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Agroecosystems are facing the adverse effects of climate change. This study explored how the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can give new insight into irrigation allocation and plant health. Leveraging the global coverage and 70-m spatial resolution of the Evaporative Stress Index (ESI) from ECOSTRESS, we processed over 200 overpasses and examined patterns over 3 growing seasons across the Maipo River Basin of Central Chile, which faces exacerbated water stress. We found that ECOSTRESS ESI varies substantially based on the overpass time, with ESI values being systematically higher in the morning and lower in the afternoon. We also compared variations in ESI against spatial patterns in the environment. To that end, we analyzed the vegetation greenness sensed from Landsat 8 and compiled the referential irrigation allocation from Chilean water regulators. Consistently, we found stronger correlations between these variables and ESI in the morning time (than in the afternoon). Based on our findings, we discussed new insights and potential applications of ECOSTRESS ESI in support of improved agricultural monitoring and sustainable water management. [ABSTRACT FROM AUTHOR]

Irrigated areas are major vectors of agricultural development and components of ecosystems. The groundwater level maintains the irrigated areas' ecology safety and sustainable development. Under the influence of irrational irrigation practices—such as flood irrigation or extreme water saving without consideration of ecological impact—different areas within an irrigation district may experience anomalies in groundwater levels (either too deep or too shallow). It is of great significance to carry out research on water resource allocation and future water-saving strategies, taking into consideration groundwater depths. In this study, a method for the optimal allocation of irrigation water resources that considered groundwater level was used to regulate irrational irrigation practices and to reveal the future direction of water saving. Helan County in Ningxia province, an ecologically fragile and arid irrigated area, was selected as a case study. Multiple scenarios of different water use and different degrees of water-saving were analyzed. The results showed that non-engineering water-saving measures (such as adjusting the planting structure and controlling the amount of irrigation for rice) had better benefits compared to engineering measures (such as efficient water-saving irrigation and channel lining). When implementing only one water-saving measure, the strategy of replacing 75% of the rice area with corn yielded the best results. This approach can reduce the irrigation water shortage rate to 11% and increase by 4.58% the acreage where the groundwater level is reasonable. When multiple water-saving measures are implemented together, the most effective strategy for future water-saving efforts involves the joint implementation of several measures: replacing 75% of the rice area with corn, limiting irrigation for rice to no more than 11.85 thousand m3/ha, adopting high-efficiency water-saving irrigation in 90% of the pump-diverted water irrigation region and 40% of the channel-diverted water irrigation region, and maintaining the channel's water utilization coefficient at 0.62. This strategy can keep the irrigation water shortage below 3.66% and increase the acreage where the groundwater level is reasonable, by 4.58% per year. The conclusions and research approaches can provide references for the formulation of water-saving measures for irrigated areas' sustainable development. [ABSTRACT FROM AUTHOR]

To mitigate the unfavorable effects of excessive water resources consumption, mainly induced by poor performance of irrigation practices, efficient water resource management strategies are required. In response to this need, we have, in an innovative way, enhanced the water resources management (WRM) strategies by both considering the regional conditions with the graph model for a conflict resolution (GMCR) decision support system, and linking the irrigation concept and water resources allocation theory to develop a coupled WRM simulation–optimization model. Typically, implementation of the modified WRM strategies may cause local conflicts because of losing the original water rights. To improve the current irrigation water allocation system with the minimum objections, the hypergame theory was utilized to enhance the capabilities of traditional GMCR models by including the parties' misunderstandings in the negotiation process and assessing the partial perceptions rather than crisp options. Moreover, by dynamic monitoring of available water resources and water consumption patterns, a WRM simulation model was developed, which is applicable in real agricultural conditions of multi-agricultural zones with multi-crop and intercropping systems and variable water supply sources. The genetic algorithm was utilized to allocate the water resources and determine optimal WRM strategies with the lowest irrigation water shortage. The efficiency of the proposed framework was assessed in conventional agricultural zones in Oman. The recommended strategies not only address local conflicts during the implementation of optimal WRM strategies, but also demonstrate significant potential to reduce the water shortage as a serious environmental concern. [ABSTRACT FROM AUTHOR]

Water scarcity, the intensification of agricultural nonpoint pollution, and continuous deterioration of the ecosystem are serious problems in arid areas. Optimal allocation of water resources is the key way to achieve sustainable and efficient development of irrigated agriculture. An integrated multiobjective optimization model considering water-balance processes was developed under shallow groundwater environments to enhance comprehensive objectives and obtain optimal irrigation solutions. A simulation module of the physical processes of water movement among crop root zones, soil water, and groundwater aquifers, and a multiobjective optimization module of irrigation water allocation were incorporated into a general framework. It has the following advantages in terms of (1) illustrating variations of agricultural hydrological parameters (e.g., soil water content, groundwater depth, groundwater-based evaporation, and so on) in water movement processes; (2) obtaining a set of noninferior decision-making solutions through a modified nondominated sorting genetic algorithm (NSGA-II), which can maximize the interests for multiple parties; and (3) generating optimal solutions of irrigation water allocation and alleviating water scarcity, improving irrigation water productivity, and reducing negative environmental effects. To demonstrate its applicability, it was applied to optimize irrigation water allocation in the Jiefangzha Irrigation Subarea (JIS), China. It was firstly split into 44 irrigation subsystems, and each subsystem was assumed to be a homogeneous unit in meteorology, soil texture, and groundwater. Under the water competition between subsystems and rigid constraints of water availability, optimized results enhanced net economic benefits by 14.7%, saved 7.74% of irrigation water amount, and improved irrigation water productivity by 13% compared with status quo. Meanwhile, it can reduce graywater footprint of grain production by 58% compared with the average level of Hetao Irrigation District, China. [ABSTRACT FROM AUTHOR]

