Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the ro...Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.展开更多
As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the...As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the multi-dimensional Kolmogorov-Zurbenko filter (KZ filter) to assimilate a near-global high-resolution (monthly 1°?× 1°?grid) humidity climate observation database that provided consistent humidity estimates from 1973 onwards;then we examined the global humidity movements based on different temporal scales that separated out according to the average spectral features of specific humidity data. Humidity climate components were restored with KZ filters to represent the long-term trends and El Nino-like interannual movements. Movies of thermal maps based on these two climate components were used to visualize the water vapour fluctuation patterns over the Earth. Current results suggest that increases in water vapour are found over a large part of the oceans and the land of Eurasia, and the most confirmed increasing pattern is over the south part of North Atlantic and around the India subcontinent;meanwhile, the surface moisture levels over lands of south hemisphere are becoming less.展开更多
The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns.However,cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lea...The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns.However,cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lead to data loss,which critically limits studying the mechanism of spatial-temporal patterns of chlorophyll-a concentration in response to marine environment changes.If the commonly used operational chlorophyll-a concentration products can offer the best data coverage frequency,highest accuracy,best applicability,and greatest robustness at different scales remains debatable to date.Therefore,in the present study,four commonly used operational multi-sensor multi-algorithm fusion products were compared and subjected to validation based on statistical analysis using the available data measured at multiple spatial and temporal scales.The experimental results revealed that in terms of spatial distribution,the chlorophyll-a concentration products generated by averaging method(Chl1-AV/AVW)and GSM model(Chl1-GSM)presented a relatively high data coverage frequency in Case Ⅰ water regions and extremely low or no data coverage frequency in the estuarine coastal zone regions and inland water regions,the chlorophyll-a concentration products generated by the Neural Network algorithm(Chl2)presented high data coverage frequency in the estuarine coastal zone Case 2 water regions.The chlorophyll-a concentration products generated by the OC5 algorithm(ChlOC5)presented high data coverage frequency in Case I water regions and the turbid Case Ⅱ water regions.In terms of absolute precision,the Chl1-AV/AVW and Chl1-GSM chlorophyll-a concentration products performed better in Class I water regions,and the Chl2 product performed well only in Case Ⅱ estuarine coastal zones,while presenting large errors in absolute precision in the Case Ⅰ water regions.The ChlOC5 product presented a higher precision in Case Ⅰ and Case Ⅱ water regions,with a better and more stable performance in both regions compared to the other products.展开更多
Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within wate...Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales,challenging comprehensive assessment of logging impacts at the watershed scale.Here,we developed multiple forest logging scenarios using the soil and water assessment tool(SWAT)model for the Le'an River watershed,a 5,837 km2 subtropical watershed in China,to quantify the hydrological effects of forest logging across different spatiotemporal scales.Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity.However,sensitivity varied across spatiotemporal scales,with the rainy season(15.30%-15.81%)showing higher sensitivity than annual(11.56%-12.07%)and dry season(3.38%-5.57%)periods.Additionally,the ecohydrological sensitivity of logging varied significantly across the watershed,with midstream areas exhibiting the highest sensitivity(13.13%-13.25%),followed by downstream(11.87%-11.98%)and upstream regions(9.96%-10.05%).Furthermore,the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas,with attenuated runoff changes due to scale effects.Scale effects were more pronounced during dry seasons((-8.13 to -42.13)×10^(4) m^(3)·month^(-1))than in the rainy season((-11.11 to -26.65)×10^(4) m^(3)·month^(-1)).These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions,providing valuable insights for forest management under increasing anthropogenic activities and climate change.展开更多
Precipitation and associated cloud hydrometeors have large temporal and spatial variability, which makes accurate quantitative precipitation forecasting difficult. Thus, dependence of accurate precipitation and associ...Precipitation and associated cloud hydrometeors have large temporal and spatial variability, which makes accurate quantitative precipitation forecasting difficult. Thus, dependence of accurate precipitation and associated cloud simulation on temporal and spatial scales becomes an important issue. We report a cloud- resolving modeling analysis on this issue by comparing the control experiment with experiments perturbed by initial temperature, water vapor, and cloud conditions. The simulation is considered to be accurate only if the root-mean-squared difference between the perturbation experiments and the control experiment is smaller than the standard deviation. The analysis may suggest that accurate precipitation and cloud simulations cannot be obtained on both fine temporal and spatial scales simultaneously, which limits quanti- tative precipitation forecasting. The accurate simulation of water vapor convergence could lead to accurate precipitation and cloud simulations on daily time scales, but it may not be beneficial to precipitation and cloud simulations on hourly time scales due to the dominance of cloud processes.展开更多
