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.

Spatial and temporal distribution of physicochemical parameters of water in Gorgan Bay (the southeast of the Caspian Sea - Iran): Fundamentals for the monitoring of the Caspian Sea coastal areas.

Gorgan Bay as a main part of the Miankaleh (a natural biosphere reserve registered by UNESCO) is one of the richest ecological area in the West Asia and very important internationally recognized refuge for the wildlife. To date, multi physicochemical parameters have not been examined on a large scale. To fill this knowledge gap, the present study aimed to explore the seasonal and spatial variability of water quality parameters of the bay. The results showed that except for depth and transparency, there are significant variations in most parameters across the four seasons. The patterns of these changes in the bay vary, as evidenced by a comparison of the distribution maps of the various factors throughout the year. Notably, alkalinity declined from east to west, reaching its highest levels at important entry points such as the Qarasu River, Bandar-Gaz, and the pier. TDS, on the other hand, increased westward, reaching its highest concentration in the shallow western regions. Maximum depth (310 cm) and transparency (250 cm) were observed in the central bay. While the pH was higher in deeper areas, the distribution of PO4 was more uniform. With lower levels in the east (salinity = 0.40 ‰) and higher levels in the west (salinity = 28.9 ‰), the salinity showed a coherent gradient. Agricultural land use in the basin of the bay and fluxes of nutrients and sediments of the rivers entering the bay has significant contribution to the bay pollution situation. These results will serve as a guide for improving our understanding of the Gorgan Bay ecosystem. They also have implications for informed conservation and management plans adapted to the specifics of this special region within the Caspian Sea.

The fate of nitrogen in the Zarin-Gol River receiving trout farm effluent.

This study investigated the Zarrin-Gol River ecosystem in Iran to trace organic matter in the food web and evaluate the impact of aquaculture farm effluent using stable isotopes of nitrogen (δ15N) and carbon (δ13C). Using a previously-developed model (Islam 2005), we estimated that a trout farm in the vicinity released 1.4 tons of nitrogen into the river. This was comparable to an estimated total nutrient load of 2.1 tons of nitrogen for the six-month fish-rearing period based on a web-based constituent load estimator (LOADEST). A model estimate of river nitrogen concentration at the time of minimum river discharge (100 L/s) was 2.74 mg/L. Despite relatively high nitrogen loading from the farm, isotope data showed typical food web structure. Several biological groups had elevated δ13C or δ15N values, but there was limited evidence for the entry of organic matter from the trout farm into the food web, with sites above and below trout farms having inconsistent patterns in 15N enrichment. By coupling nitrogen load modeling with stable isotope analysis we showed that stable isotopes might not be effective tracers of organic matter into food webs, depending on surrounding land use and other point sources of nutrients. The Zarrin-Gol River ecosystem, like other basins with high human population density, remains vulnerable to eutrophication in part due to trout farm effluent.

Developing an integrated land allocation model based on linear programming and game theory.

Land use configuration in any given landscape is the result of a multi-objective optimization process, which takes into account different ecological, economic, and social factors. In this process, coordinating stakeholders is a key factor to successful spatial land use optimization. Stakeholders need to be modeled as players who have the ability to interact with each other towards their best solution, while considering multiple goals and constraints at the same time. Game theory provides a tool for land use planners to model and analyze such interactions. In order to apply the spatial allocation model and address stakeholder conflicts, an integrated model based on linear programming and game theory was designed in this study. For implementing such model, we conducted an optimal land use allocation process through multi-objective land allocation (MOLA) and linear programming methods. Then, two groups of environmental and land development players were considered to implement the optimization model. The game algorithm was used to select the appropriate constraint so that the result would be acceptable to all stakeholders. The results showed that during the third round of the game, the decision-making process and the optimization of land uses reached the desired Nash Equilibrium state and the conflict between stakeholders was resolved. Ultimately, in order to localize the results, a suitable solution was presented in a GIS environment.

Hydrological connectivity assessment of landscape ecological network to mitigate development impacts.

