羅紫東,關(guān)華德,2,章新平,*,劉 娜,張賜成,王 婷
1 湖南師范大學(xué)資源與環(huán)境科學(xué)學(xué)院,長沙 410081
2 福林德斯大學(xué)環(huán)境學(xué)院,阿德萊德 5001,澳大利亞
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桂花樹冠層氣孔導(dǎo)度模型的優(yōu)化及其參數(shù)分析
羅紫東1,關(guān)華德1,2,章新平1,*,劉娜1,張賜成1,王婷1
1 湖南師范大學(xué)資源與環(huán)境科學(xué)學(xué)院,長沙410081
2 福林德斯大學(xué)環(huán)境學(xué)院,阿德萊德 5001,澳大利亞
冠層氣孔導(dǎo)度(gc)是許多陸面過程模型中的重要參數(shù),提高對冠層氣孔導(dǎo)度的模擬精度非常重要。以環(huán)境因子階乘的Jarvis形式的模型是氣孔導(dǎo)度模型中的典型代表,但研究中不同的環(huán)境因子有不同的響應(yīng)方程和參數(shù)。研究認(rèn)為不同的響應(yīng)方程有不同的模擬效果,并通過比較各環(huán)境因子的不同響應(yīng)方程組合的模型的模擬效果來確定最優(yōu)的gc模型。以桂花樹為例,測定了樹干液流、莖水勢和微氣象環(huán)境,用Penman-Monteith(PM)方程反推計(jì)算冠層氣孔導(dǎo)度并檢驗(yàn)不同方程組合的16種模型。模型的參數(shù)用DiffeRential Evolution Adaptive Metropolis(DREAM)模型優(yōu)化。結(jié)果表明這種方法能夠有效地找到各環(huán)境因子最優(yōu)的響應(yīng)方程,從而最優(yōu)化gc模型。優(yōu)化的gc模型很好地模擬了桂花樹冠層氣孔導(dǎo)度的變化,尤其是對干旱的響應(yīng),模擬值與PM計(jì)算值的相關(guān)系數(shù)和均方根誤差分別為0.803和0.000623 m/s。同時(shí)也證明了模型中溫度函數(shù)f(T)>1的現(xiàn)象并非個(gè)例,由于溫度(T)和水汽壓虧缺(D)常是高度相關(guān)的,建議在以后的gc模型研究中應(yīng)把T和D看成一個(gè)影響因子,但f(T)>1的這種現(xiàn)象是否具有全球性還有待進(jìn)一步研究證實(shí)。
冠層氣孔導(dǎo)度;模型優(yōu)化;環(huán)境因子;樹干液流;桂花樹
氣孔行為是植物生理生態(tài)研究中的重要主題,它影響著植物的生長、水分利用和相關(guān)生態(tài)功能[1]。植被冠層是植被與大氣間相互作用的重要界面,調(diào)節(jié)著生物圈和大氣圈間氣體、能量的交換。冠層氣孔導(dǎo)度是植物響應(yīng)環(huán)境變化的關(guān)鍵參數(shù),同時(shí)也是最難估算的參數(shù)[2-3]。但在許多氣候、水文、陸地生態(tài)系統(tǒng)等模型模擬中卻是不可忽視的重要參數(shù)[4-9]。
冠層氣孔導(dǎo)度(gc)可用氣孔計(jì)測量或用便攜式光合作用儀測定的單葉氣孔導(dǎo)度推算得到,但所得結(jié)果往往變化很大[10],且也難以長期連續(xù)觀測。隨著測定技術(shù)的發(fā)展,通過樹干液流測定整樹蒸騰后再利用Penman-Monteith(PM)公式可計(jì)算長期連續(xù)的gc。PM公式綜合考慮了植物生理和微氣象因素,已廣泛成功運(yùn)用到溫帶和熱帶闊葉森林和針葉林的冠層氣孔導(dǎo)度的計(jì)算中[3,7,11]。
在過去幾十年中,關(guān)于冠層氣孔導(dǎo)度的觀測模擬研究已有很多[2-3,7,12-16]。這些模型大部分都是通過冠層導(dǎo)度與環(huán)境變量(太陽輻射(Rs)、溫度(T)、水汽壓虧缺(D)、土壤水分含量(θ)等)間的函數(shù)關(guān)系計(jì)算的[2,12]。而Jarvis[12]模型是這種方法的典型代表,表達(dá)如下:
(1)
式中,gmax是不受環(huán)境因子脅迫時(shí)的最大氣孔導(dǎo)度,f(Rs)、f(T)、f(D)、f(θ)和f(Ca)分別是太陽輻射、大氣溫度、水汽壓虧缺、土壤水分含量和大氣二氧化碳濃度對氣孔導(dǎo)度影響的脅迫函數(shù),其值均在0—1范圍內(nèi)變化。
但Wang等[17]研究發(fā)現(xiàn),在氣孔導(dǎo)度模型中的脅迫函數(shù)f(T)>1,明顯有悖于模型中脅迫函數(shù)變化在0—1之間的理論假設(shè)[12,18]。許文滔等[7]用Jarvis模型模擬了華南馬占相思的冠層氣孔導(dǎo)度,但他也忽略了f(T)的范圍問題,觀察其模型中溫度函數(shù)也存在參數(shù)Kt<0從而使f(T)>1的現(xiàn)象;齊華等[19]對柑橘葉片氣孔導(dǎo)度模型的研究中也存在這種情況。因此,在參考Jarvis模型模擬氣孔導(dǎo)度的研究中,人們?nèi)菀缀鲆昮(T)的范圍問題,而f(T)>1是確實(shí)存在的現(xiàn)象,但這種現(xiàn)象是否具有普遍性尚不可知。
