زینب حزباوی

In recent years, our country has experienced an increase in the frequency and severity of natural hazards such as floods, droughts, and pests. These changes can be attributed to shifts in climate variables. Due to its influential role in other natural hazards such as drought, climate can significantly impact the economy and people's lives. This is particularly evident in the agricultural, animal husbandry, and industrial sectors, where it can cause damage and destruction in various regions. Drought is one of the biggest climatic challenges that our country has been facing in recent years. When a drought occurs, it initially manifests as a meteorological drought. If the drought persists, it can lead to other types of droughts, including hydrogeological, agricultural, and economic droughts. Each of these droughts, as well as their cumulative impact, affects various aspects of the ecosystem within a watershed. Climatic changes cause many problems due to their impact on the temporal and spatial distribution of precipitation in various regions. Due to its gradual process and slow speed, this phenomenon has been operating for a relatively longer period. Its effects may be revealed after a few years and with a longer delay than other risks. This phenomenon has more tangible effects in rural areas because rural communities are more vulnerable. Therefore, understanding this phenomenon and analyzing and evaluating its impact on ecosystems within a watershed is crucial for effective planning and decision-making. This knowledge is necessary for the implementation of appropriate adaptation strategies.

To carry out this research, the required climatic data were first collected from the Meteorology and Regional Water Department of Mazandaran Province. The SPI (Standardized Precipitation Index) index was also used to evaluate meteorological drought. Additionally, to examine the trend of changes in flow rate, the data from existing hydrometric stations in the region, which have more comprehensive data, were utilized. Lars-WG software was used to project future climate (2023-2050) conditions under two climate scenarios: an optimistic scenario (RCP2.6) and a pessimistic scenario (RCP8.5). After preparing the current and future data, the DrinC software was used to determine drought thresholds based on the standard table for the SPI. In this research, the IHACRES hydrological model was also utilized to forecast future discharge. To achieve this objective, the future precipitation and temperature data obtained from the previous stage were utilized, along with the observed discharge data (1990-2020), to project the trend of discharge in the future (2022-2040). Subsequently, the hydrological drought of the Tajan Watershed was calculated at the outlet station (Kordkhil) using the SDI (Streamflow Drought Index) in the DrinC software. After analyzing the stream flow data and using the standard table associated with the SDI, the thresholds for hydrological drought were determined.

The results of this study showed that, based on the SPI standard table, the degree of drought in the area under investigation has fluctuated between -3.3 (indicating very severe drought) and 2.4 (indicating extremely humid conditions) in recent years. Also, the future climate was projected for a period of approximately 27 years (2023-2050) under the influence of two optimistic scenarios (RCP2.6) and one pessimistic scenario (RCP8.5) at three selected stations (Kordkhil, Soleiman Tange, Rig Cheshme). The results of the climate drought conditions and the index values for the projected period (2023-2050) at Soleiman Tange station, in 12-month steps showed that there is a possibility of a severe climate drought occurring in this region during the water year 2049-2050. Also, the results show that there is no significant difference in the future drought index between the two scenarios. The results of the climate drought situation at Rig Cheshme station indicate a high likelihood of a severe climate drought in the region during the water year 2029-2030. Additionally, the results of the climate drought condition at Kordakhil station show that the condition at Kordakhil station differs somewhat from the other two stations, which are located at higher altitudes. This difference is likely due to the proximity of this station to the Caspian Sea, as the climate in that region is influenced by the coastal humidity. It can also be expected that there will be a severe drought in the water year 2032-2033 in this region. The results of the IHACRES model showed that the comparison chart of the simulated discharge and the observed discharge is in good agreement. Additionally, the error coefficient values obtained during the two stages of evaluation and recalibration were 0.48 and 0.53, respectively. These values indicate the model's acceptable ability to simulate future discharge. The results indicate that the discharge rate of the Watershed is unlikely to undergo significant changes in the future as a result of climate change. The probable cause for this is the occurrence of intense rainfall events, which lead to flooding and subsequent increases in river discharge. Considering that these results only show the effect of climate change on discharge, there is a possibility that other factors, such as land use changes, may also contribute to changes in the amount of discharge.

Conclusion :

The results of this analysis indicate that in both climate scenarios, there is a possibility of experiencing severe droughts in some years in the future. However, it is also possible for the climate to be very wet in certain years. These changes in the state of drought over approximately 30 years indicate climate fluctuations, which are one of the signs of climate change in a region. Therefore, based on the obtained results, it cannot be concluded that we will experience either a completely dry or completely wet state in the next 30 years. Thus, it is necessary to employ adaptation strategies to mitigate the impacts of drought during certain years. The hydrological drought situation of the Tajan River in the next 20 years (2020-2040) was evaluated using both optimistic and pessimistic scenarios. The drought situation in this river has also exhibited fluctuations, with a decreasing trend of flow in some years and an increasing trend in others. These fluctuations also indicate another effect of climate change in a region, which causes heavy rains and floods, leading to increased river flow during certain times of the year. However, relying solely on the study of the flow rate of a region cannot accurately indicate the drought situation. This is because certain measures implemented in watersheds can significantly impact the flow rate of a river. Therefore, considering all the conditions and climatic factors that govern our country, it is not surprising to see such changes occurring in different periods.

Water scarcity has become a critical issue in some regions. Accordingly, increasing attention has been given to the structural and functional connectivity of river networks as the primary source of surface water supply, while this can be a potential watershed-scale management solution. However, assessment of hydrological connectivity remains understudied in Iran. This pioneering research aimed to evaluate and optimize the hydrological connectivity in the Samian Watershed to enhance protection and management of water resources and improve hydrological performance. The results showed that the hydrological network in the Samian Watershed with a total length of 1254.73 km had 173 links and 176 nodes. The river chain node ratio (β), actual bonding degree (γ), Index of Integration of Connectivity (IIC), and Probability of Connectivity (PC) were calculated to be 0.983, 0.331, 5.66, and 1.151, respectively. The hydrological structure of the Samian Watershed in the plain areas exhibited weak connectivity. After optimization, the center of gravity of water circulation shifted southward at different connectivity levels. Increasing the optimization level up to Level 4 resulted in an improvement in the hydrological connectivity indices within the Samian Watershed. While the overall connectivity showed a sudden increase after the 4th optimization level, the IIC increased but then decreased beyond Level 4. Therefore, it is recommended to optimize the hydrological connectivity in the Samian Watershed up to Level 4 for effective management of water resources. 

The models for predicting the geographic distribution of plant species are static and probabilistic models and specify the mathematical relationships governing the geographical distribution of species with their current environment and important environmental factors affecting the distribution of species. The aim of the present research was to preparing a habitat prediction map of the Artemisia chamaemelifolia including the important medicinal species in the rangelands of Ardabil province. In the rangelands of Ardabil province, 449 sampling sites of the presence and absence of the studied species were considered from 2018 to 2021. Two categories of environmental factors including bioclimatic variables (19 cases) and topographic variables including primary indicators (3 cases) and secondary indicators (6 cases) were investigated in relation to the presence of the species. Maps of all environmental factors in the environment of the geographic information system were prepared and overlapped with 70% of the data. Prediction of species presence with four different modeling methods including; generalized linear model (GLM), generalized additive model (GAM), random forest model (RF) and generalized boosted regression model (GBM) were performed in the R software environment. The analysis of the importance of environmental variables for the models was done in the Biomode2 package. To evaluate the models, 30% of the species data and three statistics of Area under Curve (AUC), Kappa and True Skill Statistics (TSS) were used. The results of the modeling showed that the elevation was the most effective variable in the distribution of A. chamaemelifolia species in all four studied methods. In the GAM method, the variables of precipitation of the driest month, precipitation of the hottest season, and percentage of slope were also obtained as effective factors on the presence of this species. In addition to the elevation and the precipitation of the hottest season, the RF model also added the average daily temperature range to the factors affecting the presence of the species. Comparing the performance of the models showed that the GAM model with AUC 0.993, Kappa index 0.969, and TSS 0.985, is the best model among the studied models and was able to predict the habitat of the species at an excellent level. After that, the RF model with the AUC of 0.996, kappa 0.936, and TSS 0.941, with a small difference, is the second approved model in this connection. The results of this study showed that the species of A. chamaemelifolia has a relatively limited ecological niche and tends to grow in its own habitat conditions. Therefore, the prepared prediction maps can introduce the geographical areas in which the species is present to design and announce the protection areas of the species and can be used to suggest the species in the modification and restoration of areas with similar ecological conditions as the species' presence areas. The prediction model in the current study is expected to be effective for future conservation strategies.

