Agricultural Water Allocation Research Articles

A system dynamics approach for sustainable groundwater management considering climate change, inter-organizational relationships, and domestic water saving

This article uses system dynamic modeling to examine the relationship between water allocation from the Ilam dam and groundwater fluctuation, as well as the effects of climate change, changes in inter-organizational relationships, and water demand management on water allocation and groundwater level fluctuations. The results showed that agricultural water allocation has a direct effect on groundwater level fluctuations. In the first scenario, which examined the effects of climate change, the comparison of three climate change scenarios (SSP126, SSP245, and SSP585) was carried out. The results illustrated that with temperature increasing and precipitation decreasing occurred in SSP585, the groundwater drawdown increases about 0.8 m more than SSP126. The second scenario examined the effects of changes in inter-organizational relationships. Based on the results of centrality measures in the social network analysis method, it was found that the organizational power of the stakeholder organization in the agriculture sector is greater than the ones in the environment sector. This causes the allocation to the agriculture sector to be prioritized over the environment. This scenario was analyzed by assuming a change in organizational power and giving priority to the water allocation of the environmental sector. The results showed that environmental needs (maximum 0.3 MCM) were almost entirely met in the second scenario, compared to the basic scenario. Also, the decrease in agricultural water allocation has led to an increase in groundwater usage by farmers, resulting in a 20 cm drop in groundwater levels. Therefore, regarding the sustainable management of water resources, directing attention towards alternative livelihoods for farmers can contribute to environmental water rights and the remediation of aquifers. The third scenario examined the effects of water demand management. The results showed that saving 20–25% of water in the domestic sector can reduce 40–50 cm maximum groundwater drawdown which align with sustainable groundwater management.

Collaborative Management of Water‐Energy‐Food‐Ecosystems Nexus in Central Asia Under Uncertainty

AbstractCollaborative management of the water‐energy‐food‐ecosystems (WEFE) nexus can contribute significantly to sustainable development. However, multiple decision‐making levels with diverse preferences and multiple uncertainties in different forms pose intractable challenges to the management process. In this study, a novel optimization method named as multi‐level chance‐constrained fuzzy programming (MCFP) is developed to jointly manage the WEFE nexus. MCFP has advantages in evaluating trade‐offs among multiple competitive decision makers, solving decentralized planning problems with hierarchical structure, and tackling uncertainties expressed as randomness and vagueness. MCFP is then applied to the WEFE nexus in Central Asia, where five countries, 43 states, six water sources, and eight water users are involved over a long‐term planning horizon (2021–2050). A set of scenarios are designed to reflect decision‐making preferences based on different irrigation efficiencies, food, ecological and electricity demands as well as constraint‐violation probability and system credibility levels. The major findings are: (a) the proportion of agricultural water allocation would reduce to 45.4%–56.6% by 2050 to save more water for ensuring ecological restoration and energy supply; and (b) in order to balance water demands and support regional sustainable development, policymakers should sacrifice some of the benefits, set strict arable land limits for cereal crops, improve irrigation efficiency through adopting drip and sprinkler irrigation, and avoid the effects of the irrigation efficiency paradox. The findings are helpful for policymakers in gaining insight into the interrelationships of water, energy, food and ecosystems as well as making decisions for collaborative management of the WEFE nexus system.

Optimal agricultural water allocation for enhanced productivity of hot pepper ( Capsicum annum L) and economic gain: an experimental study from Southern Ethiopia

Optimal agricultural water allocation for enhanced productivity of hot pepper ( <i>Capsicum annum</i> L) and economic gain: an experimental study from Southern Ethiopia

Development and application of a new water-carbon-economy coupling model (WCECM) for optimal allocation of agricultural water and land resources

