مجله پژوهش آب ایران
پیاپی 38 (پاییز 1399)

With increasing population and changing climate regime, water supply systems in many cities of the world are under stress. To tackle this problem, water authorities are adopting several measures including demand management and identifying alternative water sources such as stormwater harvesting, greywater and wastewater reuse and desalination. Among all the alternative water sources, stormwater harvesting perhaps has received the highest level of attention. Regarding to excessive water consumption in large cities, the problem of water shortage can be reduced to the extent desirable by using rainwater harvesting systems. “Reliability” is percentage of total days in a year when the water collected in the reservoirs is able to supply the demand of residents. “Rainwater storage potential” is the volume of runoff that is collected and used by a series of actions in the tank, before becoming a flood. The aim of this study was to estimate the reliability of rainwater storage tanks and to investigate the potential of rainwater storage. In the current study, the daily water balance between the rainfalls as input and domestic water-consumption per capita as output were used for the performance analysis and design the optimum of rainwater tanks, at three different regions of Tehran (center, north and west). These three different regions of Tehran are distinct due to notable different rainfall and topography characteristics. Charts of reliability of rainwater storage were produced for domestic rainwater tanks due to tank volume, roof area, number of people in a house (i.e. water demand) and percentage of total water demand to be satisfied by the harvested rainwater. In this study, the reliability of rainwater harvesting system was investigated for rainwater saving and non-potable water supply in three different regions of Tehran. It was found that for a relatively small roof area (100 m2), 100% reliability could not be achieved even with applying a very large tank (10,000 L). This roof area was very small to collect the appropriate amount of rainfall. In this case, the accumulated rain was used quickly and the reservoir remained empty until the next rain event occurred. For the larger reservoirs, the reliability remained constant; in which case, part of the volume of the reservoir or the entire volume remained in most of the time because the roof area for collecting rainwater was enough, or the demand for water was not high. For a large roof area (300 m2), about 40% reliability can be achieved with a tank size of 10,000 L. Reliability was independent of tank size for the sizes larger than 5000–8000 L; in these rang of size, the reliability depends on the region (location). Also, the average rainwater storage in reservoirs was determined annually for each of the three locations. The results showed that the amount of rainwater storage increases with increasing roof area. It was found that with increasing the daily water demand of building residents, the days of water supply by rainwater saving from roofs would decrease. According to the results, it can be stated that the effect of increasing the volume of the reservoir in storing rainwater for the roofs with larger areas were greater than the roofs with small areas because, the roofs with the small area do not fill the reservoir. The runoff overflow from the reservoir was often carried out on the large roofs, which can be reduced by increasing the volume of the reservoir (reducing the runoff from the reservoir and adding to the amount of rainwater stored). The maximum rainwater storage for a roof area of 300 m2 were 100 m3, annually in north of Tehran. The minimum storage for a roof area of 100 m2 were 17 m3, annually in west of Tehran. In general, the results showed that north of Tehran has more reliability and also more rainwater storage than the center and west of Tehran. The considered rainfall stations (which are located 10 to 20 km apart) produced significantly different results due to the difference in rainfall conditions and topographic variability. The results revealed that rainwater tank reliability can vary significantly within a large city like Tehran, which emphasizes the need to change the design pattern of considering a single annual rainfall value for the purpose of rainwater tank sizing.

The combined weir-gate structure has various applications in hydraulic engineering and can eliminate some of the shortcomings of each individual application. The combined weir-gate structure, with the ability of simultaneous passage of the material to be deposited through the gate section and that of the suspended material from the weir, prevents the accumulation of the sediments and the suspended materials behind the weir and helps increase the measurement accuracy and flow passing. Some studies conducted on the labyrinth gates are related to triangular and trapezoidal labyrinth weirs. However, regarding the performance, the rectangular labyrinth gates could be built and run better in some areas. Moreover, in the studies carried out in the field of combined simultaneous flow above the weir and below the gate, the combined weir models with sliding gates have been rarely addressed. One of the significant issues in the combined rectangular labyrinth weir-gate structure is the position of the gate which has not been investigated yet. Therefore, this study aims to investigate the effect of the gate position on the discharge coefficient of the rectangular labyrinth weir-gate and that of increasing the weir height on the discharge coefficient of the rectangular labyrinth weir-gate in different gate positions. This study investigates the effect of gate position on the discharge coefficient using physical models of rectangular labyrinth weir-gate, made of Plexiglas with 15 and 20 cm heights in 15 positions of gates in different situations. All experiments in this research were conducted in a glass rectangular channel with the length of 12, width of 0.5, height of 0.7 meters, and longitudinal slope of zero degrees. The discharge varied from 10 to 50 liter per second. To measure the depth of water at the upstream weir and the triangular weir, a depth-measuring device with the precision of 0/1 millimeter was used. Considering the width of the laboratory flume and the decreasing effect of walls surface tension of the rectangular labyrinth gate-weir, two cycles were selected for the weir. The weirs with the crest of 90 ° (straight) and thickness of 10 mm were made of 1, 2, 3, and 4 sliding gates. The models were mounted on a four-meter upstream channel. In general, 390 experiments were conducted in this study. To investigate the effect of gate position on the flow discharge coefficient, experiments were done using 1, 2, 3, and 4 gates in different positions. For weir models with heights of 15 and 20 cm and a gate, when the gate is located in the forehead of the upstream weir (model 4), the discharge coefficient was more than those of two other models (2 and 3) due to the gate’s perpendicular position to the flow direction. For the two-gate mode in model 6 in both weirs with heights of 15 and 20 centimeters, the discharge coefficient was higher than other 3 models, due to both gates’ perpendicular positions to the flow direction. Models 10 and 14, with 3 and 4 gates had a higher discharge coefficient. In these cases, when the number of the gates’ perpendicular positions to the flow direction increased, they had a greater effect on the discharge coefficient than other states did. The results showed that the discharge in the combined weir-gate structure was more than that in weirs with no gates; that in fact, showed the effect of the gate in a rectangular labyrinth weir on the discharge increased. According to the results, the discharge coefficient in the rectangular labyrinth weir gate was more than that in the rectangular labyrinth weir with no gate. In other words, the gate effect on the discharge coefficient increased in a rectangular labyrinth weir. However, considering the results, the discharge coefficient increase in the low current range (less H/P) was greater than that in the high current range (higher H/P). In fact, the result revealed that the gate in the rectangular labyrinth weir-gate in the low current range (less H/P) had more impact on the discharge coefficient increase and, consequently, on the transit of discharge increase. Given that the reduction of the discharge coefficient with the increase in the H/P ratio results from the increase in water jets interference in adjacent cycles and the increase in local weir submergence, the flow below the gate also caused additional, new interference within the flow of water at the bottom of the structure and, consequently, reduced the discharge coefficient. The highest efficiency of the combined rectangular labyrinth weir-gate model in less H/P is recommended. According to the obtained results to achieve the highest efficiency, the maximum water ratio (H/P) was recommended to be less than 0.5 and 0.7, respectively for weirs with the height of 20 and 15 cm. This issue almost corresponds to Lux’s results who suggested the maximum water ratio (H/P) for a trapezoidal labyrinth of 0.45-0.5. This study investigated the effect of gate position on the discharge coefficient in the combined rectangular labyrinth weir-gate structure. According to the results in the studied states, when gates were located perpendicular to the flow direction, they had higher impact on the discharge coefficient. Moreover, the discharge and discharge coefficient in the rectangular labyrinth weir-gate were more than those in their counterpart with no gates. In fact, the gate in a rectangular labyrinth weir led to an increase in discharge and discharge coefficient.

