王丹丹,王 偉,劉壽東,邱新法,穆俊宇,莫華陽,崔叢欣,陶潘虹,闕宇杰,俞 樂,陳泓宇,薛舒航

(1:耶魯大學(xué)—南京信息工程大學(xué)大氣環(huán)境中心,南京 210044)(2:南京信息工程大學(xué)應(yīng)用氣象學(xué)院,南京 210044)(3:南京信息工程大學(xué)大氣科學(xué)學(xué)院,南京 210044)

太湖小時(shí)尺度水面蒸發(fā)特征及3種模型模擬效果對(duì)比*

王丹丹1,2,王 偉1,2**,劉壽東1,2,邱新法2,穆俊宇2,莫華陽2,崔叢欣2,陶潘虹2,闕宇杰2,俞 樂2,陳泓宇2,薛舒航3

(1:耶魯大學(xué)—南京信息工程大學(xué)大氣環(huán)境中心,南京 210044)(2:南京信息工程大學(xué)應(yīng)用氣象學(xué)院,南京 210044)(3:南京信息工程大學(xué)大氣科學(xué)學(xué)院,南京 210044)

小時(shí)尺度水面蒸發(fā)可影響水面大氣邊界層熱力和動(dòng)力結(jié)構(gòu),分析湖泊小時(shí)尺度水面蒸發(fā)主要影響因素,選取準(zhǔn)確模擬其特征的蒸發(fā)模型,將有助于改善流域天氣預(yù)報(bào)和空氣質(zhì)量預(yù)報(bào). 基于太湖避風(fēng)港站2012-2013年通量、輻射和氣象觀測數(shù)據(jù),分析太湖小時(shí)尺度水面蒸發(fā)主要影響因子和3個(gè)模型(傳統(tǒng)質(zhì)量傳輸模型、Granger and Hedstrom經(jīng)驗(yàn)?zāi)Ｐ?、DYRESM模型)的模擬效果. 結(jié)果表明：影響太湖小時(shí)尺度水面蒸發(fā)的主要因子為水氣界面水汽壓差和風(fēng)速的乘積,而非凈輻射. 傳統(tǒng)質(zhì)量傳輸模型、Granger and Hedstrom經(jīng)驗(yàn)?zāi)Ｐ汀YRESM模型模擬值與全年實(shí)測值的一致性系數(shù)分別為0.92、0.87和0.89,均方根誤差分別為28.35、41.58和38.26 W/m2. 傳統(tǒng)質(zhì)量傳輸模型對(duì)太湖小時(shí)尺度水面蒸發(fā)的日變化和季節(jié)動(dòng)態(tài)模擬效果最佳,其夜間模擬相對(duì)誤差小于3%,除秋季外,其他季節(jié)的模擬絕對(duì)誤差均小于4 W/m2. Granger and Hedstrom經(jīng)驗(yàn)?zāi)Ｐ拖到y(tǒng)性地高估太湖潛熱通量,在大氣較為穩(wěn)定的午后(高估22～32 W/m2)和冬季(高估72%)高估最為明顯,模擬效果最差. DYRESM模型也系統(tǒng)地高估太湖潛熱通量,模擬效果居中. 考慮水汽交換系數(shù)隨風(fēng)速的變化特征將有助于改善傳統(tǒng)質(zhì)量傳輸模型和DYRESM模型對(duì)太湖小時(shí)尺度水面蒸發(fā)的模擬精度.

太湖；潛熱通量；水面蒸發(fā)模擬；小時(shí)尺度;傳統(tǒng)質(zhì)量傳輸模型;Granger and Hedstrom經(jīng)驗(yàn)?zāi)Ｐ?DYRESM模型

我國內(nèi)陸湖泊約占國土面積的0.9%[1], 雖然所占比例不大, 但研究湖泊小時(shí)尺度水面蒸發(fā)的社會(huì)價(jià)值和科學(xué)意義因人類依水而居而凸顯. 首先,作為大氣水汽的重要來源,大型湖泊小時(shí)尺度水面蒸發(fā)能影響成云過程,增強(qiáng)下游降水[2-3]. 同時(shí),小時(shí)尺度水面蒸發(fā)是湖泊水分循環(huán)和能量平衡的關(guān)鍵環(huán)節(jié),與陸地蒸散晝強(qiáng)夜弱變化特征不同,夜間湖泊水面蒸發(fā)約占全年蒸發(fā)的48%[4]. 此外,小時(shí)尺度水面蒸發(fā)會(huì)改變湖面大氣邊界層(Atmospheric boundary layer,ABL)的熱力和動(dòng)力結(jié)構(gòu)[5],激發(fā)諸如湖陸風(fēng)等局地環(huán)流,進(jìn)而影響流域內(nèi)大氣污染物的擴(kuò)散與傳輸[6-7]. 因此,研究小時(shí)尺度水面蒸發(fā)特征對(duì)改善流域高時(shí)間分辨率的天氣、空氣質(zhì)量預(yù)報(bào)和精準(zhǔn)利用水資源意義重大.