The optimal allocation of agricultural water and land resources is of great significance in ensuring sustainable food production and economic benefits of farmers. However, agriculture, as an important carbon cycle ecosystem, has paid limited attention to carbon sequestration in the optimal allocation of water and land resources. Therefore, this study developed a new water-carbon-economy coupling model (WCECM) for optimal allocation of agricultural water and land resources. In this model, the minimum water scarcity, maximum carbon sequestration and maximum economic benefits are taken as the optimization objectives. In addition, surface water volume and groundwater volume and planting area etc. were defined as constraints, respectively. Then, the model was solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and the Entropy-weighted-TOPSIS evaluation method. The developed model was demonstrated in the largest Farm, Youyi Farm, which is one of commercial grain production base in China to analyze the optimization of water and land resources from 2010 to 2019. We found that the new WCECM, based on the simulation of a complex coupled water-carbon-economy system, can realize the optimal allocation of agricultural water and land resources to protect regional water resources, increase carbon sequestration and adjust the agricultural planting structure. In detail, through the multi-objective optimization model, the planting structure and the allocation ratio of surface water and groundwater irrigation water consumption are more suitable for this study area. After the optimization, the area planted with Rice was significantly reduced, the area planted with Maize was increased, and the area planted with Soybean did not change significantly compared with the first two crops. The planting structure has changed from focusing on paddy cultivation to dryland cultivation, with the ratio of Rice area, Maize area and Soybean area being 3:6:1. The water consumption is constrained within manageable limits, with an average annual irrigation water consumption of 2.01 × 108 m3. The amount of carbon sequestered has increased significantly, with an average annual increase of 7.8 × 108 kg. Meanwhile, the optimized economic benefits increased slightly, with a value of ¥2.35 billion. In short, optimization of water and land resources is beneficial for improving farmers' incomes, increasing carbon sequestration in agriculture, and conserving water resources.

Optimal allocation of agricultural water and land resources integrated with virtual water trade: A perspective on spatial virtual water coordination

Agricultural production consumes the majority of global freshwater resources. The worsening water scarcity has imposed significant stress on agricultural production when regions seek food self-sufficiency. To seek optimal allocation of spatial agricultural water and land resources in each water function zone of the objective region, a multi-objective optimization model was developed to tackle the trade-offs between the water-saving objective and the economic benefit objective considering virtual water trade (VWT). The cultivated area of each crop in each water function zone was taken into account as the decision variable, while a set of strong constraints were used to restrict land resources and water availability. Then, a decomposition-simplex method aggregation algorithm (DSMA) was proposed to solve this nonlinear, bounding-constrained, and multi-objective optimization model. Based on the quantitative analysis of the spatial blue and green virtual water in each agricultural product, the proposed methodology was applied to a real-world, provincial-scale region in China (i.e., Jiangsu Province). The optimized results provided 18 Pareto solutions to reallocate the land resources in the 21 IV-level water function zones of Jiangsu Province, considering four major rainy-season crops and two dry-season crops. Compared to the actual scenario, the superior scheme increased by 7.95% (5.6 × 109 RMB) for economic trade and decreased by 1.77% (2.0 × 109 m3) for agricultural water consumption. It was mainly because the potential of spatial blue and green virtual water in Jiangsu was fully exploited by improving spatial land resource allocation. The food security of Jiangsu could be guaranteed by achieving self-sufficiency in the superior scheme, and the total VWT in the optimal scheme was 2.2 times more than the actual scenario. The results provided a systematic decision-support methodology from the perspective of spatial virtual water coordination, yet, the methodology is widely applicable.

Incorporating economy and water demand rate uncertainty into decision-making for agricultural water allocation during droughts

Abstract Determining sources of uncertainty in the agricultural water supply helps to increase irrigation scheduling accuracy. Among the most important sources of uncertainty parameters such as evapotranspiration, coefficient of sensitivity to water stress, cultivation costs, selling prices of products, and water allocated to plants. This study was conducted to estimate the fluctuations of the response due to the uncertainty of four parameters of soil moisture, water provided for cultivation, cost of cultivation during the growing season, and coefficient of sensitivity to drought. In the concept of water and food nexus, two basic factors, including increasing economic productivity and achieving maximum product production, are known as decision criteria. Therefore, mathematical modeling of net income and the ratio of actual and potential yield production has been prepared according to the four uncertain input variables of the problem. The proposed model has used a multi-objective particle swarm optimization algorithm to estimate the best values of non-deterministic variables. The results of the uncertainty analysis showed that the range of changes in performance and net profit compared to the coefficient of sensitivity to water stress has different ranges and mechanisms and knowing the nature of each helps to improve the responses.