Hydrodynamic prediction and simulation of rivers’ qualitative parameters is very important for determining the rate of loading of pollutants and their manner of transmission in aquatic ecosystems, in relation to rivers’ self-purification capacity. From the beginning of human life, rivers and surface waters have always been considered essential because of the need for water for living purposes. Cities and industrial and agricultural centers, in fact entire civilizations, arose near the rivers in order to use sources of water. However, by developing industry and technology, human beings started destroying nature. Knowledge of the quality of water resources is one of the most important requirements in the planning and development of water resources and their conservation and control. Hence, in order to ensure the monitoring and management of the quality of this natural resource, some methods can be used that entail the least cost and time to attain these objectives. To develop these studies, sampling and environmental tests for water qualitative parameters were initially conducted. For performing this step, six stations were determined for monitoring in the basin of the Balikhlouchai. The parameters considered were then measured and the results were compared with existing standards. The purpose of conducting this study is to route the pollution of Balikhlochai River, establishing a relationship between the results of two quantitative and qualitative simulated hydrodynamic models (HEC-RAS and WASP) and comparing the results with statistical indices (mean absolute error, root mean square error, Relative volume error, and Nash–Sutcliffe model efficiency coefficient). Given their self-purification and self-regulation capabilities, rivers in normal situations can undertake the natural load of pollution imposed by the environment and solve them. In situations in which pollution has human origins and the load is more than the river’s carrying capacity, this will be associated with destruction, and the death of river ecosystems. The reasons for the increase in pollution load are the entry of surface runoff from the rainfall into the river, washing of various pesticides and organic fertilizers, phosphate and nitrate due to drainage of agricultural land, and the organic load entering the urban and rural. Wastewater into the river along the route is the source of food for algae and, as a result, increases the pollution burden and reduces the health of the river. Calibration and validation of the model were performed based on samples taken from six monitoring stations during 2015-2016. According to the validation results, MAE, RMSE, RE and NSE statistical indices for DO were 0.04, 7.2, 4.7 and 0.84, respectively, and 0.07, 8.2, 5.2 and 0.81 for the river flow rate, respectively, representing the optimal and reliable results of the output of simulation models. The results obtained from statistical indices showed that the simulation of the research variables was well performed, the simulation results were reliable and both models have good performance in predicting water quality. Due to the increasing population and growth of various kinds of pollution, the qualitative conditions of the rivers and aquifers become worse in the field of surface water. According to the results of the models’ output, the river discharge along the route ranged from 1.2 to 0.3 m³/s and DO ranged from 7.5 to 3.5 mg/l, indicating a severe reduction in the river flow rate and DO. Comparing these results gives a more accurate analysis of the pollution process in the Balkhlohai River; as the river's downstream is lowered, the loading rate of the pollutants is increased. So, the river is naturally unable to reduce pollutants and return the river to its normal state. It happens because both velocity of the water flow and the depth of the river are reduced. Therefore the amount of water mixing in the river decreases, as well as the amount of dissolved oxygen in the river due to reduced mixing. So, the pollution load and pollution of the river increases. Studying the pollution process in Balikhlochai River indicated that river pollution was due to the entry of urban wastewater and runoff from agricultural drainage-water into the basin, as well as the severe reduction in river self-purification capacity and Eutrophication phenomenon. The decade's prediction shows that if the measures needed to manage the current status of river water quality are not met, the river's health is compromised and should be stepped up to restore the river. The results indicate that the river downstream from the point of view of quality management should be prioritized and the study results can be used in determining the strategies for coping with pollution and promoting management in the Basin of Balikhlochai River.

A major part of the total cost of water supply systems is related to water transport and distribution systems. So, there have been many studies to enhance network planning. To plan water transport and distribution systems, the characteristics of the network (such as the diameter of the pipes, the height of the reservoirs, the type of pumps applied, etc.) must be known. Then to analyze the network, this information is used as well as the rules that govern pipes and hydraulic systems. Using analysis and optimization, the most cost-effective and reliable design for the network can be chosen. In recent decades, many researches have been done regarding optimizing the design of water distribution networks. So, various optimization methods have been used to minimize the costs of these networks. An appropriate optimization method for the water supply network should be an efficient mathematical approach to optimize the objective function. In the models of hydraulic analysis of the networks, the actual values of the discharges can be determined based on the relationship between pressure and discharge in the nodes. But it should be considered that the issues such as discrete variables (allowable diameters), design constraints (minimum pressure or velocity, etc.), and the complexity of the solving of hydraulic equations of networks, make it difficult to optimize. The optimization methods are only able to use the data given by the objective function and distance themselves from the complexities associated with the estimation of derivatives and other auxiliary functions. Since the pipes with the minimum cost should be within the allowable range in terms of pressure and velocity, another method is needed to solve the hydraulic network. Many researchers have been done to solve the problems of optimization of water transport and distribution systems, applying water distribution system modeling software such as EPANET. They define the objective function in ways which transmit the diameters to the EPANET software and return the hydraulic result of the pipes to the algorithm again. During this process, the EPANET software runes in terms of the evaluation of the objective function, and much time is spent on this process. But in the present study, using the Newton Raphson method, the hydraulic of the pipes was simulated in MATLAB. Thus, the problem solving time was greatly reduced because by the Newton Raphson method the diameters were accepted only if their speed and pressure were within the allowable range. In order to optimize, the combination of Big Bang Big Crunch (BB-BC) and Central Force Optimization (CFO) algorithm was applied. Each of these algorithms has the strengths and weaknesses that were adjusted and modified by combining them together and forming the BB-CFO algorithm. Despite high capabilities of CFO algorithm, it has weak points in acceleration calculation. The advantage of BB-BC is applying the best result in each replication and using inconstant parameters in the algorithm. But, the weaknesses of the two algorithms are such that they are completely complementary. In order to apply BB-CFO algorithm, the distribution networks of the Hanoi network and the Kadu network were selected to be optimized. After their hydraulic coding, the results were analyzed using the Newton-Raphson method with the BB-BC and BB-CFO algorithms. The Hanoi network is characterized by two loops and 34 pipes. After a 6360 evaluation of objective function, the Hanoi network was estimated at $ 6,210,780. As the last network increased difficulty of the problem, the Kadu network with two reservoirs, 34 pipes and 9 loops was selected. The method was able to reach Rs. 130,645,890 with 2288 evaluation of objective function. It should be noted that all pressures and velocities are within the allowable range and had better results compared with the power algorithms such as GA and PSO. Also, due to the lack of use of the software, the time of the program was less than other studies. The advantages of the proposed method in the current study were high speed, not applying hydraulic simulation software, use of inconstant parameters, and its simplicity in application. The present research showed that by understanding abilities and combining the capabilities of different algorithms, a better algorithm could be created. Also, the suggested algorithm could solve the problems more quickly without the use of hydraulic software and only by applying the rules which govern the hydraulic pipes.

