資源環(huán)境與城鄉(xiāng)規(guī)劃管理外文資料翻譯--陸面過程模式clm的穩(wěn)定同位素的季節(jié)變化仿真

1、<p>　　畢業(yè)設(shè)計外文資料翻譯</p><p>　　題 目 陸面過程模式CLM的穩(wěn)定同位素的季節(jié)變化仿真 </p><p>　　學(xué) 院 資源與環(huán)境學(xué)院 </p><p>　　專 業(yè) 資源環(huán)境與城鄉(xiāng)規(guī)劃管理 </p><p>　　班 級 資源0702班

2、 </p><p>　　學(xué) 生 包芳 </p><p>　　學(xué) 號 20072102002 </p><p>　　指導(dǎo)教師 王永森 </p><p>　　二〇一 一年 三月 二十五 日</p><p>　　Simu

3、lations of seasonal variations of stable water</p><p>　　isotopes in land surface process model CLM</p><p>　　ZHANG XinPing1?, WANG XiaoYun2, YANG ZongLiang3, NIU GuoYue3 & Xie ZiChu1</p>

4、;<p>　　1 College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China;</p><p>　　2 Qingdao Meteorological Bureau, Qingdao 266003, China;</p><p>　　3 Depa

5、rtment of Geological Sciences, the University of Texas at Austin, Texas 78721-0254, USA</p><p>　　Abstract: In this study, we simulated and analyzed the monthly variations of stable water isotopes in differen

6、t reservoirs at Manus, Brazil, using the Community Land Model (CLM) that incorporates stable isotopic effects as a diagnostic tool for understanding stable water isotopic processes, filling the observational data gaps an

7、d predicting hydro meteorological processes. The simulation results show that the δ18O values in precipitation, vapor and surface runoff have distinct seasonality with th</p><p>　　Atomic Energy Agency (IAEA)

8、 in co-operation with the World Meteorological Organization (WMO), the simulations by CLM reveal the similar temporal distributions of theδ18O in precipitation. Moreover, the simulated amount effect between monthlyδ18O a

9、nd monthly precipitation amount, and MWL (meteoric water line) are all close to the measured values. However, the simulated seasonal difference in the δ18O in precipitation is distinctly smaller than observed one, and th

10、e simulated temporal distribution</p><p>　　mismatches are possibly related to the simulation capacity and the veracity in forcing data.</p><p>　　Key word : stable water isotope, CLM, simulation,

11、 amount effect, seasonal variation</p><p>　　The modeling of the land surface and soil moisture is increasingly seen as an important component in understanding hydrological cycles. Stable water isotopes, for

12、example18O and D, are superlative tracers for the hydrological cycles because their abundance in water reflects the accumulated record of physical phase change. Using the features of stable isotopes can accurately determ

13、ine the partitioning of precipitation into transpiration, evaporation and runoff, which cannot be detected with mass </p><p>　　This study, a part of iPILPS, incorporates stable water isotopes in CLM as a dia

14、gnostic tool, simulates and analyses variations of stable water isotopes in different reservoirs on monthly time scales at Manaus, Brazil. The simulated behaviors of stable isotopes in precipitation on monthly time scale

15、 have good consistency with actual survey result at Manaus station set up by IAEA/WMO, howing that the simulation by the CLM incorporating stable water isotopes is reasonable.</p><p>　　1 Model description<

16、;/p><p><b>　　1.1 CLM</b></p><p>　　Earth’s biosphere is an important part of the Earth’s climate system. Relatedly, the dynamic, thermodynamic and physiological processes of vegetation c

17、overage are the key factors impacted climate change. These numerical models including physical process parameterizations are called as the land surface process model. Land surface model is composed of different physical

18、process</p><p>　　modes including the parameterization of dynamics characteristics, the longwave and shortwave radiation transfer and rainfall interception, etc. in canopy associated with vegetation shape; ph

