Average Monthly Maximum Temperatures Research Articles

Climate Change’s Impacts on Drought in Upper Zab Basin, Iraq: A Case Study

Iraq has a semiarid and desert climate. Also, it is predicted to be more susceptible to global warming effects. In the present study, daily climatic data from the past and projected future of the Upper Zab Basin, located in northern Iraq and considered an example of arid and semiarid climate conditions, were simulated using LARS-WG 6.0, i.e., a stochastic weather generator. The model also estimated daily rainfall and temperature. Using the RCP4.5 and RCP8.5 main emission scenarios, the future climate throughout the current century was estimated utilizing the MIROC5, CanESM2, HadGEM2-ES, ESM1-M, and CSIRO-Mk3.6.0 general circulation models (GCMs). This estimation was performed considering the significant uncertainty of future climate estimates. The model, constructed using thirty years' worth of historical data, was validated using climate data from the Upper Zab Basin in northern Iraq (1990–2021). According to the data, the average monthly maximum temperature will decline by 2.15–6.20 °C under RCP4.5 and 1.81–6.10 °C under RCP8.5 by the end of the twenty-first era for the corresponding upstream and downstream sub-basins. Precipitation projections from all GCMs showed varying patterns. Given that some models, like CanESM2, expected a rise in precipitation, while others, like MIROC5, forecasted a future with no change in precipitation or a falling trend, which illustrates the significant level of uncertainty in precipitation forecast when only one model was utilized. Also, the downstream sub-basin suffered the most during the 1999–2000 and 2007–2008 droughts, with average RDIst values of -1.97 and -1.64, respectively. However, the upstream sub-basin had moderate to severe droughts in 1999–2000 and 2007–2008, with average RDIst values of -0.81 and -1.84, respectively. The water available in the research location will be significantly impacted by variations in rainfall and temperature.

A 40-year evaluation of drivers of African rainforest change

BackgroundTropical forests are repositories of much of the world’s biodiversity and are critical for mitigation of climate change. Yet, the drivers of forest dynamics are poorly understood. This is in large part due to the lack of long-term data on forest change and changes in drivers.MethodologyWe quantify changes in tree abundance, diversity, and stand structure along transects first enumerated in 1978 and resampled 2019 in Kibale National Park, Uganda. We tested five predictions. First, based on the purported role of seed dispersal and herbivory and our quantification of changes in the abundance of frugivores and herbivores, we tested two predictions of how faunal change could have influenced forest composition. Second, based on an evaluation of life history strategies, we tested two predictions concerning how the forest could have changed following disturbance that happened prior to written history. Finally, based on a 50-year climate record, we evaluate the possible influence of climate change on forest dynamics.ResultsMore trees were present on the assessed transects in 2019 (508) than in 1978 (436), species richness remained similar, but diversity declined as the number of dominant species increased. Rainfall increased by only 3 mm over the 50 years but this had not significant effect on forest changes measured here. Annual average monthly maximum temperature increased significantly by 1.05 °C over 50 years. The abundance of frugivorous and folivorous primates and elephants increased over the 50 years of monitoring. Neither the prediction that an increase in abundance of seed dispersing frugivores increases the abundance of their preferred fruiting tree species, nor that as an increase in folivore abundance causes a decline in their preferred species were supported. As predicted, light-demanding species decreased in abundance while shade-tolerant species increased as expected from Kibale being disturbed prior to historical records. Finally, while temperature increased over the 50 years, we found no means to predict a priori how individual species would respond.ConclusionsOur study revealed subtle changes in the tree community over 40 years, sizable increases in primate numbers, a substantial increase in the elephant population and an increase in local temperature. Yet, a clear picture of what set of interactions impact the change in the tree community remains elusive. Our data on tree life-history strategies and frugivore/herbivore foraging preferences suggest that trees species are under opposing pressures.