Increasing water demand has become inevitable due to population growth, industrial development and agriculture. On the contrary, water resources limitation and observing sustainability have restricted water allocation. Therefore, supply of water for all demands is impossible. Overuse of groundwater and surface resources, rainfall reduction, drought in most parts of the world, as well as pollution of ground and surface water have severely lessened available water resources. So, using these vital resources, with sustainable manner, requires proper management. Demand management and water production are two effective approaches. But, water production projects are time-consuming and high-cost. So, water management models are appropriate tools for decision making to predict the future situation, with low time and cost. Mathematical techniques are useful tools for keeping the balance between environmental requirements and human demands, specifically for optimal utilization of water resources in complex applications such as watershed or aquifer management. Aiming to more comprehensive decision making, a combination of two powerful operational and research techniques, including simulation and optimization could lead to a possible sustainable and optimal economic management. This study was aimed to the simulate water policy in the Arazkuse watershed, Golestan province, Iran. In this regard, both system dynamics model and water balance relationships were applied. VENSIM as a system dynamics software was used to capture causal and consultative relationship among controlling factors, while water balance was applied to estimate availability of surface and subsurface water. As four sectors of water consumption (urban, industrial, environmental and agricultural) were considered in the current study, the data about landuse, cultivated crops in the watershed, hydrological information and drinking water consumption were gathered. Then, the water consumption was optimized in the agricultural sector, using linear programming technique with the two objectives of maximizing economic benefits and minimizing water consumption, by Lingo software. Finally, the effect of water optimization in the agricultural sector on the groundwater sustainability index of Arazkuse basin was investigated. The groundwater sustainability index represents the portion withdrawn water from the whole of aquifer reservoir.The infiltrated water volume and deep storage coefficient variables were used to evaluate available groundwater from the total groundwater and the total discharge of the basin during the simulation period. Accounting for as much as 75% of the water consumption in the basin is supplied from groundwater while for surface water this portion was 25%. In general, the total volume of annual available groundwater in the Arazkuse basin was about 75 million cubic meters whereas the total volume of available surface water equaled 155 million cubic meters. These values indicated that a large volume of surface water flows out of reach, which puts extra pressure on groundwater to meet the needs and may endanger the stability of the aquifer. Due to the amount of water supply and requirements by various sectors (including the drinking water, environment, agriculture and industry) the available water to each sector was calculated. A linear programming technique was used to optimize the allocation of water resources with an objective function of maximizing economic profit while minimizing water consumption in the agricultural sector. Flood irrigation is a common strategy for rice cultivation. Hence, irrigation efficiency in the studied watershed is lower than other watersheds which applied the irrigation methods such as drip and sprinkler systems. Considering the fact that about 32% of agricultural lands in Arazkuse watershed are dedicated for rice cultivation, to some extent could be concluded that it would be possible to increase irrigation efficiency, by improving conveyance and distribution coefficients. The results showed that although the available surface water was approximately double, provided water by groundwater supplied about 75% of the required water, especially for the case of agricultural needs. Despite the fact that runoff may be the source for a part of recharged water, a considerable amount of the available water exits in the form of runoff. Therefore, the supplied water for various sectors was linked to the excessive use of groundwater resources. Hence, the agricultural sector was the main water consumption that needed to be optimized. The assessment of the effect of optimization of agricultural water use on groundwater sustainability index showed under optimal condition of water required for agriculture the index shifted from 14.4 in the present condition to 1.9 for the new scenario.

Resilience is a criterion for measuring the strength and stability of a system and its ability to absorb changes and distribute them, while at the same time maintain relationships between system variables. The ability of societies to live and develop with dynamic environments is known as ecological resilience. When the accessibility of a vital resource (such as water) varies between overabundance and extreme scarcity due to natural or man-made phenomena, management should be flexible and the authorities adapted with maintaining legitimacy. So, the prediction of ecological resilience in water resources such as rivers is an important issue that needs to be considered for better management of land-use systems and water supplies. For this purpose physical and chemical parameters in rivers should be monitored to predict and understand the behavior, flexibility and interaction between living-beings of rivers. The ecological importance and reorganization features of algae, as well as being directly influenced by chemical and physical parameters make them eligible to be considered as indicators of nutrient pollution and to be the endpoints for numeric nutrient criteria developed for water quality management aims. But, most environmental models do not address water quality in relation to river biology over time and offer little prediction for future periods. Time series modeling and forecasting have importance in various applied studies, such as resilience in which resistance to chronic stress and time series are to be significant. In spite of various studies on intelligent modeling in the field of water management, no study has yet investigated the environmental resilience of the river in Iran, using the time series and artificial intelligence models. The resilience indicators examined for rivers have included four criteria: the biology, impact of pollutants, climate change, and time. The mentioned criteria were investigated in accordance with the following factors: Diatom algae, chemical parameters, discharge variations, and 10-year time series. The input data for modeling relations in the river ecosystem for Diatom were based on the factors influencing the physical and chemical parameters of these algae (EPA2017), and also on the basis of statistical methods of their correlation coefficients. Resilience index in the current study was determined based on Diatoms population with regard to Diatomic-Trophic index. In this respect, this study proposes a gene expression programming (GEP), hybrid wavelet-gene expression programming (WGEP), support vector machine (SVM) and wavelet support vector machine (WSVM) for prediction of monthly variations in Lavarak Station’s water quality that affect bio indicators. The 10-years (2002-2012) monthly data used in this study were measured from Aliabad River located in Tehran, Iran. First, the measured discharge (Q) and other quality parameters that affect the bio indicators data sets were initially decomposed into several sub-series, using discrete wavelet transform (DWT). Then, this new sub-series was imposed on the (GEP) and (SVM) models as input patterns to predict monthly bio indicator one month ahead. The results of the new proposed WGEP and WSVM models were compared with SVM and GEP models. The performance of this model was evaluated using Nash-Sutcliffe efficiency (NS), root mean square error (RMSE), and mean absolute error (MAE). A comparison between the four models showed the superiority of the hybrid models over the classic models. The achieved results even pointed to the superiority of a single SVM model over the GEP model. With regard to the studies conducted to determine the bio - resiliency index, the abundance of Diatom algae in the river within the standard of resilience the WSVM hybrid model was better while the WGEP was the second best. But due to the modeling process and the results, the WGEP model was used to determine the formula and the effect of each parameter was defined in the scenario. This model can also be effective in expressing changes in one or more independent parameter. The results of this study indicated that considering the capacity and the ability of AI models to deal with the nonlinear nature and dynamics of hydrological processes, the ability of wavelet analysis to extract certain periods of a time series is potentially more to gain reasonable prediction in different environmental processes and planning for them.