目前,國外已有研究利用渦度相關(guān)技術(shù)(Eddy covariance,EC)直接觀測湖泊潛熱通量[8-11],或采用大孔徑閃爍儀(Large aperture scintillometer,LAS)[12-13]基于能量平衡方程估算潛熱通量. 研究發(fā)現(xiàn),湖泊潛熱通量日變化幅度不明顯,最大值出現(xiàn)在下午而非太陽輻射最強(qiáng)的正午,最小值出現(xiàn)在午夜[5,14-15],得益于水體熱儲(chǔ)量釋放,夜間水面蒸發(fā)顯著[14-16]. 從影響因素而言,影響小時(shí)尺度水面蒸發(fā)特征的主因并非凈輻射(Rn)[10,15,17-22],而是水氣界面的水汽壓差(Δe)[23-25]或風(fēng)速(u)[17,20],且發(fā)現(xiàn)潛熱通量(LE)與兩者乘積uΔe的相關(guān)性比與u或Δe各自的相關(guān)性更強(qiáng),即小時(shí)尺度水面蒸發(fā)很大程度上取決于u與Δe的協(xié)同作用[21]. 基于EC技術(shù),國內(nèi)研究者也在太湖、鄂陵湖、洱海、納木錯(cuò)和鄱陽湖開展了高時(shí)間分辨率的湖泊潛熱通量觀測[26-28],但對(duì)湖泊各季節(jié)和全年小時(shí)尺度水面蒸發(fā)特征及其影響因素的研究尚需深入.

盡管EC和LAS觀測有助于理解水氣間潛熱通量交換特征及其影響機(jī)制,但昂貴的設(shè)備和復(fù)雜的操作限制了此類觀測技術(shù)的推廣[19],故模擬湖泊蒸發(fā)過程很有必要[29-30]. 已有研究評(píng)估了湖泊蒸發(fā)模型的模擬效果[10,31-33],但大多數(shù)模型(如彭曼模型、溫度—日長模型)局限于對(duì)日或更長時(shí)間尺度水面蒸發(fā)的模擬,僅有少數(shù)模型可用于模擬小時(shí)尺度水面蒸發(fā),如傳統(tǒng)質(zhì)量傳輸模型、Granger and Hedstrom經(jīng)驗(yàn)?zāi)Ｐ秃虳YRESM (Dynamic Reservoir Simulation Model)水文模型. McGloin等[34]分析了這3個(gè)模型模擬小型水庫小時(shí)尺度水面蒸發(fā)的效果,但對(duì)于大型湖泊(如太湖)的模擬效果有待評(píng)估. 隨著公眾對(duì)天氣預(yù)報(bào)和氣候預(yù)測需求的加強(qiáng),數(shù)值預(yù)報(bào)模式和氣候模型將趨于機(jī)理綜合化和尺度精細(xì)化[35],這需要對(duì)小于日尺度的水面與大氣之間的潛熱交換過程進(jìn)行準(zhǔn)確參數(shù)化[34],亟待加強(qiáng)小時(shí)尺度水面蒸發(fā)模型的評(píng)估.

本文旨在利用太湖渦度相關(guān)通量、輻射和氣象觀測資料,分析太湖小時(shí)尺度水面蒸發(fā)特征及其影響因素,選取傳統(tǒng)質(zhì)量傳輸模型、Granger and Hedstrom經(jīng)驗(yàn)?zāi)Ｐ秃虳YRESM模型模擬太湖小時(shí)尺度水面蒸發(fā),并利用EC實(shí)測潛熱通量評(píng)估各模型的模擬效果,探討其誤差來源,最終得到適用于模擬太湖小時(shí)尺度水面蒸發(fā)的模型.

1 資料與方法

1.1 研究站點(diǎn)

太湖是我國第三大淡水湖,水面面積為2400 km2,平均深度為1.9 m,為典型的亞熱帶大型淺水湖泊. 研究站點(diǎn)為太湖東部的湖泊通量觀測站——避風(fēng)港站(BFG站)(31°10′N,120°24′E)(圖1),該站觀測始于2011年12月15日,本文所用數(shù)據(jù)是2012年1月1日0:00至2013年2月28日24:00的BFG站半小時(shí)通量、輻射和小氣候觀測數(shù)據(jù),基于2012年數(shù)據(jù)進(jìn)行全年分析,冬季分析基于2012-2013跨年數(shù)據(jù). BFG站水域清澈[36]且有沉水植物(以馬來眼子菜(Potamogetonmalaianus)和輪葉黑藻(Hydrillaverticillata)為主)生長[4],四周水域開闊,風(fēng)浪區(qū)均超過4 km,2 m高度年平均風(fēng)速為4.0 m/s[15].

圖1 太湖湖泊通量觀測站——避風(fēng)港站、陸地氣象站——東山站地理位置示意圖及避風(fēng)港站平臺(tái)照片F(xiàn)ig.1 Map showing a lake flux site in Lake Taihu (Bifenggang station) and Dongshan land meteorology station; The instrument platform photo of the Bifenggang station is also shown

1.2 觀測系統(tǒng)

BFG站裝有渦度相關(guān)(EC)觀測系統(tǒng)、四分量凈輻射傳感器、小氣候觀測系統(tǒng)和水溫梯度觀測系統(tǒng)各一套[15],觀測設(shè)備均安裝在4 m×4 m的觀測平臺(tái)上.

