Uniformity Distribution and its Economic Effect on Irrigation Management in Semiarid Zones

The management of water resources in irrigation is a fundamental aspect for their sustainability. For correct management, several tools and systems for decision making are necessary. Among the large number of factors that affect the optimization of water use, we must focus on irrigation uniformity and its economic implications. The following methodology, implemented in a computer model, allows us to carry out an economic analysis of the effects of different Christiansen’s uniformity coefficients (CU), which are useful for system design and calculation and also for irrigation management in order to obtain maximize gross margin. In the zone studied (Hydrogeologic System 08.29, Castilla-La Mancha, Spain) working with a solid set system and with four crops (barley, garlic, maize, and onion), there is an economic interest in designing systems with a high CU (90%) that allows us to obtain a high application efficiency (Ea). Regarding the economic optimization of the irrigation depths, the results show that the optimum gross depths are always lower than the irrigation depths for maximum crop yield. The higher the CU, the lower the depths, while the crop yield increases and the gross margin of the crop improves. These general results present significant differences among crops, according to their water requirements and their economic profitability.     

Optimal Crop Planning and Conjunctive Use of Surface Water and Groundwater Resources Using Fuzzy Dynamic Programming

Optimal crop planning and the conjunctive use of surface water and groundwater resources are imperative for the sustainable management of water resources, especially in semiarid regions. In recent years, considerable attention has been given to crop planning and water resources management under uncertainties caused by climate changes that affect irrigation planning in terms of decisions to determine the amounts of water that can/must be allocated. In this paper, optimal crop planning and conjunctive use of surface water and groundwater are developed for the Najafabad Plain, a part of the Zayandehrood River basin in west-central Iran. The fuzzy inference system (FIS) is used to account for the experience and expert judgments of decision makers and farmers to obtain optimal crop planning and cultivation with a reliable water demand based on climate conditions. In the present work, fuzzy regression is used for considering uncertainty and ambiguity in the data used in the simulation model as well as the uncertainties in interactions between surface water and groundwater. The objective function of the optimization model is to minimize shortages in supplying irrigation demands. The results are applicable to a wide range of climate conditions.     

Optimal Irrigation Allocation: A Multilevel Approach

Optimal resources allocation strategies for a canal command in the semiarid region of Indian Punjab are developed in a stochastic regime, considering the competition of the crops in a season, both for irrigation water and area of cultivation. The proposed strategies are divided into two modules using a multilevel approach. The first module determines the optimal seasonal allocation of water as well as optimal cropping pattern. This module is subdivided into two stages. The first stage is a single crop intraseasonal model that employs a stochastic dynamic programming algorithm. The stochastic variables are weekly canal releases and evapotranspiration of the crop that are fitted to different probability distribution functions to determine the expected values at various risk levels. The second stage is a deterministic dynamic programming model that takes into account the multicrop situation. An exponential seasonal crop-water production function is used in this stage. The second module is a single crop stochastic dynamic programming intraseasonal model that takes the output of the first module and gives the optimal weekly irrigation allocations for each crop by considering the stress sensitivity factors of crops.     

Long-Term Operation of Irrigation Dams Considering Variable Demands: Case Study of Zayandeh-rud Reservoir, Iran

Dependency of water demands on the climate variation occurs especially in regions where agricultural demand has a significant share of the total water demands. The variability between demands that are based on annual climate conditions may be larger than the uncertainty associated with other explanatory variables in long-term operation of an irrigation dam. This paper illustrates certain benefits of using variable demands for long-term reservoir operation to help manage water resources system in Zayandeh-rud river basin in Iran. A regional optimal allocation of water among different crops and irrigation units is developed. The optimal allocation model is coupled with a reservoir operating model, which is developed based on the certain hedgings that deals with the available water and the water demands mutually. This coupled model is able to activate restrictions on allocating water to agricultural demands considering variation of inflow to the reservoir, variation of demands, and the economic value of allocating water among different crops and irrigation units. Using this model, long-term operation of Zayandeh-rud dam is evaluated considering different scenarios of inflow to the reservoir as well as agricultural demands. The results indicate that the use of operating rules which consider variable demands could significantly improve the efficiency of a water resources system in long-term operation, as it improves the benefit of Zayandeh-rud reservoir operation in comparison with conventional water supply approaches.     

