Seasonal Index Research Articles

Ecological Restoration Practices within a Semi-arid Natural Gas Field Improve Insect Abundance and Diversity during Early and Late Growing Season.

Insects are critical components of terrestrial ecosystems and are often considered ecosystem engineers. Due to the vast amount of ecosystem services they provide, because statistically valid samples can be captured in short durations, and because they respond rapidly to environmental change, insects have been used as indicators of restoration success and ecosystem functionality. In Wyoming (USA), ecological restoration required on thousands of acres of land surface have been disturbed to extract natural gas. In this study, we compared early seral reclamation sites to reference areas at two points within a growing season. We compared insect abundance and family richness on 6 natural gas well pads with early season perennial forbs and 6 well pads with the late season to insect communities on adjacent reference areas. A total of 237 individual insects were found on early season reclaimed sites compared to 84 on reference sites, while 858 insects were found on late season reclaimed sites compared to 38 on reference sites. Insect abundance was significantly higher on reclaimed well pads compared to reference areas at both points in the growing season, while reclaimed sites had significantly higher Shannon Diversity Index in early season and significantly higher family richness in late season compared to their paired reference sites. We also found interesting differences in abundance at family levels.

Regional changes of surface air temperature annual cycle in the Northern Hemisphere land areas

AbstractThe regional amplitude and phase characteristics of surface (2 m) air temperature (SAT) annual cycle (AC) over the Northern Hemisphere land area are analysed by the clustering method for the period 1953–2018. The five clusters are found in the nonlinear changes of AC amplitude and the six clusters are found in the AC phase. The spatial distributions of clusters are not the same for amplitude and phase, but the similarity is that both the amplitude and phase have remarkably different trends in the mainland of Eurasia and North America, even though these two regions locate over the similar latitudinal zone. Descending trends of AC amplitude are found for the whole period 1953–2018 in most areas, except in and around Greenland. However, when checking the nonlinear variation and making a piece‐wise linear fit, the abnormal increasing trend (stronger seasonality) appears in the major part of Eurasia after the year 1990. The comparisons of surface radiation fluxes in Eurasia and North America indicate that the long‐term reduction of the reflection and absorption of clouds and aerosols with the declined surface albedo after 1980–1985 may lead to the amplitude amplification after 1990 in Eurasia. In contrast, the variation of surface radiation in North America remains unchanged, where the decreasing trend of amplitude may result from other causes. The overall phase trend is increasing (toward earlier season), but in North America after 1990, the phase shift becomes delayed. The phase variations are correlated to North Atlantic Oscillation (NAO) cold season index over almost all regions, especially in Europe.

Statistical analysis of divorce status and causes in the Russian Federation

The article presents the results of a statistical analysis of the breeding rate in Russia. The annual data characterizing the number of divorces in 2000−2021 are considered, unstable dynamics is noted, which is generally characterized by a tendency of their decline. The dynamics of the indicator according to short-term data has showed the presence of seasonal fluctuations, seasonality indices have been calculated, a seasonal wave graph has been constructed. Based on the analysis of sociological research data, the reasons for divorces have been identified. On the basis of statistical data, the indicators that have statistical links with the general coefficient of divorce are identified and have an impact on it. These variables have been grouped by individual characteristics, the following groups of factors are formed: demographic factors; income and employment; housing conditions; public health; digital environment. With the help of correlation and regression analysis, linear models have been built for individual groups of factors, and a generalised model. By comprehensively influencing the indicators included in the model, it is possible to adjust the values of the total dilution coefficient for the future. Based on cluster analysis, regions with high, medium and low breeding rates have been identified. The results of the study can be useful to state bodies in the framework of their practical activities.

Molecular, Physiological and Hematological Responses of Crossbred Dairy Cattle in a Tropical Savanna Climate.

