جستجوی مقالات مرتبط با کلیدواژه ": climate change" در نشریات گروه "آب و خاک"

Forest soils are recognized as one of the most important carbon sinks on Earth, playing a critical role in climate balance and greenhouse gas mitigation. This study investigated the effects of altitude, climate change, grazing, and manure application on soil organic carbon (SOC) stock in forest soils of Talesh County, Iran. The Century model was used to investigate the effects of climate and management factors on soil organic carbon storage. The results showed that SOC stock increased with increasing altitude due to higher precipitation and lower temperature. The Century model estimated SOC stock with high accuracy. The defined scenarios for the Century model showed that climate change with reduced precipitation and increased temperature significantly decreased SOC stock. The negative effect of climate change was more pronounced at higher altitudes. Grazing also reduced SOC stock, especially at higher altitudes. In contrast, manure application increased SOC stock, and its positive effect was more pronounced at higher altitudes. In the climate change scenario with manure application, manure application largely compensated for the negative effect of climate change, but did not completely neutralize it. The results of this study indicate that soil organic carbon simulation models are accurate tools for predicting the effects of climate change, grazing, and manure application. In addition, the conservation of high- altitude forests and optimal forest management are of particular importance to prevent the loss of SOC stock and to combat climate change.

In order to adapt the agricultural systems to increased climate variability, meteorological information needs to be combined with management recommendations before the start of the cropping season. There are solutions to reduce the vulnerability of agriculture to increasing climate change through agro-meteorological advisories based on weather forecasts. Therefore, the purpose of this study is to review the services provided by agricultural meteorology to reduce the vulnerability of agriculture in climate change conditions. For this purpose, we attempt to use successful experiences from different countries, from developed countries with advanced technologies to less developed countries with local knowledge, in adapting to climate change. This includes various types of agricultural meteorological services, major obstacles, and gaps in providing them. Climatic services provide solutions to offer better climatic services to farmers, climatic services used in agricultural meteorology, as well as approaches to accelerate the adoption of agricultural meteorological technologies and consulting services by farmer.

To be resilient against the negative effects of natural disasters and events, there is a need for resilience programs and policies to be accompanied by civic participation, the development of indigenous knowledge, and the use of technology in line with indigenous knowledge and regional infrastructures, according to the geographical, social, and cultural characteristics of each region. Additionally, the decisions of governments and policymakers, in line with the establishment of laws and regulations and resilience policies, can strengthen or limit the ability of other actors to adapt to the effects of climate change. In the same vein, the results of the review of sources include a list of existing barriers and gaps, such as the lack of real climate forecasts, lack of information on crop growth and development, low participation of agricultural producers in the production and use of services, unfair access to communication channels, and insufficient adaptation of agricultural meteorological services to needs. It also highlights the need to move towards the transformation of digital agriculture, targeted training in Agro-meteorology, strengthening the role of information and communication technology in providing accurate information, increasing investment in installing automatic weather stations and radar, and holding seminars and training workshops to overcome obstacles and improve the quality of climate services in the agricultural sector.

The Persian Gulf and Gulf of Oman basin, due to its specific geographic-climatic position and location in the arid and semi-arid regions of the globe, is highly vulnerable to climatic anomalies, such that one of its most important climatic elements, precipitation, exhibits severe variability. This leads to prolonged droughts in some areas and causes floods and river overflows in other locations. Therefore, the study and prediction of climatic changes in this basin is crucial for reducing potential hazards and damages at various social, economic, and environmental levels.

In this research, daily precipitation data from 47 meteorological stations over 30 years (1993-2022) were obtained from the Iranian Meteorological Organization. After acquiring the data and creating a database, the daily Precipitation Concentration Index (PCI) was calculated for all studied stations. The PCI value is a number between zero and one. The closer the PCI is to one, the higher the concentration of precipitation within a limited number of days, increasing the likelihood of floods and heavy rainfall events in those areas. Ultimately, the long-term trends of the PCI were analyzed using Sen's slope estimator.

