نشریه آب و خاک
سال سی و پنجم شماره 3 (پیاپی 77، امرداد و شهریور 1400)

The modeling approach for the simulation of the growth and development of tomatoes in Iran has been overlooked. Calibrated crop simulation models, therefore, are increasingly being used as an alternative means for the rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly intuitive parameters to be defined compared to other crop models and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic settings. This study was conducted as a two-year field experiment with the aim of the simulation of soil water content, evapotranspiration, and green canopy cover of tomato using AquaCrop model under different irrigation regimes at two growth stages in Mashhad climate conditions.

A field experiment was conducted over two consecutive seasons (2016-2017) in the experimental field of Ferdowsi University of Mashhad, located in Khorasan Razavi province, North East of Iran. The experiment was laid out in a split-plot design with different irrigation regimes at the vegetative and at the reproductive stage as the main and subplot factors, replicated thrice. In total, 27 plots of 4.5×3 m (13.5 m2) were used at a planting density of 2.7 plants per m2. Seedlings were planted in a zigzag pattern into twin rows, with a distance of 1.5 m between them, so there were four twin rows of three meters in each plot. The distance between tomato plants within each twin-row was 0.5 meters. A buffer zone spacing of 3 and 1.5 m was provided between the main plots and subplots, respectively. The following experimental factors were studied: three irrigation regimes (100= 100% of water requirement, 75= 75% of water requirement, 50= 50% of water requirement) and two crop growth stages (V= vegetative stage and R= Reproductive stage). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. Model accuracy was evaluated using statistical measures, e.g., R2, normalized root means square error (NRMSE), model efficiency (E.F.), and d-Willmott. The 2016 and 2017 measured soil and canopy data sets were used for calibration and validation of the AquaCrop model, respectively.

For a water-driven model, such as AquaCrop, it is important to evaluate its effectiveness in simulating soil water content. During calibration (2016), the model simulated the soil water content with good accuracy. The simulated soil water content values were close to the observed values during calibration (2016) for all treatments with R2 ranging from 0.90 to 0.97, NRMSE in range of 8.47 to 17.96%, d varying from 0.76 to 0.99, and M.E. ranging from 0.87 to 0.96. Validation results indicated the good performance of the model in simulating soil water content for most of the treatments (0.79<R2<0.99, 10.04%<NRMSE<18.65%, 0.77<ME<0.92). Appropriate parameterization of canopy cover curve is critical for the model to provide accurate estimates of soil evaporation, crop transpiration, biomass, and yield. In general, the calibration results showed good agreement between simulated and observed data for canopy cover development in all treatments with high R2 values (>0.87), good E.F. (>0.61), low estimation errors (RMSE, ranging from only 4.5 to 9.2) and high d values (>0.92).

The AquaCrop model (version 6.1) was calibrated and validated for modeling soil water content, evapotranspiration, and green canopy cover for tomatoes under drought stress conditions. In general, soil water content, evapotranspiration, and green canopy cover of tomato were simulated by AquaCrop model with acceptable accuracy in both calibration and validation stages. However, the model performance was more accurate in no and/or moderate stress conditions than in severe water stress environments. In conclusion, the AquaCrop model could be calibrated to simulate the growth and soil water content of tomatoes under temperate conditions reasonably well and become a very useful tool to support the decision on when and how much irrigate. For R2, values > 0.90 were considered very well, while values between 0.70 and 0.90 were considered good. Values between 0.50 and 0.70 were considered moderately well, while values less than 0.50 were considered poor. Root mean square error ranges from 0 to positive infinity and expresses in the units of the studied variable. An RMSE approaching 0 indicates good model performance.

The development of water resources in many cases has led to increased economic welfare, improved living and health standards, food production, etc. However, in some cases due to the insufficient attention to all aspects of these projects, the irreparable environmental effects and subsequent social and economic effects have been imposed on society. Paying attention to environmental water requirements is one of the most important issues in decision making in water resources development plans. The objective of this study is to assess river environmental water requirements in upstream and downstream of Beheshtabad Dam. Beheshtabad Dam has designed to build on the Karun River for water transfer from Karun to Zayanderood basin. But it has not been implemented due to the various problems and challenges.

Protecting and restoring river flow regimes and hence, the ecosystems they support by providing environmental flows has become a major aspect of river basin management. Environmental flows describe the quantity, timing, and quality of water flows required to sustain freshwater,estuarine ecosystems,the human livelihoods, and well-being that depend on these ecosystems. Over 200 approaches for determining environmental flows now exist and used or proposed for use in more than 50 countries worldwide. In the present study, hydrological methods have been used. These methodes include Tennant and modified Tennant, Flow Duration Curve (FDC) and FDC shifting (for different environmental management classes). For this purpose, four hydrometric stations (three stations upstream and one station downstream of the dam) have been selected.

