作物灌水量随机模拟研究

针对降雨、蒸发蒸腾量的随机性,在制定作物灌溉计划时,将降雨、蒸发蒸腾量进行随机化处理,采用基于时间序列的随机水文学方法对降雨和蒸发过程进行了模拟,将随机模拟出的降水、蒸发蒸腾结果代入水量平衡方程,从而确定灌溉时间和灌水量。经过和实测序列进行对比,模拟值和实际值拟合较好,最大误差仅为4.74%,可为科学制定作物灌溉计划提供参考。     

应用极差法求年灌溉用水量频率曲线的方法

灌溉用水量是水利化区划、灌区规划设计及管理运用的基本依据。除西北干旱地区以外,在灌溉水库径流调节计算中,无论是时历法、典型年法还是统计法,都需要年灌溉水量频率曲线。确定年灌溉水量(定额)频率曲线的统计参数,需要根据逐年灌溉水量资料求得。求逐年灌溉水量(定额)要求具备大量的试验、调查数据及气象水文资料,而且需要进行多年逐日田间水量平衡计算,相当费时。通过降雨量与有效降雨量相关法计算灌溉水量(定额),比按逐日田间水量平衡计算方法简化,但仍需计算多年灌溉水量(定额),还是相当麻烦的。     

基于多年降雨资料的作物灌溉制度多目标优化

在非充分灌溉制度条件下,基于农田水量平衡模拟模型和作物产量计算模型并考虑随机降雨的影响,以灌溉日期和灌溉水量为决策变量,将多年作物相对产量均值最大、多年作物相对产量方差最小以及作物全生育期的总灌溉水量最小作为优化目标,建立了能够同时对灌溉日期和灌溉水量进行优化的多目标优化模型.以玉米的非充分灌溉制度优化为例用上述模型及算法进行了计算分析,并与典型年法得到的优化结果进行了对比,结果表明:基于多年降雨资料的优化灌溉制度具有较强的适应性和鲁棒性,可以避免由于灌溉日期安排不合理而导致的减产或绝收问题.     

基于数值模拟的太湖流域地区节水条件下水田灌排水量研究

太湖流域内农业用水粗放,水利用效率低下,农业面源污染严重,推广水稻节水灌溉势在必行。为了研究水稻节水灌溉对太湖流域用水总量控制的影响效应以及对太湖流域水资源量和水环境的影响效应,需要弄清在不同保证率下节水灌溉和淹水灌溉单位面积的灌溉水量和排水量。改进了逐日水量平衡法中蒸散发项和渗漏项的计算方法,并对该方法中各个参数的确定做了说明。最后以杭嘉湖区为例,选用1956-2000年系列资料模拟灌溉排水过程,同时采用P?Ⅲ型曲线计算不同保证率下节水灌溉和淹水灌溉的灌溉水量,并比较了两者的效益。     

非充分灌溉农田土壤水分动态模拟模型

系统阐述了非充分灌溉条件下农田土壤水分动态变化的二种模拟模型 ,即大田水量平衡模拟模型和土壤水运动模拟模型 ,提出了农田计划湿润层土壤含水量非线性变化的计算方法 ,结合实例对二种模型进行分析比较 ,为非充分灌溉决策提供了新的理论依据。     

基于改进NSGA-Ⅱ的作物灌水量与灌溉日期同步优化

在非充分灌溉条件下,基于农田水量平衡模拟模型和作物产量计算模型,以灌水日期和灌溉水量为决策变量,将作物相对产量最大和作物全生育期的总灌溉水量最小作为优化目标,建立了能够同时对灌水日期和灌溉水量进行优化的多目标优化模型.在模型求解方面,设计了适合于此类优化问题的染色体结构,在精英保留非劣排序遗传算法(NSGA-Ⅱ)的基础...     

冬小麦根层土壤水量平衡的系统动力学模型

为获取冬小麦根系层水量转化情况,该文采用系统动力学的建模思想和Vensim软件构建了冬小麦一维逐日土壤水量平衡模型。模型将2m土层概化为十个串联的水箱,计算了灌溉降雨后的土壤水分下渗、土壤蒸发、作物蒸腾、毛管上升补给和水分重分配等物理过程。利用河北省石津灌区军齐干渠北二支一斗渠2007－2009年两季冬小麦的田间试验资料对模型进行了率定和验证,结果显示率定期和验证期的平均残差比例和分散均方根比例均在15%以内。三种极端条件测试和六种参数的敏感性测试以及与Hydrus-1模型的比较表明模型假定合理,没有发生结构性错误。对灌区两季冬小麦生育期的土壤水分转化进行模拟,结果表明降雨和灌溉是主要供水水源,毛管水上升量很小,底部渗漏较大,而土壤储水量变化很小。     

