مجله علوم و مهندسی آبخیزداری ایران
پیاپی 67 (زمستان 1403)

Remote sensing has become a valuable tool for acquiring integrated spatial data regarding land cover and land use across various temporal and spatial scales. One of the primary challenges in multi-temporal land cover and land use mapping is the availability and integrity of training data for supervised classification algorithms. Collecting training samples for each class of land cover and land use over different time periods can be time-consuming and, particularly in rapidly changing environments, fieldwork can be challenging. This issue is further exacerbated by the potential spectral and phenological changes in land cover features over time, which can diminish the transferability of training samples. The concept of "migration" or "transfer" of training samples from a reference year to target years has been explored as a means to overcome the limitations of training data. In this context, the use of Google Earth Engine (GEE) has facilitated multi-temporal land cover and land use mapping. GEE's ability to integrate diverse data sources, including Sentinel-2 imagery and a wide range of spectral indices, enables researchers to develop robust and scalable applications for land cover and land use classification. Monitoring changes in land use and cover over time is crucial for understanding and managing the environment. However, when there are limitations in training data for different time periods, this can pose significant challenges. This study presents an innovative approach to classify Sentinel-2 satellite images from different years using a set of reference training samples.

In this study, an innovative application was explored using migrated training samples from a reference year (Sentinel-2 imagery from 2019), along with bands from Sentinel-2 images and spectral indices, to classify land cover and land use in the dynamic and ecologically significant mangrove region (Khoran Protected Area). The use of machine learning algorithms within the Google Earth Engine (GEE) framework was examined to achieve high classification accuracy and monitor land cover changes over time. Satellite images from Sentinel-2 covering the study area for the target years 2022 and 2024, as well as the reference year 2019, were retrieved in GEE. Ground truth data, including the location and classification of various land cover types for the reference year, were collected using the European Space Agency's land use maps. Subsequently, high-quality ground truth data and their corresponding image samples from the reference year (2019) were transferred to the target year images using the Spectral Angle Distance (SAD) algorithm. Classification algorithms such as Random Forest (RF), Gradient Boosting Trees (GBT), and Classification and Regression Trees (CART) were employed to classify the target year images using the variable training samples. The classified images were evaluated using various accuracy metrics, including overall accuracy, Kappa coefficient, producer accuracy, user accuracy, as well as commission and omission errors. Finally, the importance of different spectral bands from Sentinel-2 and spectral indices in the classification process was analyzed to identify the most suitable features for distinguishing various phenomena in the study area.

The results of this study indicated that among the classification algorithms, the highest accuracy for overall accuracy and Kappa coefficient in the classified images for 2024 and 2022 was achieved using the Random Forest classification, with accuracies of 0.9104 and 0.8742, and Kappa values of 0.8955 and 0.8570, respectively. Additionally, the findings revealed that the area of mangrove forests decreased from 7530.74 hectares to 6546.51 hectares during the study period, representing a reduction of approximately 984.23 hectares, or about 164 hectares per year.The area of residential and construction zones increased from 72.52 hectares in 2019 to 96.48 hectares in 2024, indicating rapid growth in these land uses over the last two years. The synthesis and summary of various classification methods across different years highlighted the relative importance of bands and indices. Specifically, the EMVI and mNDWI indices demonstrated greater dominance due to their effectiveness in capturing the phenomena of mangrove forests and aquatic areas within the study region. Consequently, for the band combinations aimed at distinguishing various phenomena, the use of green, red, and near-infrared bands, along with the mangrove index and optimized water body index, were identified as the most suitable for the study area. These findings are recommended for similar areas in southern Iran and other mangrove regions. This research employed a simple and effective application by transferring ground truth points from the reference year to their corresponding images as training samples for the target year images, utilizing the Google Earth Engine platform. This approach holds the potential for extension to other regions.

