The normalized water productivity parameter is one of the AquaCrop model’ s input, upon which the crop biomass yield is simulated on a daily basis. The necessitate of this research is that the amount of normalized water productivity for spinach has not been determined so far. This research was carried out at the Abourihan Campus farm of the University of Tehran which is located in Pakdasht. The experiments were performed during cultivation year of 2017-2018 with six planting densities of 12, 16, 17, 22, 25 and 33 plants per square meter and four replications with full irrigation. The biomass yield was measured seven times during the cultivation season. Considering the measured data of biomass and relative transpiration, the normalized water productivity was obtained for five treatments. The highest amount of normalized water productivity (12. 4 g/m 2 ) was related to 25 plants per square meter density. A relationship function was found using normalized water productivity and planting density data. This function was tested using the remaining treatment and placing the normalized water productivity in the Aqua Crop model. The mean root square error and the mean bias error between the measured and simulated data were 20. 9 and 6. 6 g/m 2 at the test step. The results of this study showed that the planting density affects normalized water productivity and it is increased by increasing planting density (optimum density) and then it is decreased. Finally, this study suggests that the normalized water productivity regarding to planting density is entered to the AquaCrop model.

The normalized Water Productivity (WP*) parameter is one of the inputs for the AquaCrop model upon which the daily basis biomass production is simulated. The default value determined for WP* is 33. 7 grams per square meter for the corn crop. The problem that this research intends to solve is the amount of normalized water productivity for the Single Crosses 704 cultivar of corn, which has not been determined yet. This study was conducted for two years (2015-2016) at an experimental farm of Aburaihan college belonging to the University of Tehran in Pakdashtregion. An overall of three plots were used for the experiment with an area size of 25 m2 for each one. The biomass yield of corn was measured six and seven times during the growing season in 2015 and 2016, respectively. The data measured in the first year (2015) and second year (2016) were used for calibration and verification of the model, respectively. The calibration was carried out using two methods of trial and error and the method presented by Steduto et al. (2009). The WP* was calibrated with a little more accuracy by the latter method, and its value was estimated as 32. 3 grams per square meter. The root mean square error and the mean bias error for the comparison between the measured and estimated biomass production are 0. 73 and 0. 25 ton per hectare for the tested data.

AquaCrop model simulates biomass yield under deficit irrigation management. This model developed by FAO and requires less input data than other similar models. One of the input data of this model is the normalized water productivity (wp*) that should be known for each plant. This parameter for the radish plant, which is part of the C3 plant, has not yet been determined. Therefore, the first objective of this study is to determine it for Pakdasht area and its second objective is to analyze the sensitivity of the Aquacrop model to the input data of reference evapotranspiration (ETo), Wp*, initial canopy cover (CCo) and maximum canopy cover(ccx). This research was carried out in the research center of Abourayhan Campus, University of Tehran in Pakdasht District during 2018 crop year. According to the results of this study, normalized water productivity of radish was determined to be 11. 3 g / m2. The results of sensitivity analysis showed that the AquaCrop model has the most sensitivity to the normalized water productivity parameter for full irrigation conditions, in which the sensitivity coefficient was estimated to be 0. 88. Under deficit condition, the model is most sensitive to the ETo parameter, and the less irrigation is more severe, the sensitivity coefficient increases. The sensitivity coefficient for 60% deficit irrigation was-10. 99.

Sensitivity analysis is an essential step before calibrating crop models. It helps researchers to have enough information about the effectiveness of each parameter and changes them during the calibration stage. This issue is more critical due to the increasing use of the AquaCrop model for crop simulation. Therefore, in the study, the sensitivity of AquaCrop to change some crop growth parameters,normalized water productivity (WP*), maximum transpiration coefficient (KCTrx), initial canopy cover (CC0), canopy growth coefficient (CGC), canopy decline coefficient (CDC) and harvest index (HI) were assessed using Beven (1979) method. For this purpose, the data collected in a research farm in Ahvaz were used. The studied treatments include irrigation methods (D: sprinkler irrigation using saline water, F: Sprinkler irrigation using saline water with post-irrigation with nonsaline water to wash the leaves, and S: furrow irrigation using saline water) with five irrigation water qualities (S1: 2. 5, S2: 2. 3, S3: 3. 9, S4: 4. 6 and S5: 1. 5 dS m-1). The results showed that the highest sensitivity was to changes in normalized water productivity (0. 95≤, Spi≤, 1. 04) and maximum transpiration coefficient (0. 95≤, Spi≤, 0. 67). After that, the sensitivity of the harvest index (0. 51≤, Spi≤, 0. 56) was in the middle category. The range of yield changes in different values of normalized water productivity, maximum transpiration coefficient, harvest index, and canopy decline coefficient was 1. 3-3. 3, 0. 8-1. 6, 0. 6-1. 16, and 0. 32-0. 64 ton ha-1, respectively. Sensitivity coefficients were positive for all parameters except CDC. Therefore, by increasing the CDC, AquaCrop suffers from underestimation error (between 17-33%), and by increasing the value of other parameters,the model suffers from overestimation error (between 5-70%). Therefore, in situations where the difference between observed and simulated yield is significant, it is better to consider the parameters of normalized water productivity and maximum transpiration coefficient. Otherwise, the parameters of the harvest index and canopy decline coefficient should be considered.

