應用渦流相關法探討地表水氣通量與熱通量之特徵：以亞熱帶季節性常綠闊葉林為例

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陳奕穎(Yi-ying Chen) 查詢紙本館藏

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水文與海洋科學研究所

論文名稱

應用渦流相關法探討地表水氣通量與熱通量之特徵：以亞熱帶季節性常綠闊葉林為例
(Investigating the Seasonal Variability of Surface Heat and Water Vapor Fluxes with Eddy Covariance Techniques: A Subtropical Evergreen Forest as An Example)

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摘要(中)

地表與大氣之間水、熱通量交換速度的快慢，不但影響著近地表邊界層的發展，亦同時牽動著區域性氣候變化以及流域集水區之水文循環。為了瞭解上述的自然現象，我們常常透過數值模式對未來的環境進行模擬或預測。但是對這些模式而言，邊界條件的給定是相當重要的，例如：水文模式中的蒸發散估算、氣候模式中動量、水氣與熱通量的參數化方式。因此，惟有正確的瞭解這些通量的傳輸機制以及其在時間上的變化特性，這些模式工具才能提供給我們可靠的計算結果，讓我們瞭解地表-大氣的交互作用及其影響。在台灣應用渦流相關法觀測地表通量的技術相較於歐、美、日等國起步較晚，但近期已急起直追。且台灣本島丘陵森林居多且茂密，提供了在複雜地貌下應用渦流相關法的研究空間。本研究試圖瞭解在複雜地貌下以渦流相關法進行地表水、熱通量觀測所面臨的挑戰並提供解決方式，並將通量結果進行參數化，期有助於地表通量觀測與陸面過程之研究。本文根據文獻回顧、研究測站、研究方法、結果討論以及結論進行分段，主要可成六個章節。其中，第三章至第五章分別針對不同研究議題進行撰寫。第一章對渦流相關法的基礎理論與應用層面的文獻進行回顧。第二章則是介紹蓮華池研究樣區所架設之觀測儀器以及該測站的長期氣候狀態。第三章是說明如何在地形複雜的地貌條件下進行渦流相關法，期達到對地表水氣與熱通量進行精準的觀測。第四章則是敘述本研究所發展的一個地表水氣通量資料缺補的統計模式。第五章則發展地表阻抗的參數化模型。第六章是總結本研究之相關發現，並對地表通量觀測未來研究發展提出建議。
彙整各章節的研究結果：於蓮華池測站進行渦流相關法觀測地表通量時會面臨能量不閉合的狀況；然此不閉合的狀態存在著一個明顯的季節性變化特徵，夏天能量閉合低(小於1)且較密集、而冬天較散亂能量閉合度高(大於1)。進一步使用通量源、匯模式對地表通量源、匯位置進行計算與調查。結果指出冬天的通量源位置較分散，且有部分會超出集水區的範圍；夏季通量源位置較集中，多數落於觀測塔附近。推論能量閉合度的高、低是季節性風場所造成與紊流發展強、弱有關。此外，在複雜的地形條件下以渦流相關法進行通量觀測建議採用平面擬合參考座標系統，並配合較長的平均時距，以60或120分鐘較佳，可降低短期能量閉合的不確定性，長期可達到年尺度的能量守恆。經由Ogive法分析水氣與熱通量觀測結果指出，使用較長的平均時距可以有效地觀測到紊流尺度較大的渦漩所動的可感熱通量，尤其是在午後熱對流較旺盛的期間。對於通量資料缺補模式的發展，主要結合主成分分析法與非線性內插方程式進行水氣通量資料缺補的統計模式開發，這個部分的研究結果指出：日間與夜晚應採取不同的內插樣本數，可以獲得較佳的通量資料缺補的結果。而地表參數化的研究，則是採用了Penman- Monteith方程式與實測通量結果繁衍地表阻抗。該研究資料顯示：當地表由濕變乾，地表阻抗與輻射之關聯性增加，地表阻抗與風速之關聯性減低；反之亦然。進一步嘗試將此地表阻抗特性參數化成微氣象因子與土壤水分之函數。該模式搭配標準微氣象觀測資料，可提供小時時間尺度下水氣通量或蒸發散量的推估。即當植被層上的微氣象條件與植被層下的土壤水分狀態同時取得時，地表水氣通量即可以Penman-Monteith方程式進行合理推算。
目前成果多為針對環境條件與地表水氣或熱通量傳輸速率之影響進行探討，植物生生理反應對地表通量傳輸之機制調查仍顯不足。未來應可對碳通量進行觀測，進行植物生理對地表水、熱通量傳輸機制與特徵之探討。