Optimal Irrigation Planning under Water Scarcity

In this study optimal irrigation planning strategies are developed for the Nagarjuna Sagar Right Canal command in the semiarid region of South India. The specific objective of the study is to allocate the available land and water resources in a multicrop and multiseason environment and to obtain irrigation weeks requiring irrigation of a fixed depth of 40 mm. The problem is solved in four stages. First, weekly crop water requirements are calculated from the evapotranspiration model by the Penman-Monteith method. Second, seasonal crop water production functions are developed using the single-crop intraseasonal allocation model for each crop in all seasons. Third, allocations of area and water are made at seasonal and interseasonal levels by deterministic dynamic programming, maximizing the net annual benefit from the project. And fourth, once optimal seasonal allocations have been attained, irrigation scheduling is performed by running a single-crop intraseasonal allocation model. Optimal cropping pattern and irrigation water allocations are then made with full and deficit irrigation strategies for various levels of probability of exceedance of the expected annual water available. The results reveal that the optimization approach can significantly improve the annual net benefit with a deficit irrigation strategy under water scarcity.     

Model for Estimating Evaporation and Transpiration from Row Crops

Accurate estimates of crop evapotranspiration ETc, that quantify the total water used by a crop, are needed to optimize irrigation scheduling for horticultural crops and to minimize water degradation. During early growth, accurate assessments of ETc are difficult in vegetable crops because of high soil evaporation due to frequent irrigation. A model to estimate ETc for vegetable crops, using only daily reference evapotranspiration data as an input parameter, was developed. It calculates crop transpiration and soil evaporation based on ground cover and daily radiation intercepted by the canopy. The model uses a two-stage soil evaporation method adapted to conditions of variable reference evapotranspiration. The model was evaluated against data using measurements from two seasons of lettuce crop, two tomato fields in the same season, and one season of broccoli crop production. Using all of the crop data, the root-mean-square error for measured versus modeled daily ETc was 0.72 mm day?1, indicating that the model works well.     

Performance Evaluation and Uncertainty Measurement in Irrigation Scheduling Soil Water-Balance Approach

The present work aims at approaching the study of the performance and uncertainty associated with an irrigation scheduling method based on a soil-water balance. On a daily time step, a water-balance-based irrigation scheduling model has been developed. A Monte Carlo simulation of the irrigation scheduling model is developed using a series of actual daily weather data of evapotranspiration and precipitation and bootstrapping stochastic technique to resampling them. Performance evaluation measurements and their uncertainty are studied by means of several parameters: reliability, resiliency, vulnerability, total irrigation water allocation, total water loosed by deep percolation, and actual evapotranspiration/potential evapotranspiration rate along the growing season. The behaviors of 12 different types of soils (between coarse-textured soils and fine-textured soils) are compared using pedotransfer functions. Total available water (TAW) is the most important hydraulic property of the soil as far as irrigation scheduling performance is concerned. The statistical relationship between evaluation performance measures and TAW has been calculated. Soils with high values of TAW perform better. Rooting depth (Zr) and fraction of TAW that can be depleted from the root zone before moisture stress (p) are two variables that directly affect the TAW. It has been studied how evaluation performance measurements change when Zr and p change too. High values of Zr and p perform better too.     

Allocation of Scarce Water Resources Using Deficit Irrigation in Rotational Systems

On irrigation schemes with rotational irrigation systems in semiarid tropics, the existing rules for water allocation are based on applying a fixed depth of water with every irrigation irrespective of the crops, their growth stages, and soils on which these crops are grown. However, when water resources are scarce, it is necessary to allocate water optimally to different crops grown in the irrigation scheme taking account of different soils in the command area. Allocating water optimally may lead to applying less water to crops than is needed to obtain the maximum yield. In this paper, a three stage approach is proposed for allocating water from a reservoir optimally based on a deficit irrigation approach, using a simulation-optimization model. The allocation results with a deficit irrigation approach are compared for a single crop (wheat) in an irrigation scheme in India, first with full irrigation (irrigation to fill the root zone to field capacity) and second with the existing rule. The full irrigation with a small irrigation interval was equivalent to adequate irrigation (no stress to the crop). It is found that practicing deficit irrigation enables the irrigated area and the total crop production in the irrigation scheme used for the case study to be increased by about 30–45% and 20–40%, respectively, over the existing rule and by 50 and 45%, respectively, over the adequate irrigation. Allocation of resources also varied with soil types.     

