進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-0107201910125200
論文名稱(中文) 合歡山集水區水文特徵及其對氣候變化衝擊影響評估之研究
論文名稱(英文) The hydrological characteristics of Hehuan Mountain watershed and impact assessment of climate variation
校院名稱 成功大學
系所名稱(中) 資源工程學系
系所名稱(英) Department of Resources Engineering
學年度 107
學期 2
出版年 108
研究生(中文) 陳易暄
研究生(英文) Yi-Hsuan Chen
電子信箱 cres4356@gmail.com
學號 N46061020
學位類別 碩士
語文別 英文
論文頁數 90頁
口試委員 指導教授-徐國錦
口試委員-葉信富
口試委員-江莉琦
口試委員-余化龍
口試委員-蔡瑞彬
中文關鍵字 水文系統  氣候變化  合歡山流域  SWAT-MODFLOW  GCM  傳遞函數 
英文關鍵字 hydrological system  Hehuan Mountain watershed  SWAT-MODFLOW  GCM  climate variation 
學科別分類
中文摘要 由於全球氣候的變化,水文系統在包括台灣在內的世界許多地方發生了巨大的變化。為了描繪水文系統對氣候變化影響的反應,水文模型的使用具有挑戰性。本研究選擇台灣的合歡山流域作為研究地點。該集水區的人類活動影響很少。它位於台灣的中部,面積1.52平方公里,坡度14.94°。採用SWAT模型並與MODFLOW聯合使用建立水文模型。在率定和驗證模型後,使用來自GCM模型的未來氣象預測(包括降雨和溫度),使用耦合模型混合項目第5階段(CMIP5)實驗方案。通過降尺度氣象數據,模擬了對氣候變化的水文響應。結果表明,水文成分反應不同降水變化趨勢為,濕季增加最多至100%,旱季減少至40%。溫度一致地升高約2℃。入滲量和逕流量高度對應於降水。地下水位對降水變化非常敏感。蒸發散量受溫度影響很大。所有這些水文分量都通過頻譜分析顯示時間序列的碎形。通過該特徵,可以使用傳遞函數從降雨量中推估水文分量。基於這種方法,數據驅動模型可以作為基於物理過程的水文模擬模型的替代方案。
英文摘要 Due to the variations of the global climate, the hydrological system has undergone tremendous changes in many places of the world including Taiwan. To delineate the response of a hydrological system to the impact of climate variation, the use of hydrological model is challenging. Hehuan Mountain watershed at Taiwan is chosen as the research site. The catchment is with few anthropogenic activities. It is located at central Taiwan with an area of 1.52 km2, a slope of 14.94°. SWAT model is adopted and conjunctively use with MODFLOW to establish the hydrological model. After model is calibrated and verified, future meteorological projections (including rainfall and temperature) from GCM model using Coupled Model Intercomprison Project Phase 5 (CMIP5) experiment scenarios are used. With downscaling meteorological data, hydrological response to the climate variation are simulated. Results show that hydrological components react differently Precipitation changes toward increasing in wet seasons for most 100% and decreasing in dry seasons to 40%. Temperature consistently increases for around 2℃. Percolation and outflow highly correspond to the precipitation. Groundwater is very sensitive to the change of precipitation. ET is significantly influenced by temperature. All these hydrological components show fractal in time series with spectrum analysis. The hydrological components can be inversely derived from the rainfall using the transfer function through this characteristic. Based on this approach, the data-driven model may serve as an alternative of the physics-based model for hydrological modeling.