A comprehensive study was conducted to assess the effects of seasonal transition and temperature humidity index (THI) on the adaptive responses in crossbred dairy cows reared in a tropical savanna region. A total of 40 lactating dairy cattle reared by small-scale dairy farmers in Bengaluru, India, were selected for this study. The research period comprised the transitioning season of summer to monsoon, wherein all traits were recorded at two points, one representing late summer (June) and the other early monsoon (July). A set of extensive variables representing physiological responses (pulse rate, respiration rate, rectal temperature, skin surface temperature), hematological responses (hematological profile), production (test day milk yield, milk composition) and molecular patterns (PBMC mRNA relative expression of selective stress response genes) were assessed. A significant effect of seasonal transition was identified on respiration rate (RR), skin surface temperature, mean platelet volume (MPV), platelet distribution width (PDWc), test day milk yield and on milk composition variables (milk density, lactose, solids-not-fat (SNF) and salts). The THI had a significant effect on RR, skin surface temperature, platelet count (PLT), plateletcrit (PCT) and PDWc. Lastly, THI and/or seasonal transition significantly affected the relative PBMC mRNA expression of heat shock protein 70 (HSP70), interferon beta (IFNβ), IFNγ, tumor necrosis factor alpha (TNFα), growth hormone (GH) and insulin-like growth factor-1 (IGF-1) genes. The results from this study reveal environmental sensitivity of novel physiological traits and gene expressions to climatic stressors, highlighting their potential as THI-independent heat stress biomarkers.

Spatiotemporal Evolution of Seasonal Crop-Specific Climatic Indices under Climate Change in Greece Based on EURO-CORDEX RCM Simulations

This study presents an updated assessment of the projected climate change over Greece in the near future (2021–2050) and at the end of the 21st century (2071–2100) (EOC), relative to the reference period 1971–2000, and focusing on seasonal crop-specific climatic indices. The indices include days (d) with: a maximum daily near-surface temperature (TASMAX) > 30 °C in Spring, a TASMAX > 35 °C in Summer (hot days), a minimum daily near-surface temperature (TASMIN) < 0 °C (frost days) in Spring, a TASMIN > 20 °C (tropical nights) in Spring–Summer and the daily precipitation (PR) > 1 mm (wet days) in Spring and Summer covering the critical periods in which wheat, tomatoes, cotton, potato, grapes, rice and olive are more sensitive to water and/or temperature stress. The analysis is based on an ensemble of 11 EURO-CORDEX regional climate model simulations under the influence of a strong, a moderate, and a no mitigation Representative Concentration Pathway (RCP2.6, RCP4.5 and RCP8.5, respectively). The indices related to TASMAX are expected to increase by up to 11 days in Spring and 40 days in Summer, tropical nights to rise by up to 50 days, frost days to decrease by up to 20 days, and wet days to decline by up to 9 days in Spring and Summer, at the EOC with an RCP8.5. The increased heat stress and water deficit are expected to have negative crop impacts, in contrast to the positive effects anticipated by the decrease in frost days. This study constitutes a further step towards identifying the commodities and/or regions in Greece which, under climate change, are or will be significantly impacted.

Beef Cattle Cycle in the State of Pará, Amazon, Brazil

The production and price behavior of the beef cattle market in the state of Pará, Brazilian Amazon, was evaluated with the objective of characterizing the dynamics of the market and guiding economic decisions made by agents in the beef cattle production chain. Time series of production and prices from the period 1995 to 2021 were analyzed using growth rates and the classical multiplicative method of time series. The cattle herd in Pará showed a growth rate of 4.54% per year from 1995 to 2021, and the slaughter of bovine females fluctuated throughout the historical series with the highest and lowest percentages in the years 2000 and 2011, respectively. There were three complete livestock cycles, differing in duration and amplitude. The average duration of the cycles was 7 years, with a shortening of the cycle over the period of study. Seasonal price indices are higher than 100, which is the annual average index, from September to January and lower than 100 from February to August. The highest price quotations for the arroba of fattened cattle coincide with the off-season period of beef cattle production, which is determined by the carrying capacity of pastures under the influence of weather conditions.