Khuzestan province is one of the main hotspots of dust generation in Iran due to special geomorphological and climatic conditions, which expose it to the highest amount of dust. The dust hotspots of Khuzestan are located in seven areas with an area of 350,000 hectares, among them. The dust hotspot in the south and southeast of Ahvaz (dust hotspot No. 4) is the largest and one of the most critical dust hotspots in the province. Rehabilitation projects prioritize this location because of its salinity, alkalinity, and severe climate conditions. Identification of plant species is the first step in rehabilitating these ecosystems that can withstand environmental stress, decrease ecosystem interference, and provide more ecosystem services to local communities while protecting water and soil resources. The above goals can be achieved by planting native species, which also increases the chances of species adaptation (establishment and survival). Numerous studies have been conducted to determine the most suitable species for land reclamation. A researcher is investigating the possibility of planting, establishing, and supplying fodder for four species of halophytes in the Miqan Arak desert. The study's findings indicate that Halimion verrucifera, a native species in the region, is more advantageous in terms of fodder production and palatability. Therefore, cultivation of this species is recommended in this area. In another study, researchers evaluated salt-resistant wood species for land reclamation in Australia. This study focuses on comparing several species from Eucalyptus, Melaleuca, Acacia, and Casuarina in both greenhouse and field conditions. They suggested two species, E. camaldulensis and E. occidentalis, for further and supplementary investigations. Despite the extensive plantings in the Khuzestan dust hotspots in recent years, no research has been conducted to choose the suitable species for land reclamation in these areas. On the other hand dominant species in these projects was Prosopis.juliflora, which is a non-native species. The objective of the current study is to evaluate the establishment, survival, and growth characteristics of Pr. juliflora (Sw.) with 5 species: Tamarix aphylla (L.), Tamarix passerinoides (Delile ex Desv.), Lycium depressum (L.), Eucalyptus camaldulensis (Dehnh.) Seidlitzia rosmarinus (Bunge ex Boiss).

Dust hotspot No 4, Khuzestan is located approximately 25 km southeast of Ahvaz city and along the Ahvaz-Mahshahr highway, at a geographic coordinate system of 48° 47' to 49° 17' east longitude and 30° 15' to 31° 15' north latitude. This region experiences an average rainfall of 218 mm, a maximum temperature of 27.7 degrees Celsius, and a minimum temperature of 24.4 degrees Celsius. The climate of the region is classified as hyper-arid by the de Martonne climate classification method. After selecting the plant species, three levels of salinity were selected: high (61-105 dS/m) (Zone 1), medium (16-60 dS/m) (Zone 2), and low (0-15 dS/m) (Zone 3) with an area of about 30560 m2. 288 seedlings were planted in each salinity level and 864 in total number of seedlings = 3 (different salinity levels) × 6 (different species) × 48 (repetition) (four farrow and 12 repetitions in each farrow). Seedlings were prepared from Dezful, Hamidiyeh, Ramhormoz and Bagh Malik Plant nurseries. Planting distances and dimensions of holes for four species of T.passerinoides, T.aphylla, Pr.juliflora and Eu.camaldulensis were determined as 5 meters and 50x50x50 cm, respectively, and for two species Se.rosmarinus and Ly.depressum, 3 meters and 30x30x30 cm, respectively. After planting the seedlings, the first watering was carried out immediately with an average of 20 liters per seedling hole. Then, watering was done every 10 days until the dry season, and every week during it. Until the seedlings were fully established, livestock was prevented from entering the planted areas with the help of local forces. Measurements were taken after planting the seedlings, including canopy cover (large and small diameter), basal cover, and height of seedlings for every treatment, and replicates three times immediately after planting, six months after planting, and one year after planting. The survival rates were recorded at intervals of six months and one year after planting. The investigated characteristics at different salinity levels were compared using one-way analysis of variance and least significant difference (LSD) methods. The analysis of data was done using SPSS statistical software while graphs were done using EXCEL software.

The results revealed that among six plant species, T.aphylla, T. passerinoides, Se.rosmarinus and Pr. juliflora with average survival percentage (in all three regions), 95.8, 91.6, 81.9 and 75.6% respectively, had greatest chance to be present in the adverse environmental conditions of this region. Eu. camaldulensis was able to survive in low salinity (66.6%), even though it failed to survive in high salinity and had a very low survival percentage (6.2%) in medium salinity. Ly.depressum's inability to survive in high and medium salinity and a low survival percentage (27%) in low salinity were reasons why it was not recommended for biological rehabilitation projects in similar ecological conditions. The height and canopy cover of all the studied species, except for Pr. juliflora, had significant differences at different salinity levels. It is recommended to plant native plants, such as T. aphylla, T. passerinoides, and Sei. rosmarinus, in this area and areas with similar ecological conditions, based on the results.

Determining the state of distribution of species and the habitats occupied by them is very important in species protection and management programs. The use of modeling to predict the distribution of species has increased in recent years. For this purpose, a wide range of modeling techniques has been developed. In this regard, the present research was conducted with the aim of evaluating the capability of logistic regression and the maximum entropy method in preparing a prediction map of the habitat expansion of Artemisia chamaemelifolia species and determining the factors affecting its distribution in Ardabil province.

 In the rangelands of Ardabil province, 449 study sites, including 102 sites of presence and 347 sites of non-presence of the species, were recorded from 2018 to 2021. Two categories of environmental factors, including bioclimatic variables (19 cases), three primary topographic indices (elevation, slope, and aspect), and five secondary topographic indices (topographic wetness index, topographic position index, topographic roughness index, stream power index, and plan curvature index), were investigated in relation to the presence of the species. Bioclimatic variables with a spatial resolution of 2.5 minutes, equivalent to 5 km², were downloaded from worldclim.org. The maps of topographical variables were produced using the maps of the digital elevation model in a geographic information system environment. Maps of all environmental factors were prepared and overlapped with 70% of the data. Environmental information was extracted for sample points. Collinearity between independent variables was checked. In the next step, the regression relationship between species presence and independent variables was extracted in SPSS software. Then, by combining the maps of factors affecting species distribution and applying the relevant regression relationship, the distribution map was predicted through the logistic regression method. To prepare the prediction map of A. chamaemelifolia habitat through the maximum entropy method in the MaxEnt software environment, the environmental layers were converted to ASCII format, and the species presence points were converted to CSV format. The maps prepared in both models were classified based on the optimal threshold of species presence into two classes: species presence and non-presence. The Kappa index was used to check the accuracy of prepared maps and compare their performance.

 The results of the modeling showed that elevation was the most effective environmental factor in the distribution of the species. The altitude range of presence of A. chamaemelifolia in Ardabil province was found to be from 2100 to 2900 meters. The Kappa coefficient obtained from the comparison of the predicted and real maps for the logistic regression model was 0.962, and for the maximum entropy model, it was 0.871, which are at a very good to excellent level. The logistic regression model identified slope percentage, precipitation of the coldest season, precipitation of the driest month, and the average range of daily temperature as influencing variables in the occurrence of the species in Ardabil province. Based on the jackknife test resulting from the implementation of maximum entropy in the MaxEnt environment, the most important variables affecting the suitability of A. chamaemelifolia habitat were seasonal precipitation and elevation.

 The results of the present study have provided key and important information about the range of tolerance of the A. chamaemelifolia species to the influencing environmental variables. Relying on the results of the current study, arrangements can be made to protect and restore habitats with current or potential distribution of A. chamaemelifolia species.