論文目次 Abstract I
摘要 II
Acknowledge III
List of Figure VI
List of Table VIII
Notation IX
Chapter 1 Introduction 1
1.1 Problem statement and motivation 1
1.2 Literature review 5
1.3 Flow chart 8
Chapter 2 Methodology 10
2.1 Spectrum analysis 10
2.1.1 Fourier transform and time series 10
2.1.2 Spectrum 11
2.2 Conceptual hydrological system 12
2.3 Soil and Water Assessment Tool (SWAT) model 15
2.3.1 Concept of SWAT 15
2.3.2 History of SWAT 16
2.3.3 Governing Equation in SWAT 18
2.3.4 SWAT CUP 22
2.4 MODFLOW 25
2.5 SWAT-MODFLOW 26
2.5.1 Model conception 26
2.5.2 Combining method 27
2.6 Future meteorological data generation 29
Chapter 3 Study area 32
3.1 Study Site 32
3.2 Map source 33
3.3 Data 36
Chapter 4 The impact of climate variation on hydrological response of Hehuan mountain watershed 40
4.1 SWAT model calibration and validation 40
4.2 Current water components 45
4.3 Transfer function 46
4.4 Scenario simulation 61
Chapter 5 Conclusions and Suggestions 72
Reference 75
參考文獻 1. Abbaspour, K., Yang, J., Maximov, I., Siber, R., Bogner, K., & Mieleitner, J. et al. (2007). Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal Of Hydrology, 333(2-4), 413-430. doi: 10.1016/j.jhydrol.2006.09.014
2. Adelman, H., & Haftka, R. (1986). Sensitivity Analysis of Discrete Structural Systems. AIAA Journal, 24(5), 823-832. doi: 10.2514/3.48671
3. Arnold, C., & Gibbons, C. (1996). Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. Journal Of The American Planning Association, 62(2), 243-258. doi: 10.1080/01944369608975688
4. Arnold, J. G., Allen, P. M., & Bernhardt, G. (1993). A comprehensive surface-groundwater flow model. Journal of hydrology, 142(1-4), 47-69
5. Arnold, J. G., Srinivasan, R., Ramanarayanan, T. S., & DiLuzio, M. (1999). Water resources of the Texas gulf basin. Water Science and Technology, 39(3), 121-133
6. Arnold, J., & Fohrer, N. (2005). SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrological Processes, 19(3), 563-572. doi: 10.1002/hyp.5611
7. Arnold, J., & Williams, J. (1987). Validation of SWRRB—Simulator for Water Resources in Rural Basins. Journal Of Water Resources Planning And Management, 113(2), 243-256. doi: 10.1061/(asce)0733-9496(1987)113:2(243)
8. Arnold, J., Srinivasan, R., Muttiah, R., & Williams, J. (1998). LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT. Journal Of The American Water Resources Association, 34(1), 73-89. doi: 10.1111/j.1752-1688.1998.tb05961.x
9. Beck, M. (1987). Water quality modeling: A review of the analysis of uncertainty. Water Resources Research, 23(8), 1393-1442. doi: 10.1029/wr023i008p01393
10. Bellin, A., Majone, B., Cainelli, O., Alberici, D., & Villa, F. (2016). A continuous coupled hydrological and water resources management model. Environmental modelling & software, 75, 176-192
11. Benjamin, J., Walker, D., Mylläri, A., & Mylläri, T. (2018). On the Applicability of Pairwise Separations Method in Astronomy: Influence of the Noise in Data. Mathematics in Computer Science, 1-6
12. Beven, K., & Binley, A. (1992). The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes, 6(3), 279-298. doi: 10.1002/hyp.3360060305
13. Blasone, R., Madsen, H., & Rosbjerg, D. (2008). Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling. Journal Of Hydrology, 353(1-2), 18-32. doi: 10.1016/j.jhydrol.2007.12.026
14. Blower, S., & Dowlatabadi, H. (1994). Sensitivity and Uncertainty Analysis of Complex Models of Disease Transmission: An HIV Model, as an Example. International Statistical Review / Revue Internationale De Statistique, 62(2), 229. doi: 10.2307/1403510
15. Bohigas, O., Giannoni, M., & Schmit, C. (1984). Characterization of Chaotic Quantum Spectra and Universality of Level Fluctuation Laws. Physical Review Letters, 52(1), 1-4. doi: 10.1103/physrevlett.52.1
16. Bosch, D. D., Sheridan, J. M., Batten, H. L., & Arnold, J. G. (2004). Evaluation of the SWAT model on a coastal plain agricultural watershed. Transactions of the ASAE, 47(5), 1493
17. Bouraoui, F., Benabdallah, S., Jrad, A., & Bidoglio, G. (2005). Application of the SWAT model on the Medjerda river basin (Tunisia). Physics and Chemistry of the Earth, Parts A/B/C, 30(8-10), 497-507
18. Boyer, D., & López-Corona, O. (2009). Self-organization, scaling and collapse in a coupled automaton model of foragers and vegetation resources with seed dispersal. Journal Of Physics A: Mathematical And Theoretical, 42(43), 434014. doi: 10.1088/1751-8113/42/43/434014
