فهرست مطالب behnam ababaei

The increase in water demand, especially in the production of agricultural products, has led to increased competition for fresh water. Therefore, improving agricultural water productivity and reducing water stress caused by agricultural production is an important measure to improve the sustainable use of water resources. One of the most important indicators proposed for water management is the concept of water footprint, which can be used as a useful tool to measure and predict the amount of water consumed in the agricultural sector and the required demand. In addition, water stress and water poverty indicators are among the other widely used indicators to evaluate water scarcity. Iran is an arid and semi-arid country that has faced severe water shortage and this has had adverse effects on the economy, ecosystem functions, and the welfare of the country's people. The agricultural sector is one of the most important and largest consuming sectors of water resources in Iran, so more than 92% of freshwater resources are consumed in this sector, so the knowledge of water resources allocated for the production of agricultural products is important for managers. And the country's policymakers are very important. So, one of the main solutions to reduce water shortage is to reduce water consumption in the agricultural sector. Among agricultural products, rice is one of the most important food products, which feeds more than half of the world's population. Therefore, the purpose of this study is to use the indicators of water footprint, water stress, and water poverty to evaluate the water scarcity of water in rice production in Iran.

Among the agricultural products, rice is a valuable food and the most important and widely consumed grain. The study area is the rice-producing provinces. which feeds more than half of the world's population. It accounts for about 19% of the world's dietary energy. After wheat, this product is known as one of the most important food items. The average area under rice cultivation (ha), production (ton), yield (tons/ha) as well as the necessary data and information were collected concerning the water resources available for rice production from the Ministry of Jihad Agriculture and the Water Resources Management Company of Iran. The evaluation of water footprint components, including blue, green, and gray water footprints, is based on the method provided by Hoekstra et al. (2011). The water stress index of rice is calculated as a ratio of the total water footprint in rice production to the total water resources available in the region. The amount of water poverty caused by rice production is defined by the product of the total water footprint in rice production and the value of the rice water stress index. Finally, the amount of export and import of virtual water due to rice production in Iran has been estimated.

On average, the total footprint of rice is 3037 m-3 t and the total volume resulting from its production is 4313 MCM, with the share of blue, green, and gray water footprints being 91.68, 6.93, and 1.39 %, respectively. The available water resources (AWR) for rice production in the producing provinces are 21,992 MCM, of which 6,872 and 15,210 MCM are related to blue water and green water, respectively. The results of the investigation of the water stress index (RWSI) caused by rice cultivation in Iran, which is the result of dividing the total water footprint in rice production by the available water resources, is on average equal to 0.5 (out of 1.9), which shows Iran is in moderate water stress of rice production. Changes in water stress in rice production on a provincial scale showed that the provinces of Qazvin, Zanjan, Isfahan, North Khorasan, Razavi Khorasan, and Sistan and Baluchistan have water stress with a value between 0.6 and 1.2 are high and very high water stress in rice production, while the provinces of Mazandaran, Guilan, Golestan, Fars, and Khuzestan are in the range of less than 0.3 (low water stress) Also, the water poverty caused by rice production is equal to 1073 MCM in Iran, which is the highest and lowest amount of water poverty in Guilan and Khuzestan provinces (290 and 11 MCM).

Water sustainability in rice production in Iran has been investigated using water stress, water poverty, and water footprint, as well as the amount of exports and imports. Among the components of the water footprint, the highest value is related to the blue water footprint, and the lowest is related to the green water footprint. The high blue water footprint shows that most of the surface water and groundwater is used for rice production, and the low green water footprint shows that the amount of rainfall is not enough for rice cultivation in Iran. According to the results, the amount of gray water footprint in rice production is more than the green water footprint, this issue shows that the low yield of rice in the studied provinces as well as the high consumption of fertilizers and chemical pesticides cause the increase of gray water footprint in rice has been produced. Assessing water scarcity using the water footprint approach can be useful for identifying the risks of high rice production due to the potential of water scarcity. The dependence and high consumption of blue water compared to green water have increased the water shortage related to the production of rice. Therefore, it is necessary to change the pattern of water resource allocation based on the status of water resources and water scarcity indicators.