許多研究根據(jù)Jarvis-Stewart(JS)方法[2,12]建立了冠層氣孔導(dǎo)度響應(yīng)環(huán)境變量的模型,并已在許多地區(qū)的不同森林類型中得到了很好運(yùn)用[1,16-17]。但一方面,許多氣孔導(dǎo)度模型都沒有考慮干旱的影響,從而削弱了模擬植被應(yīng)對水分虧缺的能力[20];另一方面,許多研究在氣孔導(dǎo)度模型響應(yīng)方程的選擇上顯得很隨意,沒有說明選擇的原因[17]以及是否適合所在的研究區(qū)域。因?yàn)樵诓煌瑲夂蚧蛭磥須夂蜃兓?人們還不能確定,模型的經(jīng)驗(yàn)公式或公式參數(shù)是否保持不變。
假設(shè)不同的環(huán)境變量方程的選擇會有不同的模擬效果,選擇最佳的方程組合可以提高冠層氣孔導(dǎo)度模擬的精度。本文的主要研究目的:(1)通過比較不同的響應(yīng)方程組合的模擬效果來尋找適合本地氣候的桂花樹的冠層氣孔導(dǎo)度模型;(2)主要分析模型中溫度函數(shù)方程f(T)及其參數(shù)kT,驗(yàn)證f(T)>1的現(xiàn)象在本文研究區(qū)域中是否也存在;(3)基于JS方法優(yōu)化的gc模型能否有效模擬gc對干旱的響應(yīng)。
1.1實(shí)驗(yàn)場地概況
實(shí)驗(yàn)場地位于湖南省長沙市西郊(112°53′20″E,28°09′46″N,海拔70 m),該區(qū)屬亞熱帶季風(fēng)濕潤氣候,春暖秋涼,夏熱冬冷,雨熱同期,四季分明。年均降水量1360mm,主要集中在3—6月,7—8月受副熱帶高壓控制,晴天多,高溫出現(xiàn)頻率最大,極易發(fā)生夏季干旱。實(shí)驗(yàn)場地在一片桂花園(1500m2,2003年由農(nóng)地改造而來),株行距約為3m×3 m,林分密度1040株/hm2,平均年齡為8a,平均樹高4 m,平均胸徑7.9 cm。
實(shí)驗(yàn)觀測于2013年4月至10月進(jìn)行,選擇生長良好、具有代表性的桂花樹2棵(樹齡分別為8年和9年,樹高分別為3.8 m和4.1 m,胸徑分別為7.6 cm和8.1 cm),進(jìn)行樹干液流和莖水勢的長期連續(xù)觀測。同時(shí)2013年夏季發(fā)生了嚴(yán)重干旱(7月1至8月18日),是湖南1951年以來夏季降水最少、高溫干旱最嚴(yán)重的一年[21],這為研究冠層氣孔導(dǎo)度模型對干旱的模擬效果提供了很好的條件。
1.2樹干液流的測定
樹干液流的測定采用熱比率法液流表(SFM1, ICT International Pty Ltd., Australia)每隔30min自動(dòng)監(jiān)測記錄1次數(shù)據(jù)。每套傳感器探頭由3個(gè)35mm長的探針組成(一個(gè)探針用來釋放穩(wěn)定的熱脈沖而其余兩個(gè)用來測定溫度的探針分別安裝在它上下各5 mm處)。每棵樣樹在離地面1.3m處的樹干南北兩側(cè)各裝一套傳感器,采用特定規(guī)格的鉆頭垂直于樹干打3個(gè)深為35mm的小孔,然后分別把3個(gè)探針按特定順序插入小孔。每套傳感器和數(shù)據(jù)記錄器用一塊12V的太陽能板或一節(jié)12V蓄電池供電。最后在儀器外部用鋁箔紙包好,以防曬防雨,保持探針周圍的環(huán)境穩(wěn)定。
在實(shí)驗(yàn)監(jiān)測結(jié)束后,采用生長錐鉆取木芯,以獲得邊材厚度、樹皮厚度、邊材鮮重和干重以及新鮮邊材體積等信息。這些信息都將輸入Sap Flow Tool(液流數(shù)據(jù)的分析和可視化,ICT International Pty Ltd.)[22]分析軟件,處理計(jì)算液流速率和液流通量,具體方法可參考Burgess等[22]。桂花樹的蒸騰(Ec)根據(jù)樣樹對應(yīng)的有效冠層投影面積和液流通量計(jì)算得到。
1.3莖水勢的測定
莖水勢(ψst)的測定采用熱電偶莖干濕度表(PSY, ICT International Pty Ltd., Australia)每隔30min自動(dòng)監(jiān)測記錄一次數(shù)據(jù)。這是由Dixon和Tyree[23]研制而近些年被廣泛應(yīng)用的一種植物莖水勢監(jiān)測儀器。PSY莖干濕度表的黃銅腔室內(nèi)有2個(gè)熱電偶,一個(gè)稍微凸出腔室的熱電耦與木質(zhì)部表面(在所測枝條上用刀片割出一塊約2cm2初露木質(zhì)部的平整表面)接觸,用來測量木質(zhì)部表面的溫度,位于腔室內(nèi)部的熱電偶則用來測量腔室內(nèi)的溫度,而莖水勢就依據(jù)這些所測溫度校正計(jì)算而得[23]。PSY莖干濕度表固定在枝干后,需在黃銅腔室外圍涂上乳膠,再用錫箔紙包好,防雨防曬保持腔室內(nèi)的環(huán)境穩(wěn)定。同樣每套儀器用一塊12V的太陽能板或一節(jié)12V蓄電池供電。PSY莖干濕度表測定的水勢范圍為-0.01 至-10 MPa,精度為±0.01 MPa,分辨率為0.002 MPa。
