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Holding a lasting balance between the water resources and water demands has become a challenging task for water resources managers, especially in recent years with the looming global warming crisis and its resulting climatic change effects. This paper focuses on modeling the optimized operation of the Zayandehrud Reservoir, located in west-central Iran, under two fifth-generation climate change scenarios called RCP4.5 and RCP8.5. A novel variant of the gravitational search algorithm (GSA), named the adaptive accelerated GSA (AAGSA) is proposed and adopted as the optimizer of the reservoir operation in this paper. The major advancement of the AAGSA against the original GSA is its high exploration capability, allowing the proposal to effectively tackle a variety of difficulties any complex optimization problem can face. The goal of the optimization process is the maximization of the sustainability of supplying the downstream water demands by the reservoir. The optimal results obtained by the original GSA and the proposed AAGSA algorithms suggest that the AAGSA can achieve much more accurate results with much less computational runtime, such that the proposed AAGSA is able to achieve the reservoir operation sustainability index of 98.53% and 99.46%, under RCP4.5 and RCP8.5 scenarios, respectively. These figures are higher than those obtained by the original GSA by 23.5% and 16% under RCP4.5 and RCP8.5, respectively, while the runtime of the proposal is reduced by over 80% in both scenarios, as compared to the GSA, suggesting the high competence of the proposed AAGSA to solve such a high-dimensional and complex real-world engineering problem. [ABSTRACT FROM AUTHOR]

In recent years, climate change as a consequence of global warming, has garnered significant attention. Therefore, serious measures must be considered in the development planning process to adapt to and reduce effects of climate change. This research evaluates six development plans in Isfahan, Iran in the face of climate change effects using a framework consisting of three components of the analytical Awareness-Analysis-Action (A-A-A) approach and using quantitative and qualitative content analysis methods. In fact, the evaluation of the level of attention of the development plans from the perspective of the level of reflection and attention to raising awareness about the issues of climate change, the ability to analyze the available climate information in a useful way to support planning decisions and the level of special actions to deal with these changes. Finally, out of the three elements of the approach, the highest amount of attention in the plans is related to the element of action with 67/70% and the lowest is related to the element of awareness with 39/10%. The overall evaluation of the development plans shows that their role in integrating climate change issues into urban planning in Iran is still limited and insufficient. On the other hand, air pollution and climate change are closely related, as chemicals that cause air quality deterioration often enter together with greenhouse gases. Therefore, it can be said that although indicators of the transportation dimension (A3-J) with 80.66%, resource management have been given special attention under the action component, but carbon sequestration dimension (A2-F) with 14.63%, under the analysis component that seeks to evaluate the amount of air pollutants, classification of the type and sources of emission of pollutants could not reflect more than half of the total percentage in all development plans. [ABSTRACT FROM AUTHOR]

Excessive concentration of greenhouse gases in atmosphere emitted from human activities has been considerably changing the world's climate, especially in the last 50 years. Agriculture, as humans' food production system, has undoubtedly interrelated with climate change (CC). During current decades, the impacts of CC on agriculture have been properly investigated; however, the impacts of agriculture on CC have received lower attention. This may be due to the scarcity of long-term spatiotemporal climatic and agricultural data to analyze coupling trends and interactions. Benefiting from a comprehensive database and using structural equation modeling, this study seeks to investigate the contribution of agriculture to CC in Iran for more than half a century. For this, two indicators were developed to evaluate structural characteristics of agricultural expansion (AEI) and CC at the province level. Then, the effect of AEI on CC was investigated using the structural equation modeling technique. The results showed that AEI has not had a positive contribution to raising the long-term average surface temperature. Precisely, the provinces with a higher level of surface temperature have had a lower AEI, indicating that other sectors outweigh agriculture in exacerbating long-term CC in the country. Nevertheless, Iran still needs to improve and sustain its agricultural practices and technologies. The main conclusion of this study is that if the government and policymakers aspire to manage CC, they should have a more holistic and systematic view. In other words, not only do they need to consider all drivers of CC, but they also have to pay close attention to the network of relationships among the drivers. [ABSTRACT FROM AUTHOR]