摘要(英)

his dissertation focused on using eddy covariance technique to investigate a variety of primary hydrologic, radiative and turbulent transport processes driving forest-atmosphere exchanges of heat and water vapor at a subtropical evergreen forest. A total of six chapters was described in this study. A brief literature review on theoretical and applied eddy covariance techniques was introduced in Chapter1. Climatological condition and experimental setup at Lien-Hua-Chih study site were presented in Chapter 2. Determining the adequate averaging periods and reference coordinates for measuring surface heat and water vapor fluxes over the mountainous terrain was devoted in Chapter 3. In sequent Chapter 4, a gap-filling model, combining principle component analysis and the K-nearest neighbor algorithm, for estimating latent heat gaps was developed. In Chapter 5, a surface resistance parameterizing model for the Penman-Monteith equation suitable for application on hourly time scale was proposed. Finally, some thoughts summarizing my findings and future works were given in Chapter 6.
To summarize, the seasonal energy closure variation at this study site was concluded as the result from having weak turbulence developments during wet seasons and mismatching flux footprint areas among flux sensors during dry seasons. The Ogive function analysis revealed that the energy closure was improved with the increase of averaging time by capturing sensible heat fluxes at low-frequency ranges during certain midday hours. For planar-fit rotation approach, a typical averaging period (30 min) is not suitable and a 60 min or 120 min averaging period is an adequate averaging period for calculating eddy fluxes at this study site. The developed gap-filling approach by incorporating the adaptive interpolation method resolves the eddy covariance data gaps problem on various timescales ranging from hours to years. Using an integrated hydrometeorological flux tower and field experiments, the surface resistance can be reasonably parameterized as a function of radiation forcing and environmental factors if meteorological conditions above canopy and soil moisture contents are well known.
For my future work, this framework can be broadened to investigate the carbon exchange, e.g., CO2 flux, between terrestrial ecosystem and atmosphere for better understanding the effect of biological controls on evapotranspiration.

關鍵字(中)

★ 通量觀測
★ 渦流相關法
★ 資料缺補
★ 能量閉合
★ 地表過程
★ 通量參數化

關鍵字(英)

★ eddy covariance
★ gap-filling
★ energy closure
★ land surface process
★ surface parameterization
★ evapotranspiration
★ Penman-Monteith

