Climate change projection using statistical downscaling model over southern coastal Iran.

Iran is highly vulnerable to climate change, particularly evident in shifting precipitation and temperature patterns, especially in its southern coastal region. With these changing climate conditions, there is an urgent need for practical and adaptive management of water resources and energy supply to address the challenges posed by future climate change. Over the next two to three decades, the effects of climate change, such as precipitation and temperature, are expected to worsen, posing greater risks to water resources, agriculture, and infrastructure stability. Therefore, this study aims to evaluate the alterations in mean daily temperature (Tmean) and total daily rainfall (rrr24) utilizing climate change scenarios from both phases 5 and 6 of the Coupled Model Inter-comparison Project (CMIP5 and CMIP6, respectively) in the southern coastal regions of Iran (Hormozgan province), specifically north of the Strait of Hormuz. The predictions were generated using the Statistical Downscaling Model (SDSM) and National Centre for Environmental Prediction (NCEP) predictors, incorporating climate change scenarios from CMIP5 with Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 and CMIP6 with Shared Socioeconomic Pathways (SSPs) 1, 2, and 5. The analysis was conducted for three distinct time periods: the early 21st century (2021-2045), middle 21st century (2046-2071), and late 21st century (2071-2095). The results indicated that the CMIP5 model outperformed the CMIP6 model in simulating and predicting Tmean and rrr24. In addition, a significant increase in Tmean was observed across all the scenarios and time periods, with the most pronounced trend occurring in the middle and late 21st century future periods. This increase was already evident during the base period of 2021-2045 across all scenarios. Moreover, the fluctuations in precipitation throughout the region and across all scenarios were significant in the three examined future periods. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of Tmean (+1.22 °C) in Bandar Lengeh station in 2071-2095 period. The lowest change magnitude of Tmean among CMIP5 scenarios was found in RCP4.5 (-1.94 °C) in Ch station in 2046-2070 period. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of rrr24 (+150.2 mm) in Chabahar station in 2071-2095 period. The lowest change magnitude of rrr24 among CMIP5 scenarios was found in RCP8.5 (-25.8 mm) in Bandar Abbas station in 2046-2070 period. In conclusion, the study reveals that the coastal area of Hormozgan province will experience rising temperatures and changing rainfall patterns in the future. These changes may lead to challenges such as increased water and energy consumption, heightened risks of droughts or floods, and potential damage to agriculture and infrastructure. These findings offer valuable insights for implementing local mitigation policies and strategies and adapting to emerging climate changes in Hormozgan's coastal areas. For example, utilizing water harvesting technologies, implementing watershed management practices, and adopting new irrigation systems can address challenges like water consumption, agricultural impacts, and infrastructure vulnerability. Future research should accurately assess the effect of these changes in precipitation and temperature on water resources, forest ecosystems, agriculture, and other infrastructures in the study area to implement effective management measures.

Studies on genetic diversity, gene flow and landscape genetic in Avicennia marina: Spatial PCA, Random Forest, and phylogeography approaches.

Mangrove forests grow in coastal areas, lagoons, estuaries, and deltas and form the main vegetation in tidal and saline wetlands. Due to the mankind activities and also changes in climate, these forests face degradations and probably extinction in some areas. Avicennia marina is one of the most distributed mangrove species throughout the world. The populations of A. marina occur in a limited region in southern parts of Iran. Very few genetic and spatial analyses are available on these plants from our country. Therefore, the present study was planned to provide detailed information on Avicennia marina populations with regard to genetic diversity, gene flow versus genetic isolation, effects of spatial variables on connectivity and structuring the genetic content of trees populations and also identifying adaptive genetic regions in respond too spatial variables. We used SCoT molecular markers for genetic analyses and utilized different computational approaches for population genetics and landscapes analyses. The results of present study showed a low to moderate genetic diversity in the studied populations and presence of significant Fst values among them. Genetic fragmentation was also observed within each province studied. A limited gene flow was noticed among neighboring populations within a particular province. One population was almost completely isolated from the gene flow with other populations and had peculiar genetic content.Spatial PCA analysis revealed both significant global and local genetic structuring in the studied populations. Spatial variables like humidity, longitude and altitude were the most important spatial features affecting genetic structure in these populations.