The long-term average of the daily PCI values for the Persian Gulf and Gulf of Oman basin indicates a high level of this index in the studied basin. The highest values of this index are observed along the southern to southeastern coastal strip from Bushehr to Chabahar, while the lowest values are found in the western and southwestern parts of the basin from Abadan to Piranshahr. The trend analysis results showed that, except for a few stations) Brujen, Jusk, Masjed Soleiman) with negative trends, the entire basin has been dominated by increasing trends. The increasing trend slope indicates that precipitation is concentrated within fewer rainy days, which could lead to an increase in heavy rainfall and flood events within this basin.

The PCI trend analysis results showed that most of the studied basin area has experienced increasing trends, meaning that precipitation has become concentrated within fewer rainy days. This increasing trend, which could be due to an increase in droughts in this basin, may exacerbate the occurrence of flood-like rainfall events within the basin. Therefore, the changes of this index in the sub-basins of Bandar Abbas-Sadij, South Baluchistan, Karun and Western Marzi have been statistically significant. This trend highlights the need for serious attention to flood, drought, and other hydroclimatological hazard management in this basin.

In the present study, the impact of climate change on maximum temperature and daily precipitation in 16 weather stations was investigated in the Sefidrood Basin from 2023 to 2052. 10 AOGCM models related to the sixth IPCC Assessment Report (CMIP6) were ranked based on their ability to simulate temperature and precipitation in the historical period (1980 to 2014). Then, the maximum temperature and daily precipitation outputs of the best model at each weather station were extracted using the LARS-WG downscaling model under three emission scenarios SSP126, SSP245, and SSP585 from 2023 to 2052. The Mann-Kendall test (95% confidence level) was also used to investigate the trend of changes in the average maximum temperature and maximum daily precipitation. The results showed that different AOGCMs have different accuracies in simulating temperature and precipitation in different regions of the basin, and their accuracies in simulating temperature were better than simulating precipitation. In general, the IPSL-CM6A-LR and HadGEM3-GC31-LL models had the best performance in simulating maximum temperature and precipitation, respectively. Results also indicated that the mean maximum temperature will increase between 0.9 and 2.8 °C in different emission scenarios. Also, the mean maximum daily precipitation will change between -8.6 and 7.17 mm in different emission scenarios.

Astragalus is the vegetation of many mountains of Iran's plateau and plays a major role in providing ecosystem services due to its pillow shape and deep rooting system, they facilitate the control and penetration of precipitation into the soil. The correlation of Astragalus ecosystems with arid and semi-arid climates has made them vulnerable to climate change. In this study, a runoff yield map based on the Budyco curve under current and future conditions of climate change (2050) was prepared using climate and temperature data from the Chelsea site (CanESM2 GCM) in TerrSet software and by using maps of sub-watersheds, annual precipitation, annual potential evapotranspiration, soil depth, plant accessible water and the current and future "Land Cover - Land Use" map, with a combination of field methods and species distribution models at the local scale of the Shur River watershed of Dehaghan (Central Zagros). Finally, the excess runoff damage produced due to climate change was estimated using the replacement cost method. The results indicated an increase in the annual runoff volume of the watershed from 70 million cubic meters to 105 million cubic meters under climate change conditions for the RCP26 scenario in 2050. Taking into account the cost of 10 million Rials for controlling 530 cubic meters of runoff through various watershed management projects, preventing the damages of excess runoff produced requires a credit amounting to 660 billion Rials based on the present value. This study proved the ability of TerrSet software to predict and produce an ecosystem service map of runoff yield under climate changes or land use changes and with the purpose of valuation on a local scale. Also, the above valuation can be the basis for planning and providing credit for the study and implementation of watershed management projects to deal with the threats of climate change.

Using the most accurate methods and models to simulate the impact of climate change on meteorological variables in different regions of the world is of utmost importance. In this study, the accuracy of 10 AOGCM models related to the sixth assessment report of the IPCC (CMIP6) was investigated for simulating temperature and precipitation in the Sefidrood Basin. For this purpose, observational data of temperature and precipitation from 16 weather stations located in the basin during the time period from 1980 to 2014 were compared with the output of AOGCMs. The Kling-Gupta Efficiency (KGE) index was utilized for this comparison. The comparison was conducted on both annual and monthly time scales, and the more accurate models were identified for each time period. The results indicated that the accuracy of AOGCM models in estimating temperature in the study area was higher than their accuracy in estimating precipitation. Additionally, different models exhibited varying capabilities in simulating these variables across different months. Based on the results obtained, the MIROC6 and MRI-EMS2-0 models performed better than other models in estimating the temperature of different months. Furthermore, the HadGEM3-GC31-LL model showed a better performance than other models in estimating historical precipitation for most months of the year. Based on the results obtained, it is necessary to select and use the best AOGCM models for each month before conducting climate change simulation studies in the study area.