This study proposes an optimal analysis model for carrying out the canal-based irrigation water allocation (OPA_IWA_Canal) for a schedule-based irrigation zone under consideration of the variations in the runoff-related factor and irrigation-related factors. Coupled with the modified genetic algorithm based on the sensitivity of the model parameters (GA-SA) with a nonlinear objective function relying on the branch-based supplying satisfaction index, the proposed OPA_IWA_Canal model could optimally allocate the irrigation water supply under a desired demand. The Zhudong Canal irrigation zone with 15 branches is selected as the study area with the upstream inflow from the Shanping weir and two intake-water hydraulic structures (Baoshan Reservoir and Yuandon water treatment plant). The results from the model application on a variety of upstream inflow and water demands indicate that the proposed OPA_IWA_Canal model can provide the branch-based irrigation water supplies and water intake of the hydraulic structures to enhance the irrigation efficiency significantly, on average, from 0.25 to 0.7 under the insufficient upstream inflow. Additionally, by proceeding with the proposed OPA_IWA_Canal model under the various combinations of the unirrigated branches, all reaming branches could be availably allocated irrigation water supplies with a high irrigation efficiency (nearly 0.8).

The type of single-slope solar greenhouse is mainly used for vegetable production in China. The coupling of heat storage and release courses and the dynamic change in the outdoor weather parameters momentarily affect the indoor environment. Due to the high cost of small weather stations, the environmental parameters monitored by the nearest meteorological stations are usually used as outdoor environmental parameters in China. In order to accurately predict the solar greenhouse and crop water demand, this paper proposes three deep learning models, including neural network regression (DNNR), long short-term memory (LSTM), and convolutional neural network-long- short-term memory (CNN-LSTM), and the hyperparameters of three models were determined by orthogonal experimental design (OD). The temperature and relative humidity monitored by the indoor sensors and outdoor weather station were taken as the inputs of models, the temperature and relative humidity 3, 6, 12 and 24 h in advance were taken as the output, 16 combinations of input and output data of two typical solar greenhouses were trained separately by three deep learning models, those models were trained 144, 144 and 288 times, respectively. The best model of three type models at four prediction time points were selected, respectively. For the forecast time point of 12 h in advance, the errors of the best LSTM and CNN-LSTM models in two greenhouses were all smaller than the DNNR models. For the three other time points, the results show that the DNNR models have excellent prediction accuracy among the three models. The maximum and minimum temperature, relative humidity, and ETo were also accurately predicted using the corresponding optimized models. In sum, this study provided an optimized deep learning prediction model for environmental parameters of greenhouse and provides technical support for irrigation decision-making and water allocation. [ABSTRACT FROM AUTHOR]

The type of single-slope solar greenhouse is mainly used for vegetable production in China. The coupling of heat storage and release courses and the dynamic change in the outdoor weather parameters momentarily affect the indoor environment. Due to the high cost of small weather stations, the environmental parameters monitored by the nearest meteorological stations are usually used as outdoor environmental parameters in China. In order to accurately predict the solar greenhouse and crop water demand, this paper proposes three deep learning models, including neural network regression (DNNR), long short-term memory (LSTM), and convolutional neural network-long- short-term memory (CNN-LSTM), and the hyperparameters of three models were determined by orthogonal experimental design (OD). The temperature and relative humidity monitored by the indoor sensors and outdoor weather station were taken as the inputs of models, the temperature and relative humidity 3, 6, 12 and 24 h in advance were taken as the output, 16 combinations of input and output data of two typical solar greenhouses were trained separately by three deep learning models, those models were trained 144, 144 and 288 times, respectively. The best model of three type models at four prediction time points were selected, respectively. For the forecast time point of 12 h in advance, the errors of the best LSTM and CNN-LSTM models in two greenhouses were all smaller than the DNNR models. For the three other time points, the results show that the DNNR models have excellent prediction accuracy among the three models. The maximum and minimum temperature, relative humidity, and ETo were also accurately predicted using the corresponding optimized models. In sum, this study provided an optimized deep learning prediction model for environmental parameters of greenhouse and provides technical support for irrigation decision-making and water allocation.

