本文選題：EPIC　切入點(diǎn)：水稻　出處：《浙江師范大學(xué)》2015年碩士論文

【摘要】：全球環(huán)境變化逐漸成為人們關(guān)注的熱點(diǎn)和焦點(diǎn)問題,其中最為突出的是全球變暖趨勢日益加劇。隨著全球變暖問題的凸顯,全球旱災(zāi)的發(fā)生頻率和影響范圍也在不斷擴(kuò)大。旱災(zāi)是世界上影響范圍最廣,造成農(nóng)業(yè)損失最大的自然災(zāi)害之一,它嚴(yán)重影響著全球糧食生產(chǎn)和糧食安全。水稻作為一種易受干旱影響的作物,其生產(chǎn)面臨巨大挑戰(zhàn)。在水稻生產(chǎn)研究中,作物模型是一種常用的手段。但幾乎所有的模型都很難具有普適性,在不同區(qū)域應(yīng)用時都需要對模型的參數(shù)進(jìn)行校準(zhǔn)。對于參數(shù)繁雜的模型而言,對其中所有參數(shù)進(jìn)行調(diào)整工作量巨大,難以實(shí)現(xiàn)。參數(shù)敏感性分析即是從眾多參數(shù)中識別和選擇關(guān)鍵的控制參數(shù),篩選出引起模型結(jié)果不確定性的主要因素的一種有效手段。本文以EPIC作物模型為基礎(chǔ),對一定區(qū)域內(nèi)水稻作物參數(shù)進(jìn)行敏感性分析。并以我國南方水稻主產(chǎn)區(qū)為例對敏感性分析結(jié)果的空間差異性進(jìn)行分析。具體步驟包括,從EPIC模型眾多水稻作物參數(shù)中篩選出對模擬產(chǎn)量可能有一定影響的參數(shù),并進(jìn)一步確定這些參數(shù)的取值范圍和分布形式,進(jìn)而進(jìn)行敏感性分析。本文選取了8個代表性地點(diǎn)進(jìn)行敏感性分析,分析結(jié)果顯示受不同地區(qū)環(huán)境影響作物參數(shù)的敏感性差異較大。為探究敏感性分析結(jié)果的空間差異性,本文以我國南方水稻主產(chǎn)區(qū)為例,對研究區(qū)內(nèi)水稻種植點(diǎn)的作物參數(shù)進(jìn)行敏感性分析,同時利用相關(guān)性分析進(jìn)一步探究環(huán)境要素與敏感參數(shù)之間的關(guān)系。通過研究得到以下主要結(jié)論：(1)敏感性分析可以有效地篩選出對模型輸出結(jié)果影響較大的參數(shù),用戶通過確定和調(diào)整這些參數(shù)完成模型校準(zhǔn)。因此,敏感性分析在模型校準(zhǔn)、模型簡化等方面有較大的應(yīng)用潛力。同時,EFAST作為一種簡單、高效、準(zhǔn)確的全局敏感性分析方法,可以在較少的樣本量下完成全局敏感性計(jì)算。相較于一階敏感性分析而言,全局敏感性全面考慮參數(shù)對模型結(jié)果的影響,以及參數(shù)之間相互作用對模型結(jié)果的影響。(2)本文通過對比8個不同地點(diǎn)的敏感性分析,結(jié)果發(fā)現(xiàn)其具有明顯的空間差異性。以我國南方水稻區(qū)為例,總體敏感性較高的參數(shù)為WA、HI、TBS和TOP等4個參數(shù)。同時不同地點(diǎn)的最敏感參數(shù)不同,且各參數(shù)的敏感指數(shù)有區(qū)域性差異。(3)通過計(jì)算各參數(shù)的敏感指數(shù)與環(huán)境要素的相關(guān)性,結(jié)果表明TBS與施肥量相關(guān)性最高,WA與播種日期相關(guān)性最高,HI與日最高氣溫相關(guān)性最高,TOP與日最低溫度相關(guān)性最高。從各環(huán)境要素來看,太陽輻射、日最高氣溫、日最低氣溫與TBS、WA、HI、TOP相關(guān)性較好,其中TBS與其成負(fù)相關(guān),WA、 HI、TOP與其成正相關(guān)。
[Abstract]:Global environmental change has gradually become the focus of attention, among which the most prominent is the increasing trend of global warming. The frequency and scope of global drought is also expanding. Drought is one of the most widespread natural disasters in the world, causing the greatest agricultural losses, It has a serious impact on global food production and food security. Rice, as a drought-prone crop, faces enormous challenges in its production. Crop models are a common method, but almost all models are difficult to be universally applicable, and the parameters of the models need to be calibrated when they are applied in different regions. It is difficult to realize the adjustment of all the parameters. The parameter sensitivity analysis is to identify and select the key control parameters from many parameters. An effective method for screening out the main factors that lead to uncertainty of the model results. This paper is based on the EPIC crop model. The sensitivity analysis of rice crop parameters in a certain region is carried out. The spatial differences of the results of sensitivity analysis are analyzed by taking the main rice producing areas of southern China as an example. The concrete steps are as follows:. The parameters which may affect the simulated yield were screened from many rice crop parameters in the EPIC model, and the range and distribution of these parameters were further determined. Then sensitivity analysis was carried out. In this paper, 8 representative sites were selected for sensitivity analysis. The results showed that the sensitivity of crop parameters affected by environment in different regions was different. In order to explore the spatial difference of sensitivity analysis results, In this paper, the sensitivity analysis of crop parameters of rice planting sites in southern China was carried out by taking the main rice producing areas in the south of China as an example. At the same time, the correlation analysis is used to further explore the relationship between the environmental factors and the sensitive parameters. The following main conclusions can be drawn from the study: sensitivity analysis can effectively screen out the parameters that have great influence on the output results of the model. The user completes the model calibration by determining and adjusting these parameters. Therefore, sensitivity analysis has great application potential in model calibration and model simplification. At the same time, EFAST is a simple, efficient and accurate global sensitivity analysis method. The global sensitivity can be calculated with less sample size. Compared with the first-order sensitivity analysis, the global sensitivity takes into account the influence of the parameters on the model results. By comparing the sensitivity analysis of 8 different sites, it is found that there are obvious spatial differences. Take the rice region of southern China as an example, The most sensitive parameters are different in different locations, and the sensitivity index of each parameter has regional difference. The correlation between the sensitivity index of each parameter and the environmental factor is calculated by calculating the correlation between the sensitivity index of each parameter and the environmental factor. The results showed that the correlation between TBS and fertilization amount was the highest and the correlation between TBS and sowing date was the highest. The correlation between top and daily maximum temperature was the highest, and the correlation between top and daily minimum temperature was the highest. From the point of view of various environmental factors, solar radiation and daily maximum temperature were the highest. There is a good correlation between daily minimum temperature and TBS-WAHI-TOP, in which TBS is negatively correlated with TBSWA-TOP, and HITO-TOP is positively correlated with TBSWA-TOP.
【學(xué)位授予單位】：浙江師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：S511;S423

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 梅方權(quán),吳憲章,姚長溪,李路平,王磊,陳秋云;中國水稻種植區(qū)劃[J];中國水稻科學(xué);1988年03期

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本文編號：1653729