已有研究表明,土壤植物連續(xù)體的水勢在黎明前可近似達(dá)到平衡狀態(tài)[17,24-25],因此,可用黎明前的莖水勢(ψpd)代替土壤水勢,反應(yīng)土壤的水分狀態(tài)。ψpd用黎明前4:00 —6:00的平均莖水勢值來計(jì)算。
1.4環(huán)境因子的測定
在距實(shí)驗(yàn)樣地約150m處的開闊地安裝微型自動(dòng)氣象站(WeatherHawk-232, USA),每隔30min自動(dòng)記錄環(huán)境因子數(shù)據(jù)。測定項(xiàng)目包括降雨量、太陽輻射(Rs)、空氣溫度(T)、相對濕度(RH)和風(fēng)速(U)。其中,空氣溫度和相對濕度用來計(jì)算水汽壓虧缺(D)。
1.5冠層氣孔導(dǎo)度的計(jì)算
PM方程同時(shí)考慮了植物生理和微氣象因素,是在計(jì)算冠層蒸騰方面使用最廣泛的方法[26],因此可根據(jù)PM方程反推計(jì)算冠層氣孔導(dǎo)度(gc),其表達(dá)方程如下:
(2)
式中,gc是冠層氣孔導(dǎo)度(m/s),ga是空氣動(dòng)力學(xué)阻力(m/s),λ是水蒸發(fā)潛熱(J/kg),γ是濕度常數(shù)(Pa/℃),Ec是樹的蒸騰量(mm/day),ρw和ρa(bǔ)分別是水和空氣的密度(kg/m3),Δ是水汽壓與氣溫變化斜率(Pa/℃),Rn是凈輻射(J/m2/s),G是地面熱通量(J/m2/s),Cp是空氣熱容量(J/kg/℃),D是水汽壓虧缺(Pa),kt和ke是用于單位轉(zhuǎn)換,當(dāng)Ec為mm/h時(shí),kt=3600 s/h,當(dāng)Ec為mm/d時(shí),kt=86400 s/d,ke=0.001,用來把Ec從mm/d 轉(zhuǎn)換為 m/d。
1.6gc模型構(gòu)建
以往的許多氣孔導(dǎo)度模型中只關(guān)注2—3個(gè)環(huán)境影響因素[20],綜合考慮了4個(gè)影響氣孔導(dǎo)度的環(huán)境因素D、T、Rs和ψpd。(CO2沒觀測研究,所以未考慮到模型中),參考Jarvis-Stewart的方法[2,12]構(gòu)建gc模型如下:
(3)
式中,gmax是植物在沒有脅迫的理想條件下的氣孔導(dǎo)度(m/s),LAI是葉面積指數(shù),函數(shù)f(i)是影響氣孔導(dǎo)度的環(huán)境因子脅迫函數(shù),其值介于0—1之間,這種模型的前提是認(rèn)為各環(huán)境變量是相互獨(dú)立的。
然而,環(huán)境因子的脅迫函數(shù)在不同研究中有不同的表達(dá)形式,在此,對于每個(gè)脅迫函數(shù),本文各采用兩種常用的函數(shù),然后通過不同組合構(gòu)建不同的gc模型,最后通過比較這些模型的模擬效果,得到一個(gè)最優(yōu)的gc模型。具體的函數(shù)方程形式見表1。
表1 4個(gè)環(huán)境因子脅迫函數(shù)的不同方程形式Table 1 Different equation forms in stress function of four environmental factors
1.7模型的選擇和參數(shù)優(yōu)化
由于降水對液流有影響以及部分時(shí)間段降水?dāng)?shù)據(jù)缺失,故本文剔除了降水日的數(shù)據(jù),同時(shí),不同樣樹因個(gè)體差異會造成模型參數(shù)的較大差異[7]。為了避免這些問題,保證模型及參數(shù)的穩(wěn)定,把兩棵桂花樹蒸騰及對應(yīng)環(huán)境因子的數(shù)據(jù)合并成一個(gè)數(shù)據(jù)集(第2棵樣樹數(shù)據(jù)追加到第1棵樣樹數(shù)據(jù)后面),并按該數(shù)據(jù)集的自然序列分成兩組:Dt奇數(shù)組(數(shù)據(jù)集中的1,3,5,…,等所有奇數(shù)序列的數(shù)據(jù)),Dt偶數(shù)組(數(shù)據(jù)集中的2,4,6,…,等所有偶數(shù)序列的數(shù)據(jù))。其中,Dt奇數(shù)組用來訓(xùn)練gc模型,選出一個(gè)最優(yōu)模型組合,然后用Dt偶數(shù)組檢驗(yàn)該模型,所有的數(shù)據(jù)都為日尺度數(shù)據(jù)。4個(gè)環(huán)境因子不同方程組合的16種模型見圖1。每一個(gè)gc模型的參數(shù)采用DiffeRential Evolution Adaptive Metropolis(DREAM)模型[33]來計(jì)算。DREAM可以根據(jù)所給方程自動(dòng)優(yōu)化參數(shù)[17],為了獲得可靠的優(yōu)化參數(shù)讓每一個(gè)模型在DREAM里都迭代60000次。
