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Recognizing and mapping the sensitivity of forests to fires is crucial for the preservation of ecosystems and biodiversity. This study, utilizing the time-series capability of Landsat 8 satellite imagery and developing an efficient model within the Google Earth Engine (GEE) platform, managed to map the sensitivity of Kurdistan province forests to fires over the the past decade, from 2013 to 2023, in two study areas located in the Marivan, Sarvabad, and Baneh counties. It provided valuable information for land use management and effective resource allocation to prevent and mitigate the impacts of forest fires in the Kurdistan region. The Normalized Burn Ratio (NBR) index was applied to pre- and post-fire season images to detect forest fires. To enhance classification results, areas such as vegetation, residential zones, and water bodies were highlighted as non-fire regions. The Random Forest (RF) model within the GEE platform was employed to achieve the highest classification accuracy. Appropriate training samples were derived from the highlighted results, and image classification using the RF model with 50 decision trees was performed on the GEE platform.To ensure the reliability of the selected training samples, the fire mapping results were compared with point-based fire data from the Kurdistan Province Natural Resources Department. The classification results for the two forest study areas- Marivan and Sarvabad regions in 2016, 2018, and 2020, and the Baneh region in 2018-demonstrated an overall accuracy of 99% and a Kappa coefficient of 0.97. The findings of this study underscore the capability of Landsat 8 imagery in mapping forest fire susceptibility and confirm the acceptable accuracy of the RF model in this context.

Rangelands play an important role in the sustainability of the ecosystem and providing financial support to many people in the world. Rangelands destruction has caused many concerns at the global level. The results of the studies show that the level of rangelands, rangeland productions and the satisfaction of rangeland users have decreased in Iran. Therefore, the purpose of this research is the pathology and complications of range management policies in Iran towards sustainable exploitation of rangelands. This research is applicable in terms of nature and purpose and mixed (qualitative-quantitative) in terms of research strategy. In the qualitative part, using the meta-synthesis method, the complications of pasture policy towards their sustainable use was determined, and then based on that, a research questionnaire was made and then based on the Delphi method, relying on the opinions of academic experts, for The significance of revealed complications was used. Also, in the qualitative part, articles related to the research topic in the last 15 years (2008-2023) were used, and the criteria for selecting sources was based on the theoretical saturation of the data. The statistical population of quantitative part was academic experts familiar with the subject and interested in cooperation, who formed the Delphi panel. In order to achieve the goal of the research, the opinions of 9 experts were used, and their selection was based on non-probability snowball sampling. The findings of the research indicate that 12 complications can be introduced as the main complications of range management policies in Iran.

Horul Azim Wetland is located in the southwestern Iran in Khuzestan Province, has 1180 km2. About 70% of the wetland is located in Iraq, which has faced the challenges of drought and oil drilling, and many of its southern lands have dried up and become dust center. The present study was conducted with the aim of assessing the levels of heavy metal pollution in the dried part of this wetland. At first, 15 stations were identified on the LANDSAT image and the dried soil of the wetland was sampled by quadrat and the Cd, Cu, Pb, Fe, Mn and Ni were measured by ICP-MS device. To assess the pollution levels, Igeo, ER, PLI, RI, CSI and mCd were used. The results showed that the concentration of Cd, Cu, Ni and Pb is higher than the shale standard. According to the Igeo, Cd pollution is high (moderate to severe). According to the PI, Cd and Pb pollution are high and moderate, respectively. The ER shows that the ecological pollution of Cd is significant, but other metals don't have ecological risk. The average of the cumulative indices ER and PLI were 193 and 9.7, respectively, indicating a possible ecological risk for all points. The CSI pollution safety for Cd, Cu, Ni and Pb was 0.52, 0.25, 4.02 and 0.34, and the mCd index was 0.27, 10.4, 13.9 and 8.9, respectively, indicating a very high severity of pollution safety for Ni and Cd.

Social capital is defined as a set of relationships, networks, trust, needs, and participatory systems within a society that enables it to confront challenges and improve the living conditions of its members. This study was conducted to analyze the intra-group social capital in three villages: Eskelabad, Eslamabad Kalleh-ye Espid, and Chah-e Ahmad in Tafatan city. Given the importance of participatory management and strengthening social capital, analyzing the link between trust and participation among individuals in these villages is essential. The network analysis method was used to examine the links of trust and participation among individuals and villages of interest. The results before the project implementation showed low levels of trust, participation, cohesion, and social capital. However, after the project implementation, these indicators improved, and trust, participation, and the speed of interaction among individuals increased. The project implementation has led to increased unity and cohesion among village residents, increasing the intra-group social capital. In other words, improved communications have led to increased social welfare and the expansion of trust and participation among the residents.