Prediction of Suzhou’s Industrial Power Consumption Based on Grey Model with Seasonal Index Adjustment

The accurate prediction of industrial power consumption is conducive to the effective allocation of power resources by power and energy institutions, and it is also of great significance for the construction and planning of the national grid. By analyzing the characteristics of the data of Suzhou’s industrial power consumption between 2003 and 2005, this paper proposes a grey model with a seasonal index adjustment to predict industrial power consumption. The model results are compared with the traditional grey model, as well as the real value of Suzhou’s industrial power consumption, which shows that our model is more suitable for the prediction of industrial power consumption. The lasted Suzhou’s industrial power consumption data, from 2019–2021, are also investigated, and the results show that the prediction results are in very good agreement with the real data. The highlights of the paper are that all precision inspection indexes are excellent and the seasonal fluctuations in the data changes can be reflected in the present model.

Time series evaluation of condemnation at poultry slaughterhouses enable to export in Southeastern Brazil (2009-2019): a tool for optimizing resources in the poultry production chain.

Even with the technological advances in management, health and genetics applied to poultry farming worldwide, there is still a high rate of carcasses condemnation at slaughterhouses, which result in losses for the poultry production chain. Thus, this work aimed to evaluate the condemnation occurrence index (COI) and adjusted seasonal index (ASI) of poultry (turkey, griller, and heavy chicken) between 2009 and 2019, in a slaughterhouse enable to export in southeastern Brazil. Data were obtained from official spreadsheets from the Brazilian Federal Inspection Service (FIS) and used to calculate the COI, correlation analysis between the main causes of condemnation, and ASI assessments throughout the year. Seven percent (55,594,318) of the poultry carcasses were condemned (partial or total), and the most frequent causes, contamination, and contusion/traumatic injury, amounted to 63.5% of the total condemnation. There was a trend of increasing condemnation throughout the time series evaluated, with COI varying between 45,282-149,809 condemnations per 1,000,000 poultry slaughtered. Considering the ASI, it was identified that for ascitic syndrome, July has a higher index value (1.63) than the months between January-June (P < 0.05). The main causes of condemnation were contamination and contusion/traumatic injury, both technological causes. ASI showed that in July there is a greater carcasses condemnation due to ascitic syndrome than in the months between January and June. The variations observed in the ASIs can provide subsidies for preventive measures and optimization of human and financial resources, generating positive impacts on food safety, productivity, and profitability of the sector.

A Seasonal–Trend-Decomposition-Based Voltage-Source-Inverter Open-Circuit Fault Diagnosis Method

This article considers power transistor open-circuit fault in a two-level voltage-source inverter and proposes a highly accurate, robust, and simple diagnosis method based on seasonal–trend decomposition. First, the three-phase current signal is decomposed into the trend, seasonal, and remainder components through the simplified seasonal–trend decomposition algorithm. Then, two characteristics “seasonal index” and “trend index” are defined. The theoretical values of the two characteristic quantities under different conditions are derived through simplified current waveform analysis and approximate integral calculation. Finally, the thresholds of characteristic quantities are set, and the diagnosis algorithm is designed. Experiments verify the effectiveness and the merits of the proposed method.

Climatic fingerprint of spring discharge depletion in the southern Italian Apennines from 1601 to 2020 CE

Annual mean spring discharge (ASD) is an important water supply source, essential for ecological systems and societies dependent on groundwater resources. Influenced by both regional and local climate fluctuations, the inter-annual variability of ASD represents a climate memory signal, significantly affected when the drought pattern manifests itself in changing climatic regimes. Gaining a better historical perspective on ASD changes requires extended time-series of discharge data and relevant climate drivers. Here, using a parsimonious model, we present a continuous (modelled) time-series of annual ASD for the karst spring of Caposele, in the Cervialto Massif of southern Italy, which is hitherto the longest (1601–2020 CE) such time-series for the entire Mediterranean region. The model was designed to capture the importance of large-scale seasonal (spring, autumn and winter) precipitation (hydro-meteorological factor), and flood and drought indices (climatological factor), and to be consistent with a sample (1920–2020 CE) of actual data. We show a limited overall sensitivity of ASD to climate variability, with a mean of 4.21 m3 s−1 and a drop from ∼1759 CE. With a mean value of ∼3.60 m3 s−1 after ∼1987 CE, ASD has revealed a substantial descending trend—possibly a fingerprint of recent warming—with a depletion of regional water reservoirs. These results highlight the need to strengthen the capacity of groundwater resources in the face of changing, and possibly enhanced, drought patterns in the Mediterranean region.