In the discussion of water demand management, it is important to have a predictive model of water consumption for the coming days. Such model can be useful in taking management decisions such as water rationing policies, water removal rates from wells and suitable timing for pumping water. Predicting water consumption in urban areas is of key importance for water supply management. Predictive modeling for water consumption can be used for planning water supply and expanding infrastructure for new developments and improving the control and operation of the water resources systems. In this research, the performance of Multi Linear Regression (MLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), coupled Wavelet and MLR (WR) and coupled Wavelet and ANFIS (WANFIS) were evaluated in predicting water demand in Kerman City, Iran. For this purpose, weekly time series of water consumption and meteorological parameters including maximum temperature and total precipitation were used to predict weekly water consumption based on 12 years data from 2006 to 2017. The data from 2006 to 2014 (469 weeks) were considered for the training of MLR, WR, ANFIS and WANFIS models and the remaining data from 2015 to 2017 (157 weeks) were used for the validation of various mentioned models. In WR and WANFIS wavelet-based models, the weekly time series of water consumption, maximum temperature and precipitation are decomposed by discrete wavelet transformation (DWT) to sub-series of approximations and details at various levels which are used as inputs of wavelet based models. The objective of multiple linear regression (MLR) analysis is to study the relationship between several independent or predictor variables and a dependent or criterion variable. The aim of this method is to determine the regression parameters by which the estimated values are efficient and consistent. Coupled Wavelet and Multi Linear Regression (WR) models are MLR models which use, as inputs, subseries components which are derived from the use of the Discrete Wavelet Transform (DWT) on the original time series data. Fuzzy Inference System (FIS) is a rule based system consisting of three components: (i) a rule-base, containing fuzzy if-then rules; (ii) a data-base, defining the membership functions (MF); and (iii) an inference system that combines the fuzzy rules and produces the system results. Fuzzy Logic (FL) is employed to describe human thinking and reasoning in a mathematical framework. The main problem with fuzzy logic is that there is no systematic procedure to define the membership function parameters. The construction of the fuzzy rule necessitates the definition of premises and consequences as fuzzy sets. On the other hand, an ANN has the ability to learn from input and output pairs and adapt to it in an interactive manner. In recent years, the ANFIS method, which integrates ANN and FL methods, has been developed. ANFIS has the potential benefits of both these methods in a single framework. ANFIS eliminates the basic problem in fuzzy system design, defining the membership function parameters and design of fuzzy if-then rules, by effectively using the learning capability of ANN for automatic fuzzy rule generation and parameter optimization. Coupled wavelet and Adaptive Neuro-Fuzzy Inference System (WANFIS) models are ANFIS models which use, as inputs, subseries components which are derived from the use of the Discrete Wavelet Transform (DWT) on the original time series data. In this comparative study, the performance of all predictive models was evaluated by statistical indices including coefficient of correlation (R), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). The obtained results from this study suggest that the wavelet-based models including the WR model (for training: R2 = 0.92, RMSE = 34151 m3, MAE = 23908 m3 and for simulation R = 0.97, RMSE = 23486 m3, MAE = 16788 m3) and the WANFIS (for training: R2 = 0.94 RMSE = 29179 m3, MAE = 20675 m3 and for simulation: R = 0.92, RMSE = 43698 m3, MAE = 29305 m3) have much higher performance compared to the MLR and ANFIS models. By the results, it can be concluded that the best models for predicting weakly water consumption in Kerman City are those with the imputes of water consumption, maximum temperature, and total precipitation of last two weeks and data decomposition level of 3 via discrete wavelet transformation method.

Lake Urmia is the largest interior lake in Iran. This lake, as an effective ecosystem unit in northwest of Iran, highly contribute in the ecology of the region. The water level of Lake Urmia has dropped between 6 and 7.40 meters that has reduced the area of water zone. This reduction has leaded to increase in salty marsh, since1995. By May 2017, the water level was 3.11 m lower than the ecological balance. Expansion of salt marshes is due to successive droughts and an increase in water harvesting from the surface and ground water sources. This phenomenon can promote dust and salt storm occurrence, and lead to the death of the lake ecosystem, compulsory migration and population health threats of more than 10 million people. In order to optimal long-term planning and management of the available water resources, a better understanding of the temporal and spatial variations of water balance components (especially actual evapotranspiration, surface runoff, and groundwater recharge) is essential. A review of various research findings around the world shows that the WetSpass-M model is a suitable model for the spatial simulation of surface runoff, actual evapotranspiration and groundwater recharge in basins. The main aim of this paper was to analyze spatial distribution of annual components of hydrology cycle in the basin of Lake Urmia, using WetSpass-M model during 25 years (1992-2017). The method adopted in this study was analytic. Climatic, hydrological and land use data were applied in this analysis. Climatic and hydrological data were provided from meteorological stations, hydrometric station information and observed wells respectively. In this study, the satellite images and field studies were applied to determine land use. These images were downloaded from the site of EarthExplorer. The original Wet Spass-M model is a quasi-steady state spatially distributed water balance model scripted in Avenue and used to predict hydrological processes at seasonal and annual time steps. Since the model is a distributed one, the water balance computation is performed at a raster-cell level. Individual raster water balance in this model was obtained by summing up independent water balances for the vegetated, bare soil, open- water, and impervious fraction of each raster cell. The total water balance of the given area was thus calculated as the summation of the water balance of each raster cell. Precipitation was taken as the starting point for the computation of the water balance by each of the above mentioned components of a raster cell. The rest of the processes (interception, runoff, evapotranspiration and recharge) follow in an orderly manner. To validate the results of Wet Spass-M model, the coefficients of Nash-Sutcliffe Efficiency and RMSE-Observations Standard Deviation Ratio were used. The basin of Lake Urmia has been affected by many climate and human changes that have caused Lake Urmia Crisis. During the study period, runoff declined but temperature and evaporation increased. Land use has also changed widely. These changes included dry farming and rangelands convert to settlement and increase in area of irrigated farming. About 3043 km2 of lake area has been reduced and added to salt marsh. The average depth of ground water has decreased by 7.4 meters. The analysis of simulation results indicated that Wets pass-M model works properly to simulate hydrological water budget components in the Urmia Lake Basin. According to the results, in 1992, the highest runoff occurred in the western part of the basin with the highest rainfall, and all groundwater in the southern and western parts of the basin was well recharge. In the year 2017 most of the runoff and groundwater recharge was confined to the southwestern part of the basin. In 2017, Lake Urmia experienced higher evapotranspiration than the year 1992. In 1992, 58.42% of the basin precipitation was spent on evapotranspiration, 7.20% for surface runoff, 31.18% for groundwater recharge and 3.2% for interception. In the year 2017, these changed to 55.49, 1.55, 39.77 and 3.19%, respectively. Among the simulated components during the study period, the runoff has the highest coefficient of variation and the lowest groundwater recharge. Also during the 25-year statistical period, eastern parts of Urmia Lake (including: Ajab Shir, Azarshahr, Maragheh and Shiramin) had the highest coefficient of variation in all studied components. The southwestern parts of the basin were in better condition.