In summary, this study demonstrates the potential of using migrated training samples and advanced machine learning algorithms such as Random Forest, Gradient Boosted Trees, and Classification and Regression Trees, along with spectral indices as auxiliary data, for the accurate classification of multi-temporal satellite imagery. The development of spatial tools, including the online Google Earth Engine (GEE) platform, is essential for the up-to-date management of land uses, particularly in wetland and mangrove areas. In this research, high-quality training samples were successfully migrated from the reference year to the target year, resulting in high classification accuracy using the Random Forest classification algorithm compared to other methods such as boosted regression or regression and classification trees. This method offers a viable solution in multi-temporal land use studies, especially in cases of insufficient or inadequate training samples within the GEE system. For future studies, it is recommended to employ a combination of Euclidean Distance (ED), Spectral Angle Distance (SAD), and K-means clustering for generating variable training samples, and to compare and analyze the classification results obtained through these methods. This approach presents a promising solution for producing up-to-date land use/land cover maps, even in challenging environments with limited training data. The findings of this study can guide future research aimed at monitoring land cover, managing, and effectively protecting valuable natural resources, such as mangrove forests, in other regions.

Flooding is a significant natural hazard that affects millions of people around the world every year and causes extensive damage to property, infrastructure, and ecosystems. Investigating changes in floodplains is very important to understand and reduce these effects. Scientific research reveals that floodplains are affected by various factors, including climate change, urbanization, and river management practices. Notably, recent climate shifts have intensified torrential rains, expanding flood zones and elevating flood risk. Concurrently, urban development and residential expansion contribute to increased flood volume and flow by altering surface runoff patterns and reducing natural water infiltration areas. To optimize flood management strategies, the initial step involves identifying flood-prone regions, creating flood area maps, and monitoring changes over time.  While post-event flood zone mapping and continuous monitoring pose challenges, satellite imagery remains the most practical solution. Advances in earth surface monitoring using satellite technology have significantly enhanced global flood monitoring capabilities. Leveraging satellite imagery and remote sensing technologies, platforms like Google Earth Engine provide valuable data for analyzing temporal and spatial variations in flood-prone areas.

In the current research, we discuss the continuous monitoring of flood events in relation to the instantaneous maximum flow at the hydrometric station of the Kohak Dam. The Hamuns of Sistan, especially in recent years, have relied solely on water inflow from Afghan rivers during floods. Consequently, we conducted monitoring of the water area of the Hamuns of Sistan during flood events and tracked the water area of the Sistan plain over time using a time series of satellite images spanning from 1984 to 2021. For this purpose, we utilized Landsat satellite images (employing the NDVI index, transition algorithms, and seasonality blue zone identification algorithms) within the Google Earth Engine environment. The initial step involved extracting flood dates from the hydrometric station at Kohak Dam, located on the Sistan River at the entrance of the Hirmand River to Iran. Subsequently, we downloaded Landsat satellite images for various dates, including pre-flood, the first image after the flood, and post-water withdrawal (based on the daily hydrograph of the Kohak Dam station). To process and extract information from the images, we performed pre-processing and identified catchment areas of Sistan’s Hamuns during flood events using the NDVI index and infrared and red bands. Additionally, we employed GSWE (Global Surface Water Explorer) algorithms to monitor temporal and spatial water extent distributions globally over the past three decades.

Results and Conclusion :

Using the dates of flood occurrence at the hydrometric station of Kohak Dam located on the Sistan River (at the entrance of the Hirmand River to Iran), we analyzed three distinct time periods: before the flood, the first date of the satellite image after the occurrence, and after the completion of water withdrawal (based on the daily hydrograph of the Kohak Dam station). During these periods, we downloaded Landsat satellite images.  The obtained results reveal that in the water year 1988-1989, the maximum instantaneous discharge was equal to 901.83 cubic meters per second, occurring on 1989/04/06. Monitoring Landsat satellite images during the three time periods before, during, and after the flood shows that with the arrival of flood flow from the Hirmand River to Iran, the water area of the Hamun wetlands increased. Specifically, after the flooding event, the water area of the Hamun wetlands in Sistan reached 2614.4 square kilometers. In the water years 1990-1991 and 2015-2016, the instantaneous maximum discharge was equal to 1178 and 624.6 cubic meters per second, respectively, occurring on 1991/06/01 and 2016/04/16. The extraction of water areas of Hamun wetlands using the NDVI index from Landsat satellite images shows that in these two flood events, the maximum water area of the Hamun wetlands resulted from flood flow and subsequent inundation. Specifically, the water area was 4477.6 and 1319.8 square kilometers, respectively.