19、otosynthesis, transpiration and evaporation related to plant physiology; and physical process of water-heat conduction, soil chemical processes, freezing and thawing of permafrost within soil, and so on. The Community La

20、nd Model (CLM) is currently one of well-developed and potential land surface models. CLM i</p><p>　　Transfer Scheme (BATS), the Institute of Atmospheric Physics, Chinese Academy of Sciences land model (IAP94

21、) and the NCAR land surface model (LSM). The model takes into account ecological differences among vegetation types, hydraulic and thermal differences among soil types, and allows for multiple land cover types within a g

22、rid cell. Strictly speaking, CLM is a single point model. According to different physical processes, the model structure can be separated into two parts, the biogeophysical </p><p>　　1.2 Stable water isotope

23、 parameterization</p><p>　　The stable isotopic ratio incorporated into CLM is noted as </p><p>　　R= (1)</p><p>　　The subscription w stands for reservoir

24、 water, for example precipitation, runoff or vapor, etc.</p><p>　　There are two possible ways of mixing the reservoir water with input, “total mixing” scheme and “partial mixing” scheme:</p><p>

25、　　Rw(t) = [N1Rw (t ?1) + N2Rinputs (t)] N (2)</p><p>　　N = N1 + N2 (3)</p><p>　　in a total mixing, </p><p>　　Roverflow (t) = Rw

26、(t) (4)</p><p>　　and in a partial mixing, namely as</p><p>　　max(N1)≥N (5)</p><p>　　Roverflow (t) = Rinputs (t

27、) (6)</p><p>　　where Rinputs is the stable isotopic ratio of any inputs, Roverflow is the ratio of overflow that is the water of exceeding the maximum storage capacity of the re

28、servoir, N1 is the mass of water in reservoir, N2 is the mass of input water, N is the total mass after mixing and t is the</p><p>　　time. As phase change is generated, there will appear fractionation effect

29、 of stable isotope. As water evaporating, the stable isotopic ratio in residual water is</p><p><b>　　(7)</b></p><p>　　where f = N1(t) / N1(0) is the fraction of residual water in the

30、 reservoir after evaporation event, and</p><p>　　α = Rw / Rv is the fractionation factor of stable isotopes between liquid and vapor, Rv is the ratio in evaporated vapor. As vapor condensing,</p><

31、p>　　Rd =α Ra , (8)</p><p>　　where Rd and Ra are stable isotopic ratios in dew and in atmospheric vapor, respectively, and α is the stable isotopic fractionation facto

32、r calculated between liquid and vapor phase.</p><p>　　As known, vegetation transpiration does not produce stable isotopic fractionation, thus, the stable isotopic ratio in transpiration equals to that in roo

33、t region, namely</p><p>　　Rtr = Rroot . (9)</p><p>　　1.3 Experimental scheme</p><p>　　Three sites with different geophysical and climatic condi

34、tions are selected for iPILPS Phase 1 experiment. They are Munich, Germany (48.08°N, 11.34°E), Tumbarumba, Australia (35.49°S, 148.01°E) located in middle latitudes and Manaus, Brazil (3.08°S, 60

35、.01°W) in tropical rainforest of South America. </p><p>　　Manaus, with an equatorial climate characterized by agreeable temperatures but plenty of rain and humidity, is situated in the heart of Amazons,

36、 north of Brazil more than 1450 km inland from the Atlantic. According to statistical data, the annual mean precipitation amount</p><p>　　is about 2190 mm at Manaus, with the maximal monthly mean precipitati

37、on of 308 mm in April and the minimal mean precipitation of 52 mm in August; the annual mean temperature is 26.8℃, with the highest monthly mean temperature of 27.9℃ in October and the lowest monthly mean temperature of

38、26.0℃ in March. </p><p>　　The survey of stable isotopes in precipitation shows that there is the marked negative correlation between monthly stable isotopic ratios in precipitation and precipitation amount a

39、t Manaus. In view of that some variation features of stable isotopes in precipitation at Manaus have comparability with that under monsoon climate in East Asia, the simulation experiment of stable water isotopes was carr