Data from Goddard cumulus ensemble model experiment are used to study temporal and spatial scale dependence of tropical rainfall separation analysis based on cloud budget during Tropical Ocean Global Atmosphere Couple...Data from Goddard cumulus ensemble model experiment are used to study temporal and spatial scale dependence of tropical rainfall separation analysis based on cloud budget during Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment （TOGA COARE）. The analysis shows that the calculations of model domain mean or time-mean grid-scale mean simulation data overestimate the rain rates of the two rainfall types associated with net condensation but they severely underestimate the rain rate of the rainfall type associated with net evaporation and hydrometeor convergence.展开更多
A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trend...A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air （TDSA） are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.展开更多
Atmospheric winds, air temperatures, water levels, precipitation and oceanic waves in the Charleston South Carolina (SC) coastal zone are evaluated for their intrinsic, internal variability over temporal scales rangin...Atmospheric winds, air temperatures, water levels, precipitation and oceanic waves in the Charleston South Carolina (SC) coastal zone are evaluated for their intrinsic, internal variability over temporal scales ranging from hours to multi-decades. The purpose of this study was to bring together a plethora of atmospheric and coastal ocean state variable data in a specific locale, to assess temporal variabilities and possible relationships between variables. The questions addressed relate to the concepts of weather and climate. Data comprise the basis of this study. The overall distributions of atmospheric and coastal oceanic state variable variability, including wind speed, direction and kinematic distributions and state variable amplitudes over a variety of time scales are assessed. Annual variability is shown to be highly variable from year to year, making arithmetic means mathematically tractable but physically meaningless. Employing empirical and statistical methodologies, data analyses indicate the same number of intrinsic, internal modes of temporal variability in atmospheric temperatures, coastal wind and coastal water level time series, ranging from hours to days to weeks to seasons, sub-seasons, annual, multi-year, decades, and centennial time scales. This finding demonstrates that the atmosphere and coastal ocean in a southeastern U.S. coastal city are characterized by a set of similar frequency and amplitude modulated phenomena. Kinematic hodograph descriptors of atmospheric winds reveal coherent <span style="font-family:Verdana;">rotating and rectilinear particle motions. A mathematical statistics-based</span><span style="font-family:Verdana;"> wind to wave-to-wave algorithm is developed and applied to offshore marine buoy data to create an hour-by-hour forecast capability from 1 to 24 hours;with confidence levels put forward. This </span><span style="font-family:Verdana;">affects</span><span style="font-family:Verdana;"> a different approach to the conventional deterministic model forecasting of waves.</span>展开更多
Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making.While it has been shown that time estimation is adaptive to the temporal context,it remains unclear how ...Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making.While it has been shown that time estimation is adaptive to the temporal context,it remains unclear how interval timing behavior is influenced by recent trial history.Here we found that,in mice trained to perform a licking-based interval timing task,a decrease of inter-reinforcement interval in the previous trial rapidly shifted the time of anticipatory licking earlier.Optogenetic inactivation of the anterior lateral motor cortex(ALM),but not the medial prefrontal cortex,for a short time before reward delivery caused a decrease in the peak time of anticipatory licking in the next trial.Electrophysiological recordings from the ALM showed that the response profiles preceded by short and long inter-reinforcement intervals exhibited task-engagement-dependent temporal scaling.Thus,interval timing is adaptive to recent experience of the temporal interval,and ALM activity during time estimation reflects recent experience of interval.展开更多