Linear, nonlinear, parametric and nonparametric regression models for nonstationary flood frequency analysis

In recent years, nonstationary flood frequency analysis (NFFA) has become an active area of research. It is crucial for water resources management and hydrologic engineering design to cope with the changing environment. Finding suitable and effective models could help perform better flood frequency analysis and make reliable estimates under the nonstationary environment. This study assesses different modelling techniques for nonstationary flood frequency analysis, including linear, nonlinear, parametric and nonparametric models using an extensive data set of 161 catchments across the UK. It identifies that rejection rates are generally higher for precipitation-informed NFFA models than time-varying NFFA models. For both time-varying and precipitation-informed NFFA, rejection rates for linear and cubic polynomial models are the highest. The models with the fewest rejections are fractional polynomial models followed by cubic spline models. Because of the flexibility, parsimony, and user-friendly features, fractional polynomial models could be a potential alternative for modelling the nonstationary behaviour of flood series. To investigate whether the seasonal flood variation and catchment characteristics influence the goodness-of-fit, a quantified seasonality index was calculated to illustrate the degree of seasonal variation of flooding for each catchment. It was found that the southeast of the UK has more significant seasonal flood variation than the northwest, and most of the catchments with high seasonality indexes are close to the Scotland and England border. Nevertheless, the correlation analysis shows insufficient evidence to conclude that catchment characteristics and seasonal flood variation impact the goodness-of-fit of the NFFA models.

Seasonal energy efficiency ratio of regenerative indirect evaporative coolers—Simplified calculation method

Evaporative-cooling technology is a promising alternative to conventional air-cooling systems that are based on using direct expansion units in terms of energy efficiency. Seasonal energy efficiency ratio (SEER) calculation methodologies and standards for these air-cooling systems remain limited. Therefore, the objective of this study is to develop a novel simplified method for calculating the SEER for regenerative indirect evaporative coolers (RIEC). Experimental tests were performed to obtain energy efficiency ratio models of four RIEC systems. Five climate zones in the Mediterranean region were considered for calculation of the SEER values. A detailed calculation method based on annual energy simulations was employed, and the results of the seasonal index were compared with those of a simplified calculation method based on the steady-state operation at four specific test points. The relative error between the SEER calculation methods was 4.6% for all the RIEC systems and climate zones. The highest SEER values of 5.8 and 5.5 were achieved for the Cairo and Madrid weather conditions, respectively. This study can serve as a reference for research on the seasonal performance and life-cycle analysis of RIEC based on an efficient counterflow heat exchanger with a wide range of airflow and cooling capacities.

Evaluation of the Spatiotemporal Distribution of Precipitation Using 28 Precipitation Indices and 4 IMERG Datasets over Nepal