19. Boyle, D. P., Gupta, H. V., & Sorooshian, S. (2000). Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resources Research, 36(12), 3663-3674
20. Buhl, J., Sumpter, D., Couzin, I., Hale, J., Despland, E., Miller, E., & Simpson, S. (2006). From Disorder to Order in Marching Locusts. Science, 312(5778), 1402-1406. doi: 10.1126/science.1125142
21. Burnash, R. J., Ferral, R. L., & McGuire, R. A. (1973). A generalized streamflow simulation system, conceptual modeling for digital computers
22. Campbell, J., & Barnett, J. (2010). Climate change and small island states: power, knowledge and the South Pacific. Routledge
23. Cao, W., Bowden, W. B., Davie, T., & Fenemor, A. (2006). Multi‐variable and multi‐site calibration and validation of SWAT in a large mountainous catchment with high spatial variability. Hydrological Processes: An International Journal, 20(5), 1057-1073
24. Cavagna, A., Culla, A., Di Carlo, L., Giardina, I., & Grigera, T. (2019). Low-temperature marginal ferromagnetism explains anomalous scale-free correlations in natural flocks. Comptes Rendus Physique. doi: 10.1016/j.crhy.2019.05.008
25. Central Geological Survey, MOEA. (2013). An integrated investigation of groundwater resources in Taiwan mountainous area: Phas I: Hydro-geological survey and compilation of hydro-geological map in central flow basin, Taiwan (4/4), Final Report
26. Cheng, Q. B., Reinhardt-Imjela, C., Chen, X., Schulte, A., Ji, X., & Li, F. L. (2016). Improvement and comparison of the rainfall–runoff methods in SWAT at the monsoonal watershed of Baocun, Eastern China. Hydrological Sciences Journal, 61(8), 1460-1476
27. Chiang, L. C., Chuang, Y. T., & Han, C. C. (2019a). Integrating Landscape Metrics and Hydrologic Modeling to Assess the Impact of Natural Disturbances on Ecohydrological Processes in the Chenyulan Watershed, Taiwan. International journal of environmental research and public health, 16(2), 266
28. Chiang, L. C., Wang, Y. C., & Liao, C. J. (2019b). Spatiotemporal Variation of Sediment Export from Multiple Taiwan Watersheds. International journal of environmental research and public health, 16(9), 1610
29. Chiew, F. H., Peel, M. C., & Western, A. W. (2002). Application and testing of the simple rainfall-runoff model SIMHYD. Mathematical models of small watershed hydrology and applications, 335-367
30. Chu, T. W., & Shirmohammadi, A. (2004). Evaluation of the SWAT model’s hydrology component in the piedmont physiographic region of Maryland. Transactions of the ASAE, 47(4), 1057
31. Chung, I. M., N. W. Kim, H. Na, J. Lee, S. Yoo, J. Kim, & S. Yang. (2011). Technical Note: Integrated Surface-Groundwater Analysis for the Pyoseon Region, Jeju Island in Korea. Applied Engineering In Agriculture, 27(6), 875-886. doi: 10.13031/2013.40629
32. Coe, R., & Stern, R. D. (1982). Fitting models to daily rainfall data. Journal of Applied Meteorology, 21(7), 1024-1031
33. Coffey, R., Dorai-Raj, S., O'Flaherty, V., Cormican, M., & Cummins, E. (2013). Modeling of Pathogen Indicator Organisms in a Small-Scale Agricultural Catchment Using SWAT. Human And Ecological Risk Assessment: An International Journal, 19(1), 232-253. doi: 10.1080/10807039.2012.701983
34. Cook, T. (2019). If Precipitation Extremes Are Increasing, Why Aren’t Floods?. Eos, 100. doi: 10.1029/2019eo120403
35. Corte-Real, J., Xu, H., & Qian, B. (1999). A weather generator for obtaining daily precipitation scenarios based on circulation patterns. Climate Research, 13(1), 61-75
36. Derwent, R., & Hov, Ø. (1988). Application of sensitivity and uncertainty analysis techniques to a photochemical ozone model. Journal Of Geophysical Research, 93(D5), 5185. doi: 10.1029/jd093id05p05185
37. Domenico, P. A., & Schwartz, F. W. (1998). Physical and chemical hydrogeology (Vol. 506). New York: Wiley
38. Duan, Q., Schaake, J., Andréassian, V., Franks, S., Goteti, G., & Gupta, H. et al. (2006). Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. Journal Of Hydrology, 320(1-2), 3-17. doi: 10.1016/j.jhydrol.2005.07.031
39. Edijatno, DE OLIVEIRA NASCIMENTO, N., YANG, X., MAKHLOUF, Z., & MICHEL, C. (1999). GR3J: a daily watershed model with three free parameters. Hydrological Sciences Journal, 44(2), 263-277. doi: 10.1080/02626669909492221
