Effective temperature of railway concrete bridges in plateau regions based on long-term meteorological data

Isotherm maps of temperature gradient for CFST in China: Statistical evaluation using long-term meteorological data

The frequency of heat events is likely to increase due to global climate change, posing an increasing risk to wheat production. To optimize crop management strategies for coping with future climates, it is crucial to quantify the high-temperature occurrence during cropping seasons. Here, sixty-six years (1955~2020) of meteorological data during wheat reproductive growth were collected from six meteorological stations in the Huaibei Plain of Anhui Province. These data were analyzed to quantify the pattern and characteristics of post-anthesis heat stress for wheat crops. Five levels of annual mean daily maximum temperature (Tmax) were defined, from normal to extreme temperatures. Six crop developmental phases of winter wheat, i.e., phase i to phase vi, were divided from flowering to maturity. The data suggest an annual mean temperature of 17~24 °C from flowering to maturity, with an annual effective cumulative temperature ranging from 725 °C d to 956 °C d. The mean temperature and effective cumulative temperature increased as crop growth progressed, along with more frequent heat events during phase ii (8~14 days after anthesis) and phase iii (15~21 days after anthesis). We also found that the frequency of extremely high temperatures (≥33 °C) from 1990 to 2020 was significantly greater than that from 1957 to 1990. Interestingly, it was found that the intensity of post-anthesis night temperatures also increased with crop growth, i.e., from phase i to phase vi. Wheat grain yield increased with increasing effective accumulative temperature and Tmax, but it started to decline when thresholds of effective accumulative temperature and Tmax were reached. Overall, these findings could provide guidelines for winter wheat cropping in the Huaibei Plain, China, or similar climate and cropping regions.

Due to an absence of an essential daily data set, changing characteristics, and cause of flow extremes in the Tianshan Mountains are rarely explored in depth. In this study, daily based long-term meteorological and hydrological observation data were collected in four typical watersheds in the middle Tianshan Mountains; Manne-Kendall trend analysis and Pettit’s test were used to detect the trends and alterations of extreme flow series; Generalized Extreme Value distribution (GEV) and General Pareto distribution (GDP) models were used to describe the probability distributions of annual maximum (AM) and peaks over threshold (POT) series based on daily discharge; and the relationship between extreme flow and climate indices, were also investigated. The findings indicated that, change of the AM series at five hydrological stations experienced positive trends; the POT series generally showed no significant trends, while the peaks over threshold number (POTN) present a positive trend at the five stations. Change points exist in the POT and occurrence time of maximum daily discharge in spring (AM-SPR) series at the Kensiwate (KSWT) station in Manas watershed; the mean extreme flow decreased after 1986, and the occurrence time the annual maximum daily flow in spring significant forward after 1978. The AM series can well fit the GEV distribution, while the POT series fit the GDP distribution better; the GEV model performed worse in estimating flood events with high return period than low return period events. Moreover, acceleration of glacier melting lead to the magnitude and frequency increments of flood in the north slope; intensifying and frequent precipitation extremes are dominate factors of extreme flow variations in south slope watersheds which without large amount of glacier coverage; and continually temperature rising in spring and increased precipitation in winter lead to the change on magnitude and timing of spring extreme floods.

Climate change can impact ecosystems by reshaping the dynamics of resource exploitation for predators and their prey. Alterations of these pathways could be especially intense in ecosystems characterized by a simple trophic structure and rapid warming trends, such as in the Arctic. However, quantifying the multiple direct and indirect pathways through which climate change is likely to alter trophic interactions and their relative strength remains a challenge. Here, we aim to identify direct and indirect causal mechanisms driven by climate affecting predator-prey interactions of species sharing a tundra food web. We based our study on relationships between one Arctic predator (Arctic fox) and its two main prey - lemmings (preferred prey) and snow geese (alternate prey) - which are exposed to variable local and regional climatic factors across years. We used a combination of models mapping multiple causal links among key variables derived from a long-term dataset (21years). We obtained several possible scenarios linking regional climate factors (Arctic oscillations) and local temperature and precipitation to the breeding of species. Our results suggest that both regional and local climate factors have direct and indirect impacts on the breeding of foxes and geese. Local climate showed a positive causal link with goose nesting success, while both regional and local climate displayed contrasted effects on the proportion of fox breeding. We found no impact of climate on lemming abundance. We observed positive relationships between lemming, fox and goose reproduction highlighting numerical and functional responses of fox to the variability of lemming abundance. Our study measures causal links and strength of interactions in a food web, quantifying both numerical response of a predator and apparent interactions between its two main prey. These results improve our understanding of the complex effects of climate on predator-prey interactions and our capacity to anticipate food web response to ongoing climate change.