論文目次

Table of Contents
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Literature Review 2
1.2.1 Theoretical Background of the Eddy Covariance Approach 2
1.2.2Reynolds Averaging and Taylor Hypothesis 3
1.2.3 Mass Conservation (Continuity) Equation 4
1.2.4 Momentum Conservation Equation 6
1.2.5 Historical Review of the Eddy Covariance Approach 8
1.3 Using Eddy Covariance Fluxes for Land Surface Process Parameterization 13
1.4 Scope of This Study 16
Chapter 2 Study Site 17
2.1 Site Description 17
2.2 Instruments Setup 19
2.3 EC Data Processing 23
Chapter 3 Determining Adequate Averaging Periods and Reference Coordinates 25
3.1 Data Set 26
3.2 Coordinate Rotations 26
3.3 The Energy Closure Fraction 28
3.4 Averaging Period Evaluation 28
3.5 Footprint Analysis 29
3.6 Micrometeorology 30
3.7 Energy Balance Components 32
3.8 Variations of Daily Energy Closure Fractions 34
3.9 Effects of Increasing Averaging Period 38
3.10 Variation of Energy Closure Fraction 42
Chapter 4 Developing a Gap-filling Model for Eddy Covariance Latent Heat Flux 47
4.1 Data Processing and Flux Gaps Identification 48
4.2 Gap-filling model 48
4.2.1 Environmental Variables 50
4.2.2 Principal Component Analysis 50
4.2.3 Interpolation Approaches 51
4.3 Model Calibration and Error Assessment 53
4.4 Flux Gaps Evaluation 54
4.5 Controlling Factors of Latent Heat Flux 56
4.6 Model Calibration 58
4.6.1 K values for KNN approach 58
4.6.2 Regression coefficients for MRS approach 58
4.7 Model Validation and Error Assessment 61
4.8 Evapotranspiration at the LHC site 64
Chapter 5 Parameterizing the Surface Resistance for Penman-Monteith Equation to Estimate Evapotranspiration 67
5.1 Dataset 68
5.2 Penman-Monteith Equation and Calculation of Climatic, Aerodynamic, and Surface Resistance 68
5.3 Katerji -Perrier Resistance Model and Rational Coefficient 70
5.4 Micrometeorological Condition 71
5.5 Seasonal Variation in Energy Fluxes 72
5.6 KP Model Calibration and Validation 74
5.7 Sensitivity Analysis of Using KP Model 80
5.8 Temporal Variability in Evapotranspiration 81
Chapter 6 Conclusions and Future Works 89
6.1 Adequate Averaging Periods and Reference Coordinates 90
6.2 Gapfilling Model for EC Latent Heat Flux 91
6.3 Surface Resistance Parameterizing and Seasonal Variability in Evapotranspiration 91
6.4 Future Works 92
BibliographyR1
APPENDIX
A. Data processing software (GUI and some excitable programs)
B. Resume
List of Figures
Figure 1.1 Illustration of the relationships between vertical surface flux and other components 5
Figure 1.2 Sketch of the generation of secondary circulation due to landscape heterogeneity. 10
Figure 1.3 Fishbone plot of problems in using eddy covariance system 12
Figure 1.4 Sketch of the surface parameterization using top-down approach for the Penman- Monteith equation. 14
Figure 2.1 The watershed boundary of the Zhuo-Shui River and its’ tributary, the Shu-Li River, located at the central Taiwan (a), the location of the sub-catchments, LHC WS04 and LHC WS05, at the top of the drainage area of the Shui-Li River, a 3-D topographic plot of the Shu-Li Catchment (red line) and upper part of the Shu-Li river is characterized as the gentle rolling terrain, LHC study site(yellow line) 18
Figure 2.2 The topographic contour map of the LHC study site (LHC WS05), the flux tower(red-circle) coordinates are 23o55’52’’ N., 120o53’39’’ E., the star symbols indicate locations of three soil moisture probes, the arrows indicate the flow directions of nearby ephemeral streams. 19
Figure 2.3 Climatology of LHC site, monthly precipitation, relative humidity, air temperature, pan evaporation from 1961-1998, annual precipitation: 2229.1, pan evaporation: 1021.1 (unit in mm) 20
Figure 2.3 Vegetation at LHC station, LAI~5 during the summer (Photo taken by I-Fu Yuan, 2009) 22
Figure 2.4 Eddy covariance system setup at LHC station 22
Figure 2.5 The flow chart of the EC data process program that includes several EC data process algorithms including reference coordinate, WPL correction, frequency, footprint analysis for eddy flux QC/QA process. 24
Figure 3.1 Wind rose chart at the LHC site; (a) in daytime (06:00-18:00 local time), and (b) in nighttime (18:00-06:00 local time) 31
Figure 3.2 Volumetric soil moisture variation averaged from 0 to 20 cm beneath the surface at the LHC site during the study period (71 days in 2008 and 70 days in 2009) 32