Soil organic carbon as a key factor in soil stability and fertility is considered as one of the important environmental challenges in the context of climate change. The aim of this study was to determine soil organic carbon zonation in Gonbad paired-watershed, Hamedan province. In this research, the information of meteorology, soil science and erosion and sedimentation study of Gombad watershed was used, including the information of 49 profiles in the 0-15 cm soil layer. After collecting data, tests of normality (Shapiro-Wilkα test <0.05), homogeneity of variance, and then the relationship between independent variables and organic carbon were performed using Pearson's linear correlation in SAS software. Also, determining the most effective independent variable using multivariate analysis, PCA factor analysis was used in XlStat 2.1 software. In order to determine the distribution and amount of soil organic carbon in the Gonbad representative watershed, modeling using SVM support vector machine learning algorithms and RF random forest was used in R software.The results showed that 78.18% of soil organic carbon changes depend on four components. Clay and nitrogen percentage were selected as the most effective variables on soil organic carbon content, so that the first component of clay content explained 34% and the second component nitrogen explained 18% of variations. According to the results of the implementation of the SVM and RF Models, the SVM model with a CE factor of 0.86 and RMSE of 0.05 in the test stage is a more accurate model in this study.

Soil is a vital natural resource with a multitude of functions, whose degradation leaves significant adverse effects on the beneficiary community. Given the pivotal role soil erosion plays in soil destruction and the mandatory measures prescribed by Article 15 of the Soil Protection Law to address these adverse consequences, the current study was performed to explore the prevailing methodologies along with their inherent limitations and advantages. The goal was to identify and propose solutions for economic assessment of damages ensuing from soil degradation. The types and extent of soil erosion damage pose significant challenges to practitioners and scientists in their efforts to assess and quantify soil degradation. It is, therefore, essential to develop a specialized evaluation framework and datasets for each type of damage. The majority of conventional methods used for quantifying erosion damage are cost-oriented, with the most prominent ones being characterized by replacement cost, avoidance cost, and opportunity cost. It is important to note that no single method is perfect as each is designed for a specific type of erosion or damage. Furthermore, it is likely that application of combined methods might lead to overestimation. Given these considerations, it is not feasible to provide a universal framework as a cure-all that can be used for all types of erosion under all conditions. Our literature review revealed that soil erosion damage assessment studies typically comprise three main parts: 1) measuring the amount of soil erosion, 2) identifying the types of erosion damage in each study area, and 3) pricing the damage, each entailing significant challenges. Consequently, it is essential to adopt the most effective and efficient approach tailored to the specific goals and conditions at hand. Based on the results obtained, the avoided cost method was identified as an optimal solution that might be recommended on practical grounds as it encompasses all damage types, its pricing is straightforward, and is applicable to all forms of erosion.

River flow pattern is subject to constant changes as a result of changes caused by natural factors and human activities both in the catchment and along the river course. A sound knowledge of the phenomena involved and their impacts on flow conditions can obviously play effective roles in minimizing flood-associated casualties and in improving the exploitation and operation of the river as a resource. In the Gheshlaq basin, agricultural and orchard developments along the river banks over the past three decades have escalated the density of different land uses to increase pressure on this resource. To carry out a site investigation into the current status of the land uses along the river, satellite images obtained from Landsat-8 sensor OLI/TRIS and Landsat-5 sensor TM were used in the Google Earth Engine system with a spatial resolution of 30 meters. Subsequently, the random forest classification method was employed to derive land use maps for the years 1990, 2000, 2010, and 2020 and the changes in each period were detected and explored. The biggest changes were found to belong to the third period (i.e., 2010-2020) when residential development had increased by 23.3%, rainfed agriculture by 57%, and irrigated agriculture by 52.7% while pastures decreased by 16.9%. The river bed, boundaries, and transverse span of the river were also studied to detect land use changes to find increases in residential development and encroachments in the study periods: indeed, 154.1% of the bed of the Gheshlagh river was found to have been encroached in the period 1990-2000, 60% in the period 2010–2000, and 31.3% in the period 2010-2020. Evidently, these increases warrant necessary measures to clear the occupied sites for reclamation. Another aspect of the study involved the determination of peak discharges in the river basin throughout the study years using the HEC-HMS software based on rainfall intensities with different return periods in the study sub-basins along with CN values extracted from the combined land use maps and soil hydrological group maps. The results showed that peak flood discharge increased over time such that the greatest change in peak discharge belonged to the third period (2010-2020). This drastic increase in peak flood discharge was attributed to the declining pasture use and the increasing agricultural and residential land uses that, in turn, increased runoff potential and, thereby, CN values.

