本文選題：空間變異 + 尺度效應(yīng)��； 參考：《內(nèi)蒙古農(nóng)業(yè)大學(xué)》2016年博士論文

【摘要】：土壤是高度不均一的歷史自然體,其特性參數(shù)空間分布具有一定的非均一性,且空間變異具有尺度效應(yīng),所以描述其不同尺度空間變異特性及變化過程等問題是當(dāng)前灌溉水文學(xué)研究的熱點(diǎn)。自然土壤特性在水平和垂直方向上都存在變異性,且不同土層土壤特性的空間變異存在著一定的相互關(guān)系,探求不同土層土壤特性空間變異特性及其相互對(duì)應(yīng)的關(guān)系,對(duì)于了解土壤特性在三維空間上的變化規(guī)律及模擬和預(yù)測(cè)具有一定的意義。在土壤水力參數(shù)空間變異研究中,建立水力參數(shù)與理化參數(shù)的轉(zhuǎn)換函數(shù)成為獲取水力參數(shù)的重要途徑,但不同尺度的主導(dǎo)過程并不相同,如何將某一尺度建立的模型經(jīng)過尺度轉(zhuǎn)換應(yīng)用于其它尺度,明確其精度及消除不確定性等問題值得進(jìn)一步研究。為此,論文依托于國家自然科學(xué)基金項(xiàng)目(51069006),以內(nèi)蒙古河套灌區(qū)為例,針對(duì)上述問題進(jìn)行相關(guān)研究和討論。(1)分別采用Bayesian神經(jīng)網(wǎng)絡(luò)(BNN)及BP神經(jīng)網(wǎng)絡(luò)建立土壤基本特性參數(shù)與土壤水分特征曲線模型參數(shù)、特征含水率之間的轉(zhuǎn)換函數(shù),進(jìn)行模型驗(yàn)證及適應(yīng)性比較。兩種方法均能得到較好的訓(xùn)練及預(yù)測(cè)效果,且BP模型的訓(xùn)練精度優(yōu)于BNN模型,但BNN模型的預(yù)測(cè)精度整體高于BP模型,且由于BNN模型的預(yù)測(cè)值為一個(gè)區(qū)間范圍,可更好的體現(xiàn)土壤特性參數(shù)的空間隨機(jī)性和結(jié)構(gòu)性特征。(2)通過經(jīng)典統(tǒng)計(jì)、地統(tǒng)計(jì)及多重分形進(jìn)行土壤基本物理特性參數(shù)(粘粒、粉粒、砂粒、有機(jī)質(zhì))和水力參數(shù)(飽和含水率θs、van Genuchten模型參數(shù)α、n)在不同尺度(小尺度：52.40km2、1km×1km；中尺度：1.243×103km2、4km×4km；大尺度：3.708×103km2、8km×8km)不同土層(0～20cm、20～40cm、40～70cm、70～100cm)的空間變異性分析,得到結(jié)論如下：基本物理特性參數(shù)在不同尺度不同土層表現(xiàn)為強(qiáng)烈的空間自相關(guān)性,空間分布主要受母質(zhì)、氣候等結(jié)構(gòu)性因素影響；在研究區(qū)域上均具有明顯的多重分形特征,且在中尺度呈現(xiàn)最強(qiáng)的空間變異性(除了有機(jī)質(zhì)在0-20cm、20-40cm土層),同時(shí)多重分形譜曲線(除了有機(jī)質(zhì)在小尺度的20-40cm及40-70cm土層)表現(xiàn)為左鉤狀,即在其空間分布中,數(shù)值較大的數(shù)據(jù)占主導(dǎo)地位,其概率分布較大。水力參數(shù)不同尺度不同土層整體上具有強(qiáng)烈的空間自相關(guān)性,空間分布主要受母質(zhì)、氣候、土壤類型等結(jié)構(gòu)因素的影響；van Genuchten模型參數(shù)α空間分布格局具有多重分形特征,但在3個(gè)尺度下多重分形譜譜寬沒有一致性變化規(guī)律,vanGenuchten模型參數(shù)n和飽和含水率θs分布格局的多重分形特征不明顯,其多重分形譜譜寬均較小。(3)通過聯(lián)合多重分形方法研究了基本物理特性參數(shù)表層(0-20cm)與其它土層(20-40cm、40-70cm及70-100cm)空間變異性的相關(guān)程度并建立轉(zhuǎn)換函數(shù)。整體上各參數(shù)0-20cm土層與20-40cm、40-70cm、70-100cm土層空間變異性的相關(guān)性依次遞減,且其相關(guān)性在小尺度及大尺度高于中尺度。通過一元函數(shù)建立基本物理特性參數(shù)在3個(gè)尺度下表層(0-20cm)與其它土層(20-40cm、40-70cm及70-100cm)的轉(zhuǎn)換函數(shù),各參數(shù)3個(gè)尺度下0-20cm土層與20-40cm土層回歸關(guān)系較好,決定系數(shù)在0.41～0.65之間,與40-70cm及70-100cm土層整體回歸效果較差,決定系數(shù)分布在0.038～0.401之間。(4)通過多元回歸、支持向量機(jī)及BP神經(jīng)網(wǎng)絡(luò)方法建立基于中尺度的水力參數(shù)與基本物理特性參數(shù)及基本物理特性參數(shù)與高光譜的轉(zhuǎn)換函數(shù),并將其尺度上推至大尺度與尺度下推至小尺度,并對(duì)其尺度轉(zhuǎn)化的適用性進(jìn)行評(píng)價(jià)�；谥谐叨冉⒌母吖庾V與土壤顆粒組成及有機(jī)質(zhì)的反演模型均可以較好的應(yīng)用于其它兩個(gè)尺度：多元回歸方法在其它兩個(gè)尺度上的相關(guān)性在0.33～0.60之間,支持向量機(jī)為0.41-0.52,BP神經(jīng)網(wǎng)絡(luò)為0.52-0.72,BP神經(jīng)網(wǎng)絡(luò)方法建立的模型在其它兩個(gè)尺度上具有更好的適用性。且顆粒組成的效果整體好于有機(jī)質(zhì)含量�；谥谐叨冉⒌乃�(shù)(飽和含水率θs、van Genuchten模型參數(shù)α)與基本物理參數(shù)(粘粒、粉粒、砂粒及有機(jī)質(zhì))的轉(zhuǎn)換函數(shù)在其它兩個(gè)尺度上均具有較好的適用性：多元回歸方法在其它兩個(gè)尺度上的相關(guān)性在0.535～0.944之間,支持向量機(jī)為0.602～0.968,支持向量機(jī)方法具有更好的適用性。van Genuchten模型參數(shù)n的建模及模型檢驗(yàn)效果均較差。3個(gè)參數(shù)的尺度轉(zhuǎn)換結(jié)果為飽和含水率θs效果最好,其次是van Genuchten模型參數(shù)α,而van Genuchten模型參數(shù)b效果最差。