Drought is one of the most widespread and devastating natural hazards, which is compounded by climate change. Indicators are widely used to provide an overview of drought conditions. In this study, the impacts of climate change on drought status in Tabriz station during future periods were investigated using Deciles Index (DI) and the Standardized Precipitation Index (SPI). First, the daily output data of HadGEM2 model under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by LARS-WG version 6 and the ability of the model was confirmed to simulate the past climate (1987-2016) in Tabriz. Then, the precipitation was simulated for Future periods of 2021-2040, 2041-2060, and 2061-2080. Using simulated precipitation data, drought status in Tabriz was assessed using two drought indices on an annual scale. The results show that in most of the studied years, the number of droughts decreased in all three future periods compared with the base period and the number of wet years increased. The results of drought monitoring and its prediction for future periods can be used in natural resource management as well as water resource management planning. In recent years, weather and climate researchers have identified climate change as the most important concern due to increasing greenhouse gas emissions and global warming. Drought is one of the most important and most common disasters affected by climate change that is slowly and progressively causing environmental, agricultural and economic damage in both dry and humid climates around the world (Li et al., 2013). Since drought affects different segments of society such as water resources, agriculture, industry, economy, health, etc., monitoring and evaluation of this factor in the present and in the future is necessary in order to provide proper planning in different parts of society. Climatologists are currently simulating climate variables using general atmospheric circulation models (Barrow and Yu, 2005). The main purpose of these models is to calculate three-dimensional climate indices in specific grids. The outputs of these models have low spatial accuracy. Therefore, if their output directly enters hydrological models, it increases uncertainty. Downscaling methods are used today to increase the spatial accuracy of these data. Downscaling methods are divided into two categories: dynamic and statistical (Beecham et al., 2014). Statistical methods are commonly used in climatic studies. In this study, the output of HadGEM2 model under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by statistical method and LARS-WG model. The daily climatic variables such as minimum temperature, maximum temperature, precipitation and sunshine for Tabriz station were produced for the next three periods of 2021-2040, 2041-2060 and 2061-2080. Then, using simulated rainfall data, the drought status of Tabriz station was evaluated using two decile indices (DI) and Standardized precipitation index (SPI). The study area in this research is Tabriz Synoptic Station which is geographically located in northwest of Iran. The data used include observed and simulated data. The observed data were related to 1987- 2016. The simulated data included HadGEM2 model that was downscaled by LARS-WG under RCP2.6, RCP4.5 and RCP8.5 scenarios. LARS-WG as one of the most popular models for generating stochastic data was used to produce daily minimum and maximum temperatures, precipitation and radiation for present and future climatic conditions. This model is more applicable than others due to repeated computation, less data input, simplicity and performance (Kilsby et al., 2007). After primary data analysis, daily precipitation series were generated and then LARS-WG was implemented. Subsequent to analyzing the input data and the initial statistical studies, the base state scenarios were implemented for the observed data and the precipitation data were simulated. Model validation based on observed and simulated precipitation values showed high agreement of the model with the observed data. Then precipitation data were tested for normality of distribution. The results indicated that the precipitation data followed the normal distribution. After ensuring that the model was capable to simulate precipitation data series for Tabriz station, it was run for three periods of 2021-2040, 2060-2041 and 2061-2080 using HadGEM2 output under RCP2.6, RCP4.5 and RCP8.5. Then annual drought was calculated using SPI and DI indices. Investigation of drought status using SPI index revealed that in most of the years, the number of droughts decreased compared with the base period (1987-2016) and the number of wet periods increased. Evaluation of the DI index also showed that in all future periods the number of extreme, severe and mild droughts decreased, in compare with the base period, but the number of moderate droughts increased. According to this index, the percentage of normal years would increase significantly in all three future periods, but the percentage of wet years would show a significant decrease.

In most of hydrometeorological studies and water resources management, flood and drought forecasting, irrigation planning and climate change studies, access to rainfall data and especially its spatial distribution (precipitation map), are of particular importance. Different models are used for spatial interpolation of rainfall data that generally fall into two categories of statistical and geostatistical methods. In statistical methods, interpolation is based on linear and nonlinear regression between main variable and covariate, but in geostatistical methods such as Kriging, spatial correlation of the sampled points is take in to account. There are many problems with spatial estimation of rainfall data especially in complex topography. Using correlated covariates is one of the answers to overcoming this problem. The altitudinal range of Mazandaran province fluctuates between -61 to 5610 meters, which creates various climates in this province. Besides, presence of the Caspian Sea in the north and Alborz Mountains Range in the south of the province make further complicates in spatial rainfall estimation, especially in the impassable heights of the province, which are a major water resource for large rivers. Since the elevated meteorological station in Mazandaran province is located on 2134 m, no rainfall data is available in area between 2134 to 5610 meters. So, this study was aimed to compare four interpolation methods include inverse distance weighting, Kriging, Co-kriging, and three-dimensional linear gradient. Also in this study, the role of covariates in precipitation estimation was investigated. Spatial data interpolation methods are used to estimate a variable at a particular point from actual data measured at adjacent points. The Inverse Distance Weighting method obtains the unknown quantity and performs the interpolation, by weighting the data around the estimated point. The interpolation methods use a set of points with known values around points with unknown data to estimate their values. This method is based on a geographical law that each phenomenon is related to other phenomena, but more depended to the close phenomena. Kriging as an advanced interpolation method is suitable for data with locally defined trends. This method can interpolate with the least variance of estimation that its error rate depends on the variogram specification. Co-Kriging is a suitable method when a covariate is present in all parts of network. In 3D Linear Gradient method, it is assumed that there is a linear trend along the length, width, and height of the region. In this study, in order to evaluate different interpolation methods of rainfall (Inverse Distance Weighting, Kriging, Co-Kriging, 3D linear gradient) the data from 25 synoptic and rain gauge stations were used in Mazandaran province. Surveying the statistical period of stations (1991-1991), the data of 2012 used to select the best interpolation method. Since the height of stations in Mazandaran province varies between -2120 and -21 m, precipitation data is not available for altitudes above 2200 meters. Therefore, altitude variable was used as an auxiliary variable in this study to obtain the most accurate estimation of altitude rainfall. In this study GS + and Mini tab software was used to calculate the estimated values of the models, Arc GIS software to prepare maps and Excel software for other calculations. Root Mean Square Error and Mean Bias Error were used to select the best interpolation method in this study. In variographic analysis, 5 types of semivariable models (Spherical, Gaussian, exponential, Linear and Linear to Sill models) were fitted to the data. The coefficient of determination and the ratio of structural changes to total variations were used to select the best half-variance. The best-chosen model has closer amounts of the coefficient and ratio to the number one. P-value and correlation coefficient were used to select the best covariate. Due to the importance of auxiliary variables in spatializing precipitation data, the variables of latitude, longitude, and altitude were used for the three-dimensional gradient equation. In order to better understanding of the studied methods, the map of annual precipitation changes in the province was plotted with different methods; then comparing them, the best rainfall map was selected. In this study, in order to determine the best interpolation method for monthly and annual precipitation data of Mazandaran province, four interpolation methods (Inverse Distance Weighting, Kriging, Co-kriging and three-dimensional linear gradients) were compared. Examination of Root Mean Square Error and Mean Bias Error revealed that the best interpolation method for long-term monthly precipitation was the 3D linear gradient method. But, the problem with this method and the other investigated methods in this study was overestimation of precipitation in high and low estimation stations in coastal and plain areas of the province. The overestimation was occured due to the lack of the number of station above altitudes of 2000 m in Mazandaran province. Therefore, the estimation of precipitation in the province's highlands had error. The outcomes of the best semicircle model in this study showed that the best models (except for July with low coefficient of determination) were spherical and exponential models. The results also showed that in hot months the spatial structure of precipitation data became weaker. Also, the impact of precipitation data in this province is about 30 km. The outcomes of correlation between monthly and annual precipitation data with latitude, longitude and altitude revealed that the altitude parameter had a significant correlation with other auxiliary parameters (latitude and longitude), in all months except July. Also, the correlation of latitude and longitude variables with precipitation was significant in some months. Therefore, it can be concluded that altitude parameter was the best auxiliary variable among the others to estimate monthly and annual rainfall in Mazandaran province. Distribution graph of annual rainfall variations with altitude along with regression equation indicated relatively good fit of linear equation to altitude rainfall fluctuations. Based on the results, the importance of the role of latitude and longitude variables for spatial precipitation data was determined. Therefore, latitude, longitude, and altitude variables were used for the three-dimensional gradient equation. Survey of annual precipitation maps showed that the three-dimensional linear, Co-Kriging and gradient methods had the most reasonable estimation of the spatial variability of precipitation in the province. According to the rainfall-altitude diagram, the average rainfall in the province is reduced to 500 mm of annual rainfall per 1000 meters. From the annual precipitation map survey with the selected method, it can be seen that only the western coasts of the province experience more than 1000 mm of rainfall per year. The slope of rainfall variations with altitude in the west of the province is more than east and due to the complex topography of the west of the province the west coast has more rainfall than the western altitudes.