Sustainable management of water-agriculture-ecology nexus system under multiple uncertainties

The shrinkage and ecological degradation of the Aral Sea resulting from the uncontrolled use of water resources and the unregulated expansion of agriculture activities call for sustainable management of water-agriculture-ecology (WAE) nexus from a basin perspective. However, managers face thorny challenges brought by multiple uncertainties in the management and planning processes. In this study, an interval stochastic fuzzy programming (ISF) method is developed for tackling multiple uncertainties presented as probability distributions, flexible variables and interval parameters. Then, an ISF-WAE model is formulated for Aral Sea Basin, which considers 108 planning scenarios that reflect different food-security and ecology-restoration requirements, as well as risk-response attitudes of decision maker over a long-term planning horizon (2021–2050). Results reveal that for Aral Sea Basin: (i) managers should set strict acreage benchmarks for cereal crops, in which wheat would account for a range of [29.1, 31.2] % of the total agricultural area; (ii) for promoting ecological restoration, the proportion of agricultural water allocation should decrease by a range of [12.7, 16.1] % during the planning horizon; (iii) due to low water requirement and high ecological value of grassland, it is recommended to expand grassland area to improve the sustainability of the Aral Sea Basin in the case of limited water resources.

An inexact bi-level multi-objective modeling approach for managing agricultural water-food-carbon-land-ecology-nutrition nexus in an arid area

Multiple subsystems such as water, food, land, carbon, ecology, and nutrition are closely intertwined, and the resulting complexity and uncertainty pose challenges to water resource management. This study proposes an optimization framework from a novel water-food-carbon-land-ecology-nutrition (WONDER) nexus perspective for the integrated management of agricultural and ecological water resources. Firstly, the irrigation priority ranking of decision-making units (DMU) is obtained based on the remote sensing indices from the Google Earth Engine (GEE) platform. Secondly, the ecological satisfaction degree, which aggregates carbon sink satisfaction degree and ecological water demand satisfaction degree, is proposed to characterize the degree of achieving the pre-given ecological objectives. Then, the above two indicators (i.e., the irrigation priority ranking and the ecological satisfaction degree) are integrated with a fuzzy-interval credibility-constrained bi-level multi-objective programming model to realize: (1) reconciling conflicts in food nutrition, food security, economic development, and ecological conservation; (2) making trade-offs among the positions and preferences of different decision-makers; (3) dealing with the dual uncertainties of fuzzy-interval parameters; (4) obtaining optimal agricultural and ecological water allocation schemes. The applicability of the model is verified by taking Hongyashan Irrigation District of Minqin County as an example. The results show that: (1) the irrigation priority ranking for the DMUs is: DMU 10 > DMU 9 > DMU 4 > DMU 5 > DMU 1 > DMU 11 > DMU 6 > DMU 8 > DMU 7 > DMU 2 > DMU 3; (2) the ratio of agricultural to ecological water allocation in the irrigation district is about 3:1, which is consistent with the water use characteristics of this area; (3) the total water allocation during the whole growth period for different crops is mainly determined by planting area, and the ranking of the water allocation is: sunflower > maize > wheat > seed melon. Moreover, compared with the single-level models, the inexact bi-level multi-objective model can well coordinate the upper and lower decision-makers and the multiple decision objectives under the WONDER nexus. Therefore, the proposed framework can provide the scientific basis for balancing agricultural and ecological water management and conflicting objectives in similar irrigation districts through involving multiple subsystems and perspectives.

