高志永, 汪有科, 姜 鵬

( 1.楊凌職業(yè)技術(shù)學(xué)院水利工程分院 楊凌 712100; 2.西北農(nóng)林科技大學(xué)水利與建筑工程學(xué)院/中國(guó)科學(xué)院教育部水土保持與生態(tài)環(huán)境研究中心 楊凌 712100; 3.水利部發(fā)展研究中心 北京 100038)

黃土高原露日數(shù)變化趨勢(shì)分析*

高志永1, 汪有科2**, 姜 鵬3

( 1.楊凌職業(yè)技術(shù)學(xué)院水利工程分院 楊凌 712100; 2.西北農(nóng)林科技大學(xué)水利與建筑工程學(xué)院/中國(guó)科學(xué)院教育部水土保持與生態(tài)環(huán)境研究中心 楊凌 712100; 3.水利部發(fā)展研究中心 北京 100038)

露日數(shù)是預(yù)防和控制植物病害的重要因子, 探討氣候變化條件下露日數(shù), 可為區(qū)域植物病害預(yù)測(cè)及防治提供事實(shí)依據(jù)。本文基于 52個(gè)氣象站點(diǎn) 1961—2010年逐日監(jiān)測(cè)氣象數(shù)據(jù), 計(jì)算了黃土高原不同時(shí)空尺度的露日數(shù), 利用去趨勢(shì)預(yù)置白處理(trend-free pre-whitening, TFPW)的Mann-Kendall法和Sen趨勢(shì)度估計(jì)法(Sen’ slope)分析了露日數(shù)變化趨勢(shì), 并借助相關(guān)分析法探討了露日數(shù)的成因。結(jié)果表明, 在月尺度上, 黃土高原露水發(fā)生在3—11月, 全區(qū)域月平均值為7 d, 9月露日數(shù)最長(zhǎng), 其中南部、東南部和西北部露日數(shù)達(dá)8～12 d。5.77%～25.00%氣象站點(diǎn)露日數(shù)在6月和8—11月以0.02～0.15 d·a-1顯著增加, 17.31%和7.68%氣象站點(diǎn)露日數(shù)在4月和7月以-0.09 d·a-1和-0.02 d·a-1顯著降低。在季尺度上, 黃土高原露水發(fā)生在春、夏和秋季, 全區(qū)域季平均值為15 d, 秋季露日數(shù)最長(zhǎng), 其中南部、東南部和西北部露日數(shù)達(dá)14～26 d。僅3.85%和5.77% 的氣象站露日數(shù)在夏季、秋季分別以 0.25～0.09 d·a-1和 0.15～0.09 d·a-1顯著增加, 5.77%的氣象站露日數(shù)在春季以-0.34～-0.07 d·a-1顯著降低。相對(duì)濕度和溫度是影響上述露日數(shù)時(shí)空變化的最關(guān)鍵因子。

黃土高原; 露日數(shù); 變化趨勢(shì); Mann-Kendall; Sen’slope

溫室效應(yīng)引起的全球氣候變暖影響著諸如溫度、相對(duì)濕度、降雨、日照時(shí)數(shù)等氣象因子變化[1], 氣象因子的變化嚴(yán)重制約了全球人口、經(jīng)濟(jì)和工業(yè)等發(fā)展[2], 甚至影響到中國(guó)乃至全球糧食安全和水資源供需[3-4]。黃土高原地處半干旱和半濕潤(rùn)氣候帶,是氣候變化的敏感區(qū), 又是生態(tài)環(huán)境脆弱區(qū), 水土流失嚴(yán)重[5-7], 氣候變化對(duì)生態(tài)環(huán)境和農(nóng)業(yè)生態(tài)系統(tǒng)會(huì)產(chǎn)生重要影響[8-9]。該區(qū)域以旱作雨養(yǎng)農(nóng)業(yè)為主,降雨作為水資源的輸入項(xiàng), 對(duì)黃土高原區(qū)域植物至關(guān)重要。然而, 該區(qū)域內(nèi)近50年降雨呈現(xiàn)顯著性減少趨勢(shì)[10], 且降雨存在時(shí)空不均, 呈現(xiàn)從東南向西北遞減趨勢(shì), 降雨多集中在7、8、9月, 因降雨集中出現(xiàn)大面積土壤流失現(xiàn)象[11-12]。黃土高原植被物候期因氣候變暖, 春季顯著提前, 秋季顯著推遲[13],導(dǎo)致氣候生產(chǎn)力以10.45 kg·km-2·a-1下降, 病蟲(chóng)害發(fā)育期縮短, 種群增長(zhǎng)力增加, 病害地理范圍擴(kuò)大[14-15], 如定西地區(qū)馬鈴薯晚役病發(fā)病面積比例以3.55%·a-1增加, 隴東和隴中2000年麥蚜蟲(chóng)發(fā)生面積是1980年的3.4倍和3.1倍[16]。陜北地區(qū)棗樹(shù)蟲(chóng)害種類(lèi)已占全國(guó)棗樹(shù)害蟲(chóng)種類(lèi)的53.45%, 桃小食心蟲(chóng)在嚴(yán)重年份造成產(chǎn)量損失率為50%～90%，棗銹病可使產(chǎn)量損失率達(dá)50%～70%，病葉嚴(yán)重時(shí)可達(dá)70%以上[17-18]。