In this study, a fuzzy dependent-chance interval multi-objective stochastic expected value programming model is developed for irrigation water resources management under uncertainties. It incorporates fuzzy dependent-chance programming, stochastic expected value programming, interval programming into multi-objective programming. Compared with conventional programming methods, it can quantify the relationship between the expected values of stochastic variables and the fuzzy goals of expected values set by decision-makers through the satisfactory degrees, and trade-off the relationship amid multiple satisfactory degrees selected as objective functions. Besides, it can cope with uncertainties expressed as interval numbers, fuzzy numbers, and stochastic variables. Moreover, the fairness of water allocation constraints formulated by the GINI coefficient can achieve the interactions between fair water allocation and satisfactory degrees. The model is applied to a real case study of irrigation water resources management of different water types (i.e., surface water and groundwater) under different water flow levels (high, medium, and low flow levels) in the midstream region of the Heihe River basin, northwest China. The results reveal that: (1) maximum water demands of wheat and economic crop are satisfied while that of corn is not met under three flow levels; (2) the expected economic benefit and water shortages of crops have positive relationships with water allocation while the expected canal water loss has a negative relationship with water allocation; (3) the bigger expected economic benefit results in the higher satisfactory degree of the expected economic benefit while the lower expected water shortage and canal water loss lead to higher satisfactory degrees of expected water shortage and canal water loss. It shows that the developed model can overcome the disadvantages of the single-objective programming of putting attention to the satisfactory degree of a kind of expected value, and neglecting the satisfactory degree of other associated expected benefit. It also can overwhelm the drawbacks of the two-objective programming model of more focus on the satisfactory degree of the expected canal water loss. The results can provide different water allocation schemes for decision-makers. [ABSTRACT FROM AUTHOR]

Sustainable agricultural production has encountered difficulties such as water scarcity, improper use of available water resources and climate change in arid countries like Iran. Simulation-optimization approaches are helpful tools for crop pattern planning and irrigation water allocation to ensure maximum net benefit is gained from the system. In this paper, optimum cultivation area and allocation of irrigation water in conditions compatible with climate change are obtained for the Borkhar plain in Iran. To achieve this, the network flow programming-based MODSIM, as a water allocation simulation model, is coupled with the grey wolf optimization (GWO) algorithm to obtain the optimum irrigation amounts and cultivation areas in the plain under two conditions: status quo, and with climate change-affected streamflows. The Hadley Centre coupled Model version 3 (HadCM3) and the second-generation Canadian Earth System Model (CanESM2) are used to generate the climatic parameters in the study area. The Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES) rainfall–runoff model is applied to calculate the coefficients of variation for the Zayandehroud River streamflows, as the surface water resource for irrigation of the plain. Results indicate that the agricultural net benefit gained from the plain will decrease by 1.5% in the A2 emissions scenario, and by 3.5%, 8% and 17.5% in the three representative concentration pathway (RCP) scenarios in the optimum states obtained by the GWO-MODSIM model. Moreover, the cultivation areas are decreased in the climate change scenarios. Therefore, appropriate management policies should be adopted for adaptation to the likely future situation. [ABSTRACT FROM AUTHOR]

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Agricultural water scarcity is a global problem and effective management of limited water resources for irrigation to meet socioeconomic demands for sustainable development is a huge challenge. A stochastic multi-objective non-linear programming (SMONLP) model is developed for the identification of sound irrigation water allocation schemes. The SMONLP model improves upon previous methods by tackling contradictions of society-economy-resources as well as reflecting uncertainty expressed as probability distributions in an agricultural irrigation system. The SMONLP model permits in-depth analyses of various water allocation policies that are associated with different levels of water supply and climate change. The developed SMONLP model is applied to optimal irrigation allocation in a semi-arid river basin in China. Results reveal that the model coordinates the regulation of interactions of society-economy-resources by balancing the targets of water productivity, allocation equity, profit, economic benefit risk, blue water utilization, and leakage loss. Moreover, surface water availability associated with different violation risk probabilities can lead to the changes in comprehensive benefit of society-economy-resources and irrigation shortages. Nearly each of the 17 irrigation regions suffers from water deficit, because water is insufficient to satisfy the requirement of crops, however, the degree of water shortage is gradually weakened when flow level ranges from low to high. The coordination degree is also used to evaluate the sustainability of water allocation and the results of comparison show that the irrigation water allocation under RCP 4.5 presents lower coordination of society-economy-resources which are mainly attributed to the aggravated contradiction between water supply and demand. A real world study demonstrates the practicability of the developed model, allowing the river basin authorities to determine irrigation water allocation strategies in a changing environment, thus promoting sustainable development of agricultural irrigation systems. • A stochastic multi-objective model is presented for sustainable irrigation. • Mutual contradictions of society, economy, and resources are coordinated. • Effects of uncertainty and climate change on irrigation water allocation are evaluated. • Sustainability of the studied irrigation system is evaluated. • Various managerial insights of irrigation allocation are offered. [ABSTRACT FROM AUTHOR]

The objective of this research was to study the participation of water user group in irrigated water management in Ban Vern Kham Pumping Irrigation Project, Xaithani District, Vientiane Capital, Lao PDR, through the analysis of variables and the formulation of participation equation. The study included 105 households for data collection based on the developed questionnaires. The collected data were analyzed using SPSS program and expressed in the forms of frequency, percentage, mean, and standard deviation. The analysis of participation variables and stepwise multiple regression was carried out to obtain the equation used to predict the participation level in irrigated water management. Based on the main findings, the overall participation level was reported to be high, which was equal to 3.62 (the total score of 5.00) with the standard deviation of 0.149. Specifically, the participation in planning irrigation water allocation and operation and maintenance of irrigation system obtained the same highest score of 3.67, whereas the least score was the participation in allocating the benefit from irrigation water (with the score of 3.53). Additionally, the personal factors of water users were found not affecting the participation level. However, the different education level played a role in participation level in irrigation water allocation planning with the statistical significance of 0.05. The other factors such as education level, working ability, and income obtained from water user group, were found to have a moderate relationship with participation level. The analysis revealed that the water user group was relatively well established due to a strong cooperation and collaboration in working together to find equitable ways to manage irrigation water. In conclusion, the participation level in irrigated water management was a function of working ability, income obtained from water user group, and position in water user group.

