Ranking of renewable energy resources for different climatic zones of Iran using fuzzy MCDM

This article aims at proposing an improved statistical model for statistical downscaling of monthly precipitation using multiple linear regression (MLR). The proposed model, namely Monthly Statistical DownScaling Model (MSDSM), has been developed based on the general structure of Statistical DownScaling Model (SDSM). In order to improve the performance of the model, some statistical modifications have been incorporated including bias correction using variance correction factor (VCF) to improve the computed variance pattern. We illustrate the effectiveness of the proposed model through its application to 288 rain gauge stations scattered in different climatic zones of Iran. Comparison between the results of SDSM and the proposed MSDSM has indicated superiority of the proposed model in reproducing long-term mean and variance of monthly precipitation. We found that the weakness of MLR method in estimating variance has been considerably improved by applying VCF. We showed that the proposed model provides a promising alternative for statistical downscaling of precipitation at monthly time scale. An investigation of the effects of climate change in different climatic zones of Iran by the use of Representative Concentration Pathways (RCPs) has shown that the most significant change is an increase in precipitation in fall and that the largest share of this increase belongs to arid climate.

Evapotranspiration can be considered as an indicator in evaluating the effects of climate change. It is the sole factor with the capability of concurrently balancing the ecosystem energy and water fluxes. Reference evapotranspiration (ETo) is the amount of water evaporated from reference crop that is affected by climatic parameters. The aim of this study was to assess ETo during the period of 2020–2049 in various climatic zones of Iran (including Tabriz, Bushehr, Isfahan, Sanandaj and Urmia). Climatic parameters including relative humidity, wind speed, Sunshine hours, atmospheric pressure, maximum and minimum temperatures were utilized for calculating ETo using FAO Penman–Monteith equation. The NCEP data and HADCM3 model data (under scenario A2 and B2) were used for prediction of future climatic parameters during 2020–2049. Statistical down scaling model was used as a hybrid regression model as well as a stochastic weather data generator. The course period was between 1986 and 2015 that was regarded for calibration and evaluation of the model. Subsequently, evapotranspiration was estimated using the model outputs in the FAO Penman–Monteith equation. Results indicated that the simulated data by the model has the same accuracy and validity under both scenarios A2 and B2. Results showed that in the studied areas, ETo tend to have an increasing trend in upcoming years. In scenario A2, ETo will increase about 16.81, 0.137, 17.52, 9.46 and 2.57 mm year−1 in Tabriz, Bushehr, Isfahan, Sanandaj and Urmia stations, respectively. Also our results represented that ETo will increase about 7.67, 6.52, 10.33, 7.73 and 1.99 mm year−1 in Tabriz, Bushehr, Isfahan, Sanandaj and Urmia stations, respectively based on the B2 scenario. Although no significant trend was observed in most of the climatic variables under A2 and B2 scenarios over the time period of 2020–2049, the ETo significantly increased during this period. Hence it could be concluded that ETo is a better indicator for describing the future climate change, compared to other climatic variables.

BackgroundThe genetic structure of populations can be influenced by geographic isolation (including physical distance) and ecology. We examined these effects in Leptopilina boulardi, a parasitoid of Drosophila of African origin and widely distributed over temperate and (sub) tropical climates.ResultsWe sampled 11 populations of L. boulardi from five climatic zones in Iran and measured genetic differentiation at nuclear (Amplified Fragment Length Polymorphism; AFLP) and mitochondrial (Cytochrome Oxidase I; COI) loci. An Analysis of Molecular Variance (AMOVA) for the AFLP data revealed that 67.45% of variation resided between populations. No significant variation was observed between climatic zones. However, a significant difference was detected between populations from the central (dry) regions and those from the wetter north, which are separated by desert. A similarly clear cut genetic differentiation between populations from the central part of Iran and those from the north was observed by UPGMA cluster analysis and Principal Coordinates Analysis (PCO). Both UPGMA and PCO further separated two populations from the very humid western Caspian Sea coast (zone 3) from other northern populations from the temperate Caspian Sea coastal plain (zone 2), which are connected by forest. One population (Nour) was genetically intermediate between these two zones, indicating some gene flow between these two groups of populations. In all analyses a mountain population, Sorkhabad was found to be genetically identical to those from the nearby coastal plain (zone 2), which indicates high gene flow between these populations over a short geographical distance. One population from the Caspian coast (Astaneh) was genetically highly diverged from all other populations. A partial Mantel test showed a highly significant positive correlation between genetic and geographic distances, as well as separation by the deserts of central Iran. The COI sequences were highly conserved among all populations.ConclusionThe Iranian populations of L. boulardi showed clear genetic structure in AFLP profiles, but not in COI sequence data. The transfer of fruits containing Drosophila larvae parasitized by L. boulardi appears to have caused some unexpected gene flow and changed the genetic composition of populations, particularly in urban areas. Nevertheless, our results suggest that climate, geographic distance and physical barriers may all have contributed to the formation of genetically distinct populations of L. boulardi. Inevitably, there will be overlap between the portions of variance explained by these variables. Disentangling the relative contributions of climate and geography to the genetic structure of this species will require additional sampling.