大氣中的水汽是決定地球氣候變化的重要因子之一[19], 也是全球水文循環(huán)的關(guān)鍵組成[20]。露水是空氣中水汽在物體表面的冷凝產(chǎn)物[21-22], 是一個(gè)重要的物理現(xiàn)象, 它影響著SPAC系統(tǒng)的能量平衡[23],是干旱半干旱地區(qū)重要的水資源[24-25], 是黃土高原不可缺失的水資源補(bǔ)充項(xiàng)[26-27], 具有降水所不及的普遍性和穩(wěn)定性, 約占黃土高原陸面水分來(lái)源的15%[28]。一方面, 露水能夠被植物冠層葉片直接吸收來(lái)補(bǔ)充體內(nèi)虧缺的水分[29-31], 降低其周?chē)乃麎翰? 促進(jìn)植物氣孔開(kāi)放和光和[23], 進(jìn)而影響冠層溫度[32]; 此外, 露水可以通過(guò)植物木質(zhì)部到達(dá)根部, 緩解土壤水分虧缺[33]。另一方面, 植物葉片的露水為許多病菌孢子的萌發(fā)和感染宿主提供環(huán)境條件[34]。因此,在全球變暖環(huán)境下分析露水變化趨勢(shì), 有利于全面理解黃土高原露水資源, 也為該區(qū)域生態(tài)環(huán)境評(píng)價(jià)提供一定的依據(jù)。

露水量和露水持續(xù)時(shí)間是露水研究的熱點(diǎn)[23],因缺乏監(jiān)測(cè)標(biāo)準(zhǔn)而難以測(cè)定[35]。針對(duì)以上不足, 許多經(jīng)驗(yàn)和機(jī)理模型, 如RH、CART、P-M、SWEB、ANN等模型被用于預(yù)測(cè)露水[36-39], 該類(lèi)模型通過(guò)借助氣象數(shù)據(jù)來(lái)估算露水[34,40-42]。機(jī)理模型在不同地理和氣候條件下均可應(yīng)用, 能夠準(zhǔn)確地估測(cè)露水,但需要輸入較多的參數(shù), 如云量、長(zhǎng)短波輻射率、凈輻射等, 且計(jì)算結(jié)果對(duì)于輸入?yún)?shù)變化十分敏感[39],經(jīng)驗(yàn)?zāi)Ｐ碗m然缺乏機(jī)理支撐, 但仍是露水模擬的可靠方法[38]。

目前, 一些學(xué)者利用實(shí)測(cè)或模型對(duì)黃土高原露水量進(jìn)行了研究[26-27,43-44], 但對(duì)露水持續(xù)時(shí)間的研究鮮有報(bào)道。露日數(shù)與露水持續(xù)時(shí)間密切相關(guān), 是預(yù)測(cè)植物葉片疾病的重要參數(shù)[37]。此外, 以上研究?jī)H考慮年內(nèi)某地區(qū)露水多寡, 未考慮氣候變化條件下長(zhǎng)時(shí)間序列露水變化特征, 也未對(duì)整個(gè)黃土高原區(qū)域露水狀況進(jìn)行全面分析。由于以往對(duì)露水的關(guān)注度不夠, 缺乏露水相關(guān)資料, 本文借助長(zhǎng)時(shí)間序列(1961—2010年)的氣象數(shù)據(jù), 采用經(jīng)驗(yàn)?zāi)Ｐ陀?jì)算了露日數(shù), 分析月、季尺度露日數(shù)時(shí)空變化特征, 利用去趨勢(shì)預(yù)置白處理(trend-free pre-whitening,TFPW)的Mann-Kendall法和Sen趨勢(shì)度估計(jì)法(Sen’slope)分析露日數(shù)的變化趨勢(shì), 并采用相關(guān)分析法探討露日數(shù)和氣象因子的敏感度, 以期進(jìn)一步全面了解氣候變化條件下黃土高原露日數(shù)分布特征和變化趨勢(shì), 為黃土高原植物病害預(yù)測(cè)、防治提供事實(shí)依據(jù)。

