探討地下水參數化對於臺灣地表水文過程之影響

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：32

、訪客IP：3.136.26.20

姓名

陳姿穎(Tzu-Ying Chen) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

探討地下水參數化對於臺灣地表水文過程之影響
(Effects of the groundwater parameterization on land surface hydrologic processes using WRF-NoahMP in Taiwan.)

相關論文

★ 土地利用型態對地表能量收支與海陸風模擬的影響	★ 探討邊界層參數化對氣象與空氣污染模擬結果的影響
★ 探討土地利用型態對珠江口沿岸地區氣象模擬的影響：高污染事件日之個案分析	★ 探討台灣地區在春季期間經長程傳輸所觀測之一氧化碳濃度與綜觀天氣之關係
★ 探討地表參數對台灣地區氣象模擬的影響	★ 探討區域尺度氣候變遷對台灣地區氣象場及汙染物濃度模擬的影響
★ 使用CMAQ-HDDM探討台灣地區臭氧之非線性反應及估算高臭氧區的來源貢獻量: 2011年個案分析	★ 地表水文循環過程與大氣耦合作用對土壤溼度以及氣象模擬的影響
★ 使用VVM探討陸氣交換過程對台灣地區高解析氣象模擬的影響--理想個案模擬	★ 使用群集分析分類綜觀尺度天氣型態以探討台灣北部地區午後熱對流系統局部環流結構與系統發展特性
★ 台灣中部山區局部環流結構特性與其對空氣汙染物傳送過程的影響	★ 開發適用於大氣邊界層觀測的無人機系統
★ 雲林地區細懸浮微粒的來源解析	★ 臺灣中部山區埔里盆地之局部環流與邊界層結構特性
★ 臺灣背風渦旋特性分析及其對空氣污染物傳輸過程影響	★ 臺灣西南部海洋邊界層垂直結構特性分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

過去許多研究指出，地表特徵影響陸地–大氣之間的交互作用，如：地形、土地利用形態、植被覆蓋率、土壤質地及土壤溼度等，其中土壤濕度對於陸氣交互作用的影響最為關鍵。土壤中的水分以土壤水或地下水的形式儲存在地表中，且地表水文過程影響土壤水分的分布，進而改變地表能量收支，並影響數值天氣預報的表現，但現今氣象模式中經常使用的地表模式卻過度簡化地表水文的描述，導致模式無法精確的掌握地表特徵，影響近地表天氣特徵的模擬及大氣邊界層的發展，因此本篇研究利用加入地下水參數化方案的地表模式Noah-MP LSM與大氣模式WRF進行耦合模擬，並選擇個案時間為2015年7月30日至2015年8月10日，目的是為瞭解在乾旱事件後到颱風侵襲，大環境乾濕分明的差異，探討加入地下水的處理過程LSM能否更精確的掌握土壤水分的分布及改變陸氣交互作用。
為了提供WRF-NoahMP模擬時有良好的地下水位初始場，透過觀測水井資料瞭解臺灣地下水位的分布特性，顯示地下水位變化受到降雨季節分布的影響最顯著，臺灣北部因四季有雨，地下水位淺呈現穩定無明顯年際變化，中部地區則受到5月梅雨季及8月颱風季影響，地下水位呈現雙峰值變化，高值落在6月與9月，而南部地區主要降雨來源為夏季，水位呈單峰值變化。然而因觀測水井皆落在平原地區，並無法得知臺灣山區的地下水位分布，因此利用Noah-MP LSM離線模式並依據地形高度來定義初始地下水位，並運行10個月後得到地下水位與土壤濕度達平衡的初始場，提供WRF-NoahMP進行模擬。離線模式模擬結果顯示可以模擬出與地下水井觀測結果接近的空間分布，但量值上可能還有些差異，因為地下水的變化不只受到降雨影響，還有可能受到人為活動、地質條件等影響，但整體來說，離線模式的模擬結果尚可用來當作WRF模擬所需的初始場資料。
然而WRF-NoahMP的模擬更換離線模式初始場後，結果顯示搭配MMF地下水參數化方案的模擬，其地下水位深度模擬的較淺，平原區大多位於土壤層之中，因此會有含水層補充水量到土壤層的機制產生，使土壤濕度增加、潛熱通量增加及近地面溫度降低，且透過地表水文收支的分析，瞭解當地下水的深度低於土壤層下方5公尺時，則不會對陸氣較互作用造成影響。