Rainfed cultivation is one of the best ways to rich the sustainable development with optimal use of green water. About 75 percent of the world's cultivated areas are rainfed, which forms important part of the international economy. For a long time rainfed cultivation has been a common method in low-rainfall areas of Iran and mentioned method is one of the main ways of producing crops yet. Rainfall is an important factor in rainfed agriculture, so its limitations and non-conformity with planting season can reduce the production efficiency. Wheat growing period consists of three seasons of the year. Rainfall in spring, fall and winter is effective on wheat growth. Cereal grains, especially wheat, are the most important food source in the world. After cereals, legumes are considered to be the main human food source and have significant nutritional and agricultural importance. In addition to being high in quality and valuable, suitable and complementary protein for cereals in the nutritional pattern, lentils are also among the legumes that stabilize the air nitrogen in the soil, which lends itself to crop rotation. Thus, intermittent cultivation of rainfed wheat with rainfed lentil is an important factor in stabilizing production in the developing countries. Given that Iran is in arid and semi-arid regions and its population is growing, it is important to plan, and evaluate the past in order to predict the future. Zanjan province is the fifth country with 6.9% of total grains production. In this study, the effect of precipitation in water requirement of these two important products and their water stress has been studied in the climatic conditions of Zanjan plain. Zanjan province is located at the longitude of 47º 10' to 50 º 5' East and the latitude of 35º 25'37º10' North. The area of Zanjan province is about 39369 square kilometers. According to the De Marten climate classification, the tropical, temperate and subtropical climatic zones can be identified at the applied level. Average rainfall over the past decade has been reported as 301 mm. For conducting this study, statistical data including meteorological and crop yield data 2004-2014 obtained from the Meteorological and Agriculture-Jahad Organization, respectively. Zanjan synoptic station with the latitude and longitude of 48º 31' East and 36º 39' North, respectively, is located 1663 meters above the sea level. In the last 50 year, the annual precipitation trend has been decreasing and this trend is affecting the water availability of rainfed plants in the region and can decrease yield of crops. Although in the long term the average temperature trend is slightly constant, the yield has also declined with decreasing rainfall. Regarding the importance of wheat and lentil in arid lands of Zanjan plain, these two main rainfed crops were investigated in this study by determining the regression relationship between yield and evapotranspiration with precipitation and water stress coefficient. The results showed that the average evapotranspiration values of wheat and lentil for Zanjan plain during the study period were 398 and 262 mm and the mean temporal stress coefficients were 36 and 33%, respectively. Regression analysis showed the highest correlation between actual evapotranspiration and yield. The highest correlation was observed between temporal stress coefficient and yield of rainfed wheat. But, in lentils this trend was different and the correlation between water stress coefficient (Ks) and yield was more than the precipitation and lentil yield. For 25% decrease in precipitation, wheat yield reduced almost 18%. Generalizing this issue, the response to water stress coefficient of the product or Ky become 1.14. The regression equation also indicated that in case of being precipitation about 300 mm, wheat yield in the area will be near one tone. For 25% decrease in precipitation, wheat yield reduced about 22%. According to figures, the fitted model was significant and well-acted; and during the growth period of these crops, lack of water supply caused water stress as well as severe yield loss. The occurrence of high tension time percentage of 25% for wheat and 20% for lentil in all years of the study period confirms that the climatic potential of the region does not reach the standard requirements of plants, so it is not possible to achieve optimal performance. Hence, new methods of tillage, conservation agriculture and supplemental irrigation are recommended for achieving high yield, as well as the expected cultivation method for crops.

Rainfed cultivation is one of the best ways to rich the sustainable development with optimal use of green water. About 75 percent of the world's cultivated areas are rainfed, which forms important part of the international economy. For a long time rainfed cultivation has been a common method in low-rainfall areas of Iran and mentioned method is one of the main ways of producing crops yet. Rainfall is an important factor in rainfed agriculture, so its limitations and non- conformity with planting season can reduce the production efficiency. Wheat growing period consists of three seasons of the year and rainfall in spring, fall and winter is effective on wheat growth. Cereal grains, especially wheat, are the most important food source in the world. After cereals, legumes are considered to be the main human food source and have significant nutritional and agricultural importance. In addition to being high in quality and valuable, suitable and complementary protein for cereals in the nutritional pattern, lentils are also among the legumes that stabilize the air nitrogen in the soil, which lends itself to crop rotation. Thus, intermittent cultivation of rainfed wheat with rainfed lentil is an important factor in stabilizing production in the developing countries. Given that Iran is in the arid and semi-arid regions and its population is growing, it is important to plan and evaluate the past in order to predict the future. Zanjan province is the fifth country with 6.9% of total grains production. In this study the effect of precipitation in water requirement of these two important products and their water stress in the climatic conditions of Zanjan plain has been studied.

Zanjan province is located at 47 degrees 10minutes to 50degrees 5minutes east longitude, 35degrees 25minutes 37degrees 10minutes north latitude, with areas of about 39369 square kilometers. According to the De Marten climate classification, the tropical, temperate and subtropical climatic zones can be identified at the applied level. Average rainfall over the past decade has been reported at 301 mm. For conducting this study, statistical data including meteorological and crop yield data 2004-2014 obtained from the Meteorological and Agriculture-Jahad Organization, respectively. Zanjan synoptic station with latitude and longitude 48degree 31minute east and 36degree 39minute north, 1663 meter above sea level. In the last 50year, the annual precipitation trend has been decreasing and this trend is affecting the water availability of rainfed plants in the region and can decrease yield of crops. Although in the long term, the average temperature trend is somewhat constant, but with decreasing rainfall, the yield has also decreased. in this study, due to the importance of wheat and lentil as two main crops of rainfed and their placement in arid lands of Zanjan plain, investigated the regression relationship between yield and evapotranspiration with precipitation and water stress coefficient.

The results showed that the average evapotranspiration values of wheat and lentil for Zanjan plain during the study period were 398 and 262 mm and the mean temporal stress coefficients were 36 and 33%, respectively. Regression analysis showed the highest correlation between actual evapotranspiration and yield so, the highest correlation between temporal stress coefficient and yield of rainfed wheat. But in lentils this trend was different and the correlation between water stress coefficient (Ks) and yield is more than the precipitation and lentil yield. For 25% decrease in precipitation, wheat yield reduced almost 18%. Generalizing this issue, the response to water stress coefficient of the product or Ky become 1.14. The regression equation also showed that in case of being precipitation about 300 mm, wheat yield in the area will be near one tone. For 25% decrease in precipitation, wheat yield reduced about 22%. According to figures, the fitted model was significant and well acted; and during the growth period of these crops, lack of water supply caused water stress as well as severe yield loss. The occurrence of high tension time percentage of 25% for wheat and 20% for lentil in all years of study period confirms the climatic potential of the region does not rich the standard requirements of plants and it is not possible to achieve optimal performance, so new methods of tillage, conservation agriculture and supplemental irrigation are recommended for achieving high yield, as well as the expected cultivation method for crops.