1 材料與方法

1.1 研究區(qū)域概況

黃土高原位于黃河中上游地區(qū), 地理坐標(biāo)為32°47′N(xiāo)～40°44′N(xiāo) 和 106°54′E～114°33′E, 東起太行山, 西至青海日月山, 南界秦嶺, 北抵陰山, 全區(qū)總面積為 6.285×105km2, 海拔為 1 200～1 600 m, 黃土覆蓋層厚30～80 m[6]。該區(qū)域包括山西、內(nèi)蒙、河南、陜西、甘肅、寧夏和青海7個(gè)省(自治區(qū))、341個(gè)縣(市), 是溫和半濕潤(rùn)氣候區(qū)向溫和半干旱、溫和干旱氣候區(qū)的過(guò)渡帶, 氣候變化敏感, 生態(tài)環(huán)境脆弱[5]。為全面研究黃土高原露日數(shù), 本文選擇52個(gè)具有代表性的氣象站(圖 1)。氣象數(shù)據(jù)來(lái)源于中國(guó)氣象局,氣象數(shù)據(jù)時(shí)間范圍為1961—2010年, 時(shí)間分辨率為逐日; 空間范圍為黃土高原區(qū)域, 空間分辨率為中國(guó)地面國(guó)家級(jí)基準(zhǔn)、基本站。該類(lèi)數(shù)據(jù)主要包括日序列的降雨量(P, mm), 平均、最大和最小溫度(Tm,Tmax,Tmin, ℃), 風(fēng)速(u, m·s-1)、相對(duì)濕度(RH, %)和蒸發(fā)(E, mm)。飽和水氣壓虧缺(VPD, kPa)是當(dāng)時(shí)溫度下空氣中飽和水氣壓(es)和實(shí)際水氣壓(ea)之間的差值, 由以下公式計(jì)算得出:

式中:es為飽和水汽壓, kPa;ea為實(shí)際水汽壓, kPa;Td為露點(diǎn)溫度, ℃; a、b為常數(shù), 分別為 17.625和243.04;T為大氣平均溫度, ℃; RH為大氣平均相對(duì)濕度, %; VPD為飽和水汽壓差, kPa;Tmin為大氣最小溫度, ℃;Tmax為大氣最大溫度, ℃。

圖1 黃土高原氣象站點(diǎn)分布Fig.1 Location of meteorological stations in the Loess Plateau

1.2 研究方法

1.2.1 露日數(shù)

本研究露日數(shù)(DD)指大氣溫度低于露點(diǎn)溫度的天數(shù)。為嚴(yán)格判斷露日數(shù), 排除各氣象站降雨日和結(jié)霜日。采用Magnus-Tetens方程[45]計(jì)算各氣象站非降雨非霜日逐日的露點(diǎn)溫度, 并利用計(jì)算的日露點(diǎn)溫度與該日最小溫度對(duì)比, 判斷結(jié)露與否。如果某日Pi=0, 且0＜＜Tdi, 表示該日存在露水,記Ti=1, 反之, 記Ti=0。式中:Tdi為i日露點(diǎn)溫度, ℃;Pi為i日的降雨量, mm;Timin為i日最小溫度, ℃。時(shí)段劃分: 月尺度為1—12月逐月; 季尺度以3、4、5月為春季, 6、7、8月為夏季, 9、10、11月為秋季, 12月至翌年1、2月為冬季。

月尺度露日數(shù)(DDm)為:

季尺度露日數(shù)(DDs)為:

1.2.2 趨勢(shì)性統(tǒng)計(jì)方法

為說(shuō)明月和季尺度露日數(shù)變化趨勢(shì), 借助Z統(tǒng)計(jì)值和Sen值, 并對(duì)露日數(shù)在 95%和 99%置信水平下顯著變化所對(duì)應(yīng)的氣象站進(jìn)行統(tǒng)計(jì), 通過(guò)Z統(tǒng)計(jì)值、顯著性氣象站所占總氣象站的比例和Sen值說(shuō)明露日數(shù)變化趨勢(shì)增減。因此, 本文采用去趨勢(shì)預(yù)置白處理(trend-free pre-whitening,TFPW)的Mann-Kendall法分析露日數(shù)的趨勢(shì)性變化。去趨勢(shì)預(yù)置白處理的Mann-Kendall處理過(guò)程如下[1-2,46]:

1.2.2.1 Mann-Kendall檢驗(yàn)方法

設(shè)一平穩(wěn)序列為Xt(t=1, 2, …,n,n為序列長(zhǎng)度)

則:

其中:

采用雙側(cè)檢驗(yàn), 在α顯著水平下, 如果|Z|＞Z(1-α/2), 拒絕無(wú)趨勢(shì)的假設(shè), 即認(rèn)為在顯著水平a下,序列Xt中存在有向上或向下的趨勢(shì); 否則接受序列Xt無(wú)趨勢(shì)的假設(shè)。Z(1-a/2)是概率超過(guò)α/2時(shí)標(biāo)準(zhǔn)正態(tài)分布的值。

1.2.2.2 去趨勢(shì)預(yù)置白處理的Mann-Kendall法

1)計(jì)算樣本數(shù)據(jù)的線(xiàn)性趨勢(shì) :

2)形成新序列項(xiàng):

3)計(jì)算新序列項(xiàng)的一階自相關(guān)r1:

如果r1對(duì)于0不顯著相關(guān), 則對(duì)原始序列直接進(jìn)行Mann-Kendall檢驗(yàn)方法, 如果r1對(duì)于0顯著相關(guān),則需構(gòu)建不含自相關(guān)影響的新序列。