The results of the study showed that the river water flow had not been sufficient to meet environmental water requirements in several cases, especially in years when the region was experiencing mild to moderate drought conditions. According to the Tennant method, the minimum environmental flow requirement averages based on Beheshtabad, DezakAbad, Kaj, and Armand stations data were 3.80, 5.06, 6.99, 22.01 m3/s, respectively. Using the mentioned stations data, , the minimum environmental flow requirement averages were 3.62, 6.07, 7.91, 23.67 m3/s based on the modified Tennant method. According to the flow duration curve method, minimum environmental flow requirements (Q95) were 1.96, 5.1, 8.32, 30.62 m3/s, using data collected from Beheshtabad, DezakAbad, Kaj, and Armand stations, respectively. The results of the flow duration curve shifting method indicated that the river water flow did not meet the river environmental water requirements in different environmental management classes in some months and years. Comparative results of different methods revealed that the minimum environmental flow requirement of Beheshtabad River upstream of Beheshtabad Dam was 1.22-16.75 m3/s from September to April (based on FDC shifting method, class C). The estimated minimum environmental flow for Koohrang River was 3.69-16.81 m3/s from September to April. The downstream of the dam, Karun River requires a minimum flow rate of 20.8-73.29 m3/s from September and October to April (based on FDC shifting method, class E).

According to the results of various methods used in this study, the Karun River flow is not enough to meet the minimum river environmental water requirements in some years and months. Therefore, decision-makers must pay attention to the environmental water requirements in decisions related to the development plans and water transfer from this river. It should be noted that the river environmental water requirements have not been met completely when the region has experienced moderate or mild drought, which would be more acute in cases of more severe drought conditions. Therefore, the current surplus water of this basin may not be a sustainable source to transfer to another basin.

Adeqiate water use in the agricultural sector requires accurate knowledge of crop sensitivity to environmental stresses (such as water stress). The crop sensitivity to water stress may be different at different growth stages and may have a different effect on the actual amount of crop evapotranspiration compared to the standard conditions. At different levels of water stress, studying the sensitivity of crop evapotranspiration at different growth stages can be provided management strategies for optimal water consumption. In the present research, the intra-seasonal sensitivity coefficients of maize were modeled by using the Jensen model.

In this research, the effect of water stress levels and growth stage sensitivity on the amount of maize (S.C 704) evapotranspiration was investigated. The experiment was performed as factorial based on randomized complete block design. The treatments included four irrigation levels of 100 (I0), 80 (I1), 60 (I2), and 40 (I3) percent of the crop water requirement and four growth stages of initial, development, middle and final. In between two irrigations, the amount of daily soil moisture was measured in the center of each plot and the depth of the crop root zone. Therefore, the amount of evapotranspiration of crops per unit area was estimated according to the soil water balance. Analysis of variance and mean data comparison of evapotranspiration and dry biomass yield were performed by SPSS software and using Duncan's multiple tests. By actual evapotranspiration and yield data, intra-seasonal sensitivity coefficients of maize to water stress (λ1 to λ4) were determined by SPSS software.

Evapotranspiration The effect of irrigation water amount and growth stage on the maize evapotranspiration amount was significant at the probability level of 1%. Evapotranspiration amounts at the initial, developmental, middle, and final of maize growth stages were estimated equal to 79, 201.8, 123.8 and 14.6 mm (in I0 treatment), 78.3, 196, 126.6 and 14.6 mm (in I1 treatment), 72, 173.6, 99 and 11.7 mm (in I2 treatment), 62.8, 147.5, 81.5 and 8.4 mm (in I3 treatment), respectively. Reduction of evapotranspiration compared to control treatment (I0) in the initial, developmental, middle, and final growth stages were estimated equal to 0.9, 2.8, 9, and 0 (in I1 treatment), 8.8, 14, 20, and 19.8 (in I2 treatment), 20.5, 26.9, 34.2 and 42.4 (in I3 treatment) percent, respectively. The results showed that the slope of evapotranspiration reduction was not the same at different irrigation levels. Also, the relative evapotranspiration of maize (in all growth seasons) at irrigation levels of I1, I2, and I3 were estimated equal to 95.6, 85, and 71.6 percent, respectively. Therefore, when applying water stress, the optimal evapotranspiration rate can be adjusted by selecting the suitable growth stage. Yield The effect of irrigation levels on the dry biomass yield of maize was significant at the level of 1% probability. The dry yield of maize in treatments of I0, I1, I2, and I3 were equal to 17.1, 15.8, 12.6, and 8.7 (tons per hectare), respectively. The relative yield of maize at irrigation levels of I1, I2, and I3 were estimated to be 92.4, 73.7, and 50.9 percent, respectively, in the Qazvin region. The reduction of soil available water affected the water uptake by the crop and reduced the yield of maize. Modeling of intra-seasonal sensitivity coefficients of water stress At the initial, developmental, middle, and final growth stages of maize, stress sensitivity coefficients of λ1, λ2, λ3, and λ4 were estimated in water stress treatments. The mean of mentioned coefficients in stress treatments was calculated to be 0.421, 1.37, 0.274, and 0.133, respectively. The results showed that during the development stage of maize growth, the effect of water stress on yield reduction was more. The model efficiency for estimating the amount of relative yield was evaluated. Evaluation statistics of R2, EF, RMSE, ME and CRM were estimated to be 0.998, 0.986, 2.753, 0.026 and 0.021, respectively. The results showed that the Jensen model efficiency was good, and it can be used in planning the low irrigation for different growth stages of maize. Yield-Evapotranspiration Function of Maize in all of the growth stages Across different irrigation levels, a simple linear relationship of Y=69.935ET-12281 (with a correlation coefficient of 0.999) was fitted between two parameters of evapotranspiration and dry biomass yield of maize. Therefore, using the above equation in low irrigation management, the amount of maize yield can be estimated based on the evapotranspiration amount. In this research, 175 mm evapotranspiration was needed for the production of the initial unit of maize biomass. That is, the transpiration portion in the above amount was negligible, and it was mostly allocated to the soil evaporation portion.