Solar-induced chlorophyll fluorescence(SiF)is a promising new proxy for global carbon cycle monitoring.Although many studies consider SIF to be linearly correlated with gross primary production(GPP),the relationship b...Solar-induced chlorophyll fluorescence(SiF)is a promising new proxy for global carbon cycle monitoring.Although many studies consider SIF to be linearly correlated with gross primary production(GPP),the relationship between SIF and GPP is jointly influenced by instantaneous radiation,canopy structure,and plant physiological factors,and their complex interactions lead to intricate SIF-GPP dynamics.Current research on SIF and GPP in subtropical evergreen mixed forests remains limited,primarily due to the lack of observational data from forest flux sites.Based on observations from the Dabie Mountain subtropical evergreen forest flux station from 2023 to 2024,we investigated the relative contributions of the radiative,structural,and physiological components of SiF to the SIF-GPP relationship at different temporal scales.The results revealed that:(1)At both seasonal and diurnal scales,SIF effectively tracks the changes in GPP;(2)The radiative component of SIF dominates the SIF-GPP linear relationship,with canopy structural variations driving its seasonal-scale dynamics while physiological response mechanisms reduce the correlation at hourly scales;and(3)During the growing season,as the time scale increased from half-hourly to daily,the SIF-GPP correlation strengthened(R2 rising from 0.36 to 0.44),while the radiative component contribution decreased slightly and the physiological component contribution weakened.Understanding the influences of these different factors on the SIF-GPP relationship can contribute to the development of more accurate models for GPP estimation using SIF.展开更多
Improving the accuracy of fishing ground prediction for oceanic economic species has always been one of the most concerning issues in fisheries research.Recent studies have confirmed that deep learning has achieved su...Improving the accuracy of fishing ground prediction for oceanic economic species has always been one of the most concerning issues in fisheries research.Recent studies have confirmed that deep learning has achieved superior results over traditional methods in the era of big data.However,the deep learning-based fishing ground prediction model with a single environment suffers from the problem that the area of the fishing ground is too large and not concentrated.In this study,we developed a deep learning-based fishing ground prediction model with multiple environmental factors using neon flying squid(Ommastrephes bartramii)in Northwest Pacific Ocean as an example.Based on the modified U-Net model,the approach involves the sea surface temperature,sea surface height,sea surface salinity,and chlorophyll a as inputs,and the center fishing ground as the output.The model is trained with data from July to November in 2002-2019,and tested with data of 2020.We considered and compared five temporal scales(3,6,10,15,and 30 days)and seven multiple environmental factor combinations.By comparing different cases,we found that the optimal temporal scale is 30 days,and the optimal multiple environmental factor combination contained SST and Chl a.The inclusion of multiple factors in the model greatly improved the concentration of the center fishing ground.The selection of a suitable combination of multiple environmental factors is beneficial to the precise spatial distribution of fishing grounds.This study deepens the understanding of the mechanism of environmental field influence on fishing grounds from the perspective of artificial intelligence and fishery science.展开更多
Appropriate temporal and spatial scales are important prerequisites for obtaining reliable results in studies of wildlife activity patterns and interspecific interactions.The spread of camera-trap technology has incre...Appropriate temporal and spatial scales are important prerequisites for obtaining reliable results in studies of wildlife activity patterns and interspecific interactions.The spread of camera-trap technology has increased interest in and feasibility of studying the activity patterns and interspecific interactions of wildlife.However,such studies are often conducted at arbitrary spatial and temporal scales,and the methods used impose scale on the study rather than determining how activity and species interactions change with spatial scale.In this study,we used a waveletbased approach to determine the temporal and spatial scales for activity patterns and interspecific interactions on Amur leopard and their ungulate prey species that were recorded using camera traps in the main Amur leopard occurrence region in northeast China.Wavelets identified that Amur leopards were more active in spring and fall than summer,and fluctuated with periodicities of 9 and 17 days,respectively.Synchronous relationships between leopards and their prey commonly occurred in spring and fall,with a periodicity of about 20 days,indicating the appropriate seasons and temporal scales for interspecific interaction research.The influence of human activities on the activity patterns of Amur leopard or prey species often occurred over longer time periods(60–64 days).Twodimensional wavelet analyses showed that interactions between leopard and prey were more significant at spatial scales of 1 km2.Overall,our study provides a feasible approach to studying the temporal and spatial scales for wildlife activity patterns and interspecific interaction research using camera trap data.展开更多