圖1 4個(gè)環(huán)境因子不同脅迫方程組合的16種gc模型(右側(cè)M1—M16是對應(yīng)的模型編號)Fig.1 16 gcmodels combined with different stress functions of four environmental factors, Symbols on the right are the model numbers (M1—M16)
圖2 桂花樹的日蒸騰量(Ec)及部分環(huán)境變量概況Fig.2 Daily transpiration (Ec) of Osmanthus fragrans and part of environmental variables (Rsis solar radiation, T is average air temperature, D is vapor pressure deficit, ψpdis predawn stem water potential and Rainfall); The broken on lines is due to the missing dataRs是太陽輻射,T是日平均溫度,D是水汽壓虧缺,ψpd是黎明前莖水勢,Rainfall是降雨量,圖中曲線斷開是因?yàn)閮x器故障導(dǎo)致的數(shù)據(jù)缺失
2.1冠層氣孔導(dǎo)度與環(huán)境影響因子的關(guān)系
本研究以實(shí)測樹干液流計(jì)算桂花樹整樹的蒸騰量,進(jìn)而利用PM方程計(jì)算冠層氣孔導(dǎo)度(gc)。圖2展示了2013年觀測期間環(huán)境條件及桂花樹蒸騰隨時(shí)間的變化,其中7月至8月中旬發(fā)生了嚴(yán)重的夏季干旱,高溫?zé)o雨。日均氣溫(T)和水汽壓虧缺(D)在整個(gè)觀測期間具有相似的變化趨勢。黎明前的莖水勢反應(yīng)了土壤水分狀況,在干旱前土壤水分充足,變化不大;干旱持續(xù)時(shí)土壤越來越干,干旱后才逐漸恢復(fù)水分狀況(8月10日水勢的突然上升是由桂花園主人澆水所致,因此也造成隨后的桂花樹蒸騰用水的增加)。桂花樹的蒸騰(Ec)對降水響應(yīng)很敏感,易受降水環(huán)境的干擾,因此本文在冠層氣孔導(dǎo)度模擬時(shí)剔除了有降水事件的數(shù)據(jù)。蒸騰還受其他環(huán)境因子的影響,尤其是土壤水分條件,在干旱前蒸騰總體保持較高水平,在干旱期間土壤水分虧缺加劇時(shí)隨之降低,干旱后隨水分條件好轉(zhuǎn)逐漸恢復(fù)。
冠層氣孔導(dǎo)度與溫度、水汽壓虧缺、土壤水分狀況等關(guān)系密切,由圖3可看出,gc與D呈顯著負(fù)相關(guān),尤其當(dāng)D> 1.75 kPa時(shí),gc顯著下降;gc與ψpd呈顯著正相關(guān),隨ψpd的降低而減小;gc與T的關(guān)系較復(fù)雜,當(dāng)日平均溫度超過約30℃時(shí),gc下降明顯;而gc與Rs雖未呈現(xiàn)正或負(fù)相關(guān)的關(guān)系,但作為gc的能量來源也密切影響著氣孔的開閉。由此也看出D和ψpd是影響桂花樹gc的兩個(gè)最主要影響因素。
圖3 冠層氣孔導(dǎo)度(gc)與4個(gè)影響因子(Rs、T、D、ψpd)的關(guān)系Fig.3 Relationship between canopy stomatal conductance (gc) and four influencing factors (Rs, T, D, ψpd)
2.2模型優(yōu)化及其驗(yàn)證
為比較16種模型的優(yōu)劣,選擇一種適合桂花樹的最優(yōu)模型,計(jì)算了模擬gc與PM計(jì)算的gc兩者間的相關(guān)系數(shù)(r)和均方根誤差(RMSE),并以此作為模型評價(jià)的標(biāo)準(zhǔn)。所有模型中只有M1,M9和M13表現(xiàn)較好,同時(shí)具有較高的r和較低的RMSE(圖4)。相比之下M9的組合模型是最優(yōu)的(圖4和圖5)。當(dāng)Rs和D的方程一樣時(shí),T和ψpd選擇f(T)-1和f(ψpd)-1的組合效果明顯好于其他用f(T)-2、f(ψpd)-2的方程組合(圖4)。例如,在模型1—4中,訓(xùn)練模型中的相關(guān)系數(shù)分別為0.801,0.807,0.786,0.790,均方根誤差分別為0.00062,0.00063,0.00064,0.00067 m/s。在圖5可看出,對于溫度,所有和f(T)-1的組合都優(yōu)于和f(T)-2的組合的模型;對于太陽輻射,兩種方程組合的模型效果差異不大(圖5),r和RMSE都很接近;對于水汽壓虧缺,指數(shù)形式的方程模擬效果優(yōu)于線性方程(圖5);對于黎明前莖水勢,用f(ψpd)-1的方程結(jié)果好于f(ψpd)-2的組合模型。
從圖4和圖5的統(tǒng)計(jì)分析可知,M9是本研究中的相對最優(yōu)模型,分別由4個(gè)環(huán)境因子脅迫函數(shù)中的f(Rs)-2、f(D)-1、f(T)-1和f(ψpd)-1方程組成,最優(yōu)模型方程如下:
(4)
圖6展示了最優(yōu)模型模擬的gc與PM計(jì)算gc的關(guān)系,兩組數(shù)據(jù)的相近程度總體上都較好,檢驗(yàn)?zāi)P椭幸踩〉昧溯^高的相關(guān)系數(shù)(r=0.78)和較低的均方根誤差(RMSE=0.00066 m/s)。但gc的模擬在干旱前存在明顯的低估現(xiàn)象,這可能與降雨日前后的環(huán)境條件(如陰天,本文只剔除了有降雨事件的日數(shù)據(jù))有關(guān)。而在干旱后存在一定程度的高估現(xiàn)象,干旱期間模擬效果最佳,這說明本文最佳的冠層氣孔導(dǎo)度模型能夠有效地模擬出干旱條件下的gc及其變化趨勢。
圖4 不同模型評價(jià)比較Fig.4 Comparison of different models evaluation
圖5 每個(gè)環(huán)境因子不同響應(yīng)方程的相關(guān)系數(shù)(r)和均方根誤差(RMSE)Fig.5 The correlation coefficient and root-mean-square error of different response functions for each influencing factor(a)是太陽輻射因子中分別用f(Rs)-1和f(Rs)-2兩種方程形式與其他環(huán)境因子所有不同方程組合的模型表現(xiàn),(b)、(c)和(d)也同理,數(shù)據(jù)為檢驗(yàn)?zāi)P偷臄?shù)據(jù)Dt偶數(shù)組
圖6 PM方程計(jì)算的gc與最優(yōu)模型模擬gc的比較Fig.6 Comparison of PM-calculated gcand simulated gcfrom the optimized model (M9)
2.3參數(shù)分析
Wang等[17]在南澳典型樹種DroopingSheoak的冠層氣孔導(dǎo)度模擬研究中發(fā)現(xiàn),gc模型中的溫度脅迫函數(shù)f(T)> 1,參數(shù)KT< 0,許文滔[7]和齊華[19]的研究中也存在f(T)> 1現(xiàn)象,這顯然與0 ≤f(T)≤1 的原則要求[2]不符。本文主要分析模型中溫度脅迫函數(shù)f(T)及其參數(shù)kT,驗(yàn)證這種現(xiàn)象在本研究中是否也存在。
16種模型中參數(shù)kT有正值也有負(fù)值,其中正值對應(yīng)的都是溫度函數(shù)為線性方程的模型(圖7),kT變化范圍為[-0.0086,0.0124],這與Wang[17]結(jié)論中kT全部為負(fù)值略有不同,與許文滔等[7]展示的kT有正值和負(fù)值情況類似,但不管是正值或負(fù)值都會造成f(T)> 1(圖8),這說明gc模型中f(T)> 1的現(xiàn)象不只是個(gè)例。為了驗(yàn)證參數(shù)kT這種情況是否都存在,把Dt偶數(shù)組也放入DREAM參數(shù)優(yōu)化模型中運(yùn)行,結(jié)果kT也是有正值和負(fù)值(圖7)。其他參數(shù)情況具體見表2。
一般函數(shù)f(T)的形狀是開口向下的拋物線[12,29,34-35]。而本文模型的結(jié)果是f(T)> 1,這可能是由于Jarvis形式的gc模型前提是認(rèn)為各環(huán)境影響因子相互獨(dú)立,實(shí)際上各環(huán)境因子會相互影響,如D會影響植物水勢[36],T和D經(jīng)常是高度相關(guān)的[37],很難區(qū)分影響因素各自對氣孔導(dǎo)度的影響。而且T和D在整個(gè)觀測期間都具有相似的變化趨勢(圖2)且兩者顯著相關(guān)(圖8),指數(shù)相關(guān)系數(shù)達(dá)0.83,對gc的影響都較明顯(圖3)。
圖7 不同模型中參數(shù)kT的變化情況Fig.7 Variation of parameter kTin different models虛線橢圓框內(nèi)都是f(T)-2的模型組合
因此,在gc模型中獨(dú)立考慮f(T)對冠層氣孔導(dǎo)度的影響可能會失真,本文引進(jìn)另一個(gè)影響因子f(DT)(圖8(b))。