The study of three maximum annual flood events in the Sistan plain showed that the water intake of Hamun wetlands is completely affected by the occurrence of floods. A step-by-step comparison of the beginning to the end of the flood hydrograph with the flood zones extracted from satellite images in the same time steps, shows the complete matching of the surface of the water zone with the size of the flood discharge. Also, the study of the changes in the water areas of the Hamuns of Sistan using the GSWE layer shows the changes that have occurred in these areas between 1984 and 2020. Therefore, the results obtained from the study of the water zones of Hamuns using GSW maps showed that the water area of Hamuns in the long term shows a sharp decrease, so that the changes occurred in such a way that in the year In 2020, compared to 1984, more than 74% of the area of Sistan's Hamuns is drained seasonally. From the point of view of studying the situation of monthly water intake based on the monitoring of long-term average water areas, the results showed that only a small part of the Sistan plain (about 11 thousand hectares) has water on average throughout the year, and this area also belongs to wells. It is half and covers about 0.5% of the studied area. Also, more than 2 million hectares of the surface of the studied area, on average, have water only in 1 month of the year, which is equivalent to 88.4% of the total area of the studied area (includes Hamuns, the rivers adjacent and the wells).

Natural hazards cause significant human and financial losses worldwide each year. Land subsidence is one of these hazards, and its importance has increased in recent years due to the occurrence of droughts in cities, especially in metropolises. This phenomenon has occurred in many plains of Iran due to over-exploitation of groundwater resources. The rate and spatial patterns of subsidence can change over time. Therefore, accurate measurement and predictive tools for monitoring and studying it are necessary. Radar interferometry (InSAR) is one of the new and powerful tools for determining the extent of land subsidence. This method is very effective and useful as a suitable and cost-effective solution in areas lacking leveling infrastructure. In fact, by providing high-precision satellite images, this technique enables the monitoring of the destructive phenomenon of land subsidence and plays a vital role in managing and controlling land subsidence. Given that the amount of groundwater withdrawal strongly affects the extent and distribution of subsidence, this research aimed to monitor annual land subsidence and evaluate its relationship with changes in groundwater levels in the Kermanshah aquifer. For this purpose, the temporal trend of land subsidence and fluctuations in groundwater levels during the same period were investigated and analyzed.

In this research, the land subsidence rate in the Kermanshah aquifer was monitored and investigated using the Differential Interferometric Synthetic Aperture Radar (DInSAR) method. For this purpose, seven series of Sentinel A-1 radar images were used to examine the subsidence rate during the 2015-2021 period. Image analysis was performed in the Windows operating system environment using the SNAP software. Then, to estimate the maximum groundwater level decline in the Kermanshah aquifer, data from 38 piezometric wells throughout October 2015-2021 were used. The spatial information and the average groundwater level of the observation wells were entered into the ArcGIS software, and groundwater level variation maps in the study area were calculated using the IDW interpolation method. Subsequently, using the land subsidence maps and the annual groundwater level changes, a map of the average seven-year land subsidence rate and groundwater level changes was prepared. These maps were used for a more detailed and comprehensive analysis of the trend of changes and the mutual effects between the annual subsidence rate and groundwater level changes. In the present study, the relationship and correlation between the annual subsidence rate and the groundwater level change trend, both in urban and agricultural areas and in the entire aquifer domain, were thoroughly investigated and evaluated.