4)剔除序列中的自相關(guān)項(xiàng):

5)形成不含自相關(guān)影響的新序列:

6)對(duì)不含自相關(guān)影響的新序列進(jìn)行Mann-Kendall檢驗(yàn)。

1.2.2.3 斜率(Sen’ slope)

為估算某一個(gè)時(shí)間數(shù)據(jù)序列變化趨勢(shì)的數(shù)值程度大小, 利用Sen提出的非參數(shù)化斜率估算[1,46-47]。

1)構(gòu)建新序列xi(i=1, 2,…,n,n為序列長(zhǎng)度),用下式計(jì)算組合序列斜率:

式中:xj,xk分別為數(shù)據(jù)序列在j和k時(shí)刻的數(shù)值,j＞k,i=1, 2,…,n,。

2)Sen斜率為求n個(gè)斜率估測(cè)值Qi的中值:

Qmed＞0表示上升趨勢(shì), 反之表示下降趨勢(shì)。為檢驗(yàn)數(shù)據(jù)序列變化趨勢(shì)顯著性與否, 對(duì)Qmed在100(1-α)%置信區(qū)間進(jìn)行檢驗(yàn)。

1.2.3 相關(guān)性分析

為說(shuō)明不同時(shí)間尺度露日數(shù)對(duì)氣象因子的響應(yīng),分析露日數(shù)與氣象因子在95%和99%置信水平下的相關(guān)性。相關(guān)系數(shù)表征變量和變量之間的相關(guān)性,相關(guān)系數(shù)的絕對(duì)值越大, 表明變量間相關(guān)程度越高,相關(guān)系數(shù)計(jì)算公式如下:

式中:Rxy為變量x和y的相關(guān)系數(shù),n為統(tǒng)計(jì)數(shù)據(jù)的總數(shù),i為第i個(gè)統(tǒng)計(jì)數(shù)據(jù),為x的均值,為y的均值。

1.2.4 數(shù)據(jù)處理

采用R 3.4.0和Microsoft Excel 2010進(jìn)行數(shù)據(jù)整理和統(tǒng)計(jì)分析, 借助Origin 2016進(jìn)行繪圖, 并利用arcgis 10.1中應(yīng)用廣泛差值準(zhǔn)確的Kriging插值法[48]繪制露日數(shù)時(shí)空分布圖。

2 結(jié)果與分析

2.1 露日數(shù)時(shí)空分布特征

在月尺度上, 從時(shí)間分布來(lái)看(圖 2a), 露日數(shù)在3—9月顯著增加(P＜0.05, 0.01), 在10—11月顯著降低(P＜0.05, 0.01), 9月份達(dá)最大, 平均值為7 d; 1—2月和12月露水幾乎不發(fā)生。從空間分布來(lái)看(圖 3), 南部武功和西北部臨河是露水發(fā)生最早, 持續(xù)時(shí)間最長(zhǎng)的地區(qū),其值分別為2～10 d和2～14 d; 全區(qū)域在5—10月均存在露水。從時(shí)空分布來(lái)看, 露水起始于 3月黃土高原南部, 持續(xù)時(shí)間為2～6 d, 武功最大, 達(dá)6 d; 4、5月南部和東南部大面積發(fā)生, 持續(xù)時(shí)間為 2～10 d,同時(shí), 西北方向臨河附近出現(xiàn)露水, 持續(xù)時(shí)間為2～6 d; 進(jìn)入 6月后, 南部呈現(xiàn)減弱趨勢(shì), 西北部增加, 持續(xù)時(shí)間為4～14 d; 7月西北部降低為4～10 d,南部增加1～2 d; 8月與7月露水發(fā)生情況基本一致,但東南部增加為8～12 d; 9、10月南部和東南部基本保持不變, 西北部在10月減小, 降至6～8 d; 11月西北部低于2 d, 南部縮減2 d。

在季尺度上, 從時(shí)間分布來(lái)看(圖 2b), 夏季露日數(shù)與秋季無(wú)差異(P＜0.05, 0.01), 露日數(shù)在秋季最大, 平均值為15 d, 冬季幾乎不存在露水。從空間分布來(lái)看(圖4), 南部武功和西北部臨河是露日數(shù)最長(zhǎng)地區(qū), 其值分別為20～24 d和30～34 d。從時(shí)空分布來(lái)看, 露日數(shù)主要在黃土高原春季南部、西北部, 其值分別為14～26和14～22 d, 西部和東北部低于4 d;進(jìn)入夏季和秋季, 露水存在整個(gè)黃土高原, 持續(xù)時(shí)間為 6～34 d, 其中秋季南部、東南部和西北部露日數(shù)較高, 為14～26 d; 冬季幾乎不存在露水。

2.2 不同時(shí)間尺度露日數(shù)變化趨勢(shì)