EC觀測系統(tǒng)由三維超聲風(fēng)速計(jì)(CSAT3,Campbell Scientific Inc.)和開路式紅外氣體分析儀(EC150,Campbell Scientific Inc.)組成,以10 Hz頻率分別測量三維風(fēng)速/超聲溫度和大氣中水汽、CO2密度,該系統(tǒng)安裝在離水面8.5 m(安裝時(shí))的高度處. 數(shù)據(jù)采集器(CR3000,Campbell Scientific Inc.)基于10 Hz觀測數(shù)據(jù)在線計(jì)算30 min平均動(dòng)量、感熱、潛熱和CO2通量. 為保證EC系統(tǒng)正常運(yùn)行及觀測數(shù)據(jù)的準(zhǔn)確性,紅外氣體分析儀在實(shí)驗(yàn)前進(jìn)行一次室內(nèi)標(biāo)定,野外觀測時(shí)每季度進(jìn)行一次場外標(biāo)定. 四分量凈輻射傳感器(CNR4,Kipp & Zonen B.V.)用于測量太陽向下短波、反射短波、向下長波和向上長波輻射,安裝高度距離水面2 m(安裝時(shí)). 小氣候觀測系統(tǒng)由溫濕度傳感器(HMP155A,Vaisala Inc.)和風(fēng)速風(fēng)向傳感器(05103,R.M. Young Company)構(gòu)成,其觀測高度與EC相同,用于測量空氣溫度、濕度、風(fēng)速和風(fēng)向. 降水量由自動(dòng)翻筒式雨量計(jì)(TE525-L,Campbell Scientific Inc.)測得. BFG站水溫梯度觀測(109-L,Campbell Scientific Inc.)分為水下20、50、100、150 cm 4個(gè)梯度,并用相同的溫度傳感器觀測湖泊底泥溫度[4].

1.3 數(shù)據(jù)處理過程

本文對(duì)30 min平均通量數(shù)據(jù)進(jìn)行的后處理包括：兩次坐標(biāo)旋轉(zhuǎn)[37]、超聲虛溫訂正、密度響應(yīng)校正(WPL校正)[38-39]和數(shù)據(jù)質(zhì)量控制.

首先,基于三維風(fēng)速數(shù)據(jù),通過兩次坐標(biāo)旋轉(zhuǎn)將超聲風(fēng)速的笛卡爾坐標(biāo)系轉(zhuǎn)換成自然風(fēng)坐標(biāo)系[40],使半小時(shí)平均水平風(fēng)與x軸平行,平均側(cè)風(fēng)速度和平均垂直風(fēng)速皆為零. 其次,為彌補(bǔ)濕度脈動(dòng)對(duì)超聲溫度測量的影響,基于通量數(shù)據(jù)和空氣溫度對(duì)感熱通量H進(jìn)行超聲虛溫訂正[41-42]. 再次,考慮到熱量和水汽輸送引起的空氣密度脈動(dòng),需對(duì)潛熱通量LE進(jìn)行WPL校正[39,43]. 最后,剔除降水時(shí)刻的EC通量觀測數(shù)據(jù),基于輻射和小氣候觀測,利用閾值法和滑動(dòng)平均標(biāo)準(zhǔn)差法剔除潛熱通量異常值并進(jìn)行數(shù)據(jù)質(zhì)量控制.

1.4 小時(shí)尺度水面蒸發(fā)模型

1.4.1 傳統(tǒng)質(zhì)量傳輸模型 該模型[34]依據(jù)水面與空氣之間的濕度差、風(fēng)速和水汽傳輸系數(shù)來確定水面蒸發(fā),具體表達(dá)式如下：

E=CE·ρ·u(qs-qa)

(1)

式中,E為水面蒸發(fā)量(kg·m2/s)；ρ為空氣密度(kg/m3)；qs為水面溫度(Ts)所對(duì)應(yīng)的飽和比濕(kg/kg)；qa為空氣比濕(kg/kg)；u為10 m高度處風(fēng)速(m/s),可依據(jù)風(fēng)廓線對(duì)數(shù)規(guī)律由風(fēng)速測量值計(jì)算得到；CE為標(biāo)準(zhǔn)高度10 m對(duì)應(yīng)的水汽傳輸系數(shù),Xiao等[44]綜合考慮了太湖BFG站沉水植物、風(fēng)速等對(duì)水汽傳輸系數(shù)的影響,并進(jìn)行了大氣穩(wěn)定度校正,確定BFG站的CE為1.0×10-3.

基于Stefan-Boltzmann定律,利用長波輻射觀測值計(jì)算得到水面溫度Ts(K),即：

(2)

式中,L↑和L↓分別為四分量凈輻射傳感器所觀測的向上和向下長波輻射(W/m2)；ε為水面比輻射率,為0.97[45-46]；σ是Stefan-Boltzmann常數(shù),取值為5.67×10-8W/(m2·K4).