Due to the widespread uncertainties in agricultural water resources systems and climate change projections, the traditional optimization methods for agricultural water management may have difficulties in generating rational and effective optimal decisions. In order to get optimal future agricultural water allocation schemes for arid areas with consideration of climate change conditions, the model framework established in this paper integrates a statistical downscaling model, back propagation neural networks, and an evapotranspiration model (the Hargreaves model) with inexact irrigation water allocation optimization model under future climate change scenarios. The model framework, which integrates simulation models and optimization models, considers the interactions and uncertainties of parameters, thereby reflecting the realities more accurately. It is applied to the Yingke Irrigation Area in the midstream area of the Heihe River Basin in Zhangye city, Gansu Province, northwest China. Then, water allocation schemes in planning year (2047) under multiple future Representative Concentration Pathways (RCP) scenarios and the status quo (2016) are compared, in order to evaluate the practicability of generated water allocation schemes. The results show that the water shortages of economic crops are improved compared with the status quo under all RCP scenarios while those of the grain crops present opposite results. Meanwhile, the economic benefits decrease from the status quo to planning year under all future scenarios. This phenomenon is directly related to the amount of irrigation water allocation and is indirectly related to the changes of meteorological conditions. The model framework can reveal the regular pattern of hydro-meteorological elements with the impact of climate change. Meanwhile, it can generate irrigation water allocation schemes under various RCPs scenarios which could provide valuable decision support for water resources managers. [ABSTRACT FROM AUTHOR]

This paper presents the application of a new evolutionary algorithm technique called combined Pareto multi-objective differential evolution (CPMDE) to optimize irrigation water allocation and crop distribution under limited water availability with three different crops (maize, potatoes and groundnut) planted on a 100 ha farmland at Vaalharts irrigation scheme, South Africa. The algorithm combines methods of Pareto ranking and Pareto dominance selections to implement a novel selection scheme at each generation. The ability of CPMDE in solving unconstrained, constrained and real-world optimization problems was demonstrated. The two objectives of the model are to maximize total crop net benefit (NB) over a planting season while minimizing total irrigation water allocation. A set of non-dominated solutions with the high NBs at lower irrigation water allocation for three crop types was obtained, and compromise programming approach was used in evaluating the most favourable solution. The best solution shows that maize produced the highest crop yield under limited water allocation in the study area. Comparing this result with that of a previous study which adopted a multi-objective optimization algorithm called multi-objective differential evolution algorithm, CPMDE is a good and robust alternative algorithm suitable for resolving crop distribution under limited water availability.

In this study, a fuzzy linear fractional programming (FLFP) approach with double-sided fuzziness is developed for optimal irrigation water allocation under uncertainty. The FLFP model can be derived from incorporating double-sided fuzzy chance-constrained programming (DFCCP) into linear fractional programming (LFP) optimization framework. The developed model can deal with uncertainty presented as fuzziness in both right-hand and left-hand sides of constraints. Moreover, it has advantages in: (1) addressing two objectives directly without considering subjective factors, (2) effectively reflecting economic water productivity between total system economic benefit and total irrigation water use, (3) introducing the concept of confidence levels of fuzzy constraints-satisfaction under both the minimum and maximum reliabilities to generate more flexible solutions and (4) facilitating in-depth analysis of interrelationships among economic water productivity, system benefits and varying confidence levels. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. The optimal irrigation water allocation solutions from the FLFP model can be obtained. These results can provide decision-support when deciding on selecting reasonable irrigation water resources management and agricultural production. [ABSTRACT FROM AUTHOR]

An interval linear multi-objective programming (ILMP) model for irrigation water allocation was developed, considering conflicting objectives and uncertainties. Based on the generation of interval numbers through statistical simulation, the ILMP model was solved using a fuzzy programming method. The model balances contradictions among economic net benefit, crop yield and water-saving in irrigation systems incorporating uncertainties in both objective functions and constraints that are based on the conjunctive use of surface water and groundwater. The model was applied to Hulan River irrigation district, northeast China. Tradeoffs between various crops in different subareas under different frequencies were analyzed, and scenarios with different objectives were considered to evaluate the changing trend of irrigation water allocation.Results indicated that the ILMP model provided effective linkages between revenue/output promotion and water saving, and offers insights into tradeoffs for irrigation water management under uncertainty. [ABSTRACT FROM AUTHOR]

