Layer chicken microbiota:a comprehensive analysis of spatial and temporal dynamics across all major gut sections

Background The gut microbiota influences chicken health,welfare,and productivity.A diverse and balanced microbiota has been associated with improved growth,efficient feed utilisation,a well-developed immune system,disease resistance,and stress tolerance in chickens.Previous studies on chicken gut microbiota have predominantly focused on broiler chickens and have usually been limited to one or two sections of the digestive system,under con-trolled research environments,and often sampled at a single time point.To extend these studies,this investigation examined the microbiota of commercially raised layer chickens across all major gut sections of the digestive system and with regular sampling from rearing to the end of production at 80 weeks.The aim was to build a detailed picture of microbiota development across the entire digestive system of layer chickens and study spatial and temporal dynamics.Results The taxonomic composition of gut microbiota differed significantly between birds in the rearing and pro-duction stages,indicating a shift after laying onset.Similar microbiota compositions were observed between proven-triculus and gizzard,as well as between jejunum and ileum,likely due to their anatomical proximity.Lactobacil-lus dominated the upper gut in pullets and the lower gut in older birds.The oesophagus had a high proportion of Proteobacteria,including opportunistic pathogens such as Gallibacterium.Relative abundance of Gallibacterium increased after peak production in multiple gut sections.Aeriscardovia was enriched in the late-lay phase compared to younger birds in multiple gut sections.Age influenced microbial richness and diversity in different organs.The upper gut showed decreased diversity over time,possibly influenced by dietary changes,while the lower gut,specifi-cally cecum and colon,displayed increased richness as birds matured.However,age-related changes were inconsist-ent across all organs,suggesting the influence of organ-specific factors in microbiota maturation.Conclusion Addressing a gap in previous research,this study explored the microbiota across all major gut sections and tracked their dynamics from rearing to the end of the production cycle in commercially raised layer chickens.This study provides a comprehensive understanding of microbiota structure and development which help to develop targeted strategies to optimise gut health and overall productivity in poultry production.

Spatial compartmentalization and temporal stability of associated microbiota in Pacific oyster Crassostrea gigas

The Pacific oyster Crassostrea gigas,one of the most exploited molluscs in the world,has suffered from massive mortality in recent decades,and the occurrence mechanisms have not been well characterized.In this study,to reveal the relationship of associated microbiota to the fitness of oysters,temporal dynamics of microbiota in the gill,hemolymph,and hepatopancreas of C.gigas during April 2018-January 2019 were investigated by 16 S rRNA gene sequencing.The microbiota in C.gigas exhibited tissue heterogeneity,of which Spirochaetaceae was dominant in the gill and hemolymph while Mycoplasmataceae enriched in the hepatopancreas.Co-occurrence network demonstrated that the gill microbiota exhibited higher inter-taxon connectivity while the hemolymph microbiota had more modules.The richness(Chao 1 index)and diversity(Shannon index)of microbial community in each tissue showed no significant seasonal variations,except for the hepatopancreas having a higher richness in the autumn.Similarly,beta diversity analysis indicated a relatively stable microbiota in each tissue during the sampling period,showing relative abundance of the dominant taxa exhibiting temporal dynamics.Results indicate that the microbial community in C.gigas showed a tissue-specific stability with temporal dynamics in the composition,which might be essential for the tissue functioning and environmental adaption in oysters.This work provides a baseline microbiota in C.gigas and is helpful for the understanding of host-microbiota interaction in oysters.

Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF 1-WFV satellite data

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance,especially for the Beijing-Tianjin-Hebei(BTH)region of China where water resources are relatively scarce and inland water bodies are generally small.The remote sensing data of the GF 1 satellite launched in 2013 have characteristics of high spatial and temporal resolution,which can be used for the dynamic monitoring of the water environment in small lakes and reservoirs.However,the water quality remote sensing monitoring model based on the GF 1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and reservoirs.In this paper,the typical reservoirs in BTH-Guanting Reservoir,Yuqiao Reservoir,Panjiakou Reservoir,and Daheiting Reservoir are taken as the study areas.In the atmospheric correction of GF 1-WFV,the relative radiation normalized atmospheric correction was adopted after comparing it with other methods,such as 6 S and FLAASH.In the water clarity retrieval,a water color hue angle based model was proposed and outperformed other available published models,with the R 2 of 0.74 and MRE of 31.7%.The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF 1-WFV data.Based on the products,temporal and spatial changes in clarity were analyzed,and the main influencing factors for each water body were discussed.It was found that the clarity of Guanting,Daheiting,and Panjiakou reservoirs showed an upward trend during this period,while that of Yuqiao Reservoir showed a downward trend.In the influencing factors,the water level of the water bodies can be an important factor related to the water clarity changes in this region.

Temporal and spatial differences in human activities performed in Urban Green Spaces of Vilnius(Lithuania)

Urban green spaces(UGS)are relevant to city well-being,as recognized by the United Nations’Sustainable Development Goals(SDGs).However,few studies have studied the temporal use of UGS.This work assessed the seasonal,weekly,and daily use of three urban green spaces(Vingis Park,Bernardino Garden,and Jomantas Park)in Vilnius(Lithuania).The study is based on an on-site observation-based survey,which recorded users’characteristics,activities,and weather conditions during summer and winter.The results showed that UGS’s seasonal,weekly,and daily use differed according to park and users’characteristics.Parks with a higher diversity of facilities had a high seasonal difference in the number of observed activities.User numbers were higher in the summer for activities with children,social activities,sports,and water activities than in the winter.Jomantas Park had the lowest variability in user characteristics.Weather variables were linked to changes in users’activities.Higher precipitation and lower temperature were associated with reducing the number of users and the diversity of registered activities.Most of the stationary activities were observed during summer.The diversity of the observed activities was associated with the available facilities rather than the park size.The distribution of stationary activities was spatially correlated with facility/equipment(benches,playgrounds,sports,and fitness equipment)and proximity to water features.The results of this study are relevant for UGS design,planning,and management.

Temporal and Spatial Dynamics of Vaccine-Derived Poliovirus (VDPV) in Democratic Republic of Congo from 2018 to 2023

Background: The Democratic Republic of Congo (DRC) has been facing outbreaks of VDPV since 2017. These wild poliovirus variants are responsible for poliomyelitis, which is in the process of eradication.. In the following lines, we try to show the evolution of VDPV cases across the country in order to understand their chronological dynamics and seasonal influence. Methods: We conducted a cross-sectional study of of VDPV notified in the DRC from 2018 to 2023. Maps of the spatial dynamics of VDPV cases were produced from attack rates with QGIS® (3.22.8). As for temporal dynamics, time series were decomposed and presented in the form of graphs showing the chronological evolution of VDPV cases and their seasonal trend, using R.4.0 software package. Results: A total of 1196 Cases of VDPV types 1, 2 and 3 were recorded in the biological confirmation databases of the INRB and the Expanded Program of Immunization during the study period across25 provinces. The eastern part of the country reporting the most cases. The general trend is upwards, with a peak in 2022 of 527 cases, whereas in 2021 there was a notable drop of 31 cases. Analysis of the temporal breakdown suggests a seasonal pattern, with peaks between the months of September and December, considered being rainy periods in some provinces. Conclusion: During the 6 years of our study (2018 - 2023) almost all the Health Zones were hit by VDPV epidemics. The eastern part was the most impacted. The seasonal component is well marked suggesting a rise in detection in the rainy season and during pivotal periods of climate change.

Spatial and Temporal Distribution Characteristics of Snowstorm in Ulanqab City in Recent 30 Years

Based on the data of daily snowfall and weather phenomena of 11 national meteorological stations in Ulanqab City from 1991 to 2020,the spatial and temporal distribution characteristics of snowstorm were analyzed.The results show that the snowstorm in Ulanqab had obvious seasonal distribution characteristics,mainly happening in spring(March-May)and autumn(September-November).It also had obvious regional distribution in space,and the snowstorm center appeared in Chahar Right Wing Middle Banner and Jining District,namely the east side of the Yinshan Mountain.In the past 30 years,the amount of snowstorm in the whole year in Ulanqab showed a certain fluctuation trend,and the number of snowstorm days had shown an obvious upward trend since 2011.

Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Spatiotemporal Distributions of Bacterioplankton Communities in the Qiantang River(Hangzhou Section),China

The spatial and temporal distribution of bacterioplankton communities plays a vital role in understanding the ecological dynamics and health of aquatic ecosystems.In this study,we conducted a comprehensive investigation of the bacterioplankton communities in the Qiantang River(Hangzhou section).Water samples were collected quarterly from seven sites over a one-year period.Physical and chemical parameters,including dissolved oxygen(DO),water temperature(WT),chemical oxygen demand(COD),nitrite(NO_(2)^(-)),active phosphate(PO_(4)^(3-))and other indices were determined.In this study,theαdiversity,βdiversity and abundance differences of bacterial communities were investigated using 16S rRNA high-throughput sequencing analysis.The spatial and temporal distribution characteristics and main driving factors of the bacterioplankton community structure and diversity were discussed.The results showed that a total of 57 phyla were detected in the bacterioplankton community,among which Proteobacteria and Actinomycetes were the two dominant groups with the highest relative abundance.The results of PCoA based on Bray-Curtis distance showed that the sampling season had a slightly greater effect on the changes in bacterioplankton community structure in the Qiantang River.Mantel and partial Mantel test showed that environmental variables(Mantel r=0.6739,P<0.0001;partial Mantel r=0.507,P=0.0001)were more important than geographical distance(Mantel r=0.5322,P<0.001;partial Mantel r=0.1563,P=0.001).The distance attenuation model showed that there was significant distance attenuation in all four seasons,and the maximum limit of bacterial community diffusion was found in spring.RDA analysis showed that nine environmental factors,including COD,WT and DO,significantly affected community distribution(P<0.05).This study provides valuable insights into the spatial and temporal distribution characteristics of bacterioplankton communities,shedding light on their ecological roles in the Qiantang River.The information obtained can guide future research on the interactions between bacterioplankton and their environment,enabling the development of effective conservation strategies and sustainable management practices for aquatic ecosystems.

The Spatiotemporal Distribution Characteristics of Cloud Types and Phases in the Arctic Based on CloudSat and CALIPSO Cloud Classification Products

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.

Inverse reliability analysis and design for tunnel face stability considering soil spatial variability

The traditional deterministic analysis for tunnel face stability neglects the uncertainties of geotechnical parameters,while the simplified reliability analysis which models the potential uncertainties by means of random variables usually fails to account for soil spatial variability.To overcome these limitations,this study proposes an efficient framework for conducting reliability analysis and reliability-based design(RBD)of tunnel face stability in spatially variable soil strata.The three-dimensional(3D)rotational failure mechanism of the tunnel face is extended to account for the soil spatial variability,and a probabilistic framework is established by coupling the extended mechanism with the improved Hasofer-Lind-Rackwits-Fiessler recursive algorithm(iHLRF)as well as its inverse analysis formulation.The proposed framework allows for rapid and precise reliability analysis and RBD of tunnel face stability.To demonstrate the feasibility and efficacy of the proposed framework,an illustrative case of tunnelling in frictional soils is presented,where the soil's cohesion and friction angle are modelled as two anisotropic cross-correlated lognormal random fields.The results show that the proposed method can accurately estimate the failure probability(or reliability index)regarding the tunnel face stability and can efficiently determine the required supporting pressure for a target reliability index with soil spatial variability being taken into account.Furthermore,this study reveals the impact of various factors on the support pressure,including coefficient of variation,cross-correlation between cohesion and friction angle,as well as autocorrelation distance of spatially variable soil strata.The results also demonstrate the feasibility of using the forward and/or inverse first-order reliability method(FORM)in high-dimensional stochastic problems.It is hoped that this study may provide a practical and reliable framework for determining the stability of tunnels in complex soil strata.