With the outbreak of the coronavirus and the disruption of the normal state of human life and death, the most social, economic, political, and especially educational activities changed to virtual communications.

The present study was planned with the aim of assessing the effectiveness rate of adopting Learning Management System (LMS) among students. A questionnaire method was used to obtain the perception and opinion of students of different courses and levels. The efficiency of LMS in seven different components was evaluated. The t-student test was employed to evaluate statistical results.

The results of statistical analysis has been interpretated based on the mean and standard deviation of each studied components. Except for exercises and assignments, other components were less than the average limit. The value of the univariate t-statistic was determined to be less than 1.65 (standard t-value) for all components except exercises and homework. Also, considering that the p-value level for all components (< 0.001), it is concluded that there was a significant difference in the answers of all research questions.

The results can be used to resolve the challenges of E-learning. Due to the prolongation of the outbreak of Covid-19 and the intensification of its consequences such as increasing demand for E-learning, the need for continued economic activities, and employing new tools and more effective use of IT will be necessary.

Due to the need to determine the suitability of the habitat in providing emendation suggestions in biological programs, predicting the optimal habitat of important and valuable plant species in local and academic knowledge is considered one of the important matters in the rangeland's improvement and development. Nowadays, different modeling methods have been considered in this field. In addition to finding important influencing factors in the establishment and distribution of the species, this can determine its preferential tendency towards environmental factors. Therefore, this study was carried out with the aim of preparing a habitat prediction map of the Artemisia austriaca species, which is important from the point of view of both local people and experts of the region and has multi-purpose values, at the level of rangelands of Ardabil province with machine learning methods.

In the Rangelands of Ardabil province, 675 sampling sites from the presence and absence of studied species were considered from 2018 to 2021. Two categories of environmental factors including bioclimatic variables and topographic variables, including primary and secondary topographic indicators were investigated in relation to the presence of the species. Maps of all environmental factors were prepared with 70% of the data and overlapped in geographic information software. Predicting the presence of A. austriaca with four methods; Generalized Linear Model (GLM), Generalized Cumulative Model (GAM), Random Forest Model (RF), and generalized boosted Regression Model (GBM) were performed in R software environment. The analysis of the importance of environmental variables for the models was done in the Biomode2 package. To evaluate the models, 30% of species data and three statistics of the area under the curve (AUC), kappa, and true skill statistic (TSS) were used.

The modeling results showed that the precipitation variable in the coldest season (Bio19) was the most effective variable in the spread of A. austriaca species in all four studied methods. In the GLM method, the variables of seasonal temperature (Bio4) and topographic position index (TPI) were also found to be effective factors in the presence of the A. austriaca species. In the RF model, respectively, Bio19 and seasonal precipitation (Bio15), and in the GBM model, the variables Bio19, precipitation of the wettest month (Bio13), and Bio15 were introduced as the most important variables in the presence of the species. Also, in the GAM model, the results showed that the variables of altitude above sea level, Bio19, Bio15, and Bio4 are the most important in the distribution of the species in the A. austriaca. Comparing the performance of the models showed that the GBM model with AUC 0.97, Kappa index 0.8, and TSS 0.886 is the best model among the studied models, followed by the random forest model with the index under the curve 0.96, Kappa 0.79, and TSS equal to 0.86 is the second model approved in this connection.

The results of this research showed that the studied models, especially the GBM and RF models, are able to predict the optimal habitat of the A. austriaca in the rangelands of Ardabil province with acceptable accuracy. Based on the results of the mentioned models, climatic factors have a greater effect on the occurrence of A. austriaca in Ardabil province. The use of the results of this and similar research is essential in preparing the habitat identification of any plant species and suggesting suitable native species for the improvement of rangelands. Finally, using the environmental factors of each region, the probability for the success or failure of the establishment of plant species can be predicted. Because one of the main conditions in the success or failure of such operations is their adaptation to the needs of the suggested species in that area.

The decrease in global biodiversity caused by human activities and the continuous expansion of man-made land use has deepened the need better to understand the relationship between biodiversity and human disturbance. By knowing and examining the process of disturbance changes over time, it is possible to identify the factors that cause it and make appropriate decisions to reduce disturbances. Meanwhile, the application of landscape ecology in various fields of urban planning shows the ability to analyze and quantitatively express the results of human-environment interaction. Gaining comprehensive knowledge of the behavior of this type of change is necessary to achieve systematic management and efforts to ensure the sustainability of ecosystems. In addition, identifying the main processes and structural factors in creating key changes in the landscape provides a proper understanding of the behavior and changeability of the landscape. Ecological disturbances can be caused naturally (fires, floods, avalanches, hurricanes, or volcanoes) or by human activities (road construction, pollution, land use, or mining) and can even completely change a habitat. One of the needs of planning for effective and sustainable management and protection is a better understanding and awareness of the process of spatial changes of this type of disturbance. The timely identification and monitoring of ecosystem responses to disturbance stimuli is an important step in ensuring the sustainable use of natural resources and integrated watershed management. Based on this, the current research aims to identify disturbance drivers, develop multifunctional disturbance indicators, and analyze their spatial patterns in the Samian Watershed located in Ardabil Province.

Materials and Methods  :

In the present study, according to the literature review of the watershed and research related to disturbance indicators, a total of 29 disturbance drivers were identified. Then, Spearman's test was used to identify and remove disturbance indicators with significant correlation (p-value<0.05). Finally, using the weighted sum of disturbance drivers, four multifunctional indices based on land use (DI-LU) (with criteria of agricultural area, range area, total agricultural and residential, dry farming area, irrigation area, urban area, urban 600 m buffer, urban-agricultural 600 m buffer, ndvi and road density), demographic (DI-DG) (with criteria of population density, rural household density, density of 10-year working people, density of people working in agriculture, density of people working in the industry, density of people working in services and residential density), sources of pollution (DI-CR) (with criteria of soil erosion, special sediment, LQ spatial coefficient in the agricultural, LQ spatial coefficient in the industry, river distance from residentional areas, density of mining operations, density of industrial centers, pol1utant density) and hydrology (DI-HR) with criteria (with criteria of slope, reservoirs and wetlands area, dam storage, dam density) and a total disturbance index (TDI) were developed.

In this research, the multi-functional indicators of disturbance in 27 Samian sub-watersheds were calculated and zoned based on identified disturbance drivers. The results of equal weighting showed that the variables used in the calculation of disturbance indices based on the first to fourth groups were assigned weights of 0.13, 0.50, 0.17, and 0.33, respectively. The results of zoning showed that the maximum value of disturbance index based on land use (DI-LU) related to sub-watershed 27 with a value of 0.77 in the high class, demographic (DI-DG) related to sub-watershed 27 with a value of 0.89 in the class very high, pollution sources (DI-CR) related to watershed 15 with a value of 0.61 were placed in the high category and hydrology (DI-HR) related to watershed 25 with a value of 0.82 were placed in the very high disturbance category. The average of the first to fourth multifunctional indices were 0.46±0.16, 0.40±0.16, 0.38±0.09, 0.52±0.15 and 0.44±0.08 respectively. The results of the total disturbance index showed that the mean and standard deviation of the total disturbance index (TDI) are equal to 0.44 and 0.08. This confusion index was categorized into three categories: low, medium, and high. Besides, a significant area of the entire Samian watershed was placed in the middle class (0.41-0.60).

In recent decades, natural ecosystems have experienced unprecedented negative disturbances due to global climate warming, extreme weather events, excessive human exploitation of resources, and damage to the environment. In the long run, this has led to a series of complex problems that will threaten the survival and sustainable development of society. Accordingly, the regional identification of disturbance drivers is very important to reducing the conflicts between socioeconomic development and environmental protection. In this context, according to the zoning results, most of the sub-watersheds were placed in the middle class based on the fourth group, which indicates the importance of considering factors such as slope, dam storage, and dam density. From the general point of view, the Samian watershed has a medium, low, low, and medium class, respectively, based on the disturbance indicators of the first to fourth groups. In general, the central and northern parts of the watershed have higher disturbances than other parts. The results of the spatial analysis also showed that the central parts of the Samian watershed are affected by more disturbance. The results of the present research are used in the diagnosis of the dynamics of disturbances to understand the interactions of a watershed system and develop the strategies required for sustainable management.