The main goal of this research is to simulate the interaction of surface water and groundwater by creating a connection between surface water and groundwater models in the Lor plain under climate change conditions. In this regard, the effects of climate change on surface water and groundwater sources were investigated based on the sixth report of the inter-state commission using a WEAP-MODFLOW coupled integrated model. The changes in the water level of the aquifer and the amount of the dropdown in the groundwater level were evaluated under the reference scenario assuming the continuation of the current situation and climate change scenarios, and the number of fluctuations in the entire plain for the 27-year period of 2050-2023(September 2050) in all climate change scenarios based on a model. A hybrid model, composed of different models, was predicted. The results showed that the average dropdown in the groundwater level at the end of 27-year period of 2023-2050 will be about 11 meters if the current situation (observational scenario) continues. In this scenario, the maximum dropdown in the groundwater level will be 38.7 meters in a part of the central and southwestern areas of the plain. If the climatic parameters predicted by the hybrid model are used in the coupled model of surface water and groundwater, the average dropdown in the groundwater level in the scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP4-8.5 will be 9.8, 10, 10.18 and 10.83 meters, respectively. The maximum dropdown in these scenarios will be 34.5, 35.2, 35.5 and 38.2 meters, respectively.

Given the complexity of the climate system and the non-linear relationships between the ocean and atmosphere within this system, it is imperative to comprehend and consider the uncertainties that stem from different sources. Understanding and accounting for uncertainties play a crucial role in predicting climatic variables and facilitating a comprehensive evaluation of greenhouse gas mitigation and adaptation policies. The objective of this study is to quantify the uncertainties in historical and future average monthly precipitation by employing various General Circulation Models (GCMs), bias correction methods, Shared Socioeconomic Pathways (SSPs) scenarios, and seven projection periods. To achieve this, the outputs of ten GCMs were adjusted using nine quantile mapping bias correction methods for the Rafsanjan study area, and a suitable method was chosen to analyze the uncertainties of SSPs and projection periods. Two statistical criteria, namely the standard deviation and interquartile range, were utilized to measure the uncertainties. The results revealed that the standard deviation and interquartile range of average monthly precipitation were lower during the historical period compared to the projection period. This difference was determined based on the selection of bias correction methods and GCMs. Furthermore, for both the historical and future periods, the STDEVs and IQRs of average monthly precipitation were lower depending on the type of bias correction methods rather than the type of GCMs. In general, the uncertainties associated with projection periods and the type of GCMs are higher during future periods compared to other sources of uncertainties such as bias correction methods and SSP scenarios. This highlights the necessity for a more accurate analysis. This study contributes to an enhanced understanding of the inherent uncertainties in climate change projections that arise from various sources.

Climate change is one of the fundamental challenges of mankind in the last century and the coming years, which affects the status of water resources at the global level, especially at the scale of the watershed. In addition, climate change affects the hydrological cycle at the basin level and thus the management of water supply and demand. The Intergovernmental Panel on Climate Change (IPCC) emphasized that climate change is an inevitable phenomenon. Therefore, it is necessary to estimate water demand sites under the influence of climate change and evaluate different climate scenarios. Several software and models have been developed to estimate the effects of climate change and integrated management of water resources at the watershed level, among which we can mention the Water Resources Planning and Management System (WEAP).By using the WEAP model, it is possible to investigate, manage, and allocate different policies for the optimal use of water resources under the influence of different management scenarios, including investigating the impact of climate change on the amount of water allocated to different supply and demand sites in Therefore, according to the importance of the topic in this research, the evaluation of different climatic scenarios of the Mahabad watershed was done.

In this research, IPCC data were extracted under two scenarios RCP 4.5 and RCP 8.5, and then in order to use the data extracted in the study area, these data were used using the LARS_WG model with a smaller scale. In the following, the IHACRES model has been used to simulate rainfall-runoff in the future period in the study area. The outputs of the IHACRES model were introduced as the input of the Weap model, which is a comprehensive and integrated model in water resources. Then, the impact of climate change on supply and demand sites under different climate scenarios was simulated using the Weap model.