The results of the Kermanshah aquifer study in the period 2015 to 2021 indicate significant fluctuations in the subsidence rate and changes in groundwater levels. In the early years of the study period, the subsidence rate in the stress-prone areas of the aquifer increased sharply, so that in 2015-2016 this rate ranged from 5.6 to -14.4 cm/year. This amount of subsidence occurred simultaneously with an unprecedented decline in groundwater levels of up to 12.6 meters during the same year.However, from 2016 to 2021, a trend of improvement in the aquifer's condition was observed, so that the subsidence rate decreased to 6.2 to -11.9 cm/year, and the groundwater level changes were limited to -0.19 to 11.8 meters per year. Detailed examination shows that the northern and western parts of the aquifer experienced the highest subsidence rates, averaging -3.4 to -6.3 cm/year, while the central and southern regions faced a decrease of 0 to -1.8 cm/year. Additionally, in the agricultural lands in the north of the aquifer, the groundwater level decline reached an average of 50 cm per year. Data analysis showed that the coefficient of determination between the average subsidence rate and the average groundwater level changes in urban and agricultural lands were 0.13 and 0.074, respectively, and for the entire aquifer area, it was 0.082, indicating a lack of strong correlation between these two variables in both land uses.

Radar imagery studies of the Kermanshah aquifer from 2015 to 2021 have revealed serious challenges related to declining groundwater levels and land subsidence. The results show a decreasing trend in the subsidence rate, from -14.4 cm/year in 2015-2016 to -11.9 cm/year in 2020-2021, with the highest rates observed in the northwestern and western parts of the aquifer. Concurrently, groundwater levels declined, with a maximum drop of -12.6 m recorded in 2015-2016 and a minimum of -0.19 m in 2019-2020. The maximum groundwater level decline was seen in the central and northern parts, while the southern and urban areas experienced the least decline. However, the correlation between subsidence rates and groundwater changes in urban and agricultural areas was low, suggesting that various factors, including faults, aquifer characteristics, and past groundwater changes, may have contributed to the land subsidence. The differences in the trends of subsidence and groundwater decline across the aquifer indicate the complexity of the issue. The study aims to provide a comprehensive understanding of the surface and subsurface changes in the Kermanshah aquifer. The findings can assist decision-makers in infrastructure and regional development planning, as well as in better managing groundwater challenges and addressing the land subsidence phenomenon through appropriate solutions. The present study provides valuable insights into the hydrogeological dynamics of the Kermanshah aquifer, which can help policymakers and water resource managers develop effective strategies to mitigate the impacts of groundwater depletion and land subsidence in the region.

Every year, landslides cause extensive human and financial losses, including the destruction of forests, fertile agricultural lands, residential areas, and communication networks. Considering that landslides are more manageable than other natural disasters; therefore, it is very important to know this phenomenon in order to prevent damages caused by it. Therefore, the present research was carried out in order to assess the risk of landslides and prepare a map of the severity of landslide damage in Bar watershed in Razavi Khorasan province. Landslide susceptibility assessment can help planners in the final management of environmental pollution and natural resources in preventing possible damages and ultimately developing economic activities in the watershed. The Bar watershed in the northwest of Neishabur city has the potential of landslides due to geological conditions, climate and human activities. Therefore, the need to identify areas prone to landslides, vulnerability and risk of landslides in this area can have positive effects on the life and economy of the people of the region. In this research, it will be tried to simultaneously study the risk, elements at risk, the vulnerability of elements due to the occurrence of landslides and finally the damage (risk) of landslides in the Bar watershed. As a result, the purpose of this research is to assess the risk of landslides and landslide damage using the severity of the risk, the elements at risk and the degree of vulnerability of the elements at risk, it is for the purpose of risk and damage management in the watershed area.