圖5為月尺度上Mann-Kendall分析露日數(shù)存在顯著性變化所在氣象站占總站百分比。在月尺度上,露日數(shù)具有增加趨勢(shì)的氣象站點(diǎn)多于降低趨勢(shì)的氣象站點(diǎn); 5.77%～25.00%氣象站點(diǎn)露日數(shù)在 6月和8—11月顯著增加, 其中8月顯著增加為最大, 占總站數(shù)的 25%, 露日數(shù)顯著性增加率為 0.02～0.15 d·a-1;17.31%和7.68%氣象站點(diǎn)露日數(shù)在4月和7月顯著降低, 4月顯著降低達(dá)最大, 占總站數(shù)的17.31%, 露日數(shù)顯著性降低率為-0.09～-0.02 d·a-1。

圖6為季尺度上Mann-Kendall分析露日數(shù)存在顯著性變化所在氣象站占總站百分比。在季尺度上,春、夏和秋季露日數(shù)在95%和99%置信水平下顯著變化所對(duì)應(yīng)的氣象站占總站的 7.69%、7.69%和3.85%。夏季和秋季露日數(shù)分別以0.25～0.09 d·a-1和0.15～0.09 d·a-1顯著增加, 5.77%氣象站露日數(shù)在夏季顯著性增加最大; 5.77%氣象站露日數(shù)在春季以-0.34～-0.07 d·a-1顯著性降低。

2.3 不同時(shí)間尺度露日數(shù)成因

表1和表2分別為月和季尺度露日數(shù)與氣象因子顯著相關(guān)條件下, 所在氣象站占總站百分比。在月尺度上Tm、RH、P、u、E和VPD對(duì)露日數(shù)都存在顯著性影響; 其中, 露日數(shù)與RH和P在3—11月均表現(xiàn)出顯著性正相關(guān), 顯著性相關(guān)所在氣象站占總站的變化范圍分別為50%～99.9%和15.39%～71.15%; 露日數(shù)與E和VPD在3～11月均表現(xiàn)出顯著性負(fù)相關(guān), 顯著性相關(guān)所在氣象站占總站的變化范圍分別為 25%～73.08%和28.84%～90.39%; 露日數(shù)與Tm和u在3—11月顯著性相關(guān)正負(fù)隨月份變化而變化; 露日數(shù)與Tm在3、4、10、11月顯著正相關(guān), 在5—9月顯著負(fù)相關(guān), 顯著性正、負(fù)相關(guān)所在氣象站占總站最大比例為 46.15%和63.47%; 露日數(shù)與u在5月和9月正相關(guān), 在3月、4月和6—8月和10—11月負(fù)相關(guān), 顯著性正、負(fù)相關(guān)所在氣象站占總站最大比例為71.1%和40.39%。表2顯示, 在季尺度上Tm、RH、P、u、E和VPD對(duì)露日數(shù)影響顯著; 其中, 露日數(shù)與RH和P在春、夏和秋季上均表現(xiàn)出顯著性正相關(guān), 顯著性相關(guān)所在氣象站占總站的變化范圍分別為 69.23%～96.16%和32.69%～76.92%; 露日數(shù)與u、E和VPD在春、夏和秋季上均表現(xiàn)出顯著性負(fù)相關(guān), 顯著性相關(guān)所在氣象站占總站的變化范圍分別為 21.15%～36.54%、23.08%～51.92%、57.69%～75.00%和 57.69%～88.46%;露日數(shù)與Tm在春、夏季顯著性負(fù)相關(guān), 在秋季顯著性正相關(guān), 顯著性相關(guān)所在氣象站占總站的 21.16%、57.69%和19.23%。

圖2 黃土高原月(a)和季(b)尺度露日數(shù)Fig.2 Dew days on monthly (a) and seasonal (b) scales in the Loess Plateau of China

圖3 黃土高原月尺度露日數(shù)時(shí)空分布圖Fig.3 Spatiotemporal distribution maps of dew days on monthly scale in the Loess Plateau of China

3 討論

3.1 氣象因子對(duì)露日數(shù)的影響

露水形成是一個(gè)極其復(fù)雜的過(guò)程, 受諸多氣象因子的影響[25,49-50], 與水汽分布、水分輸送、凝結(jié)過(guò)程有關(guān)的環(huán)境因素均可影響結(jié)露過(guò)程[51], 影響露水形成的氣象因子敏感性會(huì)隨時(shí)間尺度不同而發(fā)生變化[52]。本文僅借助溫度和相對(duì)濕度估算露日數(shù), 具有一定的局限性。本研究表明, 在月和季尺度上露日數(shù)與RH和P顯著正相關(guān), 與E和VPD顯著負(fù)相關(guān)。露日數(shù)與 RH顯著相關(guān)所在氣象站占總站數(shù)比例高達(dá)99.9%, 較高的RH為露水形成提供了充足的水汽[49,53], 降雨促使 RH增加, 增加了空氣中的水汽[49], 更容易結(jié)露;E與RH負(fù)相關(guān)[54], 增加的E使RH降低, 水汽來(lái)源減少; VPD反映空氣中水汽接近飽和的程度, 決定露水量的多寡[55], VPD越大, 說(shuō)明空氣中的水汽越難接近飽和, 露水難形成。此外, RH和P、E、VPD在月和季尺度上極顯著相關(guān)(P＜0.01), 相關(guān)系數(shù)分別為0.55、-0.26、-0.28和0.63、-0.14、-0.16。