當(dāng)22 圖8 溫度與水汽壓虧缺以及最優(yōu)模型M9中脅迫函數(shù)f(T)、f(D)和的關(guān)系Fig.8 Relationship between temperature and vapor pressure deficit and the response functions f(T),f(D), andof the best model M9 模型Models參數(shù)ParametersgmaxkRskDT0kψkm最優(yōu)模型M9Optimizedmodel,M90.004220.010.5024.460.61-3.39f(Rs)-1[0.0036,0.0039][14.76,59.65][0.27,0.50][22.67,29.93][0.60,0.81][-3.78,-1.49]f(Rs)-2[0.0036,0.0043][9.42,27.27][0.23,0.50][11.30,29.04][0.60.0.84][-3.87,-1.49]f(D)-1[0.0037,0.0043][9.42,42.43][0.40,0.50][11.30,29.62][0.60,0.84][-3.60,-1.49]f(D)-2[0.0036,0.0040][11.10,59.65][0.23,0.37][24.24,29.93][0.60,0.83][-3.87,-1.49]f(T)-1[0.0036,0.0043][20.01,59.65][0.25,0.50][24.23,24.92][0.60,0.84][-3.87,-1.49]f(T)-2[0.0036,0.0039][9.42,28.34][0.23,0.50][11.30,29.93][0.60,0.82][-3.85,-1.49]f(ψpd)-1[0.0036,0.0042][13.72,59.65][0.31,0.50][11.30,29.81][0.60,0.62][-3.87,-3.35]f(ψpd)-2[0.0036,0.0043][9.42,47.48][0.23,0.45][21.89,29.93][0.80,0.84][-1.50,-1.49] 氣孔導(dǎo)度受環(huán)境因素的綜合影響,在土壤-植被-大氣統(tǒng)一體中發(fā)揮著重要的作用。本文通過比較常用的不同環(huán)境脅迫方程的組合來優(yōu)化Jarvis形式的冠層氣孔導(dǎo)度模型,找到了適合本區(qū)域氣候環(huán)境下桂花樹的冠層氣孔導(dǎo)度。 通過比較不同環(huán)境脅迫方程的組合來優(yōu)化gc模型是一種非常有效的模型優(yōu)化方法。本文得到的優(yōu)化模型很好地模擬出了桂花樹冠層氣孔導(dǎo)度的變化,尤其是對干旱的響應(yīng)。同時(shí)也說明脅迫函數(shù)方程的選擇對gc模型的構(gòu)造很重要,在未來研究中,這種方法應(yīng)在更多的不同區(qū)域環(huán)境、不同森林樹種上應(yīng)用,才能找到適合研究區(qū)域不同樹種的最佳gc模型。本文研究區(qū)域中最優(yōu)化的桂花樹gc模型如公式(4)所示,4個(gè)環(huán)境影響因子中,水汽壓虧缺和黎明前莖水勢的最優(yōu)方程都為指數(shù)形式的方程,溫度為非線性的拋物線方程,對于太陽輻射而言兩種形式的方程效果相當(dāng),這點(diǎn)與Wang等[17]的結(jié)論相似。這可能是因?yàn)闅饪讓?dǎo)度受太陽輻射脅迫不明顯(圖3),但深層原因還有待進(jìn)一步探究。 模型中溫度脅迫函數(shù)f(T)> 1,這是由T和D高度相關(guān)所致,所以在模型構(gòu)造中應(yīng)把T和D作為一個(gè)影響因子f(DT)看待,結(jié)果f(DT)< 1,符合模型要求。同時(shí)也證明Wang等[17]提到的溫度脅迫函數(shù)f(T)> 1和參數(shù)KT< 0的現(xiàn)象不僅在地中海氣候中存在,在我國亞熱帶季風(fēng)性氣候區(qū)也存在。這種現(xiàn)象是否具有全球性有待進(jìn)一步的研究和證實(shí)。另一方面,本文重點(diǎn)是探索方法,由于樣樹數(shù)量較少,研究具體結(jié)果的代表性可能存在不確定性,未來將進(jìn)行更廣泛的研究。 Jarvis-Stewart氣孔導(dǎo)度模型前提是各環(huán)境影響因子間是相互獨(dú)立的,當(dāng)環(huán)境因子間存在較高相關(guān)時(shí),模型構(gòu)造時(shí)就須謹(jǐn)慎。 [1]Misson L, Panek J A, Goldstein A H. 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Stomatal control by fed or endogenous xylem ABA in sunflower: interpretation of correlations between leaf water potential and stomatal conductance in anisohydric species. Plant, Cell & Environment, 1996, 19(1): 75-84. [37]Alves I, Pereira L S. Modelling surface resistance from climatic variables? Agricultural Water Management, 2000, 42(3): 371-385. Optimization of canopy stomatal conductance models forOsmanthusfragransand