摘要(英)

The land surface hydrologic processes such as surface/subsurface runoff and soil-groundwater interaction strongly affect soil moisture. In order to understand how the land surface hydrologic processes affect the land-air interactions in Taiwan, the WRF model coupled with Noah-MP land surface models were applied and chosen three different runoff options. The difference in the three runoff option in Noah-MP LSM is that a free drainage is specified as the lower boundary condition in option 3 (FD), option 1 (SIMGM) add the groundwater parameterization which considers the water content in aquifer to cacluate the water table depth changing and the option 5 (MMFGW) is more complicated groundwater parameterization which considers the aquifer interact with the soil column and river.
Based on observation wells, we can understand the distribution characteristics of Taiwan’s ground-water level. The shallow groundwater level in northern Taiwan is stable and there is no obvious annual change. The groundwater level of central area shows two peak change and the high values in June and September because of the mei-yu at May and typh-oon at August. While the main source of rainfall in the southern region was summer, and the water level showed a single peak change. However, the observation wells are located at plain area, it could not know the groundwater level in mountain area clearly. Therefore, the offline Noah-MP LSM is used to simulate the initial data of groundwater table based on the rough topography criterion and the output is used for WRF simulation.
The study period is from 30 July to 10 August 2015 during which a moderate intensity typhoon made a landfall in Taiwan. Before the influence of the typhoon, the atmospheric conditions were relatively dry and calm and soils were also dry. During the dry atmospheric conditions, the SIMGM simulates the less soil moisture in three runoff options because the soil would tranfer the water to aquifer. The MMFGW simulates the most soil moisture because the shallower water table depth in soil column which make aquifer transfer the water to soil column. Therefore, the MMFGW simulates higher latent heat flux and lower 2m temperature. In addition to considering that the groundwater parameterization will affect the meteorological simulation, the depth of the water table would also affect. When the offline model product is replaced as the initial data and compared with the water table provided by the WRF official, there will be a deeper groundwater level in the central and southern plain. This would affect the soil moisture trier and reduce the latent heat flux.

關鍵字(中)

★ 地表水文過程
★ 地下水
★ 土壤濕度
★ 陸氣交互作用

關鍵字(英)

★ hydrologic processes
★ groundwater
★ soil moisture
★ land-air interaction

論文目次

摘要 .............................i
Abstract.............................iii
目錄 .............................v
表目錄 .............................vi
圖目錄 .............................vii
第1章緒論 .....................1
1.1 前言 .....................1
1.2 文獻回顧 .....................2
1.3 研究目的 .....................5
第2章模式介紹與實驗設計.............6
2.1 模式介紹 ......................6
2.1.1 氣象模式WRF....................6
2.1.2 Noah-MP地表模式(Noah-MP land surface model)..................................6
2.2 實驗設計與模式設定...............10
2.2.1 Noah-MP 離線模式的實驗設計與設定..11
2.2.2 WRF模式實驗設計與模式設定 .........12
2.3 觀測資料 .........................13
第3章個案介紹與離線模式結果分析.........15
3.1 個案介紹 .........................15
3.2 觀測資料分析.......................15
3.2.1 土壤濕度觀測.......................15
3.2.2 地下水觀測資料分析..................17
3.3 離線模式模擬結果....................19
第4章實驗結果討論........................21
4.1 不同地下水參數化方案的比較...........21
4.2 地下水位初始場對地表水文過程的影響分析.24
4.3 模式結果與觀測資料比對驗證...........27
第5章結論與展望..........................30
5.1 結論................................30
5.2 未來展望 ............................32
第6章參考文獻 ............................33
附表 ....................................40
附圖 ....................................44