Urmia Lake is the largest inland water body in Iran and the second hyper-saline lake in the world. It has been designated as a “conserved region of biosphere” by UNESCO due to its environmental importance and unique aquatic ecosystem. The lake has faced a variety of natural and anthropogenic hazards in recent years and has encountered dramatic changes in its natural hydrodynamic condition. There are a number of reasons for these changes, where primary causes are increased water consumption, especially in the agricultural sector, development of water storage structures in the lake basin, construction of the causeway, climate change, global warming, and droughts. Due to changes in the lake's natural condition, the study of its hydrodynamic pattern is inevitable. In this regard, the current study aims to simulate changes in the water temperature of Lake Urmia in order to study its hydrodynamic. MIKE hydrodynamic models are developed by DHI Water and Environment for simulation of flows in estuaries, bays, coastal areas, lakes, and oceans. To perform the simulations in this study, MIKE3 model was used. In the modeling process, the flow simulation is carried out simultaneously with the heat transfer model, covering all the hydrodynamic conditions of the lake. In order to simulate water temperature and its effects on the lake hydrodynamics in the MIKE model, density is considered as a function of temperature, then the heat transfer equations are solved at each time step. The forces governing the hydrodynamic equations include wind, air pressure, tide, wave, and Coriolis forces. However, the forces governing heat transfer/diffusion equations are of a different nature. Air temperature, relative humidity, and clearness coefficient are important inputs for the simulation of changes in the water temperature in the lake. Due to the high accuracy of the obtained values, ECMWF model data were used in the model. The validity of the numerical model was also assessed by comparing the simulated results against satellite data. The information is provided by the Group for High Resolution Sea Surface Temperature (GHRSST). In this group, global sea surface temperature data are generated using a multi-scale two-dimensional variational (MS-2DVAR) blending algorithm. These sea surface temperature data are obtained from various satellites with multiple sensors (such as AVHRR, AATSR, SEVIRI, AMSRE, TMI, MODIS, GOES, MTSAT-1R, etc.). In this study, changes in the water temperature of Lake Urmia were simulated in order to study its hydrodynamic condition. Initially, the data used to simulate water temperature changes are presented. These data included air temperature, relative humidity, and clearness coefficient. Due to the high accuracy and generalizability of the ECMWF model output to the entire computational domain, the output of this model were used to obtain the above data. MIKE3 hydrodynamic model was used to perform the simulations. In order to investigate the effects of precipitation, evaporation, and rivers discharge on water temperature changes, two models, one with and the other without considering these factors were implemented. The water temperatures were compared in these two models. The results showed that water temperature values were approximately the same for the two cases. Also, a comparison between the water temperature output results at different depths revealed that due to the low depth of the lake, the temperature difference between the surface layer and the near-bed layer was low and reached a maximum of 0.2 °C. In addition, GHRSST satellite data was used to validate the model results. Evaluations indicated that the model results were in good agreement with the measured data, and seasonal variations in lake surface temperature were also well simulated. Moreover, the effect of causeway on the spatiotemporal distribution of lake water temperature has been investigated. For this purpose, simulation of temperature changes was considered over a one year period. The results demonstrated that the water temperature of the lake did not change significantly in both with and without causeway, and the temperature exchange between northern and southern parts of the lake occurred in both conditions. Hence, this model can be used as an efficient tool to assess the effect of causeway on the flow pattern, salinity distribution and sedimentation process in both parts of the lake

Temperature variations have a very strong impact on ecosystems and pose many challenges for economic, social and agricultural developments. According to reports from the Intergovernmental Panel on Climate Change (IPCC) in the 20th century, the global temperature could rise by about 0.4 to 0.8 degrees Celsius. By the way, forecasts with six different scenarios showed that the temperature rise may reach 1.8 to 4 degrees Celsius at the end of the 21st century. Researches indicated that despite uncertainties in climate forecasts, the increase in temperature caused by human activities is bound to result in sudden and irreversible changes in the global water cycle. It has been confirmed in several studies that the temperature changes affect the precipitation parameter. Finding the relationship between the marginal distributions of the various variables to understand the laws governing these dependencies can be very effective in recognition of the observed hydrological events. Therefore, the assumption of independence between the variables can cause doubt in the accuracy of the results, so using bivariate data analysis methods could be very helpful. Due to the correlation between different hydrological parameters, the use of methods that can take into account the interdependence of variables and jointly model them could gain more reliable accuracy. In this regard, the lack of studies is strongly observed, thus the purpose of this research is to use copula functions in simultaneous modeling of annual precipitation and temperature in Khuzestan province. Copulas are multivariate distribution functions which their one-dimensional margins are uniform in (0, 1). Sklar (1959) introduced the theory of the copula function and showed how univariate distribution functions could be interconnected using this method to create multivariate distribution functions. An important advantage of copula functions is that they allow the transfer of multivariate functions from to . For this purpose, the marginal functions of each variable are calculated and the copula function is construct using the dependency structure between the parameters. Therefore, it presents a full description of inner dependency structure In other words, the Sklar’s theorem claims that the dependency structure explained by copula function when the inner dependency among the variables is considered and univariate marginal distributions are calculated. The copula functions are made up of many families, including the Archimedean copulas. Archimedean copulas have been used in various fields of science such as economics, environmental studies and hydrological events due to their specific mathematical explicit formula. Many copulas do not have explicit formula, and this poses many challenges to their application. Applications of multivariate techniques for frequency analysis of hydrological parameters can be very useful and provide more reliable results. The most important part of using copulas is calculating the copula parameter. Many methods have been developed for this purpose. Ultra-innovative optimization algorithms can be very useful.In this study, four different copula functions (including, Ali - Mikhail – Haq, Clayton, Frank and Farlie- Gumbel- Morgenstern) were used for multivariate analysis of annual precipitation and temperature in Khuzestan province. In order to select the suitable copula function for forming the bivariate distribution, after fitting the suitable marginal distribution on every variables and estimating the distribution parameters, seven copula function used to link the marginal functions and the dependency parameter of every copula function was estimated by WOA method. Finally, the best fitted copula function was selected by comparing the CDF values of every copula function with corresponding values obtained from empirical copula. For selecting the best copula function, the Cramér–von Mises (Sn) and Normalized Root Mean Square Error (NRMSE) criteria were used. Copula is a flexible approach for constructing joint distribution with different types of marginal distributions. Indeed, the copula is a function which links univariate marginal distributions to construct a bivariate or multivariate distribution function. In this study, the annual precipitation and temperature in Khuzestan province during 1988-2018 were investigated, using copula functions. For this purpose, six synoptic stations were selected because of their sufficient statistics, including: Dezful, Ahvaz, Ramhormoz, Mahshahr, Abadan and Masjed Soleiman. In the next step, nine different distribution functions were fitted on considered series and the best fitted distributions was selected for studid stations. After specifying the marginal distributions, four different copula functions (including: Ali- Mikhail- Haq, Clayton, Frank and Farlie- Gumbel- Morgenstern) were used for constructing multivariate frequency analysis of temperature and rainfall series. The most important part of applying multivariate functions is to determine the coefficient of copula function. Thus for this purpose, the Whale Optimization Algorithm (WOA) was used. After determining the coefficients of the copula functions, the joint distributions were constructed. The results showed that for the precipitation and temperature series of the Abadan, Dezful, Mahshahr and Masjed Soleiman stations Clayton function, and in other stations the Frank function had the best accuracy.