Bar Neyshabur watershed is one of the sub-watersheds of the Bar River watershed, which is located in the north of Neyshabor city. In terms of geographical location, Bar watershed is located in the range of 58°23'35° to 58°4'52°E and 36°28°9' to 36°7'N latitude and is located in the southern slopes of Binalud. In order to carry out this study, first, through library studies and field visits, the factors affecting the occurrence of landslides were identified and collected. The distribution map of landslides in the region was prepared with the help of the data provided by the General Directorate of Natural Resources and Watershed Management of Razavi Khorasan Province and modified using Google Earth satellite images and field visits. Then, 14 key and effective parameters on the occurrence of landslides in the region were selected. Next, the landslide risk map was prepared using the maximum entropy (ME) machine learning method, and then, the accuracy of the model was evaluated using the ROC index. In order to model the risk of landslides, 70% of landslide points were used to train the model and 30% of landslide data were used to validate the model. Next, the information layer of the elements at risk and the degree of vulnerability of the elements were extracted. Finally, the landslide damage map was prepared by combining maps of risk intensity, elements at risk and degree of vulnerability of elements based on the general equation of risk.

Based on the results, the amount of surface area under the ROC index diagram in the validation stage was 0.854, which indicates the very good capability of the model in zoning and determining landslide prone areas in the Bar watershed. The results of the vulnerability map of the studied area showed that 56.9% of the Bar watershed were in the class of high and very high elements at risk. Meanwhile, 17.8% of this basin was placed in the high and very high class of landslide risk vulnerability. The reason is the absence of important facilities, large factories, highways, important structures and large recreational complexes in this basin. Also, the results of the risk zoning map of the studied area showed that 9.1% of the Neyshabur watershed, equivalent to 2503 hectares in the high and very high class, and 77.8% of the area, equivalent to 21450 hectares, were damaged in the low and very low class. The interpretation of the results showed that due to the presence of elements at risk in the region, most of the region has low and very low risk. This helps to focus the management work in the sectors that have a lot of damage and reduces the waste of time and money.

In this study, sensitivity, vulnerability, and risk of landslides were zoned in the Bar Neyshabur watershed in Razavi Khorasan province as a landslide-prone basin with an area of about 27,643.8 hectares. The natural conditions of the Bar Neyshabur watershed, such as geology, uneven conditions, geomorphology and tectonics, as well as human aggravating factors such as land use change and rural roads, have created a suitable platform for the occurrence of landslides, and a total of 73 cases were identified in the study area. In this research, it was tried to use all the effective factors in the landslide susceptibility assessment in the Neyshabur Bar watershed. According to the risk zoning map of the studied area, 9.1% of the Neyshabur watershed was damaged in the high and very high class and 77.8% of the area was in the low and very low class. The interpretation of the results of the number of landslides in each risk class showed that 41 landslides were found in the low and very low risk class. As a result, due to the presence of elements at risk in the region, most of the region has low risk and very low risk. The results of this study help to focus management work in the sectors that have a lot of damage and reduce the waste of time and money.

Climate change creates changes in precipitation amount and pattern, temperature and evapotranspiration, which influences water resources availability, increases floods and droughts frequency and intensity, leads to degradation of the environment, which serves the water quality and leads to the dynamic change of runoff. On the other hand, land cover change and population increase impact on hydrological system too. Understanding the hydrological response of watersheds to physical change (land use) and climate change (precipitation and temperature) and quantifying the water balance change due to environmental change are very important for water resources sustainable development and an important component in water resources management and planning.  This research aims to manage water supply and demand in the future under change conditions at a river basin-scale. In line with this goal, the situation of water supply and demand in the Gorganrud River basin was evaluated under the conditions of climate change, land use change and population change. The Gorganrud river basin is located in the northeast of Iran in the three provinces of Golestan, North Khorasan and Semnan, with an area of about 11,220 square kilometers. The studied area is an important source of water supply for the domestic, agricultural, industrial and environmental sectors in Golestan province, so it is important to investigate the water resources condition in this area in the future period.