Establish a reliable rainfall-runoff relation capable of predicting runoff in ungauged basins is a matter of interest across the world for a long time and has been taking importance during the past decades.Regionaliza...Establish a reliable rainfall-runoff relation capable of predicting runoff in ungauged basins is a matter of interest across the world for a long time and has been taking importance during the past decades.Regionalization approaches,hydrological models and machine learning techniques have been used to estimate runoff.However,returning some simplicity to the predictions might be necessary for practical uses.In this paper,we re-introduce C.E.Grunsky approach,developed in the early 1900s to predict runoff from values of precipitation on a two-equations system.Here,we analyze the Grunsky generalized method applied for 716 Brazilian catchments,on an interannual and monthly scales.First,we established the best method to find the rainfall-runoff relation coefficient for each catchment.Then,we evaluate the performance of the method on a local scale,i.e.,catchment by catchment.Lastly,we analyze the method of regionalization,by grouping the catchments into six hydrologically similar classes.For local scale,the Kling-Gupta Efficiency(KGE)values range from 0.87 to 0.93 on the interannual scale and is greater than 0.50 on the monthly scale.For the regionalized approach,KGE varies from 0.60 to 0.84 on an interannual scale.We also found suitable KGE values on a monthly scale,with more than 22%of catchments with KGE greater than 0.50,being the best performances in the Non-seasonal and Extremely-wet groups,and the worst performance in the Dry group.Our findings indicate that Grunsky approach is suitable to predict streamflow for Brazilian catchments on interannual and monthly scales.This simple and easy-to-use equation presents a reliable alternative to more complex methods to compute runoff by only using rainfall data.展开更多
基金funded by National High Technology Research and Development Program of China (863 Program,2012AA092303)Project of Shanghai Science and Technology Innovation (12231203900)+2 种基金Industrialization Program of National Development and Reform Commission (2159999)National Science and Technology Support Program (2013BAD13B01)Shanghai Leading Academic Discipline Project
文摘Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.
文摘As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the multi-dimensional Kolmogorov-Zurbenko filter (KZ filter) to assimilate a near-global high-resolution (monthly 1°?× 1°?grid) humidity climate observation database that provided consistent humidity estimates from 1973 onwards;then we examined the global humidity movements based on different temporal scales that separated out according to the average spectral features of specific humidity data. Humidity climate components were restored with KZ filters to represent the long-term trends and El Nino-like interannual movements. Movies of thermal maps based on these two climate components were used to visualize the water vapour fluctuation patterns over the Earth. Current results suggest that increases in water vapour are found over a large part of the oceans and the land of Eurasia, and the most confirmed increasing pattern is over the south part of North Atlantic and around the India subcontinent;meanwhile, the surface moisture levels over lands of south hemisphere are becoming less.
基金funded by the Project for Fostering Outstanding Young talents of Henan Academy of Sciences(No.210401001)Special Project for Team Building of Henan Academy of Sciences(No.200501007)+1 种基金Science and Technology Research Project of Henan Province(Nos.212102310424,222102320467,and 212102310024)Major Scientific Research Focus Project of Henan Academy of Sciences(No.210101007).
文摘The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns.However,cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lead to data loss,which critically limits studying the mechanism of spatial-temporal patterns of chlorophyll-a concentration in response to marine environment changes.If the commonly used operational chlorophyll-a concentration products can offer the best data coverage frequency,highest accuracy,best applicability,and greatest robustness at different scales remains debatable to date.Therefore,in the present study,four commonly used operational multi-sensor multi-algorithm fusion products were compared and subjected to validation based on statistical analysis using the available data measured at multiple spatial and temporal scales.The experimental results revealed that in terms of spatial distribution,the chlorophyll-a concentration products generated by averaging method(Chl1-AV/AVW)and GSM model(Chl1-GSM)presented a relatively high data coverage frequency in Case Ⅰ water regions and extremely low or no data coverage frequency in the estuarine coastal zone regions and inland water regions,the chlorophyll-a concentration products generated by the Neural Network algorithm(Chl2)presented high data coverage frequency in the estuarine coastal zone Case 2 water regions.The chlorophyll-a concentration products generated by the OC5 algorithm(ChlOC5)presented high data coverage frequency in Case I water regions and the turbid Case Ⅱ water regions.In terms of absolute precision,the Chl1-AV/AVW and Chl1-GSM chlorophyll-a concentration products performed better in Class I water regions,and the Chl2 product performed well only in Case Ⅱ estuarine coastal zones,while presenting large errors in absolute precision in the Case Ⅰ water regions.The ChlOC5 product presented a higher precision in Case Ⅰ and Case Ⅱ water regions,with a better and more stable performance in both regions compared to the other products.
