Daily Temperature Research Articles

Trend and teleconnection analysis of temperature extremes in New South Wales, Australia

AbstractThis study investigates possible trends and teleconnections in temperature extremes in New South Wales (NSW), Australia. Daily maximum and minimum temperature data covering the period 1971–2021 at 26 stations located in NSW were used. Three indices, which focus on daily maximum temperature, daily minimum temperature, and average daily temperature in terms of Excessive Heat Factor (EHF) were investigated to identify the occurrence of heatwaves (HWs). The study considered HWs of different durations (1-, 5-, and 10-days) in relation to intensity, frequency, duration, and their first occurrence parameters. Finally, the influences of three global climate drivers, namely – the El Niño/Southern Oscillation (ENSO), the Southern Annular Mode (SAM), and the Indian Ocean Dipole (IOD) were investigated with associated heatwave attributes for extended Austral summers. In this study, an increasing trend in both hot days and nights was observed for most of the selected stations within the study area. The increase was more pronounced for the last decade (2011–2021) of the investigated time period. The number, duration and frequency of the heatwaves increased over time considering the EHF criterion, whereas no particular trend was detected in cases of TX90 and TN90. It was also evident that the first occurrence of all the HWs shifted towards the onset of the extended summer while considering the EHF criterion of HWs. The correlations between heatwave attributes and climate drivers depicted that heatwave over NSW was positively influenced by both the IOD and ENSO and negatively correlated with SAM. The findings of this study will be useful in formulating strategies for managing the impacts of extreme temperature events such as bushfires, floods, droughts to the most at-risk regions within NSW.

Detection of changes in local-scale statistical distribution of daily temperature and precipitation in dryland region: a case study of Ningxia, China

Identifying alterations in the local-scale statistical distributions of daily temperature indices and precipitation is crucial for adapting to climate change and combating desertification in arid regions. This study focuses on Ningxia, a representative dryland area in China, where we investigated changes in the statistical distributions of daily temperature and precipitation across ten weather stations over two 30-year periods: 1991–2020 compared to 1961–1990. From 1961 to 2020, annual air temperature indices (mean, maximum, and minimum) exhibited a significant upward trend at all ten weather stations (P < 0.05). Specifically, when comparing the decades of 1961–1970 with those of 2001–2020, annual air temperature indices increased by 1.2–2.2 °C for annual air temperatures, by 0.91–1.97 °C for maximum air temperature, and by 1.26–3.28 °C for minimum air temperature; these corresponded to rates of increase ranging from 0.20–0.40 °C per decade for mean air temperatures, from 0.15–0.33 °C per decade for maximum air temperatures, and from 0.21–0.55 °C per decade for minimum air temperatures, respectively during this period. The observed differences in the statistical distributions of daily temperature indices between the two periods were statistically significant (P < 0.05), indicating an increase rate of approximately 1 °C during the period from 1991 to 2020 relative to that from 1961 to 1990 across various weather stations. In contrast, there were no significant increasing or decreasing trends in annual precipitation (P > 0.05). Similarly, the changes in statistical distributions of daily precipitation also did not reach a significance level (P > 0.05) when comparing data from both periods within these stations. Furthermore, the correlation coefficients between daily temperature indices and daily precipitation have decreased across these stations during the later period compared to earlier years. These findings suggest that while there has been a notable shift toward higher values in the statistical distribution of daily temperature indices—indicating rising temperatures—the corresponding distribution patterns for daily precipitation have remained relatively stable between these time frames. Consequently, this research confirmed an escalation in localized drought conditions within certain dryland areas despite broader regional trends toward humidification observed over recent decades; thus implying an increased risk of desertification as aridity intensifies.