analysis of its parameters LUO Zidong1, GUAN Huade1,2, ZHANG Xinping1,*, LIU Na1,ZHANG Cicheng1, WANG Ting1 1CollegeofResourceandEnvironmentScience,HunanNormalUniversity,Changsha410081,China 2SchooloftheEnvironment,FlindersUniversity,Adelaide,SA5001,AUS Canopy stomatal conductance (gc) controls transpiration and photosynthesis processes. Thus, the simulation ofgcand its environmental variation forms a significant component of many land surface models. A Jarvis-type model, which calculatesgcfrom a reference value multiplied by scaling (or response) functions of influencing environmental variables, is a typical representation ofgcin land surface modeling. Influential environmental factors often include solar radiation, vapor pressure deficit, and temperature and soil water conditions. Studies have applied different response functions to each individual environmental factor, often without rigorous evaluation. Thus, there is a need to determine which combination of response functions is most appropriate for a specific vegetation cover. In this study, an optimization model ofgcwas determined forO.fragrans, an evergreen tree species in the southern China,based on field measurements. Sapflow, stem water potential, and microclimatic variables were recorded at anO.fragransplantation site in 2013, where a severe drought occurred in July and August of that same year. Sap flow data were used to calculate transpiration, from whichgcwas estimated from the inversed Penman-Monteith (PM) equation, based on micrometeorological data. Predawn stem water potential data were used to estimate root zone water potential, one of the environmental variables influencinggc. Other environmental variables were available or could be derived from the micrometeorological measurements. A total of sixteengcmodels composed of different response functions were examined. Parameters of each candidate model were optimized using the DiffeRential Evolution Adaptive Metropolis(DREAM)model. DREAM runs multiple different chains simultaneously for global exploration and automatically tunes the scale and orientation of the distribution in randomized subspaces during the search for the optimized