Measurement of water resources carrying capacity in Gugang Town of Central China based on human-water-agriculture framework

More research has revealed that central China is one of the most vulnerable areas to a water crisis because of limited and unequally distributed water resources as well as rising water demands. Also, climate change is expected to reduce water resources dramatically, resulting in increased competition among users and severe water shortages for irrigated agriculture. The township, the most basic level of the Chinese government system, urgently needs to compare water allocation based on different management strategies to define the most optimized water management plan. The concept of a “human-water-agriculture” evaluation system was proposed in this study to couple and coordinate human well-being, sustainable water resource use, and agricultural development in Gugang Town, China. Within this framework, twenty-five indicators were chosen from the criterion layers to assess and optimize the carrying capacity of water resources for historical periods and the future year 2030. The results showed that the WRCC was bearable in previous years, except for 2018 when the percentage of water shortage reached 19.5 % because of a sharp reduction in dynamic surface water availability. The static surface water, represented by the water restored in hilly ponds in village and town areas, was discovered to account for up to 17 % of the total amount for the dry year. The WRCC would be overburdened in 2030, with 61.6 % and 18.1 % water-deficient underwater supply at 90 % and 75 % guarantee rates, respectively. The WRCC states that effective optimization of agricultural water allocation for crops to maximize economic benefits with limited water consumption could significantly improve human and agricultural systems. This study investigated a route for studying WRCC in village and town areas, providing theoretical and practical guidelines for local water-adapted development.

Multi-layer multi-objective cooperative regulation of agricultural water resources in large agricultural irrigation areas based on runoff prediction

Reasonable and optimal allocation of irrigation water is of great significance to improve agricultural water use efficiency for large-area agricultural irrigation systems. To solve the problems of large agricultural production scale, unreasonable water distribution structure, many rivers in the region and large changes in runoff volume, this paper takes the Sanjiang Plain in Heilongjiang Province, China, as the research object to develop an efficient and accurate water optimal allocation method. First, the runoff volume of many rivers in the region was obtained through the ARIMA-SVM-BP combined runoff prediction model, based on which a ‘regional-irrigation district-farmland’ multilayer multiobjective water resource cooperative deployment system model was established to achieve the synergistic enhancement of farmers' interests and the economic-social and regional economic-engineering volume of the irrigation district. The joint optimization method, fuzzy mathematical planning, and large system decomposition-coordination coupling were used to solve the multilayer multiobjective regulation system. The results show that the optimal agricultural water allocation volumes for the current year (2020) and the forecast year (2025) of 37 irrigation districts in the Sanjiang Plain are 3.61 × 109 m3 and 3.89 × 109 m3, saving 17% of water and resulting in a 24% decrease in the agricultural water use ratio compared with the actual situation, respectively. In the process of repeated coordination of the large system, the water distribution error is controlled within 1%. The fairness of water distribution in the multilayer system was improved by 34.7% while obtaining high economic benefits. The method proposed in this study helps the coordinated regulation and planning of multiple water sources in large agricultural irrigation areas, improves the ability of water distribution in irrigation areas to cope with different levels of incoming water, and promotes the sustainable development of regional irrigated agriculture.

Robust optimization for sustainable agricultural management of the water-land-food nexus under uncertainty

Global climate change has led to a severe loss of water and land resources and has exacerbated food crisis concerns. The water-land-food nexus in agricultural irrigation systems is receiving increasing attention. However, for sustainable food production, land and water resources must be managed effectively. The allocation of agricultural water and land resource components involves a large amount of uncertainty regarding readily available water. A robust optimization model of water-land-food nexus for agricultural irrigation systems is presented in this study, which comprehensively considers the uncertainty of the system and optimizes the allocation of scarce water and land resources for diverse crops to maximize irrigation system productivity. Using the Yellow River Basin as a case study, the results revealed that the developed optimization model can successfully resolve the system uncertainty perturbation and provide the finest potential allocation of water and land resources. Its irrigation system productivity can attain 1.6843–1.7143 kg/m3, which is improved by 6.6%–8.5%, and it can also reveal the interaction between WLF-nexus systems. This study also discovered that crop type and proportion can be modified to improve resource-use efficiency based on regional water and land resource endowment. Finally, policy implications are proposed to enhance resource utilization efficiency.