Association between heat exposure and Kawasaki disease: A time-stratified case-crossover study

Nationwide studies investigating the association between daily mean temperature and Kawasaki disease are lacking. This study aimed to examine the association between ambient temperature and Kawasaki disease by utilizing daily data from nationwide administrative claims databases. The daily number of Kawasaki disease patients younger than 15 years old, who were hospitalized from 2011 to 2022, was extracted from the nationwide administrative claims database of hospitalizations in Japan. Daily mean temperature data and relative humidity data were obtained from the Japan Meteorological Agency. Since the exposure of interest includes heat, hospitalizations during the five warmest months (May through September) were used for analysis. A time-stratified case-crossover study with conditional quasi-Poisson regression analysis was used to estimate the relative risk (RR) of weather exposure for Kawasaki disease hospitalization with a lag of 0–5 days by prefecture. Relative humidity was included in the model simultaneously to control for its potential confounding effect. Random-effects meta-analysis was used to estimate pooled RRs. There was a total of 48,784 cases of Kawasaki disease hospitalization during the study period, of which 87.9% were under 5 years of age. Exposure to high daily mean temperatures was associated with an increased risk of hospitalization for Kawasaki disease. Specifically, exposure to extreme high daily mean temperatures (99th percentile high temperature) was associated with higher risk of hospitalization by 33% (RR 1.33, 95% confidence interval (CI):1.08, 1.65). Similar results were obtained from sensitivity analysis. Future research should elucidate the mechanisms by which high temperature is associated with hospitalization for Kawasaki disease.

Effects of High Tunnel Soil Solarization on Sclerotinia sclerotiorum in the Temperate Climate of Central Kentucky.

Diseases caused by Sclerotinia spp. can affect a wide range of plants, including vegetables, with yield losses ranging from 10 to 50%. Sclerotinia diseases can be especially problematic in high tunnels where high-value vegetable crops are planted in early spring to extend the growing season, achieve earlier harvest, and bring higher profits. Fungicide applications and crop rotations are limited due to product application restrictions and constraints on time, crop resistance, and profitability. Soil solarization is a cultural management method that uses transparent polyethylene to raise soil temperatures via solar irradiation to kill pathogens, pests, and weeds. A two-year study was conducted in a Kentucky high tunnel to determine the maximum temperature potential of solarization at various soil depths at different durations during different seasons and to identify temperatures at which S. sclerotiorum sclerotia lose viability. The experiment included solarization treatments of 2, 4, and 6 weeks and a non-solarized control implemented in spring, summer, and fall. Sclerotia and temperature data loggers were buried at 5.1, 10.2, and 15.2 cm soil depths. The number of hours at which soil temperatures reached ≥ 40 °C was greatest in summer in both years, followed by fall, and then spring. The highest average daily maximum soil temperature reached was 48.9°C, which occurred during the summer 6-week solarization in Year 1. The viability of buried sclerotia was overall lower in solarized treatments compared to non-solarized treatments in both years. In general, the 2-week solarization treatment had significantly higher percent sclerotial germination than the 4-week and 6-week treatments, which were not significantly different from one another. The viability of sclerotia was progressively higher with burial depth. In both years, sclerotia germination was significantly lower in summer compared to spring and fall.

HiCPC: A new 10-km CMIP6 downscaled daily climate projections over China

Accurate climate projections are critical for various applications and impact assessments in environmental science and management. This study presents HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China), a novel dataset tailored to China’s specific needs. HiCPC leverages outputs from 22 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). To address inherent biases in daily GCM simulations, an advanced Bias Correction and Spatial Disaggregation (BCSD) method is employed, using the China Meteorological Forcing Dataset (CMFD) as a reference. HiCPC offers detailed daily precipitation and temperature data across China at an enhanced spatial resolution of 0.1° × 0.1°. It covers both the historical period (1979–2014) and future projections (2015–2100) based on four CMIP6 Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Upon validation, HiCPC demonstrates good performance, surpassing CMIP6 GCMs across the historical period. This reinforces its significance for essential research in climate change evaluation and its associated implications within China.

