Modeling the Effects of Spatial Variability of Irrigation Parameters on Border Irrigation Performance at a Field Scale

The interaction between surface and subsurface water flows plays an important role in surface irrigation systems. This interaction can effectively be simulated by the physical-based models, which have been developed on the basis of the numerical solutions to the Saint-Venant and Richards’ equations. Meanwhile, the spatial variability of field physical properties (such as soil properties, surface micro-topography, and unit discharge) affects the interaction between surface and subsurface water flows and decreases the accuracy of simulating surface irrigation events at large scales. In this study, a new numerical methodology is developed based on the physical-based model of surface irrigation and the Monte Carlo simulation method to improve the modeling accuracy of surface irrigation performance at a field scale. In the proposed numerical methodology, soil properties, unit discharge, surface micro-topography, roughness, border length, and the cutoff time for the unit discharge are used as the stochastic parameters of the physical-based model, while field slope is assumed as the constant value because of the same field tillage and management conditions at a field scale. Monte Carlo simulation is used to obtain the stochastic parameter sample combinations of the physical-based model to represent the spatial variability of field physical properties. The updated stochastic simulation model of surface micro-topography, which is developed to model the spatial distribution of surface elevation differences (SED), is used to obtain the surface micro-topography samples at a field scale. Compared with the distributed-parameter modelling methodology and the field experimental data, the proposed numerical methodology presents the better simulation performance

Optimizing the planting structure in Daxing District in 2020 based on inaccurate two-stage planning model and grey model

In order to optimize the planting structure and use water more efficiently, an inaccurate two-stage planning model is proposed in this paper. This model can not only reflect uncertainty of the probability distribution in the form of the possible distribution interval, but also build a recourse relationship between expected benefits and penalties for failing to achieve target goals. The two-stage planning model, combined with the gray GM (1.1) model, is applied to Daxing district of Beijing to optimize and adjust planting areas of the grain crops, fruits and vegetables, and garden plots in 2020. In the meantime, three scenarios were established for comparative analysis. Results show that after optimization, the economic benefits of above-mentioned three planting areas in Daxing district in 2020 is 3.71 billion CNY, an increase of 348 million from 2016 CNY; the total water consumption is 64.17 million cubic meters, a decrease of 62.79 million cubic meters from 2016. Results indicate that this model method is feasible for optimizing planting structure, and to some extent, can provide decision-making support and a theoretical basis for planting structure optimization and prediction in similar areas to Daxing district

Optimizing the planting structure in Daxing District in 2020 based on inaccurate two-stage planning model and grey model

In order to optimize the planting structure and use water more efficiently, an inaccurate two-stage planning model is proposed in this paper. This model can not only reflect uncertainty of the probability distribution in the form of the possible distribution interval, but also build a recourse relationship between expected benefits and penalties for failing to achieve target goals. The two-stage planning model, combined with the gray GM (1.1) model, is applied to Daxing district of Beijing to optimize and adjust planting areas of the grain crops, fruits and vegetables, and garden plots in 2020. In the meantime, three scenarios were established for comparative analysis. Results show that after optimization, the economic benefits of above-mentioned three planting areas in Daxing district in 2020 is 3.71 billion CNY, an increase of 348 million from 2016 CNY; the total water consumption is 64.17 million cubic meters, a decrease of 62.79 million cubic meters from 2016. Results indicate that this model method is feasible for optimizing planting structure, and to some extent, can provide decision-making support and a theoretical basis for planting structure optimization and prediction in similar areas to Daxing district