This research aims to analyze the spatial water footprint of agricultural products in four categories of blue, green, gray, and white water in Khiavchai sub-watersheds. To this end, the meteorological data (monthly rainfall and temperature) were used as input data for the CROPWAT program. The Google Earth images were used to map the land use areas and estimate the area of agricultural and rangelands. It was found that apples, sour cherries, cherries, walnuts, and grapes are the dominant crops produced in the orchard area. The four types of virtual waters of agricultural products were analyzed in four land uses, i.e., orchards, rainfed agriculture, irrigated agriculture, and rangelands. The total water consumed by the apple product is more than other products in all sub-watersheds and the amount of virtual water of walnut is also high in most sub-watersheds. The total consumption of blue, green, white, and gray water in sour cherry, cherry, and grape was the same and was evaluated as less than the other two products. In addition, the average amount of blue water for rainfed agriculture was estimated as 2788.85 m3 t-1, among which lentils and chickpeas had the higher blue water among the rainfed crops. According to the results, sub-watersheds 7 and 14 located in western parts of the study area had the highest of virtual water. In sub-watershed 7, a large part of its area is used for rangeland and only a small part is related to rainfed agriculture, and all area of sub-watershed 14 is used for rangeland. Therefore, the appropriate cultivation pattern can be determined based on the amount of virtual water and its spatial changes.

Sand dunes are one of the most important landforms in arid and semi-arid regions, whose mobility varies depending on climatic factors, especially the speed and frequency of erosive winds, rainfall, and the amount of evaporation and transpiration, which always causes many problems for the activity of these dunes. Therefore, monitoring climatic factors and analyzing dust events can be a positive step toward knowing the factors affecting their activity and predicting their mobility status in the future. Lut desert, with its huge amount of sand masses, is one of the most active places concerning changes in wind speed and direction. The eastern part of this region, which includes the largest sand mass of Iran, the Yalan sand, is dominated by the 120-day winds, which doubles the importance of investigating the winds and their shaping role during the blowing season of these winds. Based on this, the current research was conducted to analyze the mobility of quicksands in Lut plain stations in the past and predict their conditions in the future.

The study area includes the Lut desert located in Kerman province. To conduct this research, average monthly values of climatic elements including temperature, rainfall, hourly data related to horizontal field of view, wind speed and direction, and the code of various dust phenomena were used. These data were received from the Meteorological Organization of seven synoptic stations that have statistics for 20 years (2002 to 2021). Investigating the changes of climatic parameters over 20 years, investigating the temporal and spatial mechanism pattern of dust, wind and storm rose, calculating the amount of evaporation and transpiration using the Torrent-White method, investigating the condition of dunes using the Lancaster index were the main steps of the current research. The mobility of quicksands was texted using a sensitivity analysis and at the end, the zoning maps of the final sand transport potential were prepared using ArcGIS software.

The results showed that the stations leading to Lut plain are facing a decrease in rainfall and an increase in temperature, evaporation, and transpiration. The changes in wind speed are high in Shahdad station and low in Baft station, and the prevailing wind direction in most of the studied stations is west and north. The highest frequency of local and extra-local winds is related to Nehbandan station with 1090 days and Bam station with 791 days, and the lowest frequency of winds is Local and extra-local corresponding to Baft station with 23 and 31 days, respectively. The calculation of the aridity index of the stations showed that the index of prevailing climatic conditions in the region is dry, ultra-arid, and semi-arid due to the activeness of wind deposits and the peak time of local events in the stations located in plain areas, and this means Shahdad station has become a real desert in terms of desertification risk, Baft station is under severe desertification risk, and other five stations are under very severe desertification risk. With the reduction of the UNEP aridity index and the increase of the aridity of the environment, the amount of sand mobility in the whole Lut plain is increased and the risk of desertification in the region is intensified. Based on the sensitivity analysis, if in the future the frequency of winds exceeds the erosion threshold by 30%, the activity and movement of dunes will increase by 30%, while with a 30% increase in rainfall, the dune activity will decrease by 43%. Also, the sand mobility index has a positive relationship with changes in evaporation and transpiration. With a 30 % increase in evaporation and transpiration and the speed of erosive winds, the activity and movement of dunes increase by up to 70 %, and with a 30 % decrease in these two meteorological indicators, the activity and movement of dunes decrease by 50 %.

By calculating the Lancaster index, the potential activity of dunes has been determined and the active and inactive areas have been separated. The results of the zoning of this index showed that in the studied area, the activity level of dunes is mostly completely active and active at the top of the dunes. It is inactive only in Bam station, which has been done to stabilize and reduce dust activity and management measures such as planting trees, hand-planting desert forests, dunes, and mulching. However, due to the activity of the dunes in other stations, these measures are not enough and need new management and planning. The results of the present research can be used to predict the future condition of dunes and determine appropriate management measures to control and reduce possible damage to other land uses in the region.

The rainfall system of a major part of Iran is mediterranean, where the precipitation amount during the vegetation period is low. In addition, the occurrence of precipitation in the non-vegetation period or beginning of the vegetation period, which does not cover the surface of the earth well, is one of the important reasons for water erosion in Iran. Since vegetation has a special role in soil erosion control and runoff retention, any change in the vegetation structure and pattern, which expresses the landscape pattern and function, can have a significant effect on changing hydrological processes. Therefore, the assessment of soil and water loss and the quantification of its relationship with landscape metrics provide key information for the development of water and soil quality management strategies.

The current research was conducted to investigate the hydrological component changes with landscape metrics on 2 m2 plots using simulated rainfall at an intensity of 32 mm.h-1 in a part of rangelands of Ardabil County. At first, considering the type and percentage of vegetation as the main variable, eight groups of vegetation composition along with one group without vegetation (control) were considered with three replications. The composition (and percentage) of the vegetation from the first to the eighth groups, respectively, include low-height graminea predominance (45), the composition of dense bushes with graminea (43), bushes with low-height and medium-distribution (37), sparse bushes mostly with low and medium height (31), the composition of sparse bushes with graminea (56), dense bushes in upper parts (54), low-height bushes with very low distribution (15), and dense bushes with almost uniform distribution (56). After measuring the runoff and sediment at the plot outlets, different hydrological components were calculated. Then, plots with nine different vegetation combinations were imaged in three replicates before and after rainfall simulation. After transferring the images prepared from the plots to the Arc/Map10.8 environment, nine important landscape metrics were calculated.

Changes in the mean patch density (4.43-26.90), largest patch index (54.16-86.75), edge density (17.12-107.38), landscape shape index (1.50-4.47), mean shape area (4.16-37.46), mean Euclidean nearest neighbor distance (0.00-1.65), landscape division index (0.19-2.31), mean patch shape index (1.24-22.85), and the effective mesh size (15.80-43.96) indicate their different influence from different percentage and composition of vegetation cover. Spearman's correlation matrix analysis showed a nonsignificant relationship between the mean soil loss, runoff volume, runoff coefficient, and sediment concentration with landscape metrics (r<0.26 and p-value>0.10). The small scale of the studied plots, the lack of diversity in the vegetation composition, and the uniformity in terms of vegetation height can be cited as the reasons for the lack of correlation. In general, groups with vegetation values above 50% had a better condition in terms of LPI, AREA_MN, and MESH, which indicates more connectivity and less degradation. The increase in vegetation cover and spatial heterogeneity above the landscape surface can change the path of sediment transport, reduce sediment connectivity, and lead to a decrease in sedimentation.