Soil, as the bed and medium of plant growth, plays a vital role in providing services to communities that depend on natural ecosystems. Its qualitative characteristics, in turn, strongly depend on the vegetation cover in the area. The present study explores the effects of different types of vegetation cover on soil organic matter components and biological characteristics, using the mountainous region of Central Alborz in Nowshahr, Mazandaran Province, as its study site. To achieve this, forest, forest-rangeland ecotone, and rangeland habitats were identified in summer 2023 and three one-hectare plots were selected in each to collect four soil samples (to make a total of 12 soil samples per habitat) from a 30 × 30 cm surface area and a depth of 10 cm. Variance analysis revealed significant differences (p<0.05) among the soils taken from the three different habitats. Compared to rangeland soil, forest soils exhibited a higher soil moisture (by 11%) but a lower temperature (by 8°C). Moreover, samples from the forest habitat showed the lowest bulk density (1.16) but the highest porosity (0.56), stability (53.72), and mean weight diameter (0.39). Meanwhile, phosphorus, potassium, calcium, and magnesium levels in the forest soils were by 168%, 108.3%, 91.1%, and 221%, respectively, higher than those in rangeland soils. Similarly, the activities of urease, acid phosphatase, arylsulfatase, and invertase enzymes in forest soil were by 1.69, 2.35, 2.02, and 1.88 times, respectively, higher than those in rangeland soil. Moreover, forest soils were characterized by higher microbial indices, including basal respiration and stimulated respiration as well as carbon, nitrogen, and phosphorus contents, which were by 2.84, 1.6, 1.9, 2.35, and 2.29 times greater than those recorded for rangeland soil. Earthworm population and biomass in the forest soil were by 5.84 and 7.19 times greater than those observed in the rangeland one, which recorded the lowest among the three soils tested. Additionally, mites, springtails, and nematodes in the forest soil outnumbered those in rangeland soil by 3.46, 3.57, and 6.13 times, respectively. The results of principal component analysis revealed that the first and second axes together explained 52.37% of the variation. These findings confirm that forest vegetation cover establishes greater correlations with higher levels of organic matter components, fertility indices, and biological activities. Overall, it may be claimed that, due to their high capacity for maintaining soil fertility and biodiversity, forest vegetation cover can be considered a suitable option for the restoration of degraded habitats in the study region and similar areas, and that this finding should receive due consideration by managers and decision-makers.

In forest ecosystems, throughfall, a pivotal component of the hydrological cycle, exhibits spatial variations that have received scant attention. The redistribution of rain beneath the forest canopy gives rise to distinct rainfall patterns, resulting in substantial spatial disparities across diverse forest ecosystems. Forests assume a pivotal role in the water balance of the Zagros ecosystem. However, the increasing trend of afforestation using both native and non-native species, particularly fast-growing ones, necessitates an assessment of its impact on rainfall and its components compared to natural stands. This assessment is crucial as it affects the water cycle significantly. The present study aims to estimate the spatial variability of throughfall in natural stands of Persian oak (Quercus brantii) and afforested areas with Pinus eldarica and Cupressus arizonica in Zagros forests, with a specific focus on Chaghasbez Forest Park in Ilam county.