作物非充分灌溉制度优化的01规划模型建立与应用

以农田水量平衡模型及作物水分生产函数模型为基础,引入0-1变量描述在可能的灌溉期内是否进行灌溉,建立了非充分灌溉制度优化的0-1规划模型。模型利用Microsoft Excel的规划求解工具进行求解。将该模型应用于山西省潇河灌区冬小麦灌溉制度优化,结果与现有模拟-优化模型比较接近,但求解过程更为简单。结果表明研究区冬小麦灌溉的关键期是拔节末期(5月上旬);初始土壤含水率较低时,适宜灌水时间有所提前,灌溉的增产效果也更明显;冬小麦蒸散发量及相对产量均随灌水量的增加而增加,但边际产量却逐渐降低。     

逐日降水的随机模拟模型及其在黑龙江省的应用

在模拟逐日降水数据方面,近些年来的一种通用方法是联合应用一阶马尔科夫链和伽玛分布函数建立随机模拟模型,这种方法在国外很大的环境范围内被证明是有效的。为此,应用黑龙江省14个站点56年的实测降水数据,针对马尔科夫两状态转移概率和伽玛分布参数的不同来源,即统计分析实测降水数据直接获得和利用实测降水数据建立回归模型间接推求,分别模拟了各站点的1 000年逐日降水过程。结果表明:应用一阶马尔科夫链和伽玛分布函数可以很好地模拟黑龙江省14个站点的逐日降水过程,模型的模拟精度很高,证明了这种方法是有效的;但国外推求转移概率和伽玛分布参数的经验模型和长系列法在黑龙江省不完全适用,应用重新建立的回归模型推求的转移概率和伽玛分布参数模拟的逐日降水数据的精度同样很高。     

回归水模拟的系统动力学模型

以水量平衡原理为基础,运用系统动力学方法,并以Vensim软件包为工具,建立了稻田田间尺度水平衡转化过程的模拟模型,同时将此模型应用于稻田回归水量的模拟分析。通过对漳河灌区稻田田间尺度水量平衡转化过程的检验表明,模拟的地表出流过程与实测值吻合;模拟的田面水位的涨落趋势也与实测过程一致,田间水位动态模拟峰值最大误差为10%;模型能反映稻田水量平衡转化过程。对田间尺度回归水量的动态模拟表明:地表回归水与来水量的变化趋势一致,地下回归水变化比较平稳,逐日回归水量动态变化随着来水量的增加表现出增加的趋势,同样累积回归水量随累积来水量的增加而增加。     

Evaluation of irrigation schemes for sugarcane in Sindh,Pakistan, using SWAP93

A computer simulation model, SWAP93, was used to simulate the soil water balance of sugarcane (Saccharum officinarum L.) over a period of 6 years, in order to develop an efficient irrigation scheduling scheme for Sindh, Pakistan. Given the limitations and inflexibility of the existing warabandi irrigation system, which does not allow on-demand irrigation, only irrigation depth and irrigation interval were varied in order to assess the best irrigation depth/interval combination for sugarcane production. Twelve irrigation treatments were simulated. These treatments were four irrigation amounts (900, 1200, 1650 and 1800 mm) and three irrigation frequencies (7, 10 and 15 days). Three seasons with rainfall totaling less than 20 mm were compared with three seasons of over 200 mm rainfall. Two approaches were used in assessing the irrigation schemes: yield parameters and water management response indicators. Treatment parameters (e.g. irrigation amounts, weather conditions, soil characteristics, etc.) served as input for SWAP93, actual transpiration was calculated and then used in a crop water production function to predict yield and water use efficiency. Additionally, water management response indicators were derived from model outputs, and used to assess the impact of the schemes on soil salinity and water logging. Both these indicators and the yield and water use efficiency indicated that a seasonal total of 1650 mm, applied at a 15-day interval was the best irrigation scheme for the region.     

Reducing peak supplemental irrigation demand by extending sowing dates

Supplemental irrigation (SI) is a common practice in the dry environments and aims at improving and stabilizing rainfed crops by adding small amounts of water to rainfed crops during times when rainfall fails to provide sufficient moisture for normal plant growth. Results from long-term research in experimental stations and farmer fields showed substantial increases in rainfed crop yields and water use efficiency in response to SI. Nevertheless, SI comes at a cost.The date of sowing winter wheat in a rainfed Mediterranean-type environment depends upon the onset of rainfall. The optimal date for achieving highest yield under rainfed conditions is around mid-November. However, farmers tend to sow wheat later than this date because of the delay and/or unreliability of initial rains. With SI, early sowing and crop establishment can be ensured. However, early sowing of all the fields’ results in higher water demand during a very short period in spring because all the fields will be at the peak use rate. Spreading out dates of sowing allows peak water demand to occur over a longer period, thus reducing the discharge and the size of irrigation system needed, and hence improves the economics of this practice. In this paper, the impact of adopting a multi-sowing date strategy on farm water demand and crop production is considered. A simplified optimization model solved by linear programming is presented. Four-years’ data (1992–1996) from field experimental research conducted on bread wheat in northern Syria have been used in the analysis.We showed that a multi-sowing date strategy has reduced the peak farm water demand rate by more than 20%, thus potentially reducing irrigation system capacity and/or size. Alternatively, the water demand rate of a larger area can be met with the same water supply. However, optimal sowing dates that minimize farm water demand rate do not always maximize total farm production. The outcome depends on crop water requirements and yield for each sowing date. Furthermore, this selection is greatly influenced by the level of water scarcity. The approach used can help in reducing the cost of irrigation and improving the efficiency of water use in SI.     