參考文獻

江昭輝，「台灣地區土壤含水率觀測網之建置與模擬」，國立中興大學，碩士論文，西元2015年
楊竣凱，「台灣地區地下水位，土壤含水率與微氣象關係之模擬與研究」，國立中興大學，碩士論文，西元2015年
陳宗顯，「降雨引致地下水位變化之研究-以那菝、六甲與東和地下水位站為例」，國立成功大學，碩士論文，西元2006年
陳奕全，「水文模式結合季長期天氣展望推估鳳山溪流域地下水位變化」，國立中央大學，碩士論文，西元2017年
蘇紹昆，「評估NOAH陸地過程模式在石門水庫集水區模擬之水文循環過程」，國立中央大學，碩士論文，西元2008年
洪于珺, 洪景山, 蔡佳伶. (2014). 高解析土壤資料同化系統之效能評估. 大氣科學，42(1), 29-47.
王鈺慈，「臺灣中部山區埔里盆地之局部環流與邊界層結構特性」，國立中央大學，碩士論文，西元2019年
Barlage, M., Tewari, M., Chen, F., Miguez-Macho, G., Yang, Z.-L., and Niu, G.-Y. (2015). The effect of groundwater interaction in North American regional climate simulations with WRF/Noah-MP. Climatic Change, 129(3-4), 485-498.
Cai, X., Yang, Z. L., David, C. H., Niu, G. Y., and Rodell, M. (2014). Hydrological evaluation of the Noah‐MP land surface model for the Mississippi River Basin. Journal of Geophysical Research: Atmospheres, 119(1), 23-38.
Chen, F., and Dudhia, J. (2001). Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review, 129(4), 569-585.
Chen, F., Janjić, Z., and Mitchell, K. (1997). Impact of atmospheric surface-layer para-meterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Boundary-Layer Meteorology, 85(3), 391-421.
Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H. L., Koren, V., . . . Betts, A. (1996). Modeling of land surface evaporation by four schemes and comparison with FIFE observations. Journal of Geophysical Research: Atmospheres, 101(D3), 7251-7268.
Cheng, F.-Y., Hsu, Y.-C., Lin, P.-L., and Lin, T.-H. (2013). Investigation of the effects of different land use and land cover patterns on mesoscale meteorological simulations in the Taiwan area. Journal of applied meteorology and climatology, 52(3), 570-587.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A. (1991). Physiological and environ-mental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest meteoro-logy, 54(2-4), 107-136.
Cosby, B., Hornberger, G., Clapp, R., and Ginn, T. (1984). A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Research, 20(6), 682-690.
Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J. (2000). Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408(6809), 184-187.
Dickinson, E., Henderson-Sellers, A., and Kennedy, J. (1993). Biosphere-atmosphere transfer scheme (BATS) version 1e as coupled to the NCAR community climate model.
Ek, M., Mitchell, K., Lin, Y., Rogers, E., Grunmann, P., Koren, V., . . . Tarpley, J. (2003). Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. Journal of Geophysical Research: Atmospheres, 108(D22).
Fan, Y., Miguez‐Macho, G., Weaver, C. P., Walko, R., and Robock, A. (2007). Incorporat-ing water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations. Journal of Geophysical Research: Atmospheres, 112(D10).
Hong, S.-Y., Noh, Y., and Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 134(9), 2318-2341.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D. (2008). Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research: Atmospheres, 113(D13).
Jiang, X., Niu, G. Y., and Yang, Z. L. (2009). Impacts of vegetation and groundwater dynamics on warm season precipitation over the Central United States. Journal of Geophysical Research: Atmospheres, 114(D6).
Kain, J. S. (2004). The Kain–Fritsch convective parameterization: an update. Journal of applied meteorology, 43(1), 170-181.
Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., . . . Lawrence, D. (2004). Regions of strong coupling between soil moisture and precipitation. Science, 305(5687), 1138-1140.
Lin, T.-S., and Cheng, F.-Y. (2016). Impact of soil moisture initialization and soil texture on simulated land–atmosphere interaction in Taiwan. Journal of Hydrometeoro-logy, 17(5), 1337-1355.
Lo, M. H., and Famiglietti, J. S. (2013). Irrigation in California′s Central Valley streng-thens the southwestern US water cycle. Geophysical Research Letters, 40(2), 301-306.
Mahrt, L., and Ek, M. (1984). The influence of atmospheric stability on potential evaporation. Journal of Climate and Applied Meteorology, 23(2), 222-234.
Manabe, S. (1969). Climate and the ocean circulation1: I. The atmospheric circulation and the hydrology of the earth’s surface. Monthly Weather Review, 97(11), 739-774. doi:10.1175/1520-0493(1969)097<0739:CATOC>2.3.CO;2