Water is a basic element for human stability and social economic activities. However, water scarcity is one of the biggest problems facing many societies around the world. Today, many regions of the world are affected by water shortages. Population growth in the future has caused a greater demand for food, which has a direct impact on water consumption in the agricultural sector. Hormozgan province is in the south of Iran and has an arid and extra-arid climate, and the problem of lack of water resources is an important and undeniable fact. Therefore, it is necessary to investigate the changes in the agricultural water footprint and the lack of water resources for providing an optimized cultivation model to reduce the water footprint and preserve water resources. Based on this, the main goals of this research are: (1) Estimation of water footprint components of the agricultural section, (2) Calculation of water scarcity indicators including water stress, agricultural water stress, and Blue Water Scarcity (BWS), (3) Estimation of water poverty and (4) Calculation of self-sufficiency and water dependence indicators during from 2008 to 2019.

In this study, agricultural water footprint components, including green, blue, and gray water footprints were estimated based on the method described by Hoekstra et al (2007) in Hormozgan Province. Also, regional agricultural water shortage in this research is measured using the water stress index, agricultural water stress, water shortage, water poverty, self-sufficiency index, and water dependency, which is the known method for assessing water scarcity. The ratio of the use of water resources (water consumption) to the number of available water resources.Hormozgan province has an area of about 68000 km2, which is the eighth province of the country. In terms of climate condition, this province is located in the hot and dry region of Iran and its climate is influenced by desert and semi-desert climate. The average annual temperature of this area is about 27˚c. The average rainfall in Hormozgan province is 188 mm. Information related to cultivated area, production per unit area, yield, planting and harvesting dates, growth cycle length, fertilizer consumption for the studied agricultural products was prepared from the Agricultural Jihad Organization and the information related to water resources such as available water resources, total water consumption, water consumption in the agricultural sector in two sectors, surface and underground water of the province for the statistical period of 2017 to 2018 was prepared from the Regional Water Company of Hormozgan province. Also, information related to the population and per capita consumption of each product has been collected from the Program and Budget Organization.

The total volume of the water footprint of Hormozgan province is 1698.02 MCM, of which 1484.64 MCM (79.19 %) is blue water, 75.51 MCM (6.65 %) is green water, and 137.86 MCM (14.16 %) is related to gray water. The average water resources available during the studied period is 2583.70 MCM, of which 1584.55 MCM is related to blue water resources and 999.15 MCM to green water resources. The comparison of water stress, agricultural water stress, and blue water scarcity indices in crop productions showed that the average WSI is 0.91 and its value is always higher than 0.85 during the study period, which shows that Hormozgan province facing severe and extreme water stress. The average AWSI and BWS during the studied period are 1.38 and 1.19. These indicators highlight the fact that Hormozgan Province is facing a critical level in terms of water shortage the agricultural production systems. The average water poverty in agricultural section is 4919.59 MCM. In the following, regarding the indicators of water dependency (39.21 %) and water self-sufficiency (60.79 %), despite the severe water shortage in Hormozgan Province, it has a high level of water self-sufficiency in the production of agricultural products. It is necessary to develop crops that have less water footprint by modifying the cultivation pattern.

Bluewater resources are the main water resources available in the agricultural sector in Hormozgan Province. This issue can be a reason for the high-water self-sufficiency index compared to the water dependency index. On the other hand, AWSI is higher than WSI, which indicates high water stress in the agricultural sector. Since the share of the blue footprint is more than other components of the water footprint, BWS is more than WSI. As a result, the water resources of this province are not rich and this province has high water poverty. However, the AWSI can reveal the situation of agricultural water shortages in arid agricultural areas more clearly. Strategies for agricultural development and water use formulation in the Iranian South-producing areas should be made based on the areas' AWSI performance. Moreover, it should be noted that the intensification of water resources in certain areas is caused by producing agricultural products for other regions due to the mismatch of agricultural production and population. This phenomenon has not been quantified or analyzed in this paper but needs to be studied in the future.

