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The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0–30 and 30–60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority. [ABSTRACT FROM AUTHOR]

The enormous heterogeneity and complexity of landscape patterns and their linkage with the hydrological responses have rarely been quantified and cataloged, especially in ungauged regions. This research therefore linked the landscape characteristics to hydrological processes using a newly developed runoff landscape index (RLI) at the watershed scale in Ardabil Province, northwest Iran. First, 11 common landscape metrics were calculated using Fragstats 4.2.1 software. Then, a runoff landscape index (RLI) was developed based on land cover (λC), soil (λK), and topography (λS) factors in 28 watersheds. Correlation and regression analyses were also conducted to determine the relationship between RLI, commonly used landscape metrics, and mean base flow. The spatial variations of all meaningful landscape metrics and RLI were considerable throughout the study watersheds. The mean values of λC, λK, and λS were found to be 2.78 ± 1.08, 0.50 ± 0.10, and 1.22 ± 0.30, respectively. The mean RLI varied from 0.00009 in the Lay Watershed with an area of 19.09 km2 to 0.28 in the Boran Watershed with 10,268.95 km2. The correlation coefficient (r > 0.42; p-value < 0.05) was obtained significantly between RLI and only five landscape metrics, including the largest patch index (LPI), landscape shape index (LSI), landscape division index (DIVISION), splitting index (SPLIT), and Shannon's diversity index (SHDI). In addition, a regression model with R2 of 0.97 and 0.67, respectively, in calibration and validation steps was established between river base flow as the dependent variable and main waterway length, LPI, LSI, SPLIT, modified Simpson's diversity index (MSIDI), and λS as independent variables. The result confirms the significant interdependence of RLI and landscape characteristics, which can be used to interpret the landscape's dynamic and its effects on hydrological processes. [ABSTRACT FROM AUTHOR]

In Iran, similar to other developing countries, groundwater quality has been seriously threatened. Therefore, this study aimed to apply Machine Learning Algorithms (MLAs) in Groundwater Quality Modeling (GQM) and determine the optimal algorithm using the Best–Worst Method (BWM) in Ardabil province, Iran. Groundwater quality parameters included calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), chlorine (Cl−), sulfate (SO4−), total dissolved solids (TDS), bicarbonate (HCO3−), electrical conductivity (EC), and acidity (pH). In the following, seven MLAs, including Support Vector Regression (SVR), Random Forest (RF), Decision Tree Regressor (DTR), K-Nearest Neighbor (KNN), Naïve Bayes, Simple Linear Regression (SLR), and Support Vector Machine (SVM), were used in the Python programming language, and groundwater quality was modeled. Finally, BWM was used to validate the results of MLAs. The results of examining the error statistics in determining the optimal algorithm in groundwater quality modeling showed that the RF algorithm with values of MAE = 0.28, MSE = 0.12, RMSE = 0.35, and AUC = 0.93 was selected as the most optimal MLA. The Schoeller diagram also showed that various ion ratios, including Na+K, Ca2+, Mg2+, Cl−, and HCO3+CO3, in most of the sampled points had upward average values. Based on the results of the BWM method, it can be concluded that a great similarity was observed between the results of the RF algorithm and the classification of the BWM method. These results showed that more than 50% of the studied area had low quality based on hydro-chemical parameters of groundwater quality. The findings of this research can assist managers and planners in developing suitable management models and implementing appropriate strategies for the optimal exploitation of groundwater resources. [ABSTRACT FROM AUTHOR]

The study of soil erosion is one of the necessities of watershed management, and erosion control requires understanding the behavior of runoff and sediment production in different conditions. This study aims to evaluate the effect of soil particle percentage, soil organic carbon (OC) content, slope, and initial moisture on runoff components and the amount of sediment at the plot scale. In this study, a rainfall simulator has been used to generate rainfall in the Shamsabad area, Ardabil province, Iran, and was performed at 58 points through synthetic rainfall with 20-min duration. Also, soil samples were taken and their properties were determined through laboratory analysis. The GIS was used to produce slope layers and Topographic Wetness Index. The average percentage of soil OC was 0.90, and the average of clay, sand, and silt was 24.39%, 50.26%, and 24.35%, respectively. Taylor diagram and triple diagram models were used to determine the dependence of runoff and sediment behavior on independent variables. The average percentage of soil OC was 0.90, and the average of clay, sand, and silt was 24.39%, 50.26%, and 24.35%, respectively. The results showed that runoff termination time is directly related to soil clay content and inversely related to soil sand content; in other words, with increasing sand to more than 35%, runoff production time became longer. The amount of sediment or soil clay has shown a positive relationship with the percentage of sand, and the amount of slope is negatively related. By increasing the amount of soil clay to above 40%, the amount of sediment has reached its highest level in gentle slope values (< 10 degrees). Considering the complexity of sediment production behavior and the role of various factors on runoff and sediment production in interaction with all influential factors should be considered. [ABSTRACT FROM AUTHOR]