Coupled Crop and Solid Set Sprinkler Simulation Model. II: Model Application

In this work, applications of the coupled solid set sprinkler irrigation and crop model AdorSim introduced in the companion paper are presented. The sprinkler irrigation model is based on ballistic theory, while the crop model is based on CropWat. AdorSim was used to evaluate the effect of sprinkler spacing on seasonal irrigation water use (WU) and crop yield. The most relevant results were related to the characterization of advanced irrigation scheduling strategies. The differences in crop yield and WU derived from irrigating at different times of the day were estimated for two locations strongly differing in wind speed. Irrigation guidelines were established in these locations to relate gross water use and water stress induced yield reductions. Simulations were also applied to estimate adequate wind speed thresholds for irrigation operation. In the experimental conditions, thresholds of 2.0–2.5?m?s?1 proved effective to control yield reductions and to minimize WU.     

Optimal Reservoir Operation for Irrigation of Multiple Crops Using Genetic Algorithms

This paper presents a genetic algorithm (GA) model for obtaining an optimal operating policy and optimal crop water allocations from an irrigation reservoir. The objective is to maximize the sum of the relative yields from all crops in the irrigated area. The model takes into account reservoir inflow, rainfall on the irrigated area, intraseasonal competition for water among multiple crops, the soil moisture dynamics in each cropped area, the heterogeneous nature of soils, and crop response to the level of irrigation applied. The model is applied to the Malaprabha single-purpose irrigation reservoir in Karnataka State, India. The optimal operating policy obtained using the GA is similar to that obtained by linear programming. This model can be used for optimal utilization of the available water resources of any reservoir system to obtain maximum benefits.     

Robust Optimal Design of Activated Sludge Bioreactors

This paper proposes an algorithm for a robust optimal design of the biological reactor and secondary settling facilities in suspended growth nitrogen and phosphorus removal systems. Robust optimization includes uncertainty in the decision-making procedure and seeks a solution that remains “close” to optimal for all potential operation scenarios. It thus differs fundamentally from the deterministic and stochastic approaches, where uncertainty is ignored or a solution based on either the most likely scenario or the average performance over all potential scenarios is produced. The robust optimization of a suspended growth system is a multiobjective optimization problem concerned with minimization of the global costs and variability of the system’s performance around the optimal. The proposed robust optimization approach uses the ASM3 model, making use of its performance prediction capabilities to produce a powerful tool for designing activated sludge systems. The algorithm was applied to the design of the biological reactor and secondary settling facilities for the Vila Real municipal wastewater treatment plant (Portugal).     

Modeling Reservoir Irrigation in Uncertain Hydrologic Environment

In a detailed model for reservoir irrigation taking into account the soil moisture dynamics in the root zone of the crops, the data set for reservoir inflow and rainfall in the command will usually be of sufficient length to enable their variations to be described by probability distributions. However, the potential evapotranspiration of the crop itself depends on the characteristics of the crop and the reference evaporation, the quantification of both being associated with a high degree of uncertainty. The main purpose of this paper is to propose a mathematical programming model to determine the annual relative yield of crops and to determine its reliability, for a single reservoir meant for irrigation of multiple crops, incorporating variations in inflow, rainfall in the command area, and crop consumptive use. The inflow to the reservoir and rainfall in the reservoir command area are treated as random variables, whereas potential evapotranspiration is modeled as a fuzzy set. The model’s application is illustrated with reference to an existing single-reservoir system in Southern India.     