The crop sensitivity to water stress and different needs to transpiration at different growth stages were the reasons for the different reduction of maize evapotranspiration. Reduction of soil available water reduced the water uptake and transpiration, and crop biomass. The results showed that reducing the water stress was effective in increase of maize evapotranspiration efficiency. In order to produce the maximum crop biomass, the sensitivity of the maize growth stage and the water stress level must be considered.

The occurrence of successive droughts, along with increasing water needs and lack of proper management of water resources has caused a water crisis that has various environmental and economic consequences. In addition to the drought, the change in the cropping pattern towards water crops has also made the water crisis the first critical phenomenon in recent years in the community, which has a direct impact on the agricultural sector as the largest consumer of water. Therefore, optimizing the cropping pattern is one of the most important factors in managing water resources and coping with water shortages. In this study, to determine the optimal cropping pattern of major crops in Silakhor plain in the next three years using two approaches using Linear Programming and Meta-Heuristic Algorithms.

In the first step, in order to determine the optimal cropping pattern with the aim of maximizing farmers' incomes in the next three years and the limited water and land available, the amount of rainfall recharge is used as a criterion to determine the water exploitation interval and determine the minimum and maximum exploitation each year. In order to forecast rainfall, SARIMA time series models and Genetic Programming were used considering the data of the last 10 years in both seasonal and monthly modes, and according to RMSE and D.C. criteria, a better model was selected. Then, for each crop year, 100 exploitation scenarios were determined according to the amount of groundwater recharge caused by rainfall and the amount of exploitation in previous years. In the second step, Linear Programming was used to determine the optimal cropping pattern with the aim of maximizing farmers' incomes and limitations of exploitable water in each scenario and arable land. The price of each product is projected according to the average long-term inflation of the country, i.e., 20%, and the profit from the cultivation of each product was calculated as a proportion of the price of the product in each year by examining the previous years. Finally, the performance of three types of Static, Dynamic, and Classified Dynamics Penalty Functions into two algorithms, Differential Evolution and PSO was investigated to achieve the results obtained from Linear Programming. Static penalty functions use a constant value during the optimization process, whereas in dynamic penalty functions, the fines are modified during the process and depend on the number of generations. In the classified dynamics penalty, groups of violations are also determined, and the penalty of each response is determined according to the amount of violation of the restrictions and the generation number.

The results show that with increasing groundwater exploitation, farmers' incomes also increase; However, in the exploitation of more than 223.5, 222.2, and 225.1 million cubic meters for the cropping years 2020-2021, 2021-2022, and 2022-2023, respectively, the limitation of the total arable land has prevented the increase of the area under cultivation, and by increasing exploitation, farmers' incomes remain stable. Also, in order to cultivate four crops of wheat, barley, rice, and corn with the current area under cultivation in Silakhor plain, 142 million cubic meters of water is harvested annually from underground sources. By optimizing the cropping pattern for the four crops studied, with the current water exploitation, the income of farmers in the region will increase by 18%. In general, the PSO algorithm answers this problem much faster. The average number of iterations of the PSO algorithm to solve each scenario in this problem is 38% of the number of iterations of the Differential Evolution algorithm. Overall, in solving this problem, the PSO algorithm has performed better in 84% of the scenarios. In penalty functions, the best performance in both algorithms belongs to the classified dynamics, dynamic, and static penalty functions, respectively. By changing the penalty function from static to classified dynamics penalty function, the number of iterations of the Differential Evolution algorithm to achieve the Linear Programming solution is reduced by an average of 11%; In contrast, the PSO algorithm did not react significantly to the change in the penalty function, and its repetitions decreased by an average of only 3%.