Research on the optimal allocation of agricultural water and soil resources in the Heihe River Basin based on SWAT and intelligent optimization

In the context of global climate change, changes in precipitation and temperature conditions, especially the increasing frequency of extreme climate events, have a severe impact on agricultural production. Predicting climate change and adjusting planting structures over time are critical to sustainable agricultural development. In this study, the Coupled Model Intercomparison Project Phase 6 (CMIP6) and Soil and Water Assessment Tool (SWAT) models were used to simulate the future climate and runoff in the middle reaches of the Heihe River Basin (MHR). Then, the MOPSO and NSGA-II were used to plan future planting structures. The results showed that the temperature and precipitation in the MHR showed an increasing trend. By the 2070 s, the temperature was projected to increase by more than 2 °C under SSP245, while that under SSP585 was more than 4 °C. The annual mean precipitation was projected to increase, but the distribution was uneven, and the increased precipitation was mainly concentrated in spring and autumn. Under the interaction of temperature and precipitation, the runoff of the Heihe River will increase in the future, especially under SSP585, and the runoff of the Heihe River is projected to reach 2.48 billion m³/a in the 2070 s. Crop water demand was projected to increase with temperature, and crop irrigation water demand would increase by 14.4 % in the 2070 s under SSP245. Planting vegetables helps to improve agricultural economic benefits and carbon sinks, but to control soil erosion, the area of vegetables is best kept at approximately 16 %. Fertilizers and agricultural films are the primary carbon sources in agricultural production activities in MHR, accounting for more than 60 % of the total carbon. This study suggests promoting sustainable agricultural development by improving irrigation efficiency, using low-carbon fertilizers, controlling soil erosion, and gradually expanding the vegetable planting area with a considered ecological environment.

Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China.

We quantified and evaluated the allocation of soil and water resources in the Aksu River Basin to measure the consequences of climate change on an agricultural irrigation system. We first simulated future climate scenarios in the Aksu River Basin by using a statistical downscaling model (SDSM). We then formulated the optimal allocation scheme of agricultural water as a multiobjective optimization problem and obtained the Pareto optimal solution using the multi-objective grey wolf optimizer (MOGWO). Finally, optimal allocations of water and land resources in the basin at different times were obtained using an analytic hierarchy process (AHP). (1) The SDSM is able to simulate future climate change scenarios in the Aksu River Basin. Evapotranspiration (ET0) will increase significantly with variation as will the amount of available water albeit slightly. (2) To alleviate water pressure, the area of cropland should be reduced by 127.5 km2 under RCP4.5 and 377.2 km2 under RCP8.5 scenarios. (3) To be sustainable, the allocation ratio of forest land and water body should increase to 39% of the total water resource in the Aksu River Basin by 2050.

GNSS-R monitoring of soil moisture dynamics in areas of severe drought: example of Dahra in the Sahelian climatic zone (Senegal)

ABSTRACT With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals’ waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought.

Optimal Agricultural Water Allocation for the Sustainable Development of Surface and Groundwater Resources

Optimal Agricultural Water Allocation for the Sustainable Development of Surface and Groundwater Resources

Agricultural Water Optimal Allocation Using Minimum Cross-Entropy and Entropy-Weight-Based TOPSIS Method in Hetao Irrigation District, Northwest China