Figure 3.3 The diurnal cycle of hourly averaged Rn, G, , H, and LE at the LHC site and the bar indicates the standard deviation (NR with 60 min averaging period for H and LE fluxes); (a) dry season; (b) for wet season 33
Figure 3.4 Variations of daily energy closure fraction computed with different averaging periods and rotation approaches; solid blue line with circle is for the Planar-Fit rotation, dash-dot red line with square is for the double rotation and dash green line with delta is without any rotation; the bar indicates the standard deviation of daily energy closure for each run; (a) dry season, (b) wet season, and (c) both dry season and wet season 37
Figure 3.5 The Ogive curves of kinematic latent heat fluxes for every 2 hours local time (PFR with 2 hr averaging period) 40
Figure 3.6 The Ogive curves of kinematic sensible heat fluxes for every 2 hours local time (PFR with 2 hr averaging period) 40
Figure 3.7 The hourly energy closure fraction with PFR at different friction velocity classes; dashed-dot line with circle is 15 min averaging period, dashed line with square is 30 min averaging period, solid line with delta is 60 min averaging period. Data are binned at a friction velocity interval of 0.05 m s-1 41
Figure 3.8 The daily energy closure fraction with the PFR approach for 15 min and 60 min averaging periods; square and delta symbols represent 15 min and 60 min averaging periods, respectively; the solid blue and dash red lines are fitted curves with the second order polynomials 41
Figure 3.9 Relationship between volumetric soil moisture and daily (a) energy closure fraction, (b) wind speed, and (c) net radiation. The symbols represent binned average for each soil moisture class. The bars show the standard deviation of variables at each soil moisture class. The soil moistures are classified into four classes: Class 1 is less than 0.175; Class 2 is between 0.175 and 0.275; Class 3 is between 0.275 and 0.375; and Class 4 is greater than 0.375 (PFR with 60min averaging period) 44
Figure 3.10 Spatial distribution of maximum footprint locations for different soil moisture classes, (a) Class 1, (b) Class 2, (c) Class 3, and (d) Class 4; the flux tower is located at the origin; colors give the footprint density values in percentage (%), with the same color scales used for all graphs. The maximum footprint locations are parameterized with atmospheric stability and a length scale along the mean wind direction after Hsieh et al. (2000) (PFR with 60min averaging period) 45
Figure 4.1 Flow chart of the proposed LE gapfilling model 49
Figure 4.2 Distribution of flux gaps by day and hour through 2008 (a) and 2009 (b); for each time step, the total score higher than 2 points was identified as the flux gap (warm color states the score over 2 points); the eddy flux will be rejected and replaced with gap-filled flux 55
Figure 4.3 Relationship between LE flux and PCs for clear sky condition, PC1 (a), PC2 (b), PC3 (c), and PC4 (d). The error bars show the stand deviation of the mean LE value within each PC class. The trend lines are derived from second order polynomial curves 59
Figure 4.4 Relationship between LE flux and PCs for nighttime/cloudy condition, PC1 (a), PC2 (b), PC3 (c), and PC4 (d). The error bars show the stand deviation of the mean LE value within each PC class. The trend lines are derived from second order polynomial curves 59
Figure 4.5 Performance of the developed LE gap-filling model with different k values, presented in circle symbols (clear sky runs) and in rectangular symbols (nighttime/cloudy runs); the bars indicate the standard deviation in each class 61
Figure 4.6 Error assessment of the developed LE gap-filling model at various time scales, daily (a), weekly (b), monthly (c), and bimonthly (d) at different percentage of artificial gaps from 5% to 40% 63
Figure 4.7 Gap-filled LE fluxes for year 2008 (a) and year 2009 (b) at LHC study site, annual evapotranspiration for 2008 and 2009 are 736 mm and 728 mm, respectively 65
Figure 5.1 Overview of the daily micrometeorological conditions over the study site during 2008-2011: (a) soil moisture and cumulative daily rainfall; (b) average wind speed; (c) average vapor pressure deficit; (d) average temperature 72
