Investigation of the effect of climate change on heat waves

Abstract

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.

Evaluation of hydro-meteorological drought indices for characterizing historical and future droughts and their impact on groundwater

“…Also, the minimum and maximum discharges, according to the lowest and greatest precipitation values, respectively, were forecasted in summer and in winter. Results indicated a decreasing runoff trend which was in concurrence with the outcomes of Khosravi et al, [42] Rahimi et al [43] in some areas of Iran, however, in comparison with the results of Han et al [17] in China, and Kitoh [48] in Turkey they showed an increasing trend of runoff. It appears human actions can effect the decrease of runoff more than hydro-climatic elements.…”

“…This outcome was in concurrence with the outcomes from other parts of Iran. [42][43][44] The main reason for this drought was identified to be the population increase and unsuitable spatial population dispersal, disorganized agricultural seg-…”

How groundwater level can predict under the effect of climate change by using artificial neural networks of NARX

“…In this study, modelling using neural network was used indirectly in a long-term prediction structure. This trend is also seen in the [27][28][29][30][31][32].…”

Building socio-hydrological resilience “improving capacity for building a socio hydrological system resilience”

“…Two of the most important extreme events are drought and flood. Drought can caused by climate change, land use change, over irrigation, or water over abstraction from surface and groundwater resources (Javadinejad et al 2020e). Therefore, analyzing both drought dissemination and responses and the influences of climatic and social parameters in shaping the processes for resilience is necessary (Javadinejad et al 2019h).…”