 To project the climatic condition in future the data related to the CanESM2 large-scale global climate model under two scenarios of RCP2.6 and RCP8.5 were prepared. Then, these data were downscaled using Statistical Down Scaling Model (SDSM4.2) for local climatic gauging stations. Twelve climatic stations were considered for precipitation and nine for minimum and maximum temperatures. Land use maps for 1999, 2009 and 2017 were prepared using Landsat satellite images and IDRISI software. Then, land use changes were predicted using the Land Change Modeler (LCM) tool in the TerrSet software. The population size and water demand for domestic, industrial, agricultural, and environmental sectors were predicted for the future period. Then, HEC-HMS SMA was calibrated and validated using rainfall and runoff data of 2007-2009 and January 2013- March 2014, respectively.The calibrated and validated hydrological model was used to simulate the hydrological components (runoff, groundwater recharge, and volumes of inflow, outflow, and storage in dam reservoirs) under climate and land use changes. Finally, the value of the Water Supply Stress Index (WaSSI) was calculated under two climate change scenarios, RCP2.6 and RCP8.5.

 Investigations show that the weighted average of the Water Supply Stress Index has increased from 1.003 for the period 1966-2006 to 1.033 for the period 1966-2011. The study of climate change shows changes in precipitation, minimum and maximum temperature in different climate stations under both RCP2.6 and RCP8.5 scenarios. It should be noted that the impact of climate change scenarios is not the same in all climate stations. The prediction of land use change indicates the reduction of agricultural lands and their conversion into residential areas. In addition, the conversion of dense forests to semi-dense forests and rangelands is significant. The forecast of population changes and water demand indicates the increase of these variables. The comparison of the average annual rainfall in the past (2006-2017) and the future (2020-2040) at the Aqqla hydrometric station shows that this variable will have values of 207.038 and 210.8 million cubic meters under RCP2.6 and RCP8.5 scenarios, respectively which is significantly more than its amount in the last period with the amount of 179.64 million cubic meters. The total water supply components for the RCP2.6 scenario is equal to 930.49 and for the RCP8.5 scenario is equal to 935.85 million cubic meters. The sum of water demand components according to RCP2.6 scenario is equal to 1495.04 and according to RCP8.5 scenario it is equal to 1379.2 million cubic meters. The median value of WaSSI in the future under two climate change scenarios RCP2.6 and RCP8.5 was predicted to be 1.61 and 1.45, respectively.

 In this research, the impact of climate change, land use change and population change on the water resources of the Gorganrud River Basin was investigated. By calculating the Water Supply Stress Index, it was found that based on the used index, the lack of water security in the Gorganrud River Basin will be a challenge in the future. In the management of water resources, it is necessary to focus on both sides of water supply and demand but, since it is possible to manage water supply less, it is necessary to focus more on water demand management. In this regard, we should look for solutions that are more likely to be implemented. Water demand management measures in the agricultural sector such as changing the cropping pattern, changing the cropping date, reducing agricultural sector waste and reducing evaporation from the soil surface are among the appropriate solutions in the field of water demand management. In addition, the observation of land use changes and especially the increase in the area of residential areas and the decrease in the area of dense forests in this research is a warning for policy makers, decision makers, managers and executive departments in order to further protect natural resources and manage the development of residential areas. The methodology and outputs of this research can be useful for decision makers and managers of water resources in the conditions of change and uncertainties related to it, in the Gorganrud River Basin and similar conditions.

One of the important steps in the framework of watershed conservation programs and sustainable management of natural resources is the identification of vulnerable areas. This step serves as a solid foundation for the measures needed for environmental management. Vulnerability assessment provides necessary standards for implementation priorities and helps identify and prioritize sensitive and vulnerable areas for sustainable management of natural resources. Among natural hazards at the watershed level, floods are one of the most frequent and destructive types of hazards that cause many human and financial losses. Due to being situated in a vast ecosystem of arid and semi-arid regions, Iran is frequently exposed to the destructive effects of floods. The country's vulnerability to these events is exacerbated by climate change and unsustainable human activities. It is predicted that due to climate changes, rapid unplanned urbanization, expansion of impervious surfaces, changes in land-use patterns, and poor management of watersheds, floods will occur more intensely in the future. Therefore, the study and identification of vulnerable areas, as one of the important variables in flood risk management, is a key part of non-structural measures to prevent and reduce the destructive effects of floods. This research seeks to analyze the spatial vulnerability of the Zarrinehrood Watershed to flood events. By considering vulnerability functions, including exposure, susceptibility, and resilience, the degree of vulnerability of this watershed to flood was investigated, and vulnerable areas were identified.