Martinez, J. A., Dominguez, F., and Miguez-Macho, G. (2016a). Effects of a groundwater scheme on the simulation of soil moisture and evapotranspiration over southern South America. Journal of Hydrometeorology, 17(11), 2941-2957.
Martinez, J. A., Dominguez, F., and Miguez-Macho, G. (2016b). Impacts of a ground-water scheme on hydroclimatological conditions over southern South America. Journal of Hydrometeorology, 17(11), 2959-2978.
Maxwell, R. M., Chow, F. K., and Kollet, S. J. (2007). The groundwater–land-surface–atmosphere connection: Soil moisture effects on the atmospheric boundary layer in fully-coupled simulations. Advances in Water Resources, 30(12), 2447-2466.
Maxwell, R. M., Lundquist, J. K., Mirocha, J. D., Smith, S. G., Woodward, C. S., & Tompson, A. F. (2011). Development of a coupled groundwater–atmosphere model. Monthly Weather Review, 139(1), 96-116.
Miguez‐Macho, G., Fan, Y., Weaver, C. P., Walko, R., and Robock, A. (2007). Incorp-orating water table dynamics in climate modeling: 2. Formulation, validation, and soil moisture simulation. Journal of Geophysical Research: Atmospheres, 112(D13).
Mitchell, K. (2005). The community Noah land-surface model (LSM). User’s Guide Public Release Version, 2(1).
Niu, G. Y., and Yang, Z. L. (2004). Effects of vegetation canopy processes on snow surface energy and mass balances. Journal of Geophysical Research: Atmospheres, 109(D23).
Niu, G. Y., Yang, Z. L., Dickinson, R. E., and Gulden, L. E. (2005). A simple TOPMOD-EL‐based runoff parameterization (SIMTOP) for use in global climate models. Journal of Geophysical Research: Atmospheres, 110(D21).
Niu, G. Y., Yang, Z. L., Dickinson, R. E., Gulden, L. E., and Su, H. (2007). Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data. Journal of Geophysical Research: Atmospheres, 112(D7).
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., . . . Rosero, E. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. Journal of Geophysical Research: Atmospheres, 116(D12).
Noilhan, J., and Planton, S. (1989). A simple parameterization of land surface processes for meteorological models. Monthly Weather Review, 117(3), 536-549.
Roesch, A., Wild, M., Gilgen, H., and Ohmura, A. (2001). A new snow cover fraction parametrization for the ECHAM4 GCM. Climate Dynamics, 17(12), 933-946. doi:10.1007/s003820100153
Schaake, J. C., Koren, V. I., Duan, Q. Y., Mitchell, K., and Chen, F. (1996). Simple water balance model for estimating runoff at different spatial and temporal scales. Journal of Geophysical Research: Atmospheres, 101(D3), 7461-7475.
Sellers, P. J., Mintz, Y., Sud, Y. C., and Dalcher, A. (1986). A Simple Biosphere Model (SIB) for Use within General Circulation Models. Journal of the Atmospheric Sciences, 43(6), 505-531. doi:10.1175/15200469(1986)043<0505:ASBMFU>-2.0.CO;2
Seuffert, G., Gross, P., Simmer, C., & Wood, E. F. (2002). The influence of hydrologic modeling on the predicted local weather: Two-way coupling of a mesoscale weather prediction model and a land surface hydrologic model. Journal of Hydrometeorology, 3(5), 505-523.
Wagner, S., Fersch, B., Yuan, F., Yu, Z., and Kunstmann, H. (2016). Fully coupled atmospheric‐hydrological modeling at regional and long‐term scales: Develop-ment, application, and analysis of WRF‐HMS. Water Resources Research, 52(4), 3187-3211.
Yang, R., and Friedl, M. A. (2003). Modeling the effects of three‐dimensional vegetation structure on surface radiation and energy balance in boreal forests. Journal of Geophysical Research: Atmospheres, 108(D16).
Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., . . . Tewari, M. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 2. Evaluation over global river basins. Journal of Geophysical Research: Atmospheres, 116(D12).
Zhao, W., and Li, A. (2015). A review on land surface processes modelling over complex terrain. Advances in Meteorology, 2015.
Zheng, D., van der Velde, R., Su, Z., Wang, X., Wen, J., Booij, M. J., . . . Chen, Y. (2015). Augmentations to the Noah model physics for application to the Yellow River source area. Part II: Turbulent heat fluxes and soil heat transport. Journal of Hydrometeorology, 16(6), 2677-2694.
Zhuo, L., Dai, Q., Han, D., Chen, N., and Zhao, B. (2019). Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application. Hydrology and Earth System Sciences, 23(10), 4199-4218

指導教授

鄭芳怡

審核日期

2020-8-24

推文