An inexact nonlinear m λ -measure fuzzy chance-constrained programming (INMFCCP) model is developed for irrigation water allocation under uncertainty. Techniques of inexact quadratic programming (IQP), m λ -measure, and fuzzy chance-constrained programming (FCCP) are integrated into a general optimization framework. The INMFCCP model can deal with not only nonlinearities in the objective function, but also uncertainties presented as discrete intervals in the objective function, variables and left-hand side constraints and fuzziness in the right-hand side constraints. Moreover, this model improves upon the conventional fuzzy chance-constrained programming by introducing a linear combination of possibility measure and necessity measure with varying preference parameters. To demonstrate its applicability, the model is then applied to a case study in the middle reaches of Heihe River Basin, northwest China. An interval regression analysis method is used to obtain interval crop water production functions in the whole growth period under uncertainty. Therefore, more flexible solutions can be generated for optimal irrigation water allocation. The variation of results can be examined by giving different confidence levels and preference parameters. Besides, it can reflect interrelationships among system benefits, preference parameters, confidence levels and the corresponding risk levels. Comparison between interval crop water production functions and deterministic ones based on the developed INMFCCP model indicates that the former is capable of reflecting more complexities and uncertainties in practical application. These results can provide more reliable scientific basis for supporting irrigation water management in arid areas. [ABSTRACT FROM AUTHOR]

Water scarcity causes conflicts among natural resources, society and economy and reinforces the need for optimal allocation of irrigation water resources in a sustainable way. Uncertainties caused by natural conditions and human activities make optimal allocation more complex. An intuitionistic fuzzy multi-objective non-linear programming (IFMONLP) model for irrigation water allocation under the combination of dry and wet conditions is developed to help decision makers mitigate water scarcity. The model is capable of quantitatively solving multiple problems including crop yield increase, blue water saving, and water supply cost reduction to obtain a balanced water allocation scheme using a multi-objective non-linear programming technique. Moreover, it can deal with uncertainty as well as hesitation based on the introduction of intuitionistic fuzzy numbers. Consideration of the combination of dry and wet conditions for water availability and precipitation makes it possible to gain insights into the various irrigation water allocations, and joint probabilities based on copula functions provide decision makers an average standard for irrigation. A case study on optimally allocating both surface water and groundwater to different growth periods of rice in different subareas in Heping irrigation area, Qing’an County, northeast China shows the potential and applicability of the developed model. Results show that the crop yield increase target especially in tillering and elongation stages is a prevailing concern when more water is available, and trading schemes can mitigate water supply cost and save water with an increased grain output. Results also reveal that the water allocation schemes are sensitive to the variation of water availability and precipitation with uncertain characteristics. The IFMONLP model is applicable for most irrigation areas with limited water supplies to determine irrigation water strategies under a fuzzy environment. [ABSTRACT FROM AUTHOR]

The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production. [ABSTRACT FROM AUTHOR]

Due to different responses of crop growth stages to the water deficit, it is necessary to optimize water allocation between different growth stages to obtain the maximum food production in reservoir irrigation systems which are widely distributed throughout Southern China and India. In order to address the inputs uncertainties and dynamics existing in the above agricultural water management, an interval multistage water allocation model is developed. By incorporating multistage stochastic programming and interval parameter programming, the developed model can deal with uncertain inputs both expressed as interval parameters and probability distributions, and realize a dynamic irrigation among different growth stages from a reservoir. In the model, water requirement targets are first treated as first-stage decision variables to tackle the unique problem of agricultural water management. Additionally, given that net benefit and penalty of each growth stage are key parameters due to their determinative roles for allocation between different growth stages, a crop water production function is introduced into the calculation to make them factually reflect the competition among different growth stages. The model is then applied to the Yangshudang Irrigation District to plan rice irrigation and demonstrate its applicability. Rainfall has been divided into five levels with probability distributions in each growth stage and parameters have been characterized as interval numbers to show system uncertainty. Five scenarios that represent different initial active storage levels of the reservoir are set to acquire more detailed results. Through the parameter estimation, net benefits are [1.08, 1.29], [5.04, 6.01], [11.79, 14.08] and [1.61, 1.92] RMB/m 3 , and penalties are [2.39, 2.48], [11.13, 11.54], [26.05, 27.01] and [3.55, 3.68] RMB/m 3 for tillering stage, booting stage, heading stage and milky stage respectively. Through the model simulation, water requirement targets in booting stage and heading stage under all scenarios are set at their upper bound, while this figure in tillering stage reaches its upper bound only when initial active storage is under high or very high level. The results show that irrigation water can be optimally allocated between different growth stages of a single crop in a single reservoir system under inputs uncertainty. Although there is a limitation to regard rainfall as to be uniform in the whole area, the solutions of water requirement targets under different scenarios, as well as water allocation patterns among different growth stages, are valuable for optimizing irrigation water use in meso- and micro-scale agricultural system under inputs uncertainty. [ABSTRACT FROM AUTHOR]