圖4 黃土高原季尺度露日數(shù)時(shí)空分布圖Fig.4 Spatiotemporal distribution maps of dew days on seasonal scale in the Loess Plateau of China

圖5 黃土高原月尺度上Mann-Kendall分析露日數(shù)存在顯著性變化所在氣象站占總站百分比Fig.5 Percentage of stations with significant tends in monthly dew days by Mann-Kendall test in the Loess Plateau of China

圖6 1961—2010年黃土高原季尺度上Mann-Kendall分析露日數(shù)存在顯著性變化所在氣象站占總站百分比Fig.6 Percentage of stations with significant tends in seasonal dew day from 1961 to 2010by Mann-Kendall test in the Loess Plateau of China

表1 黃土高原月尺度露日數(shù)與氣象因子顯著相關(guān)所在氣象站占總站百分比Table 1 Percentage of stations with significant correlation between dew day and meteorological factors on monthly scale in the Loess Plateau of China %

溫度影響露水形成, 較低的氣溫易于物體表面冷卻而結(jié)露[56], 較高的溫度使得物體表面溫度高于露點(diǎn)溫度, 促使E加快, 降低物體表面RH, 露水不易形成[49]。然而, 本研究表明,Tm在 3、4、10、11月和秋季與露日數(shù)顯著正相關(guān), 顯著性正相關(guān)所在氣象站占總站最大比例為46.15%。黃土高原地區(qū)在此時(shí)段內(nèi)Tm為 1.30～10.20 ℃, 相應(yīng)露點(diǎn)溫度為-5.46～0.15 ℃, 露點(diǎn)溫度低于 0 ℃時(shí), 空氣中過(guò)剩的水汽直接以霜的形式體現(xiàn), 增加的Tm會(huì)影響到露點(diǎn)溫度, 促使露點(diǎn)溫度從負(fù)變?yōu)檎? 空氣中達(dá)到飽和的水汽才能以露水的形式凝結(jié)在物體表面。

風(fēng)速是影響露水形成的重要?dú)庀笠蜃覽57]。本文u與露日數(shù)僅在5月和9月顯著性正相關(guān), 顯著性正相關(guān)所在氣象站占總站最大比例為71.1%, 該時(shí)段u均值為2.01～2.76 m·s-1;u與露日數(shù)在其他月份和季尺度上顯著負(fù)相關(guān), 顯著負(fù)相關(guān)所在氣象站占總站的最大比例為40.3%, 該時(shí)段u均值為2.08～2.92 m·s-1。Monteith認(rèn)為小于0.5 m·s-1的風(fēng)速易于露水形成[58],微風(fēng)或風(fēng)速為零時(shí), 水汽分子擴(kuò)撒到穩(wěn)定邊界層可任意產(chǎn)生小液滴, 在液滴周?chē)纬伤麧舛忍荻?在濃度梯度的作用下露水量增加[21], 小的風(fēng)速有利于冷卻凝結(jié)面同時(shí)帶來(lái)水汽[59]。Jackson等[60]認(rèn)為露水形成的適宜風(fēng)速為 0.5～2 m·s-1, Muselli等[61]、Hanisch等[62]研究表明限制露水形成的風(fēng)速分別為4.7 m·s-1、3.3～5.3 m·s-1和 5.7 m·s-1。大風(fēng)可以使近地層大氣發(fā)生湍流運(yùn)動(dòng), 也可以使近地面的溫度輪廓線(xiàn)絕熱,在不受對(duì)流加熱運(yùn)動(dòng)影響時(shí), 可以很清楚地觀(guān)測(cè)到露水量[63]。因此, 風(fēng)速對(duì)露水的影響存在不確定性。

表2 黃土高原季尺度露日數(shù)與氣象因子顯著相關(guān)所在氣象站占總站百分比Table 2 Percentage of station with significant correlation between dew day and meteorological factors on seasonal scale in the Loess Plateau of China %

綜上, 月尺度和季尺度上的溫度和相對(duì)濕度是影響黃土高原區(qū)域露日數(shù)的最關(guān)鍵因子, 這與已有關(guān)于濕潤(rùn)區(qū)露水成因的結(jié)論相一致[64]。黃土高原南部及東南部溫度和相對(duì)濕度高于西北部[65], 露日數(shù)與溫度在3、4、10、11月及秋季顯著正相關(guān)氣象站占總站數(shù)的 13.47%～46.15%和 13.47%; 露日數(shù)與相對(duì)濕度在月和季尺度上顯著正相關(guān)氣象站占總站數(shù)的51.4%～99.9%和69.23%～96.16%, 因此, 南部和東南部在3、4、10、11月及秋季較高的溫度和相對(duì)濕度促使露日數(shù)大于西北部。

