The Impact of Future Climate Change and Human Activities on Hydro-climatological Drought, Analysis and Projections: Using CMIP5 Climate Model Simulations

2020

Saf. Extreme Environ.

Self Cite

In this study, in addition to considering the two dominant climatic domains during extreme condition in the study area, temperature and humidity, and determining the correlation and correlation of these two factors with the prevalence of rotavirus gastroenteritis, a statistical model was proposed to predict the prevalence of the disease. Although this research, like many earlier studies, has been based on statistical data for a period of three years, according to its primary objectives, the short-term risk of the disease has been zoned using GIS software, and this point is new. The distinction between the present study and previous studies is considered. Previous studies did not consider the relationship between climate change and gastroenteritis GE. So, the purpose of this study was to investigate the correlation between the number of patients admitted in the hospital with symptoms of gastroenteritis GE and the monthly climatic variables of temperature and humidity of rotavirus, maximum, mean during extreme condition, at least in the city of Ahvaz, in three levels and its suburbs. The results showed a high prevalence of rotavirus infection in children in Ahvaz at low temperature. The maximum rotavirus activity was determined at 13°C. Also, the highest number of patients with symptoms of rotavirus gastroenteritis was observed in autumn and early winter. It is suggested that the results of this study should be considered in determining the timing of vaccination during extreme condition.

Section: Introductionmentioning

confidence: 99%

Potential impact of climate change on temperature and humidity related human health effects during extreme condition

2020

Saf. Extreme Environ.

Self Cite

“…The output of the general circulation models can be divided into two types: dynamic and scalable. Heat waves are the most important atmospheric disasters, and the annual mortality rate from climatic hazards shows that thermal waves cause the highest mortality rates compared to other climatic events [15][16][17] . Reducing the consequences of heat waves depends on identifying thermal waves, their predictions, discovering ways to reduce their effects on general health and identifying areas that are vulnerable to heat waves [18][19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

Investigation of the effect of climate change on heat waves

2020

Resour Environ Inf Eng

Self Cite

The purpose of this research is to identify the heat waves of the South Sea of Iran and compare the conditions in the present and future. To reach this goal, the average daily temperature of 35 years has been used. Also, in order to predict future heat waves, the maximum temperature data of four models of the CMIP5 model series, according to the RCP 8.5 scenario, has been used for the period 2040-2074. In order to reverse the output of the climatic models, artificial neural networks were used to identify the thermal waves, and the Fumiaki index was used to determine the thermal waves. Using the programming in MATLAB software, the days when their temperature exceeded 2 standard deviations as a thermal wave were identified. The results of the research show that the short-term heat waves are more likely to occur. Heat waves in the base period have a significant but poorly developed trend, so that the frequency has increased in recent years. In the period from 2040 to 2074, the frequency of thermal waves has a significant decreasing trend, but usually with low coefficients. However, for some stations from 2040 to 2074, the frequency of predicted heat waves increased.

“…Analysis of groundwater levels (GWL) within hydrological and hydraulic studies, particularly in developing countries where overexploitation is a problem, is crucial. This will also lead to effective and integrated management and planning for groundwater resources in the future (Javadinejad et al, 2019a).…”

Section: Introductionmentioning

confidence: 99%

Modelling groundwater level fluctuation in an Indian coastal aquifer

2020

WSA

Estimating groundwater level (GWL) fluctuations is a vital requirement in hydrology and hydraulic engineering, and is commonly addressed through artificial intelligence (AI) models. The purpose of this research was to estimate groundwater levels using new modelling methods. The implementation of two separate soft computing techniques, a multilayer perceptron neural network (MLPNN) and an M5 model tree (M5-MT), was examined. The models are used in the estimation of monthly GWLs observed in a shallow unconfined coastal aquifer. Data for the water level were collected from observation wells located near Ganjimatta, India, and used to estimate GWL fluctuation. To do this, two scenarios were provided to achieve optimal input variables for modelling the GWL at the present time. The input parameters applied for developing the proposed models were a monthly time-series of summed rainfall, the mean temperature (within its lag times that have an effect on groundwater), and historical GWL observations throughout the period 1996–2006. The efficiency of each proposed model for Ganjimatt was investigated in stages of trial and error. A performance evaluation showed that the M5-MT outperformed the MLPNN model in estimating the GWL in the aquifer case study. Based on the M5-MT approach, the development of this model gives acceptable results for the Indian coastal aquifers. It is recommended that water managers and decision makers apply these new methods to monitor groundwater conditions and inform future planning.