Влияние на климатичните промени върху хематологичния и биохимичен статус на стадо крави от порода Лимузин в средата на два последователни пасищни сезона

The aim of the present study was to evaluate the status in the red and white blood count, together with the values of some enzymes and metabolites - ALT, AST, ALP, serum iron and glucose in selected animals from a herd of Limousine beef cattle in the middle of two consecutive grazing seasons, with large differences in the average daily temperature, the amount and distribution of precipitation and of the quantity and quality of the grass at the end of its vegetation. The study was conducted in the experimental base “Zlatusha” of the IAS-Kostinbrod with 15 pregnant cows equalized by BCS and age, from which blood samples were taken in July, 2022 and 2023, by venipuncture of the tail vein. The analysis of hematological indicators in whole blood was performed with a 5-Part-Diff Automated Hematology Analyzer URIT-5160, and the analysis of biochemical indicators - in plasma extracted from it with a Semi-automatic Biochemical Analyzer BTS-350. Results are expressed as mean values ± S.E.M. and were analyzed statistically by the analysis of variance (ANOVA) method. Although there was almost no difference in the levels of hematocrit and the number of erythrocytes in the middle of two grazing seasons, in July, 2022, the studied cows had a significantly higher level of hemoglobin than in July, 2023 (P&lt;0.001), which reflected also on the significance of the difference in mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC) – also at P&lt;0.001. There were no significant differences in leukocyte counts and leukocyte subpopulations and in iron and glucose concentrations in the middle of two consecutive years. ALT and AST enzyme activities were significantly lower (P&lt;0.05), and ALP activity was significantly higher in July, 2022 (P&lt;0.01).

Youth suicidality risk relative to ambient temperature and heatwaves across climate zones: A time series analysis of emergency department presentations in New South Wales, Australia.

Youth suicidality prevalence continues to rise alongside hot weather severity. Links between these two variables are underexplored. We examined associations between daily temperature and emergency department suicidality presentations by young people. We assessed these associations for five regions covering New South Wales as determined by 'climate zone' and analysed for heatwave effects as well as based on demographic subgroups. Daily emergency department presentations for suicidality by people aged 12-24 years across New South Wales, Australia, during warmer months (November to March) from 2012 to 2019 were examined in relation to daily mean temperature and heatwaves (⩾3 consecutive days ⩾ 95th percentile of long-term daily mean temperature) and by climate zone, using a generalised additive model with negative binomial distribution. Risks for age- and sex-based subgroups were also calculated. New South Wales youth suicidality presentation rates were significantly higher on hotter days. For every 1°C rise above average daily mean temperature, youth suicidality presentations to New South Wales emergency departments increased by 1.3%. Heatwaves did not increase presentation rates beyond single-day daily mean temperature effects. These findings were predominantly replicated across climate zones and demographic subgroups, though the association between suicidality and ambient temperature was weaker in coastal regions including Eastern Sydney. There is a positive linear association between ambient temperature and youth suicidality presentations to emergency departments. Risks are increased on single hot days, not only during heatwaves. Public health, broader societal approaches to heat and health system planning should consider impacts on youth suicidality of predicted increases in hot weather severity and frequency.

Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India

This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T30AMT > 38 °C) and Zone-2 (T30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.

Statistical-empirical pavement temperature prediction models based on data from road weather stations in Estonia

Asphalt pavement properties are greatly affected by seasonal temperature variation. Binder grade selection needs to be aligned with forecasted operational pavement temperatures to maximise pavement longevity. Different widely adopted models for determining binder Superpave Performance Grading have been shown to provide inaccurate results for the Nordic-Baltic region, leading to under- or overestimation of operational pavement temperatures and erroneous selection of a suitable binder grade. This study proposes empirically formulated pavement temperature prediction models for Estonia based on temperature data collected from six different road weather stations in Estonia from 2019 to 2023. Models were derived using measurements from daily maximum and minimum air temperatures combined with pavement temperature profiles with depth.

Genetic variants associated with chronic fatigue syndrome predict population-level fatigue severity and actigraphic measurements.

The diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (CFS) is based on a constellation of symptoms which center around fatigue. However, fatigue is commonly reported in the general population by people without CFS. Does the biology underlying fatigue in patients with CFS also drive fatigue experienced by individuals without CFS? We used UK Biobank actigraphy data to characterize differences in physical activity patterns and daily temperature rhythms between participants diagnosed with CFS compared to controls. We then tested if single nucleotide variants (SNVs) previously associated with CFS are also associated with the variation of these actigraphic CFS correlates and/or subjective fatigue symptoms in the general population. Participants diagnosed with CFS (n = 295) had significantly decreased overall movement (Cohen's d = 0.220, 95% CI of -0.335 to -0.106, p-value = 2.42x10-15), lower activity amplitudes (Cohen's d = -0.377, 95% CI of -0.492 to -0.262, p-value = 1.74x10-6), and lower wrist temperature amplitudes (Cohen's d = -0.173, 95% CI of -0.288 -0.059, p-value = 0.002) compared to controls (n = 63,133). Of 30 tested SNVs associated in the literature with CFS, one was associated in the control population with subjective fatigue and one with actigraphic measurements (FDR < 0.05). The genetic overlap of CFS risk with actigraphy and subjective fatigue phenotypes suggests that some biological mechanisms underlying pathologic fatigue in CFS patients also underlie fatigue symptoms at a broader population level. Therefore, understanding the biology of fatigue in general may inform our understanding of CFS pathophysiology.

Identification of drought-salinity combined stress in tomato plants by vegetation indices

A major issue in several farming areas of the Mediterranean basin consists of drought and salinity stress. This stress is mainly due to a steady exposition of warm daily temperature and heatwaves, moreover with inevitable irrigation with saline water. Therefore, detecting the stress is essential to minimise significant yield loss and preserve agricultural sustainability. In this context, remote and proximal sensing can play a crucial role in allowing fast, not destructive, extensive, and reliable assessment of crop status. In this work, the effectiveness of several multispectral indices in detecting salinity and water stress in tomato plants, grown under controlled green-house conditions, was investigated. Three different classifiers (fine tree model, linear discriminant model, and linear support vector machines model) were used to verify whether, and the extent to which, the adopted multispectral indices can be adopted to identify a stress condition of the tomato plants. In the experimental campaign, the stress occurrence on tomato plants was assessed on the base of a set of ecophysiological measurements, such as transpiration, stomatal conductance, and photosynthesis rate. Obtained results showed that a classification model based on linear support vector machines, exploiting the combination of Photochemical Reflectance Index and the Chlorophyl Index, can detect drought and salinity stress in tomato plants with an accuracy higher than 94%.

Linking regional economic impacts of temperature-related disasters to underlying climatic hazards

Abstract Temperature-induced disasters lead to major human and economic damage, but the relationship between their climatic drivers and impacts is difficult to quantify. In part, this is due to a lack of data with suitable resolution, scale and coverage on impacts and disaster occurrence. Here, we address this gap using new datasets on subnational sector-disaggregated economic productivity and geo-coded disaster locations to quantify the role of climatic hazards on economic impacts of temperature-induced disasters at a subnational scale. Using a regression-based approach, we find that the regional economic impacts of heat-related disasters are most strongly linked to the daily maximum temperature (TXx) index. This effect is largest in the agricultural sector (6.37% regional growth rate reduction per standard deviation increase in TXx anomaly), being almost twice as strong as in the manufacturing sector (3.98%), service sector (3.64%), and whole economy (3.64%). We also highlight the role of compound climatic hazards in worsening impacts, showing that in the agriculture sector, compound hot-and-dry conditions amplify the impacts of heat-related disasters on growth rates by a factor of two. In contrast, in the service and manufacturing sectors, stronger impacts are found to be associated with compound hot and wet conditions. These findings present a first step in understanding the relationship between temperature-related hazards and regional economic impacts using a multi-event database, and highlight the need for further research to better understand the complex mechanisms including compound effects underlying these impacts across sectors.

