Climatic Regions Of Iran Research Articles

Analysis of factors affecting evapotranspiration zoning.

Reference evapotranspiration (ETo) has a significant role in water resource planning and management as well as analysis of crop production and other agricultural tasks. Methods for estimating ETo may require diurnal/monthly assessments to perceive the consequences of climatic changes on local regions. The spatial and temporal patterns of ETo were analyzed in the current work using data from 340 weather stations in Iran. The entropy theory was used to assess the uncertainty of the utilized variables and the modified Kendall test was applied for temporal trend analysis. The interpolation (e.g., kriging) and ordinary least squares (OLS) methods were used for spatio-temporal ETo classification/modeling. The spatial analysis demonstrated that the OLS method with a good fit measure (R2 = 0.985) successfully simulated the spatial relationships of ETo with climatic parameters. After examining error indices, the cokriging method with an exponential variogram was introduced as the best method of seasonal and annual ETo classification in Iran. Spatially and temporally calculated ETo patterns using modified Hargreaves (MHGR) and MODIS methods closely resembled the standard FAO Penman-Monteith (FPM-56) method, all indicating a gradual increase in ETo. MHGR and MODIS methods serve as suitable alternatives for estimating ETo in various climatic regions of Iran, provided data availability.

Flood process types and runoff coefficient variability in climatic regions of Iran

ABSTRACT This study analysed the spatiotemporal variability of runoff coefficients (RCs) in four climatic regions based on 18 468 events recorded in 963 Iranian catchments. Five flood process types were identified using a classification scheme. The results show that winter and spring have higher mean RCs of 0.46 and 0.42, respectively, confirming the role of snowmelt and heavy precipitation in flood runoff generation in these seasons. Event saturation conditions (i.e. event rainfall depth) had a stronger impact on RC variability than pre-event saturation conditions (i.e. antecedent rainfall depth). Flood occurrence varies significantly by season and region, with short rains being the most common type of flooding. Rain-on-snow floods, snowmelt, and long-rain floods had higher RCs than other types, and significant differences in RCs were observed across the four climate regions using the non-parametric Kolmogorov-Smirnov test. The median flood time scale is between 1 and 20 days in all catchments.

Assessment of morpho-physiological and biochemical profiles of Stevia rebaudiana Bertoni in different climatic regions of Iran

Assessment of morpho-physiological and biochemical profiles of Stevia rebaudiana Bertoni in different climatic regions of Iran

Techno–Econo–Enviro Energy Analysis, Ranking and Optimization of Various Building-Integrated Photovoltaic (BIPV) Types in Different Climatic Regions of Iran

Iran is one of the most energy-consuming countries, especially in the construction sector, and more than 40% of its energy consumption is in the construction sector. Therefore, considering the very high potential of Iran in the field of solar energy, the need to pay attention to providing part of the energy required by buildings by solar energy seems necessary. The study of the effect of climate on the performance of a BIPV has not been done in Iran so far. Also, the use of ranking methods using the weighting of parameters affecting the performance of BIPV has not been done so far. The purpose of this study is to investigate the power supply of BIPV connected to the grid in the eight climates of Iran. Technical–economic–environmental energy analyses were performed by HOMER 2.81 software. In order to study different types of BIPV, four angles of 0°, 30°, 60°, and 90° were considered for the installation of solar cells. The effective output parameters of HOMER software were weighted by the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on the opinion of experts, and it was observed that payback time (year) has the highest weight among the studied criteria. Then, different cities were ranked using the evaluation based on distance from the average solution (EDAS) method. The results showed that Jask is the most suitable and Ramsar is the most unsuitable city. Also, the results of the EDAS method were confirmed by Additive Ratio Assessment (ARAS), Weighted Aggregates Sum Product Assessment (WASPAS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods.

Impacts of climate change on occupational health indicators in the three climatic regions of Iran

ABSTRACT Climate change has increased the exposure risk of workers to occupational health risk factors and diseases. This study aims to investigate the impacts of climate change on the occupational health indicators at the workplaces in Iran. This study was conducted during 2021 in three climatic regions of Iran. Required data was collected from Health Deputies of Medical Universities and Iran Meteorological Organization. Stepwise linear regression model used for data analysis and predictions were done based on three scenarios of SSP1–2.6, SSP3–7.0, and SSP5–8.5 during the period of 2021–2100. This indicated 2.6 and 2.9 times higher percentage of workers who were exposed to heat stress and Ultra Violet (UV) radiation, respectively, in the provinces understudy. This study suggests a holistic approach to address potential impacts of climate change on workers’ health and safety that would benefit in making decisions on climate-related planning and developing the adaptation strategies at workplaces.

Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to the presence of model hyperparameters. Conventional approaches rely on manual adjustment, which is time consuming and often unsatisfying. Therefore, the goal of this study has been to optimize the parameters of the support vector machine (SVM) algorithm for the generation of land use/cover maps from Sentinel-2 satellite imagery in selected humid and arid (three study sites each) climatic regions of Iran. For supervised SVM classification, we optimized two important parameters (gamma in kernel function and penalty parameter) of the LU/LC classification. Using the radial basis function (RBF) of the SVM classification method, we examined seven values for both parameters ranging from 0.001 to 1000. For both climate types, the penalty parameters (PP) showed a direct relationship with overall accuracy (OA). Statistical results confirmed that in humid study regions, LU/LC maps produced with a penalty parameter >100 were more accurate. However, for regions with arid climates, LU/LC maps with a penalty parameter >0.1 were more accurate. Mapping accuracy for both climate types was sensitive to the penalty parameter. In contrast, variations of the gamma values in the kernel function had no effect on the accuracy of the LU/LC maps in either of the climate zones. These new findings on SVM image classification are directly applicable to LU/LC for planning and environmental and natural resource management.

Satellite-based monitoring of meteorological drought over different regions of Iran: application of the CHIRPS precipitation product.

In the present study, the spatiotemporal evaluation of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product is performed in capturing meteorological drought over different climatic regions of Iran. The performance of the product as a high spatial resolution dataset in monitoring drought is evaluated against the 68 meteorological stations from short to long scale (i.e., SPI1, SPI3, SPI6, SPI9, and SPI12) in the period of 1987 to 2017. Besides, the capability of the CHIRPS in detecting drought events is assessed in different drought classes. The results suggest that the climate type, the time scale, and the drought class affect the quality of the CHIRPS performance. The CHIRPS offers the best performance in the detection of all drought events with SPI < - 1 over the SPI1 (0.69 < POD < 0.85). However, the product provides the worst performance for SPI12 (0.50 < POD < 0.70). At the country level, the highest agreement between the CHIRPS- and observation data-based SPI is found over the SPI6 (CC = 0.56), while the lowest is observed over the SPI12 (CC = 0.47). Based on the temporal evaluation, the G6 (0.18 < CC < 0.44, 1.06 < RMSE < 1.28) and G8 (0.17 < CC < 0.43, 1.06 < RMSE < 1.29) regions located in the southern coast of the Caspian Sea have an inadequate performance. However, the southern parts (G4 region) (0.38 < CC < 0.65, 0.83 < RMSE < 1.27) and the northwestern area (G3 region) (0.53 < CC < 0.62, 0.87 < RMSE < 0.97) of the country offer the best performance. The spatial evaluation describes the high accuracy (CC > 0.7, RMSE < 0.5) in some regions, including the western parts of G1, the northern area of G3, and the southern parts of G4. The research findings provided an important opportunity to advance the understanding of drought monitoring over the different climatic regions based on the high-resolution satellite precipitation products.

Physical properties of straw bale and its effect on building energy conservation and carbon emissions in different climatic regions of Iran

Straw has been used as a building material for many centuries, and due to its outstanding physical properties such as great thermal and acoustical insulation, energy efficiency and low embodied carbon emission, it has become more popular within the last decade. In this study, first the physical properties of straw bale such as apparent density, thermal conductivity, short-term water absorption and compressive strength are measured through laboratory tests. Based on experimental data, Inventory of Carbon & Energy (ICE) database and using EnergyPlus software, the energy performance and carbon emissions of three different types of straw bale buildings in four climatic regions of Iran are evaluated, and the results are compared to that of a conventional building in the same climates. The results show that using straw bale as a construction material, the energy consumption of buildings will reduce up to about 83.12%. Additionally, it was observed that the straw bale buildings, can decrease embodied carbon up to 76% compared to the conventional buildings.

Recent changes in physical properties of the land surface and their effects on dust events in different climatic regions of Iran

The physical characteristics of the Earth’s surface play an important role in controlling the velocity of the wind erosion threshold. Therefore, to reduce the risks of dust storms, it is necessary to determine the impact of changes in this characteristic on wind erosion events in different regions. In this regard, this study was carried out to examine the effects of soil moisture (SM), land surface temperature (LST), and vegetation cover (VC) on wind erosion phenomenon using a combination of satellite data and terrestrial observations from 2010 to 2018. In this work, the dust storm index (DSI) was selected to monitor the wind erosion across the country. The multivariate regression method was utilized to explore the effect of alterations in these parameters on changes in soil erosion at both local and regional scales in Iran. In the local scale, regression coefficients between LST-DSI, SM-DSI, and VC-DSI were estimated in the range of 0.002 to 0.77, −0.029 to −0.51, and − 0.001 to −0.82, respectively. On the regional scale, alterations in the LST and EVI could show about 40% of changes in the wind erosion activity over arid regions of Iran (R2 = 0.43; P value β2LST = 0.15). In semi-arid regions, 36% of changes in DSI was due to alterations in LST and SM (R2 = 0.36; P value 0.05). These results can be used by planners and managers to develop programs to combat wind erosion and reduce the risks posed by dust storms in arid and semi-arid regions of Iran.