The obtained results are applicable in explaining the appropriate reference to optimize water and soil protection measures on the watershed scale. However, It is suggested that similar and more comprehensive research be done in different scales of erosion plots and even in the landscape (slope) scale so that by considering a wide range of vegetation, topography, climatic conditions, as well as successive rains, it is possible to compare the results, optimum selection of study scale, and finally planning to manage and protect vegetation and water and soil resources.

The main purpose of this study was to mapping the canopy cover (CC) and the aboveground net primary production (ANPP) of plant life forms by the independent factor of physiographic in the rangelands of Ardabil province.Material &

First, according to plant types and environmental factors, sampling was taken at the peak stage of plant growth from 2016 to 2020 using one square meter plots. To determine the most effective environmental factor in the changes of plant parameters, the correlation between dependent and independent variables was used. Linear regression equation was used for modeling by the most effective factor and the maps were simulated. The accuracy of the equations was also calculated by RMSE. Then, the correlation between CC and ANPP of life forms and the total was obtained.

The results showed that the elevation factor had the highest correlation with the dependent variables. The difference between the estimated average of the model with the measured values for CC of grasses equal to +1%, forbs –1%, shrubs -1%, and total CC +2%; and these values for the ANPP of grasses equal to +21 kg/ha, forbs was equal to zero, shrubs were -13 and the total ANPP was +274 kg/ha.

Predicting the studied parameters will be necessary to manage the balance between supply and demand of rangeland products.

Remote sensing data provide a high ability to represent habitat characteristics and use in species distribution models. The purpose of this study is to determine the most important of remote sensing predictors, including climatic indices (precipitation and temperature), primary and secondary topographic indices (elevation, slope, direction, Topographic Position Index (TPI) and Terrain Roughness Index (TRI) and remote sensing indices (Global Environmental Monitoring Index (GEMI), Leaf Area Index (LAI), Modified Normalized Difference Water Index (MNDWI), Modified Simple Ratio Index (MSR), Normalized Burn Ratio Index (NBR) and Visible Atmospherically Resistant Index (VARI)) using two species distribution models (Boosted Regression Tree and Random Forest) to predict the presence of J. excelsa in Khalkhal County of Ardabil province and northern part of Zanjan province using SAHM software. The evaluation of prediction models using AUC chart (Area under curve) showed that it is at an excellent level for both the BRT model (0.991) and the RF model (0.974). The most important affecting habitat desirability based on the BRT method include annual precipitation, slope, digital elevation model, temperature, GEMI index and TRI index variables respectively. The most important variables affecting habitat desirability based on the RF method, respectively, include annual precipitation, digital elevation model, GEMI index, slope, VARI index, temperature, MSR index, TRI index, TPI index, NBR index, MNDWI index, LAI index and aspect. The region mapped in the study as suitable habitats’ for the species could be used in the planning strategies with the aim of evaluating the susceptible habitats, the possibility of conservation, reproduction and breeding. Considering that the modeling method choice is the main source of variability in predictions and choosing the best prediction model is not simple, therefore, it is suggested to use a combination of these models instead of relying on the outputs of a single model.

The natural abundance of phytoplankton in the Persian Gulf has changed, creating a constant threat to biodiversity. Monitoring and predicting the spatial-temporal behavior of this phenomenon is crucial to prevent negative social-economic consequences and environmental planning. On this basis, the current research aims to assess the spatial-temporal changes of chlorophyll and analyze its relationship with the sea surface temperature in the Persian Gulf. To this end, we estimated and analyzed changes in the concentration of chlorophyll as an organic compound produced by algae and plants using MODIS sensor images from 2000 to 2021 (monthly and seasonally). Spatial indices of the barycenter were also calculated in addition to accuracy assessment using error evaluation criteria. Global Moran and G-star (G*) statistics were used to identify the degree of spatial autocorrelation and the magnitude of spatial patterns. The results showed that the intensity of this phenomenon on average varies between 0.05 and 16.38 mg m-2. The northwest and southwest have the maximum values and the central parts of the Persian Gulf have the minimum values. The northern coast of the Persian Gulf is located in Iran, and the northern parts, i.e., areas with less population and industries, have a low amount of this phenomenon. The ellipse line shows three standard deviations in the northwest and southeast directions following the cities and industries. The time series of changes showed that as the temperature decreases in the cold season, especially in January, the concentration increases, and as the temperature of the water surface increases in the hot season the amount of chlorophyll in the water decrease. Statistical analysis of the spatial distribution also indicates a significant cluster spatial pattern. Slope changes verified that the southern regions of the Persian Gulf, especially in the countries of Bahrain, UAE, and Saudi Arabia, have the most positive slope of changes. In the main part of the area, including the northern part of the Persian Gulf, whose coastline is entirely owned by Iran, except for the northwest, which includes the Mahshahr port and Asaluyeh, the slope of changes has been very low or decreasing. This research could identify areas exposed to adverse changes in phytoplankton concentrations and can be used in environmental planning.

Ecosystem health is a concept that is often used to evaluate ecosystems (Lu et al., 2015). A healthy ecosystem can be considered as resilient against disturbance. On the other hand, degradation occurs when different states of ecosystems related to energy flow, nutrient cycling, and hydrological regimes are negatively affected. Especially for forest areas, it is necessary to protect the structure and function of ecosystems and actively restore destroyed ecosystems to ensure their integrity and maintain their health. As a result, a wide range of forest ecosystem health indicators has been developed with an emphasis on the use of quantitative and qualitative indicators (Chen et al., 2016; Fei et al., 2013). Assessing the health of ecosystems based on these indicators is a prerequisite for ecological restoration and sustainable development.Forest management requires information about the state of the forest with a focus on forest health. Because healthy forests are able to perform well compared to unhealthy forests. Forest health monitoring is especially important in creating sustainable forest management and also the integrated watershed management. However, today, the protection and monitoring of forests concerning forest health still do not exist and are limited to small scale studies. Therefore, this study assesses the health conditions of forest ecosystems at the watershed level in the north of Iran. In this research, by using remote sensing data and preparation of vegetation indices along with field data obtained from forest health monitoring in the Siahkol Shenrud watershed, the analysis of these indices and their grading at the forest health level is discussed. The protection and monitoring of forest conditions requires an extensive investigation of different forest types that are located inside a watershed. Therefore, in this study, a method suitable to the conditions of the forests of northern Iran is presented, which is necessary for detecting forest health levels based on the different types in a watershed. This study is conducted to provide useful knowledge in the field of creating a sustainable management strategy in the forests of northern Iran, which is based on watershed management.

Study area This research was carried out in the Siahkol Shenrod watershed located in Gilan province with a total area of 190 km2 (Figure 1). This basin is located in a plain where a small river named Shamrod passes through the middle and its height increases from north to south. This region has a mild and humid climate, which is also influenced by the cooler climate of the hills. The average annual temperature is 18°C. The average rainfall varies between 800 mm (southern part) and 1200 mm (northern part) per year.

This research is a combination of remote sensing survey and analysis methods and a geographic information system (GIS) to describe the health status of forest ecosystems in the Siahkol Shenrod watershed. Land use classification was done using the Landsat 8 OLI satellite image of 2021. Supervised classification method and maximum likelihood algorithm were used to prepare land use maps. In order to evaluate the accuracy of the classification, Google-Earth images were used by selecting 30 educational samples. For accuracy assessment, the high value of the Kappa coefficient (0.92) was obtained.  Vegetation indices calculation After pre-processing, the Landsat 8 image was used to calculate vegetation indices according to Table 1. Reference Equation Vegetation Index Frampton, Dash, Watmough & Milton (2013) NDVI Huete et al. (2002) EVI Bannari et al. (1995) L=0.5 SAVI Gao, 1996 NDWI Gitelson et al. (2001) ARI1 Gitelson et al. (2002) CRI1  Forest health index extraction The six calculated vegetation indices (Table 1) were combined and classified for the input of forest health index (FHI) in ENVI software. In this classification, ten classes were defined for all six indices, and then in calculating the FHI index, it was reduced to five classes by combining both related classes. The FHI index (Gupta and Pandey, 2021) is available in the ENVI forest health toolbox, as follows:• Field data collection To validate the forest health levels in different forest types of the watershed obtained from remote sensing data, it is necessary to conduct a field survey. Field survey data were obtained from the study area in September and October 2021. A stratified sampling method was used before the field survey in the GIS environment. Based on this method, 40 square-shaped samples (30 m * 30 m) were built. Observational activities were conducted using the Forest Health Monitoring (FHM) method (USDA Forest Service, 2020). The area with less than 10% damage was classified as a very healthy forest, and the forest damage between 10 and 25% was classified as a healthy forest. 25-50% damage was classified as medium health forest, 50-75% as poor forest health, and 75-100% damage was classified as very poor forest health.