According to the average annual rainfall of 250 mm in Iran, this country among the can be specified among the top arid countries. Increasing population and consequently increase in water demand have threatened the water balance during recent decades. Withdrawal of water from the aquifer in some regions has led to a quick drop in groundwater levels. This fact resulted in several problems such as: drying of wells, reducing the flow of rivers and canals, and also land subsidence. Ground water recharge is an important process in the hydrological balance of water. It is essential for regions where the proactively conservation of groundwater and surface water is strongly required. This study proposed a methodology to delineate as well as to identify favorable artificial recharge sites, using integrated remote sensing (RS), geographical information system (GIS) and multi-criteria decision making (MCDM) techniques for augmenting groundwater resources. Studied area was located in the Maran plain of the Northwest of Tabriz, Iran. This plain has been suffered from water shortage problems since the last decade. Regarding that most of the water demand in Iran is supplied by aquifers, awareness of the condition of aquifers could help manage groundwater sources. Remote Sensing (RS) and Geographical Information System (GIS) play a crucial role in analyzing groundwater. In this study, using GIS and RS and multi-criteria decision-making models, areas with the potential of containing groundwater were identified in Tasuj plain. Analytical Hierarchical Process (AHP) was used as a MCDM technique to normalize the weights of various thematic layers and their classes for delineating the groundwater potential and recharge zone maps. In the current study, areas were classified due to of potential of containing groundwater, in four classes as good, middle, poor and very poor. According to the results of AHP and ANP models, areas of alluvial fans were the best parts, due to having appropriate thickness of alluvium, transmissivity, hydraulic conductivity, water level depth, and vegetation coverage. Suitable areas are located in the northern and eastern parts of the studied plain, according the models estimations. While in the southern parts in which thickness of the alluvium was the lowest, salt marsh has been created. By the results of ANP, percentage of the plain area which belongs to each classification of groundwater potential were estimated as 33.7%, 29.41%, 25.91%,11.58%, respectively for good, middle, poor and very poor. Therefore the summation area of good and middle classes were estimated as 62.48% of the total plain area. This part contained 91% of total wells. The parameter of wells density was calculated in each class and then the results by AHP and ANP were compared. The density in the middle class by AHP and ANP model was 0.648 and 0.458. The greater amount of wells density revealed the higher groundwater potential. It was considered that the best model was that in which the density of wells in the poor and very poor classes was the less. Hence, ANP model had the better results than AHP. Field data from piezometric wells, as well as distribution of wells confirmed these results. By the results of the current study for determination of suitable areas for groundwater artificial recharge, both models of AHP and ANP revealed that the most appropriate area in Tasuj plain was the alluvial fan and low slope areas. Susceptible zones of groundwater storage places were classified in the four classes as good, middle, poor and very poor. The results of the two models were compared with the amount of density of wells. So, the ANP model showed the more acceptable results than the AHP. The detected areas in the current study were in northen parts and eastern. These parts contained gardens and appropriate vegetation coverage, hydraulic conductivity, transmissivity, alluvial thickness, groundwater depth and slope that confirmed the acceptable estimation of the models. Also, the results of this study confirmed the previous studies in the region which indicated that the most appropriate places for artificial recharge were in low-altitude areas and coarse-grained alluvial sediments. The map of groundwater high-potential zone was finally validated using the groundwater depth data from 25 wells. Consequently, the results of this study by ANP could be used in efficiently manage and artificial charge of groundwater in Tasuj plain, so as to ensure sustainable utilization of scarce groundwater resources.

Using stable organic modifiers is a way to improve soil quality. In developed countries in the field of sustainable agriculture, due to high attention to the issue of carbon stabilization in the soil and its role in reducing greenhouse gas emissions, and therefore minimizing damage to the environment, the production of soil modifiers has been taken into consideration. In recent years, Biochar has been used as a soil modifier and a tool for carbon sequestration in agricultural soils. In fact, Biochar is a carbon-rich organic material that produced by pyrolysis of biomass under oxygen limited conditions, that can improve the physical and chemical characteristics of soils. Biochar is one of the most stable organic compounds used nowadays to modify soil properties. Saturated hydraulic conductivity and soil moisture coefficients are the most important physical properties of soils that affect water and solute transport and plant growth. Despite the importance of soil physical properties in increasing soil production and quality, less attention has been paid to the effects of Biochar on these properties compared to the chemical. This study was carried out to investigate the effects of two types of Biochar (cow manure and sugarcane residues) on saturated hydraulic conductivity and soil moisture coefficients of sandy loam texture in greenhouse conditions. For this purpose, Biochar was produced from cow manure and sugarcane residue at temperature of 400° C, for 3 hours. Biochar which was prepaird in powdered form evenly added to the soil that was air-dried and passed through a 4-mm sieve. In this study, according to the number of experimental treatments, 27 plastic pots were used, that 3 kg of soil was added to each one. In the bottom of the pots, holes were made for drainage and a sand filter (with a grain size of 2 to 4 mm) with a thickness of 2 cm was placed in the bottom of the pot. The pots were kept in the greenhouse for 60 days, at an average daily temperature of 35c. During the experimental period, irrigation was performed based on the discharge of 50% of soil moisture content at field capacity. The experiment was conducted in a completely randomized factorial design with two types of Biochar (cow manure and sugarcane residues) and application rates of five levels (zero, 0.5, 1, 1.5 and 2% by weight), in 3 replications. Saturated hydraulic conductivity (Ksat) and soil moisture coefficients including field capacity (FC), permanent wilting point (PWP), plant Available water (PAWC) and relative holding capacity (RFC) were determined. The results showed that the effect of Biochar application on saturated hydraulic conductivity and soil moisture coefficients including field capacity, permanent wilting point, plant Available water and relative holding capacity was significant (p<0.01). The effect of sugarcane Biochar was greater on saturated hydraulic conductivity, field capacity moisture and plant available water, in compare with cow manure. Application of 2% Biochar weight in both types (produced from cow manure and sugarcane) had the highest effect on increasing saturated hydraulic conductivity and moisture coefficients in sandy loam soil, compared to control treatment. Although the consumption level of 2% had the greatest effect, no significant difference was observed in the studied characteristics between the consumption level of 2% and 1.5%. Therefore, it can be stated that in order to select the optimal and economic level, the level of 1.5% of Biochar can be introduced as the optimal consumption level. In the optimum amount, the best results can be obtained due to time and low cost. Therefore, according to the results of this study, it can be concluded that apply of Biochar as a modifier is an appropriate method, especially in light textured soils that have low water storage capacity. Therefore, adding Biochar in dry and semi-arid areas that have shortage of water resources can also be beneficial. However, it should be noted that increasing levels of Biochar in soil can have negative effects such as increasing soil salinity. Regarding the positive effect observed due to adding Biochar on the physical parameters measured in sandy loam texture, the best level of Biochar should be chosen in such a way that is economically viable to use. Hence, in order to obtain the optimum level in field conditions further research is required.