Solar Passive House Conceptand Thermal System Design

This aricle puts forward a concept of family home enclosed in a greenhouse. Inspired by Naturhus and Earthship ideas, it leverages thermofluid technologies inspired by nuclear power passive cooling systems. The solar gain of the greenhouse allows for enhanced heating fuel efficiency in winter, while a system of solar-drive heat exchangers dissipates excess heat in summer. This general living qualities of such innovative housing concepts are discussed, followed by an outline of the computational challenges that need to be overcome by experiment-driven modelling, aiming for the demonstrator that successfully controls summer daily temperature peaks.

Relationship between urban traffic crashes and temporal/meteorological conditions: understanding and predicting the effects

Urban traffic accidents pose significant challenges to public safety and transportation management. Previous studies have revealed that temporal and meteorological factors are the key contributors to accident rate. Besides the inconsistent observations or lack of exploration in some aspects such as snowfall, fog, wind and daily temperatures, it has been shown that these factors are essentially entangled. Furthermore, existing methodologies of analysis or prediction have been limited to relative risk or traditional models. Hence, this study is centered on understanding the detailed correlations between temporal and meteorological factors and accident rate of two types of crashes – moving vehicle and fixed-object crashes using the traffic accident data from Dalian. Further, by incorporating a diverse set of the features, a prediction model leveraging the random forest algorithm is proposed and proved effective in anticipating accident occurrences on the district level. The feature importance analysis has shown that time period and factors such as holiday, temperature and season are most important factors. The rate is higher on working days and in spring, and collisions of both types peak at 6–7 am. When the highest daily temperature is 27 °C and the lowest is 20 °C or -8 °C, the incidence is relatively higher. On the basis, the recommendations are aimed at optimizing transportation systems, mitigating accident risks, and enhancing public safety in urban environments.

Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran

The rise of industrial societies leads to higher greenhouse gas emissions, profoundly affecting the climate in coastal regions. Consequently, air temperature readings from standard meteorological stations are key indicators of the Earth's environmental condition. Therefore, accurate estimation of daily temperature in each region is one of the important prerequisites for agricultural planning as well as water resources management and drought prevention, which can be done in different ways such as experimental, semi-experimental and intelligent models. In this research, WSVR, AIG-SVR, GWO-SVR and BAT-SVR hybrid models were investigated and evaluated in order to estimate the average daily air temperature on the shores of the Caspian Sea located in the north of Iran. For modeling, weather station data from Babolsar meteorological station located in Mazandaran province were used. During the water year from 2012 to 2022, daily parameters including relative humidity, maximum temperature, minimum temperature, wind speed, and evaporation were selected as network inputs, with the average daily air temperature as the network output. To assess and compare model performances, several criteria were employed including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percentage bias. Comparative analysis revealed that the WSVR model surpassed other models, demonstrating the highest correlation coefficient (0.992), lowest RMSE (0.096), and lowest MAE (0.042). The highest Nash Sutcliffe criterion (0.996) and bias percentage (0.001) were prioritized in the validation stage.

Climate-Related Risks and Agricultural Yield Assessment in the Senegalese Groundnut Basin

Climate change and variability pose significant threats to agricultural production, particularly in regions heavily dependent on rainfed agriculture like Senegal. The problem addressed in this study revolves around the impact of climate-related risks on agricultural yields in the Senegalese Groundnut Basin as a key agricultural region. Daily rainfall, temperatures, and yield over 1991–2020 were used. The data were analyzed using multiple regression, trend analysis, and correlation approaches. The results indicate that the overall seasonal precipitation increases over time (98 mm in the north and 103 mm in the south). However, we found that the south Groundnut Basin has a much slower seasonal precipitation rate than the northern zone. Our results also show that the northern zone exhibits a more consistent and predictable growing season, with onset and offset, in contrast with the southern zone, which shows higher variability. The analysis further reveals that both the northern and southern zones are experiencing a warming trend, with the southern zone showing a more pronounced increase in maximum temperatures (+0.7 °C) than to the northern zone (+0.4 °C). Estimates from the regression analysis revealed that total seasonal precipitation and maximum temperature positively and significantly influence groundnut, millet, and maize yields in the northern and southern zones. All the other weather-related parameters have different influences depending on the zone. These findings highlight the heterogeneous nature of the study area and the significant role climatic factors play in crop yield variability in the Groundnut Basin. Understanding these influences is crucial for developing targeted agricultural strategies and climate adaptation measures to mitigate risks and enhance regional productivity. The study provides valuable insights for policymakers and farmers aiming to improve crop resilience and sustain agricultural outputs amidst changing climatic conditions.