This study was conducted in Chaghasbez Forest Park in Ilam Province, with the aim of measuring rain components, including throughfall and stemflow, in stands of Persian oak, Pinus eldarica (Eldar pine) and Cupressus arizonica (Silver cedar). Rainfall was measured using five rain gauges located in the open space (outside the canopy) adjacent to the studied stands. Throughfall was measured using 27 collectors in Persian oak stands underneath the canopy, and 36 collectors in each Eldar pine and Silver cedar stand. To minimise measurement errors in throughfall at the stands, after every five rain events, four of the collectors were randomly relocated under the crown of the stand. The remaining collectors were stationed at fixed points throughout the study period. This approach increased the number of samples and reduced the measurement error of throughfall. Geostatistical methods were employed to investigate the spatial patterns and distribution of rainfall, and statistical analyses were conducted using GS+ software (version 5.1.1).

The mean throughfall depths for oak, Eldar pine, and Silver cedar were calculated as 207.32 mm, 129.21 mm, and 152.47 mm, respectively. The results indicate that the throughfall percentage of oak is higher than that of Eldar pine and Silver cedar. The average leaf area index (LAI) and the percentage of canopy gap of the studied stands were 1.4 m²/m² (CV= 53%) and 43.85% for Persian oak stands, 1.04 m²/m² (CV= 88.78 %) and 57.04% (CV= 46.26%) for Eldar pine, and 1.2 m²/m² (CV= 80.78%) and 54.71% (CV= 52.47%) for Silver cedar. The spatial pattern analysis of throughfall using variogram analysis revealed that under the canopy of Persian oak (RSS= 0.021, C0= 0.001, and r²= 0.51) and Silver cedar (RSS= 0.0, C0= 0.125, and r²= 0.92), the distribution is anisotropic with a strong spatial structure. Conversely, under the canopy of Eldar pine, the distribution exhibited isotropic characteristics with an average spatial structure (RSS= 0.102, C0= 0.54, and r²= 0.66). The analysis identified the exponential, linear, and spherical variogram models as the most suitable for Persian oak, Eldar pine, and Silver cedar, respectively.

This study found that the spatial continuity range of throughfall was estimated to be 10.8 metres in eldar pine, 8 metres in silver cedar, and 2.4 metres in Persian oak. A review of the literature revealed that the type of forest, tree density, and biomass can affect the spatial change and correlation structure of throughfall. Consequently, it is imperative to take into account the distinct characteristics of the forest type when investigating the spatial patterns of throughfall and their ecological ramifications. Subsequent research, which involves a comparison of the spatial correlation structure of throughfall in evergreen and deciduous forests, is expected to provide a more precise understanding of this subject.

Surface runoff is one of the main reasons for erosion, sedimentation and reduction of river water quality. Therefore, it is important to predict the watershed response to precipitation events. Selecting an appropriate rainfall and runoff model for the basin is crucial for effectively planning and managing water resources. Moreover, the increase in greenhouse gases can lead to numerous adverse effects on all systems interacting with the climate. In this research, the effect of chandes in precipitation and temperature investigated using BCSD downscaling method based on a combination of the output of AOGCM models of the 6th IPCC report under two emission scenarios of SSP245 and SSP585. This study projected future climate change scenarios and investigated the simulation of runoff entering the reservoir based on these scenarios to forecast river flow to the Dez Dam reservoir located in Khuzestan province, Iran.

To examine the effects of climate change on streamflow in Dez dam station, the output of six AOGCM models from the 6th IPCC assessment report was utilized. The output of these models includes temperature and precipitation data for the base period of 1991-2020 and the future period of 2030-2059 and under SSP245 and SSP585 scenarios were extracted. Also, the simulation of the runoff entering the reservoir is based on two future climate scenarios in order to produce the river flow to the Dez dam reservoir.

The results of current research showed that the combined global climate model performs better than the other 6 individual models and also has a better fit with the observational data. Also, rainfall reduction in most months in SS245 scenario is more than SSP585. Temperature increases are more pronounced in the warmer months of the year than in the colder months. Furthermore, the results indicate that the IHACRES model effectively simulates flow during wet periods, or high flow rates, whereas its performance is less consistent during periods of low flow. The results indicate that the highest amount of increase in runoff in both scenarios compared to the observation period is in February at the rate of 248.20 m3/s in the SSP585 scenario and the lowest amount in January at the amount of 194.26m3/s in the SSP245 scenario.