水面蒸發(fā)量E乘以汽化潛熱L(J/kg)即可轉(zhuǎn)換為潛熱通量(LE)(W/m2),汽化潛熱(L)根據(jù)空氣溫度(Ta,K)通過下式算得：

L=(2.501-0.002361(Ta-273.15))×106

(3)

1.4.2 經(jīng)驗(yàn)關(guān)系模型(Granger and Hedstrom模型) Granger and Hedstrom[19]提出了一種用于估計(jì)湖泊小時(shí)尺度LE的簡單經(jīng)驗(yàn)?zāi)Ｐ?具體如下：

LE=[b+m(Ta-Ts)+n(esw-ea)]u2

(4)

式中,u2是2 m高度處風(fēng)速(m/s)；esw為水面溫度Ts所對(duì)應(yīng)的飽和水汽壓(hPa)；ea是空氣水汽壓(hPa)；Ta和Ts含義同上. 該模型考慮了大氣穩(wěn)定度對(duì)水面蒸發(fā)的影響,大氣穩(wěn)定時(shí)(Ta>Ts),湖泊蒸發(fā)受到抑制；大氣不穩(wěn)定時(shí)(Ta

大氣穩(wěn)定時(shí)：

(5)

大氣不穩(wěn)定時(shí)：

(6)

式中,X(m)為風(fēng)浪區(qū)長度,本文取4000 m.

1.4.3 DYRESM模型 DYRESM是一維水文模型,當(dāng)湖泊或水庫滿足一維假設(shè),即水體層結(jié)較明顯時(shí),該模型可用于模擬小于日時(shí)間尺度的水體溫度、鹽度和密度的垂直分布[47-48]. DYRESM模型無需水面溫度作為初始值輸入,其計(jì)算潛熱通量的方法為[48]：

(7)

式中,Qlh為Δt時(shí)間段內(nèi)水面蒸發(fā)所對(duì)應(yīng)的熱量(J/m2)；水汽傳輸系數(shù)CE為1.3×10-3；汽化潛熱L取2.453×106J/kg；其他變量同上.

2 結(jié)果與討論

2.1 小時(shí)尺度太湖水面蒸發(fā)的影響因素

水面蒸發(fā)主要受三個(gè)環(huán)節(jié)控制：為蒸發(fā)提供多少可利用能量(凈輻射),維持蒸發(fā)進(jìn)行的濕度梯度大小(水氣界面水汽壓差)以及水面與大氣之間的水汽交換能力(風(fēng)速和大氣穩(wěn)定度),故合理的小時(shí)尺度水面蒸發(fā)模型應(yīng)包含以上關(guān)鍵過程和主要影響因子. 通過相關(guān)分析可知(表1),影響太湖LE最重要的環(huán)境單因子是水面與空氣之間的水汽壓差Δe(相關(guān)系數(shù)R=0.64),其次是平均風(fēng)速u(R=0.52),且LE與Δe和u乘積的相關(guān)性更好(R=0.85). 在小時(shí)尺度上,凈輻射Rn不是影響LE的重要因子(R=0.30),這與凈輻射和潛熱通量日變化存在相位差異的結(jié)論一致[10,15,17-21,44]. 盡管LE與水氣界面溫度差ΔT和uΔT的相關(guān)系數(shù)分別僅為0.05和0.18,但ΔT會(huì)通過大氣穩(wěn)定度間接影響潛熱交換[19],不穩(wěn)定大氣邊界層(ΔT>0)能促進(jìn)湖泊潛熱交換,穩(wěn)定大氣邊界層(ΔT<0)會(huì)抑制潛熱蒸發(fā)[19-21,49-51]. 四季潛熱通量與環(huán)境因子的相關(guān)性與全年分析結(jié)果相似,LE與uΔe最相關(guān),其相關(guān)系數(shù)在春、夏、秋、冬季分別為0.85、0.78、0.88和0.87. 冬季LE與uΔT、ΔT的相關(guān)系數(shù)(R=0.76,R=0.57)明顯大于其他3個(gè)季節(jié)的結(jié)果,尤其大于夏季結(jié)果(R=0.03,R=-0.04),而冬季LE與Rn的相關(guān)系數(shù)(R=0.07)明顯小于其它3個(gè)季節(jié)的結(jié)果,尤其小于夏季結(jié)果(R=0.38),這與冬、夏季的大氣穩(wěn)定度和輻射差異有關(guān).

表1 太湖避風(fēng)港站潛熱通量在半小時(shí)時(shí)間尺度上與環(huán)境因子的相關(guān)系數(shù)*

*R是相關(guān)系數(shù),均通過了0.05的顯著性檢驗(yàn).

圖2 渦度相關(guān)觀測的LE與uΔe的幾何平均回歸關(guān)系Fig.2 Geometric mean regression relationship between latent heat flux measured by eddy covariance and the product of wind speed and the vapor pressure difference at water-air interface

由于LE與uΔe均存在觀測誤差,故本文采用幾何平均回歸方法(Geometric mean regression,GMR)[52]對(duì)BFG站的LE與uΔe進(jìn)行線性回歸(圖2),回歸方程為y=1.94(±0.01)x-5.48(±0.57),相關(guān)系數(shù)R高達(dá)0.85,即uΔe對(duì)LE變化的解釋程度達(dá)到72%. 綜合表1和圖2可知,影響太湖小時(shí)尺度水面蒸發(fā)最重要的因素是uΔe,而其他環(huán)境因子的貢獻(xiàn)在冬、夏兩季存在差異,冬季LE與uΔT相關(guān)性較強(qiáng),夏季LE和Rn存在一定的相關(guān)性.