Understanding the distribution pattern of the Fusarium species can help prevent crop diseases and large yield losses. While several approaches have been used to control soil-borne pathogens, soil solarisation has shown promising results in managing these pathogens. The main objectives of this study were to: (i) describe the biogeography of Fusarium species in four different climatic zones in Iran and (ii) explain the effect of soil solarisation on main pathogenic Fusarium species in wheat grains, beans and date palms. A total of 12 sub-samples were collected from four different climatic zones including, Rasht (humid), Zanjan (semi-arid), Isfahan (extra-arid) and Ahwaz (arid). For precise identification, molecular-phylogenetic analyses of the species were also performed. From these four sites 17 Fusarium species were recovered. F. solani complex, F. oxysporum and F. equiseti were the only species found in all four regions; whereas F. compactum, F. sambucinum and F. fujikuroi were restricted to Ahwaz, Zanjan and Rasht, respectively. Furthermore, soil solarisation treatments were applied to F. pseudograminearum, F. solani and F. oxysporum, as the main cause of root rot pathogens and wilt disease of wheat, bean and date palm, respectively. After 6 weeks of soil solarisation application, the population densities of these species were decreased from 900 to 100 CFU g−1 in F. solani, from 600 to 50 CFU g−1 in F. oxysporum and from 550 to 0 CFU g−1 in F. pseudograminearum showing a promising result in controlling soil-borne pathogens. Mycogeography of Fusarium species and the effect of soil solarisation can help improve the management control strategies of these soil-borne fungi.

Seasonal total precipitation is one of the important meteorological variables and its prediction is useful for the supply of water to different sectors. This study aims to compare Seasonal Autoregressive Integrated Moving Average (SARIMA), Multilayer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System-Subtractive Clustering (ANFIS-SC), and ANFIS-Fuzzy Cluster Means (ANFIS-FCM) for the prediction of seasonal precipitation. The precipitation data were obtained for the 1951–2018 period from 8 stations located in different climatic zones of Iran. The stations and their climates are Anzali (per-humid moderate climate), Babolsar (humid moderate climate), Kermanshah (semi-arid cold climate), Shiraz (semi-arid moderate climate), Bushehr (arid warm climate), Shahroud (arid cold climate), Isfahan (extra-arid cold climate), and Zahedan (extra-arid moderate climate). The time-lagged precipitation as input for all models was chosen using the autocorrelation function (ACF), and the data were divided into two periods: 1951–2001 for training (75%) and 2002–2018 for testing (25%). Based on the evaluation criteria (root mean squared error [RMSE], normalized root mean squared error [NRMSE], Wilmott Index [WI], Akaike Information Criterion [AIC], and Bayesian Information Criterion [BIC]), results showed that the SARIMA stochastic model was more accurate than the artificial intelligence methods and had the least over- and under-estimations. MLs exhibited good prediction accuracy, but ANFIS-FCM had a little higher accuracy. Consequently, due to the high accuracy and simplicity, the stochastic model is reported as the best predictor for seasonal precipitation in all climates. In terms of the R2 values, the models showed better fitting in wet and normal years than in drought years. Further, the model predictions were more accurate in per-humid and humid areas than in arid and extra-arid climates. Also, the NRMSE values were in the range of 0.1 and 0.2, which indicated that SARIMA’s performance was medium and well. A significant result of this study was that results for different climates based on RMSE were completely opposite to those based on NRMSE, WI, and R2. This contrast was caused by the neglect of data range in the RMSE equation, so it is not a good choice to compare the results under different climates and it is better to use its normalized form “NRMSE.”