Improving the accuracy of global precipitation measurement integrated multi-satellite retrievals (GPM IMERG) using atmosphere precipitable water and altitude in climatic regions of Iran

ABSTRACT The global precipitation measurement integrated multi-satellite retrievals (GPM IMERG) provide a new opportunity to estimate precipitation remotely. However, the accuracy of estimations varies in different regions. Therefore, the aim of this study is to improve the IMERG precipitation estimations using two influencing factors including total precipitable water (TPW) and altitude data. The investigations were performed in six rainfall climatic regions of Iran using daily and six-hourly IMERG estimations in years 2015 to 2017. TPW was extracted from a locally developed algorithm for the moderate resolution imaging spectroradiometer (MODIS) measurements. Different models named as M1 to M4 were investigated by considering combinations of the linear and quadratic forms of the IMERG precipitation, TPW, and altitude. The results were evaluated by root mean square error (RMSE), correlation coefficient (r), and Nash-Sutcliffe (NSc). The results showed that the M1 represents the best performance among the proposed models, which uses linear relationship of the IMERG estimations and TPW along with the constant value. The mean values of r, RMSE, and NSc criteria for daily precipitation estimations of IMERG were acquired as 0.46, 5.88, and −2.77 mm, respectively. Those values were improved by M1 to 0.52, 0.18, and 0.27 mm, respectively. Moreover, the evaluation results of IMERG six-hourly precipitation estimations showed r, RMSE, and NSc of 0.65, 11.26, and −33.27 mm. The values of these criteria were improved by M1 to 0.65, 0.07, and 0.32 mm. In general, the results proved the ability of TPW to improve IMERG precipitation estimations in the study area.

Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison

This study analyzes the performance of precipitation estimates from historical runs of the CMIP6 (Climate Model Intercomparison Project Phase 6) over the climatic regions of Iran. In order to capture the spatio-temporal precipitation patterns, using a set of evaluation metrics, 12 GCMs (General Circulation Models) are compared to the observation data from the GPCC (Global Precipitation Climatology Centre) at the common 1° spatial resolution for 1950–2014. A comprehensive assessment is performed at different temporal scales including monthly, seasonal and annual. Results indicate that the reliability of precipitation estimates varies significantly across space and time. The CMIP6 models best reproduce the climatological features of precipitation and its spatio-temporal changes over the arid and hyper arid areas of the country. The outputs of the models exhibit less systematic biases in the arid zone. In addition, a strong underestimation is detected throughout the rainiest zone, indicating high uncertainty in wet regions. All models tend to show some level of underestimation in summer months with the lowest rainfall. The findings illustrate substantial inter-model variability over different climatic zones. Each of the CMIP6 models appears to be more suitable in a specific climatic zone. The models that performed reasonably well in the humid zone (CNRM-CM6-1 and MRI-ESM2-0), did not perform well in the hyper arid and arid zones. Similarly, models (HadGEM3-GC31-LL, BCC-CSM2-MR, and CanESM5) that performed well in the arid and hyper arid zones did not perform as well in the humid zone. Results inform what types of models are suitable for different climate zones.

Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran

Satellite precipitation products are important data sources in different spatial resolutions, time scales, and spatio-temporal coverage. In this study, the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product with a high spatial resolution (0.05°) is evaluated in the period of 1987 to 2017 over different climate regions of Iran. The accuracy of the satellite product is compared with the 68 ground-based meteorological stations over different time scales (i.e., daily, monthly, and annual) and precipitation classes. Results show that the performance of CHIRPS depends on the time scale, precipitation depth, and climate type. The best performance of the product (CC = 0.80, FRMSE = 0.57, NSE = 0.63) across the country is observed in the annual time scale, while the monthly product offers the best performance in the regional scale. The product provides inadequate performance (CC = 0.34, FRMSE = 5.72, NSE = − 0.2) in daily time scale across the country and most of the climatic regions. The product is found to be most accurate in the south and southwest of the country, while the lowest performance is observed over the Caspian coast. The CHIRPS satellite provides the best performance in detection of no/tiny precipitation (POD > 0.90) and the worst performance in light and low, moderate precipitation (POD < 0.10). It is expected that the findings of the current study can be used to manage the water resources and mitigate the disaster at the national level.