基金supported by the National Natural Science Foundation of China(No.31660234).
文摘Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales,challenging comprehensive assessment of logging impacts at the watershed scale.Here,we developed multiple forest logging scenarios using the soil and water assessment tool(SWAT)model for the Le'an River watershed,a 5,837 km2 subtropical watershed in China,to quantify the hydrological effects of forest logging across different spatiotemporal scales.Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity.However,sensitivity varied across spatiotemporal scales,with the rainy season(15.30%-15.81%)showing higher sensitivity than annual(11.56%-12.07%)and dry season(3.38%-5.57%)periods.Additionally,the ecohydrological sensitivity of logging varied significantly across the watershed,with midstream areas exhibiting the highest sensitivity(13.13%-13.25%),followed by downstream(11.87%-11.98%)and upstream regions(9.96%-10.05%).Furthermore,the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas,with attenuated runoff changes due to scale effects.Scale effects were more pronounced during dry seasons((-8.13 to -42.13)×10^(4) m^(3)·month^(-1))than in the rainy season((-11.11 to -26.65)×10^(4) m^(3)·month^(-1)).These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions,providing valuable insights for forest management under increasing anthropogenic activities and climate change.
基金supported from the National Key Basic Research and Development Projectof China(2009CB421505)the National Natural Sciences Foundation of China(40775031)the Project(No.2008LASW-A01)
文摘Precipitation and associated cloud hydrometeors have large temporal and spatial variability, which makes accurate quantitative precipitation forecasting difficult. Thus, dependence of accurate precipitation and associated cloud simulation on temporal and spatial scales becomes an important issue. We report a cloud- resolving modeling analysis on this issue by comparing the control experiment with experiments perturbed by initial temperature, water vapor, and cloud conditions. The simulation is considered to be accurate only if the root-mean-squared difference between the perturbation experiments and the control experiment is smaller than the standard deviation. The analysis may suggest that accurate precipitation and cloud simulations cannot be obtained on both fine temporal and spatial scales simultaneously, which limits quanti- tative precipitation forecasting. The accurate simulation of water vapor convergence could lead to accurate precipitation and cloud simulations on daily time scales, but it may not be beneficial to precipitation and cloud simulations on hourly time scales due to the dominance of cloud processes.
基金supported by the National Key Basic Research and Development Project of China under Grant No.2011CB403405the National Natural Science Foundation of China under Grant Nos.41075039 and 41175065the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘Data from Goddard cumulus ensemble model experiment are used to study temporal and spatial scale dependence of tropical rainfall separation analysis based on cloud budget during Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment （TOGA COARE）. The analysis shows that the calculations of model domain mean or time-mean grid-scale mean simulation data overestimate the rain rates of the two rainfall types associated with net condensation but they severely underestimate the rain rate of the rainfall type associated with net evaporation and hydrometeor convergence.
文摘A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air （TDSA） are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.
