本文選題：水熱通量 + 尺度效應(yīng)��； 參考：《中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)》2017年博士論文

【摘要】：土壤和植被與大氣間的水熱通量交換是陸面大氣動(dòng)力和熱力過程的基礎(chǔ),決定著全球能量平衡及水分循環(huán),在氣候變化適應(yīng)策略、生態(tài)保護(hù)和農(nóng)業(yè)水資源管理等領(lǐng)域的實(shí)際應(yīng)用中也占有重要地位。目前,用于模擬地表與大氣間水熱通量交換的遙感模型和陸面過程模型在大尺度上應(yīng)用時(shí)仍存在較大的不確定性。主要表現(xiàn)為:首先,基于均勻地表?xiàng)l件發(fā)展起來的模型參數(shù)及參數(shù)化方案在異質(zhì)性地表?xiàng)l件下的適用性存在一定的不確定性;其次,在進(jìn)行地表水熱通量模擬時(shí),中高空間分辨率遙感模型逐像元的模擬方式以及粗網(wǎng)格陸面過程模型通常采用的網(wǎng)格/次網(wǎng)格均質(zhì)假設(shè)均不能充分表達(dá)地表的異質(zhì)性,在模型非線性結(jié)構(gòu)的影響下便會給地表水熱通量真實(shí)空間異質(zhì)性的表達(dá)以及像元尺度、網(wǎng)格\次網(wǎng)格尺度水熱通量均值的模擬帶來一定的不確定性;最后,地面水熱通量觀測源區(qū)空間范圍與模型模擬單元空間范圍之間往往存在較大差異,使得利用地面水熱通量觀測的直接模型精度驗(yàn)證存在一定的不確定性。這些不確定性形成了地表水熱通量模擬中尺度效應(yīng)的核心問題。本文分析了遙感高分辨率尺度(100 m)至陸面過程模型粗網(wǎng)格尺度(10km~50 km)地表水熱通量模擬中的尺度效應(yīng),旨在提高對地表水熱通量模擬中尺度依賴的認(rèn)識,進(jìn)而為陸面過程模型粗網(wǎng)格尺度下參數(shù)化方案及驗(yàn)證方法的改進(jìn)提供理論和依據(jù)。主要研究內(nèi)容及結(jié)論如下:1、從分析本論文所采用的遙感蒸散發(fā)模型ETMonitor出發(fā),揭示不同地表土壤水分SSM和植被特征LAI條件下控制蒸散發(fā)過程的主要因子。該部分針對不同土壤水分和植被狀態(tài)分布特征,發(fā)展了在同時(shí)考慮多種輸入變量的高維空間中對地表蒸散發(fā)模型進(jìn)行全局敏感性分析的方法。研究結(jié)果顯示:蒸散發(fā)模擬對葉面積指數(shù)LAI和地表土壤水分SSM的敏感性在稀疏植被覆蓋且土壤水分較低時(shí)更加明顯;蒸散發(fā)模擬對空氣溫度和空氣相對濕度的敏感性在不同的葉面積指數(shù)LAI和地表土壤水分SSM異質(zhì)性分布條件下均較高;蒸散發(fā)模擬對風(fēng)速的敏感性在高植被覆蓋且地表土壤水分SSM較高時(shí)更加明顯;蒸散發(fā)模擬對下行短波輻射的敏感性在葉面積指數(shù)lai和地表土壤水分ssm均較高時(shí)稍弱。2、中高空間分辨率的混合像元存在條件下,遙感模型模擬地表水熱通量時(shí)逐像元的模擬往往不能充分表達(dá)地表的復(fù)雜異質(zhì)性,在水熱通量遙感模型非線性結(jié)構(gòu)作用下便會導(dǎo)致水熱通量模擬的尺度效應(yīng)問題,本文對遙感模型水熱通量模擬的尺度效應(yīng)問題進(jìn)行分析。該部分研究利用基于二維離散小波變換的圖像多尺度分析方法分析了不同尺度的地表植被、土壤水分和水熱通量交換異質(zhì)性特征,并基于etmonitor模型框架,通過比較逐級空間尺度(90m、180m、360m、720m至1440m)下“參數(shù)聚合”與“通量聚合”的差異,分析了中高分辨率遙感逐像元蒸散發(fā)估算的“聚合誤差”。結(jié)果顯示:在所選研究區(qū)研究時(shí)段內(nèi),地表植被、土壤水分及蒸散發(fā)的異質(zhì)性可存在于不同的空間尺度上,且各自的空間分布特征不盡相同,與蒸散發(fā)空間異質(zhì)性分布高度相關(guān)的高空間分辨率地表特征數(shù)據(jù)的利用對蒸散發(fā)的高精度模擬至關(guān)重要;當(dāng)前研究中,因與et空間分布特征高度相關(guān)的地表特征(ssm)及et自身的異質(zhì)性信息較少出現(xiàn)在較小的空間尺度上,高空間分辨率(90m)輸入數(shù)據(jù)逐級聚合到千米級空間分辨率的過程中,在研究區(qū)內(nèi)多數(shù)像元的像元尺度聚合誤差并不明顯,僅在地表土壤水分和植被狀態(tài)差異較大的黑河沿岸、綠洲內(nèi)部作物與城鎮(zhèn)交界處以及研究區(qū)東南部的滴灌區(qū)周圍等區(qū)域出現(xiàn)了較大的聚合誤差;因不同情景下蒸散發(fā)模擬與地表狀態(tài)的非線性關(guān)系存在差異,像元尺度聚合誤差的分布與地表狀態(tài)異質(zhì)性信息密度的分布不完全一致。3、陸面過程模型模擬地表水熱通量時(shí)通常采用網(wǎng)格\次網(wǎng)格均質(zhì)假設(shè),對地表狀態(tài)及氣象條件異質(zhì)性做簡化處理,本文對該簡化處理導(dǎo)致的水熱通量模擬尺度效應(yīng)問題進(jìn)行了分析。該部分研究通過情景模擬和實(shí)例分析兩種方式展開。情景模擬分析中,基于所模擬的模型次網(wǎng)格內(nèi)多種ssm和lai的空間異質(zhì)性分布情景(分別對應(yīng)植被分布從稀到密,表層土壤水分從干到濕的情景)以及多種氣象條件,分別分析了當(dāng)忽略ssm和lai異質(zhì)性時(shí)的單獨(dú)及協(xié)同影響,并討論了氣象條件對蒸散發(fā)模型中蒸散發(fā)模擬與地表狀態(tài)之間非線性及聚合誤差的影響。情景模擬分析結(jié)果顯示:在不同的地表特征異質(zhì)性分布情景下,陸面過程模型次網(wǎng)格地表特征均質(zhì)假設(shè)方案導(dǎo)致的蒸散發(fā)估算偏差表現(xiàn)不同,當(dāng)ssm和lai為正偏態(tài)分布(干旱半干旱的稀疏植被覆蓋區(qū))時(shí)易出現(xiàn)較大的估算誤差;多種氣象條件下的統(tǒng)計(jì)結(jié)果顯示,忽略ssm異質(zhì)性比忽略lai異質(zhì)性更易產(chǎn)生較大的聚合誤差;忽略SSM和LAI的異質(zhì)性在風(fēng)速較高、空氣溫度較高、空氣較干燥的氣象背景下易產(chǎn)生低估,在風(fēng)速較低、空氣溫度較低、空氣較濕潤的氣象背景下易產(chǎn)生高估。在實(shí)例分析部分,以我國西北部黑河流域中游和上游為研究區(qū),采用三種陸面過程模型模擬常用的網(wǎng)格尺度(10 km、25 km和50 km),以遙感模型ETMonitor的模擬結(jié)果為依托,分析了不同網(wǎng)格大小下陸面過程模型中采用的次網(wǎng)格內(nèi)地表狀態(tài)均質(zhì)假設(shè)和網(wǎng)格內(nèi)氣象條件均質(zhì)假設(shè)方案對模擬結(jié)果的影響。實(shí)例分析結(jié)果顯示:對次網(wǎng)格及格網(wǎng)尺度水熱通量估算,忽略同一次網(wǎng)格內(nèi)地表特征異質(zhì)性與忽略同一網(wǎng)格內(nèi)氣象條件異質(zhì)性對子流域尺度平均值的估算影響不大,但可導(dǎo)致較大的次網(wǎng)格、網(wǎng)格尺度日均蒸散發(fā)估算誤差,且較大的估算誤差易出現(xiàn)在地表較干旱地區(qū),因此基于地表覆蓋類型次網(wǎng)格劃分的陸面過程模型的應(yīng)用效果依不同的應(yīng)用目的及研究區(qū)而異;在研究區(qū)內(nèi)忽略同一次網(wǎng)格內(nèi)地表狀態(tài)異質(zhì)性和忽略同一網(wǎng)格內(nèi)氣象條件異質(zhì)性導(dǎo)致的聚合誤差多呈同向疊加形式,且二者交互作用在網(wǎng)格尺度較大時(shí)更加明顯。