Linear programming the Ramsar convention's criterion IV (case study: Shadegan Wetland, West Asia).

Wetland ecosystems provide critical ecological services to both humans and wildlife. However, many wetlands around the world are facing challenges that threaten their ecological integrity and jeopardize their status as Ramsar Convention sites. The Shadegan Wetland, a Ramsar site since 1975, has been on the Montreux Record since 1993 due to changing conditions in the wetland. This study aims to utilize linear programming (LP) techniques to evaluate the status of criterion IV of the Ramsar Convention within the Shadegan Wetland. Using mathematical language and Excel software, we defined criterion IV and developed a linear model. The Lingo software was employed to verify the model by setting constraints for proxy variables (X variables). We selected constraints based on extreme climatic conditions, such as energy and water limitations, during the study period while considering the trend of each variable. By identifying effective interventions for promoting sustainable use of the wetland while preserving its ecological balance, the LP can support the efforts to re-nominate the Shadegan Wetland as a Ramsar site. Considering the critical conditions, the lowest value of Z in the studied period unravels the critical year as the target. Based on the result, 2015 with the lowest value of the Z index (- 0.36) was identified as the critical year in the entire study period starting from 2001-2019. In the critical year itself, the population of birds equals 50,000 birds, while the average population of birds over the course of the past 20 years was nearly 37,000 birds.

Putative Local Adaptive SNPs in the Genus Avicennia.

The genus Avicennia with eight species grow in intertidal zones of tropical and temperate regions, ranging in distribution from West Asia, to Australia, and Latin America. These mangroves have several medicinal applications for mankind. Many genetic and phylogenetic studies have been carried out on mangroves, but none is concerned with geographical adaptation of SNPs. We therefore, used ITS sequences of about 120 Avicennia taxa growing in different parts of the world and undertook computational analyses to identify discriminating SNPs among these species and to study their association with geographical variables. A combination of multivariate and Bayesian approaches such as CCA, RDA, and LFMM were conducted to identify the SNPs with potential adaptation to geographical and ecological variables. Manhattan plot revealed that many of these SNPs are significantly associated with these variables. The genetic changes accompanied by local and geographical adaptation were illustrated by skyline plot. These genetic changes occurred not under a molecular clock model of evolution and probably under a positive selection pressure imposed in different geographical regions in which these plants grow.

Using Artificial Intelligent to Model Predict the Biological Resilience With an Emphasis on Population of cyanobacteria in Jajrood River in The Eastern Tehran, Iran.

Prediction of bio-resilience in water resources such as rivers is important for better management of land-use systems and water resources. This study has proposed the use of artificial intelligent (AI) models for assessing the relationship among the biological conditions in Jajrood River. For this purpose, the qualitative monthly data of the river related to 2008-2018 were applied. Different resilience indicators for preparation of scenarios were determined using the canonical correlation analysis (CCA) method. Appropriate time-series scenarios (5scenarios) were modelled via Gene Expression Programming (GEP) plus Support Vector Machine (SVM), the bio-indicators were predicted. In order to reduce the error, the wavelet hybrids (W-GEP and W-SVM) were also used for modelling. Validation of the models was performed using Nash-Sutcliffe efficiency (E), root mean square error (RMSE), and mean absolute error (MAE). In all the models investigated, Scenario 3 and Scenario 4 had the highest and lowest accuracies as 0.98 and 0.33 in validation, respectively. The third scenario combined with NO3 -, BODt-1, BOD, PO3-, and Q provided the best results. Then, the values of 0.98, 0.94, 0.82, and 0.78 were obtained for its validation by WSVM, WGEP, SVM, and GEP models, respectively. These findings suggested the superiority of hybrid models and SVM over classical models and GEP in water quality assessment respectively. Examination of the scenarios revealed that NO3 - and DO had the highest and the lowest impact on Shannon index of Cyanophyceae algae over time, as a bio-indicator of water quality in the river, respectively.