文摘Atmospheric winds, air temperatures, water levels, precipitation and oceanic waves in the Charleston South Carolina (SC) coastal zone are evaluated for their intrinsic, internal variability over temporal scales ranging from hours to multi-decades. The purpose of this study was to bring together a plethora of atmospheric and coastal ocean state variable data in a specific locale, to assess temporal variabilities and possible relationships between variables. The questions addressed relate to the concepts of weather and climate. Data comprise the basis of this study. The overall distributions of atmospheric and coastal oceanic state variable variability, including wind speed, direction and kinematic distributions and state variable amplitudes over a variety of time scales are assessed. Annual variability is shown to be highly variable from year to year, making arithmetic means mathematically tractable but physically meaningless. Employing empirical and statistical methodologies, data analyses indicate the same number of intrinsic, internal modes of temporal variability in atmospheric temperatures, coastal wind and coastal water level time series, ranging from hours to days to weeks to seasons, sub-seasons, annual, multi-year, decades, and centennial time scales. This finding demonstrates that the atmosphere and coastal ocean in a southeastern U.S. coastal city are characterized by a set of similar frequency and amplitude modulated phenomena. Kinematic hodograph descriptors of atmospheric winds reveal coherent <span style="font-family:Verdana;">rotating and rectilinear particle motions. A mathematical statistics-based</span><span style="font-family:Verdana;"> wind to wave-to-wave algorithm is developed and applied to offshore marine buoy data to create an hour-by-hour forecast capability from 1 to 24 hours;with confidence levels put forward. This </span><span style="font-family:Verdana;">affects</span><span style="font-family:Verdana;"> a different approach to the conventional deterministic model forecasting of waves.</span>
基金supported by the National Science and Technology Innovation 2030 Major Program of China(2021ZD0203700/2021ZD0203703)the National Natural Science Foundation of China(31771151 and 32171030)+2 种基金Lingang Lab(LG202104-01-03)a Shanghai Municipal Science and Technology Major Project(2018SHZDZX05)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32010200)。
文摘Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making.While it has been shown that time estimation is adaptive to the temporal context,it remains unclear how interval timing behavior is influenced by recent trial history.Here we found that,in mice trained to perform a licking-based interval timing task,a decrease of inter-reinforcement interval in the previous trial rapidly shifted the time of anticipatory licking earlier.Optogenetic inactivation of the anterior lateral motor cortex(ALM),but not the medial prefrontal cortex,for a short time before reward delivery caused a decrease in the peak time of anticipatory licking in the next trial.Electrophysiological recordings from the ALM showed that the response profiles preceded by short and long inter-reinforcement intervals exhibited task-engagement-dependent temporal scaling.Thus,interval timing is adaptive to recent experience of the temporal interval,and ALM activity during time estimation reflects recent experience of interval.
基金The National Natural Science Foundation of China(42250205)The‘CUG Scholar’Scientific Research Funds at China University of Geosciences(2019004)。
文摘Solar-induced chlorophyll fluorescence(SiF)is a promising new proxy for global carbon cycle monitoring.Although many studies consider SIF to be linearly correlated with gross primary production(GPP),the relationship between SIF and GPP is jointly influenced by instantaneous radiation,canopy structure,and plant physiological factors,and their complex interactions lead to intricate SIF-GPP dynamics.Current research on SIF and GPP in subtropical evergreen mixed forests remains limited,primarily due to the lack of observational data from forest flux sites.Based on observations from the Dabie Mountain subtropical evergreen forest flux station from 2023 to 2024,we investigated the relative contributions of the radiative,structural,and physiological components of SiF to the SIF-GPP relationship at different temporal scales.The results revealed that:(1)At both seasonal and diurnal scales,SIF effectively tracks the changes in GPP;(2)The radiative component of SIF dominates the SIF-GPP linear relationship,with canopy structural variations driving its seasonal-scale dynamics while physiological response mechanisms reduce the correlation at hourly scales;and(3)During the growing season,as the time scale increased from half-hourly to daily,the SIF-GPP correlation strengthened(R2 rising from 0.36 to 0.44),while the radiative component contribution decreased slightly and the physiological component contribution weakened.Understanding the influences of these different factors on the SIF-GPP relationship can contribute to the development of more accurate models for GPP estimation using SIF.
基金supported in part by the National Natural Science Foundation of China under Grant 41876141 and Grant 42006159in part by the National Key R&D Programme of China under Grant 2019YFD0901404.