In this study focused on Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and RCP8.5) for 2020–2039 were used. The results obtained in the mentioned scenarios for the statistical period of 2020-2039 show a decrease in precipitation and an increase in temperature. In order to estimate the productive runoff of the basin under the influence of climate change, the IHACRES rainfall-runoff model was used and the performance of the model in the study area was evaluated with correlation coefficient (R) and bias error. It was found that in the calibration and validation period, the correlation coefficient and bias error were 72%, 0.08, 69%, and 0.59, respectively, which shows the acceptable performance of the model in the region. In the following, the outputs of the IHACRES model were introduced as input to the WEAP model and it was determined that with the priority of allocating one for the drinking site and two for the agricultural site of the MIROC 8.5 scenario, the highest amount of unmet demand with the amount of 121.4 million cubic meters, especially for the year 2039 will have. On the other hand, the GFDL 4.5 scenario will have the highest reliability with 72% volume reliability and 42% time reliability.

By examining the results, it can be concluded that according to the MIROC 8.5 scenario, the amount of rainfall will decrease by 4.8% compared to the observation period. This reduction trend was also observed in three other scenarios. Then, using the IHACRES model, the monthly average amount of flow for the periods of 2020 to 2039 was produced and it was observed that the amount of runoff produced by the model will decrease from 2036 onwards. In the following, the runoff produced by the IHACRES model as the input of the simulator-optimizer modelWEAP entered. Then, the information related to climate change was checked under four scenarios and it was found that the highest unsupplied water needs for drinking sites considering allocation priority one and agriculture with priority allocation two are related to the MIROC 8.5 scenario with the amount of unsupplied demand of 121.4 million. Is cubic meters. Of course, it is worth mentioning that in this study, underground water supply and water allocation to the industrial sector have not been modeled.Keywords: Integrated water resources management, Climate Change, IHACRES, WEAP, Mahabad river basin.

In this study, the combination of the Improved Water Optimization (IWO) algorithm and the Water Evaluation and Planning System (WEAP) simulation model was employed to investigate the potential increase in the economic optimum height of the Zarineh Rood Reservoir Dam. The WEAP model's results indicated deficiencies in meeting the drinking, industrial, agricultural, and environmental needs of the study area under current conditions. Furthermore, the WEAP-IWO modeling results revealed an economically optimal increase in the height of the Zarineh Rood dam by 6.3 meters, resulting in a new reservoir volume estimated at 913.4 million cubic meters. By incorporating this increased reservoir volume into the WEAP model, there was an average 17.76 (precent ) enhancement in demand coverage and water supply system reliability across the study area. Additionally, the study assessed the impact of climate change on inflows to the Zarineh Rood reservoir for the future period (2022-2040), indicating an overall decreasing trend in the average annual river discharge compared to the baseline period. Furthermore, under both the SSP1-2.6 (optimistic) and SSP5-8.5 (pessimistic) scenarios, water scarcity for meeting agricultural demands in the study area is projected to worsen relative to current conditions. Reductions in demand coverage and reliability index results for the study area were observed under both SSP1-2.6 and SSP5-8.5 scenarios compared to current conditions. Therefore, increasing the dam's height to mitigate the effects of climate change appears necessary.

Investigating the effects of climate change on improving agricultural productivity is of great importance. It is predicted that the rising temperature trend will continue in the 21st century, leading to changes in the climatic conditions of  regions. Changes in precipitation distribution ، temperature and water resources are considered as harmful consequences of climate change، which could have detrimental effects on agricultural production. . In this study، the impact of climate change and cultivation on different dates was examined in terms of the yield of the 'Parsi' spring wheat variety in the Qazvin Plain. This study covered the period from 2021to 2100, comparing two information sources, LARS-WG and DKRZ, for producing annual climate change data and using the Aquacrop model to simulate the plant’s response to the mentioned changes. During the periods 2021-2040، 2041-2060، 2061-2080، and 2081-2100، the best planting date (February 4, February 20, March 5, March 20, and April 4) for increasing wheat yield was evaluated.  According to the model results in both scenarios 4.5 and 8.5، in all four periods (2021-2040، 2041-2060، 2061-2080 and 2081-2100), the spring wheat yield will increase compared to the baseline.. The highest yield in all of these periods and models is predicted for the period 2081-2100 under the LARS-WG climate model of in scenario 8.5,  assuming planting on February 4, with an estimated yield of 12.43 tons per hectare and a standard deviation of 0.31 tons per hectare. the lowest yield is expected for the period 2021-2040 under LARS-WG climate conditions in  scenario4.5, with planting on April 4, with an estimated yield of 7.87 tons per hectare and a standard deviation of 0.36 tons per hectare. In all four periods (2021-2040، 2041-2060، 2061-2080 and 2081-2100), February 4 is recommended as the most suitable planting date to increase wheat yield in the Qazvin plain.