The results show that the cropping pattern of the region is not optimal, and with the increase of water exploitation, it will move towards the cultivation of water products. Also, by optimizing the cultivation pattern of the region, farmers' incomes can be increased. Examination of Differential Evolution and PSO algorithms with three types of penalty functions also show that using the classified dynamics penalty function in the PSO algorithm can have good results.

Erosion and sediment production studies along with other natural resources studies in decision making and success and efficiency of watershed plans are of great importance. In order to plan and be aware of the destructive situation of the watershed, it is necessary to have erosion and sediment production from each watershed. The information about sediment load of basins can show the prospect of erosion. Sediment scatter from the soil surface by the impact of raindrops and shear force of runoff and is transported to downstream by spraying from raindrops and mainly by runoff. Also, the stress characteristics of soil particles are important in the process of effective transport. In recent decades, soil erosion has been intensified due to the human interference, inappropriate land management and land use. This is much more important in developing countries, because soil erosion is a serious risk to sustainable development in these countries. Soil erosion on farmland occurs due to the interaction between nature and human activities that have been being intensified in recent years. Estimation of sedimentation in watersheds, dealing with sediment accumulation risks in water structures and reservoirs of dams are the main objectives in water resources management that leads to sustainable development. One of the most erodibility of Iran is the Gachsaran formation. Gachsaran formation is about 1600 meters thick. A viewpoint of lithology is consisting of salt, anhydrite, colorful lime, and some shale. Gachsaran formation age is lower Miocene.

In this study, in order to determine sediment estimation by using different erosion components in different land uses of Gachsaran formation deposits, a part of Kuhe Gypsum watershed of Izeh city with an area of 1202 hectares was selected. In this study, the relationship between produced sediment and different erosion components such as runoff,soil permeability,runoff, and erosion threshold in different land uses of Gachsaran formation was determined by multivariate regression. Then, sampling of erosion different components was done at 6 points with 3 replicates and at rainfall different intensities of 0.75, 1 and 1.25 mm/min in three land uses of rangeland, residential area and agricultural using rain simulator. SPSS and EXCEL softwares were used for statistical analysis.

The results showed that sediment estimation using different erosion components presents acceptable results and can be used for other watersheds. The results also showed that in sediment estimation by erosion different components, runoff and erosion threshold had the most positive and negative effect and in eight cases played a role in modeling. Then, soil permeability has the average effect of positive and negative and has played a role in modeling in seven cases. In addition, runoff has not played a role in modeling in any of the three different land uses and intensities of precipitation.

Sediment estimation by erosion different components, the runoff and erosion threshold had the highest effect. Soil permeability had a moderate influence and runoff rate has not played a role in modeling in any of different land uses and precipitation intensities, it indicated the much more important role of runoff and erosion threshold and soil permeability in this modeling method in estimating sediment production. Finally, sediment estimation method by using erosion different components showed that it could be more applicable in sediment estimation in hard-to-reach watersheds in the future and be more effective in soil conservation and erosion reduction with appropriate and rational estimates in more appropriate implementation of watershed projects.

Playa, as an important geomorphic position in arid areas, covers about 1% of the continents and has attracted attention of soil scientists and geomorphologists. Soil genetic processes related to landforms and geomorphic processes are of great importance. Micromorphology is among necessary techniques in soil studies which has been used by several researchers. Micromorphological features together with other soil characteristics provide invaluable data for reconstructing soil genetic processes. Moreover, classification and identifying characteristics of soils are pre-requisites for the optimum use and management of soil resources. Soil Taxonomy and World Reference Base (WRB) is among the most extensively used classification systems worldwide. Since no data about soils of the Jazmoorian Playa is available, the present research was performed with the following

1) studying physical, chemical, and micromorphological properties of soils in the Jazmoorian Playa related to different geomorphic surfaces, and 2) classifying soils of the region by Soil Taxonomy (2014) and WRB (2015) systems.