2.2 小時(shí)尺度太湖水面蒸發(fā)模型模擬

2.2.1 全年模型模擬效果 從統(tǒng)計(jì)結(jié)果而言,傳統(tǒng)質(zhì)量傳輸模型模擬結(jié)果的均值(61.37 W/m2)和中位數(shù)(47.57 W/m2)最接近于實(shí)測結(jié)果(均值為60.49 W/m2,中位數(shù)為46.52 W/m2),Granger and Hedstrom和DYRESM模型模擬值的均值分別為84.28和79.83 W/m2,中位數(shù)分別為68.56和61.58 W/m2,均高于實(shí)測結(jié)果,且以Granger and Hedstrom模型為甚. 線性回歸分析發(fā)現(xiàn),傳統(tǒng)質(zhì)量傳輸模型模擬的LE與ECLE的回歸方程斜率為0.90,表明該模型有低估太湖水面蒸發(fā)的趨勢,而Granger and Hedstrom和DYRESM模型模擬值與實(shí)測值的回歸方程斜率分別為1.15和1.17,表明這2個(gè)模型都系統(tǒng)性地高估太湖小時(shí)尺度水面蒸發(fā).

圖3 3種模型的LE模擬值與EC實(shí)測值的散點(diǎn)圖和幾何平均回歸關(guān)系(EC為渦度相關(guān)觀測,TM表示傳統(tǒng)質(zhì)量傳輸模型,GH表示Granger and Hedstrom模型,DR表示DYRESM模型)Fig.3 Scatter plots and geometric mean regression relationship between hourly latent heat flux measured by eddy covariance and simulated by three models(EC represents eddy covariance,TM represents traditional mass transfer model,GH represents Granger and Hedstrom empirical model,DR represents DYRESM model)

本文選取相關(guān)系數(shù)(R)、均方根誤差(RMSE)、Willmott一致性系數(shù)(I)、Nash-Sutcliffe效率(NSE)、平均誤差(MBE)、平均絕對(duì)誤差(MABE)、對(duì)稱性平均絕對(duì)百分比誤差(SMAPE)(公式見表2)來綜合評(píng)價(jià)3種模型對(duì)太湖小時(shí)尺度水面蒸發(fā)的模擬效果(表3). 傳統(tǒng)質(zhì)量傳輸模型模擬值與實(shí)測值的相關(guān)系數(shù)(R=0.86)、I(0.92)和NSE(0.73)均最大,4個(gè)誤差指標(biāo)均最小,即該模型模擬效果最佳,這與BFG站LE與uΔe的強(qiáng)相關(guān)性(R=0.85)(見2.1節(jié))有關(guān),諸多內(nèi)陸水體EC觀測都得到類似結(jié)論[21,53]. Granger and Hedstrom模型模擬結(jié)果的誤差指標(biāo)最大,如RMSE高達(dá)41.58 W/m2,其模擬值與實(shí)測值相關(guān)系性最差(R=0.84)、I值最小(0.87)、NSE效率最低(0.41),即該模型模擬值偏離實(shí)測值最大. DYRESM模型模擬效果的評(píng)價(jià)參數(shù)介于傳統(tǒng)質(zhì)量傳輸模型和Granger and Hedstrom模型之間,模擬效果居中.

表2 太湖小時(shí)尺度水面蒸發(fā)模擬效果統(tǒng)計(jì)參數(shù)公式*

2.2.2 四季連續(xù)5日模擬結(jié)果 四季連續(xù)5日(1月1-5日、4月4-8日、7月21-25日和10月12-16日)潛熱通量觀測值和模擬值的時(shí)間序列見圖4. 7月連續(xù)5日潛熱通量呈現(xiàn)顯著的日變化特征,即午后達(dá)到峰值、凌晨降至谷值,其他各月5日潛熱通量日動(dòng)態(tài)并不明顯,而1月3-4日冷鋒過境帶來的大風(fēng)降溫天氣能顯著增強(qiáng)太湖水面蒸發(fā). 綜合分析3個(gè)模型的模擬結(jié)果發(fā)現(xiàn)(表3),各模型在四季均能較好地模擬BFG站潛熱通量時(shí)間變化特征,R均超過0.75,I均高于0.72. 3個(gè)模型模擬值的MABE和SMAPE最大值基本分別出現(xiàn)在7月和1月,這與湖泊潛熱通量夏季高冬季低的季節(jié)變化特征相關(guān). 對(duì)比分析3個(gè)模型模擬結(jié)果發(fā)現(xiàn)(表3),所有統(tǒng)計(jì)指標(biāo)都顯示傳統(tǒng)質(zhì)量傳輸模型在4個(gè)時(shí)段的模擬效果最佳,DYRESM模型次之,Granger and Hedstrom模型模擬誤差最大. 3種模型模擬效果的對(duì)比差異在湖泊水面蒸發(fā)較大時(shí)更為明顯(圖4),如7月21-25日的午后時(shí)段和1月3-4日冷鋒過境時(shí),且傳統(tǒng)質(zhì)量傳輸模型的優(yōu)勢在7月的連續(xù)5日更為突出,其RMSE不足另外2個(gè)模型的1/6.