Epidemiological study on Anaplasma phagocytophilum in cattle: Molecular prevalence and risk factors assessment in different ecological zones in Iran

Estimated Effective Precipitation (Pe) in dryland areas is an essential element of water resource management. Itrepresents the amount of precipitation available in the crop root zone to meet the needs of evapotranspiration. Thecurrent study compared different approaches for estimating Pe in different climatic zones of Iran. A two-layer soil–water balance (SWB) model was adopted based on the proposed approach in which a portion of the previous day’sprecipitation saved between the previous and current root-zone development is added to the Pe of the current day. Tothis end, we used three groups of data (meteorological, phenological, and soil characteristics data) related to 21 agrometeorologicalstations representing arid, semi-arid, semi-humid, and humid regions of the country. The results ofthis study indicated that, in spite of data limitations, the new procedure performed appropriately in estimating thatpart of the wheat yield which could be explained by Pe only. Coefficients of determination (R2) between annualprecipitation and Pe ranged from 0.50 in the humid climatic zone to 0.82 in the arid climatic zone. Ultimately, usingannual precipitation data collected from 181 Iranian synoptic stations and its correlation with Pe, the first annual Pemap of Iran was produced.

Assessing the performance of rainwater harvesting systems, which is one of the effective ways to cope with the water shortage crisis, leads to better management of these systems. In this paper, the reliability of rainwater harvesting systems and the overflow ratio of storage tanks were investigated for different climatic conditions, different volumes of tanks, and different roof areas. The results indicated that in the cities of Rasht, Sari, Tabriz, and Yazd, using a 10-m3 tank, non-potable water needs of a four-person family can be supplied from a 100-m2 roof area for 67.3, 42.98, 12.07, and 1.35% of the days during a year. Further, if a 1-m3 tank is used in Rasht, 50.67% of the total harvested water will be overflowed, which would decrease to 19.21% if the 10-m3 tank is replaced. For Tabriz, the ratio of overflow from the 5-m3 tank was zero, but for the city of Sari, even with use of a 10-m3 tank, 0.73% overflow occurred. In addition, for Yazd city, using a 1-m3 tank, an overflow of 48.4% occurred, but when the volume of the tank was increased to 2 m3, there was no overflow. For the city of Tabriz, the ratio of overflow from the 5-m3 tank was zero, but for the city of Sari, even using a 10-m3 tank, 0.73% overflow occurred. For a constant volume of storage, as long as the average rainfall of the area was high, the ratio of overflow was also elevated. The ratio of overflow, from the highest to the lowest, was related to the temperate climate of the Caspian Sea and the pseudo-Mediterranean climate, moderate and humid climate, mountainous climate, and desert hot and dry climate, respectively.

Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.

To investigate the effects of water withholding on 17 tomato (Solanum lycopersicum L.) landraces collected from different climatic zones of Iran and two commercial hybrids, the polyphasic OJIP fluorescence transient, relative water content (RWC), electrolyte leakage (EL) and vegetative growth parameters were analyzed. Duncan’s multiple range test (DMRT) for all the studied parameters and drought factor index (DFI) based on performance index on the absorption basis (PIabs) were used for screening the plants based on their tolerance to drought condition. Result showed that compared to the control plants, vegetative growth parameters, RWC, PIabs, relative maximal variable fluorescence (FM/F0), maximum quantum efficiency of PSII (FV/FM), quantum yield of electron transport (ΦE0) and electron transport flux per reaction center (RC) (ET0/RC) were decreased, whereas, EL, quantum yield of energy dissipation (ΦD0), specific energy fluxes per RC for energy absorption (ABS/RC) and dissipated energy flux (DI0/RC), which are closely related to the incidence of photoinhibition were increased in plants exposed to water withholding. DMRT and DFI screening results clearly categorized the landraces into three groups (tolerant, moderately sensitive and sensitive). Tolerant landraces showed less change for most of the measured parameters compared to sensitive and moderately sensitive landraces. We found that adapted landraces to dry climates had a higher tolerance to drought stress. Principal component analysis (PCA) revealed that FM/F0, FV/FM, ΦE0, ΦD0, PIabs, ABS/RC, ET0/RC and DI0/RC parameters are the most sensitive parameters for detection of impact of drought stress on tomato plants. In conclusion, the eight parameters have the potential to identify the drought injury in tomato seedlings.