The Jazmoorian playa is located in Kerman and Sistan Baloochestan provinces. The Jazmoorian Playa is a continental depression of late Pliocene. The playa is about 360 m above sea level with about 65 km length and 45 km width located between 58 ˚ to 60 ˚ longitudes and 27 ˚ to 28 ˚ latitudes. The area extends to the igneous Bazman Mountains to the northeast, the igneous Jebalbarez Mountains (granodiorite, andesite, granite) to the north and northwest, the Beshagard Ophiolite Mountains of Cretaceous and Paleocene to the south, and the colored Mélanges to the Oman Sea. Soil moisture and temperature regimes of the area were aridic (and aquic in limited areas) and hyper thermic, respectively. Wet zone, fan delta, clay flat, puffy ground clay flat, sodic clay flat, and salt crust were among the geomorphic surfaces investigated in the playa. In order to study the maximum soil variations in the area, eight representative pedons were described and sampled. Collected soil samples were air dried, grounded, and passed through a 2 mm sieve, and routine physical and chemical soil properties were then analyzed. Undisturbed soil samples were used for micromorphological observations. The soils were classified according to Soil Taxonomy (33) and WRB (11) systems.

Results showed that EC contents of the saturated extracts ranged from 0.5 (fan delta) to 222.2 (salt crust) dS/m. The soils of the playa in Kerman Province affected by the Halilrood River had less salinity compared to the soils on playa surfaces in Sistan Baloochestan Province under influence of the Bampoor River. In addition, salt crust was only formed in parts of the playa located in Sistan Baloochestan Province. Clay coating and lenticular gypsum crystals were among the micromorphological features observed in the Jazmoorian Playa’s soils. The clay coating was formed due to high Na content. However, lenticular gypsum was formed due to small volume pore spaces as well as high salinity of the area. High soluble salts (Table 3) caused a salt coating around pore spaces to be formed due to evaporation of saline water table. WRB system could better classify soils into Solonchak and Solonetz RSGs compared to Soil Taxonomy system which classifies all soils as the Salids sub order. Natric Aquisalids, Typic Natrisalids, Natric Haplosalids, and Puffic Haplosalids sub groups and Natrisalids great group are recommended to be added to Soil Taxonomy system for more harmonization between the two classification systems. Furthermore, the definition of salic horizon in WRB system (EC of at least 15 dS/m and the EC multiplied by thickness of at least 450) is recommended to be included in Soil Taxonomy, because of limitations induced by salts and for a better correlation of the two systems.

Results of physicochemical properties clearly showed that electrical conductivity of soil saturated extracts was in the range of 0.5 to 222.2 dS/m. The part of the playa located in Sistan Baloochestan Province is more saline than the part in Kerman Province. More salinity of playa in Sistan Baloochestan Province was attributed to the Bampoor River which passes through evaporative formations located in east and southeast of the area. Micromorphological observations showed clay coatings and lenticular gypsum crystals as pedogenic features. The soils of the area were classified as Aridisols and Entisols (according to Soil Taxonomy system) and Solonetz, Solonchaks, Fluvisols, and Regosols Reference Soil Groups based on WRB classification system. Moreover, WRB system was capable of separating saline from saline-sodic soils, however, Soil Taxonomy classifies both soils as Salids suborder. Therefore, WRB system is better suited for classification of the soils of our study area as compared with Soil Taxonomy.

Iran is one of the most important countries in citrus (oranges) production. Citrus fruits are grown in different soils with a wide range of physical, chemical and fertility properties in the country, although some restrictions in the cultivated lands cause yield loss. In this regard, the present study was conducted to investigate the effect of physical, chemical and soil fertility characteristics on citrus yield in important areas under cultivation, the regression relationships of characteristics with yield, and the rating of soil and land parameters.

The 138 oranges orchards (118 orchards for rating and 20 orchards for validation) were selected in Fars, Mazanderan, Kerman and Guilan provinces. In each garden, a questionnaire was completed, a soil pedon was studied and soil samples were taken to carry out the appropriate physicochemical analyses. The selected soil and land characteristics were soil salinity (EC), exchangeable sodium percentage (ESP), pH, gypsum content, soil calcium carbonate (TNV), organic carbon (OC), clay, sand, silt, gravel, and soil available phosphorus and potassium contents. From the whole obtained data, 20 data were considered for validation purpose and the remaining data were used for modeling based on stepwise multivariate and simple regression methods. In these equations, the relationship between yield, as dependent variable, with soil and land characteristics, as independent variables, was investigated. Finally, land characteristics rating was obtained by the FAO method and the proposed crop requirements table was evaluated using the validation dataset.