Agricultural irrigation accounts for nearly 70% of the total water use around the world. Uncertainties and climate change together exacerbate the complexity of optimal allocation of water resources for irrigation. An interval-fuzzy two-stage stochastic quadratic programming model is developed for determining the plans for water allocation for irrigation with maximum benefits. The model is shown to be applicable when inputs are expressed as discrete, fuzzy or random. In order to reflect the effect of marginal utility on benefit and cost, the model can also deal with nonlinearities in the objective function. Results from applying the model to a case study in the middle reaches of the Heihe River basin, China, show schemes for water allocation for irrigation of different crops in every month of the crop growth period under various flow levels are effective for achieving high economic benefits. Different climate change scenarios are used to analyze the impact of changing water requirement and water availability on irrigation water allocation. The proposed model can aid the decision maker in formulating desired irrigation water management policies in the wake of uncertainties and changing environment. [ABSTRACT FROM AUTHOR]

An interval linear fractional irrigation water allocation (ILFIWA) model is developed in response to the complexity of errors in estimating crop yields, fluctuating hydrological elements as well as varying economic profits in an irrigation system. The model is capable of quantitatively solving multi-objective problems, i.e. to obtain the maximum system net benefit and the minimum irrigation water use. Particularly, it can handle multi-objective functions expressed as ratios, such as irrigation water productivity. Moreover, it can reflect the uncertainties of the variables/parameters and functions involved. The potential of the developed model is shown by applying to a case study in northwest China. Results of the model can help make irrigation water allocation decisions for different time periods under varying flow levels. Comparison between ILFIWA model and ordinary interval linear programming model shows that the developed model is conducive to improving irrigation water productivity and saving irrigation water, and helping decision makers formulate desired irrigation water resources management policies under uncertainty. [ABSTRACT FROM AUTHOR]

In this study, the multi-dimensional critical regulation control (MCRC) modes and water allocation for irrigation system were studied in the middle reaches of Heihe River basin, China, to coordinate the water contradiction of agricultural irrigation and ecological irrigation. Based on the key indexes of five dimensions, including water resources, ecological environment, society, economy and water-saving measures, 320 schemes of three regulation control levels were generated and analyzed, then the optimal modes were obtained by introducing entropy theory and synergetic theory, thus the corresponding thresholds were obtained meanwhile. Based on the parameters of the most optimal irrigation system regulation control modes, an inexact multi-stage stochastic programming (IMSP) model for water resources optimal allocation of the 12 major irrigation districts in the middle reaches of Heihe River basin was developed and the corresponding results were obtained. This paper gives the optimal MRCR modes and water resources allocation plans from a different perspective by considering the ecological environment protection and agricultural water-saving. The results can help decision makers to develop a more sustainable water resources planning program in study areas and similar areas. [ABSTRACT FROM AUTHOR]

Abstract: To improve the water resource management of the inland river basins of northwestern China, a Decision Support System (DSS) is developed to provide an operative computer platform for decision makers. The DSS is designed according to actual water resource management problems and is seamlessly integrated into a user-friendly interface implemented in the Visual C# programming language. The DSS comprises an information management system that performs data collection, verification, management, and visualization, and models estimated crop water demand and water allocation for different levels of water use units. The objective of this study is to aid in the decision-making process related to water allocation scheme planning and implementation and to aid real-time responses to changes in water supply, allowing a new water allocation scheme to be developed based on the actual relationship between the supply and demand for water. The system is tested to allocate water to different levels of water use units as a standard decision support tool by means of the actual total available water from rivers, reservoirs, and groundwater. More than 60 water decision makers use the system at more than 40 locations along the middle reaches of the Heihe River Basin. [Copyright &y& Elsevier]

Because of political risk, economic feasibility, and cultural concerns, it has been a great challenge for economists to provide palatable remedies to governments to promote water allocation efficiency. Considering the limitation of water pricing to irrigation water, this research addresses questions of which strategic policy alternatives to water pricing might improve irrigation water allocation efficiency An empirical framework is provided to compare irrigation policies for allocating scarce water to agricultural production in Egypt and Morocco. Partial-equilibrium agricultural sector models specific to Egypt and Morocco were employed for policy tests. Consumer and producer surplus from agricultural based commodities is maximized subject to various resources, technical, and policy constraints. Positive Mathematical Programming (PMP) was used to calibrate the model. Water pricing policy, water complementary input factor tax policy, and output tax policy are tested using these two agricultural sector models. Results suggest that effective policy depends on the social, economic, and environmental contexts of specific regions. For countries like Egypt where most agricultural land is irrigated, taxes on Nitrogen (W) fertilizer and energy and output tax on water-intensive and low profit crop production may be more effective than others. For the Moroccan case, taxation on crop inputs and outputs not only affect water use in the public irrigation sector, but also private irrigation sector and rain-fed as a whole. Water pricing and output tax policies are better suited and effective than water complementary input factor taxation. Findings from Morocco might be generalized to other countries with similar irrigation characteristics and diversity in irrigated (public and private) and rain-fed land. The results for both countries demonstrate that some of the strategic irrigation policies can work towards directing cropping decisions to less water intensive crops and also generating revenues for governments in situations where governments choose not to price water. [ABSTRACT FROM AUTHOR]