Construction of a comprehensive meteorological risk index for wheat Fusarium head blight prediction based on more than a half-century of monitoring data.

Fusarium head blight (FHB) represents a critical threat to wheat production globally, not only reducing yields but also contaminating crops with harmful mycotoxins. This study aimed to elucidate new spatiotemporal patterns of FHB incidence and to develop a comprehensive meteorological risk index to enhance scientific prevention and control of the disease. Through the analysis of annual and decadal variations from 1965 to 2023, the study assessed FHB trends across four agricultural regions (I, II, III, and IV) in Jiangsu Province, located in the middle and lower reaches of the Yangtze River-a hotspot for FHB in China. Key findings include: Since 1965, Regions I and III consistently exhibited higher FHB incidence rates compared to Regions II and IV. Post-2000, there was a notable increase in years with high incidence rates, with Region III overtaking Region I as the region with the highest incidence. Since 2010, occurrences of FHB reaching the most severe grade (Grade 5) have surpassed those in previous decades across all regions. The study also revealed a stronger correlation between meteorological factors (cumulative precipitation, number of days with rainfall ≥ 0.1 mm, total rainy days with ≥ 2 and ≥3 consecutive days of rain, total rainy days with both average daily air temperature ≥ 15 °C and daily rainfall ≥ 0.1 mm, days with average daily relative humidity ≥ 85%, cumulative sunshine hours, and cumulative cloudy days) and the FHB incidence rates during the heading-flowering-grain filling period in Regions I, II, and III, compared to the heading-flowering period alone. This indicates that optimal temperature and high humidity during the grain filling stage significantly contribute to the final FHB incidence rates. Despite the less apparent correlation between temperature changes and disease rates, the significant warming trend observed since 2000 has likely fostered conditions conducive to the proliferation of FHB. The comprehensive meteorological risk index, constructed to incorporate key meteorological factors during the heading-flowering-filling period, showed a strong correlation with actual disease incidences. The index demonstrated fitting accuracy rates of 84.7% for Region I, 72.9% for Region II, 83.1% for Region III, and 90.9% for Region IV, underscoring its effectiveness in predicting FHB occurrences. This tool offers both convenience and practicality, providing valuable insights for strategically managing FHB risks based on local weather conditions.

Analysis of spatio-temporal variability of temperature, relative humidity and temperature humidity index in Naivasha Sub-county, Kenya

The aim of the study was to understand the environmental conditions impacting the reproductive performance of Sahiwal cattle, focusing on temperature, relative humidity, and relative humidity index (RHI). These factors significantly influence reproductive rates, highlighting the importance of spatio-temporal variability studies for informed herd management decisions. This study analyzed climate variability trends from 1998 to 2019 at the Kenya Agricultural and Livestock Research Organization (KALRO) Dairy Research Institute (DRI)-Naivasha, in Malewa Ward, Naivasha Sub-County, Kenya. Daily minimum and maximum temperature (T) and relative humidity (RH) data sourced from the Kenya Meteorological Department were utilized. The Temperature-Humidity Index (THI) was computed using the THI equation developed by Mader. Time series analyses, including the Mann-Kendall test and Sen's slope estimates, were employed to assess seasonal variability. Results indicated significant decreasing trends in temperature across all the four seasons studied (DJF, MAM, JJA, and SON), with rates of cooling at -0.0625, -0.0175, -0.0125, and -0.0342 °C per year, respectively. In contrast, relative humidity showed statistically significant increases in all seasons, with rates of +0.5977, +0.999, +1.4493, and +1.0499 for DJF, MAM, JJA, and SON, respectively. Seasonal THI exhibited significant decreases in DJF and JJA, potentially impacting livestock reproductive performance. These findings are crucial for policymakers to create adaptation and mitigation methods to address climate change effects on livestock and livelihoods.