parameters. The measurement data points were separated to form two sets of data, one for parameter optimization using DREAM, and the other for model testing. The best model was determined based on the statistics of model testing results. The results indicate that this method is useful in determining the appropriate response function for each environmental factor in order to optimize thegcmodel. ForO.fragrans, an exponential function of vapor pressure deficit and root zone water potential, and a parabolic function of air temperature are the most appropriate response functions, whereas no significant difference is observed between different functions of solar radiation. The optimized model shows a significantly improved estimation of thegcofO.fragrans, especially for the drought period. The correlation coefficient and root-mean-square error based on the model testing result were 0.803 and 0.000623 m/s, respectively. The results also suggest that the temperature stress function can be larger than one, a finding that is inconsistent with the conceptual definition of a stress function. Similar findings have been reported in previous studies. This discrepancy is likely attributed to the fact that air temperature and vapor pressure deficit are often strongly interdependent. Thus, to be conceptually consistent, the function of temperature and that of vapor pressure deficit should be combined into one single stress function. Further studies are required to examine if this result applies to other vegetation types globally. canopy stomatal conductance; model optimizing; environmental factors; sap flow;Osmanthusfragrans 10.5846/stxb201506171230 湖南省百人計(jì)劃項(xiàng)目(2010004);湖南省重點(diǎn)學(xué)科建設(shè)項(xiàng)目(2011001);國家自然科學(xué)基金項(xiàng)目(41571021);湖南省研究生科研創(chuàng)新項(xiàng)目基金(CX2015B167) 2015-06-17; 2015-09-28 Corresponding author.E-mail: zxp@hunnu.edu.cn 羅紫東,關(guān)華德,章新平,劉娜,張賜成,王婷.桂花樹冠層氣孔導(dǎo)度模型的優(yōu)化及其參數(shù)分析.生態(tài)學(xué)報(bào),2016,36(13):3995-4005. Luo Z D, Guan H D, Zhang X P, Liu N,Zhang C C, Wang T.Optimization of canopy stomatal conductance models forOsmanthusfragransand analysis of its parameters.Acta Ecologica Sinica,2016,36(13):3995-4005.3 結(jié)論與討論