表3 3種模型模擬太湖小時(shí)尺度水面蒸發(fā)效果的統(tǒng)計(jì)參數(shù)

注：TM,GH,DR含義同圖3.

圖4 2012年1、4、7和10月連續(xù)5日潛熱通量的渦度相關(guān)觀測值和3種模型模擬值的時(shí)間序列圖Fig.4 Hourly time series of latent heat flux measured by eddy covariance and simulated by three models in 5 consecutive days during January,April,July and October in 2012

2.2.3 季節(jié)平均和年平均日變化模擬 EC觀測的LE季節(jié)平均和年平均日變化特征相似,即在午后達(dá)到最大值,凌晨達(dá)到最小值(圖4). 年均日動(dòng)態(tài)最大值為81.57 W/m2(14:00),最小值為40.89 W/m2(5:30),其日變化幅度(40 W/m2)明顯小于太湖凈輻射(460 W/m2)和東山站陸地潛熱通量(130 W/m2)[4]. 冬季LE日變化較其他3個(gè)季節(jié)波動(dòng)更明顯,可能與冬季冷鋒頻繁過境有關(guān)[22]. 太湖LE日變化與Rn存在相位差異,通常較多的可利用能量(Rn-ΔQ,ΔQ為水體熱儲(chǔ)量)在午后主要用于潛熱蒸發(fā)[10],這一結(jié)論也被諸多研究所證實(shí)[5,14-15]. 同時(shí),季節(jié)和年平均后的LE全天均為正值,表明太湖湖面全天均發(fā)生蒸發(fā),與鄱陽湖EC觀測結(jié)果類似[26]. 此外,得益于水體熱儲(chǔ)量釋放,太湖夜間(19:00-7:30)蒸發(fā)顯著,約占全年蒸發(fā)總量的50%,與Liu等[22](49%)和Li等[5](41%～51%)的觀測結(jié)果相近,略高于McGloin等[14]的觀測結(jié)果(40%).

3個(gè)模型模擬的四季平均和年平均LE日變化趨勢與EC實(shí)測值相近,但模擬值的峰谷出現(xiàn)時(shí)間均落后于觀測結(jié)果約0.5～1.5 h不等. 春、冬季,傳統(tǒng)質(zhì)量傳輸模型上午對(duì)潛熱通量的低估比例分別為14%和9%,下午分別高估15%和10%,夜間模擬效果較好,模擬誤差不足3%. 該模型在夏季的模擬效果最好,誤差僅為3%,而在秋季模擬效果最差,全天系統(tǒng)性地低估LE,低估程度約為13%. Granger and Hedstrom模型和DYRESM模型的模擬效果相近,四季均系統(tǒng)性地高估LE日變化. Granger and Hedstrom模型在四季對(duì)LE的高估比例分別為48%、38%、18%和72%,而DYRESM模型在四季的高估程度分別為38%、35%、12%和35%. Granger and Hedstrom模型在冬季的高估程度約為DYRESM模型的2倍,與該模型在冬季高估大氣穩(wěn)定度對(duì)湖面蒸發(fā)的影響有關(guān)[34]. Granger and Hedstrom模型對(duì)上午水面蒸發(fā)的高估程度大于DYRESM模型,下午兩者高估程度相當(dāng). 對(duì)于年平均日變化而言,傳統(tǒng)質(zhì)量傳輸模型對(duì)LE日變化幅度模擬誤差<1 W/m2,夜間模擬值與實(shí)測值近乎相等,僅在白天上午略微低估(6%),下午略微高估(4%). Granger and Hedstrom模型對(duì)太湖年平均LE日變化的高估程度(38%)大于DYRESM模型的高估程度(29%),午后2個(gè)模型高估程度相近,上午和夜間DYRESM模型的高估偏差較Granger and Hedstrom模型小.

圖5 渦度相關(guān)觀測和模型模擬的太湖潛熱通量(a～d)季節(jié)平均和(e)年平均日變化(EC,TM,GH和DR含義同圖3)Fig.5 Seasonally (a-d) and annually (e) averaged diurnal variations in latent heat flux measured by eddy covariance and simulated by three models in Lake Taihu(EC,TM,GH and DR have the same meanings to Fig.3)

圖6 3種模型LE模擬誤差(模擬值減去實(shí)測值)的年平均日變化(A)和季節(jié)變化(B)(b圖底部給出了各季節(jié)實(shí)測LE的平均值)Fig.6 Annually averaged diurnal variations(a) and seasonally averaged variations(b) in simulation errors (simulation minus measurement) of latent heat flux modelled by three models(The mean EC LE values for each season are also included at the bottom of Fig.(b))

2.3 模型模擬誤差分析

2.3.1 模型模擬誤差的時(shí)間變化特征 傳統(tǒng)質(zhì)量傳輸模型的LE模擬值在夜晚(21:00-5:30)與ECLE值一致性最佳；在6:00-15:00模擬值小于實(shí)測值,且在上午(7:00-9:00)低估程度最大(5～7 W/m2)；在15:30-20:30模擬值大于實(shí)測值,日落前后(16:00-19:00)高估最明顯(2～5 W/m2). Granger and Hedstrom模型和DYRESM模型模擬誤差的日變化特征相似,其模擬值始終大于LE觀測值,兩者均在16:00左右高估程度最大,上午8:00左右高估程度最小. 具體而言,Granger and Hedstrom模型和DYRESM模型午后對(duì)LE高估值分別為22～32和20～29 W/m2,上午高估程度分別為15～20和7～16 W/m2(圖5e和圖6a).