Scenario-based planning for reduction of emitted CO2 from the Pars Special Economic Energy Zone (PSEEZ) of Iran.

The Pars Special Economic Energy Zone (PSEEZ) is one of the largest gas reserves in the world. To reduce the CO2 emission from the area in line with the international climate commitments, two groups of scenario have been developed: the first is using the ecosystem services of mangrove forests, and the second is technology development for oil and gas industries. For the first scenario, the carbon sequestrated by the trees was calculated using the allometric equation, and the carbon of sediments was determined by using the Walkley-Black method. For the second scenario, CO2 emitted from the PSEEZ was calculated by using the Iranian Petroleum Ministry Guidelines. CO2 emission from the PSEEZ was 0.030 Gt year-1. The maximum CO2 sequestered by mangrove scenarios was 0.12% of the PSEEZ emissions, while the flaring elimination technology reduces 36% of the PSEEZ emissions. Generally, the scenario of using oil and gas industry technologies is more effective.

Applying landscape metrics and structural equation modeling to predict the effect of urban green space on air pollution and respiratory mortality in Tehran.

Green space and its spatial formation are important elements of public welfare in urban environments and green ecosystems in big cities largely contribute to the mental and physical health of citizens. Tehran is Iran's biggest and most polluted city and air pollution in this city causes loss of human lives due to respiratory diseases. The effect of green area has been less studied in former researches in Tehran, and the reducing effects of green landscape on the mortality of respiratory diseases have not yet been evaluated. To measure the effects of green area landscape patterns (fragmentation, area-edge, shape, and aggregation) on public health, the current study evaluated the pathways and effects of green space on air pollution and the mortality of respiratory diseases using structural equation modeling approach and the partial least squares method. The results of the study indicated green space has a significant mitigating effect on air pollution and mortality of respiratory diseases and also air pollution has a meaningful increasing effect on mortality due to respiratory diseases in Tehran. The most important latent variable in green space is class area that indicates more area of green space is correlated with less mortality of respiratory diseases. The most important indicator of air pollution was the PM2.5 that needs to be considered and controlled by urban policymakers. Accordingly, maximizing the green area and its cohesion and minimizing fragmentation and green patch edge can contribute to a reduction in air pollution and consequently lower mortality of citizens.

A new ecosystem-based land classification of Iran for conservation goals.

Land management and biodiversity protection are highly dependent on ecosystem classification. To identify the ecosystems, often ecologically homogenous areas are distinguished based on physical and biological features at various scales. These areas can also be considered as biodiversity surrogates for protection policies and planning. We classified the terrestrial areas of Iran into ecosystems using revised and updated layers of landform and climate as our two main criteria. Moreover, we applied a revised vegetation layer as the confirmatory criterion. At a scale of 1:1,000,000, we obtained a total of 119 homogenous ecological units, and based on the dominant vegetation types, we classified them into 21 terrestrial ecosystems at the national level. Of these ecosystems, 11 were dominated by vegetation, and the remaining 10 had sparse nondominant vegetation. Evaluation of the least and most frequent ecosystem patches and ranking of their size classes using landscape metrics provided an information basis for better land protection planning. We maintain that each ecosystem needs to be represented by a protected area and its size and distribution also helps us form a comprehensive and effective protection network in Iran. Graphical abstract.

Capability assessment and tourism development model verification of Haraz watershed using analytical hierarchy process (AHP).