Agriculture has the largest share of total water consumption in the world. Among the cereals, the high consumption and demand of wheat has made it a strategic product. Due to the high level of wheat cultivation in the world, the amount of water used for its production can significantly affect the total water used in the agricultural sector and pose a serious threat to access to water resources. This study was conducted with the aim of evaluating the water shortage in wheat production in Iran, emphasizing the framework of the water footprint in the country during the statistical period of 2007-2018. The results of this study showed that the share of blue, green and gray water footprints is 71.76, 15.87 and 13.11% of the total water footprint in the production of Faryab wheat and 55 and 38.34% of the green water footprint respectively. And it forms gray in dry wheat in Iran. Also, the results showed that the average water stress index of wheat production in the country is 0.6, and the range of spatial changes of this index varies from 0.01 in Guilan to 3.01 in Ardebil at the North. The highest level of water self-sufficiency index (lowest dependency index) is related to North Khorasan, Khuzestan and Fars provinces with an average value of 70% and the lowest level of self-sufficiency index is related to Guilan province (2%), Mazandaran 9% and Tehran 16% (highest dependency index). The present study will be very helpful for making decisions about the sustainable management of water resources for wheat production in Iran.

Precipitation affects quantity and quality of water resources and agricultural production. Therefore, the estimation and analysis of its spatial-temporal variations is of great importance. In many regions of Iran, limited spatial-temporal information is available due to sparse distribution of monitoring stations and short observational records. On the other hand, dependency of rain-fed and irrigated production systems on precipitation increases the importance of the analysis of spatiotemporal variations of this weather variable. One way to address this limitation is to use regional/global gridded datasets. In this study, monthly precipitation data were obtained from the CRU dataset (developed principally by the UKchr('39')s Natural Environment Research Council (NERC) and the US Department of Energy) and used to investigate temporal trends in annual, seasonal and monthly precipitations in 675 grid cells (0.5°×0.5°) across Iran over two periods, 1957-1986 and 1987-2016. The results of the previous studies showed that the CRU gridded dataset offers quality data in Iran, especially for trend analysis. Also, the accuracy of the CRU dataset was validated in 14 selected stations regarding monthly precipitations and temporal trends over two different periods, pre-1987 and post-1987. The significance of temporal trends was assessed using a modified version of the rank-based nonparametric Mann-Kendall (MK) test. Trend magnitudes (i.e. slope) were estimated with the Theil-Sen approach and the Trend Free Pre-whitening (TFPW) procedure was applied to remove the effect of serial correlation. The results confirm the acceptable accuracy of the CRU dataset for trend analysis purposes, especially over the last three decades, except in the northern strip of the country (RMSE=10.71mm, R2=0.84). Two 30-year periods (1957-1986 and 1987-2016) were compared in terms of spatial patterns and temporal trends. Annual precipitation over the last three decades (1987-2016) has decreased as compare to the previous 30-year period (1957-1986) in most parts of the country. Over the last three decades, around 42% and 50% of the country’s total area experienced significant and insignificant decreasing trends in annual precipitation, respectively. National average annual precipitation has decreased by 15.78 mm/decade over the same period. Three important regions regarding agricultural production experienced the most significant reductions in annual precipitation: (1) Ardebil, East Azerbaijan, Kurdistan, Kermanshah, Ilam, Lorestan, Zanjan, Hamadan, and parts of West Azerbaijan, Markazi and Gilan (in the west and northwest), (2) Sistan and Baluchestan, Kerman, and southern parts of South Khorasan (in the south and south east), and (3) North Khorasan, northern parts of Razavi Khorasan and east of Golestan (in the east and north east). Reduced annual precipitation was mainly attributed to the reduction in seasonal precipitations in winter and spring, which have critical role in agricultural production and domestic water supply. Temporal trends were also analysed at the monthly scale. January, February, March and December revealed the largest number of grid cells with significant decreasing trends over 1987-2016 while November is the only month with significant number of grid cells experiencing significant increasing trends. The results of this study show that the monthly time series of the CRU TS 4.01 dataset, which has an almost complete spatial and temporal coverage in Iran over the last 60 years, are promising alternatives to weather station observations especially in data-scarce regions of Iran. Analysis of variations and the seasonal and monthly scales help understand the recent climate change and target the most crucial features of it when it comes to formulating adaptation strategies.

