the Open Research Fund of Innovation and Open Laboratory of Eco-meteorology in Northeast China,China Meteorological Administration(stqx2019zd02)Heilongjiang Meteorological Science and Technology Research Project(HQGG202004)Heilongjiang Provincial Natural Science Foundation of China(LH2020C105)。
文摘The dry and windy climate and low ground cover in spring in the black soil region of Northeast China make the soil strongly affected by wind erosion,which seriously threatens the food security and ecological security of this region.In this paper,based on the daily observation data of 124 meteorological stations in study area from 1961 to 2020,seasonal and monthly wind erosion climate factor(C)in spring(March to May)were calculated by using the method proposed by the Food and Agriculture Organization of the United Nations(FAO),the wind erosion characterization in spring were systematically analyzed based on C by various statistical analysis methods.The results showed that in the past 60 years,spring wind erosion climate factor(CSp)and monthly C of the whole region and each province(region)all showed highly significant decreasing trend,but they began to show rebounded trend in the middle or late 2000s.CSp of the study area showed a significant upward trend since 2008 with an increase of 4.59(10a)^(-1).The main contributors to this upward trend are the changes of C in March and in April.For the four provinces(regions),CSp in Heilongjiang,Jilin,Liaoning and eastern Inner Mongolia all showed rebounded since 2008,2011,2008 and 2009,respectively.The rebounded trend of CSp in eastern Inner Mongolia was the most obvious with a tendency rate of 11.27(10a)^(-1),and its mutation occurred after 1984.The rebound trend of CSp in Heilongjiang Province takes the second place,with a trend rate of 4.72(10a)^(-1),but there’s no obvious time mutation characteristics.The spatial characteristics of CSpand monthly C are similar,showing decreasing characteristics centered on the typical black soil belt of Northeast China.Compared with 1961-1990,in the period from 1991 to 2020,the proportion of high value areas(CSp>35,monthly C>10)has decreased to varying degrees,while the proportion of low value areas(CSp≤10,monthly C≤4)has increased.The trends of seasonal and monthly C in 82.2%~87.7%of the stations show significant decreases at 95%confidence level.CSp is closely related to wind speed at 2m height,temperature difference,minimum temperature and precipitation in the same period,of which the correlation between CSp and wind speed is the strongest,indicating that the main control factor for CSp in the study area is wind speed,but the impact of the change of temperature and precipitation on CSp cannot be ignored.
文摘China＇s dryland region has serious wind erosion problem and is sensitive to climate change due to its fragile ecological condition. Wind erosion climatic erosivity is a measure of climatic factors influencing wind erosion, therefore, evaluation of its intensity and response to recent climate changes can contribute to the understanding of climate change effect on wind erosion risk. Using the FAO equation, GIS and statistical analysis tools, this study quantified the climatic erosivity, analyzed its spatiotemporal variations, and detected the trend and sen- sitivity to climate factors during 1961-2012. The results indicate that mean annual climatic erosivity was 2-166 at 292 stations and 237-471 at 6 stations, with the spatial distribution highly in accordance with wind speed （R^2 = 0.94）. The climatic erosivity varied greatly over time with the annual variation （CV） of 14.7%-108.9% and monthly variation （concentration degree） of 0.10-0.71 in the region. Meanwhile, annual erosivity showed a significant down- ward trend at an annual decreasing rate mostly above 1.0%. This significantly decreasing trend was mainly attributed to the obvious decline of wind speed during the period. The results suggest that the recent climate changes were highly possible to induce a decrease of wind erosion risk in China＇s dryland region.
文摘A method was introduced to assess the sustainability of energy production over the lifetime (~20 y) of wind turbines. Community Earth System Model simulations were downscaled for the tourist seasons (mid-May to mid-September) of 2006 to 2012 (CESM-P1) and 2026 to 2032 (CESM-P2) to obtain a reference and projected wind-speed climatology, respectively. The wind speeds served to calculate the potential power output and capacity factors of seven turbine types. CESM-P1 wind-speed climatology, power output, and capacity factors were compared to those derived from wind speeds obtained by numerical weather forecasts for reference to known standard to wind-farm managers. Juneau, Alaska served as a virtual testbed as this region is known to experience changes in wind speeds in response to the Pacific Decadal Oscillation. CESM-P2 suggested about 2% decrease for wind speeds between the speeds at cut-in and rated power, and about 8% - 10% decrease in potential wind-power output. This means that in regions of decadal climate variations, the sustainability of wind-energy production should be part of the decision-making process. The study demonstrated that using mean values of wind-speeds can provide qualitative knowledge about decreases/increases in potential energy production, but not about the magnitude. Using the total individual wind-speed data of all seasons provided the same amount of total power output than summing up the power outputs of individual seasons. The main advantage of calculating individual seasonal wind-power outputs, however, is that it theoretically permits assessment of interannual variability in power output and capacity factors. Comparison to a known standard may help stakeholders in understanding of uncertainty and interpretation of projected changes.
文摘The ocean wave climate has a variety of applications in Naval defence.However,a long-term and reliable wave climate for the Indian Seas(The Arabian Sea and The Bay of Bengal)over a desired grid resolution could not be established so far due to several constraints.In this study,an attempt was made for the simulation of wave climate for the Indian Seas using the third-generation wave model(3g-WAM)developed by WAMDI group.The 3g-WAM as such was implemented at NPOL for research applications.The specific importance of this investigation was that,the model utilized a“mean climatic year of winds”estimated using historical wind measurements following statistical and probabilistic approaches as the winds which were considered for this purpose were widely scattered in space and time.Model computations were carried out only for the deep waters with current refraction.The gridded outputs of various wave parameters were stored at each grid point and the spectral outputs were stored at selected locations.Monthly,seasonal and annual distributions of significant wave parameters were obtained by post-processing some of the model outputs.A qualitative validation of simulated wave height and period parameters were also carried out by comparing with the observed data.The study revealed that the results of the wave climate simulation were quite promising and they can be utilized for various operational and ocean engineering applications.Therefore,this study will be a useful reference/demonstration for conducting such experiments in the areas where wind as well as wave measurements are insufficient.
文摘Global climate change may have serious impact on human activities in coastal and other areas.Climate change may affect the degree of storminess and,hence,change the wind-driven ocean wave climate.This may affect the risks associated with maritime activities such as shipping and offshore oil and gas.So,there is a recognized need to understand better how climate change will affect such processes.Typically,such understanding comes from future projections of the wind and wave climate from numerical climate models and from the stochastic modelling of such projections.This work investigates the applicability of a recently proposed nonstationary fuzzy modelling to wind and wave climatic simulations.According to this,fuzzy inference models(FIS)are coupled with nonstationary time series modelling,providing us with less biased climatic estimates.Two long-term datasets for an area in the North Atlantic Ocean are used in the present study,namely NORA10(57 years)and ExWaCli(30 years in the present and 30 years in the future).Two distinct experiments have been performed to simulate future values of the time series in a climatic scale.The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.