To mitigate the negative effects of land use developments, the current study focused on the hydrological connectivity within the landscape ecological network of Gharesou watershed, Iran, using Graph theory. Thus, scenarios of the future land use arrangements were used for the objective assessment of the effects of patterns on the ecological structures and functions, the main target being runoff control. Hydrological connectivity was analyzed using runoff source network, stream network and its buffer zone. Also, functions like permeability and runoff production potential were analyzed for the future scenarios. Following the ranking of the connectivity significance of the hydrological graphs elements, the ecosystem services hotspots and incompatible land uses were demonstrated. Subsequent assessments of the elements of runoff source networks using Circuit Theory helped identify the future critical areas. Analyses of the hydrological graphs and the runoff source network represented the amount and location of critical areas in each development scenario as well as the imposed hydrological costs. The hydrological and ecological land use costs were used in the process of land use optimization through Simulating Annealing algorithm (SA). Using these costs in the land use planning process resulted in detecting areas which may experience disturbance later in future. Finally, the results of the optimization of scenarios showed how land use arrangements in each scenario can be optimized to simultaneously include the ecological suitability (vertical relationships) and the ecological network relationships (horizontal relationships).

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.

Evaluation of the relationship between soil erosion and landscape metrics across Gorgan Watershed in northern Iran.

Soil erosion is one of the most serious environmental threats strongly influenced by the spatial pattern of land uses. This study was designed to evaluate the relevance of land use pattern and soil erosion using landscape metrics across Gorgan Watershed in northern Iran. Therefore, the revised universal soil loss equation was applied to evaluate and model soil loss and sedimentation in the region. Then, soil erosion relationship to land use pattern was analyzed using a variety of metrics including percentage of landscape, number of patches, largest patch index, and landscape shape index. The results revealed that potential of soil loss, sediment retention, and sediment yield for the whole watershed were 6.6, 2.4, and 1.5 t ha-1 year-1, respectively. The quantity of sediment retention was estimated at 4.3, 3.2, 1.0, and 1.2 t ha-1 year-1 in forest, rangelands, agriculture, and built-up areas, respectively. Similarly, sediment yield was 0.6, 1.6, 1.5, and 2.1 t ha-1 year-1, respectively. The results revealed that the soil loss increased with decreasing metrics of forest and rangelands while increasing metrics of built-up regions and agricultural lands accelerated the process. Moreover, we showed that land use type of patches was an important factor on soil erosion, and soil loss was also affected by area, number, shape, and density of landscape patches. Result of this study can facilitate monitoring of erosion-sensitive areas in the watershed which can help managers and decision makers to design more suitable measures for soil conservation.

Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting.

Efficient land use management requires awareness of past changes, present actions, and plans for future developments. Part of these requirements is achieved using scenarios that describe a future situation and the course of changes. This research aims to link scenario results with spatially explicit and quantitative forecasting of land use development. To develop land use scenarios, SMIC PROB-EXPERT and MORPHOL methods were used. It revealed eight scenarios as the most probable. To apply the scenarios, we considered population growth rate and used a cellular automata-Markov chain (CA-MC) model to implement the quantified changes described by each scenario. For each scenario, a set of landscape metrics was used to assess the ecological integrity of land use classes in terms of fragmentation and structural connectivity. The approach enabled us to develop spatial scenarios of land use change and detect their differences for choosing the most integrated landscape pattern in terms of landscape metrics. Finally, the comparison between paired forecasted scenarios based on landscape metrics indicates that scenarios 1-1, 2-2, 3-2, and 4-1 have a more suitable integrity. The proposed methodology for developing spatial scenarios helps executive managers to create scenarios with many repetitions and customize spatial patterns in real world applications and policies.

Environmental monitoring and assessment of landscape dynamics in southern coast of the Caspian Sea through intensity analysis and imprecise land-use data.