The results of descriptive statistics analysis showed that the variance values for available potassium, sand, clay, gravel and TNV were high and for pH and OC and gypsum were negligible. Therefore, most soil properties have a wide range of variation which could be related to the fact that oranges are grown in a wide range of soil types. The value of TNV varied between 10 and 33.3%. The presence of carbonate in soil reduces the availability of macro- and micronutrient elements in direct and indirect manners. The average of EC in the studied orchards was 5.4 dS.m-1. Minimum, maximum and average of ESP were 1.7, 28 and 10.7, respectively. The lowest and highest salinity and sodicity were observed in Mazandaran and Kerman soils, respectively. Maximum, minimum and average percentage of gypsum were 12, 0.36 and 3.54%, respectively. The highest amount of gypsum was observed in Bam and Shahdad regions of Kerman province and the lowest gypsum content was observed in Mazandaran and Guilan provinces. The soil pH varied from 6.63 to 8.8 with the average of 7.8. The soil OC values were between 0.05 and 3.53% and its average was 0.89%, showing the fact that the most studied soils were poor in organic matters. The average of soil available phosphorus and potassium in the studied orchards for citrus was less than the critical level. The average, minimum and maximum of available potassium were 224, 100 and 360 mg.kg-1, respectively. The mean, minimum and maximum amounts of available phosphorus were 21.6, 8 and 45.9 mg.kg-1, respectively. According to the multivariate regression model, among soil properties, EC, ESP, TNV, gypsum, gravel, available phosphorus and potassium were selected by the model. The determination coefficient of the model was 0.95, indicating that these properties have the greatest effect on citrus yield. Simple regression equations demonstrated that TNV, gypsum, EC, ESP, sand, clay, gravel, available potassium and phosphorous had the highest correlation (R2 > 0.6); and soil OC and pH had the lowest correlation (R2<0.2) with yield. The equations also revealed that soil EC, ESP, gypsum, TNV and gravel percentage had the greatest effect in yield loss, and soil organic carbon, absorbed phosphorus and potassium had the greatest effect on increasing citrus yield. As stated in equations, reported permissible and critical thresholds for effective soil properties on citrus yield, were 2.4 dS.m-1 for EC, 5 for ESP, 1.5% for gypsum, 20% for TNV, 22 mg.kg-1 for available phosphorus, 280 mg.kg-1 for available potassium, 110 cm for soil depth, and >2 m for groundwater level. Finally, evaluating the proposed crop requirements table with validation dataset fitted between citrus yield and soil index, resulted in the determination coefficient value of 0.79, denoting the acceptable accuracy of proposed table.

Overall results showed that the main land limiting characteristics for orange production were soil salinity and sodicity, high amount of soil calcium carbonate and gypsum. Among unsuitable physical and fertility properties of soil, salinity and sodicity are the most effective factors affecting yield reduction. Consequently, proper management practices such as introducing cultivars compatible with these soil conditions, soil remediation and leaching operations to reduce soil salinity and sodicity are necessary. Furthermore, in most areas under orange cultivation such as Fars and Kerman provinces, the soil calcium carbonate content is more than the critical level for plant growth. In addition, the averages of soil available phosphorus and potassium were less than the critical levels, which should be considered for nutrient management of orchards. The proposed table of crop requirements seems to be accurate enough to conduct land suitability studies for orange varieties, especially cultivars grown in the north and south of the country.

Drought is a climatic phenomenon and an integral part of climate fluctuations that occurs periodically and intermittently throughout the world and across all climates. However, the magnitude of this natural hazard in arid and semi-arid regions, such as most parts of Iran, is more acute due to the high sensitivity and weakness of these areas, and its effects may persist for years after the occurrence of drought. Drought is a multifaceted phenomenon as precipitation, temperature, evaporation, wind and relative humidity play important roles in the drought characteristics such as occurrence, severity, and magnitude. Climate change and global warming, and in some cases displacement of meteorological stations cause heterogeneity in time series of meteorological data. Therefore, the purpose of this study was to investigate the homogeneity and break point in precipitation time series data and the effects of a break point in drought characteristics in some synoptic stations in Iran.

In this study, homogeneity of rainfall time series data at two time scales of annual (water year) and plant growth periods in some selected synoptic stations of Iran with different climatic conditions was investigated. For this purpose, four tests including Standardized Normal Homogeneity test (SNH), Buishand’s Range test (BHR), Buishand’s U test (BUR) and Petite’s test were applied and the break points were determined. Then, at the stations with break points in the precipitation data series, the drought severity values were determined using four indices of SPI, SPEI, RDI and eRDI, for two periods, (before and after of the break points). Then drought characteristics based on Markov Chain Model and Transition probability matrix including vulnerability, reliability, reversibility and stationary of three condition of droughts (dry, normal and/or wet condition) were determined for the two time scales periods (annual and plant growth periods). Then, the differences between the characteristics for the two periods were investigated. Also, the characteristics of drought-free time intervals for the two periods based on Run’s theory were determined and compared.