Coastal areas are of paramount importance due to their pivotal role in facilitating a wide range of socio-economic activities and providing vital environmental services. These areas, as the meeting points of land and sea, face significant risks of flooding due to the ongoing rise in sea levels caused by climate change. Additionally, they are susceptible to extreme events like king tides and large waves in the future. This paper introduces a framework for estimating the extreme total water level (TWL) by considering the effects of regional sea level rise (RSLR) resulting from a warming climate under RCP 8.5. It also incorporates the contributions of high tides, 100-year storm surge, and 100-year wave setup and run-up. The proposed framework is utilized to evaluate the occurrence of extreme coastal flooding along the Persian Gulf coast of Iran, an area that is home to significant industries in the country. The results offer an estimated increase of RSLR by 0.23 m from 2020 to 2050 considering an ensemble of climate model projections. Extreme wave setup values are estimated to range between 0.19 and 0.66 m, while storm surge is projected to vary from 0.4 to 1.44 m across the studied coastline. These together yield in a projected extreme TWL along the coastline within the range of 3.18 and 3.90 m above the current sea level. This significant increase in sea level could lead to the inundation of approximately 513 km2 of low-lying coastal land, which accounts for about 16% of the studied domain and could pose serious flooding threat to the people and their assets in this region. Finally, relative ranking of flooded zones (i.e., 6 zones) helps determine the areas with higher chance of flood exposure, at which investment in flood mitigation measures should be prioritized. [Display omitted] • Regional sea level rise is quantified using GCMs under RCP 8.5. • Flooding maps are shown under regional SLR, tides, storm surge, wave setup and run-up. • Extreme total water level is expected to inundate important ports in Persian Gulf coast of Iran by 2050. • Research projects that 513 km of low-lying coastal areas will be inundated due to extreme total water level. [ABSTRACT FROM AUTHOR]

This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement: The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty. [ABSTRACT FROM AUTHOR]

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Extended periods of hydro-climate extremes with excessive or scarce rainfall associated with high or low temperatures have resulted in an imbalanced water cycle and inefficient socio-economic systems in several regions of Iran. However, there is a lack of comprehensive investigations on short-term to long-term variations in timing, duration, and temperature of wet/dry spells. This study bridges the current gap through a comprehensive statistical analysis of historical climatic data (1959–2018). Results indicated that the negative tendency of the accumulated rainfall (− 0.16/ − 0.35 mm/year during the past 60/30 years) in 2- to 6-day wet spells had made significant contributions to the ongoing downward trend in annual rainfall (− 0.5/ − 1.5 mm/year during the past 60/30 years) owing to a warmer climate condition. Warmer wet spells are likely responsible for precipitation patterns changes in snow-dominated stations since their wet spells temperature has more than threefold growth with increasing distance to coasts. The most detected trends in climatic patterns have started in the last two decades and become more severe from 2009 to 2018. Our results confirm the alteration of precipitation features across Iran due to anthropogenic climatic change, and suggest expected increase in air temperature would likely result in further dry and warm conditions over the coming decades. [ABSTRACT FROM AUTHOR]

The 1641 Dehkhwargan-Tabriz earthquake (Ms 6.8) is the only destructive earthquake to have occurred in Azarshahr-Khosrowshahr-Osku region of northwestern Iran. Ambiguities regarding the causative fault of this earthquake were a motivation to someway declare its mechanism. In this study, by conducting a field survey in an area between Tabriz and Azarshahr, and use of satellite imagery and aerial photographs, we provide geologic and geomorphic indications to identify the poorly known Azarshahr-Tabriz fault zone (ATF), which passes through the meizoseismal region of the 1641 earthquake. Our observations highlight the presence of a sinistral active fault zone that extends ~40 km in length from south of Tabriz to the northeast and north of Azarshahr to the southwest. The ATF is a structural assemblage of several fault strands with a variety of extensional and compressional mesoscale structures. We estimated slip rates ranging from ~0.01 mm yr-1 to <1 mm yr-1, based on drainage offset measurements, which demonstrate a relatively stable tectonic environment located immediately south of the well-known seismically active dextral North Tabriz fault. [ABSTRACT FROM AUTHOR]