Multi-objective simulation-based optimization of controlled blind specifications to reduce energy consumption, and thermal and visual discomfort: Case studies in Iran

This paper aims to present a multi-objective simulation-based optimization of architectural specifications and control parameters of a smart shading blind. Using the proposed method, implementation of control strategies on the window shading device and simultaneous optimization leads to a significant reduction in building energy consumption, and occupants' thermal and visual discomfort. Simulations are carried out using EnergyPlus, objective functions and decision-making parameters are identified by jEPlus, and multi-objective optimization is done by jEPlus + EA through NSGA-II. Optimization of the controlled blind is implemented in a typical office room located on a middle floor of a building, and the results are evaluated for four window orientations in six different climatic regions of Iran according to Köppen-Geiger climate classification. The decision variables are shading control strategy and its set-points and shading location, dimensions, angle, and material. The annual total building energy consumption, the predicted percentage of dissatisfied (PPD) and the discomfort glare index (DGI) are also considered as three objective functions minimized simultaneously. The weighted sum method is used to select the final answer from Pareto solutions. According to the results, based on the climate and the window orientation, the proposed optimization method leads to 2.8–47.8% decrease in the annual total building energy consumption compared to the initial design simultaneously with 15.5–69.9%, and 8.5–56.3% reduction in DGI and PPD indexes, respectively. The results clearly show how appropriate selection of the shadings specifications and their control strategy parameters can significantly not only prevent energy losses but also provide better occupants’ thermal and visual comfort.

Estimate of outdoor thermal comfort zones for different climatic regions of Iran

This study objective is to estimate high and low thresholds of the outdoor thermal comfort category for different thermal comfort indices in Iran. By using a statistical method that considers foure bioclimatic indices: SET*, PT, PET, and UTCI, the paper estimates their thermal comfort zone to gauge difference in terms of local climate. In this study, 154 stations were selected across the country and daily data period from 1995 to 2014 were considered. The results show that the estimated thermal comfort zones for the indices studied for Iran are: SET*, from 20.5 (BWh or Hot desert climate) to 25.5 °C (BSh or Hot semi-arid climate); PT, from 15.9 (Cfa or Humid subtropical climate) to 19.1 °C (Aw or Tropical wet and dry); PET, from 18.9 (BWh) to 22.5 °C (Aw); and UTCI, from 18.5 (BWh) to 25 °C (Csb or Warm-summer Mediterranean climate). A comparison between the original thresholds and estimated thresholds of each one of the four indices considered provides an insight to assist the future decision making of outdoor activities planning and design.

Distribution of Nosema Spp. in climatic regions of Iran.

‎Nosemosis is one of the most prevalent bee diseases in the world causing significant ‎economic losses in the global bee-keeping industry. This cross-sectional study was conducted during April-September, 2016 to investigate the prevalence of nosemosis in different climatic regions of Iran. A total of 183 ‎apiaries were selected based on cluster sampling and the climate of apiaries under study was classified using Domarten method. In each apiary, five percent of the colonies were ‎ randomly sampled. A total of 183 adult bee samples were taken and examined by microscopic and polymerase chain reaction (PCR) methods for the presence of Nosema infections. According to the results, infection caused by Nosema ceranae was observed in all regions under study. The prevalence of N. ceranae was 46.40% (42.70–50.10). However, infection with Nosema apis was not observed in the samples in either pure form or as ‎associated infection. Based on the results of PCR, the prevalence of N. ceranae was ‎53.80% (46.60– 61.00) ‎ in ‎humid, ‎71.00% (53.70–77.50) ‎ in semi-humid‎, ‎68.10% (61.40–74.80) ‎ in very humid, ‎29.40% (22.70–36.10) in arid, ‎34.30% (27.40–41.20) in semi-‎arid and ‎24.00% (17.90–30.00)‎ in Mediterranean climates. The prevalence of ‎infection in different climatic zones of the country was found to have significant ‎differences (p < 0.001). ‎ According to the findings, N. ceranae ‎ was the only Nosema species in honeybees with a broad geographical dispersion in Iran. It seems that climate can influence the prevalence of mentioned parasite. ‎

An Investigation on the Role of Iwan as a Sustainable Element in the Traditional Houses of Different Climatic Regions of Iran

An Investigation on the Role of Iwan as a Sustainable Element in the Traditional Houses of Different Climatic Regions of Iran

Economic optimization of PCM and insulation layer thickness in residential buildings