傳統(tǒng)質(zhì)量傳輸模型在春、夏、冬季模擬效果明顯優(yōu)于其它模型,對(duì)LE的高估分別為3.7、2.3和1.5 W/m2. 但該模型在秋季對(duì)LE低估了9.0 W/m2,與DYRESM模型模擬效果(高估8.7 W/m2)相當(dāng),仍優(yōu)于Granger and Hedstrom模型(高估12.6 W/m2). 傳統(tǒng)質(zhì)量傳輸模型秋季模擬效果較差,可能與秋季BFG站沉水植物生長旺盛降低了水汽界面的動(dòng)量粗糙長度有關(guān)[28,44]. Granger and Hedstrom模型和DYRESM模型在四季均高估太湖水面蒸發(fā),2個(gè)模型夏季模擬值的絕對(duì)誤差最大,對(duì)太湖蒸發(fā)的高估分別為32.5和29.4 W/m2；而2個(gè)模型冬季模擬值的相對(duì)偏差最大,分別達(dá)到LE實(shí)測值(28.22 W/m2)的72%和35%. 另外,Granger and Hedstrom模型高估程度在春、夏、秋季比DYRESM模型大3～5 W/m2,而冬季卻高達(dá)10 W/m2(圖5a～d和圖6b). Granger and Hedstrom模型引入空氣與水面的溫度差來表征大氣穩(wěn)定狀況,該模型高估了大氣穩(wěn)定度對(duì)潛熱通量的重要性[34],低估了水汽界面濕度差對(duì)湖泊潛熱交換的貢獻(xiàn). 太湖水面上方大氣全天超過90%的時(shí)間都處于不穩(wěn)定狀態(tài)[4],僅在太陽落山前呈微弱的逆溫現(xiàn)象,故強(qiáng)調(diào)大氣穩(wěn)定度貢獻(xiàn)的Granger and Hedstrom模型會(huì)全天高估太湖潛熱通量,以太陽落山前大氣穩(wěn)定時(shí)為甚. 冬季水面經(jīng)常出現(xiàn)大氣穩(wěn)定狀況[34],這也解釋了為什么冬季Granger and Hedstrom模型模擬值與ECLE值相關(guān)性最差.

圖7 避風(fēng)港站CE值隨u的變化特征(大圓點(diǎn)為1 m/s間隔內(nèi)的平均值,誤差線表示各間隔內(nèi)觀測值的1倍標(biāo)準(zhǔn)差)Fig.7 Variation in transfer coefficient of water vapor (CE) with wind speeds at 10 m height at the BFG station(Big circle denotes the bin average (bin width of 1 m/s),Error bars are one standard deviation of measurements for each bin)

2.3.2 水汽傳輸系數(shù)取值帶來的誤差 傳統(tǒng)質(zhì)量傳輸模型和DYRESM模型對(duì)湖泊水面蒸發(fā)的模擬效果依賴于水汽交換系數(shù)的取值[44]. 圖7顯示了BFG站水汽交換系數(shù)CE值隨10 m高度風(fēng)速的變化關(guān)系. BFG站CE值在0.8×10-3～2.1×10-3之間變化,當(dāng)u<4 m/s時(shí),CE隨風(fēng)速增加而迅速減小至1.0×10-3；當(dāng)u>4 m/s時(shí),CE值幾乎不隨風(fēng)速變化,在0.8×10-3上下波動(dòng),與Xiao等[44]研究結(jié)果相似. 對(duì)比觀測值和Garratt模型模擬值發(fā)現(xiàn)[25],在低風(fēng)速(u<2 m/s)不穩(wěn)定大氣狀況下,Garratt模型對(duì)CE值低估了0.2×10-3～0.8×10-3；在中高風(fēng)速(u>5 m/s)穩(wěn)定或中性大氣狀況下,Garratt模型對(duì)CE值高估了0.25×10-3.

綜上分析,傳統(tǒng)質(zhì)量傳輸模型CE全天取定值(1.0×10-3),在上午中等風(fēng)速(u≈ 4 m/s)條件下,模型的CE值接近實(shí)際值,該模型上午對(duì)LE的低估(6%)主要是其他因素引起；而午后高風(fēng)速(u>5 m/s)條件下,CE值約為0.8×10-3,模型高估的0.2×10-3CE值使其午后高估了4%的LE. 該模型秋季系統(tǒng)性地低估LE,與秋季BFG站沉水植被生長旺盛,降低水面粗糙度,模型CE取值偏低有關(guān)[44]. 對(duì)于DYRESM模型,CE取值(1.3×10-3)偏大,使得該模型系統(tǒng)性地高估LE. 此外,該模型中汽化潛熱L取常數(shù)(2.453×106J/kg),忽略了其隨溫度而變化的特征,對(duì)模擬結(jié)果具有一定影響.