Mapping local climates in highly heterogeneous mountain regions: Interpolation of meteorological station data vs. downscaling of macroclimate grids

Software is currently a key part of many safety-critical applications. But the main problem facing the computer industry is how to develop a software with (ultra) high reliability on time, and assure the quality of software. In the past, some researchers reported that the Pareto distribution (PD) and the Weibull distribution (WD) models can be used for software reliability estimation and fault distribution modeling. In this paper we propose a modified PD model to predict and assess the software fault distribution. That is, we suggest using a special form of the Generalized Pareto distribution (GPD) model, named the bounded Generalized Pareto distribution (BGPD) model. We will show that the BGPD model eliminates several modeling issues that arise in the PD model, and perform detailed comparisons based on real software fault data. Experimental result shows that the proposed BGPD model presents very high fitness to the actual fault data. In the end, we conclude that the distribution of faults in a large software system can be well described by the Pareto principle.

ObjectiveThis study aimed to analyze the clinicopathological features of pediatric renal biopsies from plateau regions of China.MethodsClinicopathological features of pediatric renal biopsies were compared between plateau and non-plateau regions in patients who were admitted to West China Second University Hospital, Sichuan University between April 2001 and March 2017. Patients were children younger than 18 years.ResultsThe proportion of primary glomerular disease in the plateau group was lower than that in the non-plateau group (45.56% vs 62.09%, respectively). In the plateau group, IgA nephropathy (IgAN) was the major primary glomerulonephritis (GN) pathology. IgAN accounted for 36.54% and 21.63% of GN cases with nephrotic syndrome and hematuria, respectively. Henoch-Schönlein purpura nephritis was the most common secondary GN in both groups. The proportion of hepatitis B virus-associated GN was higher and that of lupus nephritis was lower in the plateau group than in the non-plateau group.ConclusionsThere are differences in renal pathological types between children in plateau regions and those in non-plateau regions. Among children in plateau regions, IgAN and Henoch-Schönlein purpura nephritis were the most common kidney diseases.

A statistical method to analyze the uncertainties of strong earthquake hazard estimation is proposed from Generalized Pareto distribution (GPD) model using the northeastern Tibetan Plateau earthquake catalogue (1885–2017) data. For magnitude threshold of 5.5, the magnitude return levels in 20, 50, 100, 200, and 500 years are 7.19, 7.70, 7.99, 8.22, and 8.45, respectively. The corresponding 95% confidence intervals are [6.77, 7.60], [7.27, 8.12], [7.53, 8.44], [7.71, 8.72], and [7.84, 9.06], respectively. The upper magnitude limit obtained from this GPD model is 9.07 and its 80% confidence interval is [8.16, 9.98]. The sensitivity analysis by the Morris method indicates that the input magnitude threshold has a relatively large influence on the estimation results. Thus, threshold selection is important for the GPD model construction. The sensitivity characteristic ranking of input factors become increasingly stable with the increasing of return period, which implies that GPD model is more suitable for estimating strong earthquakes magnitude return levels and upper magnitude limit. The GPD modeling approach and qualitative uncertainty analysis methods for strong earthquake hazard estimations proposed in this paper can be applied to seismic hazard analysis elsewhere.

Lakes under natural conditions are potential indicators of climate change, but limited evidence has been proposed to explicitly explain the mechanisms of large-scale lake changes associated with climate. In this paper, satellite imageries from the 1970s to 2010 were compared to track the dynamic change patterns of plateau lakes. It aims to identify the forcing of climate change on the lakes in plateau regions of China. Results revealed that: (1) widespread expanding trends in the area and abundance of lakes on wetter plateau regions, by increasing precipitation and snow presented a positive effect on lake levels, as well as warming resulted glacier melting and permafrost degrading contributed to expansion of lakes in the regions covered by glaciers on the Tibetan Plateau by 58–62 % and of 13–20 % on the western Inner Mongolia and Xinjiang Plateau; (2) shrinkage lakes in the main portion of the arid regions were mainly caused by the decreasing wind speed resulted water balance change, irrigation and other human utilization of water resources. Although precipitation decreased, wind speed also has the most dominant impact on decreasing evaporation, followed by solar radiation, both of which offset the effect of increasing temperature, especially in eastern Inner Mongolia and Xinjiang Plateau; (3) thawing permafrost led to shrinkage of lakes in an area of seasonal permafrost, the shrinking and splitting of lakes in a discontinuous permafrost zone, and the rising of lake levels in an area of continuous permafrost; (4) the future of the plateau lakes under climate change must be in question, because the glacier and snow melting water is not sustainable for wetter regions, and the increasing water consumption caused by the farmland expansion for arid regions.