Analysis of Spatiotemporal Changes in Integrated Land Productivity in Chongqing from 1997 to 2023

[Objectives]Integrated land productivity can reflect the comprehensive utilization of land and the overall output level,which is the most basic and commonly used indicator in assessing land use efficiency.This thesis aims to analyze the spatial and temporal changes of integrated land productivity in Chongqing from 1997 to 2023 in order to assess its land use efficiency.[Methods]This study measured the integrated land productivity of Chongqing Municipality,the only municipality directly under the central government in the western part of China,over the past 26 years(1997-2023)through relevant surveys and statistical data,and analyzed in depth the integrated land productivity of the 38 districts and counties under the jurisdiction of Chongqing,as well as the functional sub-districts of the"one district and two clusters"and the"one district and two clusters"in Chongqing.It also analyzes the characteristics of spatial and temporal differences in land productivity in 38 districts and counties under the jurisdiction of Chongqing and"one district and two clusters".[Results]The results of the study show that over the past 26 years,the integrated land productivity of Chongqing has shown an annual growth trend,and the integrated land productivity of the 38 districts and counties and the functional subregions of"one district and two clusters"has also increased significantly,but the average annual growth rate of the integrated land productivity varies among different regions.From the perspective of spatial differences,there are significant differences in land productivity among the 38 districts and counties of Chongqing and the functional subregions of"one district and two clusters",which are mainly due to the different natural conditions,economic development levels and functional positioning of each region.[Conclusions]Based on the results of the study and the actual situation of Chongqing,this paper puts forward the leading measures to improve the integrated land productivity,with a view to providing a reference basis for Chongqing to improve the efficiency of land use and promote the sustainable use of land resources.

Flight parameter calculation method of multi-projectiles using temporal and spatial information constraint

The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision field.Therefore,finding efficient solutions to extract knowledge from these sources is imperative.Recently,the BlazePose system has been released for skeleton extraction from images oriented to mobile devices.With this skeleton graph representation in place,a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action.We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest,it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks.Hence,in this study,we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition.Moreover,we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor.Additionally,we propose different skeleton detection thresholds that can improve the accuracy performance even further.We reached a top-1 accuracy performance of 40.1%on the Kinetics dataset.For the NTU-RGB+D dataset,we achieved 87.59%and 92.1%accuracy for Cross-Subject and Cross-View evaluation criteria,respectively.

Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms,presenting risks for numerous countries,societies,and individuals,and posing a serious threat to cyberspace security.To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection,we propose a detection method based on improved CapsNet and temporal–spatial features(iCapsNet–TSF).First,the dynamic routing algorithm of CapsNet is improved using weight initialization and updating.Then,the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal–spatial features.Finally,the iCapsNet model is employed to fully learn the temporal–spatial features of facial videos,and the results are fused.Experimental results show that the detection accuracy of iCapsNet–TSF reaches 94.07%,98.83%,and 98.50%on the Celeb-DF,FaceSwap,and Deepfakes datasets,respectively,displaying a better performance than most existing mainstream algorithms.The iCapsNet–TSF method combines the capsule network and the optical flow algorithm,providing a novel strategy for the deepfake detection,which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.

The Influence of Climate Change and Variability on Spatio-Temporal Rainfall and Temperature Distribution in Zanzibar