Persian architecture has a deep history of designing buildings with respect to nature. This paper will deal with the classification and analysis of various types of traditional buildings in the different climatic zones of Iran. Finally, interesting criteria will be introduced to help with contemporary architecture design. These lessons from the past will not only improve energy conservation but will also result in pleasing architecture in harmony with nature.

Determining new threshold temperatures for cooling and heating degree day index of different climatic zones of Iran

Historical changes of extreme temperature in relation to soil moisture over different climatic zones of Iran

Climate classification provides a framework for a better understanding of the dominant weather patterns in different regions of the Earth. This study aims at identifying climate zones in Iran based on the analysis of monthly temperature and precipitation over 139 synoptic stations across Iran during the period 1991–2020. Based on the application of the principal component analysis, we identified six distinct climate zones in Iran: mild and humid, cool and sub‐humid, cold and temperate semi‐arid, warm and semi‐arid, cool and arid, and warm and hyperarid. The highest precipitation occurs in the southern coastal plains of the Caspian Sea, characterized by a mild and humid climate. The climate of western Iran is identified as cool and sub‐humid, while northwestern Iran is characterized by a cold and temperate semi‐arid climate. Southwestern Iran is identified as a region with a warm and semi‐arid climate, while northeastern Iran has a cool and arid climate. Southeastern and central Iran are both characterized by a warm and hyperarid climate. The highest monthly and seasonal precipitation values over Iran occur in March (48.6 mm) and winter (134.2 mm), respectively, while the highest monthly and seasonal mean temperature values occur in July (29.1°C) and summer (28.0°C), respectively. In terms of seasonal variation, the maximum precipitation occurs in the southern coastal plains of the Caspian Sea in autumn, while the minimum occurs in southwestern Iran in summer. Our results have important implications for better understanding and analysing the climatic characteristics across Iran.

Many efforts have been made to increase the utilization of renewable energy resources (RESs) in Iran. This paper aimed to evaluate the techno‐economic performance of an introduced hybrid microgrid (HMG) in eight climate zones of Iran. Therefore, ten cities are selected from the eight climate conditions of Iran. An electricity pricing strategy is also implemented according to the electricity tariffs defined by the Ministry of Energy (MOE) of Iran. The proposed electricity pricing strategy is applied to the HOMER software for investigating the optimal system configuration, RES electricity generation, and the economics of each understudy city. Optimization results indicate that Urmia (in moderate and rainy climate zone) has the least net present cost (NPC) (−5839$) and levelized cost of energy (COE) (−0.0122 $/kWh), whereas Golestan (in semimoderate and rainy climate zone) has the highest NPC (4520 $) and COE (0.012 $/kWh). It is shown that the combination of photovoltaic (PV)/wind turbine (WT)/converter in the grid‐connected operation mode is the most economical configuration. Moreover, the cities with higher potentials of wind speed and solar irradiance have lower NPC and COE. It is concluded that the utilization of the battery energy storage (BES) is technically and economically infeasible for all eight climate zones, even if the stored electricity is sold to the grid. Two sensitivity analyses are conducted to the electricity feed‐in‐tariff (FiT) and solar module price, respectively. The first sensitivity analysis indicates that by increasing FiT, more contribution of RESs is seen, which leads to lower COE and NPC. Furthermore, the two cities of Urmia and Yazd have the highest NPC and COE reductions. The second sensitivity analysis studies the module price impacts on the NPC and COE of each understudy city. It is revealed that the PV module price has a considerable effect on NPC and COE. However, this effect is more significant in some cities such as Bam, where a linear relationship is seen between the module price and economic results (NPC and COE).