 Vegetation indicators Six vegetation indices extracted for forests in the study area are presented in Figure 3. Based on the results, 95% of the forests in the study area showed an NDVI index of more than 0.5, which indicates healthy vegetation. Also, healthy vegetation with high chlorophyll content was shown in 91% of the studied area with an EVI index between 0.2-0.8. In this research, according to the multiple linear regression analysis of the effect of vegetation indices used in calculating forest health, the NDVI index with an R2 value of 0.77 has the greatest effect on the level of forest health in the watershed, which is the greenness level. It also shows the density level of forest stands. Similar results of the influence of vegetation indices have been presented by Tuominen et al. (2009). Furthermore, the higher effectiveness of NDVI in this study is similar to the findings of Barkey and Nursaputra (2017) and Oliech (2019) in assessing forest health. After NDVI, R2 values for EVI, SAVI, and AR1 indices were 0.75, 0.73, and 0.72, respectively, and for NDWI and CRI1 indices were 0.63. Forest health index The relationship of six vegetation indices calculated from the Landsat 8 image was used to determine the level of forest health in the Shenrod watershed. Forest health was divided into five categories: 1) very good (very healthy); 2) good (healthy); 3) moderate (under stress); 4) poor (unhealthy); 5) very poor (dead). Based on the results, the overall accuracy was 86%. Also, the kappa value of 0.81 was obtained. Figure 4 presents the health status of forest stands in the Shenrod watershed.Based on Figure 4, the distribution of forest health classes in the study area is not uniform. The central areas of the watershed have higher levels of forest health, while the southern areas of the watershed have lower levels of forest health. This can be due to the history of heavy wood harvesting, selective cutting, and as a result, the widespread opening of forest stands (Jahdi and Arabi, 2023), in the form of forestry plans implemented in the southern areas of the watershed during the last two decades. Wildfires in this area also affect the structure and composition of the vegetation and are a great threat to the long-term productivity and overall health of the forest. In general, 56.8% of the forest area is in the medium health category. Healthy and unhealthy forests with levels almost close to each other make up 41.7% of the total forest area. 1.5% of the forest area is also in the very poor health category (Figure 4).The information on the forest health levels in the forest types of the watershed is presented in Table 3. Plantation, especially broadleaf plantations, has the largest forest area in unhealthy conditions (approximately 33%). In contrast, natural forests, including mixed broadleaf forest and beech forest, have the largest forest area in healthy conditions (approximately 21%). On average, almost 60% of all existing forest types in the study area are in the medium health category.In general, many parameters affect the health conditions of forest types in the watershed, so it is important to identify the factors that caused the forest health levels in the survey with more observational activities.

In this study, the quantitative index of forest health was evaluated using broadband spectral indices and stress-related pigments before tree fall using remote sensing data. Using forest health analysis, the quantitative health conditions of the forest were described in 2021 with a forest area of 122.2 km2. Of this amount, there are healthy forests with an area of 23.8 km2 and unhealthy forest conditions with an area of 29 km2. The maximum area of the watershed, i.e. 69.4 km2, is also in medium health conditions. The health status of forest ecosystems is mostly average health, which is the result of forest destruction, soil erosion, and human overexploitation. Forest conservation programs can effectively control forest degradation and improve the health of local ecosystems. Forest health detection based on remote sensing in the study area was consistent with the field survey results. This study guided the systematic approach and provided remote sensing techniques for forest health monitoring programs and sustainable forest management.

Drought is one of the natural hazards whose consequences and effects on social, economic, water resources and agriculture can be significantly revealed. Although the occurrence of drought is inevitable, it can be planned by anticipating a reduction in its devastating effects on the economy, society and the environment. The purpose of this study was to explain the types of drought indicators and introducing the important and widely used indicators in assessing and quantifying meteorological and hydrological droughts. Meteorological drought is usually defined by the degree of dryness (compared to the normal or average value) and the duration of the dry period. Meteorological drought definitions should be considered separately for each specific region; Because the weather conditions that lead to deficit of rainfall vary from one region to another. Drought, in its meteorological sense, means a decrease in rainfall for a certain period of time on a specific area compared to the long-term average of the same area's rainfall in the same period of time.

While introducing the benefits, limitations and scope of different drought classes, the relationships used by droughts are presented. Standardized precipitation indices (SPI), percentage of normal rainfall index (PNPI), rainfall anomaly index (RAI), Bhalme and Mooly drought index (BMDI), decile index (DI), evapotranspiration deficit index (ETDI), Palmer Drought Severity Index (PDSI), reclamation drought index (RDI) and Soil Moisture Drought Index (SMDI) were introduced in the category of meteorological drought. In the category of hydrological drought, surface water supply indices (SWSI) were also examined.

Various drought indicators along with variables, time scales and their concepts are presented in the results section. The results showed that the SPI index has a high comparative advantage for monitoring meteorological drought. Also, RDI index is more sensitive to climatic variables than SPI index and PNPI index is not recommended for drought assessment due to high error. The standard precipitation index (SPI) is known as the most suitable index for drought analysis, especially spatial analysis, due to the simplicity of calculations, the use of available rainfall data, the ability to calculate for any desired time scale, and the very high ability to compare results spatially. Due to its simplicity and practicality, Rainfall Anomaly Index (RAI) has been often used in drought estimation to deal with drought in different stages for different climatic regions. The time scale for calculating this index is monthly and yearly. The calculation method of BMDI drought index is similar to Palmer's drought severity index and the index works recursively; That is, in calculating the drought intensity of a given month, a coefficient of the previous month's drought intensity is also considered. The DI index is defined as a rating of the amount of precipitation in a specific period of time and is presented in order to solve the deficiencies in the percentage of normal method. The Decimal Index provides a statistically accurate measure of precipitation, provided long-term climate data are available. The need for low input variables, including all components of water balance in index calculations, and comparability in different times and places are considered strengths of the PDSI index. This index is able to monitor drought in short-term and long-term periods (one to 48 months). This index is increasing due to the need for low data, high sensitivity and high flexibility of its use; Due to the fact that the RDI index is calculated based on rainfall and potential evaporation and transpiration, it is more sensitive to climate variables and changes than drought indicators that are based only on rainfall (such as the standardized precipitation index). The purpose of the SWSI index is to obtain a standard for determining the amount of water available in mountainous areas and the possibility of comparing different areas with each other. The SWSI index determines the severity of ongoing droughts in the region and the future situation can be predicted with the help of this index. SMDI drought index is an index that is based on the total soil moisture daily for one year and the only climatic factor used in it is soil moisture data.