The average weather condition in a specific region is defined as climate. The diversity of climatic variables is effective in determining the climate of a region and causes the formation of diverse and different climates. One of the effects of climate change is that causes an increase or decrease in a climate zone and, as a result, a shift in climate zones. Climate classification is an attempt to identify and recognize the differences and similarities of climate in different regions and to discover the relationships between different components of the climate system. Climate classification indicators are used to visualize current climate and quantify future changes in climate types as predicted by climate models. The studies conducted on these methods show that climatic variables affecting experimental methods such as temperature and precipitation should be considered effective variables in determining climatic boundaries in a new way. The De Martonne aridity index is an empirical index for climate classification based on two components, precipitation and temperature. Due to its high accuracy, and the use of variables that are more accessible and can be measured at most meteorological stations, De Martonne’s index has received more attention from researchers and has been used in many studies of climate change. Therefore, the purpose of this research is to evaluate the effects of climate change on the climatic classification of Iran.

To investigate the effects of climate change on the climatic classification of Iran, the De Martonne aridity index has been used. To show the effects of climate change in the past and the future on Iran's climate, data from 120 meteorological stations of Iran, which are distributed in different locations with different climates, were collected and analyzed in the statistical period of 1933-2022. The climatic condition of Iran in the base period was determined according to the De Martonne aridity index. In addition, to investigate the effects of climate change in the coming periods on the climatic classification of Iran, the data related to the output of the CanESM2 model, which is one of the CMIP5 models that is hybridized by the Canadian Center for Climate Modeling and Analysis (CCCMA) by combining CanCM4 and CTEM models, were used. To examine the changes in climatic classes of Iran under different scenarios and conditions, the output of two release scenarios, RCP2.6 and RCP8.5, were utilized. Due to the large-scale output of General Circulation Models (GCM), the output of this model was downscaled using the LARS-WG model. The LARS-WG model, which is considered one of the most famous and widely used models for downscaling weather data, was used to generate precipitation values, minimum and maximum temperatures, as well as daily radiation, under base and future climate conditions.

According to the results, the majority of Iran (90.49%) has an arid and semi-arid climate. The percentage of arid climate is 68.82%, while that of semi-arid climate is 21.97%. Therefore, Iran should be called an arid and semi-arid country in terms of climate. By analysis of the effects of climate change indicates that in future periods, the precipitation and average temperature will increase. This increase will be greater under the RCP8.5 scenario than the RCP2.6 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The sum of arid and semi-arid climates will reach its lowest level in the period of 2020-2041. This is following the RCP2.6 scenario, after which these climates are expected to expand once more. According to the RCP8.5 scenario, during the periods of 2021-2040, 2041-2060, and 2061-2080, the total area of arid and semi-arid climates will decrease. However, from 2081 to 2100, this trend will be reversed, increasing in these climates. According to the results of this research and according to the forecast, although according to different release scenarios, the difference in the area of different classes can be seen, in the future, arid and semi-arid climatic zones will still form the majority of Iran.

In this research, by using the latest available data, Iran's climate is classified by the De Martonne aridity index, and then the changes in Iran's climate classes under the effects of climate change in the future periods, according to the output of the CanESM2 model from the CMIP5 modes, which is downscaled using the LARS-WG model. It has been investigated according to two emission scenarios, RCP2.6 and RCP8.5. The results indicated that the arid climate with 68.82% and the semi-arid climate with 21.97% constitute the largest area of Iran. The remaining climatic classes collectively comprise less than 10% of Iran's area. Therefore, Iran should be called an arid and semi-arid country in terms of climate. Investigating the effects of climate change on precipitation and temperature showed that both precipitation and average temperature will increase in future periods. However, the increase in both variables will be greater under the RCP8.5 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The findings of this study indicate the necessity of addressing the issue of climate change and the importance of involving experts and macro planners in the analysis of the effects of climate change. It is suggested to use the output of other GCM models in future research due to the uncertainty of climate scenarios. Also, the use of diverse climate classification methods that incorporate other variables is suggested for more precise identification of climate characteristics.