文摘Improving the accuracy of fishing ground prediction for oceanic economic species has always been one of the most concerning issues in fisheries research.Recent studies have confirmed that deep learning has achieved superior results over traditional methods in the era of big data.However,the deep learning-based fishing ground prediction model with a single environment suffers from the problem that the area of the fishing ground is too large and not concentrated.In this study,we developed a deep learning-based fishing ground prediction model with multiple environmental factors using neon flying squid(Ommastrephes bartramii)in Northwest Pacific Ocean as an example.Based on the modified U-Net model,the approach involves the sea surface temperature,sea surface height,sea surface salinity,and chlorophyll a as inputs,and the center fishing ground as the output.The model is trained with data from July to November in 2002-2019,and tested with data of 2020.We considered and compared five temporal scales(3,6,10,15,and 30 days)and seven multiple environmental factor combinations.By comparing different cases,we found that the optimal temporal scale is 30 days,and the optimal multiple environmental factor combination contained SST and Chl a.The inclusion of multiple factors in the model greatly improved the concentration of the center fishing ground.The selection of a suitable combination of multiple environmental factors is beneficial to the precise spatial distribution of fishing grounds.This study deepens the understanding of the mechanism of environmental field influence on fishing grounds from the perspective of artificial intelligence and fishery science.
基金funded by the Fundamental Research Funds for the Central Universities(2572017PZ14)the National Key Programme of Research and Development,Ministry of Science and Technology(2016YFC0503200)+1 种基金NSFC(31872241,31572285)to G.J.full-time postdoctoral support program of Northeast Forestry University(60201103)to J.Q.
文摘Appropriate temporal and spatial scales are important prerequisites for obtaining reliable results in studies of wildlife activity patterns and interspecific interactions.The spread of camera-trap technology has increased interest in and feasibility of studying the activity patterns and interspecific interactions of wildlife.However,such studies are often conducted at arbitrary spatial and temporal scales,and the methods used impose scale on the study rather than determining how activity and species interactions change with spatial scale.In this study,we used a waveletbased approach to determine the temporal and spatial scales for activity patterns and interspecific interactions on Amur leopard and their ungulate prey species that were recorded using camera traps in the main Amur leopard occurrence region in northeast China.Wavelets identified that Amur leopards were more active in spring and fall than summer,and fluctuated with periodicities of 9 and 17 days,respectively.Synchronous relationships between leopards and their prey commonly occurred in spring and fall,with a periodicity of about 20 days,indicating the appropriate seasons and temporal scales for interspecific interaction research.The influence of human activities on the activity patterns of Amur leopard or prey species often occurred over longer time periods(60–64 days).Twodimensional wavelet analyses showed that interactions between leopard and prey were more significant at spatial scales of 1 km2.Overall,our study provides a feasible approach to studying the temporal and spatial scales for wildlife activity patterns and interspecific interaction research using camera trap data.
文摘Establish a reliable rainfall-runoff relation capable of predicting runoff in ungauged basins is a matter of interest across the world for a long time and has been taking importance during the past decades.Regionalization approaches,hydrological models and machine learning techniques have been used to estimate runoff.However,returning some simplicity to the predictions might be necessary for practical uses.In this paper,we re-introduce C.E.Grunsky approach,developed in the early 1900s to predict runoff from values of precipitation on a two-equations system.Here,we analyze the Grunsky generalized method applied for 716 Brazilian catchments,on an interannual and monthly scales.First,we established the best method to find the rainfall-runoff relation coefficient for each catchment.Then,we evaluate the performance of the method on a local scale,i.e.,catchment by catchment.Lastly,we analyze the method of regionalization,by grouping the catchments into six hydrologically similar classes.For local scale,the Kling-Gupta Efficiency(KGE)values range from 0.87 to 0.93 on the interannual scale and is greater than 0.50 on the monthly scale.For the regionalized approach,KGE varies from 0.60 to 0.84 on an interannual scale.We also found suitable KGE values on a monthly scale,with more than 22%of catchments with KGE greater than 0.50,being the best performances in the Non-seasonal and Extremely-wet groups,and the worst performance in the Dry group.Our findings indicate that Grunsky approach is suitable to predict streamflow for Brazilian catchments on interannual and monthly scales.This simple and easy-to-use equation presents a reliable alternative to more complex methods to compute runoff by only using rainfall data.