SEASONAL VARIATION OF PHENOLS AND FLAVONOIDS IN NON-GALLED AND GALLED LEAVES OF CARYOCAR BRASILIENSE (CARYOCARACEAE)

Introduction: Galling insects manipulate the development of their host plants. Galls are rich in secondary metabolites, such as phenolic compounds and terpenes. Caryocar brasiliense acts as a host of galling insects. Few studies report the impacts of this plant-insect interaction on the species' morphology, physiology, and secondary metabolism. Objective: The purpose of this study was to verify if there is a correlation between the levels of total phenols and flavonoids in C. brasiliense leaf extracts and environmental variables, such as temperature and precipitation, throughout the year, and to evaluate how galling insects can interfere in this seasonality. Methods: Ethanol extracts from leaves were obtained by soxhlet extraction. The main classes of secondary metabolites were detected by phytochemical prospecting. The levels of total phenols and flavonoids were quantified by the Folin-Dennis tests and complexation reaction with aluminum chloride, respectively. Pearson 's correlation coefficient shows that the levels of phenols and flavonoids were correlated with the average monthly maximum temperatures and precipitation accumulation, respectively, recorded in Gurupi - Tocantins. Results and Discussion: Phytochemical prospecting indicated flavonoids, saponins, tannins, triterpenes, steroids, and alkaloids in both extracts. In C. brasiliense, the samples of healthy leaves suffered a greater variation in the levels of phenols (68.39±2.3 to 279.34±3.1 mg GAE g-1 extract) and flavonoids (164.06± 3.0 at 269.03±3.6 mg QE g-1 extract) compared to leaf samples with galls. A strong positive correlation was found between the contents of total phenols and maximum temperatures and a strong negative correlation with precipitation through Pearson's correlation. Conclusions: The variations observed in the levels of phenols and flavonoids in the extracts of C. brasiliense seem to be related to abiotic factors and the herbivory promoted by the galling insect.

Ten years of monitoring malaria trend and factors associated with malaria test positivity rates in Lower Moshi

BackgroundHigh altitude settings in Eastern Africa have been reported to experience increased malaria burden due to vector habitat expansion. This study explored possible associations between malaria test positivity rates and its predictors including malaria control measures and meteorological factors at a high-altitude, low malaria transmission setting, south of Mount Kilimanjaro.MethodsMalaria cases reported at the Tanganyika Plantation Company (TPC) hospital’s malaria registers, meteorological data recorded at TPC sugar factory and data on bed nets distributed in Lower Moshi from 2009 to 2018 were studied. Correlation between bed nets distributed and malaria test positivity rates were explored by using Pearson correlation analysis and the associations between malaria test positivity rates and demographic and meteorological variables were determined by logistic regression and negative binomial regression analyses, respectively.ResultsMalaria cases reported at TPC hospital ranged between 0.48 and 2.26% per year and increased slightly at the introduction of malaria rapid diagnostic tests. The risk of testing positive for malaria were significantly highest among individuals aged between 6 and 15 years (OR = 1.65; 1.65 CI = 1.28–2.13; p = 0.001) and 16–30 years (OR = 1.49; CI = 1.17–1.89; p = 0.001) and when adjusted for age, the risk were significantly higher among male individuals when compared to female individuals (OR = 1.54; 1.00–1.31; p = 0.044). Malaria test positivity rates were positively associated with average monthly minimum temperatures and negatively associated with average monthly maximum temperatures (incidence rate ratio (IRR) = 1.37, 95% confidence interval (CI) = 1.05–1.78, p = 0.019 and IRR = 0.72, 95% CI = 0.58–0.91, p = 0.005, respectively). When analysed with one month lag for predictor variables, malaria test positivity rates were still significantly associated with average monthly minimum and maximum temperatures (IRR = 1.67, 95% CI = 1.28–2.19, p = 0.001 and IRR = 0.68, 95% CI = 0.54–0.85, p = 0.001, respectively). Average monthly rainfall and relative humidity with or without a one month lag was not associated with malaria test positivity rates in the adjusted models. Explopring possible associations between distribution of long-lasting insecticidal nets, (LLINs) and malaria test positivity rates showed no apparent correlation between numbers of LLINs distributed in a particular year and malaria test positivity rates.ConclusionIn Lower Moshi, the risk of being tested positive for malaria was highest for older children and male individuals. Higher minimum and lower maximum temperatures were the strongest climatic predictors for malaria test positivity rates. In areas with extensive irrigation activity as in Lower Moshi, vector abundance and thus malaria transmission may be less dependent on rainfall patterns and humidity. Mass distribution of LLINs did not have an effect in this area with already very low malaria transmission.