Temperature and rainfall affect the quantity and quality of agricultural products. Therefore, it is important to estimate its spatial-temporal changes. In many region of the country, due to the low density of meteorological stations or the small statistical period of new stations, limited time and space information is available. Therefore, this study aims to use the data of CRU, AgMERRA, AgCFSR and GPCC gridded weather datasets in estimation of yield and water requirement of wheat and comparing with the estimated values with the information of Qazvin Synoptic Station. For this purpose, monthly weather time series of Qazvin synoptic station were extracted from 1980 to 2010 along with the data from the selected gridded datasets extracted from the closest grid cell to the synoptic station (K1), the average of four closest grid cells to the synoptic station (K4), and the average of eight closest grid cells to the synoptic station (K8). The quality of the gridded datasets was assessed with four statistical indices (R2, RMSE, NRMSE, ME) in indirect way (the latter using the outputs of the AquaCrop model). In estimating wheat water requirement, GPCC database with four points (K4) and one point (K1) showed the best performance. Wheat yield simulated with AgMERRA data with K1 and K4 closest grid cells had the highest correlation with the simulated values with data of synoptic station. Results showed that selected gridded datasets can be used to simulated grain yield, but only data from GPCC-CUR would result in reliable estimation of water requirement.

Application of crop models is essential for numerous purposes including prediction of crop yield and water requirement, evaluation of the impact of climate change, drought and irrigation and agronomic management on crop growth and development. One challenge with the application of these models is the large number of input parameters. Measurement of input parameters can be time-consuming, costly and sometimes practically impossible and they are usually estimated using calibration and inverse modelling. Sensitivity Analysis is a procedure during which the impact of input parameters on target output variables is investigated. During model calibration, sensitive parameters must be measured or estimated with higher accuracy. Analysis of the sensitivity of closed-source models is not as straightforward as it is with open- source models. In this research, the impact of 47 crop parameters on five output variables of AquaCrop, a closed-source crop model, namely soil evaporation, crop transpiration, evapotranspiration, biomass at harvest and grain yield, were studied for wheat and maize in Qazvin Plain and Moghan Pars-Abad in Iran. The sensitivity of the selected parameters was evaluated with the relative Nash–Sutcliffe Efficiency Index. Increase in canopy cover, degree-days from sowing to maturity, degree-days from sowing to start of senescence and maximum canopy cover in fraction of soil cover were identified as sensitive parameters for both crops. Therefore, accurate determination of crop growth stages, in calendar days or degree-days, which are easier to measure than most of the other parameters, is of greater importance. In Qazvin and for all output variables, around half of the selected parameters were ineffective and considered unimportant during calibration. Despite that the methods of local SA are computationally and conceptually simpler than the methods of global SA, the results showed that this method could lead to similar outcomes to previous studies in which global methods were used.

The common methods for estimation of evapotranspiration and water requirement are generally based on the ideal and without stresses conditions. In field conditions, different stresses such as salinity, dryness, temperature, soil fertility and other field managements effect on evapotranspiration and water requirement. In this study, AquaCrop model is used for estimation of real evapotranspiration and water requirement in Moghan Plain. The results showed that the evapotranspiration and water requirement of this model is less than the Cropwat model, which is due to consideration of the region conditions such as soil moisture, salinity, fertility and other field managements by AquaCrop model. The Cropwat model only consider climate condition. According to the results obtained from the AquaCrop model, in comparison with the Cropwat model, water requirements of Wheat, Barley and Maize were reduced about 41, 69 and 790 m3/ha, respectively. Also the evapotranspiration and water requirement of the AquaCrop model were compared with NetWat. These parameters of AquaCrop are higher for wheat and maize, and for less barley data. NetWat data has a low degree of reliability, due to being old of weather data.