Figure 5.2 The surface energy components measured by the flux tower and eddy covariance approach; (a)surface net radiation flux, Rn; (b) ground heat flux, G; (c) latent heat flux, LE; and sensible heat flux, SH ; over the study site during 2008-2010 73
Figure 5.3 Analysis of the regression relationship between inverted surface resistance rc and critical resistance r* for the calibration data set. ra is the aerodynamic resistance; different θ classes (a) θclass:15; (b) θclass:18; (c) θclass: 21; (d) θclass:24; (e) θclass:27; (f) θclass:30; (g) θclass:33; (h) θclass:36; (i)θclass: all classes. 76
Figure 5.4 The relationship between the regression coefficients, slope a and interception b, and soil moisture for 8 different soil moisture classes from 15% to 36%. 77
Figure 5.5 Regression between measured and modeled hourly evapotranspiration values by KP model using constant approach 78
Figure 5.6 Regression between measured and modeled hourly evapotranspiration values by KP model using soil moisture approach 78
Figure 5.7 The sensitivity of latent heat flux estimation in function of net radiation (right triangle), air temperature (delta), wind speed (square), and relative humidity (gradient) 80
Figure 5.8 Daytime (08:00-18:00) average (a) αPT coefficient, (b) surface resistance, (c) critical resistance, and (d) aerodynamic resistance from 2008 to 2011; red square is αPT coefficient; blue delta is surface resistance; green delta is the critical resistance; black circle is the aerodynamic resistance 82
Figure 5.9 Diurnal course of PT coefficient α (a) and equilibrium evapotranspiration LEeq (b) for the dry season and PT coefficient αPT (c) and equilibrium evapotranspiration LEeq (d) for the wet season, during the study period (2008 to 2011). The dash-dot lines indicated the daytime hours from 08:00-18:00 83
Figure 5.10 Diurnal patterns in aerodynamic resistance (a), critical resistance (b), and surface resistance (c) over the dry season; and diurnal patterns in aerodynamic resistance (d), critical resistance (e), surface resistance (f) over the wet season, for the entire study period (2008 to 2011). The dash-dot lines indicated the daytime hours from 8:00-18:00 LT 85
List of Tables
Table 2.1 Descriptions of models, sampling rates and measurement heights of instruments installed at the LHC site 21
Table 3.1 Average daily integrated latent heat (LE) and sensible heat (H) fluxes with different averaging periods and coordinate rotations for all seasons, the (std) stands for the standard deviation of daily energy closure fraction (CF), and B is the Bowen ratio for whole available data from 2008-2009 (71 +70 days) 36
Table 4.1 Weighting coefficients of each environmental variable and PC loading for clear sky and nighttime/cloudy conditions 57
Table 4.2 Model calibration with the Nash-Sutcliffe efficiency for KNN approach with different K values or for MRS approach, during clear sky or nighttime/cloudy in 2008 60
Table 4.3 Regression coefficients of the second order polynomial equation for clear sky and nighttime/cloudy conditions 60
Table 4.4 Daily average latent heat fluxes of random-selected days during clear sky or nighttime/cloudy in 2009, calculated by LE gap-filled model by KNN with different K values or by MRS as the LE predictions (Prd.); and QC/QA passed LE noted as observations (Obs.) 62
Table 4.5 Estimation errors of the LE gap-filling model at daily (a), weekly (b), monthly (c), bimonthly (d) time scales, containing different percentage of artificial gaps from 5 % to 40 % in 2009 63
Table 5.1 Summary the coefficients, slope a and interception b, calibrated by constant approach and soil moisture approach 75
Table 5.2 Regression coefficients for soil moisture approach 79
Table 5.3 Seasonal evapotranspiration estimated by KP model with soil moisture approach on hourly time scale from 2008 to 2011, ET units in (mm) 81
Table 5.4 Seasonal average value of equilibrium evaporation LEeq, PT parameter αPT, aerodynamic resistance ra, critical resistance r*, surface resistance rc, and temperature gradient parameters used in Equation(8), LEeq unit in (MJ m-2); ra, r*,and rc units in (s m-1); Δ and γ units in (Pa oC-1) 83

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指導教授

李明旭(Ming-hsu Li)

審核日期

2012-7-12

推文