A hierarchical intensity analysis of land-use change is applied to evaluate the dynamics of a coupled urban coastal system in Rasht County, Iran. Temporal land-use layers of 1987, 1999, and 2011 are employed, while spatial accuracy metrics are only available for 2011 data (overall accuracy of 94%). The errors in 1987 and 1999 layers are unknown, which can influence the accuracy of temporal change information. Such data were employed to examine the size and the type of errors that could justify deviations from uniform change intensities. Accordingly, errors comprising 3.31 and 7.47% of 1999 and 2011 maps, respectively, could explain all differences from uniform gains and errors including 5.21 and 1.81% of 1987 and 1999 maps, respectively, could explain all deviations from uniform losses. Additional historical information is also applied for uncertainty assessment and to separate probable map errors from actual land-use changes. In this regard, historical processes in Rasht County can explain different types of transition that are either consistent or inconsistent to known processes. The intensity analysis assisted in identification of systematic transitions and detection of competitive categories, which cannot be investigated through conventional change detection methods. Based on results, built-up area is the most active gaining category in the area and wetland category with less areal extent is more sensitive to intense land-use change processes. Uncertainty assessment results also indicated that there are no considerable classification errors in temporal land-use data and these imprecise layers can reliably provide implications for informed decision making.

Tailoring a non-path-dependent model for environmental risk management and polycentric urban land-use planning.

This study attempts to develop a non-path-dependent model for environmental risk management and polycentric urban land-use planning in Gorgan Township area, Iran. Applying three suitability layers of environmental risk (soil erosion, flood risk, fire risk, and land susceptibility), urbanization potential, and integrated surface (environmental risk plus urbanization potential layers), a non-path-dependent Cellular Automata-Markov Chain (CA-MC) model was configured to execute three scenarios of polycentric urban growth allocation. Specifically, the modeling approach improved the traditional functionality of the CA-MC model from a prediction algorithm into an innovative land allocation tool. Besides, due to its flexibility, the non-path-dependent model was able to explicitly include different characteristics of the landscape structure ranging from physical land attributes to landscape functions and processes (natural hazards). Accordingly, three polycentric urban growth allocation efforts were undertaken and compared in terms of connectivity and compactness of the resultant patterns and consumption of other land resources. Based on results, the polycentric allocation procedure based on integrated suitability layer produced a more manageable pattern of urban landscape, while the growth option based on environmental risk layer was more successful for protecting farmlands against excessive urbanization. This study suggests that polycentric urban land-use planning under the strategy of rural land development programs is an available option for designing an urban landscape with lower exposure to natural hazards and more economic benefits to rural residents. Finally, the non-path-dependent modeling is a recommended approach, when highly flexible and interactive decision-support systems as well as trend-breaking scenarios are desired.

Optimizing cultivation of agricultural products using socio-economic and environmental scenarios.

The combination of degrading natural conditions and resources, climate change, growing population, urban development, and competition in a global market complicate optimization of land for agricultural products. The use of pesticides and fertilizers for crop production in the agricultural fields has become excessive in the recent years and Golestan Province of Iran is no exception in this regard. For this, effective management with an efficient and cost-effective practice should be undertaken, maintaining public service at a high level and preserving the environment. Improving the production efficiency of agriculture, efficient use of water resources, decreasing the use of pesticides and fertilizers, improving farmer revenue, and conservation of natural resources are the main objectives of the allocation, ranking, and optimization of agricultural products. The goal of this paper is to use an optimization procedure to lower the negative effects of agriculture while maintaining a high production rate, which is currently a gap in the study area. We collected information about fertilizer and pesticide consumption and other data in croplands of eastern Golestan Province through face-to-face interviews with farmers to optimize cultivation of the agricultural products. The toxicity of pesticides according to LD50 was also included in the optimization model. A decision-support software system called multiple criteria analysis tool was used to simultaneously minimize consumption of water, chemical fertilizers, and pesticides and maximize socio-economic returns. Three scenarios for optimization of agricultural products were generated that alternatively emphasized on environmental and socio-economic goals. Comparing socio-economic and environmental performance of the optimized agricultural products under the three scenarios illustrated the conflict between social, economic, and environmental objectives. Of the six crops studied (wheat, barley, rice, soybeans, oilseed rape, and maize), rice ranked second in the social and fifth in the economic scenarios. Soybeans had the lowest rank for economic and social scenarios and its cultivation in the study area, in terms of economic and social goals, was rejected by the model. However, cultivation of soybeans continues in the area as a responsibility to cater for the major need of the country. Because of subsidized prices of water, fertilizers, and pesticides, the use of these items are far from optimized in the current agricultural practices in the area.

Rules versus layers: which side wins the battle of model calibration?