Trend and Pattern in Average Monthly Maximum Temperatures in Thailand from 1986 to 2015

This research aims to study the trends and patterns of average maximum temperature change data of monthly surface in Thailand during the 30 year-period from 1986 to 2015 that has already eliminated seasonal variations and autocorrelation. The data set obtained from Climatological center, Meteorological Development Division, Thailand Meteorological Department for 73 completed recording stations. The climatically regions of Thailand meteorology are divided into 5 regions: Central, Eastern, North, Northeast and South. After the process of parameter estimation using simple linear regression of average maximum temperature data that has already eliminated influences of variations, the study results showed that over the last 10 years, the northern, central, eastern, northeastern and southern regions of Thailand had trends of the average maximum surface temperature increasing by 0.164°C 0.137°C 0.127°C 0.194°C 0.068°C, respectively. For over the whole country, the average maximum surface temperature increasing by 0.141°C in the last 10 years at a significant level of 0.05.

Comparison Performance Prediction between some Artificial Neural Networks (Box Jenkins) methodology with application

This paper compares a prediction accuracy between the statistical time series method that use (Box – Jenkins) methodology, and some artificial neural networks, which differ from them through the feedback in their structure. These networks are Feed Forward Neural Network (FFNN), Elman Neural Network (ENN), and Nonlinear Autoregressive with Exogenous Input (NARX). By using a set of data, the average monthly maximum temperatures in Mosul for the years (1983– 2009), which numbered 324 observations, twelve observations were kept as Truncated samples in order to compare with the results of prediction models for the above two methods. The results of prediction with performance of neural networks with feedback is better than others, and the performance methodology of (Box - Jenkins).

Investigation into the Effects of Climatic Change on Temperature, Rainfall, and Runoff of the Doroudzan Catchment, Iran, Using the Ensemble Approach of CMIP3 Climate Models

This study investigated the effects of climatic changes on temperature, rainfall, and runoff in the Doroudzan catchment in the northeast of Fars province, Iran. Temperature and rainfall changes in three periods including 2011–2030, 2046–2065, and 2080–2099 were downscaled and studied using 15 Coupled Model Intercomparison Project, Phase 3 (CMIP3) climatic models, under three scenarios of greenhouse gas emissions A2, B1, and A1B, from the database of the LARS-WG model. The difference in the amount of changes in temperature and rainfall in these three periods and the observational amounts under the 15 models indicated the uncertainty of the changes values. To reduce this uncertainty and limit the results to the management and planning of water resources, ensemble approach was considered. For the preparation of the ensemble approach, the parameters from the files of the 15-model scenarios were averaged so that a climatic ensemble model could be obtained for each period. Then, the runoffs of the next three periods, under the second approach and three emission scenarios, were produced using the feedforwad neural network. The results indicated an increase in the average monthly maximum temperature and the minimum temperature in all three periods under the three scenarios. The results also showed a decrease in the rainfall in the early months of the year as well as an increase in the rainfall in the spring in most scenarios. Generally, the average annual rainfall in all these three periods under the climatic ensemble model, and three emission scenarios showed a reduction in the average annual rainfall in the three periods. The maximum amount of reduction was in 2080–2099 (101 mm) under the scenario B1. Besides, a reduction occurred in the average runoff of the catchment under three ensemble models and the emission scenario in all three periods, as compared to the average of the long-term observational values in most years.