In recent years, advancements in computer technologies, remote sensing systems, software, and various models have enabled the prediction of ecological niches for diverse plant and animal species. Over the past decades, alterations in human lifestyles, industrialization, and production processes have resulted in increased atmospheric pollutants, leading to severe climate change. Global climate change has induced shifts in plant growth ranges, with an expansion of warm-weather-adapted plants and a decline in cold-weather-adapted ones. These changes consequently modify the structure and ecosystems of the entire planet, directly and indirectly impacting ecosystem services crucial for human well-being and economic prosperity. Consequently, predicting the effects of climate change on plant distribution has emerged as a pivotal research area to inform conservation strategies and programs. Species distribution models primarily predict the impact of climate change on plant growth ranges. Accurate predictions of species distribution are essential for effective conservation planning and sustaining forest ecosystem services in the face of climate change. Given the significance of this issue, this research aimed to identify the most critical climatic and environmental factors influencing the distribution of Rhume ribes L. species and ascertain its current geographical range within Razavi Khorasan Province, located in northeastern Iran.

For this purpose, 68 bioclimatic variables including soil characteristics (45 cases), topographical factors (four cases), and climatic factors (19 cases) were first subjected to correlation analysis as predictive variables and variables with high correlation (above 80%) were removed. Due to the large size of the studied area, sampling of presence points was done with field visits during the period of 1400-1401 of the introduced areas, and a total of 232 presence points from eight regions were registered as presence points using the global positioning system (GPS). Then all the environmental data and presence points in R software using Biomed 2 package models which include GLM, GBM, GAM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt models in determining the relationship between vegetation and environmental factors in pastures Razavi Khorasan Province was predicted in the present time. The accuracy of the models was evaluated using the values of KAPPA, TSS, and ROC indices, which are prominent and widely used indices for determining and identifying the potential areas.

The results of this research showed that according to the accuracy evaluation index, the best modeling for the current time is the random forest model with an accuracy of 95.5%, which indicates the accuracy of the modeling at an excellent level. Also, the relative importance of the selected models and the variables that have had the greatest impact at present include digital elevation model (DEM), Average monthly (BIO2), This is the sum of all total monthly precipitation values (BIO12), The average temperatures experienced during the wettest quarter (BIO 8) and the amount of sand at a depth of 15-30 cm from the soil surface (Sand 15-30), which indicates the great influence of climatic factors on the distribution of this species, and in the next stage, the height above sea level and finally the soil factors have the greatest influence. The most distribution of Rhume ribes L. species at present is in the east of Razavi Khorasan Province including the cities of Bakharz, Torbat Jam, Taibad, Zaveh, Khaf, and Rashtkhwar in the form of a strip on their border and in the west of the Province on the border of Koh Sorkh and Neishabur cities and the north of the Province on the border Binaloud, Zabarkhan and Mashhad cities and the south of the Province in Gonabad city has spread in a strip and limited way.

The results of this research can be used to improve, protect, and economically exploit and expand the habitat of the Rhume ribes L. species. Destructive human activities, such as livestock grazing and the corrupt exploitation of rhubarb, combined with climate change, have endangered the current habitats of this species in Razavi Khorasan Province. These unprincipled exploitations, disregarding environmental capacities in natural resource management, are a significant problem in Razavi Khorasan Province and the country, gradually leading to water, soil, and plant loss in the region. While this study sufficiently examined current climatic and soil factors to identify areas suitable for rhubarb species, a deeper understanding is required to effectively restore damaged areas, preserve those at risk, and enhance the predictive capabilities of ecological models. In addition to climatic and soil factors, the potential habitats of plant species are influenced by various factors, including human activities, exploitation methods, livestock grazing, wildlife, economic and social conditions, and other direct and indirect impacts on distribution. Numerous studies have been conducted on different plant species. This research evaluated various machine learning-based species distribution models, selecting random forests as the most suitable. Species distribution models are valuable, cost-effective tools for natural resource managers, increasing their awareness and decision-making abilities regarding the effects of climate change on species.