The present study aimed at assessing tourism potential of a place to meet requirements of sustainable development policies. We studied the Haraz watershed because of its particular environmental characteristics and a high potential for ecotourism. The required data for this descriptive-analytical research were collected by combining field and desktop studies. First, the ecotourism capability assessment of the area was done using Arc GIS 10.3 software based on the Hyrcanian Forest Tourism Development Model for concentrated tourism and extensive tourism. Next, the most important effective indices included (i.e., 19 indices) were determined by Delphi questionnaire and SPSS 17. Finally, AHP technique was applied to analyze the body mass of the indices in order to verify the validity of the model. The results show that 0.0044, 01.3, 3.52, and 37.71% of the study area is suitable for concentrated ecotourism (grade 1), concentrated ecotourism (grade 2), extensive ecotourism (grade 1), and extensive ecotourism (grade 2), respectively. Based on the model applied, slope, direction, and fundamentals (infrastructure) with the body masses of 0.232, 0.116, and 0.115 were identified as the first priorities. Comparing the results of this model and AHP confirms the validity of the model. To strengthen the tourism development potential of the watershed and protect its ecosystems and biodiversity, it is necessary to choose a proper development model. Failure to identify the existing capacities and the field's sensitivities can cause dissatisfaction of local residents and also damage to the ecosystem of the area.

Combining biological and geomorphological data to introduce biotopes of Bushehr Province, the Persian Gulf.

Identification and classification of intertidal areas provides the basic knowledge needed for studies of biogeography, macro-ecology, and faunal populations, as well as for conservation planning and managing human activities in coastal areas. In this research, the eastern coast of Bushehr Province was classified using the Coastal and Marine Ecological Classification Standard (CMECS). Seven substrate subgroups, five geoform types in unconsolidated mineral substrate, five micro-habitats in rock substrate, and nine biotic groups were identified in study stations. The non-metric multidimensional scaling (nMDS) ordinations and one-way analysis of similarity (ANOSIM) showed that macroinvertebrate species composition differed significantly among different geoform types (habitat types). Eight biotopes are introduced for the eastern coast of Bushehr Province based on observational and statistical methods. The results presented here show that identifying intertidal biotopes using CMECS is an appropriate method both for classifying the southern coastal areas of Iran and for integrating biotic and abiotic components.

Investigation of Great Egret (Casmerodius albus) breeding success in Hara Biosphere Reserve of Iran.

Study of Great Egret breeding success was carried out for the first time in Hara Biosphere Reserve of Iran. Since Great Egret is considered as wading bird as well as wetland-dependent species which is located on top of the food chain in this ecosystem, its breeding study is an appropriate means for evaluating food supply fluctuations and environmental threatening factors by comparison of different years. On the other hand, Great Egret is considered a suitable indicator to examination of biological changes, impact of pollutions, and other effective human activities on Hara Biosphere Reserve. Therefore, read-ahead is required for area management planning in order to maintain the health of mangrove ecosystem and control threatening factors of the sensitive biodiversity of area. The results indicate that the average breeding success of Great Egret in different stages of hatching success, fledging success, and breeding success were equal to 0.54, 0.61, and 0.50 in 2008 and 0.61, 0.59, and 0.42 in 2009, respectively.

Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj).

Selection of landfill site is a complex process and needs many diverse criteria. The purpose of this paper is to evaluate the suitability of the studied site as landfill for MSW in Karaj. Using weighted linear combination (WLC) method and spatial cluster analysis (SCA), suitable sites for allocation of landfill for a 20-year period were identified. For analyzing spatial auto-correlation of the land suitability map layer (LSML), Maron's I was used. Finally, using the analytical hierarchy process (AHP), the most preferred alternative for the landfill siting was identified. Main advantages of AHP are: relative ease of handling multiple criteria, easy to understand and effective handling of both qualitative and quantitative data. As a result, 6% of the study area is suitable for landfill siting and third alternative was identified as the most preferred for siting MSW landfill by AHP. The ranking of alternatives were obtained only by applying the WLC approach showed different results from the AHP. The WLC should be used only for the identification of alternatives and the AHP is used for prioritization. We suggest the employed procedure for other similar regions.