In many parts of the world, teleconnection patterns are one of the climate phenomena that significantly change the causative climate anomalies, especially temperature and precipitation. Thus, statistical analysis and modeling of their effects are of great importance in order to understand the fluctuations and climate variability in a region. In this study, 52 teleconnection indices were utilized to perform statistical analysis and conceptual modeling as well as simulation of temperature and precipitation fluctuations in Iran. For this purpose, temperature and precipitation data of 36 synoptic stations in Iran and 52 teleconnection indices for the period 1951–2019 were used. For the analysis, the relevant data were classified into four groups of global (GLB), regional (RGN), El Niño–Southern Oscillation (ENSO), and all indices (ALL). Then, the correlation of the aforementioned indices with temperature and precipitation was calculated using the teleconnectional–statistical model (TSM). Afterward, 5 years with the highest correlation coefficient was selected and considered as the forecasting parameters. The results revealed that the local temperature forecasting using RGN indices and the precipitation forecasting using GLB and ALL indices were more accurate than those using other indices. Our findings highlighted the prominent role of the indices with broader geographical regions that mainly were evolved across the oceans. Moreover, the effect of ENSO teleconnection on Iran climate was explained by the dynamic mechanism of the atmospheric bridge. Comparing TSM outputs and the Climate Forecast System Version 2 (CFSV2), TSM outperformed CFSV2 in the conducting experiment. Overall, the findings of this study emphasized on the existence of coincided synergy in the fluctuation or trend within almost all indices and atmospheric parameters, and most of the fluctuations in the indices occurred simultaneously or right after the ENSO events, including specific super El Niño in 1997–1998 (turning point). Also, the dramatic effect of sunspots on the average temperature of Iran is shown in inverse harmony, which is not easily revealed by correlation analysis. Also, the influence of global warming and especially synchronization with El Niño event on climatic change was briefly discussed in the Middle East. In this work, for the first time in the Middle East, physical mechanisms of how linkages affect Iran's climate and rainfall were proposed in a synoptic conceptual model. [ABSTRACT FROM AUTHOR]

Climate change and global warming are often considered the main reason for water scarcity in Iran. However, there is little evidence showing that the arid/wet regions get drier/wetter due to climate change. Some researchers believe that parts of water challenges in Iran arise from bad governance and mismanagement of water resources. To address the role of climate change on the water scarcity, this study aims to detect the drought trends in the southeast of Iran to investigate drought characteristics changes during 1981–2020. The nonparametric Mann–Kendall test was used for this purpose. CHIRPS product was collected as an alternative source of ground data for trend analysis of drought characteristics. The evaluation metrics show that the CHIRPS product performs better at monthly and annual scales (correlation higher than 0.8) than daily (correlation less than 0.4). The results also illustrate that the duration and severity of short-term droughts (3, 6, and 9 months) have decreased, while their intensity has increased. Conversely, duration, severity, and intensity changes for long-term droughts (12, 18, and 24 months) are insignificant. The trend in the Standardized Precipitation Index (SPI) showed that, in general, the southeast of Iran has not been getting drier during the last four decades. One may conclude that the change in precipitation is not the only reason for water challenges in this area, and both natural and anthropogenic drought might cause water scarcity. Accordingly, it is suggested that the effects of human activities and governmental plans should be considered as well. [ABSTRACT FROM AUTHOR]

Background and Objective: Researchers investigate and identify the gaps and frequency of studies related to specific knowledge by using scientometrics. The purpose of this study was to analyze the trend of scientific research in the field of climate change in Iran. Materials and Methods: All articles affiliated with Iran in the field of climate change have been cited in Scopus databases from 1991 to December 9, 2019, and were retrieved by using keywords like "climate change", "global warming", "global change", "climate variability", "greenhouse gas". The data were analyzed using R software. Results: The total number of documents was 2253. The trend of document production in the field of climate change in Iran follows an increasing exponential trend. Iran's collaboration with other countries in the field of climate change studies was significant so around 25% of scientific productions have been produced jointly with other countries. Keywords analysis showed a greater frequency for keywords including “downscaling”, “energy”, “global warming” and “uncertainty”. However, despite the importance of issues such as “adaptation” and “vulnerability” concerning climate change in Iran, the frequency of these keywords in climate change studies in Iran is low. Conclusion: Based on the results of this study and considering the importance of climate change and its effects on the country, also the importance of greenhouse gases including CO2, and the rank of Iran as the eighth country in the emission of these gases, it is recommended to pay more attention to issues such as “vulnerability”, “adaptability”, analysis of “greenhouse gases production”, “management of energy consumption” and the use of “renewable energies” in future studies. [ABSTRACT FROM AUTHOR]