Affected by the temporal and spatial changes of natural resources, human activities, and social economic system policies, there are many uncertainties in the development, utilization, and management process of irrigation district agricultural water resources, which will increase the complexity of the use of irrigation district agricultural water resources. Decision makers find it challenging to cope with the complexity of fluctuating water supplies and demands that are critical for water resources’ allocation. In response to these issues, this paper presents an optimization modeling approach for agricultural water allocation at an irrigation district scale, considering the uncertainties of water supply and demand. The minimum cross-entropy method was used to estimate the parameters of hydrologic frequency distribution functions of water supply and demand, which are the basis for agricultural water resources’ optimal allocation and the evaluation of water resources’ carrying capacity in the Hetao Irrigation District. Interval Linear Fractional Programming was used to find water availability, shortage, and use efficiency in different irrigation areas of the Hetao Irrigation District (HID) under different scenarios. The denominator of fractional planning is the environmental goal, and the numerator is the economic goal; so, the objective function of fractional programming is the utility rate required in the post-optimization analysis. Future water availability and shortage scenarios are adopted consistent with the Representative Concentration Pathways’ (RCPs’) framework, and future water use scenarios are developed using the Shared Socioeconomic Pathways’ (SSPs’) framework. Results revealed that under SSP1, the annual water consumption increased from 30 billion m3 to 60 billion m3, almost doubling in Urad. The annual water consumption under SSP2 and SSP3 increased slightly, from 30 billion m3 to about 50 billion m3. The amount of water available for well irrigation in Urad decreased from 300 to 250 billion m3, while the amount of water available for canal irrigation in Urad remained at 270 billion m3 from 2010 s to 2030 s, only dropping to 240 billion m3 in 2040 s. The entropy-weight-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method was applied to evaluate agricultural water resources’ allocation schemes because it can avoid the subjectivity of weight determination and can reflect the dynamic changing trend of irrigation district agricultural water resources’ carrying capacity. The approach is applicable to most regions, such as the Hetao Irrigation District in the Upper Yellow River Basi with limited precipitation, to determine water strategies under the changing environment.

Risk regulation of water allocation in irrigation areas under changing water supply and demand conditions

The uncertainty of the hydrological environment and unbalanced water resource allocation result in a high risk of irrigation water shortages in regional agriculture, which seriously affects the sustainable development of agricultural systems. In this paper, we propose a risk regulation based modeling approach for the optimal allocation of agricultural water resources in a complex stochastic environment. The approach includes a conditional value-at-risk (CVaR) model, two-stage stochastic programming (TSP) model, two-dimensional joint distribution probability (JP) model, fractal criteria, and a multiple forms of chance-constrained programming (CCP) model. The model can weigh the contradiction between the intended target and associated penalties attributed to unknown hydrological events, measure the risk between system benefits and expected losses in agricultural water allocation at different confidence levels, and address the randomness in the objective function and constraints (including the left end term, right end term, and left and right end terms). To verify the applicability of the method, it is applied to the Jinxi Irrigation District in China to optimize the allocation and risk regulation of limited water resources under the variable runoff conditions of the Songhua River and crop water demands in the irrigation area. By adjusting parameters such as risk preference and probability of violation, the risk of water shortages in the irrigation area can be regulated, and the multidimensional impacts of different water allocation schemes on agricultural economic benefits, social benefits, ecology and environment can be determined. The case study reveals that the CTSP-CCJP method is sensitive, applicable to complex and uncertain environments and important for the efficient use of agricultural water resources and risk reduction.

Study on the Matching Method of Agricultural Water and Land Resources from the Perspective of Total Water Footprint

The matching status of agricultural water and land resources is a prerequisite for grain production. The influence of gray water footprint has not been paid attention to in the study of agricultural water and land resources matching based on water footprint. To measure the matching status of agricultural water and land resources more comprehensively, the total water footprint (including blue, green and gray water footprint) and the cultivated land area was taken as the characterization parameters of water and land resources, respectively. The Gini coefficient model, and the agricultural water and land resources matching coefficient model were constructed to calculate the matching degree of agricultural water and land resources in a cold region (Heilongjiang Province) of China. Based on the amount of agricultural water consumption, the equivalent coefficient model was used to evaluate the degree of agricultural water and land resources shortage or to be developed. The result of agricultural water and land resources matching coefficient model showed that the matching degree of agricultural water and land resources in Heilongjiang Province is getting better year by year, which is consistent with the calculations determined from the Gini coefficient. The result of the equivalent coefficient method based on agricultural water consumption was consistent with the result of the Gini coefficient method based on total water footprint, which is verified that it is scientific and reasonable to take the total water footprint as the characterization parameter of water resource. The findings may provide implications for the spatial optimal allocation of regional agricultural water and land resources.