In this research, the latest emission scenarios compiled in the 6th report of the IPCC were used and are more compatible with the climatic conditions of the planet. The results showed that the combined model performs better than the other 6 individual models; Therefore, the combined model was used to forecast the climate parameters of the study area under two emission scenarios of SSP245 and SSP585 in the future period (2030-2059). The results showed that the highest amount of rainfall occurs in the winter months. The temperature has increased in most months in both scenarios compared to the observation period. Therefore, upon reviewing the results, it is evident that there is good agreement between the measured and predicted values in the downscailing process. The BCSD model demonstrates strong performance in simulating precipitation and temperature at the Dez Dam station, making it suitable for estimation purposes. Also, based on the results, the IHACRES model has a good ability to simulate the flow in wet periods or, in other words, high discharges, while in low discharges, this adaptation is less. Investigating the impact of climate change on underground water resources and dam useful life is essential for water resources management. The results of this research can be useful in analyzing droughts, controlling destructive floods, allocating surface and underground water resources, increasing water regulation for drinking and agriculture, drought analysis, and comprehensive management of water resources at the basin level.

Drought, as an important climatic condition, has affected extensive areas of the world. Vegetation cover is also affected by low precipitation, high temperatures, and evaporation in dry ecosystems. These impacts can be defined as ecological drought on vegetation cover. Long-term droughts can have dangerous effects on vegetation cover. The SPEI index, which is based on the difference between precipitation and potential evapotranspiration, has been used in various studies to examine the spatiotemporal patterns of drought. Additionally, the use of satellite images with high spatial and temporal resolution is an effective tool for studying vegetation cover changes and the impacts of drought. 

To investigate the impact of drought on vegetation cover in the southern Kerman regions, the Enhanced Vegetation Index (EVI) from MODIS satellite imagery and the Standardized Precipitation Evapotranspiration Index (SPEI) were used. In this research, EVI derived from the MOD13Q1 MODIS sensor product with a spatial resolution of 250 meters and a temporal resolution of 16 days was used. First, the monthly average of this index was estimated for the study area from the beginning of 2001 to the end of 2022 on the Google Earth Engine platform, and then the monthly average over the study period was calculated. Based on the results of this section, the two months with the highest EVI values were selected. To investigate the impact of meteorological drought on vegetation cover, the SPEI index with different time scales of 3, 6, 9, and 12 months for the months of March and April over 22 years (2001-2022) was calculated using MATLAB software. The sensitivity of EVI to SPEI in March and April was calculated by the Pearson correlation coefficient in the Trend Analysis Module (ETM) of TerrSet software. Then, the slope of the relationship between the 3, 6, 9, and 12-month SPEI and EVI based on the Chatfield (2016) linear regression equations in the ETM model of TerrSet software was calculated to investigate the impact of SPEI fluctuations at different time scales on vegetation cover. 

According to the results, the months of April and March have the highest EVI values throughout the year, indicating maximum growth and vitality of vegetation during these months. The months of January and December have the lowest average EVI values. The months of April and March, which had the highest EVI values, were selected to investigate the impact of drought on vegetation cover. According to the results obtained in March, the highest correlation of EVI was with the 12-month SPEI, covering 40.4% of the study area. Furthermore, in this month, the lowest correlation of EVI was with the 9-month SPEI, covering around 15.55% of the study area. In April, the highest correlation of EVI was with the 12-month SPEI, followed by the 6-month SPEI, covering 25.53% and 20.57% of the study area, respectively. In most areas of Qalat, Roudbar Jonob, Jiroft, Faryab, Manoojan, Amberabad, and Arzueie, especially from the central regions of the study area towards the south, the 12-month SPEI had the highest correlation with EVI. The lowest correlation of EVI was with the 9-month SPEI, followed by the 3-month SPEI, covering 10.75% and 15.44% of the study area, respectively. In March, the positive high, moderate, and low classes accounted for 29.02%, 19.41%, and 15.03% of the study area, respectively. These percentages for April were 41.89%, 28.1%, and 14.69%. The lowest percentage of area in both months belonged to the very high negative and high negative classes. In March, areas in the north, northeast, and parts of the west of the study area, including most areas of Fahraj, Narmashir, Bam, and Rigan had very low and low sensitivity to drought, while areas in the central regions towards the south, southwest, and southeast, including the south regions of Jiroft, west of Roudbar Jonob, and most areas of Kahnuj, showed the highest sensitivity of EVI to SPEI. In April, EVI in major parts of the western, northwestern, central, east, southeast, south, and southwest regions of the study area showed very high and high sensitivity to drought compared to SPEI. The lowest sensitivity of EVI to drought in this month was related to the northeastern parts. 