[Abstract]:The exchange of water and heat flux between the soil and the atmosphere is the basis of the dynamic and thermodynamic processes of the terrestrial atmosphere, which determines the global energy balance and water cycle. It also plays an important role in the practical applications of climate change adaptation strategies, ecological protection and agricultural water resources management. The exchange of remote sensing model and land surface process model still have great uncertainty when applying the land surface process model on large scale. The main manifestations are: first, the model parameters and parameterization schemes based on the uniform surface conditions have certain uncertainty in the applicability of the heterogeneous surface conditions; secondly, when the surface water heat flux is simulated, the surface water heat flux is simulated. The simulation of high spatial resolution remote sensing model and the assumption that the grid / subgrid homogenization assumption usually used in rough grid land surface process model can not fully express the heterogeneity of the surface. Under the influence of the model nonlinear structure, the expression of the real spatial heterogeneity of the surface water heat flux and the pixel scale, the grid subnet will be given. There is a certain uncertainty in the simulation of the mean of the mean of the thermal flux of the grid scale. Finally, there is a large difference between the spatial range of the surface water heat flux and the space range of the model simulation unit, which makes the accuracy of the direct model of the surface water heat flux observed. The core problem of mesoscale effect in the surface water heat flux is simulated. This paper analyzes the scale effect of the remote sensing high resolution scale (100 m) to the surface water heat flux simulation of the rough grid scale (10km~50 km) of the land surface process model, which aims to improve the understanding of the scale dependence of the surface water heat flux simulation, and then to the rough grid scale of the land surface process model. The main research contents and conclusions are as follows: 1, from the analysis of the remote sensing evapotranspiration model ETMonitor used in this paper, the main factors to control the evapotranspiration under different soil moisture SSM and vegetation feature LAI are revealed. This part is aimed at different soil moisture and soil moisture. The results show that the sensitivity of Evapotranspiration to leaf area index LAI and surface soil moisture SSM is more obvious when it is covered with sparse vegetation and the soil moisture is low; The sensitivity of the