Detection of changes and trends in climatic variables in Bangladesh during 1988–2017

Due to the importance of climatic variability, an assessment detecting the changes and trends has been carried out over different time series of major climatic variables from the records of meteorological stations over Bangladesh from 1988–2017. Linear regression, the Mann-Kendall test, and Sen's slope method were used to analyze the significant trends and magnitude of the variables' changes, while the Pearson and Spearman rho correlation test have been applied to correlate between the variables. The results show that the average monthly maximum temperature (Tmax) and minimum temperature (Tmin) have increased significantly by 0.35 °C/decade and 0.16 °C/decade, respectively. However, the increase in Tmax is comparatively higher than Tmin and caused significant increases in the monthly temperature range (MTR) at a higher rate in winter than in the monsoon season. The trend patterns of Tmax, Tmin, and MTR reveal that most of the regions of the country (especially the south-eastern and north-eastern) have been colder during winter and hotter during the monsoon. In contrast, the wind speed (WS) has decreased significantly all over the country and decreased by a higher rate in the north-western (NW) region (monsoon, 0.60 and annual, 0.51 kt/decade) than other regions, while the monsoonal and annual precipitation have decreased by 87.35 mm/decade and 107 mm/decade, respectively. The monsoonal Tmax and Tmin (0.47 °C/decade and 0.38 °C/decade, respectively) have increased significantly in the NW; consequently, this region has been warmed by 0.27 °C/decade. The increase in temperature and decrease in WS may cause a decrease in rainfall in the NW region. Humidity changes are not significant except in the monsoon season across the country. Precipitation, WS, and humidity are negatively correlated with the temperature variables. The declination of WS may influence the rising trend in temperature and the falling trend in precipitation and humidity, suggesting the need for further advanced study on the negative effects of climate change in Bangladesh.

Hospitalizations for heat-stress illness varies between rural and urban areas: an analysis of Illinois data, 1987\u20132014

BackgroundThe disease burden due to heat-stress illness (HSI), which can result in significant morbidity and mortality, is expected to increase as the climate continues to warm. In the United States (U.S.) much of what is known about HSI epidemiology is from analyses of urban heat waves. There is limited research addressing whether HSI hospitalization risk varies between urban and rural areas, nor is much known about additional diagnoses of patients hospitalized for HSI.MethodsHospitalizations in Illinois for HSI (ICD-9-CM codes 992.x or E900) in the months of May through September from 1987 to 2014 (n = 8667) were examined. Age-adjusted mean monthly hospitalization rates were calculated for each county using U.S. Census population data. Counties were categorized into five urban-rural strata using Rural Urban Continuum Codes (RUCC) (RUCC1, most urbanized to RUCC5, thinly populated). Average maximum monthly temperature (°C) was calculated for each county using daily data. Multi-level linear regression models were used, with county as the fixed effect and temperature as random effect, to model monthly hospitalization rates, adjusting for the percent of county population below the poverty line, percent of population that is Non-Hispanic Black, and percent of the population that is Hispanic. All analyses were stratified by county RUCC. Additional diagnoses of patients hospitalized for HSI and charges for hospitalization were summarized.ResultsHighest rates of HSI hospitalizations were seen in the most rural, thinly populated stratum (mean annual summer hospitalization rate of 1.16 hospitalizations per 100,000 population in the thinly populated strata vs. 0.45 per 100,000 in the metropolitan urban strata). A one-degree Celsius increase in maximum monthly average temperature was associated with a 0.34 increase in HSI hospitalization rate per 100,000 population in the thinly populated counties compared with 0.02 per 100,000 in highly urbanized counties. The most common additional diagnoses of patients hospitalized with HSI were dehydration, electrolyte abnormalities, and acute renal disorders. Total and mean hospital charges for HSI cases were $167.7 million and $20,500 (in 2014 US dollars).ConclusionElevated temperatures appear to have different impacts on HSI hospitalization rates as function of urbanization. The most rural and the most urbanized counties of Illinois had the largest increases in monthly hospitalization rates for HSI per unit increase in the average monthly maximum temperature. This suggests that vulnerability of communities to heat is complex and strategies to reduce HSI may need to be tailored to the degree of urbanization of a county.