Based on the results of the correlation analysis of EVI with SPEI at different time scales, in both months of March and April, the highest correlation of EVI with SPEI has been with SPEI 12, 6, 3, and 9 months respectively. Therefore, the greatest impact of drought on vegetation cover in southern Kerman is related to SPEI 12 months, with the least impact related to SPEI 9 months. These results are due to different environmental conditions in the study area, which have led to different results in each region. The results of the sensitivity of EVI to SPEI show that the highest sensitivity is allocated primarily to high positive, medium positive, and low positive classes, mainly related to central, western, southwestern, south to southeast and eastern parts of the study area. Additionally, the results of sensitivity of EVI to SPEI show that the lowest sensitivity of vegetation cover to drought is related to the northeastern regions, some parts of the southeastern and northern areas of the study area. These regions mainly include barren lands or pastures with poor vegetation cover. Therefore, due to the lack and scarcity of vegetation cover, the sensitivity of EVI to SPEI is at its lowest level. According to the results of this research, environmental conditions such as climatic characteristics, topography, type of vegetation cover, human management, and so on have a significant influence on determining the relationship between vegetation cover index and meteorological drought index. It is suggested that future research, considering these factors, prioritize predicting this phenomenon and modeling changes in vegetation cover under the influence of drought.

Various tools have so far been developed with different efficiency to measure the flow of wind deposits. In this regard, two types of absorption and maintenance efficiency tools are commonly used as technical criteria in selecting sediment traps, bearing the lowest amount of wastage. Accordingly, this study set out to compare and contrast the adsorption and retention efficiency of BSNE and Siphon (3rd generation) as wind erosion sediment trap tools.

The efficiency of siphon traps (3rd generation) and BSNE were investigated and measured at different wind speeds of 7, 8, 9, 11, and 13 m/s using the wind tunnel laboratory of the Faculty of Natural Resources and Desertology. The efficiency was also measured in different events within the natural environment using Wind Erosion Monitoring stations. On the other hand, to check the absorption efficiency, the test tray was filled with air sediments, and the sediment catchers were placed inside the device. Then the sediments collected in the device were measured in terms of different speeds. Moreover, to measure the retention efficiency, the sediment traps were filled with 100 grams of wind-induced sediment, and the amount of lost sediment was measured at different wind speeds. After the sediments were granulated with the help of a shaker machine, the granulation indices of the sediments were obtained using the grain gauge software G.R. Graph.2. Finally, the data collected regarding the efficiency and performance of the two types of sediment traps were analyzed. It should be noted that the results were obtained in terms of 7, 8, 9, 11, and 13 m/s speed rates under different laboratory conditions (wind tunnel) and natural environment or desert at the wind erosion measurement station of Yazd University.

According to the results of the study, the absorption efficiency of the siphon trap was found to be 2 to 5 percent greater than that of the BSNE trap. Moreover, the study found that while the increasing trend of the siphon sediment trap’s efficiency against the wind speed of 13 m/s reached its maximum at roughly 94.6%, the maximum efficiency of the BSNE sediment trap was recorded at the speed range of 11 m/s (92%). Both sediment traps experienced a decline in wind-borne sediment capture efficiency at wind speeds of 11 meters per second. In both traps, the capture efficiency decreased with increasing height above the surface. However, the siphon sediment trap consistently demonstrated a higher capture efficiency compared to the BSNE trap. On the other hand, it was found that at heights lower than 50 cm from the ground, the absorption efficiency of 3rd generation siphon traps was approximately 1.5 times greater than that of BSNE traps. While at higher altitudes, the absorption efficiency of both approaches. Furthermore, the 3rd generation siphon sediment trap could maintain almost 100% retention efficiency due to its special tank design when the wind speed increased to rates over 11 meters per second under super-critical conditions. However, due to the design of the BSNE sediment trap, approximately 3% of the total captured and trapped sediment was lost from the reservoir and escaped into the surrounding environment.The results suggested that the numerical index of the particles collected in siphon sediment traps was lower than that of BSNE, which could partly be attributed to the exit of some of the fine particles when trapped in the reservoir of BSNE sediment traps. Moreover, the numerical value of the skewness index of the sediments collected in each type of sediment trap decreased with an increase in wind speed, tending towards fine particles. In other words, the increased population of fine particles caused a decrease in the average value of the median diameter or D50.