[Abstract]:The soil is a highly heterogeneous historical natural body. The spatial distribution of its characteristic parameters has a certain heterogeneity, and the spatial variation has the scale effect. So it is a hot spot in the current research of irrigation hydrology to describe the spatial variability and change process of different scales. The natural soil characteristics vary in the horizontal and vertical direction. The spatial variability of soil characteristics of different soil and different soil layers has a certain relationship, exploring the spatial variation characteristics of soil characteristics and their corresponding relationship in different soil layers. It has certain meaning for understanding the change law of soil characteristics in three-dimensional space and the simulation and prediction of soil characteristics. The transformation function of the hydraulic parameters and the physical and chemical parameters becomes an important way to obtain the hydraulic parameters, but the leading processes of different scales are different. How to apply the scale transformation to the other scales through the scale transformation, and to clarify its accuracy and eliminate the uncertainty are worth further studying. The Natural Science Fund Project (51069006) takes Inner Mongolia Hetao irrigation area as an example to study and discuss the above problems. (1) Bayesian neural network (BNN) and BP neural network are used to establish the parameters of soil basic characteristic parameters and soil moisture characteristic curve model, and the conversion function between the characteristic water content and the model is verified. Adaptability comparison. The two methods can get better training and prediction effect, and the training precision of BP model is better than that of BNN model, but the prediction accuracy of BNN model is higher than that of BP model as a whole, and the spatial randomness and structural characteristics of soil characteristic parameters can be better reflected by the prediction value of BNN model. (2) pass through the channel. The basic physical properties of soil parameters (clay, powder, sand, organic matter) and hydraulic parameters (saturated water content [theta] s, van Genuchten model parameter alpha, n) at different scales (small scale: 52.40km2,1km * 1km; mesoscale: 1.243 * 103km2,4km x 4km; large scale: 3.708 x 103km2,8km x 8km) in different soil layers are carried out by the canonical statistics and multifractal. The spatial variability of (0 to 20cm, 20 to 40cm, 40 to 70cm, 70 to 100cm) is analyzed. The results are as follows: the basic physical parameters are strongly influenced by the spatial autocorrelation in different soil layers, and the spatial distribution is mainly influenced by the structural factors such as the parent material and the climate. The mesoscale has the strongest spatial variability (except organic matter in 0-20cm, 20-40cm soil layer), and the multifractal spectrum curve (except for the organic matter in the small scale 20-40cm and the 40-70cm soil layer) is left hook like, that is, in its spatial distribution, the large number of data is dominant and its probability distribution is larger. The hydraulic parameters are different scales and different soil. There is a strong spatial autocorrelation on the whole layer, and the spatial distribution is mainly influenced by the structural factors such as the parent material, climate, soil type