Due to the significant effects of CO 2 emissions on climate change and global warming, as well as its serious hazards to human health, the prediction of CO 2 emission is a vital issue. The main aim of this paper is to evaluate the power of the Inclusive Multiple Model (IMM) as a novel approach to predict CO 2 emission in the agriculture sector of Iran. For the same, we implemented the environmental Kuznets curve (EKC) specification and data from 2003 to 2017 for 28 provinces of Iran. In the first level, various specifications were implemented for each of the Multiple Regression (MLR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) models. In the second level, an IMM model was implemented for treating the outputs of the best specification out of the MLR, GPR, and ANN models as inputs to an ANN model. The performance of the models was compared with the Taylor diagram and innovation and unique graphs. Findings indicated that the IMM model with C C = 0.81 , RMSE = 0.69, the highest residuals between −5 and 5 (37.84%), and the lowest distance from observation points (1.857) estimated CO 2 emission values more precisely. These improvements indicate that there are possible directions for future research activities. Due to the most accuracy of the IMM, it is recommended to use this method to predict CO 2 emission to adopt appropriate policies for reducing air pollution. Image 1 • We developed Inclusive Multiple Model (IMM) as a novel approach to predict CO 2 emission in the agriculture sector of Iran. • We utilized Environmental Kuznets Curve (EKC) hypothesis literature to predict CO 2 emission. • We used the Taylor diagram with distance and new graphs to determine the most accurate model. • A measure of income inequality could improve results in investigating EKC. • The IMM was the most accurate model according to all of performance metrics and diagnostic graphs. [ABSTRACT FROM AUTHOR]

The study area, with arid to semi-humid climates, is located in northern Fars Province, southern Iran. Daily precipitation and temperature data from 22 models of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) are used to study climate change for period 2026–2085 under RCP2.6, RCP4.5, and RCP8.5. Daily outputs of each CMIP5 model are downscaled to climatic stations over the study area using LARS-WG tool and transient change factors approach. Daily precipitation is downscaled to seven climatic stations while daily temperature downscaled to two stations. Downscaling results indicate that temperature will increase by + 1.85, + 2.5, and + 3.45 °C under the RCP2.6, RCP4.5, and RCP8.5, respectively, while precipitation will reduce by 14, 22, and 20.8%, respectively, within the study area. Standardized precipitation index (SPI) analysis, performed for 3-, 6-, 9-, and 12-month time scales, shows that frequency (duration) of normal periods will increase (decrease) under climate change, however, 12-month SPI analysis shows decreased frequency and increased duration for normal periods. Frequency, duration, severity, and intensity of wet periods may increase, decrease, or not change under climate change, depending on the SPI time scale. The study area will experience more frequent, longer-duration, severed, intensified droughts in the future due to global warming. Extreme storm analysis shows that precipitation depth of most frequent storms (2-year return period storms) will increase, but precipitation depth of less frequent storms (10- and 20-year return period storms) will decrease under climate change. Temperature enhancement, precipitation reduction, and longer-duration, severed, intensified droughts will increase water shortage in the future, resulting in water crisis and reduced security of food production over northern Fars Province. [ABSTRACT FROM AUTHOR]

Contrary to expectations that global warming will be accompanied by an increase in terrestrial evaporation, pan evaporation (PE) has decreased in several parts of the world during the last decades. This opposing relationship is known as the pan evaporation paradox phenomenon that can be affected by climate change. For this purpose, this study identifies spatial and temporal trends in PE and its associated climate variables (Cvars) in Iran at seasonal and annual time scales. Hence, PE and Cvars data from 68 meteorological stations distributed over Iran during the period 1987–2016 were selected. The analyses of the temporal and spatial trends in PE, Cvars, and relationships among them were examined using the statistical techniques to identify the causes of PE trends. Results indicated that stations located in the subtropical region (latitude below 32°), which is mostly located in the southern half of Iran, showed significant negative PE trends (about 20–30% of stations) in spite of an increasing trend in temperatures indicating the "pan paradox" phenomenon. However, stations with significant positive PE trends (about 40% of stations), which are mostly located in the northern half of Iran, are placed at latitudes above 32°. Trend analysis of other Cvars indicated significant increases in temperatures, sunshine duration, and wind speed in more than half of the stations on all time scales. In addition, minimum, maximum, and mean temperatures, and sunshine duration were the most dominant variables affecting PE in annual, spring, autumn, and summer, respectively, in Iran. [ABSTRACT FROM AUTHOR]