40、ied out at Manaus.</p><p>　　The CLM simulation requires forcing that contains isotopes in precipitation and atmospheric vapor etc. at high resolution (Table 2). These forcing data, commended in iPILPS Phase

41、1 exclusively, are derived from output of REMOiso (Regional Model with isotopes) at 15-min time step for one ideal year (360 days). For the details on the forcing data see ref. In this experiment, the model iterates a 1-

42、year calculation until differences between the initial and final values decrease below 0.01 mm/a for </p><p>　　1.4 Stable isotopic balance</p><p>　　Water balance is the base of calculating water

43、 amount in land surface scheme. Similarly, stable isotopic balance is the base of stable isotopic simulation. </p><p>　　The magnitude of stable isotopic ratio in water is related to that in initial origin, e

44、.g. in atmospheric precipitation or in vapor. By averaging the δ18O in reservoirs and the specific humidity as well as aggregating the water budgets at 15-min time step, the daily variations of the δ18O in reservoirs and

45、 corresponding water budgets are obtained (Figure 1). </p><p><b>　　，</b></p><p>　　Figure 1 The daily variations of theδ18O in precipitation (a), vapor (b), with the corresponding wa

46、ter budgets from REMOiso as inputs at Manaus, Brazil. In Figure 1, theδ18O in precipitation and in vapor show all obvious seasonality and the typical isotopic signature in evergreen tropic forest: the heavy rain or

47、the moist atmosphere (great q) is usually depleted in stable isotopes, whereas the light rain or the dry atmosphere (small q) is usually enriched in stable isotopes. Compared with </p><p>　　The soil column i

48、s discredited into ten layers with different depths from 0.0175 m to 1.437 m in vertical direction. In this study, the variations of stable water isotopes and water budgets are concerned in super-surface (0―0.0175 m, the

49、 first layer in CLM) and root-region (0.0175―3.433 m, the 2nd―10th layer in CLM).</p><p>　　2 Simulation results by CLM</p><p>　　2.1 Seasonal variations of 18O and water budgets</p><p&

50、gt;　　in land surface reservoirs</p><p>　　On the monthly time scale, the simulated precipitation, specific humidity and surface runoff show all the obvious bimodal seasonality, which characterizes the climati

51、c regime of equator zones (Figure 2). The primary maximal and minimal precipitation appear respectively in April and in July with their amount difference of 528 mm, and the second maximal and minimal precipitation respec

52、tively in December and in January with the amount difference of 170 mm, merely 1/3 of the former amount and only o</p><p>　　Figure 2 The monthly variations of the O in precipitation (a),vapor (b), surface

53、runoff (c), surface dew (d) and surface evaporation (e), with the corresponding water budgets at Manaus, Brazil.</p><p>　　The magnitude of surface evaporation is related to atmospheric humidity. Compared Fig

54、ure 2(e) with 2(b), the evaporation is relatively small at two maximal specific humidity in April and in December, but relatively great at the minimal specific humidity in July. Unlike the behaviors of precipitation, spe

55、cific humidity and condensation, evaporation shows the weak seasonality and indistinctive correlation with theδ18O in evaporation.</p><p>　　2.2 Seasonal variations of the 18O and water</p><p>　　

56、The surface infiltration water, originated primarily from atmospheric precipitation, shows a very good consistency with precipitation . As a result, 18the monthly meanδ18O in infiltration water is positively correlated t

57、o that in precipitation, but negatively to infiltration water in accordance with the amount effect. Compared with precipitation, the infiltration water is isotopic ally enriched markedly due to evaporation action.</p&

58、gt;<p>　　The variation of super-surface soil water is influenced not only by infiltration water but also by mass exchange with root region water and surface evaporation action. Impacted by the storage regulation a

59、nd peak attenuation actions of soil, the seasonality of super-surface soil water is weakened. Correspondingly, theδ18O in superurface reservoir displays unclear seasonality and un-marked correlation with super-surface wa