An improved fuzzy sorting algorithm coupling bi-level programming for synergetic optimization of agricultural water resources: A case study of Fujian Province, China

The uneven allocation of water resources and the shortage of regional water resources pose great challenges to the economic development and regional development balance of the Fujian province. Optimizing the water allocation structure in different regions can effectively alleviate water pressure. In this study, a type-2 fuzzy bi-level programming (T2FBL) method is proposed to plan the agricultural water resource system in the Fujian province. This method uses an improved fuzzy sorting algorithm to deal with uncertain parameters in the system and combines the bi-level programming method to balance the trade-off between two levels of decision-makers, the uncertain information contained in the secondary membership function omitted in the interval type-2 fuzzy theory is considered in the new ranking algorithm. Multiple scenarios related to different food security needs and different risk indices are examined. The major findings are as follows: (i) With an average tolerance of 75%, the average gross agricultural output value under various scenarios increased (0.4% ∼ 7%) (average 3.89%) after optimization. (ii) The regional water allocation scheme under different food demands and different water availability scenarios is calculated, and the results show that prioritizing adjustments to the industrial water distribution structure of Fuzhou and Zhangzhou will greatly relieve the water pressure in the Fujian province. (iii) The relationship between the availability of system water resources and economic benefits is given through the calculation results of the T2FBL model. These findings can provide an in-depth understanding of the interaction between agricultural, industrial and tertiary industry water allocation and provide technical support for agricultural water resource planning issues.

An interval multi-objective fuzzy-interval credibility-constrained nonlinear programming model for balancing agricultural and ecological water management

This study presents an interval multi-objective fuzzy-interval credibility-constrained nonlinear programming (IMFICNP) model combined with spatial water requirement of ecological vegetation (SEWR) estimation for solving the problem of allocation of agricultural and ecological water in irrigation districts under uncertainties. Through techniques of remote sensing (RS) and geographic information system (GIS), the ecological vegetation is subdivided into three types including forest land, grassland and shrubland and the water requirement of ecological vegetation is extended from site-specific sample to spatial decision-making unit (DMU), which provides a set of spatial data for input parameters of constraints. The IMFICNP model can be formulated through combination of interval parameter programming, multi-objective programming and fuzzy-interval credibility-constrained programming, which can handle the conflicts of multiple objectives under uncertainties such as single uncertainty (interval and fuzzy parameters) and dual-uncertainties (fuzzy-interval sets), and finally generate optimal water allocation schemes for crop and ecological vegetation under different credibility levels. The interval quadratic crop water production functions (IQCWPFs) are introduced to express the nonlinear relationships between crop yield and irrigation amount. Then, this model is applied to a case study of Huangyang Irrigation District (HID) in Shiyang River Basin to demonstrate its applicability. The results indicate that a higher credibility level is accompanied by less amount of water allocation and lower system benefit. The amount of water allocation at the DMU is dominated by planting area of crops and ecological vegetation, but there are few exceptions that optimal solutions are determined by the economic value. In addition, SEWR enables to reflect spatial heterogeneity of the DMU at a larger scale. IMFICNP model can coordinate conflicts among multiple objectives and it can tackle the violation of system constraints with fuzzy-interval sets. Therefore, these results can effectively balance the agricultural and ecological water management in irrigation districts, and provide valuable basis for the sustainable development of arid and semi-arid areas.