Global warming has substantial impacts on the hydrologic cycle and precipitation characteristics. This study aimed to explore variability in the form of precipitation by analysis of trends in the snowfall-day (SD)/total precipitation-day (PD) ratio across Iran. To this end, the PD and SD records were acquired from 33 synoptic stations during 1965–2014. The Mann-Kendall (MK) test was the main method used for trend detection. Analyzing data exposed that the annual SD/PD ratio was less than 25% in a plethora of the stations. The MK test findings indicated downward tendencies in the SD/PD ratio over most regions of the country; although in many cases, the observed trends were not statistically significant. The changes in the annual SD/PD ratio were estimated down to − 2% per decade over ~ 70% of Iran's total area. Further studies are required to ascertain the linkages between the large-scale climate oscillations and the form of precipitation across this region. Also, using remotely sensed data (e.g., obtained from the MODIS) and fine-resolution gridded data (e.g., global and regional reanalysis data) in future research could improve the findings of such studies and dwindle the uncertainties caused by the scanty site-based observations. [ABSTRACT FROM AUTHOR]

Aim: The nested pattern in the geographical distribution of three Indian owlets, resulting in a gradient of endemicity, is hypothesized to be an impact of historical climate change. In current time, the Forest Owlet Athene blewitti is endemic to central India, and its range is encompassed within the ranges of the Jungle Owlet Glaucidium radiatum (distributed through South Asia) and Spotted Owlet Athene brama (distributed through Iran, South and Southeast Asia). Another phylogenetically close species, Little Owl Athene noctua, which is largely Palearctic in distribution, is hypothesized to have undergone severe range reduction during the Last Glacial Maximum, showing a postglacial expansion. The present study tests hypotheses on the possible role of Quaternary climatic fluctuations in shaping geographical ranges of owlets. Methods: We used primary field observations, open access data, and climatic niche modeling to construct climatic niches of four owlets for four periods, the Last Interglacial (~120–140 Ka), Last Glacial Maximum (~22 Ka), Mid‐Holocene (~6 Ka), and Current (1960–1990). We performed climatic niche extent, breadth, and overlap analyses and tested if climatically suitable areas for owlets are nested in a relatively stable climate. Results: Climatically suitable areas for all owlets examined underwent cycles of expansion and reduction or a gradual expansion or reduction since the Last Interglacial. The Indian owlets show significant climatic niche overlap in the current period. Climatically suitable areas for Little Owl shifted southwards during the Last Glacial Maximum and expanded northwards in the postglaciation period. For each owlet, the modeled climatic niches were nested in climatically stable areas. Main Conclusions: The study highlights the impact of Quaternary climate change in shaping the present distribution of owlets. This is relevant to the current scenario of climate change and global warming and can help inform conservation strategies, especially for the extremely range‐restricted Forest Owlet. [ABSTRACT FROM AUTHOR]

Global climate change leads to an increasing in the number and severity of weather events such as floods. Floods have been reported one-half of all weather-associated disasters with high impacts on countries. Global warming causes a different pattern of rainfall in Iran caused long-term drought since 30 years ago and recent heavy raining which lead to a massive flood in this country. It is predicted that health subsequences of Iran 2019 flood such as communicable diseases vary due to the geographical extent and different climates of flooded areas. However, observing long term and short term preventive measures can be effective to reduce the high impact of flood in Iran. [ABSTRACT FROM AUTHOR]

As a preliminary and major step for land use planning of the coming years, the study of variability of the past decades' climatic conditions with comprehensive indicators is of high importance. Given the fact that one of the affected areas by climatic change includes variability of thermal comfort, this study uses the physiologically equivalent temperature (PET) to identify and evaluate bioclimatic conditions of 40 meteorological stations in Iran. In this study, PET changes for the period of 1960 to 2010 are analyzed, with the use of Mann-Kendall non-parametric test and Pearson parametric method. The study focuses particularly on the diversity in spatio-temporal distribution of Iran's bioclimatic conditions. The findings show that the mean frequency percentage of days with comfort is 12.9 % according to the total number of selected stations. The maximum and minimum frequency percentage with values of 17.4 and 10.3 belong to Kerman and Chabahar stations, respectively. The findings of long-term trend analysis for the period of 1960-2010 show that 55 % of the stations have significant increasing trend in terms of thermal comfort class based on the Pearson method, while it is 40 % based on Mann-Kendall test. The results indicate that the highest frequency of days with thermal comfort in the southern coasts of Iran relates to the end of autumn and winter, nevertheless, such ideal conditions for the coastal cities of Caspian Sea and even central stations of Iran relate to mid-spring and mid-autumn. Late summer and early autumn along with late spring can be identified as the most ideal times in the west and northwest part of Iran. In addition, the most important inhibiting factors of thermal comfort prove to be different across the regions of Iran. For instance, in the southern coasts, warm to very hot bioclimatic events and in the west and northwest regions, cold to very cold conditions turn out to be the most important inhibiting factors. When considering the variations across the studied period, an increase in the frequency of thermal comfort condition is observed in almost half of the stations. Moreover, based on Pearson and Mann-Kendall methods, the trend of changes in monthly averages of PET has decreased in most stations and months, which can lead to different consequences in each month and station. Thus, it is expected that due to PET changes in recent decades and to the intensified global warming conditions, Iran's bioclimatic conditions change in a way that transfers the days with comfort to early spring and late autumn. [ABSTRACT FROM AUTHOR]