As suggested by previous studies, in economic optimization of buildings equipped with phase change materials, insulation layer and PCM highly affect each other’s performance. To consider these effects, in the present study, the thickness of the insulation and PCM layer of a residential building are optimized simultaneously. Coupled with EnergyPlus as the thermal simulation tool, genetic algorithm is applied to optimize the thicknesses for different climatic regions of Iran. Tehran, Isfahan, Shiraz, Bandar Abbas, Yazd, and Tabriz were selected for optimization. The fitness function is the total cost of the building throughout its operation years. In each city, the optimum PCM and insulation layer thicknesses are obtained with different present worth factors each accounting for a different scenario regarding the economic outlook of the country. Results show that the optimum PCM thickness is zero for all cases. On the other hand, the optimum insulation thickness varies significantly by region and PWF. While in southern parts of the country it was approximately zero, in cold regions, the optimum thickness was more than 6cm. Moreover, results show that with current economic situation of the country, insulation layers are far more cost effective than phase change materials.

Genetic relationships of Iranian Hypericum perforatum L. wild populations as evaluated by ISSR markers

St. John’s wort (Hypericum perforatum L.), as the most important medicinal species of the genus Hypericum, is medicinally used for many purposes such as treatment of mild and moderate depression. Considering importance of knowledge on genetic structure of wild relatives of crop plants, the present study was aimed to assess genetic variability among ten wild populations of H. perforatum growing in different climatic regions of Iran via ISSR markers. In general 15 selected primers generated 191 polymorphic fragments with an average of 12 in each primer. According to the UPGMA constructed dendrogram and PCoA, studied populations were classified into four main groups which was, to the some extent, in accordance with their geographical origins. Genetic distances of these populations ranged from 0.109 (Nor and Nowshahr) to 0.345 (Ardebil and Galogah). Also, Nei gene diversity (H e) values were calculated to be from 0.066 to 0.216 and Shannon’s information indices (I) varied from 0.321 to 0.097. Nei’s genetic differentiation index (G st) was high (0.573) indicating the high levels of genetic variation among the studied populations. Finally, the gene flow (N m) value was found to be 0.372. Obtained information is useful for use of Iranian H. perforatum germplasm for breeding programs and for development of effective conservation strategies.

Comparison of surface functional groups and metal uptake efficiency of rice husk harvested from different climatic zones

Rice husk (RH) is a very effective natural adsorbent for fast removal of heavy metal cations from water solutions. Application of RH for removal of some heavy metal ions, such as Ni, Zn, Mn, Co, Cu, Pb and Cd from water solutions has been studied and different maximum adsorption capacities and a variety of optimized conditions were reported in the literature. In this work, the efficiency of RH harvested from different climatic regions was studied. For this proposal, different RH samples were collected from three different climatic regions of Iran (nominated as RH1 to RH3); their removal efficiencies of heavy metal cations of Ni(2+), Cu(2+) and Cd(2+) were investigated and compared. The adsorption data at optimum conditions could be assessed well by both Langmuir and Freundlich models. Statistical analysis of the results of adsorption isotherms showed that different RH samples have different efficiencies in uptake of these heavy metal ions. The RH samples were characterized using Fourier transform infrared spectroscopy and Boehm titration, which indicated that amounts of functional groups differed between RHs that are grown in different climatic conditions.

Essential oil, phenolic compounds and antioxidant activity of Thymus daenensis Celak. at different harvest times

Thymus daenensis Celak. is an important medicinal plant grows in different climatic regions of Iran. A pot experiment was conducted to evaluate the essential oil yield and oil composition of thyme as affected by harvest time during 2010. Isolate the essential oil of the plant samples were determined by hydro-distillation in Clevenger Apparatus with three hours. Composition of the essential oils was determined by Gas chromatography mass spectrometry (GC-MS). Twenty six compounds, representing 98.5% of the essential oil were identified. The major compounds were thymol, p-cymene, γ-terpinene, β-caryophyllene and carvacrol. The results showed that thymol was the most abundant compound in thyme oil that a significant difference in its content due to harvest time was observed. The higher concentration of thymol was detected at full-flowering stage. In contrast, the higher concentration of γ-terpinene, a precursor of p-cymene, and p-cymene, a precursor of thymol was achieved at pre-flowering stage. Also, obtained results illustrated that phenolic compounds was higher in plant samples harvested at full-flowering stage. In addition, a significant increase in antioxidant activity of thyme was found to be due to harvest at full-flowering stage. Key words: Thymus daenensis Celak., essential oil, hydro-distillation, gas chromatography mass spectrometry (GC-MS), harvest time.