Due to population growth and human activities, water shortages have become an increasingly serious concern in the North China Plain, which has become the world's largest underground water funnel. Because the yield per unit area, planting area of crops, and effective precipitation in the region are uncertain, it is not easy to plan the amount of irrigation water for crops. In order to improve the applicability of the uncertainty programming model, a hybrid LSTM-CPP-FPP-IPP model (long short-term memory, chance-constrained programming, fuzzy possibility programming, interval parameter programming) was developed to plan the irrigation water allocation of irrigation system under uncertainty. The LSTM (long short-term memory) model was used to predict crop yield per unit area, and CPP-FPP-IPP programming (chance-constrained programming, fuzzy possibility programming, interval parameter programming) was used to plan the crop area and the effective precipitation under uncertainty. The hybrid model was used for the crop production profit of winter wheat and summer corn in five cities in the North China Plain. The average absolute error between the model prediction value and the actual value of the yield per unit area of winter wheat and summer maize in four cities in 2020 was controlled within the range of 14.02 to 696.66 kg/hectare. It shows that the model can more accurately predict the yield per unit area of crops. The planning model for the benefit of irrigation water allocation generated three scenarios of rainfall level and four planting intentions, and compared the planned scenarios with the actual production benefits of the two crops in 2020. In a dry year, the possibility of planting areas for winter wheat and summer corn is optimized. Compared with the traditional deterministic planning method, the model takes into account the uncertain parameters, which helps decision makers seek better solutions under uncertain conditions. [ABSTRACT FROM AUTHOR]

An interval nonlinear double-sided fuzzy chance-constrained programming (INDFCCP) approach is formulated to effectively allocate irrigation water among competing water users. The INDFCCP approach is formulated by combining inexact quadratic programming (IQP) and double-sided fuzzy chance-constrained programming (DFCCP) within a general optimization framework. This approach has the following features. (1) It's able to handle interval and fuzzy uncertainties, and nonlinearity existing in the objective functions. (2) It's capable of addressing these fuzzy constraints and fuzzy variables where different confidence levels and satisfaction degree levels should be satisfied. (3) Each fuzzy chance-constraint can be further analyzed with the maximum and minimum reliability scenarios, which makes it possible to reflect variations of system conditions. (4) Interval quadratic crop water production functions (IQCWPFs) are employed in place of deterministic ones to quantitatively describe the mathematical relationships between crop yields and actual crop evapotranspiration (or irrigation water applied). Then, to demonstrate its applicability and feasibility, the INDFCCP approach is applied in the Yingke Irrigation District (YID), northwest China for allocating irrigation water to three crops in three subareas under uncertainty. Finally, more flexible decision solutions regarding optimal irrigation water allocation have been generated and analyzed under different predetermined confidence levels, showing several advantages of the INDFCCP approach with respect to the deterministic one. Under the same confidence level, system benefits under the minimum reliability scenario (e.g. [499.6, 909.7] × 106 Yuan, α = 0.5) are higher than that under the maximum reliability scenario (e.g. [498.7, 908.9] × 106 Yuan, α = 0.5). From above outcomes, the INDFCCP approach provides more appropriate results and reliable scientific bases needed for better managing irrigation water in irrigated agricultural areas. • An interval nonlinear double-sided fuzzy chance-constrained programming approach is developed. • Interval quadratic crop water production functions are estimated based on interval regression method. • The model is applied to a real case study in an arid area in northwest China for irrigation water resources allocation. • Cost-effective solutions can be generated by introducing confidence levels and the minimum and maximum reliability scenarios. [ABSTRACT FROM AUTHOR]

• A new irrigation-drainage collaborative optimization model is developed. • It explores the more applicable irrigation and drainage water allocation solutions. • The developed model will lead to greater benefits than conventional optimal model. Agricultural production in arid and semi-arid area faces a global problem of water resources shortage and land salinization. Irrigation and drainage are the important measure to enhance crop yield and control soil salinity. Generally, at field scale only irrigation is optimized to pursue the higher water use efficiency and crop yield, while drainage is difficult to optimize owing to controlled by dynamic groundwater levels. Here, we develop a new collaborative optimization model of irrigation and drainage to improve irrigation water use efficiency and to control soil salinity. The model is formulated by integrating simulation of physical processes of field water - salt balance and a genetic algorithm-based optimization model. The new model is to search optimized irrigation and drainage strategic decision for enhancing field economic benefit with the condition controlling salinity with limited water resources. Then, a case study on optimally allocating irrigation and drainage water to different growth stages of maize field in the Hetao Irrigation District, arid area of northwest China shows enhanced applicability of the developed model. Five groundwater depth levels (1 m, 1.5 m, 2 m, 2.5 m and 3 m) and five groundwater salinity levels (2 g/L, 2.25 g/L, 2.5 g/L, 2.75 g/L and 3 g/L) are provided to show and compare the solutions of the optimal irrigation and drainage water allocation. Results indicate the developed model can supply reasonable field monthly irrigation and drainage decision with considering field hydrology, especially contribution of groundwater to crop water demand and groundwater role to soil salt accumulation. The contrary relationship between system benefit and irrigation water use efficiency was described successfully by the developed model. And compared with traditional single irrigation optimization model, the developed irrigation-drainage collaborative optimization model can enhance drainage function to keep the optimal groundwater levels and improve the system benefit by 0–8%. Overall, the developed model can provide more applicable irrigation water and drainage strategies to the sustainable development of irrigation agriculture. [ABSTRACT FROM AUTHOR]