The results indicated that the highest absorption efficiency rates were 94.6 and 92% for 3rd generation siphon trap and BSNE, respectively. Also, compared to the BSNE, the siphon trap performed relatively better in terms of sediment storage and retention under physical and aerodynamic conditions. On the other hand, as for the retention efficiency of both types of sediment traps, it was found that the 3rd generation siphon trap performed 100% successfully at all investigated speeds. However, the efficiency of the BSNE sediment trap decreased from 100% to 97% when the speed increased from 11 m/s to 15 m/s, indicating that by increasing the speed and creating a twisting current inside the chamber of the BSNE sediment trap, part of the fine-grained sediments were thrown out of the trap, thus reducing the retention efficiency of the trap. It could generally be argued that the absorption and retention efficiency of the siphon trap is about 10 to 40% greater than that of the BSNE. Therefore, as the siphon trap is designed and built in Iran, it bears more advantages than its American counterpart.

As one of the greatest natural disasters faced by mankind since time immemorial, drought can be defined as a long period of reduced precipitation (a season or a year, for instance) occurring in almost all climate zones, including areas with high and low precipitation rates. Moreover, drought is a globally intensifying phenomenon, with no region or country in the world remaining safe from its consequences. On the other hand, the extent, frequency, intensity, and duration of drought are increasing in many parts of the world due to climate change. In addition, the phenomenon influences a diverse range of biophysical resources in catchment areas, including the discharge and sedimentation of rivers. Therefore, awareness of the drought status, prediction of drought, and zoning of its severity can significantly reduce the risk of its potential damage. 

Covering an area of ​​3516 km2, Behbahan is located in the southeast of Khuzestan province between 50˚ to 50˚ 21´ east longitude and 30˚ 30´ to ˚31 north latitude. Various indicators have so far been developed for monitoring drought, each of which considers one or more parameters contributing to drought. As a widely applied drought index worldwide, The Standardized Precipitation Index (SPI) time series can be used to describe the intensity of historical and current drought events at a site and to estimate the potential prospective magnitude of drought.Auto-correlated integrated ARIMA model is frequently used in analyzing time series. The model was first proposed by Box and Jenkins when studying time series, inspiring many related models that have since been developed and applied in various branches of geosciences. To investigate and predict drought in Behbahan City, this study sought to model annual SPI time series, six-month SPI, and nine-month SPI using the ARIMA model. 

The results indicated that the ARIMA (5,0,11) was the best choice for modeling annual drought index time series in the validation stage, with its errors being R2=0.64 and RMSE=0.81 MAE=0.66 and MAPE=213.27 errors. The ARIMA (2,0,5) was also found to be the best model for modeling the six-month SPI drought index time series, where the R2 value of the model was equal to 0.50 (greater than that of other models), and its RMSE, MAE, and MAPE errors were reports as 0.84, 0.64 and 100.31, respectively (less than those of other models). Moreover, due to the non-normality of the residuals of the selected models, a suitable model was not found for modeling the nine-month SPI time series. Therefore, other methods such as wavelet analysis are recommended to be used in this regard. Discussion and

As a complex and often underestimated phenomenon, drought strongly influences different aspects of human life. Therefore, early prediction of drought plays a crucial role in strategic planning and management of water resources. In this regard, time series models are considered a suitable tool for predicting climatic events. This study used the time series of the SPI index to model drought in Behbahan City at annual, six-month, and nine-month scales, seeking to identify appropriate models in this regard. It appears that managers and relevant decision-makers should take similar studies into account to access a clear analysis of the region and develop optimal plans for the management of water resources. This is especially important in Iran, where a large area of the country is characterized by arid and semi-arid climates. Considering that the phenomenon of drought can be predicted and managed, it is necessary to implement basic measures to reduce the adverse effects of drought in the coming years.