emission simulation to air temperature and air relative humidity was higher under the different leaf area index LAI and the surface soil moisture SSM heterogeneity. The sensitivity of the evapotranspiration simulation to the wind speed was more obvious when the high vegetation was covered and the surface soil moisture SSM was higher; the sensitivity of the Evapotranspiration to the downlink short wave radiation was high. The leaf area index (LAI) and the surface soil moisture SSM are both slightly weaker and slightly.2, and under the presence of mixed pixels with high spatial resolution, the simulation of the surface water heat flux can not fully express the complex heterogeneity of the surface when the remote sensing model simulates the surface water heat flux, and it will lead to the hydrothermal flux model under the action of the nonlinear structure of the water heat flux remote sensing model. In this paper, the scale effect problem of water heat flux simulation in remote sensing model is analyzed in this paper. In this part, the multi-scale analysis method based on two-dimensional discrete wavelet transform is used to analyze the surface vegetation of different scales, the characteristics of the exchange of soil moisture and water heat flux, and based on the etmonitor model framework. After comparing the difference between "parameter aggregation" and "flux aggregation" in 90m, 180m, 360m, and 720m to 1440m, the "aggregation error" of the estimation of the medium and high resolution pixel steaming is analyzed. The results show that the heterogeneity of soil vegetation, soil moisture and evapotranspiration can exist in different period of study area. On the spatial scale, the spatial distribution characteristics are different, and the high spatial resolution surface feature data, which is related to the spatial heterogeneity of the evapotranspiration, is very important for the high precision simulation of the evapotranspiration. In the current study, the surface feature (SSM) and the heterogeneity of the ET itself are highly correlated with the spatial distribution characteristics of the ET. In the process of small spatial scale, high spatial resolution (90m) input data converge to kilometer spatial resolution, the pixel scale aggregation error of most pixels in the study area is not obvious, only in Heihe along the surface soil moisture and vegetation state, and the border between crops and towns in oasis. There are large aggregation errors in the area around the drip irrigation area in the southeastern part of the study area, and the nonlinear relationship between the evapotranspiration simulation and the surface state under different scenarios, the distribution of the pixel scale aggregation error and the distribution of the information density of the surface state heterogeneity is not exactly the same as.3, and the land surface process model simulates the surface water heat flow of the surface. The grid subgrid homogenization assumption is usually used to simplify the surface state and the meteorological condition heterogeneity. This