Transition from endogenous to exogenous feeding in longfin yellowtail Seriola rivoliana larvae under simultaneous effects of daily temperature fluctuation and rotifer Brachionus rotundiformis enrichment.

Temperature and nutrition are suggested as the primary factors affecting larval survival during the transition from endogenous to exogenous feeding in fish. However, little is known about its simultaneous impact during this period. In this study, Seriola rivoliana eggs were subjected to a constant 24°C (CTE) and a daily temperature fluctuation (DTF) between 22.8 and 25.2°C until oil droplet exhaustion (5.5days after hatching). On the other hand, marine fish larvae mostly rely on live feed, with certain nutritional deficiencies such as poor long-chain fatty acids. Thus, rotifer Brachionus rotundiformis enrichment was simultaneously evaluated with temperature using three enrichment diets: Ori-green, S.presso, and a Domestic emulsion. For this purpose, the five experimental groups were established in triplicate using six 100-L tanks with three 10-L containers inside (18 experimental units in total). One hundred eggs were incubated, using a green water system, and 10 rotifers mL-1 were offered at mouth opening. After oil droplet exhaustion, survival was only affected by temperature (P < 0.01), being higher at DTF compared to CTE. At the same stage, Domestic emulsion resulted in bigger larvae than Ori-green. In a further assay at 3.7 DAH, the relative expression of the trypsin gene was higher at Domestic emulsion compared to S.presso and Ori-green. This study indicates that daily temperature fluctuation can improve larval performance and low levels of EPA and DHA in Domestic emulsion enriched rotifers were not critical for Seriola rivoliana at first feeding.

Short-term associations of diarrhoeal diseases in children with temperature and precipitation in seven low- and middle-income countries from Sub-Saharan Africa and South Asia in the Global Enteric Multicenter Study.

Diarrhoeal diseases cause a heavy burden in developing countries. Although studies have described the seasonality of diarrhoeal diseases, the association of weather variables with diarrhoeal diseases has not been well characterized in resource-limited settings where the burden remains high. We examined short-term associations between ambient temperature, precipitation and hospital visits due to diarrhoea among children in seven low- and middle-income countries. Hospital visits due to diarrhoeal diseases under 5 years old were collected from seven sites in The Gambia, Mali, Mozambique, Kenya, India, Bangladesh, and Pakistan via the Global Enteric Multicenter Study from December 2007 to March 2011. Daily weather data during the same period were downloaded from the ERA5-Land. We fitted time-series regression models to examine the relationships of daily diarrhoea cases with daily ambient temperature and precipitation. Then, we used meta-analytic tools to examine the heterogeneity between the site-specific estimates. The cumulative relative risk (RR) of diarrhoea for temperature exposure (95th percentile vs. 1st percentile) ranged from 0.24 to 8.07, with Mozambique and Bangladesh showing positive associations, while Mali and Pakistan showed negative associations. The RR for precipitation (95th percentile vs. 1st percentile) ranged from 0.77 to 1.55, with Mali and India showing positive associations, while the only negative association was observed in Pakistan. Meta-analysis showed substantial heterogeneity in the association between temperature-diarrhoea and precipitation-diarrhoea across sites, with I2 of 84.2% and 67.5%, respectively. Child diarrhoea and weather factors have diverse and complex associations across South Asia and Sub-Saharan Africa. Diarrhoeal surveillance system settings should be conceptualized based on the observed pattern of climate change in these locations.