Continuous surface of urbanization suitability, as an input to many urban growth models (UGM), has a significant role on a proper calibration process. The present study evaluates and compares the simulation success of the Cellular Automata-Markov Chain (CA-MC) model through multiple methods. For this, a series of mapping algorithms are applied ranging from empirical methods such as multi-criteria evaluation (MCE) to statistical algorithms without spatially explicit suitability mapping rules such as logistic regression (LR) and multi-layer perceptron (MLP) neural network and finally statistical and spatially explicit rule-based methods such as SLEUTH-Genetic Algorithm (SLEUTH-GA) model. The CA-MC model was calibrated in three study locations including Azadshahr, Gonbad, and Gorgan cities in northeastern Iran. Applying Kappa-based indices (Kappa, K location, K Simulation, and K Transloc) and computing relative error (RE) values of landscape metrics, performance of the model was quantified and compared across the three study sites. The MCE and SLEUTH-GA methods, as the most data-demanding and the most computationally complex methods, respectively, yielded approximately similar results (especially in case of Kappa-based indices) and these methods were less successful compared to LR and MLP models. LR and MLP models were less data-demanding, while they produced approximately equal results. This study concludes that, when historical growth patterns feed an urbanization suitability mapping process, neither rules (SLEUTH-GA) nor layers (MCE) are effectively efficient when applied in a separated manner. Instead, methods with statistical rules and least-correlated input layers (LR and MLP) provide better simulation outputs. In contrast, methods such as MCE are more applicable when a non-path-dependent mapping procedure is desired since this method does not require training data (dependent variable) and the provided flexibilities in urbanization suitability mapping under various scenarios can improve the functionality of land-use change prediction algorithms into innovative land allocation tools.

An integrated spectral-textural approach for environmental change monitoring and assessment: analyzing the dynamics of green covers in a highly developing region.

The present study compares the effectiveness of two common preclassification change detection (CD) methods that use two-dimensional data space of spectral-textural (S-T) change information. The methods are principal component analysis (PCA) and change vector analysis (CVA) in the Gorgan Township area, Golestn Province, Iran. A series of texture-based information was calculated mainly to separate those land use/land cover (LULC) conversions that are spectrally indistinguishable and also to provide a basis for automatic classification of S-T data space. Both methods were evaluated in terms of accuracy and the required time and expertise. Having the two-dimensional S-T data space generated, support vector machine (SVM) classifier was implemented to automatically extract changed pixels and the receiving operator characteristic (ROC) was employed to assess the accuracy of the output. According to the results, the study area has witnessed substantial mutual transformations between various LULCs among agricultural lands were the most dynamic category in the region. The PCA method applied to the S-T information achieved a ROC of 0.90-indicating an acceptable performance-while the S-T CVA method achieved a lower value of 0.75. The S-T PCA method was considerably less time-consuming and less expertise demanding as well as more accurate in our study area.

Performance assessment of geospatial simulation models of land-use change--a landscape metric-based approach.

Performance evaluation is a critical step when developing land-use and cover change (LUCC) models. The present study proposes a spatially explicit model performance evaluation method, adopting a landscape metric-based approach. To quantify GEOMOD model performance, a set of composition- and configuration-based landscape metrics including number of patches, edge density, mean Euclidean nearest neighbor distance, largest patch index, class area, landscape shape index, and splitting index were employed. The model takes advantage of three decision rules including neighborhood effect, persistence of change direction, and urbanization suitability values. According to the results, while class area, largest patch index, and splitting indices demonstrated insignificant differences between spatial pattern of ground truth and simulated layers, there was a considerable inconsistency between simulation results and real dataset in terms of the remaining metrics. Specifically, simulation outputs were simplistic and the model tended to underestimate number of developed patches by producing a more compact landscape. Landscape-metric-based performance evaluation produces more detailed information (compared to conventional indices such as the Kappa index and overall accuracy) on the model's behavior in replicating spatial heterogeneity features of a landscape such as frequency, fragmentation, isolation, and density. Finally, as the main characteristic of the proposed method, landscape metrics employ the maximum potential of observed and simulated layers for a performance evaluation procedure, provide a basis for more robust interpretation of a calibration process, and also deepen modeler insight into the main strengths and pitfalls of a specific land-use change model when simulating a spatiotemporal phenomenon.