Accurate accounting of spatiotemporal variability of precipitation is essential for understanding the changing climate. Among the available precipitation estimates, the Global Precipitation Measurement (GPM) is an international satellite network providing advanced global precipitation estimates. The integrated multi-satellite retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation and yields a better performance in detecting precipitation events and spatial resolution. Here, we used twenty years (2001–2020) of IMERG Final data over the entire Nepal to analyze the spatial and temporal distribution of precipitation. This study evaluates the dynamic characteristics of the precipitation amounts, intensities, frequencies, and other relevant data across Nepal, using four IMERG datasets: (i) microwave only, (ii) infrared only, (iii) multi satellites gauge uncalibrated, and (iv) multi satellites gauge calibrated. A total of 28 precipitation indices was computed: threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, proportion-based indices, and additional seasonal indices. Results show that all four IMERG datasets are promising in capturing spatial details. The frequency of wet days corresponds with ground-based precipitation. Still, most indices, including consecutive wet days, annual and monsoon precipitation, and days when precipitation equaled or exceeded 20 and 50 mm, were substantially underestimated. In addition, the microwave-only dataset highly underestimated the precipitation amount. Notably, a substantial proportion of false alarms is a problem for all four IMERG datasets. Moreover, our results demonstrate that the IMERG uncalibrated dataset tends to overestimate precipitation during heavy precipitation events. These advantages and shortcomings of IMERG datasets over the rugged terrain of Nepal can provide useful feedback for sensor and algorithm developers to overcome limitations and improve retrieval algorithms. The study findings are helpful to the broader data users and practitioners for effective water decision applications.

Small but voracious: invasive generalist consumes more zooplankton in winter than native planktivore

In recent years, Lake Constance has experienced an invasion and domination of three-spined stickleback (Gasterosteus aculeatus) in the pelagic zone, which has coincided with a decline in the native whitefish (Coregonus wartmanni) population. Similar massive invasions of sticklebacks into pelagic zones have been recognized also in marine areas or small lakes worldwide. However, their diet overlaps with native species is rarely evaluated, especially in the winter season, which often presents a bottleneck for fish survival. In this study, we compared the diet of pelagic sticklebacks with the diet of the substantially larger native whitefish in different seasons, to evaluate the threat of the recent stickleback invasion on whitefish populations. By monthly sampling of zooplankton and both fish species diets, we could demonstrate that sticklebacks select similar prey throughout most of the year and consume more prey than whitefish during the winter. With relations between prey availability and prey selection, interspecific and intraspecific seasonal diet variability and indices like a prey-specific index of relative importance, we discuss the importance of zooplankton species traits and abundance for whitefish and stickleback predation. This study shows that sticklebacks, despite their small size, represent a serious potential diet competitor to native planktivorous fish. Sticklebacks quickly adapt to new environments, and thus we advocate precautions regarding their introduction into similar lakes as Lake Constance, as this could cause irreversible ecological changes.

Attribution of the human influence on heavy rainfall associated with flooding events during the 2012, 2016, and 2018 March-April-May seasons in Kenya

The changing probabilities of extreme climate and weather events, in terms of frequency, intensity, spatial extent, duration, and timing is one of the most noticeable and damaging manifestations of human-induced climate change. During the March-April-May (MAM) rainfall season of 2012, 2016 and 2018, Kenya experienced high rainfall that caused both widespread and localised flooding, resulting in human and livestock deaths, destruction of infrastructure and property, bursting of riverbanks, submerging of farmlands and emergence of isolated cases of water-borne diseases. Here, we aim to quantify how the magnitude of heavy rainfall during these seasons may have been altered by human-induced climate change. We undertake a probabilistic attribution analysis using three different approaches utilising two observational datasets and two independent climate model experiment set-ups. We analyse three different seasonal heavy rainfall indices, maximum consecutive 5-day, 10-day, and 20-day rainfall, to compare the magnitude of maxima recorded in MAM 2012, 2016 and 2018 with the magnitude of maxima in a pre-industrial climate (with little or no anthropogenic influence). We find a shift towards intensification of extreme rainfall in today's climate, although these increases are not in all cases statistically distinguishable from our estimates of magnitudes in the preindustrial climate. Although we find no significant anthropogenic climate change influence, the intensification of extreme rainfall amid the observed drying trend and the projected increases in rainfall in the MAM season in Kenya, leave the already vulnerable societies with uncertainties about how to prepare for a changing climate. This study, therefore, provides a basis for an in-depth assessment of current and future trends of extreme rainfall in East Africa in adapting to changing climate risks for sustainable development in the already vulnerable and less resilient society.