60、ter. However, the surface evaporation keeps isotopic consistency with super-</p><p>　　The root-region water and the subsurface runoff have all weak seasonality with slightly later time phase than precipitati

61、on. Usually, in the rainy season, bigger aquiclude and stronger sub-surface runoff corresponds to the higher water table; and in the dry season, smaller aquiclude and weaker subsurface runoff to the lower water table. Co

62、rrespondingly, δ18O in reservoirs shows that, in the rainy season, the heavy precipitation and the produced strong infiltration have the marked impact on δ18O </p><p>　　Seasonal variations of the O and wate

63、r budgets in canopy reservoir </p><p>　　The canopy storage water mainly comes from the precipitation interception by canopy, the replenishment from condensation is less. Therefore, the seasonal variation of

64、theδ18O in canopy reservoir is consistent with that in precipitation. In accordance withδ18O in canopy reservoir is in-the amount effect, the versely proportional to the canopy storage water: in the rainy season, more c

65、anopy storage water corresponds toδ18O in reservoir, and in dry season, less canopy lowerδ18O in reservoir. Compa</p><p>　　Because vegetation transpiration process is considered not to generate isotopic frac

66、tionation, theδ18O variation in canopy transpiration keeps consistent with that in root-region water that furnishes the most of the canopy transpiration. By comparing ,the canopy transpiration varies with contrary to can

67、opy evaporation. In the dry season, the water furnishing to canopy evaporation is less for lighter precipitation, but canopy transpiration is more due to drier atmosphere; in the rainy season, the</p><p>　　3

68、 Comparison between CLM simulated and actual results </p><p>　　Manaus is one of sampling stations attached to the global survey network set by the International Atomic Energy Agency (IAEA) in co-operation w

69、ith the World Meteorological Organization (WMO). There have been 26-year stable isotopic survey records from 1965 to 1990 (absent from 1993 to 1995) at Manaus (http://www iaea.org.programs/ri/gnip/gnipmain.htm).</p>

70、;<p>　　On the monthly timescale, there is the marked amount effect between the actualδ18O in precipitation and precipitation amount with the confidence level above 0.001,and the simulated amount effect has good co

71、nsistency with the actual that.</p><p>　　The relationship betweenδD andδ18O in atmospheric precipitation is called as the meteoric water line (MWL). The actual global MWL by Craig is D = 8.0δ18O +10.0. The s

72、lope item of 8.0 stands for comparative relationship of fractionation rates between deuterium and oxygen-18, and the constant item of 10.0 does the deviation degree of the deuterium from that in equilibrium state. They a

73、re controlled by all of these phase-change processes from vapor evaporating in its origins to raindrops falling on</p><p>　　Compared with the global MWL, the actual MWL at Manaus has the slightly great slope

74、 and constant items, but the simulated one has the slightly small slope and constant items. </p><p>　　4 Conclusions </p><p>　　(1) Similar to the simulated variations of precipitation, specific

75、 humidity and surface runoff, the simulatedδ18O in these reservoirs also shows the bimodal seasonality with the marked negative correlations with corresponding water amount. The variation of theδ18O in dew has </p>

76、<p>　　very good consistency with that in vapor because dew is condensed from vapor directly. The surface evaporation amount is related to atmospheric humidity. However, theδ18O in evaporation shows the indistincti

77、ve correlation with evaporation amount. </p><p>　　(2) The seasonal variation of theδ18O in surface in-filtration water has a very good consistency with that in precipitation because of originating primarily

78、from atmospheric precipitation. Impacted by storage regulation and peak attenuation actions of soil, the seasonal differences of theδ18O in super-surface and root-region reservoirs are weakened. Theδ18O in subsurface run

79、off equals to that in root-region water because the mass complement mainly comes from root-region water. </p><p>　　(3) The seasonal variation of theδ18O in canopy reservoir is consistent with that in precipi