Optimal Reservoir Operations for Irrigation Using a Three Spatial Scales Approach

A three-step computational model for the optimal weekly interseasonal operation of a multipurpose (irrigation, environmental, domestic/industrial) reservoir is developed. Environmental and domestic/industrial uses are evaluated and considered as priority uses that induce deficit irrigation conditions. The spatiotemporal variability of the irrigation water demand at the basin level is accounted for. The objective is the maximization of the interseasonal agricultural profitability at the basin level. The optimal allocation process solves the competition for water on different temporal scales (weekly, seasonal, and interseasonal) and on different spatial scales (in basins among irrigation areas and in irrigation areas among crops). The three steps are simulation model operating at the soil-crop unit level, optimization model operating at the multicrop area level, and optimization model operating at the basin level. This consists of parametric dynamic programming for which an analytical objective function was defined and an analytical solution was determined. This solution replaces the iterative procedure, so that it is possible to account for all the variables without running into the “curse of dimensionality” problem. The environmental use allocation is expressed as a function of a parameter, the variations of which give different environmental protection levels. The validation case study emphasizes the importance of considering the spatiotemporal variability of the demand. This is consistent with the “computationally tractable” model algorithm.     

Coupled Crop and Solid Set Sprinkler Simulation Model. I: Model Development

The development of a coupled crop model (Ador-Crop) and solid set sprinkler irrigation model (Ador-Sprinkler) is reported in this work. The crop model incorporates many of the features developed in the well-known CropWat model. Improvements include the use of thermal time and the input of daily ET0. The solid set sprinkler model applies ballistic theory to determine water distribution resulting from water droplets subjected to a wind vector. Regarding the validation of the coupled model (AdorSim), the plot of soil available water versus measured and simulated yield reduction resulted in similar features. AdorSim explained 25% of the variability in measured yield reduction. Most of the unexplained variability is due to the effect of nonwater-related factors affecting crop yield. In a companion paper, AdorSim is used to investigate optimum water management options in the middle Ebro basin in the NE of Spain.     

Multilevel Approach for Optimizing Land and Water Resources and Irrigation Deliveries for Tertiary Units in Large Irrigation Schemes. I: Method

This paper presents the area and water allocation model (AWAM), which incorporates deficit irrigation for optimizing the use of water for irrigation. This model was developed for surface irrigation schemes in semiarid regions under rotational water supply. It allocates the land area and water optimally to the different crops grown in different types of soils up to the tertiary level or allocation unit. The model has four phases. In the first phase, all the possible irrigation strategies are generated for each crop-soil-region combination. The second phase prepares the irrigation program for each strategy, taking into account the response of the crop to the water deficit. The third phase selects the optimal and efficient irrigation programs. In the fourth phase of the model, irrigation programs are modified by incorporating the conveyance and the distribution efficiencies. These irrigation programs are then used for allocating the land and water resources and preparing the water release schedule for the canal network.     

Effects of Different Levels of Irrigation Water Salinity and Leaching on Yield and Yield Components of Wheat in an Arid Region

Soil salinity is a major environmental factor limiting the productivity of agricultural lands. To determine the effects of irrigation water salinity and leaching on soil salinity and consequently wheat yield, a field experiment was conducted on a silty clay soil, a typical soil of Rudasht region, Isfahan province, Iran, with three irrigation water salinity levels of 2, 8, and 12?dS/m with/without leaching levels of 4, 19, and 32% with two different irrigation water managements, using factorial design with four replications for each treatment. The results showed that as the irrigation water salinity and consequently soil salinity increases, the yield components such as grain yield, straw yield, 1,000-grain weight, crop height, spike length, and leaf area index decreases significantly. Leaching caused the yield components to increase significantly. An increase in seed protein percentage was noted as the salinity of irrigation water increased. The interaction effects of irrigation management and leaching on yield and yield components was significant. The results of best fit line to relative yield data versus soil ECe showed that the parameters of the above linear relation are site specific, and there is no significant difference between the parameters obtained in this study as compared to the other researchers’ results and the study validates the established relationships between wheat yield and salinity obtained by other researchers. The recycled drainage water could be used in combination with less saline river water as an alternative and less expensive irrigation water to grow salt-tolerant crops such as wheat, to produce profitable yield and to improve the agricultural economy of arid land regions.     