Extreme Rainfall Indices in Southern Levant and Related Large-Scale Atmospheric Circulation Patterns: A Spatial and Temporal Analysis

This study aims to provide a comprehensive spatio-temporal analysis of the annual and seasonal extreme rainfall indices over the southern Levant from 1970 to 2020. For this, temporal and spatial trends of 15 climate extreme indices based on daily precipitation at 66 stations distributed across Israel and Palestine territories were annually and seasonally analyzed through the nonparametric Mann–Kendall test and the Sen’s slope estimator. The annual averages for frequency-based extreme indices exhibited decreasing trends, significantly for the Consecutive Dry Days. In contrast, the percentiles- and intensity-based extreme indices showed increasing trends, significant for extremely wet days, Max 1- and 3-day precipitation amount indices. The study area had expanding periods of extreme dry spells for spring and correspondingly shortening extreme wet spells for spring, winter and the combined winter–spring. Moreover, most of spring indices showed negative trends. Conversely, most winter indices displayed positive trends. Regarding the influence of large-scale circulation patterns, the North Sea Caspian pattern, the Western Mediterranean Oscillation, and ENSO were the primary regulators of the winter, spring, and autumn extreme indices, respectively. These findings contribute to a better understanding of extreme rainfall variability in the Levant region and could be utilized in the management of water resources, drought monitoring, and flood control.

Production, Fishing Season and Fishing Ground of the Dominant Fish (Euthynnus affinis, Mene maculata, Leiognathus equulus) Caught by Boat Seine in Palabuhanratu Indonesia

Boat Seine net is a very productive fishing gear in Nusantara Fisheries Port (NFP) Palabuhanratu. Boat Seine was a fishing unit that has the third largest contribution to the provision of fish stocks in 2019, which is 9% of the total production volume of NFP Palabuhanratu after lift net (40%) and longline (35%). The purpose of this study was to analyze the composition of the main catch, productivity, fishing season and to analyse the fishing ground in Palabuhanratu Bay. The type of data collected in this study was quantitative data. Primary data from interviews and field observations of fish landing activities at Palabuhanratu in October 2020 were used to compile this study's data, which included the type of fishing gear used, the composition of the types, and the production of the catch. Secondary data collected were monthly catches and trips made by Boat Seine fishing units. By analyzing the data of Boat Seine net fishing from 2015 to 2019, descriptive analysis method was used to investigate the composition of Boat Seine net catch. The results of the research showed that mackerel (Euthynnus affinis), moonfish (Mene maculata), and ponyfish (Leiognathus equulus) are the dominant fishes caught at the Palabuhanratu waters. The productivity level of Boat Seine net was 348 Kg/Trip in 2015, which increased to 603 Kg/Trip in 2019. The mackerel season occurs around May to September and November. Moonfish fishing season occurs in March, September and October. Ponyfish fishing season occurs in January, March, May, October, November, and December. Boat Seine net fishing grounds in the waters of Palabuhanratu Bay are relatively stationary around the waters of Palabuhanratu Bay.Keywords: CPUE, Fishing Area, Fishing Season Index, Productivity

Spatiotemporal variations of precipitation concentration influenced by large-scale climatic factors and potential links to flood-drought events across China 1958–2019