80、tation. Compared with precipitation, canopy reservoir is isotopically enriched due to evaporation action. Similar to that in surface dew, the seasonal variation of theδ18O in canopy</p><p>　　dew is consisten

81、t with that in vapor. Compared with vapor, the variation range of theδ18O in canopy dew is distinctly smaller although canopy dew is isotopically enriched. </p><p>　　(4) Based on the available data from IAE

82、A/WMO, the actual precipitation amount and the</p><p>　　δ18O in precipitation have all distinct seasonality at Manaus. Moreover, the simulated amount effect between monthlyδ18O and monthly precipitation amou

83、nt, and MWL (meteoric water line) are all close to the actual results.</p><p>　　1 Aleinov I, Schmidt G A. Water isotopes in the GISS Model E land surface scheme. Glob Planet Change, 2006, 51(1-2): 108-120 &

84、lt;/p><p>　　2 Yoshimura K, Miyazaki S, Kanae Sh, et al. Iso-MATSIRO, a land surface model that incorporates stable water isotopes. Glob Planet Change, 2006, 51(1-2): 90-107</p><p>　　3 Gat J R. At

85、mospheric water balance in the Amazon basin: an isotopic evapotranspiration model. J Geophys Res, 1991, 96: 13179-13188 </p><p>　　4 Hoffmann G, Werner M, Heimann M. Water isotope module of the ECHAM atmosph

86、eric general circulation model: a study on time-scales from days to several years. J Geophys Res, 1998, 103: 16871-16896 </p><p>　　5 Yoshimura K, Oki T, Ohte N, et al. A quantitative analysis of δ18O variab

87、ility with a Rayleigh-type isotope circulation short-term model. J Geophys Res, 2003, 108(D20): doi: 10.1029/ 2003JD003477 </p><p>　　6 Fischer M J. iCHASM, a flexible land-surface model that incorporates st

88、able water isotopes. Glob Planet Change, 2006, 51(1-2): 121-130 </p><p>　　7 Henderson-Sellers A, MeGuffie K, Hang Z. Stable isotopes as validation tools for global climate model predictions of the impact of

89、 Amazonian deforestation. J Climate, 2002, 15: 2664-2677 </p><p>　　8 Henderson-Sellers A, Fischer M, Aleinov I, et al. Stable water isotope simulation by current land-surface schemes: results of iPILPS Phas

90、e 1.Glob Planet Change, 2006, 51(1-2): 34-58 </p><p>　　9 Dai Y J, Zeng X B, Dickinson R E, et al. The Common Land Model. Bull Amer Meteor Soc, 2003, 84(8): 1013-1023 </p><p>　　10 Oleson K W, D

91、ai Y J, Bonan G, et al. Technical description of the Community Land Model (CLM). NCAR/TN-461+STR, 2004 </p><p>　　11 Sturm K, Hoffmann G, Langmann B, et al. Simulation of δ18O in precipitation by the regiona

92、l circulation model REMOiso. Hydrol Process, 2005, 19: 3425-3444 </p><p>　　12 Craig H. Isotopic variations with meteoric water. Science, 1961, 133: 1702-1703</p><p>　　Chinese Science Bulletin ，

93、2009, 1007(10): 1765-1772.</p><p>　　陸面過程模式CLM的穩(wěn)定水同位素的季節(jié)變化仿真</p><p>　　章新平1，王小云2，楊宗輛3，牛國月4，謝子出5</p><p>　　1湖南師范大學(xué)資源與環(huán)境科學(xué)系，長沙410081，中國;</p><p>　　2青島市氣象局，青島266003，中國;</p&

94、gt;<p>　　3三部地質(zhì)科學(xué)院，美國德克薩斯大學(xué)奧斯汀分校，得克薩斯州78721-0254，美國.</p><p>　　摘 要 本文模擬和分析了不同穩(wěn)定水同位素組成在馬瑙斯、巴西的月變化，運用區(qū)域土地模型（CLM的）即將穩(wěn)定同位素技術(shù)作為一個了解穩(wěn)定水同位素診斷的工具，填補(bǔ)了觀測水文氣象數(shù)據(jù)和預(yù)測過程的空白。仿真結(jié)果表明，通過標(biāo)記相應(yīng)的負(fù)值水量，含δ18O的降水其降水量和地表徑流有明顯的季節(jié)