Impact of atmosphere CO2 concentration on crop transpiration and photosynthesis is undeniable. In this study, the impact of increased CO2 concentration on wheat, barley and maize, as the main cereals of the Qazvin Plain, was studied under A1B, A2, B1 and B2 scenarios using the calibrated and validated AquaCrop model. Under these scenarios (increased CO2 concentration, unchanged weather data and the assumption of unchanged level of irrigation water supply), average wheat yield will increase between 11.5 and 14.4% for the 2010-2035 period as compared to the 26-year base period (1984-2009). For barley and maize, this average increase will be 8.5 to 11.3% and 3.7 to 4.3%, respectively. Total crop transpiration will decrease on average by 7, 5 and 1% for wheat, barley and maize, respectively. Moreover, increases in water productivity of the studied crops were estimated as 13, 6 and 4% respectively. These results confirm more significant impact of increased atmosphere CO2 concentration on C3 crops (like wheat and barley) as compared with C4 crops (like maize).

Study of climate change helps in the management of water resources and agricultural production. Temperature increase is one of the most definite aspects of climate change. Limited number of weather stations and the quality and duration of records of weather variables are among the most important limiting factors in climate change studies. Application of gridded regional/global datasets is a promising option to overcome these limitations. In this study, temporal trends in annual, seasonal and monthly averages of daily minimum, mean and maximum temperatures were analysed using the data obtained from the CRU TS 4.01 gridded dataset and the Mann-Kendall (MK) test. The results show that over 1987-2016 (second period), long-term national annual average of daily mean temperature has increased by 0.4-1.2 °C across the country as compared to 1957-1986 (first period). Over the last here decades, temporal trend in annual average daily mean temperature was estimated at 0.39 °C.dec-1 (1.17 °C increase in 30 years). The observed increase in daily minimum temperature (1.23 °C in 30 years) over the second period was larger than the increase observed in daily mean temperature. Analysis of temporal trends in seasonal averages reveals that winter and spring, especially in the west and northwest, experienced the strongest positive trends over the last three decades. The results of this study confirm the occurrence of significant temperature changes in winter, spring and summer, which are important to the country’s agriculture sector.

Modelling moisture redistribution in soils after irrigation cutoff and utilizing this parameter in system design can be considered as one of the criteria for improving water productivity in surface and subsurface irrigation systems. In this study، experiments were carried out in a transparent plexy-glass tank (0. 5m. 1. 22m. 3m) using three different soil textures (fine، heavy and medium). The drippers were installed at 4 different soil depths (surface، 15cm، 30ch and 45cm). The emitter outflows were considered 2. 4 and 4 lit/hr with irrigation duration of 6hr. Then، using the- theorem of Buckingham and Dimension Analysis (DA)، equations were developed to estimate the pattern of moisture redistribution (horizontal and vertical) after irrigation cutoff in surface and subsurface drip irrigation systems. The results of the comparisons between simulated and observed values showed that these equations are very capable in predicting the pattern of moisture redistribution in different directions. Considering these equations in designing surface and subsurface drip irrigation systems could improve system performance.