Evapotranspiration variations (ET0) were investigated and analyzed using Minitab16 software for the 2010-2019 period using the Nizab system's data in Yazd province, and then ET0 was predicted until 2027. Based on the results, the increase of ET0 in cities of Yazd province was affected by the enhancement in wind speed and weather temperature, and the decrease in relative humidity from 2010 to 2019. To determine the appropriate model, Ardakan, Abarkooh, and Taft cities were selected as a representative in each climatic group, and ET0 data for the years 2010 to 2015 were considered as the input data of the software and ET0 data for the years from 2016 to 2019 were used to validate the determined model. The prediction of the determined models showed an increasing trend of ET0 for cold seasons in Ardakan and Abarkoh by 2027. Also, the model prediction showed a decreasing trend of ET0 for hot seasons in Taft by 2027. Also, the ET0 will not change significantly in cold seasons. In Abarkoh and Ardakan cities, autumn-spring crops such as wheat and in Taft city, spring-summer crops such as sunflower will be more affected by ET0 variations.

 The sharp drop in the water level of the underground aquifers in the plains shows the lack of a suitable approach to create synergy between all the stakeholders in the water sector in the management of water resources. On the other hand, the undeniable limitation of water resources has led to the adoption of integrated water resources management as a requirement. In this regard, in an agricultural plain, there should be a matching of underground water resources with programmable water in order to reduce the severity of the created crisis and help to balance the plain over time. Therefore, avoiding acreage expanding in the agricultural sector and increasing water productivity should be on the agenda of decision makers, especially in the planning of cropping patterns.

This research has been carried out with the aim of improving the cultivation pattern in improving the physical and economic productivity of water in the Qaderabad-Madarsolaman plain of Fars province. The required data of the products include crop calendar, price at the time of harvest, production cost, yield, monthly programmable amount of plain water and monthly irrigation water amount of each crop related to crop year 2021-2022, which is in the form of documents from Jihad- Agriculture Organization. and the Regional Water Company of Fars province was collected. Data analysis was done by mathematical programming method using GAMS software.

The results showed that after implementing the model and determining the optimal cultivation pattern, the number of agricultural products in the plain increased by 50%. So that the number of products reached 18 products from 12 products. Also, the area under cultivation of crops in the optimal cultivation pattern decreased by 27% and reached 3861 ha from 5303 ha. Meanwhile, the amount of irrigation water of the optimal pattern caused a 26% reduction in the consumption of underground water resources. In addition, the amount of crop production in the current and optimal model was 8,469,3000 and 6,947,060 kg, respectively, and the decrease in the cultivated area has caused an 18% decrease in this index in the optimized model. However, the economic efficiency of the entire plain in the current and optimal model was calculated as 1975.37 and 2035.60 billion rials, respectively, which indicates a three percent increase in the optimal model. With regard to the direct effects of the modification of the cultivation pattern of the Qaderabad-Madarsolaman plain, including the increase in the number of agricultural products of the plain and the introduction of six crops with the highest economic water productivity into the optimal cultivation pattern, as well as the reduction of the area under cultivation of crops in the optimal cultivation pattern, and the increase of the economic efficiency of the entire plain in this pattern, it is expected that the index of physical productivity and economic productivity of the whole plain will also change. So, the physical water productivity index of the whole plain in the current and optimal model was obtained as 2.56 and 2.84 kg/m3, respectively. In the same way, the water economic productivity index of the whole plain was calculated as 59,607 and 83,086 rials per cubic meter, respectively.

The modification of the cultivation pattern in the Qaderabad-Madarsolaman plain resulted in the annual saving of 8.64 million cubic meters of water, an 11% increase in physical productivity, and a 39.4% increase in the economic productivity of water. Based on this, focusing on the water productivity index in the form of improving physical and economic productivity can be followed as a suitable approach to create synergy between all stakeholders and beneficiaries of the water sector in the management of water resources in the agricultural sector. In this approach, by focusing on increasing water productivity, in a defined time period, while minimizing the amount of production reduction, and by increasing the livelihood level of the users, it is possible to reduce the agricultural water consumption in the plains to the amount of programmable water.