AquaCrop model is one of the water-driven models that has been developed to simulate the growth of crops under different amounts of irrigation water. However, to use this model, it is necessary to perform calibration. For calibration, it is necessary to determine the sensitivity of changing in input parameters. Therefore, the present study performed to evaluate the sensitivity of AquaCrop in simulating safflower biomass to changes in crop growth parameters. So, normalized water productivity (WP*), maximum transpiration coefficient (KcTrx), initial canopy cover (CC0), crop growth coefficient (CGC) and crop reduction coefficient (CDC) was evaluated using Baven method. In this study, data collected from an agricultural research station in Kermanshah, Iran, was used. Data consisted of surface drip irrigation at three levels (T1, T2 and T3 represent the supply of 100, 66 and 33% of water requirement, respectively), furrow irrigation at two levels (T4: 100% water supply and T5: application of 50 mm irrigation water at the same time in flowering period) and rainfed (T6). The results showed that AquaCrop was the most sensitive to change WP*. The lowest sensitivity was assigned to CDC. The sensitivity of this model to CDC parameter changes was negative and for other treatments was positive. Therefore, increasing the amount of CDC decreased safflower biomass while increasing other parameters increased safflower biomass. The sensitivity of each parameter depended on the irrigation treatment. So that increasing the amount of irrigation water for WP* and Kc increased the sensitivity.

Crop Simulation models are used for water management in farms and are widely used for optimization of water use efficiency. AquaCrop model, developed by FAO, is based on yield response to water. Compared to other similar models, AquaCrop requires fewer input parameters. The objective of this study was evaluation of this model for barley under deficit irrigation in Pakdasht region. The experiment was done in 2014-15 growing season and included three irrigation treatments and three sowing dates. The irrigation treatments included full irrigation and two treatments of 80% and 60 percent of full irrigation. Sowing dates included early, normal, and late planting. Comparing the estimated values of AquaCrop model and measured values showed that the model is well capable of simulating the barley biomass production. Average R2, RMSE and MBE for the comparison between measured and estimated values were calculated to be 0.96, 8.4 %, and 2.6 %, respectively.

The Organisation for Economic Co-operation and Development (OECD) have predicted from the years 2000 to 2050 the industrial demand for water will increase by 400 %. This manuscript will discuss water stewardship as an aid to water productivity. The benefits of the integration of water stewardship and water productivity will be portrayed in this paper. The fundamentals of water productivity will be outlined. The stages of water stewardship namely operational, context, strategy and engagement will be introduced. The concept of the green economy and ecolabel products will be discussed. Other synergies including the life cycle analysis, water footprint assessment, multibarrier designs, citizen science and policy development as core needs within the integration will be outlined. The bigger goal of aiding the sustainable development goals (SDGs) to achieve clean water and water security as the main reason for society and corporate business to move in the direction of this integration will be highlighted. Water hydrology and catchment understanding are also the core benefits of the integration of water stewardship with water productivity. Improving water productivity by integrating water stewardship into its practices can improve business practices, environmental water flows, supply chain sourcing, policies, and water-efficient technologies. This manuscript highlights the range of different synergies that can strengthen the integration of water stewardship and water productivity. Water stewardship as an aid to water productivity can place water as a game changer for more eco-economical and environmental practices.

Phytoremediation is widely viewed as the ecologically responsible alternative to the environmentally destructive physical and chemical remediation methods currently practiced. Soil and water pollution is due to many kind of contaminants from various anthropogenic origins such as agricultural, industrial, wastewater; activities which involve the addition of nutrients, pesticides and on the other hand, industry and urbanization pollute the water with solid wastes, heavy metals, solvents, and several other slow degrading organic and inorganic substances. Dispersion of these contaminants from the source can be through the atmosphere, via the waterbodies and water channels, and/or into the soil itself, and from there they enter the food chain and adversely affects the human life. Important progresses have been made in the last years developing native plants for phytoremediation and/or nano-phytoremediation of environmental contaminants. Generally it is a technology that utilizes plants and their associated rhizosphere microorganisms to remove and transform the toxic chemicals located in soils, sediments, groundwater, surface water, and even the atmosphere. Phytoremediation applied to wetlands is an effective, nonintrusive, and inexpensive means of remediating wastewater, industrial water and landfill leachate. It highly increases water productivity.

Over the last three decades, climate change has emerged as one of the most crucial issues for humankind, with serious implications for sustainable development. In recent decades, changes in climate have caused the impacts on natural and human systems on all continents and across the oceans. Human activities are estimated to have caused approximately 1. 0° C of global warming above pre-industrial levels, with a likely range of 0. 8° C to 1. 2° C. Global warming is likely to reach 1. 5° C between 2030 and 2052 if it continues to increase at the current rate. Climate change increases variability in the water cycle, inducing extreme weather events such as droughts and more erratic storms, reducing the predictability of water availability, affecting water quality and threatening sustainable development and biodiversity worldwide. Agriculture is the sector most vulnerable to climate change due to its high dependence on climate and weather. It is important to determine the impacts of climate change on water resources in order to develop possible adaptation strategies to improve water productivity. This paper discusses the observed climate change over the past few decades; the climate change-induced impacts, such as rising sea levels, changing rainfall patterns, increased droughts, and more erratic storms; the future climate change; the climate change impacts on water productivity; and the strategies to improve the water productivity such as improved policies, emphasis on sustainability, improving water resource management and use of appropriate models.