基于贝叶斯神经网络的农田分区灌溉需水量模拟分析

针对农田分区灌溉需水量模拟过程中普遍存在的求解过程易陷入局部最小化、出现过度拟合,以及过度依赖历史用水数据,导致最终模拟结果存在显著误差的问题,研究基于贝叶斯神经网络的农田分区灌溉需水量模拟分析方法。以前一周需水量、年内月需水量占比、日内温度上限值及日降雨量为指标,通过聚类分析获取指标数据均值,对农田分区灌溉历史用水的样本数据进行聚类分析。构建贝叶斯神经网络模型,将指标数据均值输入模型,根据BP神经网络原理与贝叶斯规则训练指标数据,然后输出农田分区灌溉需水量模拟结果。试验结果显示数据聚类结果中数据间关联度高于95%,数据拟合效果较好,模拟需水量时具有更高的精度与稳定性。      

A mathematical model for simulating water balances in cropped sandy soil with conventional flood irrigation applied

In this paper, a model that integrates various complex model components for the purposes of water balance modeling throughout crop development in arid inland region under the conventional flood irrigation practiced is presented. These components are modules for calculating dynamic soil water content based Richard's equation, potential and actual evapotranspiration, and crop root water uptake. Soil water content in the active root zone and soil evaporation simulation obtained from the model were test using field data in 2003. The low values of MARE and high values of R² and PE in the active root zone of soil profile as well as daily soil evaporation indicated that the soil water balance simulation model presented in the paper can be used with reliable accuracy to simulate the components of water balance in cropped sandy soil under the conventional flood irrigation condition in arid inland regions. The model simulation on components of water balance using observed field data in 2004 indicated that large quantities – about 43% of irrigation water (amounting to 840 mm) – were consumed by deep percolation, only small (less than 41%) proportions of irrigation water used by the plants for transpiration. The current irrigation scheme is characterized by the unreasonable agricultural water management with the waste of water in the irrigational system in this region. The impact of irrigation scheduling on water balance presented in this paper showed that the reasonable irrigation scheme with more frequent irrigation and less amounts is more suitable for the irrigation of spring wheat in Heihe River basin, northwest China. Therefore, to establish a decision-making system for agricultural irrigation scheme and to utilize the limited water resources in this region have become an urgent problem that needs to be solved.     

Regional analysis of irrigation water requirements using kriging: Application to potato crop (Solanum tuberosum L.) at Trás-os-Montes

Aiming at the analysis of the regional variation of potato crop irrigation water requirements over the Trás-os-Montes region, data from 106 rainfall stations and eight weather stations were utilized in an irrigation scheduling simulation model to estimate net irrigation water requirements of the potato crop. The simulation model was first validated using a field experiment which allows to derive the required crop data to be used in the simulations. The reference evapotranspiration (ET₀) was estimated using the FAO Penman–Monteith method. The model was applied to all 106 locations, each with a data set spanning a 19-year period. As a result of this application, series of the net irrigation water requirements for a 19-year period were obtained for each location. The resulting 106 point values of the net water requirements of the potato crop have been treated as a regionalized variable. The respective semivariograms have been computed and the kriging method then applied to estimate the spatial distribution of the water requirements in the region. Contour lines of this regionalized variable have been drawn using a GIS system. Results show an estimation error averaging 5% for the entire region.     

Application of EPIC model for irrigation scheduling of sunflower in Southern Italy

A crop simulation model was used to optimize irrigation scheduling for sunflower grown in Southern Italy. The EPIC model, calibrated and validated in previous studies, was run for a 45-year simulation with climatic data measured daily, using 66 scenarios involving a combination of irrigation times (at five fixed data), seasonal irrigation amounts (from 0 to 200 mm) and irrigation frequency.The results obtained from the simulation indicate: (i) the superiority of single or double irrigation in the central phase (bud flower opening, flowering) for seed and biomass yield; (ii) the optimal value of seasonal irrigation water to be about 250–300 mm for the highest water use efficiency value; (iii) the highest profitability for the farmer achieved with a single irrigation of 200 mm at bud flower phase and a reduction of net income with irrigation at sowing or at seed ripening phases.The EPIC model, due to its sub-models (growth, weather, soil, management, water, erosion, economic, etc.), can be considered a useful tool for comparing several management strategies, and requires a minimum investment of time and money. This approach could be used at the farm level or on a larger scale.     