Background: The climate change fact is intensive among the Middle East countries and especially Iran. Among the Middle East countries, Iran will experience an increase of 2.6 °C in mean temperatures and a 35% decline in precipitation in the next decades. In vice versa, Iran by total greenhouse gas (GHG) emissions nearly to 616,741 million tons of CO2 is the first responsible country to climate change in the Middle East, and seventh in the world. The high-level contribution of Iran to emissions of GHG depends on a significant production of oil, gas, and rapid urbanization. The present study aimed to reveal an overview of climate change facts and statistics in Iran.Results: In this manuscript, the evidential facts on climate change were investigated in global, regional, and national scales. For this purpose, the main increasing annual temperature and GHG emissions were considered. Besides, the variations of meteorological characteristics such as surface temperature, total precipitation, and upward longwave radiation (ULR) were reviewed in Iran indicating an anomalous decrease in precipitation events and anomalous increase in ULR and temperature characteristics confirming the global warming/climate change effects. Afterward, the legislative agreements on climate change concerning international adoptions and conventions were reviewed from Rio 1992 to NY 2016.Conclusions: The results showed that further research and development should be considered the novel methods to explore renewable energy applications and to mitigate GHG emissions in order to overcome the increased risk of climate change effects. Technological affairs and international participants should support this target. [ABSTRACT FROM AUTHOR]

The present study examined the forest floor, soil properties, and dynamics of greenhouse gas fluxes in 23-years old stands of alder (Alnus subcordata C. A. M.), maple (Acer velutinum Bioss.), cypress (Cupressus sempervirens L. var. horizontalis), and pine (Pinus nigra) sites in northern Iran. A degraded natural forest (DNF) with sparse trees including Parrotia persica C. A. M., Carpinus betulus L., and Diospyros lotus L. located near the afforested stands was selected as the control region. Fifteen forest floor and topsoil (0–15 cm) samples per site were taken and analyzed in the laboratory. In addition, the seasonal changes in the fluxes of CO 2 , CH 4 , and N 2 O were studied over a year. The quality of the forest floor differed among the sites, indicating the highest N, and the lowest C concentrations in the alder stand. The soil bulk density, pH, available nutrients (P, Ca, and Mg), earthworm activities and microbial biomass N were significantly higher in the alder stand with organic C being the least. The microbial biomass C did not differ among the studied sites, whereas the total N was significantly higher in the alder ≈ maple sites having the minimum C/N ratios. High contents of water were found in the DNF site. The highest CO 2 (0.31 mg CO 2 m−2 d−1) and N 2 O (0.42 mg N 2 O m−2 d−1) fluxes were observed in the DNF site. The CH 4 flux showed higher amounts in the DNF ≈ cypress ≈ pine (0.12, 0.11 and 0.07 mg CH 4 m−2 d−1, respectively) sites than in the maple ≈ alder (−0.05, and − 0.04 mg CH 4 m−2 d−1, respectively) plantations. The highest values of all fluxes were measured in August, whereas the lowest were detected in February. This research recommends a plantation with suitable native broad-leaved species along with a forestation management plan to rehabilitate the forests of the DNF area. • Land cover encourage a specific signature in terms of forest floor quality which alters the topsoil properties. • Conversion of degraded natural forest to plantations improved the soil fertility. • The planting of cypress and pine species were not as successful as broad-leaved plantations. • Plantation decrease soil GHG fluxes than degraded natural forest. • The amounts of GHG emitted from the field were the most in August and the least in February. [ABSTRACT FROM AUTHOR]