Drinking water is considered to bean important exposure pathway for humans to ingest trace elements; human urine samples are widely accepted as biometric substrates that can reflect human exposure to trace elements. The current study aimed at investigating the concentrations of trace elements including selenium (Se), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) in drinking water and human urine in plateau region of China, determining the association among trace elements in drinking water and urine, and analyzing their associations with age and gender. The results showed that the majority of trace element concentrations were in the range of the World Health Organization (WHO 2011) guideline values, in both urine samples of male and female, and the median values were descending in the order: Zn > Cu > As > Se > Cr > Ni > Mn > Pb > Cd > Co. Selenium contributed to the excretion of As, Cr, Cu, Cd, and Zn in human body, group of 31-40years appeared to present the greatest excretion ability in most of the trace elements. Weak positive correlations were observed between age and Mn in female urine samples, and negative correlations were observed between age and Se, As, Co, and Cu in male urine samples and between age and Co in female urine samples, respectively. Significant positive correlation was observed in As between drinking water and the whole human urine. In the same family, female seemed to show higher proportions of urinary As levels than male. This study will provide elementary information regarding trace element levels in drinking water and human urine in residents in plateau region of China and is helpful to provide reference for dietary nutrient trace element intake and effective control for local resident.

Arctic human settlements experience formidable challenges from accelerating climate change and environmental transformations. While these towns have demonstrated adaptive resilience, the looming threat of local climate extremes raises concerns about the results of adaptation and mitigation efforts. With the further development of Arctic settlements, it is necessary to consider changes in local climatic conditions, shifting the adaptation focus from regional to local scales. The local climate perspective in this literature synthesis study is built upon constraints from physical climatology, focused on the climate and environment within and around the town of Longyearbyen, Svalbard. The study provides insights into Longyearbyen’s local climate dynamics, including physical mechanisms, climate localisation, factors and trends, as well as their implications. Three model pathways for development are discussed, centred on (1) industrial development, (2) public services, and (3) tourism and conservation. This categorisation is introduced to distinguish development scenario sensitivity to the local climate effects. The synthesis indicates that any development concentrated spatially will amplify local warming and climate change, as positive climate feedback predominate. The study emphasises the need for a comprehensive understanding of the environmental factors sustaining local climatic anomalies.

The correlation of dairy products and radiographic progression of knee osteoarthritis of Tibetan in plateau region

Aiming at the refined value of effective temperature of steel-concrete composite girder jointless bridge, the historical meteorological data of 38 national benchmark weather stations in Inner Mongolia, China for more than 40 years were investigated. The most unfavorable weather conditions of composite girder once in 100 year’s period were simulated according to the measured historical meteorological data. Based on the finite element model and section weighted average method, the extreme effective temperature of composite girders at various weather stations was calculated. Then the isothermal map of the extreme effective temperature of Steel-concrete composite girders is obtained by spatial interpolation, which is compared with the Chinese Specifications. The results show that the effective temperature of composite girders is significantly affected by climate and environment. The maximum effective temperature and the minimum effective temperature in Inner Mongolia, China range from 37.46 ℃ to 46.07 ℃ and −41.92 ℃ to −10.13 ℃, respectively. The regional differences reach 8.61 ℃ and 31.79 ℃, and the influence of low temperature is more obvious. The results of this paper show that the effective temperature in most areas is higher than specifications, which may lead to the serious underestimation of temperature effect in the design of jointless bridges in Inner Mongolia, China.KeywordsBridge engineeringSteel-concrete composite girderEffective temperature extreme valueIsotherm mapSeamless bridge

A semiparametric Bayesian method with birth-death Markov Chain Monte Carlo algorithm for extreme mooring tension analysis

The total retail sales of goods and services in the US is modelled using generalized pareto distribution (GPD) model from Extreme Value Theory. The trend test which is Mann-Kendall (MK) suggests that retail sales data exhibit a non-stationarity. Thus, the non-stationary GPD with a linear dependence in time is proposed. The maximum likelihood estimation (MLE) method is used to estimate the parameters of GPD model. The return level of US annual retail sales for the next 20, 50 and 100 years are being calculated. Results showed that both stationary and non-stationary GPD produce the commendable results in fitting the data. The assessment on Q-Q plot suggest that GPD distribution provides a good fit to the annual retail sales data using MLE method. However, non-stationary GPD with time as a linear covariate does improve the model. This is supported by the goodness-of-fit test where the AIC, BIC and LRT conclude the non-stationary performed quite well compared to the stationary model. Consequently, the non-stationary GPD model is the most appropriate, comprising linear effect in the location of extremal behavior, but homogeneity in all other aspects. The return levels provided in this study could provide some useful information to the country where the government should focus more on the retail industry as the retail industry has high potential to be primary sector of the economy in the country. In addition, the government should be more creative in strategizing the marketing plan into a global market and building up the alternative way to increase the profits while coping the challenges during the pandemic period.