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Volume 3, Issue 1 (4-2016)
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=
Here 
is the outputs of ANN models (downscaled data) and 
is 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:
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.
=(i,j,n)=T(i,j,n)-T(I,j)
The departure from the climatic mean is given by
=
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).
Volume 7, Issue 1 (5-2020)
Volume 10, Issue 2 (9-2023)
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
Volume 11, Issue 1 (5-2024)
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.
Volume 11, Issue 4 (2-2025)
Subsidence as an environmental hazard is caused by various natural and human factors. The drastic changes in land use, the increase in the number of deep wells, and the effects of the subsidence phenomenon in Dehgolan plain show the need to investigate these influencing factors. In such a situation, adequate understanding of the degree of vulnerability and investigation of the influencing factors in that process provides the opportunity for planning and environmental preparation of the space in order to reduce vulnerability. In this research, first, the NDVI index of the plain was investigated with the help of 15 Sentinel-2 and Landsat 8 satellite images, and the best date was selected for the Sentinel-1 images. In this way, 8 Sentinel-1 satellite images were analyzed over a period of 8 years (2014-2021) and all the images were analyzed and processed in eight stages with the help of SNAP software. 3 Landsat 7 and 8 satellite images were used to investigate land use changes (2000-2021).By applying atmospheric and radiometric corrections and finally performing the supervised classification method using Arc GIS software, land use was extracted and its changes were checked. The interferometric results showed that the Dehgolan plain suffered a total of 480 mm of subsidence. So that 60 mm of subsidence has occurred in this plain every year. In the end, with the preparation of the map of land use changes, the classes of irrigated agricultural and residential lands increased by 6.98, 1.47 percent, and the uses of pasture, forest and rainfed lands were faced with a sharp decrease, so that irrigated lands increased by 8477 and residential by 672 hectares. Is. The results obtained from the analysis of the relationship between water use and subsidence showed that rapid subsidence occurs mainly in water and urban land use classes. This is a consequence of increasing water extraction for agriculture and drinking. Usually, the pattern of land use conversion with more human influences has increased the rate of subsidence.
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