Based on the homogeneity tests, precipitation data of Arak and Tabriz stations for two scales of annual and plant growth periods have break points. According to the results, in the most cases, the second period's reversibility was lower than the first period. Reliability and vulnerability also decreased and increased in all cases in the second period, respectively, compared with the first period. In most cases, there was an increase in stationary of drought in the second period relative to the first period. The rate of change in the probability of survival of the normal and wet condition in both periods was increasing and in some cases decreasing. Regarding the results of Run’s theory at the growth periods scale, the average and maximum duration of drought periods increased in all cases in the second period. The minimum, average and maximum severity of drought periods also increased in most cases in the second period. The minimum, average, and maximum values increased in most cases in the second period. On an annual basis, the number of drought periods in most cases has increased in the second period. The average and maximum duration of drought periods increased in all cases in the second period. The minimum, average, and maximum severity of drought periods also increased almost in all cases in the second period. Minimum, average, and maximum of drought magnitude values increased in most cases in the second period with respect to the first one. The minimum, average and maximum values of the drought-free durations (interval time without drought conditions) in most cases were lower in the second period. At the annual scale, the minimum duration of drought was one year in all cases and no change was found between the time slices. The average duration in most cases was lower in the second period.

The results show that the rainfall data of Arak and Tabriz stations have break points in the time scales of plant growth period and annual periods. The reliability was decreasing while the vulnerability of drought was increasing in the second period, indicating an increase in drought occurrence in recent decades. Moreover, the probability of drought stability (stationary) in the second period increased in most cases. The average and maximum duration of drought periods also increased in the second period. The minimum, average, and maximum drought severity, and the minimum, average, and maximum of magnitude of drought periods were higher during the second period. In most cases, the minimum, average, and maximum of severity and magnitude of drought-free time intervals were lower in the second period. In general, difference in the characteristics of drought before and after of precipitation break point can be due to increased evapotranspiration, as a result of global warming, intensifying the effects of drought. Moreover, based on the results of the eRDI index, the climatic conditions became drier in both stations and time periods. In other words, it can be stated that the effective rainfall has decreased to some extent in recent years compared to the early years of the study period. Further studies are needed to assess the changes in drought characteristics in all synoptic stations in the country having long-term data.

The behavior of daily changes of relative humidity is quite variable. We first draw the curve of this variable on a normal day. And it can be seen that the distribution of this variable is not normal. The curve of this variable is a skewed curve to the right. Therefore, the equal coefficients could be used only as an approximation for estimating the daily average of relative humidity. Climatic conditions of the meteorological stations are also another parameter to be considered. This research presents a new method for estimating the daily average of relative humidity in three climatic regions of Iran. The patterns for the sample stations in each climatic region were presented separately.

E. Eccel (2012) developed an algorithm to simulate the relative humidity of the minimum daily temperature in 23 weather stations in the ALP region of Italy. In this research, the base pattern was calibrated by temperature and precipitation measurement. Ephrath, et al. (1996) developed a method for the calculation of diurnal patterns of air temperature, wind speed, global radiation, and relative humidity from available daily data. During the day, the air temperature was calculated by: (1) (2) Where S(t): Dimensionless function of time, DL: Day Length h, LSH: the time of maximum solar high h, ta: current air temperature, P: the delay in air Tmax with respect to LSH h. Farzandi, et al. (2012) presented more accurate patterns for estimating daily relative humidity from the humidity of Iranian local standard hours and daily precipitation variables, the minimum, maximum, and average daily temperature in coastal regions. The purpose was to present linear and nonlinear patterns of daily relative humidity separately for different months (12 patterns) and annually in coastal regions (the Caspian Sea, the Persian Gulf, and the Oman Sea).Mirkamandar, et al. (2020) modified new patterns of diurnal temperature based on climatically clustering in Iran. The final pattern has an interception and new coefficients to estimate the daily average of temperature. Rezaee-Pazhand, et al. (2008) introduced new patterns for estimating the daily average temperature in arid and semiarid regions of Iran. The final pattern has an interception and new coefficients to estimate the daily average of temperature. (3) Veleva, et al. (1996) showed that the atmospheric temperature-humidity complex (T-HC) of sites located in a tropical humid climate cannot be well characterized by annual average values. Better information is given by the systematic study of daily changes of temperature (T) and relative humidity (RH), which can be modeled by linear and parabolic functions. Farzandi et al. (2011) divided Iran into three climatic clusters. Which were used in the present work. First, a classification that provides climatological clustering. This clustering was used the data of annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation, and three indices of Demartonne, Ivanov, and Thornthwaite. Iran was partitioned into three clusters i.e. coastal areas, mountainous range, arid and semi-arid zone. Several clustering methods were used and the around method was found to be the best. Cophenetic correlation coefficient and Silhouette width were validation indices. Homogeneity and Heterogeneity tests for each cluster were done by L-moments. The “R”, software packages were used for clustering and validation tests. Finally, a clustering map of Iran was prepared using “GIS”. The data of 149 synoptic stations were used for this analysis. Systematic sampling was done to select sample stations. The linear regression model was fitted after screening and data preparation. A model was presented for estimating the daily average temperature in each climatic region and sampling stations in each cluster. The best models were presented by reviewing the required statistics and analyzing the residuals. The calibration and comparison of the presented patterns in this paper with commonly applied models were undertaken to calculate the mean squared error. “SPSS.22” software was used for analysis.