paper analyzes the scale effect of the hydrothermal flux simulation caused by the simplified treatment. This part of the study is carried out through two ways of scenario simulation and example analysis. In the scenario simulation analysis, the simulated model is based on the simulated model. The spatial heterogeneity of multiple SSM and Lai in the subgrid (corresponding vegetation distribution from sparse to dense, surface soil moisture from dry to wet) and a variety of meteorological conditions were analyzed, respectively, to analyze the individual and synergistic effects of ignoring SSM and Lai heterogeneity, and discussed the simulation of evapotranspiration and the surface of the evapotranspiration model in the evapotranspiration model. The results of the situation simulation analysis show that the estimation deviation of evapotranspiration in the land surface process model is different, when SSM and Lai are positive partial distribution (arid and semi-arid sparse vegetation cover) under different surface characteristic heterogeneity distribution scenarios. The statistical results under various weather conditions show that neglecting SSM heterogeneity is more likely to produce larger aggregation errors than neglecting Lai heterogeneity; ignoring the heterogeneity of SSM and LAI is higher in wind speed, higher air temperature and less air temperature, lower air temperature and air temperature, air temperature is lower, air temperature is lower, air temperature is lower, air temperature is lower, air temperature is lower, air temperature is lower, air temperature is lower, air temperature is low In an example analysis, three land surface process models are used to simulate the common grid scale (10 km, 25 km and 50 km). Based on the simulation results of remote sensing model ETMonitor, the process models for different grid size land surface are analyzed. The results show that the estimation of the water and heat flux of the grid and grid scale, neglecting the heterogeneity of the surface features in the same grid and neglecting the heterogeneity of the meteorological conditions in the same grid to the sub basin scale. The estimation of the average value has little influence, but it can lead to larger subgrid and the estimation error of the daily evapotranspiration on the grid scale, and the larger estimation error is easy to appear in the arid area. Therefore, the application effect of the land surface process model based on the subgrid submeshing of the surface cover type varies according to the different application purpose and the research area; in the study area, the application effect of the land surface process model is different. Neglecting the heterogeneity of the surface state in the same grid and ignoring the meteorological conditions heterogeneity in the same grid results in the same direction superposition, and the interaction between the two is more obvious when the grid scale is larger.

【學(xué)位授予單位】：中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：P422.4

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