Forests are the only ecosystems that offer all the classified services. However, such environmental services and functions are not provided freely, bearing seemingly latent economic values that are not fully considered within the framework of the market system. Moreover, these services are not sufficiently and quantitatively recognized in comparison to other economic services. In other words, the value of non-market services in natural ecosystems is far greater than that of goods. On the other hand, To streamline decision-making processes regarding the utilization of available resources, it is imperative to both quantify the quantifiable costs and benefits (exchangeable goods and services) and to incorporate non-market and intangible costs and benefits, which have primarily been considered from a consumptive standpoint, into the relevant economic framework and calculations Covering 31% of the land area, forests bear economic value and offer environmental services that must be valued through the economic valuation of biological resources.More than half of the world's forests are being deforested in just five countries, that is, an estimated 10 million hectares per year between 2015 and 2020, indicating that six million hectares decrease per year compared to similar statistics recorded in the 1990s. Characterized by its geographical and climatic conditions, Iran also deals with the issue of forestry, with annual afforestation reducing the net amount of deforestation within the country, highlighting the necessity of development and estimation of the economic and environmental values of planted forests. As most of the studies conducted in the field of conservation value are mostly related to the past decade and few studies have addressed this issue in recent years, the present study sought to estimate the conservation value of the planted forests of the Isfahan Steel Mill Factory using the contingent valuation method. 

As one of the preferred methods, which is also applied in the current study, the Contingent Valuation Method can theoretically be used to evaluate the resources and continuity of the existence of what people care about, even if they never visit it in person. Moreover, the method includes the development of a questionnaire to select appropriate options and increase the accuracy of the results, thus preventing potential prospective deviations. The population of the study comprised the areas that were most affected by planted forests, including a collection of seven cities adjoining the factory with a population of 2 million people, among whom 384 people were selected through random sampling. Then, the relevant questionnaire was developed and administered to participants in seven regions in 2017-2018.In this method, proposed amounts as annual payments for individuals to protect the afforestation projects of Isfahan Steel Company were presented to the selected individuals. Given that individuals maximize their utility, the logit model follows the cumulative logistic distribution function.The parameters of the logit model were estimated using the maximum likelihood method. It should be noted that predicting the effects of non-distributive variables on the probability of acceptance is of great significance in estimating logit models. The amount offered by an individual also bears special importance. After estimating the logit model, the expected value of the willingness to pay was calculated as the highest acceptance offer by numerical integration in the range of zero.

The results of the study in terms of social, economic, and environmental characteristics of the respondents indicated that the average age of the respondents was 31 years, who provided more acceptable answers in terms of income due to their independence. In addition, 64% of the respondents were male, 54% were married, and only 5% were members of an environmental organization. On the other hand, 84% of the respondents assessed the significance of protecting the forests of the factory as high and very high, and after reading the information provided in the brochure, only 18% of the respondents stated that they knew all the information provided, suggesting a lack of sufficient information regarding the environmental activities carried out by the factory. Furthermore, 41 percent of the respondents were highly concerned about the destruction of forest plantations, and almost 50 percent of the respondents evaluated the percentage of possible damage to forestry as relatively severe. The variables of income, education, membership in environmental organizations, number of employed people, taxes, and the level of concern about the destruction of forestry were found to have a direct relationship with the proposed price. In addition,The negative sign of the estimated coefficient for the proposed price variable is consistent with the current research theory and indicates that if the proposed amount for protection increases, the likelihood of acceptance in respondents' willingness to pay decreases.Considering the elasticity of the proposed amount variable (0.7736), a one percent increase in the price offered to respondents decreases the probability of acceptance in their willingness to pay for the conservation value of afforestation by 0.77%. Additionally, based on the marginal effect of this variable (0.02059), a one-rial increase in the amount proposed to individuals decreases the probability of accepting a payment for conservation by 0.02%.Furthermore, the annual conservation value of each hectare of Isfahan Steel plantation was equal to IRR 6048875. In other words, each family was willing to pay an average of IRR 193560.4 annually (according to the size of the household) for the protection of forestry. 

This study has achieved promising results. First, the study showed that people in the study population were aware of the significance of forest resources. Second, the results indicate that there is a willingness to pay significantly for the improvement and development of such resources, providing justification for policymakers and officials to support the quality of the environment and natural resources and prevent the underestimation and trivialization of natural resources. It is recommended that future studies address different functions of planted forests and the effect of various parameters such as tax on willingness to pay for the protection of ecosystems and reduction of damages. In addition, the ecosystem enjoys various other values, such as water and soil conservation, carbon dioxide absorption and oxygen production, and purification of air pollutants, each of which needs to be carefully estimated.