A real-time water allocation model for large irrigation systems

In this study a simulation model for real-time irrigation scheduling of water deliveries at the tertiary and secondary canal levels of large irrigation systems has been developed. The model is responsive to current season changes in weather and other variables. The irrigation scheduling of the subsequent week is found out at the end of each week by updating the status of the system with real time data up to that week and then by solving the model for the new conditions. The model is based on water balance approach for lowland paddy and a soil moisture simulation approach for determining the irrigation requirements of upland crops. Expected rainfall at different probability levels during the irrigation season was used based on past rain fall data and Leaky law. The model was applied to an irrigation system in Thailand for determining the required irrigation deliveries. Result of the application indicate that the model can be used for determining water deliveries in a real-time basis.     

Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing

As water resources are limited and the demand for agricultural products increases, it becomes increasingly important to use irrigation water optimally. At a farm scale, farmer's have a particularly strong incentive to optimize their irrigation water use when the volume of water available over a season is production limiting. In this situation, a farmer's goal is to maximize farm profit, by adjusting when and where irrigation water is used. However, making the very best decisions about when and where to irrigate is not easy, since these daily decisions require consideration of the entire remaining irrigation season. Future rainfall uncertainty further complicates decisions on when and which crops should be subjected to water stress. This paper presents an innovative on-farm irrigation scheduling decision support method called the Canterbury irrigation scheduler (CIS) that is suitable when seasonal water availability is limited. Previous optimal scheduling methods generally use stochastic dynamic programming, which requires over-simplistic plant models, limiting their practical usefulness. The CIS method improves on previous methods because it accommodates realistic plant models. Future farm profit (the objective function) is calculated using a time-series simulation model of the farm. Different irrigation management strategies are tested using the farm simulation model. The irrigation strategies are defined by a set of decision variables, and the decision variables are optimized using simulated annealing. The result of this optimization is an irrigation strategy that maximizes the expected future farm profit. This process is repeated several times during the irrigation season using the CIS method, and the optimal irrigation strategy is modified and improved using updated climate and soil moisture information. The ability of the CIS method to produce near optimal decisions was demonstrated by a comparison to previous stochastic dynamic programming schedulers. A second case study shows the CIS method can incorporate more realistic farm models than is possible when using stochastic dynamic programming. This case study used the FarmWi$e/APSIM model developed by CSIRO, Australia. Results show that when seasonal water limit is the primary constraint on water availability, the CIS could increase pasture yield revenue in Canterbury (New Zealand) in the order of 10%, compared with scheduling irrigation using current state of the art scheduling practice.     

Evaluating irrigation performance in a Mediterranean environment

Assessment of irrigation performance is a prerequisite for improving water use in the agricultural sector to respond to perceived water scarcity. Between 1996 and 2000, we conducted a comprehensive assessment of the performance of the Genil–Cabra irrigation scheme (GCIS) located in Andalusia, southern Spain. The area has about 7,000 ha of irrigated lands distributed in 843 parcels and devoted to a diverse crop mix, with cereals, sunflower, cotton, garlic and olive trees as principal crops. Irrigation is on demand from a pressurized system and hand-moved sprinkler irrigation is the most popular application method. Six performance indicators were used to assess the physical and economic performance of irrigation water use and management in the GCIS, using parcel water-use records and a simulation model. The model simulates the water-balance processes on every field and computes an optimal irrigation schedule, which is then checked against actual schedules. Among the performance indicators, the average irrigation water supply:demand ratio (the ratio of measured irrigation supply to the simulated optimum demand) varied among years from 0.45 to 0.64, indicating that the area is under deficit irrigation. When rainfall was included, the supply:demand ratio increased up to 0.87 in one year, although it was only 0.72 in the driest year, showing that farmers did not fully compensate for the low rainfall with sufficient irrigation water. Nevertheless, farmers in the area made an efficient use of rainfall, as indicated by the relatively high values (0.72–0.83) for the ratio of actual:attainable crop yields. Water productivity (WP) in the GCIS oscillated between 0.72 €/m³ and 1.99 €/m³ during the 4 years and averaged 1.42 €/m³ of water supplied for irrigation, while the irrigation water productivity (IWP) averaged 0.63 €/m³ for the period studied. WP is higher than IWP because WP includes production generated by rainfall, while IWP includes only the production generated by irrigation.Communicated by A. Kassam