3.2 溫度和濕度對(duì)露日數(shù)的影響

溫度是氣候變化的“指示器”[47], 相對(duì)濕度影響生物圈和地表水文[66], 黃土高原區(qū)域溫濕度變化決定露日數(shù)變化趨勢(shì)。5.77%～55.77%氣象站的溫度在4—11月顯著增加(表3), 遞增率為0.018～0.090 ℃·a-1, 平均值為0.049 ℃·a-1, 高于近50年間全球0.013 ℃·a-1[67]和全國(guó) 0.022 ℃·a-1[68]增溫率。9.62%～15.39%氣象站的濕度在 7—9月顯著降低(表 3), 遞減率為-0.3～-0.1%·a-1,平均值為-0.2%·a-1; 11.54%～23.02%氣象站的濕度在4月和6月顯著增加(表3), 遞增率為0.1～0.3%·a-1, 平均值為0.3%·a-1。季尺度溫度和相對(duì)濕度變化顯著性不大。盡管黃土高原區(qū)域溫度存在上升趨勢(shì), 部分月份相對(duì)濕度為降低趨勢(shì), 但未影響到露日數(shù)上升趨勢(shì)這一狀態(tài), 其主要原因是月和季尺度上的平均溫度和相對(duì)濕度目前還處于易于結(jié)露(露水發(fā)生率大于 70%)的溫度和相對(duì)濕度范圍內(nèi)(圖7)。

表3 黃土高原不同時(shí)間尺度Mann-Kendall分析溫度和相對(duì)濕度存在顯著性變化所在氣象站占總站百分比Table 3 Percentage of stations with significant trends in monthly and seasonal relative humidity and mean temperature in the Loess Plateau of China

圖7 黃土高原月(a, b)和季(c, d)尺度適宜結(jié)露溫度(a、c)和相對(duì)濕度(b、d)及露水發(fā)生頻率Fig.7 Suitable temperature (a, c) and relative humidity (b, d) for dew formation in monthly (a, b) and seasonal (c, d) scales and dew occurrence frequency in the Loess Plateau of China

3.3 存在不足及展望

全球氣候變暖愈演愈烈, 新的間歇性干旱、半干旱區(qū)域?qū)映霾桓F。露水對(duì)干旱和半干旱地區(qū)植物生長(zhǎng)極其重要。由于黃土高原地區(qū)露水實(shí)測(cè)資料缺失, 該地區(qū)露水對(duì)植物生態(tài)效應(yīng)的研究匱乏。葉片和根系吸收水分策略是植物獲取水分的兩種有效途徑, 水資源的獲得性和限制性會(huì)影響到植物對(duì)兩種吸水策略的偏重。黃土高原長(zhǎng)期處于土壤水分虧缺狀態(tài), 使植物根系較難獲取水分, 這勢(shì)必影響到植物獲取水分的途徑, 植物通過(guò)葉片能夠獲得多少水分？所獲得的水分能否維持其生長(zhǎng)？露水與植物葉片相互機(jī)制如何？這是今后研究的重點(diǎn)。此外, 露水為真菌和細(xì)菌等病原體提供感染宿主植物的環(huán)境條件, 促使植物發(fā)病。然而如何利用這一特點(diǎn)采用相應(yīng)措施(如制造微生物試劑等)對(duì)植物病蟲(chóng)害進(jìn)行防治還需進(jìn)一步研究。

4 結(jié)論

基于黃土高原 52個(gè)氣象站長(zhǎng)時(shí)間序列的氣象數(shù)據(jù), 分析月、季尺度露日數(shù)時(shí)空變化特征、變化趨勢(shì)及成因。在月尺度上, 黃土高原露水首先發(fā)生在3月武功, 終止于11月份, 5—10月全區(qū)域均存在露水; 9月露日數(shù)最長(zhǎng), 南部、東南部和西北部露日數(shù)達(dá)8～12 d, 全區(qū)域平均值為7 d; 5.77%～25.00%氣象站點(diǎn)露日數(shù)在 6月和 8—11月以 0.02～0.15 d·a-1顯著增加; 17.31%和7.68%氣象站點(diǎn)露日數(shù)在4月和7 月以-0.09～-0.02 d·a-1顯著降低。在季尺度上, 黃土高原露水發(fā)生在春、夏和秋季, 夏季和秋季露日數(shù)無(wú)顯著差異; 露日數(shù)在秋季最長(zhǎng), 南部、東南部和西北部露水持續(xù)達(dá) 14～26 d, 全區(qū)域平均值為15 d;3.85%和 5.77%的氣象站露日數(shù)在夏季和秋季分別以 0.25～0.09 d·a-1和 0.15～0.09 d·a-1顯著增加; 5.77%氣象站露日數(shù)在春季以-0.34～-0.07 d·a-1顯著性降低。相對(duì)濕度、降雨、蒸發(fā)、飽和水汽壓差和溫度在不同時(shí)間尺度上對(duì)露日數(shù)影響不同, 但相對(duì)濕度和溫度是影響露日數(shù)變化的最關(guān)鍵因素。