The coefficient of determination (R2) and the Fisher statistics showed that the patterns had a good ability to estimate the daily average of relative humidity. The daily average of relative humidity patterns confirmed an interception in the equations. Standardized coefficients showed that predictor variables were not weighted in all of the patterns. The mean squares errors were a measure of the applicability of patterns. The accuracy of the estimating daily average of relative humidity recommended models in three climates was confirmed by calculating the mean squared errors. The proposed patterns of this study had less error than the common patterns.

The relative humidity at 3 pm was more effective in estimating the daily average. The independent assumption of the residual was confirmed with the acceptable value of Durbin-Watson statistics. The averages of the residuals in each pattern was zero. According to the graphs, stabilization of variance can be seen based on the residual on each pattern in each cluster. Proposed patterns were calculated according to mathematical principles. But the common patterns did not observe these mathematical principles. The mean squares errors (MSE) of proposed patterns were less than common patterns. Therefore, the patterns presented in this study are more powerful than common patterns.

Precipitation is one of the most important climatic variables playing a decisive role for different purposes. Temporal changes in precipitation affect many climatic and environmental phenomena (such as runoff, floods, air temperature, and humidity) as well as many human activities (such as agriculture and housing). The precipitation regime includes all characteristics and statistics of precipitation in relation to its distribution throughout the year, and the temporal distribution of precipitation according to the months or seasons of the year is called the "Precipitation Regime".

The daily data of 385 stations were obtained from Iran Meteorological Organization and the Ministry of Energy for the period of 2016-1966 (51 years). The hidden aspects of precipitation and precipitation regime of the Iranian coast of Caspian Sea were studied. At first, these stations were used in order to create maps with a spatial resolution of 3 × 3 km, and the general specifications of the monthly and annual precipitation were presented. Sinusoidal behaviors of monthly precipitation in each pixels were then investigated. Accordingly, first to sixth harmonics were extracted. Finally, the cluster analysis method was used based on the Euclidean distance and the "Ward" method of linkage to identify the spatial patterns of precipitation based on the contribution of different periodic and its zoning. Then, the homogeneity and seasonal index of precipitation was estimated.

The results show that the mean annual precipitation is higher on the coastline, especially in the southwest of the Caspian Sea, and decreases as it passes from the coast. In the southwest parts of the Caspian Sea, maximum precipitation occurs in the autumn. At the Alborz highlands, the maximum and minimum precipitation fall during winter and summer, respectively. The monthly precipitation coefficient of variation indicates that with seasonal changes from winter to spring, precipitation changes in the Caspian region are declining, and with changes in seasonal precipitation from summer to autumn, precipitation changes are in the ascendant. The largest variability coefficient of the month to month of the precipitation (60 to 70 percent) was calculated at the coastline of the Caspian Sea. This shows notable month to month precipitation changes and seasonal instability in these areas. The coefficient of variation is gradually reduced by distance from the coastline. The lowest coefficient of variation was obtained in the southern parts of the Caspian Sea (the Alborz altitudes) between 15% and 30%. This suggests a small difference in rainfall over the course of the months. In other words, they indicate the activity of various rainy systems, or at least the continuity of rainy systems in these areas, and the stability of the precipitation season. The homogeneity index indicates that the precipitation distribution is more concentrated in the coastal areas of the Caspian Sea, and it becomes more uniform with the advance towards the southern parts of the area (part of the Alborz heights). The seasonal precipitation index of the Caspian Sea region indicates three types of precipitation regime. The lowest spatial extent (6.28%) is related to the uniform precipitation regime, found in small parts of the Alborz heights. The most abundant regime has a wetter season. This precipitation regime, which includes 76.13% of the Iranian coast of Caspian Sea, is observed in the eastern and western regions of the Caspian Sea. The third regime (13.25% of the study area), which is mainly seasonal with a short dry season, covers the Caspian Sea coastline, parts of the Talesh heights, and a small part of the eastern region.

The results revealed that the precipitation classes obtained based on the seasonal index were closer to the reality due to the similarity of these classes with the average monthly and annual precipitation. Therefore, this index seems to be the most optimal tool for determining precipitation regimes in the Caspian region. According to this precipitation regime classification, there are three classes of precipitation regime in the Caspian region. The existence of these three classes indicates the presence and activity of different synoptic and local systems in the Caspian region.