• A full fuzzy-interval credibility-constrained nonlinear programming (FFICNP) model is proposed under uncertainty. • This approach is applied to a case study for planning irrigation water allocation in the Zhanghe Irrigation District. • A lower credibility level corresponds to higher net system benefit and system efficiency. • The results support in-depth analysis of interrelationships among water allocation schemes, system benefits, and credibility levels. To address the water shortage caused by various natural conditions and ineffective irrigation water management in the Zhanghe Irrigation District (ZID) of the Yangtze River basin in China, a full fuzzy-interval credibility-constrained nonlinear programming (FFICNP) model is developed under uncertainty. Derived through incorporating fuzzy credibility constrained programming into the Jensen model optimization framework, FFICNP can not only address intervals (single uncertainty) and fuzzy-interval sets (dual uncertainties) in the model objectives and double-sided constraints, but also reflect nonlinear responsive relationships between the crop yields and irrigation levels by introducing the crop water production functions (CWPFs) under different growth stages. Moreover, an expected-value-based (EVB) approach is introduced to solve the FFICNP model. The FFICNP model is then applied to the case study of irrigation water allocation in the ZID for demonstrating its applicability. Optimal solutions can be generated from the FFICNP model for solving the irrigation water allocation problem under uncertainty. The results indicate that a lower credibility level corresponds to a higher level of system benefits and system efficiency. The system benefits of ZID in a wet year are [17.72, 24.23] × 109 CNY when λ = 1.0 and [17.79, 25.03] × 109 CNY when λ = 0.6. These findings from the FFICNP model can support in-depth analysis of interrelationships among irrigation water allocation schemes, system benefits, and credibility levels, and thus contribute to the effectiveness of irrigation water management under various inflow levels and complex uncertainties. [ABSTRACT FROM AUTHOR]

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha−1, and 0.85 t ha−1, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity. [ABSTRACT FROM AUTHOR]

• An interval multiobjective multi-stage stochastic programming (IMMSP) model is proposed. • A modified minimum deviation (MMD) method was proposed for solving IMMSP. • The developed approach is applied to a case study for planning seasonal irrigation water-storage scale and allocation. • The results guide local water managers identify the optimal management strategies and project construction scale of water conservancy. Water managers in arid and semi-arid areas must allocate limited irrigation water to different water use sectors considering the conflicting objectives, seasonal runoff inflow, and multiple uncertainties. To deal with these problems, an interval multiobjective multi-stage stochastic programming (IMMSP) model was proposed for finding reasonable water-storage scale and optimizing limited irrigation-water resources. Key factors in planning irrigation-water resources, such as random seasonal runoff, interval uncertainty in data collection, economic benefits, water leakage loss, and water deficit, were fully considered in the IMMSP model. Additionally, as an important indicator to describe the seasonal water supply capability of local water supply project, including reservoirs, agricultural ponds and etc., the water-storage scale, defined as the ratio of water storage capacity over total streamflow, is proposed to obtain the quantitative relationship between this indicator and objective of IMMSP. This study attempted to obtain the relationship between water-storage scale and objective of IMMSP as well as provide a reference of determining water-storage scale from the perspective of optimization. In addition, to solve the IMMSP model, a modified minimum deviation (MMD) method was proposed for dealing with uncertainties, making tradeoff among conflicting objectives, and reflecting the different importance of objectives. Both the IMMSP model and MMD method were applied to a real-world water-allocation problem in the middle reaches of the Heihe River basin for verifying its validity. The solutions generate a set of decision alternatives under different seasonal runoff scenarios and further guide local water managers identify the optimal management strategies and project construction scale of water conservancy. Moreover, a sound discussion of contribution to water-storage scale planning is made and the comparisons between IMMSP and each single-objective model (economic benefits, water leakage loss, and water deficit) in this study demonstrate that the results obtained by the proposed approach are more practical than a single objective with the same constraints. These results can not only effectively contribute to local irrigation water management and ecological restoration, but also provide more information to plan regional water-storage scale values. [ABSTRACT FROM AUTHOR]

This paper presents the application of a new evolutionary algorithm technique called combined Pareto multi-objective differential evolution (CPMDE) to optimize irrigation water allocation and crop distribution under limited water availability with three different crops (maize, potatoes and groundnut) planted on a 100 ha farmland at Vaalharts irrigation scheme, South Africa. The algorithm combines methods of Pareto ranking and Pareto dominance selections to implement a novel selection scheme at each generation. The ability of CPMDE in solving unconstrained, constrained and real-world optimization problems was demonstrated. The two objectives of the model are to maximize total crop net benefit (NB) over a planting season while minimizing total irrigation water allocation. A set of non-dominated solutions with the high NBs at lower irrigation water allocation for three crop types was obtained, and compromise programming approach was used in evaluating the most favourable solution. The best solution shows that maize produced the highest crop yield under limited water allocation in the study area. Comparing this result with that of a previous study which adopted a multi-objective optimization algorithm called multi-objective differential evolution algorithm, CPMDE is a good and robust alternative algorithm suitable for resolving crop distribution under limited water availability. [ABSTRACT FROM AUTHOR]