Regional Salinity Modeling for Conjunctive Water Use Planning in Kheri Command

A one-dimensional water and solute transport UNSATCHEM model is calibrated and validated with a saline water use experiment for wheat and cotton crops. The model is further employed for regional scale salinity modeling with distributed data on soil, irrigation water supply, and its quality from six representative locations from the Kheri command of the Bhakra irrigation system. The wheat–cotton crop rotation, the main rotation in the command, is considered during long-term simulations. The CROPWAT model is used to determine the evapotranspiration requirements of different wheat and cotton crops, while soil water retention parameters are estimated by the RETC model. Atmospheric water and solute boundary conditions are assumed at the top boundary, while free drainage is considered for the lower boundary, as the watertable in the command is sufficiently deep. Simulated salinity and yield values are compared with observed values for regional validation of the model. Critical areas in the command are identified using regional scale modeling results, and applying irrigation water availability and root zone salinity criteria. Guidelines for sustainable conjunctive water use planning are for the Kheri command to get optimum agricultural production despite the use of saline water for irrigation under prevailing scenarios of water availability and its quality.     

Optimal Allocation of Irrigation Water Supplies in Real Time

This paper presents an evaluation of the potential of an optimization approach in improving real-time irrigation water management in systems with complex distribution networks. The optimization approach is based on quadratic programming. The operational objective is to maximize crop production through appropriate water allocation, while maintaining equity between different irrigation schemes and units within schemes. The approach has been evaluated through application to the irrigation system in the Lower Ayung River Basin in Bali, Indonesia. A simulation model of this irrigation system was available, and it has been possible to measure the effectiveness of the optimization approach by comparing the results of simulation runs incorporating optimization with the results of runs representing existing water allocation practice. The results indicate that the optimization approach does have potential and can significantly improve crop production at the basin scale. This paper presents a preliminary assessment of the potential of the approach and describes the development of a more sophisticated optimization approach based on real-time evaluations of crop water requirements. Considerations for practical implementation are discussed.     

Drinking Water Treatment Plant Design Incorporating Variability and Uncertainty

Both inherent natural variability and model parameter uncertainty must be considered in the development of robust and reliable designs for drinking water treatment. This study presents an optimization framework for investigating the effects of five variable influent parameters and three uncertain model parameters on the least-cost treatment plant configuration (contact, direct, or nonsweep conventional filtration) that reliably satisfies an effluent particulate matter concentration constraint. Incorporating variability and uncertainty within the decision-making framework generates information for investigating: (1) impacts on total cost and treatment reliability; (2) shifts on the least-cost treatment configuration for providing reliable treatment; and (3) the importance of the individual variable and uncertain parameter distributions for reliably satisfying an effluent water quality constraint. Increasing the magnitude of influent variability and model parameter uncertainty results in a greater expected design cost due, generally, to increases in process sizing required to reliably satisfy the effluent concentration constraint. The inclusion of variability and uncertainty can also produce a shift in the locations of the least-cost configuration regions, which are dependent on the expected influent water quality and the magnitude of variability and uncertainty. The additional information provided by incorporating the variable and uncertain parameters illustrates that parameter distributions related to the primary removal mechanism are critical, and that contact and direct filtration are more sensitive to variability and uncertainty than conventional filtration.     

气候变率影响下博茨瓦纳河流流量的时空变化

The fourth assessment report of the IPCC highlights that the global average surface temperature is projected to increase by 1.8 to 4.0℃ by the year 2100 compared to current climate. Given that climate is the most important driver of the hydrological cycle, the rise in temperature could cause changes in occurrence patterns of extreme hydrologic events like streamflow droughts. An increase in frequency and severity of these events could pose sed-ous challenges for sustainable management of water resources particular in add regions.However, the understanding of water resources dynamics and the possible impacts of climate change on these dynamics is hindered by uncertainties in climate change models and com-plex hydrological responses of streams and catchments to climatic changes. Therefore ob-servational evidence of streamflow dynamics at the local scale could play a crucial role in addressing these uncertainties and achieving a fuller reconciliation between model-based scenarios and ground truth. This paper determines spatial and temporal changes in stream-flow volumes and their association with climatic factors based on the non-parametric Mann-Kendall test and ANOVA to determine possible changes in streamflow over the years and their relation to climatic factors. Streamflow is generally stochastic highlighting the im-portance of factoring in temporal flow variability in water resources planning. There is no clear evidence that changes in climatic variables are related to streamflow behaviour.