Understanding the irregularity of precipitation variations at both temporal and spatial scales is critical for assessing flood and drought risks. In this study, the statistical characteristics of precipitation variations in eight subregions were detected based on daily precipitation records from 798 meteorological sites during 1958–2019, and Long-term precipitation Concentration Index (LCI), Annual precipitation Concentration Index (ACI) and Seasonal precipitation concentration index (SPCI) values were used to analyse temporal heterogeneity of precipitation concentrations. Results showed that the LCI and ACI values were generally characterized by “high in southwest, low in Tibet, high in summer, and low in winter.” Specifically, ACI presented significant increasing trends in 5 regions (Northeast, North, Tibetan Plateau, Western Northwest, and Eastern Northwest), insignificant increasing trends in 2 regions (Jianghuai and Southwest), and a significant decreasing trend in 1 region (South), indicating the potential of increased drought and flood risks. SPCI exhibited an increasing trend in the spring months in Northeast China (p < 0.01), and significant increasing trends in the Tibetan Plateau and Western Northwest China during autumn months (p < 0.05). Attribution analyses on 11 large-scale climatic factors showed that the El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) strongly influenced the precipitation concentration indices with distinct zonal characteristics. ENSO, which is the most significant factor affecting the precipitation concentrations, had the strongest positive effect in South China and coastal areas. Meanwhile, the NAO presented a notable negative influence on the ACI, while the AMO exhibited a positive impact in Northern China. Our work added to the body of evidence that the extreme rainfall events have been significantly increasing in the Tibetan Plateau, Western Northwest China, and Eastern Northwest China (p < 0.05), and that the north-western regions of China have been experiencing significant wetting trends. Furthermore, climate change would potentially lead to more extreme drought-flood events in the future.

Comparison of the Two Most Common Phenology Algorithms Imbedded in Land Surface Models

AbstractA reasonable representation of plant phenology in land surface models (LSMs) is necessary to accurately simulate the momentum, heat, and mass interchanges between land and the atmosphere from ecosystem to global scales. Many process‐based phenology algorithms have been developed and coupled to LSMs to describe seasonal vegetation changes. The growing degree day (GDD) and the growing season index (GSI) algorithms are the two most well‐known algorithms used in LSMs for simulating phenophases. However, assessments of these two most commonly used phenology algorithms in LSMs are quite scarce. In this study, these two phenology algorithms were respectively coupled with the Community Land Model (CLM) and the Dynamic Land Model (DLM) to obtain four modeling scenarios. The simulation accuracy of phenophases and gross primary production (GPP) in the four scenarios was assessed against observations at the site scale, focusing on deciduous forests and grasses. The three main findings were as follows: (a) the difference in simulated phenological events between different LSMs coupled with the same phenological algorithm was small and less than 1 day, DLM performed better than CLM; (b) compared with the GSI algorithm and regardless of whether it was coupled with the DLM or CLM model, the GDD model performance was better for spring phenology and worse for autumn phenology; (c) GSI performance was better than GDD for GPP simulation over different vegetation function types across different bioclimatic zones: on average, the root mean square error and the index of agreement were about 8.0% higher and about 6.5% lower, respectively.

Spatiotemporal Distribution and Epidemiological Characteristics of Hospital Admissions for Carbon Monoxide Poisoning in Guangdong, China, 2013–2020

ObjectiveThis study aimed to determine the spatiotemporal distribution and epidemiological characteristics of hospital admissions for carbon monoxide poisoning (COP) in Guangdong, China, from 2013 to 2020. MethodsData on age- and sex- specific numbers of hospital admissions due to COP in Guangdong (2013–2020) were collected. Daily temperatures were downloaded through the China Meteorological Data Sharing Service System. We analyzed temporal trends through time series decomposition and used spatial autocorrelation analysis to detect spatial clustering. The distributed lag nonlinear model was used to quantify the effects of temperature. ResultsThere were 48,854 COP admissions over the study period. The sex ratio (male to female) was 1:1.74. The concentration ratios (M) ranged from 0.73–0.82. The highest risk occurred in January (season index = 3.59). Most cases were concentrated in the northern mountainous areas of Guangdong with high-high clustering. COP in the study region showed significant spatial autocorrelation, and the global Moran's I value of average annual hospital admission rates for COP was 0.447 (P < 0.05). Low temperatures were associated with high hospital admission rates for COP, with a lag lasting 7 days. With a lag of 0 days, the effects of low temperatures [5th (12 °C)] on COP were 2.24–3.81, as compared with the reference temperature [median (24 °C)]. ConclusionCOP in Guangdong province showed significant temporal and spatial heterogeneity. Low temperature was associated with a high risk of COP, and the influence had a lag lasting 7 days.