The focus of this research is the Zarrinehrood watershed, located in the provinces of West Azarbaijan and Kurdistan, Iran. To identify flood-prone areas in this region, we first collected relevant data and information on factors contributing to flood vulnerability in the study area. Then, the grid layers of factors were prepared in three sectors including exposure (i.e., factors such as average annual rainfall, frequency of heavy rainfall, maximum daily rainfall, distance from waterways, and frequency of floods), susceptibility (i.e., factors like the percentage of farmer and rancher households, distance from industrial areas and mines, distance from roads, land use, population density, and distance from residential areas), and resilience (i.e., factors such as the number of rural cooperative companies, the number of health and treatment centers, and the number of transportation routes). Next, the importance of the variables was evaluated using the method of Analytic Network Process (ANP), taking into account the internal connections between the variables. The pixel value of the data was then determined by analyzing the relationship of each factor with flood vulnerability and employing fuzzy value functions. Finally, the vulnerability map was prepared and analyzed for the study area by applying the variable weights to the normalized layers.

Examining the importance of variables using the ANP method for each vulnerability function revealed that among the variables used to extract the exposure map, the two variables 'distance from the waterway' and 'frequency of floods' were assigned the highest importance with weights equal to 0.343 and 0.337, respectively. For the susceptibility map, the factors 'population density,' 'distance from residential areas,' and 'percentage of farming and ranching households' held the highest weights (0.361, 0.235, and 0.178, respectively). The resilience study indicated that the 'number of health and treatment centers' held the greatest importance among the variables, with a weight of approximately 65.5%. The exposure map showed that areas located at the watershed outlet were at a higher exposure. Areas near the outlet and in the basin's northeast region exhibited higher susceptibility to floods. Meanwhile, the central areas of the basin showed the highest resilience to floods, while the northern and southern parts had the least resilience. By combining the exposure, susceptibility, and resilience maps, it was found that the most vulnerable areas of the watershed (with a very high vulnerability class) were mainly located at the watershed's outlet, covering about 2.06% of the watershed. These zones exhibit a combination of high exposure to flood hazards, increased susceptibility, and low resilience, which may hinder their ability to recover from flooding events.

Conclusion and discution:

Assessing the flood vulnerability map revealed that the southern areas of the Zarrinehrood Watershed, located near the watershed outlet, exhibit the highest vulnerability levels. This can be attributed to the combination of high flood exposure, high susceptibility, and low to moderate resilience in these areas. One of the advantages of this research can be attributed to the use of fuzzy value functions for data pixel valuation instead of classification. The provision of continuous values based on fuzzy functions preserves the  data variability, offering a more realistic approach compared to methods such as input classification. Additionally, fuzzy values can help overcome ambiguity and reduce uncertainty. The main limitation of this research lies in the insufficient statistics and information available on flood resilience. Important criteria, such as the number of flood warning systems, rescue centers, past experiences with flood hazard, flood insurance, statistics of flood dams, shelters, and emergency services, were identified as significant factors in previous studies but were not accessible. Therefore, future studies should consider incorporating these factors if they become available. In general, it can be acknowledged that prioritizing the implementation of management policies with an emphasis on preventive and risk-based approach can take a more precise direction in relation to the results of this study and include the protection and restoration of vulnerable areas.