Climate change has resulted in serious social-economic ramifications and extremely catastrophic weather events in the world, Tanzania and Zanzibar in particular, with adaptation being the only option to reduce impacts. The study focuses on the influence of climate change and variability on spatio-temporal rainfall and temperature variability and distribution in Zanzibar. The station observation datasets of rainfall, Tmax and Tmin acquired from Tanzania Meteorological Authority (TMA) and the Coordinated Regional Climate Downscaling Experiment program (CORDEX) projected datasets from the Regional climate model HIRHAM5 under driving model ICHEC-EC-EARH, for the three periods of 1991-2020 used as baseline (HS), 2021-2050 as near future (NF) and 2051-2080 far future (FF), under two representative concentration pathways (RCP) of 4.5 and 8.5, were used. The long-term observed Tmax and Tmin were used to produce time series for observing the nature and trends, while the observed rainfall data was used for understanding wet and dry periods, trends and slope (at p ≤ 0.05) using the Standardized Precipitation Index (SPI) and the Mann Kendall test (MK). Moreover, the Quantum Geographic Information System (QGIS) under the Inverse Distance Weighting (IDW) interpolation techniques were used for mapping the three decades of 1991-2000 (hereafter D1), 2001-2010 (hereafter D2) and 2011-2020 (hereafter D3) to analyze periodical spatial rainfall distribution in Zanzibar. As for the projected datasets the Climate Data Operator Commands (CDO), python scripts and Grid analysis and Display System (GrADS) soft-wares were used to process and display the results of the projected datasets of rainfall, Tmax and Tmin for the HS, NF and FF, respectively. The results show that the observed Tmax increased by the rates of 0.035℃ yr-1 and 0.0169℃ yr-1, while the Tmin was increased by a rate of 0.064℃ yr-1 and 0.104℃ yr-1 for Unguja and Pemba, respectively. The temporal distribution of wetness and dryness indices showed a climate shift from near normal to moderate wet during 2005 at Zanzibar Airport, while normal to moderately dry conditions, were observed in Pemba at Matangatuani. The decadal rainfall variability and distributions revealed higher rainfall intensity with an increasing trend and good spatial distribution in D3 from March to May (MAM) and October to December (OND). The projected results for Tmax during MAM and OND depicted higher values ranging from 1.7℃ - 1.8℃ to 1.9℃ - 2.0℃ and 1.5℃ to 2.0℃ in FF compared to NF under both RCPs. Also, higher Tmin values of 1.12℃ - 1.16℃ was projected in FF for MAM and OND under both RCPs. Besides, the rainfall projection generally revealed increased rainfall intensity in the range of 0 - 25 mm for Pemba and declined rainfall in the range of 25 - 50 mm in Unguja under both RCPs in perspectives of both NF and FF. Conclusively the study has shown that the undergoing climate change has posed a significant impact on both rainfall and temperature spatial and temporal distributions in Zanzibar (Unguja and Pemba), with Unguja being projected to have higher rainfall deficits while increasing rainfall strengths in Pemba. Thus, the study calls for more studies and formulation of effective adaptation, strategies and resilience mechanisms to combat the projected climate change impacts especially in the agricultural sector, water and food security.

Temporal and spatial variation of cloud cover in arid regions of Central Asia in the last 40 years

Water resources are one of the key factors restricting the development of arid areas,and cloud water resources is an important part of water resources.The arid region of central Asia is the core region of the current national green silk road construction,and is the largest arid region in the world.Based on cloud cover data of ECMWF,the current study analyzed temporal and spatial characteristics of cloud properties in arid regions of Central Asia between 1980 and 2019.Our findings show that:(1)From the point of view of spatial distribution,total cloudiness in arid regions of Central Asia was low in the south and high in the north.The distribution of high cloud frequency and medium cloud frequency was higher in the south and lower in the north,while low cloud frequency distribution was low in the south and high in the north.(2)In terms of time,the variation of cloud cover and cloud type frequency had obvious seasonal characteristics.From winter to spring,cloud cover increased,and the change of cloud type frequency increased.From spring to summer,cloud cover continued to increase and the change of cloud type frequency increased further.Cloud cover began to decrease from summer to autumn,and the change of cloud type frequency also decreased.(3)Generally,average total cloud cover decreased in most of central Asia,and high and medium cloud cover increased while low cloud cover decreased.This study provides a reference for the rational development of cloud resources in the region.

Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process

The preceding and succeeding precipitation(PSP)often act together with extreme precipitation(EP),in turn,causing floods,which is an objective component that is often overlooked with regard to summer flood hazards in the arid region of Northwest China.In this study,event-based extreme precipitation(EEP)was defined as continuous precipitation that includes at least one day of EP.We analyzed the spatiotemporal variation characteristics of four EEP types(front EEP,late EEP,balanced EEP,and single day EEP)across the Loess Plateau(LP)based on data acquired from 87 meteorological stations from 1960 to 2019.Precipitation on the LP basically maintained a spatial pattern of"low in the northwest region and high in the southeast region",and EP over the last 10 a increased significantly.The cumulative precipitation percentage of single day EEP was 34%and was dominant for 60 a,while the cumulative precipitation percentage of front,late,and balanced EEP types associated with PSP accounted for 66%,which confirms to the connotation of EEP.The cumulative frequencies of front and late EEP types were 23%and 21%,respectively,while the cumulative frequency of balanced EEP had the lowest value at only 13%.Moreover,global warming could lead to more single day EEP across the LP,and continuous EEP could increase in the northwestern region and decrease in the eastern region in the future.The concept of process-oriented EP could facilitate further exploration of disaster-causing processes associated with different types of EP,and provide a theoretical basis for deriving precipitation disaster chains and construction of disaster cluster characteristics.

Eff ects of environmental factors on the temporal and spatial variations in branch and leaf CO_(2)effl ux of Larix gmelinii var.principis-rupprechtii Mayr

The CO_(2)effl ux of branches and leaves plays an important role in ecosystem carbon balance.Using a carbon fl ux system,the effl ux of Larix gmelinii var.principisrupprechtii(Dahurian larch)was investigated in 27 years(immature),31 years(near-mature),and 47 years(mature)stands at diurnal,seasonal,and spatial scales(direction and height)as well as its connection with environmental factors from May to October 2020.Diurnal variation in effl ux was a single peak,and the maximum occurring between 14:00 and 16:00.Seasonal variation also exhibited a single peak,with the maximum in late July and the minimum in early October.From May to September,effl ux on the south side was the largest among the three stands,and mean values on the south side of 27 year-old,31 year-old,and 47 year-old trees were 0.50,0.97 and 1.05μmol·m^(–2)·s^(–1),respectively.The minimum occurred on the north side.Except for the maximum in July and September in the 27 year-old stand in the middle of the canopy,the maximum effl ux in the upper canopy,and the means in the 27 year-old,31 year-old,and 47 year-old stands were 0.49,0.96 and 1.04μmol·m^(-2)·s^(-1),respectively;the minimum occurred in the lower canopy.Temperatures and relative humidity infl uenced seasonal variations in effl ux.Seasonal variation in temperature sensitivity coeffi cient(Q 10)was opposite that of temperature,increasing with decreasing temperature.At the spatial scale,maximum Q 10 occurred in the mid canopy.With the effl ux and temperature data in diff erent locations,it is possible to better estimate effl ux variations in each stand.

Spatial and Temporal Evolution Characteristics of Extreme Precipitation and Isohyets in Ningxia

The multi-scale spatial and temporal evolution characteristics of extreme precipitation and isohyets in Ningxia were studied using daily,monthly,and annual precipitation data from 20 meteorological stations in Ningxia over the last 60 years.The results revealed that the intensity of rainstorms in Ningxia had decreased slightly over the previous 60 years,with the intensity of rainstorms in southern and central Ningxia being higher than in northern areas.Ningxia's annual and seasonal precipitation varied regionally,declining from the southeast to the northwest.Annual,spring and autumn precipitation exhibited a significantly declining trend from 1960 to the early 21^(st)century;summer precipitation displayed a slightly decreasing trend;and winter precipitation showed a significantly increasing trend.Nevertheless,there was a noticeable increase in annual and seasonal precipitation after 2005.From the 1960s to the 2000s,the 200 mm isohyet moved slowly southward,while the 400 mm isohyet jumped southward twice in the 1970s and 2000s before jumping considerably northward in the 2010s to reach their northernmost region.