and so on. The van Genuchten model parameter alpha spatial distribution pattern has multi fractal characteristics, but the multifractal spectrum width is not consistent at the 3 scales, vanGenuchten model parameter n and saturated water cut. The multifractal characteristics of the rate theta s distribution pattern are not obvious, and their multifractal spectrum spectrum is smaller. (3) the correlation degree of the spatial variability of the basic physical characteristic parameters (0-20cm) and other soil layers (20-40cm, 40-70cm and 70-100cm) is studied by the combined multifractal method. The whole parameter 0-20cm soil layer and 20-40cm are established. 40-70cm, the correlation of spatial variability of 70-100cm soil layer decreases in turn, and its correlation is higher than the mesoscale in small scale and large scale. Through one element function, the transformation function of the basic physical characteristic parameters at 3 scales (0-20cm) and other soil layers (20-40cm, 40-70cm and 70-100cm), and the 0-20cm soil layer and 20-40cm under the 3 scales of each parameter The regression relation of soil layer is good, the coefficient of decision is between 0.41 and 0.65, and the overall regression effect of 40-70cm and 70-100cm soil layer is poor, the coefficient of decision is distributed between 0.038 and 0.401. (4) through multiple regression, support vector machine and BP neural network method to establish the hydraulic parameters and basic physical properties parameters and basic physical properties of the mesoscale parameters and the basic physical properties. The number and hyperspectral conversion function are pushed to the large scale and the scale to the small scale, and the applicability of the scale transformation is evaluated. The hyperspectral and soil particle composition and the organic matter inversion model based on Mesoscale can be better applied to its two scales: multiple regression method in the other two The correlation on the scale is between 0.33 and 0.60, the support vector machine is 0.41-0.52, the BP neural network is 0.52-0.72, the model established by the BP neural network has better applicability on the other two scales. And the effect of the particle composition is better than the organic matter content. The hydraulic parameters based on the medium scale (saturated water content [theta] s, van Gen The conversion functions of uchten model parameters and basic physical parameters (clay, particle, sand and organic matter) have good applicability on the other two scales: the correlation of multiple regression methods on other two scales is between 0.535 and 0.944, support vector machine is 0.602 to 0.968, support vector machine method has better applicability. .van Genuchten model parameter n modeling and model test results are poor, the result of poor.3 parameter scale conversion result is the best effect of saturated water content theta s, followed by van Genuchten model parameter alpha, and van Genuchten model parameter B effect is the worst.

【學(xué)位授予單位】：內(nèi)蒙古農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：S152.7

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