The occurrence and intensity of climatic and hydrologic extreme events, as indicators of climate change, are increasing in most parts of arid and semi-arid regions, including Ardabil province, Iran. Recent studies have revealed the need for the multidimensional assessment of flood disasters in this area. Consequently, this present study was conducted to provide comprehensive information on the flood vulnerability of 26 watersheds in Ardabil province. Here, six components, including meteorological, hydrological, physical-environmental, social, economic, and countermeasures, were computed at a watershed scale based on 19 different criteria. Finally, the flood vulnerability index (FVI) for each watershed was calculated. The results revealed the need for different management approaches for flood hotspots based on the vulnerability to the six components studied. The integrated FVI showed that 46.97%, 33.63%, 18.10%, 1.20%, and 0.10% of the province have very high, high, medium, low, and very low flood vulnerability, respectively. The spatial mapping also revealed that all study areas were under flood stress, except small parts in central, east, and north. The preliminary version of the flood vulnerability atlas is presented, which estimates the flood disaster risk throughout the province. In addition, the developed regional framework in this study also allows for more comprehensive and extensive dataset analysis. [ABSTRACT FROM AUTHOR]

Assessing and monitoring ecosystem health are critical components of ecosystem management as they provide information on natural resource destruction and the causes and factors leading to it. The need for comprehensive and multidimensional indices for assessing and predicting the status of ecosystems has become increasingly important. The ecosystem health index (EHI) was calculated by assessing multiple criteria, including vigor, organization, and resilience criteria, in an ecotone ecosystem in Ardabil province. Different weighting approaches were used to evaluate the index value, and sub-watersheds were prioritized based on the index weighting. The EHI values varied among the studied sub-watersheds, ranging from 0.32 to 0.79, depending on the weighting method. Sub-watershed SW1 had a higher health status with a value of 0.77 compared to other studied sub-watersheds in the Iril watershed, while SW5 had a lower health status with a value of 0.34 in the equal weighting approach. The average value of the EHI for all studied sub-watersheds in the Iril watershed was found to be 0.50, indicating a relatively undesirable ecosystem health status. The EHI is a useful tool for prioritizing areas and an effective step in ecosystem management, considering the multidimensional nature of the index and the factors affecting ecosystem restoration or destruction. Assessing the extent of ecosystem degradation across various regions depends on the health status of the studied ecosystem, the feasibility of index calculation, and may involve different selection criteria. [ABSTRACT FROM AUTHOR]

Invasive plants can alter the function and structure of ecosystems resulting in social, economic, and ecological damage. Effective methods to reduce the dominance of invasive plants are needed. The present study was aimed at modeling the invasive species Leucanthemum vulgare Lam. in the rangelands of the Namin region in northwest Iran, as well as predicting the habitat connectivity of this species to detect areas with high habitat connectivity. Modeling of potential habitats was performed using logistic regression (LR) and maximum entropy (MaxEnt); the ensemble map which resulted from these was used to predict habitat connectivity using the electrical circuit method. Topography (elevation, slope, and aspect), climate (precipitation and temperature), and soil (acidity, electrical conductivity, soil texture, calcium, magnesium, sodium, phosphorus, potassium, organic carbon, organic matter, saturation percentage, and total neutralizing value) were used in this study. The presence and absence points of the L. vulgare were recorded using a stratified-random sampling method by means of a global positioning system. Soil samples were collected at a depth of 0 to 30 cm where L. vulgare was present and also where it was absent. According to the results, in LR, the variables of temperature, phosphorus, organic matter, and sand and in the MaxEnt, the variables of sand, total neutralizing value (TNV), and silt were the most influential factors on the distribution of L. vulgare. The appraisal of the MaxEnt performance and the precision of the model prediction were 0.97. The Kappa indices for the predicted map obtained from the LR and MaxEnt models were 0.80 and 0.73, respectively. The models' evaluation indicated that both models were able to predict the distribution of L. vulgare habitats with a high level of accuracy; however, LR was more reliable. According to the LR prediction, 9.91% (10,556.25 ha) of the Namin region was attacked by L. vulgare. Connectivity assessment showed that the current density spread of L. vulgare continued from the northeast of the Namin region toward the southeast. On the other hand, the higher current density spread was demonstrated in the eastern region (rangelands adjacent to Fandoghlu forests), and other rangelands which are more threatened by the invasion of L. vulgare. Identifying sites exposed to invasive species can help implement programs to prevent invasive species from invading areas where management and prevention should be implanted to prevent and/or reduce the spread. [ABSTRACT FROM AUTHOR]