3 結(jié)論

用3種模型對(duì)太湖小時(shí)尺度水面蒸發(fā)進(jìn)行模擬，發(fā)現(xiàn)傳統(tǒng)質(zhì)量傳輸模型適用性最佳，DYRESM模型模擬效果次之,Granger and Hedstrom模型經(jīng)驗(yàn)性強(qiáng),適用性最差. 具體結(jié)論如下：

1)影響太湖小時(shí)尺度水面蒸發(fā)的主要單因子是水—?dú)饨缢麎禾荻群惋L(fēng)速,且兩者乘積與潛熱通量觀測值相關(guān)性最強(qiáng)(0.85),對(duì)其解釋程度達(dá)到72%.

2)傳統(tǒng)質(zhì)量傳輸模型在春、夏、冬季模擬的絕對(duì)誤差和相對(duì)誤差均分別小于4 W/m2和7%,而秋季的模擬誤差(低估9 W/m2,13%)與DYRESM模型(高估8.7 W/m2,12.7%)的數(shù)值相當(dāng),Granger and Hedstrom模型四季模擬誤差均最大,以冬季高估最為明顯(20 W/m2,72%). 傳統(tǒng)質(zhì)量傳輸模型模擬值與實(shí)測值日變化最為接近,夜間模擬相對(duì)誤差小于3%,Granger and Hedstrom模型在午后高估最為明顯(22～32 W/m2).

3)傳統(tǒng)質(zhì)量傳輸模型和DYRESM模型忽略了水汽交換系數(shù)隨風(fēng)速的變化特征,給太湖小時(shí)尺度水面蒸發(fā)模擬帶來誤差,以低風(fēng)速和高風(fēng)速下最為明顯. Granger and Hedstrom模型過于強(qiáng)調(diào)大氣穩(wěn)定度對(duì)水面蒸發(fā)的影響,使其在大氣穩(wěn)定時(shí)段(日落前后和冬季)的模擬誤差最大.

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CharacteristicsofmodellinghourlywatersurfaceevaporationinLakeTaihuandcomparisonofsimulationresultsbythreemodels

WANG Dandan1,2, WANG Wei1,2, LIU Shoudong1,2, QIU Xinfa2, MU Junyu2, MO Huayang2, CUI Congxin2, TAO Panhong2, QUE Yujie2, YU Le2, CHEN Hongyu2& XUE Shuhang3

(1:Yale-NUISTCenteronAtmosphericEnvironment,NanjingUniversityofInformationScienceandTechnology,Nanjing210044,P.R.China)(2:CollegeofAppliedMeteorology,NanjingUniversityofInformationScienceandTechnology,Nanjing210044,P.R.China)(3:CollegeofAtmosphericScience,NanjingUniversityofInformationScienceandTechnology,Nanjing210044,P.R.China)

Water surface evaporation on hourly timescale can affect the thermal and dynamic structure of the aloft atmospheric boundary layers. Understanding the main drivers of hourly evaporation and accurate evaporation model can improve weather forecast and air quality prediction in catchment. Based on half-hour flux, radiation and micrometeorological observations at the Bifenggang site in Lake Taihu in 2012 and 2013, the main drivers of Lake Taihu hourly evaporation were investigated. Then the performance of three models (traditional mass transfer model, Granger and Hedstrom model and DYRESM model) was evaluated against latent heat flux measured by eddy covariance. The results showed that the main driver for Taihu hourly evaporation was the product of the water vapor pressure difference at water-atmosphere interface and wind speed, rather than the expected net radiation. The Willmott index of agreement between simulated values and measured values were 0.92, 0.87, 0.89 for traditional mass transfer model, Granger and Hedstrom model and DYRESM model, respectively, with the corresponding root mean square error of 28.35 W/m2, 38.26 W/m2and 41.58 W/m2. The traditional mass transfer model showed the best performance on the diurnal time scale, especially at night when the simulation relative error was less than 3%. Except autumn, the absolute errors of traditional mass transfer model were less than 4 W/m2. Granger and Hedstrom model performed worst and systematically overestimated the latent heat flux at Lake Taihu, particularly in the afternoon (overestimate of 22-32 W/m2) and winter (overestimate of 72%) when the atmospheric boundary layer was stable. Although with overestimation, DYRESM model still performed considerably better than Granger and Hedstrom model and ranked middle. The parameterization of transfer coefficient for water vapor with wind speed can improve the hourly evaporation simulation at Lake Taihu by traditional mass transfer model and DYRESM model.

Lake Taihu;latent heat flux; modelling water surface evaporation; hourly time scale; traditional mass transfer model; Granger and Hedstrom model; DYRESM model

*國家自然科學(xué)基金青年項(xiàng)目(41505005)、江蘇省自然科學(xué)基金青年項(xiàng)目(BK20150900)、國家自然科學(xué)基金項(xiàng)目(41475141,41575147)、南京信息工程大學(xué)人才啟動(dòng)經(jīng)費(fèi)項(xiàng)目(2014r046)和南京信息工程大學(xué)2015年度大學(xué)生實(shí)踐創(chuàng)新訓(xùn)練計(jì)劃項(xiàng)目(201510300023)聯(lián)合資助. 2016-11-10收稿；2017-02-24收修改稿. 王丹丹(1993～)，女，碩士研究生；E-mail：18751971206@163.com.

**通信作者; E-mail： wangw@nuist.edu.cn.