Optimal Crop Water Allocation Based on Constraint-State Method and Nonnormal Stochastic Variable

Abstract

Integrated and holistic approach of water resources management is important for sustainability. Since the optimum use of water resources needs taking into account different environmental issues. Accordingly, the use of supportive models in decision making as an effective tool is significantly important. To addressing uncertainty in crop water allocation, several methodologies have been proposed. The most of these models consider rainfall as a stochastic variable affecting soil moisture. Applying a new methodology/model while considering the stochastic variable in nonnormal state and uncertainties for both irrigation depth and soil moisture looks more realistic. In this research, a mathematical model was developed based on Constraint-State equation optimization model and Beta function. The first and the second moments of soil moisture are used as constraints in optimization process. This model uses the soil moisture budget equation for a specific plant (winter wheat) on a weekly basis, considering the root depth, soil moisture, irrigation depth, rainfalls, evapotranspiration, leaching depth, soil physical properties and a stochastic variable. The model was written in MATLAB and was run for winter wheat in Badjgah, south of Iran. The results were compared with the results obtained from a simulation model. Based on the results, the optimum net irrigation depth of winter wheat including the rainfall was 306.2 mm. The insignificant difference of simulation and optimization results showed that, the optimization model works properly and is acceptable for optimization of irrigation depth, as its reliability index is 96.86 %.

An Interval Two‐Stage Classified‐Allocation Model for Regional Water Management under Nonstationary Condition

“…2002; Kaviani et al. 2015), reservoir system operation management (Mousavi and Ramamurthy 2000; Ahmed and Sarma 2005; Reddy and Kumar 2006), groundwater management (Ahlfeld et al. 1988; Ayvaz 2009), and conjunctive surface groundwater planning (Haimes and Dreizin 1977; Montazar et al.…”

“…Various deterministic optimization models have been employed for water resources management and allocation over the past decades (Singh 2012). For example, many linear, nonlinear, and dynamic programming optimization models have been used for irrigation management (Shangguan et al 2002;Kaviani et al 2015), reservoir system operation management (Mousavi and Ramamurthy 2000;Ahmed and Sarma 2005;Reddy and Kumar 2006), groundwater management (Ahlfeld et al 1988;Ayvaz 2009), and conjunctive surface groundwater planning (Haimes and Dreizin 1977;Montazar et al 2010). However, the above deterministic optimization models have difficulties in handling various uncertainties existing in the water resource management problems, such as the apparent randomness of water inflow and the inexactness of economic parameters which can only be determined as interval numbers.…”

Two Monthly Continuous Dynamic Model Based on Nash Bargaining Theory for Conflict Resolution in Reservoir System

“…Inefficiency of classical conflict resolution techniques [ 1 – 6 ] in dealing with conflicting issues in water allocation among different users have drawn attention to utilizing innovative alternatives for resolving conflict. A number of approaches such as Nonlinear Programing [ 7 , 8 ], Compromise Programming [ 9 – 11 ], Analytic Hierarchy Process [ 12 , 13 ] and Fuzzy Set Analysis [ 14 – 16 ] have been utilized to deal with conflict situations.…”