A major part of Iran is located in dry and semi-arid areas, and the drought phenomenon is an inseparable part and is considered one of the characteristics of dry and semi-arid areas. Based on this, a two- to three-year drought period is experienced in the country almost every five years. These droughts have reduced surface and underground water sources and reduced usable water. The occurrence of drought and its continuation also affects the quantity and quality of ground water resources. The reduction of precipitation, which is one of the most important parameters of feeding the underground water aquifers, can cause the destruction and loss of the ground water aquifers. There are different drought indicators, each of which has advantages and disadvantages. Meanwhile, the Comprehensive Drought Index (RDI) is more sensitive to climate variables and changes compared to the SPI index. In this regard, it can be said that the RDI index is calculated based on rainfall and potential evaporation and transpiration, but the SPI index is only calculated based on rainfall. RAI index has the ability to evaluate drought in short-term and long-term time periods (one to 48 months). Overall, a review of the indicators provided can help determine the appropriate indicator to assess drought. An appropriate indicator to provide information on drought management challenges. In addition, it quantifies the practical aspects of drought, such as the severity, duration, and frequency of drought, along with possible, and statistical characteristics. Among the studied indices, the PNPI index is not recommended for drought evaluation due to its high error; Also, the DI index is not suitable for areas having stations with a short-term data period because it requires long-term data to evaluate drought. The PDSI index is calculated from the data of precipitation, temperature and soil moisture and considers any type of precipitation as precipitation. In PDSI index, the average precipitation is not the same as the median, which is one of the disadvantages of this index.

A healthy forest ecosystem is essential to provide a wide range of environmental, social, and economic services. To evaluate forest health level, it is necessary to quantify ecosystem conditions using a variety of indicators. In this study, a combination of six indices: NDVI, EVI, SAVI, NDWI, ARI1, and CRI1 were used to derive the forest health condition map around Shenroud watershed, Siahkal using Landsat 8 OLI image of 2021 and ENVI Forest Health Tool. We evaluated the forest health with five grades (A; very good to E; very poor). Forest health data validation in various forest types in the study area is conducted via field crosscheck and assessment regarding the forest health level using 40 sample plots and FHM (Forest Health Monitoring) method. In this work, based on the multiple linear regression model of the effect of vegetation indices in forest health assessment, the NDVI vegetation index with an R2 value of 0.77 has the highest effect on the forest health level in the watershed, which shows the greenness level and the density level of forest stands. The results of the analysis show that most of the forests in the study area were moderately healthy. 19.4% of the forest areas were classified as “Healthy”, 56.8% as “Moderately healthy”, and 23.8% as “Unhealthy”. Furthermore, different forest types in the study area have different percentages of healthy forests. Plantations, especially broadleaf plantation, has the largest forest area in unhealthy conditions (weak and very weak) (approximately 33%). In contrast, natural forests, including mixed broadleaf forest and beech forest, have the largest forest area in healthy conditions (very good and good) (approximately 21%). In general, the current state of the ecosystem in the study area is mainly in middle health, which is a result of long-term deforestation, soil erosion, and inappropriate human exploitation. Our RS-based health diagnosis of the area was consistent with field survey results. The process could be very useful in mapping the health conditions of the forests of the country accurately. It is suggested to study the spatial and temporal variation of forest health in the watershed under future climate change.

Watershed degradation had negative effects on ecological and anthropologic functions at different scales. Therefore, strategic planning and conserving watershed resources is the main goal for managers and policy-makers. To achieve this goal, it is essential to provide a scientific roadmap concerning the health degree of the watershed in terms of its multi-functions. A healthy watershed improves the resilience of local ecology to climate change and provides essential services for human and ecological functions. Identifying healthy watersheds could be an effective managerial tool for monitoring natural and human phenomena and impacts. Although, in recent decades, there have been numerous types of research on watershed health and its assessment methods in different water and soil environments and in relation to environmental and social processes with economic models for decision-making in different fields. But regarding to the interpretation of different watershed health assessment models with fuzzy logic, limited studies have been carried out. This is the fact that fuzzy science has been well-considered in various sciences. In recent years, fuzzy logic has been mentioned as a powerful technique in hydrological component analysis and resource decision-making. Hydrological problems are associated with uncertainty, which is managed by fuzzy logic-based models. Fuzzy logic is based on the language of nature.  To this end, the present study was planned to accomplish our previous information on the KoozehTopraghi Watershed health and develop a new PSR-Fuzzy-based framework.

To do this research, firstly the pressure, state-response (PSR) model was conceptualized and customized for the study watershed. Secondly, the main criteria of road density, watershed slope, runoff coefficient, agriculture area with a slope of more than 25%, precipitation, and temperature were computed for building the pressure indicator. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) also were computed for building the state indicator. Then, the specific erosion (m3 y-1), erosion intensity coefficient, river density, and rangeland area were computed for building the response indicator. Thirdly, these criteria are converted to fuzzy bases using Fuzzy Linear membership functions in the ArcGIS 10.8 environment. Fuzzification is a method in which each pixel in the map is given a value between zero and one. This amount expresses its value according to the goal it pursues, and the higher it is in terms of value, the higher it is awarded to it as a result. Six operators including AND, OR, SUM, PRODUCT, Gamma 0.9, and Gamma 0.5 were used for incorporating three indicators of PSR and watershed health zoning. Fourthly, to evaluate and classify the output results of the operators used in the estimation of watershed health, the Quality Sum (QS) was used.

The results proved the better performance of two operators of Gamma 0.9 and PRODUCT. The Qs was 0.46 for PRODUCT as the first priority, followed by Gamma 0.9 operators with a Qs of 0.37 in the second priority as the most efficient operators in mapping watershed health. The pressure indicator results showed that 33.84, 0.16, 9.45, 50.51, and 6.04% of the total area of the KoozehTopraghi watershed were classified as very high, high, medium, low, and very low, respectively. The results of the state indicator, 7.55, 52.71, 39.67, and 0.07% of the total area of the study watershed were classified as very high, high, medium, and very low, respectively. The response indicator results indicated that 15.16, 13.30, 29.99, 34.80, and 6.76% of the total area of the KoozehTopraghi watershed were classified as very high, high, medium, low, and very low, respectively. According to the results of the PRODUCT operator, 67, 23, 9, and 1 % of the study watershed were classified as unhealthy, relatively unhealthy, medium, and relatively healthy, respectively. For Gamma 0.9 operator 0.9, 46, 1, 17, and 36% of watersheds were classified in unhealthy, medium, relatively healthy, and healthy classes. Based on this, it is a priority to provide suitable solutions for basic land management. Because it may be intensified the continuation of the irreparable process at the watershed level.

The results confirmed the spatial changes in health status throughout the KoozehTopraghi Watershed. Therefore, different scientific and rational programs need to be adapted to improve health to various degrees. It is highly suggested to prioritize nature-based solutions, integrated participatory management, and adaptive co-management for improving the KoozehTopraghi watershed health. Acquaintance with modern management patterns in the world, of course, with the different climatic and social conditions of our country, we can open up in the field of comprehensive watershed management compared to the past.  The watershed health index as a practical tool in watershed management can be used to determine priorities and monitor watershed status changes. In addition, since the factors affecting the management of ecosystems are considered in the health index, it can be considered as a tool for analyzing the vegetation, water, and soil resources for use with the needs of the living organism.

Ecological quality is an inclusive measurement unit of the elements, configuration, and performance of an ecosystem in the temporal and spatial scales. In addition, assessing ecological quality could provide valuable information for experts and managers in the first step of directed planning and comprehensive watershed management. Therefore, the current research was conducted to calculate the ecological quality index (EQI) in one of the upland watersheds of Yamchi Dam, which is not without the effect of human interference.

Nir Watershed as a study area is located in the southwestern part of Ardabil Province. To carry out the current research, first, the essential variables of ecological quality assessment (normalized difference vegetation index (NDVI), fractional vegetation coverage (FVC), leaf area index (LAI), net primary production (NPP), moisture index (IM), land surface temperature (LST), soil erosion, ecological integrity index, and runoff retention index) were collected and calculated. The variables were chosen to represent the three functions of ecosystems maintaining, supporting, and regulating. After standardization, weighting of the variables was done using the advanced multi-criteria, i.e., Projection Pursuit Model (PPM). Afterward, the EQI was calculated based on the sum weight of all variables for 11 sub-watersheds of Nir.