Windbreaks act as natural or artificial barriers that, by reducing wind speed, prevent soil erosion, reduce humidity, and damage to agricultural products, and increase crop efficiency. Due to the specific climatic conditions of Fars Province, where drought and inappropriate temporal and spatial distribution of rainfall are undeniable realities, it can be said that irrigation water is the most important input for agricultural production, and any sustainable food production and agriculture depends on the correct and rational use of the province's limited resources. A review of research conducted at the national level shows that despite the increase in the efficiency and productivity of irrigation water in the field in recent years, for various reasons, including disregard for climate potential and water resources and unplanned development of agricultural lands to meet the food needs of the country's growing population, excessive extraction of groundwater resources and the destruction of aquifers have been witnessed. The aim of this study was to investigate the effect of density and distance from windbreak on the production of wheat and dry fodder, as well as soil moisture retention at a depth of 10 cm. This study was conducted in the 1401-1402 crop year on four homogeneous and uniform wheat plots with identical varieties, planting dates, planting distances, soil characteristics, and irrigation and fertilization conditions in the Shahed area of ​​the Faculty of Agriculture of Shiraz.

 This research was conducted in the 1401-1402 crop year in four homogeneous and uniform wheat crop plots at the Shiraz University of Agriculture farm (crop area about 547 hectares). For this study, three plots with different densities of tree windbreaks (density above 90%, between 40 and 90% and also less than 40%) and one plot without windbreaks were used. To investigate the effect of distance from windbreaks on soil moisture storage parameters and crop yield, distances of 1, 5, 10 and 15 times the height of the windbreak (average height 12 m) were determined in lands with high, medium and low density and without windbreaks, and soil samples and crop yield were collected in three replications from each distance based on a completely randomized block design. To measure the moisture content after sample collection and transfer to the laboratory, the samples were first weighed, then placed in an oven for 24 hours and weighed again. Also, to measure the yield after harvesting a plot (one square meter) of wheat in completely random replications and at specific intervals from different treatments, samples were collected separately and kept in the environment for four days until the moisture in their forage was completely dried. First, the harvested crop was weighed, then the wheat was separated from the forage, and the net weight of wheat and the weight of dry forage were weighed.

Results and discution:

The parameters studied were analyzed using SPSS software at a significant level of 5% using Duncan's test. The results of comparing the average percentage of soil moisture retention in three densities, high, medium and low, showed a similar trend with respect to height, such that the lowest moisture loss was at a distance of one height from the windbreak and in the plot of land with a density above 90%. The plot of land with a density below 40% at a distance of 10 meters from the windbreak had a similar performance to the land without a windbreak and did not show an effect in reducing moisture loss. In relation to the amount of crop production, the density above 90% has a more significant difference with other densities, so that the average crop production at different distances (1, 5, 10 and 15 times the height of the windbreak) was 2.24 kg/m2, which decreased to 2.13 kg/m2 at medium density, 1.97 kg/m2 at low density, and 1.94 kg/m2 in the plot of land without a windbreak. In other words, under the same agricultural conditions, the amount of crop produced in the plot of land with medium density was 4.9%, in the plot of land with low density was 12.05%, and in the plot of land without a windbreak was 13.4% less than the plot of land with high density. In the plot of land with low density at all distances from the windbreak, the amount of crop produced in the measured area behaved similarly to the plot of land without a windbreak and did not differ significantly from each other.

 The present study investigated the effect of density and distance from windbreaks on the production of wheat and dry fodder, as well as the maintenance of soil moisture at a depth of 10 cm from the soil surface. Windbreaks can reduce water evaporation from the soil and plant surface by reducing wind speed in the areas around them. Therefore, soil moisture is maintained more in lands with windbreaks, which will benefit plant growth and crop production efficiency. As shown in the results, the production and moisture from the soil surface are a function of the height of the distance from the windbreak, and the maximum production was at a distance of five times the height of the windbreak. It is suggested that in studies related to this subject, a plot of land without wheat cultivation and only to examine the soil moisture at different distances from the windbreak should be investigated. Also, for the construction of windbreaks around Shiraz farms, educational and promotional programs should be provided for farmers on the benefits of windbreaks and how to construct them; cultivated fields should be identified, parceled, and prioritized; Also, multipurpose and economically suitable native species for creating windbreaks should be identified and introduced. It is worth noting that creating financial and advisory facilities while encouraging farmers can help accelerate the implementation of this strategy.