95、相關(guān)性。相比之下，由國際原子能總署（IAEA）與世界氣象組織（氣象組織）合作研究，由CLM的模擬揭示了δ18O的時空分布規(guī)律。此外，δ18O月含量和月降水量之間的關(guān)系、模擬量和大氣降水線效果（大氣降水線）都接近實測值。然而，在模擬中降水中δ18O的季節(jié)差異明顯小于觀察結(jié)果之一，而在模擬的時空分布降水中對δ18O的觀察顯示其模擬不是單一的一個理想的雙峰季節(jié)性，這些不匹配可能與仿真能力和迫使數(shù)據(jù)的準(zhǔn)確性有關(guān)。</p><

96、p>　　關(guān)鍵詞 穩(wěn)定水同位素，CLM的，仿真，降水量效應(yīng)，季節(jié)變化</p><p>　　對陸地表面和土壤水分的建模越來越被視為是水文循環(huán)中一個重要的組成部分。穩(wěn)定水同位素，例如18O和D是水文循環(huán)中最合適的示蹤劑，因為它們的豐度反映了物相累積并紀(jì)錄了其變化。利用穩(wěn)定同位素的特征可以準(zhǔn)確地確定降水量轉(zhuǎn)化為蒸騰，蒸發(fā)和徑流的部分，而不能被被檢測物質(zhì)影響。最近，地表水參數(shù)穩(wěn)定同位素計劃進(jìn)行了項目比對iPILP

97、S（同位素陸面參數(shù)化方案），該實驗旨在確定并測試不同的陸地表面的iPILPS，納入穩(wěn)定水同位素計劃，在水文數(shù)據(jù)和氣候?qū)W研究水資源調(diào)查中評價穩(wěn)定同位素的適用性，找出評估陸面計劃觀測數(shù)據(jù)與應(yīng)用穩(wěn)定同位素和水的具體同位素數(shù)據(jù)預(yù)報水文氣象過程的差距。 在本研究中，iPILPS的一部分包括以CLM的穩(wěn)定水同位素作為診斷工具，模擬和分析穩(wěn)定水同位素在馬瑙斯和巴西水庫每月不同的變化。在馬瑙斯站設(shè)立的原子能機(jī)構(gòu)/氣象組織的調(diào)查結(jié)果顯示該模擬中降水量

98、變化每月變化與穩(wěn)定同位素每月實際變化有很好的一致性，結(jié)果表明由CLM來模擬穩(wěn)定水同位素是合理的。</p><p><b>　　1 模型描述</b></p><p><b>　　1.1 CLM</b></p><p>　　地球生物圈是地球氣候系統(tǒng)的重要組成部分,與此相關(guān)的動態(tài)、溫度及植被覆蓋度的生理過程是影響氣候變化的關(guān)鍵

99、因素。這些模型包括物理參數(shù)模型被稱為陸面過程模型。陸面過程模型是由不同的物理過程包括動態(tài)參數(shù)化模式特點、長波和短波輻射、轉(zhuǎn)讓和降雨截留、樹冠等相關(guān)植被的形狀、光合作用、蒸騰和蒸發(fā)與植物生理和物理過程中的土壤水熱傳導(dǎo)、土壤化學(xué)進(jìn)程、凍結(jié)和凍土融化土壤等等。區(qū)域土地模式（CLM的）是目前是比較發(fā)達(dá)的和有發(fā)展?jié)摿Φ牡乇砟Ｐ汀?CLM是由大氣研究所物理中科院土地模型（IAP94）和NCAR的陸面過程模式（LSM的）在生物圈大氣調(diào)遷計劃（