該研究對(duì)于全面了解氣候變化條件下黃土高原露日數(shù)分布特征及變化趨勢(shì)提供事實(shí)依據(jù), 也為未來(lái)黃土高原植株病害預(yù)測(cè)、防治及風(fēng)險(xiǎn)評(píng)估提供一定參考價(jià)值。

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Spatiotemporal analysis of dew days in China’s Loess Plateau*

GAO Zhiyong1, WANG Youke2**, JIANG Peng3
(1.Department of Water Conservancy, Yangling Vocational & Technological College, Yangling 712100, China; 2.College of Water Resources and Architectural Engineering, Northwest A&F University / Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences & Ministry of Education, Yangling 712100, China; 3.Development Research Center of the Ministry of Water Resources of China, Beijing 100038, China)

Global warming due to greenhouse effect has altered meteorological variables such as temperature, relative humidity,rainfall and sunshine hours.The resulting change of these variables could have strong effects that threaten population, agriculture, environment, economy and industry.It could even affect global food security and supply/demand of water resources in the world.The Loess Plateau in North China is a semiarid and sub-humid climate region and is well-known for severe soil erosion, fragile ecological environment and sensitivity to climate change.Climate change will have a major impact on the ecological environment and agricultural ecosystems.Given the above, temporal and spatial distribution of meteorological elements for the Loess Plateau region has been analyzed.However, there was little information on dew days on the plateau.DewDew day was a key parameter of hydrologic cycle and plant disease prevention.Analysis of the spatial distribution and long-term temporal trends of dew days and the relatedness with climatic variables may provide the basis for plant disease prediction and prevention in local areas.In this study, dew day data from 52 meteorological stations for the period 1961–2010 were calculated using a model.The spatial distribution of seasonal and monthly dew days was interpolated by Kriging and the temporal trends of the days examined using trend-free pre-whitening (TFPW) and Sen’s slope estimator.Correlation analysis explained the dew-day formation.The results showed that at monthly scale, dew days started in March and ended in November,with a monthly mean of 7 dew days.The maximum dew days were in the south, southeast and northwest of the Loess Plateau in September, with a range of 8–12 days.Analysis of dew days indicated significant positive trends for 5.77%–25.00% of the stations, with a variation of 0.02–0.15 d·a-1during the periods from August through November and June.Dew days with significant negative trends were found too, with the decrease in July and April by 0.02–0.09 d·a-1and for 7.68%–17.31% of the stations.At seasonal scale, dew days occurred in spring, summer and autumn, with a seasonal mean of 15 dew days.The maximum dew days were in autumn, with 14–26 dew days in the south, southeast and northwest of the plateau.Dew days with significant positive trends were observed in summer and autumn, which varied respectively by 0.09–0.25 d·a-1and 0.09–0.15 d·a-1for 3.85% and 5.77% of the stations.Dew days with significant negative trends were evident in spring, which varied by ?0.34 to ?0.07 d·a-1for 5.77% of the stations.Relative humidity and temperature had clear and dominant effects on the spatiotemporal trend of dew days.The study provided a quantitative basis for understanding dew day distribution and trend in the Loess Plateau under global climate change.It also provided a vital reference for future plant disease forecast, prevention and risk assessment.

May 11, 2017; accepted Jun.21, 2017

Loess Plateau; Dew day; Trend analysis; Mann-Kendall test; Sen’ slope

S161.9; S165+.28; P467

A

1671-3990(2017)11-1718-13

10.13930/j.cnki.cjea.170435

高志永, 汪有科, 姜鵬.黃土高原露日數(shù)變化趨勢(shì)分析[J].中國(guó)生態(tài)農(nóng)業(yè)學(xué)報(bào), 2017, 25(11): 1718-1730

Gao Z Y, Wang Y K, Jiang P.Spatiotemporal analysis of dew days in China’s Loess Plateau[J].Chinese Journal of Eco-Agriculture, 2017, 25(11): 1718-1730

* 國(guó)家科技支撐計(jì)劃項(xiàng)目(2015BAC01B03)和陜西統(tǒng)籌項(xiàng)目(2014KTCG01-03)資助

** 通訊作者: 汪有科, 主要從事作物高效用水和水土保持研究。E-mail: gjzwyk@vip.sina.com

高志永, 主要從事近地表汽態(tài)水研究。E-mail: GZYstruggling@163.com

2017-05-11 接受日期: 2017-06-21

* This study was supported by the National Key Technologies R&D Program of China (2015BAC01B03) and Shaanxi Science & Technology Co-ordination and Innovation Project (2014KTCG01-03).

** Corresponding author, E-mail: gjzwyk@vip.sina.com