Predictors of mammal species richness in KwaZulu-Natal, South Africa

The management of multi-functional landscapes warrants better knowledge of environment-richness associations at varying disturbance levels and habitat gradients. Intensive land-use patterns for agricultural purposes lead to fragmentation of natural habitat resulting in biodiversity loss that can be measured using landscape metrics to assess mammalian richness. Since carnivores and herbivores are likely to show different responses to disturbance, we calculated carnivore, non-carnivore, and total mammal species richness from camera surveys using a first order Jackknife Estimator. Richness was compared along a habitat gradient comprising coastal forest, Acacia thicket, and highland in KwaZulu-Natal, South Africa. We used standardized OLS regression models to identify climatic and disturbance variables, and landscape metrics as predictors of species richness. The estimated total and non-carnivore species richness were highest in coastal forest, while carnivore species richness was highest in highland followed by coastal forest and Acacia thicket. Average monthly maximum temperature was a significant predictor of all richness groups, and precipitation of the wettest month and isothermality determined total and non-carnivore species richness, respectively. These climatic variables possibly limit species distribution because of physiological tolerance of the species. Total mammal richness was determined by mean shape (+) and habitat division (−) while diversity (+) and patch richness (−) explained carnivore species richness. Mean shape index (+) influenced non-carnivore richness. However, habitat division and patch richness negatively influenced total mammal richness. Though habitat patch size and contiguity had a weak positive prediction, these metrics demonstrated the importance of habitat connectivity for maintaining mammal richness. The identification of these climatic and landscape patterns is important to facilitate future landscape management for mammal conservation in forest-mosaics.

Habitat Use by Nile Crocodiles in Ndumo Game Reserve, South Africa: A Naturally Patchy Environment

Distribution patterns reflect the interactions of organisms with their environment. We discuss the distribution patterns of Nile Crocodiles (Crocodylus niloticus) in a naturally patchy floodplain environment with the use of aerial survey data collected over the last 40 yr. Although only 10,000 ha in size, Ndumo Game Reserve (NGR) supports one of the largest wild crocodile populations in South Africa, largely because of landscape complementation with neighboring Mozambique. Distributions within the NGR were influenced by landscape physiognomy and composition as well as connectivity and corridor quality. To quantify the effects of environmental conditions on crocodile distribution, we conducted 40 diurnal counts at Lake Nyamithi in the NGR between 2009 and 2012. Average monthly maximum temperature had an effect on the number of crocodiles in Lake Nyamithi; however, environmental variables influenced different size classes of Nile Crocodiles to a varying extent. Anthropogenic disturbances influenced the functionality of the floodplain landscape negatively, with impacts on habitat use and connectivity. It is considered essential that a cross-border conservation program be initiated in order to conserve the current population of Nile Crocodiles in the greater NGR area.

Understanding Climate Change on the California Coast: Accounting for Extreme Daily Events among Long-Term Trends

The majority of weather station records indicate that surface air temperatures have been warming in California between 1950 and 2005. Temperature data from the mid-1990s to the present were analyzed for stations on California Central Coast near Big Sur (Monterey County) to better understand potential for climate change in this biologically unique region. Results showed that daily temperatures in both the winter and summer seasons have cooled the Big Sur coast, particularly after 2003. A current hypothesis is that observed coastal California cooling derives from greenhouse gas-induced regional warming of the inland Central Valley and Sierra Nevada foothill areas, resulting in stronger sustained on-shore sea-breeze flow. Closer examination of daily temperature records at a station location near the Big Sur coast revealed that, even as average monthly maximum temperatures (Tmax) have decreased gradually, the number of extreme warm summer days (Tmax > 37 °C) has also increased by several fold in frequency. Overall patterns in the station records since the mid-1990s indicated that diurnal temperature ranges are widening on the Big Sur coast, with markedly cooler nighttime temperatures (frequently in the wet winter season) followed by slightly higher-than-average daytime temperatures, especially during the warm, dry summer season.

