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Gelaleh Molodi, Asadolah Khorani, Abbas Moradi,
Volume 3, Issue 1 (4-2016)
Abstract

Climate change is one of the most significant threats facing the world today. One of the most important consequences of climate change is increasing frequency of climate hazards, mainly heat waves. This phenomena has a robust impacts on human and other ecosystems. The aim of this study is investigating changes of heat waves in historical (1980-2014) and projected (2040-2074) data in northern cost of Persian Gulf.

The focus here is on Mean daily maximum temperature and Fujibe index to extract heat waves. For this purpose 6 weather stations locating in north coast of Persian Gulf, Iran, are used (table 1).

Table1: weather stations

Station

Latitude

Longitude

Elevation(m)

Abadan

30° 22' N

48° 20' E

6.6

Boushehr

28° 55' N

50° 55' E

9

Bandarabbas

27° 15' N

56° 15' E

9.8

Bandarlengeh

26° 35' N

54° 58' E

22.7

Kish

26° 54' N

53° 54' E

30

  In addition, 4 model ensemble outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to project future occurrence and severity of heat waves (2040 to 2070), under Representative Concentration Pathways 8.5 (RCP8.5), adopted by the Intergovernmental Panel on Climate Change for its Fifth Assessment Report (AR5) (table 2).

Table2: List of the AR5 CMIP5 Used Models

Model

Modeling Cener

Country

CanESM2

Canadian Earth System Model

Canada

MPI-ESM-MR

Max-Planck-Institut für Meteorologie

Germany

CSIRO-Mk3-6-0

Commonwealth Scientific and Industrial Research Organization

Australia

CMCC-CESM        

CMCC Carbon Earth System Model

Italy

The output of models is downscaled using artificial neural network method (ANN). A feed-forward network of multi-layer perceptron with an input layer, a hidden layer and an output layer is used for this purpose. 73 percent (1980 – 2000) of the data is used for training and 27 percent (2000-2005) for testing ANN models. Root Mean Square Error (RMSE) is used as an indicator of the accuracy of Models.

RMSE=AWT IMAGE

Here  AWT IMAGE is the outputs of ANN models (downscaled data) and AWT IMAGEis the observation data.

Fujibe et all (2007) used an index based on Normalized Thermal Deviation (NTD) for extracting long-term changes of temperature extremes and day to day variability using following equations:

AWT IMAGE

Where N is the number of days in the summation except missing values. Then nine-day running average was applied three times in order to filter out day-to-day irregularities.

AWT IMAGE=(i,j,n)=T(i,j,n)-T(I,j)

The departure from the climatic mean is given by

AWT IMAGE=AWT IMAGE

AWT IMAGE

If NTD >2 and at least lasts for 2 days it determine as a heat wave.

Results

Table 3 shows the results of downscaling selected GCM models.

nodes

RMSE

Average RMSE

Sigmoid function

Linear function

Abadan

Bushehr

Bandarabbas

Bandar-e-Lengeh

Kish

CanESM2

5

1

9.6

6.1

4.85

4.7

4.5

5.97

MPI-ESM-MR

5

1

9.3

7.1

3.9

5

4.3

5.9

CSIRO-MK3-6-0

15

1

8.8

5.6

3.6

3.4

3.6

5

CMCC-CESM

10

1

9.2

5.8

3.9

4.7

3.9

5.5

Table 4 compares the frequency of heat waves for GCMs and historical data.

CanESM2

MPI-ESM-MR

CSIRO-Mk3-6-0

CMCC-CESM

Historical data

Abadan

434

401

448

387

430

Bushehr

376

423

420

406

407

Bandarabbas

441

405

457

382

410

Bandar-e-Lengeh

380

414

388

401

400

Kish

421

442

415

442

399

For historical data, heat waves are more frequent in Abadan station than other stations. There is an increasing trend in the occurrence of heat waves in historical data and monthly frequency of heat waves show the highest amounts for summer.

For both historical and future data 2 days listening heat waves are more frequent.

Table 5 shows seasonal changes of heat waves for historical data and GCMs.

season

The ratio of heat waves from total historical data (percent)

The ratio of heat waves from total projected data (percent)

Abadan

Spring

30.43

24.02

Summer

29.19

27.87

Autumn

17.39

22.61

Winter

22.98

25.48

Bushehr

Spring

21.42

24.23

Summer

25

26.21

Autumn

28.57

24.82

Winter

24

25.32

Bandarabbas

Spring

21.73

24.7

Summer

26.81

27.01

Autumn

25.81

25.17

Winter

24.1

24.63

Bandar-e-Lengeh

Spring

23.55

23.74

Summer

23.33

29.82

Autumn

23.74

25.81

Winter

25.17

20.8

Kish

Spring

24.27

24.8

Summer

25.53      

28.32

Autumn

23.35

25.21

Winter

23.1

23.8

In recent years the frequency of heat waves is increasing in all studied stations. Coincide with Russia and Europe, the highest amounts of heat waves is occurred in 2010 in northern coast of Persian Gulf and this is adopted Esmaeilnezhad et all (2013), Gavidel (2015) and Azizi (2011).


