EGI OpenIR
基于 RZWQM2 模型的预报式灌溉条件下绿洲棉田土壤水热特征
Alternative TitleSoil Water and Heat Characteristics of Oasis Cotton Field under Predictive Irrigation Based on RZWQM2 Model
李文珍
Subtype硕士
Thesis Advisor齐志明 ; 桂东伟
2019-06-30
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Discipline理学硕士
Keyword墒情灌溉 预报式灌溉 RZWQM2 土壤水热 经验漫灌 Soil Moisture Irrigation Predicting Irrigation RZWQM2 Soil Water and Heat Experience Flooding
Abstract土壤水热条件是影响作物生长发育的重要生态因子之一,不同灌溉制度及灌溉方式下的土壤水热状况不同,进而对土壤水热运移分布产生影响。为了探讨新疆策勒绿洲地区基于 RZWQM2 模型(根区水质模型)的新型预报式灌溉条件下的棉田土壤水热特征,研究预报式灌溉系统下的节水效果。本研究在策勒绿洲地区进行了三年(2016~2018)的田间试验,设置了 2 种灌水模式:基于计算机模型的预报灌溉与基于土壤墒情的灌溉,每种灌水模式设置 2 种灌溉梯度:充分灌溉(100%)和非充分灌溉(75%的充分灌溉)。首先对两种灌溉制度进行田间实验对比,突出新型预报式灌溉的优势,其次着重对新型预报式灌溉进行研究,利用田间实测数据对模型进行率定与验证,并利用十年的气象数据输入 RZWQM2 模型模拟田间变量,并与传统漫灌进行了长期的对比研究。得出以下结论:(1)不同灌溉处理的作物产量在一定范围内随灌溉量的增加而增加,预报充分的产量较预报亏缺,墒情充分,墒情亏缺分别提高 13.7%、 12.1%、 47.6%。水分利用效率表现为预报亏缺最高,且产量与预报充分的产量无显著差异。本结果表明,预报亏缺灌溉与其他 3 种灌溉方式相比,可达到节水增产的目的。因此该灌溉方式可为农业节水增产提供科学依据。(2)根据实测土壤水分、温度、株高、叶面积指数、生物量和作物产量数据对 RZWQM2 模型进行了率定与验证。结果表明:除深层土壤含水量模拟效果略差些外,土壤含水量总体模拟效果较好。模型对土壤温度、生物量和产量的模拟效果均较好,模拟值与实测值能够较好的吻合,尤其是对生物量与产量的模拟,PBIAS 分别在-22.9%~20%, -3.8 %~10%之间; IOA≥0.94。 本研究表明 RZWQM2模型能够较准确地模拟绿洲棉田的土壤水分、温度、生物量和产量,有助于准确的评估绿洲作物水分胁迫状况,从而提高预报系统的精确性, 从而为使用该模型指导农业管理以实现节水增产的目标提供科学依据。(3)利用 RZWQM2 模型对田间变量进行了长期模拟,分析了土壤水热变化特征,结果表明: 2006~2015 年的土壤水分、温度、冠层温度变化不明显,蒸散发有下降的趋势。预报灌溉相比于传统漫灌有所减产,但却能节约较多的灌溉用水,这对新疆干旱地区的水资源管理具有一定的指导意义。综上,基于 RZWQM2 模型新型预报式灌溉相比于墒情灌溉在节约水资源的同时增加作物产量,且相比于传统的大田经验漫灌能够在策勒绿洲缺水地区节约较多田间用水。同时,能够实现自动化灌溉,可节约劳动成本和时间成本。但在RZWQM2 模型在模拟产量的精度方面有待完善与提高。
Other AbstractSoil water and heat conditions are one of the important ecological factors affectingcrop growth and development. Soil water and heat conditions are different underdifferent irrigation schemes and modes, thus affecting soil water and heat transport anddistribution. In order to study the soil water and heat characteristics of cotton field undera new type of predictive irrigation based on RZWQM2 model (root zone water qualitymodel) in Cele Oasis, Xinjiang, and to study the water-saving effect of predictiveirrigation system. In this study, a three-year field experiment was conducted in Celeoasis region (2016-2018). Two irrigation modes were set up: predictive irrigation basedon computer model and irrigation based on soil moisture. Two irrigation gradients wereset for each irrigation mode: full irrigation (100%) and deficit irrigation (75%). Firstly,field experiments were conducted to compare the two irrigation schemes, highlightingthe advantages of the new type of predictive irrigation. Secondly, the new type ofpredictive irrigation was studied. The model was calibrated and validated using fieldmeasured data, and the field parameters were simulated by using 10-yearmeteorological data input RZWQM2 model, and compared with traditional floodingirrigation for a long time. The following conclusions are drawn:(1) The crop yield of different irrigation treatments increased with the increase ofirrigation amount in a certain range. The yield of the full irrigation of the forecasting isbetter than the deficit irrigation of the forecasting, and full and deficit irrigation for soilmoisture is increased by 13.7%、 12.1%, and 47.6%, respectively. The water useefficiency showed full irrigation of the forecasting was the highest, and the yield hadno significant difference with full irrigation of the forecasting. The results show thatcompared with the other three irrigation methods, the predicted deficit irrigation canachieve the goal of saving water and increasing production. Therefore, this irrigationmethod can provide scientific basis for water saving and yield increase in agriculture.(2) The RZWQM2 model was calibrated and validated based on measured soil moisture, temperature, plant height, leaf area index, biomass and crop yield data. Theresults show that except for the slightly worse simulation effect of deep soil watercontent, the overall simulation effect of soil water content is better. The simulationresults of soil temperature, biomass and yield of the model are good, and the simulationvalues are in good agreement with the measured values. Especially for the simulationof biomass and yield, PBIAS is between - 22.9%-20%, - 3.8%-10%. IOA is more than0.94. This study shows that RZWQM2 model can accurately simulate the soil moisture,temperature, biomass and yield of oasis cotton field, help to accurately assess the waterstress of oasis crops, and thus improve the accuracy of forecasting system, so as toprovide scientific basis for guiding agricultural management to achieve the goal ofwater saving and yield increase.(3) Using RZWQM2 model, the field parameters were simulated for a long time, andthe characteristics of soil water and heat were analyzed. The results showed that soilmoisture, temperature and canopy temperature did not change significantly from 2006to 2015, and evapotranspiration had a downward trend. Compared with the traditionalflooding irrigation, the predicted irrigation can reduce the yield, but save moreirrigation water, which has a certain guiding significance in the management of waterresources in the arid areas of Xinjiang.In conclusion, compared with soil moisture irrigation, the new forecasting irrigationbased on RZWQM2 model not only saves water resources but also increases crop yield,but also saves more field water in the water-deficient area of Cele Oasis compared withtraditional field experience flooding irrigation. At the same time, it can realizeautomatic irrigation and save labor cost and time cost. However, the accuracy ofRZWQM2 model in simulating production needs to be improved.
Subject Area生态学
Language中文
Document Type学位论文
Identifierhttp://ir.xjlas.org/handle/365004/15288
Collection中国科学院新疆生态与地理研究所
研究系统
Affiliation中国科学院新疆生态与地理研究所
First Author Affilication中国科学院新疆生态与地理研究所
Recommended Citation
GB/T 7714
李文珍. 基于 RZWQM2 模型的预报式灌溉条件下绿洲棉田土壤水热特征[D]. 北京. 中国科学院大学,2019.
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