Population growth can greatly impact on greenhouse gas emission pattern in the future. Considering the importance of population growth in climate change studies, the present research concentrates on the population growth of the Middle East in the late 21th century based on the IIASA population scenarios. Under the projections of A1-B1 scenario, the world’s population will grow up to 8.7 billion and according to A2 scenario it will be almost doubled by 2050. The results indicate that under both of these scenarios, which are considered among principle climate change scenarios, the population of the Middle-East would increase from 312 million in 1995 to 809 million and 2 billion, respectively by 2100. According to population rates assumed in these scenarios, the domestic water demand of Taleghan reservoir was estimated for the period of 2055 and 2100. The results indicated that under these two scenarios, by 2055 (2040-2069) the mean of monthly domestic water demand of the reservoir will be respectively increased by 80 and 161%. Furthermore, the analysis of the data for 2006-2011 revealed that during all these years as well as all their dry months (July – November), the total average of the domestic and agricultural water use rate has been greater than the average reservoir inflow rate, resulting in the decrease of the storage volume of the reservoir in these periods. Therefore, considering the severity of water shortage in this period, a reevaluation of the current reservoir management methods and formulation of a change adaptation plan seems to be essential.

In this research، Data Fusion (DF) method was applied to simulate the hydrological process of Taleghan reservoir daily inflow. Two different DF algorithms were proposed and assessed based on K-nearest neighbors (KNN) algorithm. Four artificial neural network models and two Hammerstein-Wiener (HW) models were used as the individual simulation models. Comparison of the results between individual models and DF algorithms revealed the superiority of the DF method. The performances of the two DF algorithms were comparable in simulating monthly mean inflow values، but AL1 overestimated the monthly standard deviations. Then، the daily time series of Temperature and Precipitation were generated by a well-tested weather generator model and were used as the inputs to the individual models. The results showed that the individual models can result in different or even inconsistent variations under climate change scenarios. It was also revealed that the performance of the AL2 data fusion algorithm was proved by the best HW model and this algorithm resulted in more logical results. Moreover، regarding considerable diversity among the individual models، the DF method can increase the reliability of the simulations related to the predicted variations of reservoir daily inflow under climate change scenarios.

Hammerstein-Wiener (HW) models are capable in describing nonlinear dynamic systems. These models are nonlinear and have been widely used in a wide range of sciences due to their simplicity and having a physically-based concept. In this research، for the first time in hydrology and water resources management، three different structures of these models using daily temperature and precipitation data as model inputs were applied to simulate Taleghan Reservoir daily inflow using R2، RMSE، SRMSE، MAE، d and PEP statistics and criteria. To do this، the reservoir data from 2006 to 2011 were utilized. The results obtained with (HW1) and without (HW2) data pre-processing were compared with the results achieved from two different structures of artificial neural networks (ANNs) including (i) Feed-Forward ANN with two Hidden Layers (FeedF2) and (ii) Generalized Regression Neural Network (GRNN2). The results revealed that the HW models outperformed the ANN models. In particular، the mean and standard deviation of the inflow time series were simulated very accurately. The SRMSE values of the HW1 model were 33% and 37% and while these values for the HW2 model were 28% and 43% over calibration and validation phases، respectively. Meanwhile، the accuracy obtained over calibration and validation phases were 50% and 71% for FeedF2 and 58% and 50% for GRNN2، respectively.

Crop simulation models are used for understanding plants response to the change of surrounding environment and to evaluate crop physiological and morphological characteristics to enhance crop production and to contribute to the efficient use of water and soil resources. Yet, the evaluation of these models is a prerequisite for their use in assessing different management strategies. In this study, CERES-Barley from DSSAT software package (version 4) was calibrated by using inverse modeling method and PEST model. This study was conducted with 11 experimental treatments each with three replicates in the form of complete randomized block design. The irrigated treatments were consisted of 100 (T100), 90 (T90), 80 (T80), 70 (T70), 60 (T60), 50 (T50), 40 (T40), 30 (T30), 20 (T20), and 10 (T10) percent of crop water requirement. The rainfed treatment was consisted of one irrigation at planting (November 1st) and the other at first spring (April 8th) before introducing water stress. The results indicated that the model could reasonably well simulate these amounts up to T50 with acceptable accuracy. The calculated SRMSE statistics between T50 and T100 treatments for the simulated grain yield, straw yield, and total crop yield was 5.5, 10.9, and 3.6 percent, respectively. The corresponding R2 values were obtained to be 95, 69, and 93 percent, respectively.