Aydin Moradi, Somaye Emadodin, Saleh Arekhi, Khalil Rezaei,
Volume 7, Issue 1 (5-2020)
Abstract

 
 
Dr Masoud Moradi, Dr Mohammad Hosein Gholizadeh, Mr Meysam Rahmani,
Volume 10, Issue 2 (9-2023)
Abstract

Investigation of the Temporal and Spatial Variation of Maximum Soil Temperature in Iran

Extended Abstract
Introduction
The study of soil temperature in different depths of soil is important in climatology, hydrology, agrometeorology and water resource management. Different depths has a different temporal and spatial soil temperature variation. It represents the regional ground temperature regime. Furthermore, due to its rapid response to environmental changes, soil temperature is one of the most important indicators of climate change. The increase in soil temperature because of global warming can promotes disasters such as drought by increasing the water demand of agricultural products during the plant growth period. The increase in soil temperature also have a various consequences, include increasing evaporation from the soil surface, soil salinity in susceptible areas, which can lead to a decrease in soil yield and failure in plant growth. Therefore, knowledge of soil temperature changes in different environments is very important in climate studies. The aim of the current research is to analyze the spatial and temporal variations of soil temperature at different depths from five to 30cm of the ground and to investigate the existence of any kind of increasing or decreasing trend at different climates of Iran.
Methodology
Hourly soil temperature data (depths of 5, 10, 20 and 30 cm) were used in this research for the period of 1998-2017. The soil depth temperature is measured three times a day at 6:30 am, 12:30 pm, and 6:30 pm local time (3, 9, and 3 p.m. UTC). These data have been received for 150 synoptic stations of Iran on a daily basis from the Iran Meteorological Organization (IRIMO). IRIMO monitored the quality of soil temperature for data entry, data recording, and data reformatting errors. Data availability, discrepancies, errors, and outliers were identified during the second stage.
At the first step, temporal coefficient of variation were calculated for available soil temperature time series from five to 30 cm depths of each station. For this purpose, the average of three daily measurements of soil temperature was calculated and then the temporal coefficient of variation was obtained. In the next step, trend analysis of soil temperature has been investigated using the non-parametric Mann-Kendal test. The trend slope was calculated using Sen’s slope for each station in seasonal time scale. Trend analysis has been done for all three observations of the day.
Results and Discussion
The studied stations show significant spatial patterns in the temporal variability of soil temperature. In all four investigated depths, from five to 30 cm, the northwest parts of Iran, and some parts of Zagros and Alborz mountain ranges have high temporal coefficient of variation. In contrast, the stations located on the southern coasts and southern islands had the lowest temporal variability. In warm and cold seasons (summer and late autumn to mid-winter), the spatial changes of soil temperature at different depths are lower than spring and early autumn. However, in the warm period of the year, the soil temperature experiences lower spatial variations at different depths. Spring and autumn seasons, as the transition period from cold to warm and warm to cold seasons, show the most spatial temperature variations in Iran. Detected trends do not have significant differences among the three observations of the day. Soil temperature Trend analysis at different depths showed positive values for two seasons of summer and winter over most of the stations throughout Iran. Extreme trends are more frequent in the summertime of Zagros and Alborz mountainous regions, while in the winter season the stations located at the southern latitudes of Iran have experienced the most positive trends. In the summer season, higher trends with 99% confidence are more frequent in the mountainous areas. These positive trends in soil temperature have occurred in all studied depths. The negative trend at different depths is a distinct feature of the autumn season, which is significantly more prevalent than other seasons throughout Iran. The analysis of soil temperature trends in different depths shows that values above 1 degree Celsius often occur in 5 to 20 cm deeps. The increasing trend of soil temperature in winter shows a greater spatial expansion, which is indicate increasing annual minimum soil temperatures and the increasing trend of Iran's soil temperature.
Keywords: Soil Temperature, Spatiotemporal Variations, Man-Kendal Test, Sen's Slope, Iran

 
Mrs Mozhgan Shahriyari, Dr Mostafa Karampoor, Dr Hoshang Ghaemi, Dr Dariush Yarahmadi, Dr Mohammad Moradi,
Volume 11, Issue 1 (5-2024)
Abstract

Flash floods are one of the most dangerous natural events and often cause loss of life and damage to infrastructure and the environment. This research investigated the occurrence of the most intense continuous monthly floods (October-March) from 1989 to 2021. Precipitation data from 115 synoptic stations were selected. Then, the total rainfall of 1 to 9 days was sorted according to intensity. Using Minitab statistical software and the Andersen-Darling index, heavy rains were extracted based on the 95th percentile. Then, based on the criteria of the highest and lowest number of rainy days, the highest and lowest accumulated rainfall, the wettest and driest months were determined. Considering the three criteria of intensity, continuity, and rainfall coverage, the strongest storms in the wettest months were selected. The data used for synoptic analysis include the average sea level pressure data, the height and vertical component of the wind at 500 hPa, the wind and humidity field specific to the pressure levels 925, 850, and 700 hPa, and the horizontal moisture flux values specific to the pressure level 925, 850 and 700 hPa. The probability of the occurrence of atmospheric rivers was identified by the moisture flux extracted from the specific, meridional, and meridional wind components. The results showed that the storms of October 27-31, 2015, November 5-7, 1994, December 12-16, 1991, January 11-15, 2004, February 3-9, 1993, and March 13-15, 1996 were the strongest in the wettest months. During the storms of October, November, February, and March, moisture has been transported from the southwest of the Red Sea by atmospheric rivers to the western, southwestern, southern, and southeastern regions of Iran.
 

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