The importance of protected areas (PAs) is mainly to protect biodiversity that is affected by the interactions between climate changes, landscape connectivity, and land use. The objectives are (1) identifying land use/cover classes and their changes occurred in the studied period, (2) quantifying landscape pattern to analyze the diversity, complexity, and fragmentation characteristics at class and landscape levels. Land use/cover (LU/LC) changes of the Marakan protected area (MPA), as a biodiversity hotspot in the northwest of Iran, were investigated with post-classification method, and also landscape pattern changes were analyzed using landscape metrics at the class and landscape levels in a time span of 31 years. The most obvious observations were as follows: (1) incremental changes occurred in the area of human-dominated LU/LC types and a tendency for development of merging patches and emerging new patches was detected. (2) Rangelands as the dominant LU/LC types in MPA depicted mainly a trend for conversion into low-quality rangelands, (3) MPA at the landscape level witnessed a decrease in the fragmentation due to decreasing of the patches number specifically in high and low-density rangeland and merging process of patches in human-dominated LU/LC types. The main net changes are considered as negative from the ecological point of view, mainly in high-density rangeland that undoubtedly affects ecosystem process and function. Therefore, the decision makers and planners may consider and manage these undesirable changes on the landscape scale, which can be easily understood by local people. [ABSTRACT FROM AUTHOR]

Predicting the impacts of flood control measures to determine the best spatial distribution and specifications of check dams assists managers and engineers in planning flood control projects. The focus of this paper is on scenario analysis of check dam construction using a multi-criteria decision-making (MCDM) technique in the Jafar-Abad Watershed, Golestan Province, Iran. Based on spatial distribution, number and elevation of check dams, eight structural management scenarios were developed. For each scenario, flood hydrographs for different return periods were simulated using the HEC-HMS model. To predict the impacts of implementing the management scenarios, some hydrologic and hydroeconomic indices were quantified for each management scenario. To weight the indices, expert knowledge was elicited using the Delphi process. A MCDM approach was employed to choose the best management scenarios. The analysis shows that Scenario 7 (increasing the number of check dams from 58 to 69) is the best management scenario from the hydrologic perspective. In addition, best management scenarios from hydroeconomic perspective are Scenario 1 (current condition), and Scenario 5 (with only 15 check dams on an upstream sub-watershed), respectively. The approach implemented in this research is a useful way to allocate flood control measures efficiently and effectively. [ABSTRACT FROM AUTHOR]

Urban planning is essential for managing the diverse impacts of urban green spaces, such as public access, stormwater control, urban life quality, and landscape aesthetics, promoting sustainable urban development and urban residents' well-being by integrating green space considerations into city planning. The aim of this study is to use graph-based metrics to calculate the connectivity of UGS across the main municipal zones of Ardabil city over consecutive periods under different population growth rates. Another objective of this study is to compare the connectivity values of UGS in the four municipal zones and to evaluate changes in the connectivity indices at various distance thresholds of UGS patches. After identifying UGS in different periods, the changes in graph-based connectivity indices at various distance thresholds of UGS patches were analyzed. Additionally, the changes in connectivity indices over different periods and across various municipal zones were compared and analyzed. The findings reveal that UGS areas were larger in the past but have recently had smaller patch sizes. Connectivity between UGS nodes (dNL) decreased at various distances over the study years, showing a declining trend in different connectivity indices. UGS connectivity decreased in municipal zones 1, 2, and 3 but increased in recent years after a decline until 2012 across all four zones of Ardabil city. Zone 4 had the highest UGS connectivity due to newly developed urban areas and well-allocated UGSs. Integrating the ecological impacts of UGS connectivity in urban development and design will enhance trade-offs between conservation, public health, and social equity. New urban areas should allocate sufficient land for UGS and parks, ensuring accessibility to support health and leisure through municipal planning. The study highlights the need for sustainable urban development policies that prioritize the allocation and maintenance of UGSs., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)