Predicting the distribution of the air pollution sensitive lichen species Usnea hirta

AbstractUsnea hirta, an important member of the lichen family Parmeliaceae, has long been used as a bio-monitor of air pollution, particularly of sulphur dioxide in North America. Although U. hirta has a wide geographical distribution, it is important to be able to identify accurately the optimal habitat conditions for air pollution-sensitive species, thus making it possible to more effectively and efficiently establish air quality bio-monitoring stations. We modelled the distribution of U. hirta as a function of nine variables, five macroclimatic variables: average monthly precipitation, average monthly minimum temperature, average monthly maximum temperature, solar radiation, and integrated moisture index, and four topographic variables: elevation, slope, aspect, and land forms and uses for the White River National Forest, Colorado. The response variable was developed based on the presence or absence of U. hirta at each of 72 bio-monitoring baseline sites established in selected portions of four intermountain area states. Our model was developed using Non-Parametric Multiplicative Regression (NPMR) analysis, a modelling approach that analyzes environmental gradients, or predictor variables, against known locations for individuals of the model species. Finally, we evaluated our model on the basis of log β values and overall improvement over a naïve model and the Monte Carlo Permutation Test with 1000 randomized runs. The best model for U. hirta included four variables – solar radiation, average monthly precipitation, and average monthly minimum and maximum temperatures (log β=3·68). Among these four variables, average monthly maximum temperature was the most influential predictor (sensitivity=0·71) for the distribution of U. hirta. The occurrence rate for U. hirta, based on field validation, was 45·5%, 65·4%, and 70·4% for low, medium, and high probability areas, respectively. This study showed that our model was successful in predicting the distribution of U. hirta in the White River National Forest. Based on these results, the north-eastern and western portions of the forest appear to offer the most favourable conditions for the installation of future air quality bio-monitoring baseline sites.

P81 The changing incidence of paediatric empyema in NE England 2006–2010

Introduction and objectivesThe incidence of empyema has been increasing in children. We investigated trends in the incidence of empyema cases over the time period 2006–2010 in relation to temperature, rainfall...

Ridge regression technique to determine the environmental influences on tef (Eragrostis tef) grain yield

Tef grain yield is dependent on a number of component characters such as plant height and panicle length. These characters and consequently yield are governed by a large number of factors including environmental factors. The objective of this paper was to determine the environmental influences on tef grain yield. The effects of eleven environmental variables on tef yield were studied using least squares and ridge regression analyses. The results revealed that the least squares estimates of regression coefficients for seven environmental variables did not give a correct indication of the influence of the variables on tef yield but that the estimates from ridge regression were stable. In addition, the ridge regression model has a lower mean square error. Tef grain yield is positively correlated to rainfall, average monthly minimum temperatures, soils dominated by silt with adequate quantities of available nitrogen while it is negatively influenced by average monthly maximum temperatures and clay soils. The results obtained from this study are in accordance with previous literature about the effects of environmental factors on yield of tef cultivars.

Ethnicity and glaucoma: Higher environmental temperatures may accelerate the onset, and increase the prevalence, of primary open-angle glaucoma

Publications on the age-related prevalence of primary open-angle glaucoma (POAG) show that African and African-descended patients and others living in warm climates present a higher prevalence of POAG than do those in temperate zones. The data have been analysed in order to discover whether temperature plays a role. The relation between AOO, the youngest age diagnosed with POAG, and prevalence has also been examined. Prevalences at the age of 70 years P(70) were calculated from logarithmic plots, and logP(70), plotted as a function of the average monthly maximum temperature T degrees C, and tested with the LINEST programme. AOO declined, but logP(70) rose, with T degrees C. The data are consistent with the view that high temperatures accelerate the onset, and increase the prevalence, of POAG. A consideration of evolution suggests that the reduced Caucasian prevalence may result from originally African tribes migrating to locations with more temperate climates: this would have caused a constriction of superficial capillaries to preserve body heat; hence the aqueous outflow channels might dilate, and reduce a predisposition for POAG. Suggestions are made for testing the hypothesis.

Effects of Clear‐Cutting on Stream Temperature

The principal source of energy for warming streams is the sun. The amount of sunlight reaching the stream may be increased after clear‐cut logging. Average monthly maximum temperatures increased by 14°F and annual maximum temperatures increased from 57° to 85°F one year after clear‐cut logging on a small watershed in Oregon's coast range. In a nearby watershed where strips of brush and trees separated logging units from the stream, no changes in temperature were observed that could be attributed to clear‐ cutting.