The wide range of variability of the variables used in the entire Nir Watershed area was confirmed. In general, sub-watershed 1 had the maximum mean of the first to fourth variables (FVC, NDVI, NPP, and LAI) and the minimum mean LST. While sub-watershed 11 had the opposite situation in terms of these five variables. All five variables reflect the structure and function of vegetation and therefore the ecological quality. The status of other indicators except ecological continuity (EC) was also evaluated in sub-watershed 1 in a medium status. Based on the PPM weighting method, the FVC (0.39) has the highest importance, and moisture and runoff retention indices with an equal weight of 0.01 have been assigned the least priority. In addition, the ecological quality results showed that watersheds 11 (0.10) and 1 (0.90) respectively have the lowest and highest levels of EQI. In general, according to the zoning resultd, it was found that the northeastern and southeastern parts of the watershed have the lowest EQI which covered 29% of the area.

The results can be used as one of the main goals of managing watersheds regarding preserving the ecosystem's health and integrity. Moreover, the priority of rehabilitation budget allocation can be assigned according to the resilience degrees of studied sub-watersheds.

The aim of this study was to evaluate the relative resilience of the Samian watershed located in Ardabil province using five indicators including specific erosion and sediment, drought, flood potential, climate, and socio-economic in different weighting conditions. At the equal weighting, the average relative resilience index of Samian Watershed was 0.53, which indicates the average relative resilience. Considering the weight of each indicator at the same level in 27 different sub-watersheds of Samian, sub-watershed 8 located in the east with priority 1 had the highest relative resilience. Considering the equal weight and with emphasis on climate, flood potential and socio-economic indicators, sub-watershed 27 located in the central part of Samian Watershed was at the lowest level of relative resilience and was given priority 27 and sub-watershed 8 has been recognized as the most resilient sub-watershed. Sub-watershed 27 has been permanently in utilization due to the use of rainfed cultivation. Emphasizing erosion and sediment, and drought indicators, respectively sub-watersheds 25 and 23 located in the center of Samian Watershed were ranked in 27 priority.

The present study was conducted to conceptualize and evaluate the health of the Asiabrood Watershed in Chalus Township, Mazandaran Province, Iran.

The existing regional data, including climatic, hydrologic, erosion, sedimentation, and economic and social data, were collected and analyzed. In addition, several field visits were also made to make a general assessment of the ecological, economic, and social situation of the region and to complete the information on the study watershed. Then, the watershed health was assessed using the conceptual model of pressure, state, and response at the sub-watershed scale. Accordingly, the pressure index was first investigated by analyzing the driving forces of human activities and climate change over the study watershed. Then, the current state of the natural environment and the watershed performance to the pressures was analyzed as the status index. Further, the response index of the Asiabrood Watershed was calculated as a criterion for expressing the degree of community response or different outcomes of the watershed to the changes and concerns imposed on the watershed system.

 The results showed that the total health index of the Asiabrood Watershed with respective and relative contributions of pressure, state, and response indices of 18, 39, and 43% and an overall health index of 0.50 has a moderate health condition.

Although the health status of the Asiabrood Watershed is in moderate condition, the health status of the study watershed may decline, and, of course, adverse responses are possible if appropriate management policies are not adopted.

Daily flow data are a prerequisite for water resources management, but it is not possible to measure it in many upstream watersheds. In this study, different optimization algorithms have been used to evaluate the efficiency of the SIMHYD model. Therefore, the discharge data of Kouzetopraghi rive gauge station was selected as the study data (805 km2) located in Ardabil province. The daily data of rainfall, evapotranspiration of the meteorological stations in the study area were used to simulate the daily river flow data. Optimization methods including genetic algorithm, comprehensive competitive evolution, search pattern, multi-start search pattern, uniform random sampling, Rosenbrook, multi-start Rosenbrook optimization were evaluated based on statistical efficiency criteria. The mean value of discharge values by genetic algorithms, multi-year pattern search, uniform random sampling, multi-start Rosenbark, Rosenbork, comprehensive competitive evolution, search pattern were 0.031, 0.023, 0.085, 0.032, 0.024, 0.032, 0.031, respectively. The results showed that the change of optimization algorithms has a significant effect on the calibration accuracy of the model, so that the values of the Nash-Sutcliffe efficiency criteria for the employed algorithms were 0.42, 0.31, -8.55, 0.38, 0.56, 0.023, and 0.24, respectively. The Rosenbrook algorithm had higher accuracy in calibrating the SIMHYD hydrological model compared to other algorithms used. A part of the modeling error can be related to the inconsistency of precipitation and runoff data due to the multiplicity of stations.

The ecological security of the watershed depends on the land capacity and the level of demand expected to ensure the survival of the population living in it and and its regional assessment is necessary to advance the strategic planning and poliy-making. Towards this, the aim of this study was to analyze the heterogeneity of ecological security of the Samian Watershed located in the center of Ardabil Province. Accordingly, a comprehensive evaluation method based on the analysis of various dimensions of supply and demand of material and spiritual security for 27 sub-watersheds was conceptualized and implemented. It was found that the mean value of both supply and demand indicators of ecological security in the whole watershed are equal to 0.53. In general, this watershed has marginal security. However, in terms of spatial heterogeneity, the maximum value of the ecological security index (0.49) was allocated to sub-watershed 22 located in the central part of the watershed. One of the general features of this sub-watershed is the existence of the irrigated agriculture, which has caused the region to be at a high level in terms of ecological security and supplied the demands of region population. Also, the minimum value of this index (-0.43) is related to sub-watershed 27 located in the northwest. The Ardabil City, the capital of Ardabil Province, is located in this sub-watershed, and the urban nature and lack of agriculture, forest, and garden land use are among the characteristics of this sub-watershed, which has led to the sub-watershed being in an unsafe situation. The spatial distribution of the ecological security index showed that 12, 51, 33, and 4% of the region, respectively characterized by security, marginal security, marginal insecurity, and insecurity states cover.

The present review has been compiled to monitor management practices and hydrological response in 12 representatives and paired watersheds of the country including Tangeshol (Fars), Khamesan (Kurdistan), Dehgin (Hormozgan), Zidasht (Alborz), Shush (Khuzistan), Kakhk (Khorasan Razavi), Kechik (Golestan), Kasilian (Mazandaran), Gonbad (Hamedan), Goorband (Sistan & Baluchestan), Herris (East Azarbaijan), and Tahooneh (Yazd). For this purpose, about 161 studies were used and analyzed. Only 40 sources have specifically studied and compared the control and treatment watersheds. Therefore, it can be concluded that the applicability of the practices results and studies conducted in the paired watersheds have not been adequately described or documented. The first research was carried out in 2001 in Shush and Kasilian paired watersheds and most of them were observed in Kasilian (44 studies) and Kechik (28 studies) paired watersheds. The existence of the least research in Herris, Goorband, and Tahooneh watersheds (less than four studies) can be attributed to their new establishment and lack of extracted data. Most of the studies of paired watersheds in the country were related to the years 2015, 2016, and 2021 with 18, 17, and 17 cases.

Soil moisture is an important variable in climatic, hydrological, and ecological systems that link atmospheric processes to the earth's surface. The rainfall systems of the water year 2017-2018 abnormally caused more than normal rainfall in Fars province and caused major changes in the surface moisture of the soil. The purpose of this research is to analyze the spatial changes of soil moisture before and after heavy spring rains in Fars province using Downscale RADAR images. Using the backscatter bands of VV and VH polarizations as well as the incident angle band (𝜃) extracted from Sentinel 1 radar images and land use extracted from the MODIS sensor, a training layer was created. Furthermore, using the support vector machine algorithm, the downscaling soil moisture map was obtained. The results showed that the volumetric soil moisture with high resolution is between 0.18 and 0.38 in the rainy year and between 0.12 and 0.24 in the long term. An anomaly map showed that between 0.14 and 0.18 m3 increased soil moisture. The positive anomaly is more in the east and south of the province, and less humid areas have experienced a greater share of positive anomaly. Moran's index statistic with a value of 0.99 has also confirmed the spatial autocorrelation of soil moisture anomaly and clustering of moisture increase. In general, it can be concluded that by using the results of this method, it is possible to monitor areas with low or high soil moisture anomalies after different rainfalls and to improve the decision-making process.