40. Faleiro, E., Kuhl, U., Molina, R., Muñoz, L., Relaño, A., & Retamosa, J. (2006). Power spectrum analysis of experimental Sinai quantum billiards. Physics Letters A, 358(4), 251-255. doi: 10.1016/j.physleta.2006.05.029
41. Freeze, R. A., & Cherry, J. A. (1979). Groundwater: Englewood Cliffs. New Jersey
42. Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, J. G. (2007). The soil and water assessment tool: historical development, applications, and future research directions. Transactions of the ASABE, 50(4), 1211-1250
43. Goldberger, A., Amaral, L., Hausdorff, J., Ivanov, P., Peng, C., & Stanley, H. (2002). Fractal dynamics in physiology: Alterations with disease and aging. Proceedings Of The National Academy Of Sciences, 99(Supplement 1), 2466-2472. doi: 10.1073/pnas.012579499
44. Green, W. H., & Ampt, G. A. (1911). Studies on Soil Phyics. The Journal Of Agricultural Science, 4(1), 1-24. doi: 10.1017/s0021859600001441
45. Grizzi, F., Castello, A., Qehajaj, D., Russo, C., & Lopci, E. (2019). The complexity and fractal geometry of nuclear medicine images. Molecular Imaging and Biology, 21(3), 401-409
46. Grizzetti, B., Bouraoui, F. D. M. G., & De Marsily, G. (2005). Modelling nitrogen pressure in river basins: A comparison between a statistical approach and the physically-based SWAT model. Physics and Chemistry of the Earth, Parts A/B/C, 30(8-10), 508-517
47. Groves, D. G., Lempert, R. J., Knopman, D., & Berry, S. H. (2008). Preparing for an uncertain future climate in the Inland Empire. Rand Corporation
48. Hahn, G., & Shapiro, S. (1994). Statistical models in engineering. New York: Wiley
49. Haq, R., Pandey, A., & Bohigas, O. (1982). Fluctuation Properties of Nuclear Energy Levels: Do Theory and Experiment Agree?. Physical Review Letters, 48(16), 1086-1089. doi: 10.1103/physrevlett.48.1086
50. Hargreaves, G., Hargreaves, G., & Riley, J. (1985). Agricultural Benefits for Senegal River Basin. Journal Of Irrigation And Drainage Engineering, 111(2), 113-124. doi: 10.1061/(asce)0733-9437(1985)111:2(113)
51. Harmel, R. D., Cooper, R. J., Slade, R. M., Haney, R. L., & Arnold, J. G. (2006). Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Transactions of the ASABE, 49(3), 689-701
52. Hoyos, F. T., Martín–Landrove, M., Navarro, R. B., Villadiego, J. V., & Cardenas, J. C. (2019). Study of cervical cancer through fractals and a method of clustering based on quantum mechanics. Applied Radiation and Isotopes, 150, 182-191
53. Huang, K., & Hwang, R. (2016). Future trends of residential building cooling energy and passive adaptation measures to counteract climate change: The case of Taiwan. Applied Energy, 184, 1230-1240. doi: 10.1016/j.apenergy.2015.11.008
54. Huang, Z., & Yang, H. (2015). Dominant climatic factor driving annual runoff change at catchments scale over China. Hydrology & Earth System Sciences Discussions, 12(12)
55. IPCC Fifth Assessment Report: CSIROexperts comment. (2013). ECOS. doi: 10.1071/ec13228
56. Izaurralde, R., Williams, J., McGill, W., Rosenberg, N., & Jakas, M. (2006). Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecological Modelling, 192(3-4), 362-384. doi: 10.1016/j.ecolmodel.2005.07.010
57. Jaramillo, F., & Destouni, G. (2015). Comment on “Planetary boundaries: Guiding human development on a changing planet”. Science, 348(6240), 1217-1217
58. Jha, M. K. (2011). Evaluating hydrologic response of an agricultural watershed for watershed analysis. Water, 3(2), 604-617
59. Jones, J. A., Creed, I. F., Hatcher, K. L., Warren, R. J., Adams, M. B., Benson, M. H., Boose, E., Brown, W. A., Campbell, J. L., Covich, A., Clow, D. W., Dahm, C. N., Elder, K., Ford, C. R., Grimm, N. B., Henshaw, D. L., Larson, K. L., Miles, E. S., Miles, K. M., Sebestyen, S. D., Spargo, A. T., Stone, A. B., Vose, J. M., & Williams, M. W. (2012). Ecosystem processes and human influences regulate streamflow response to climate change at long-term ecological research sites, BioScience, 62(4), 390-404
60. Kammler, D. (2000). A first course in Fourier analysis. Upper Saddle River, NJ: Prentice Hall
61. Karl, T., Arguez, A., Huang, B., Lawrimore, J., McMahon, J., & Menne, M. et al. (2015). Possible artifacts of data biases in the recent global surface warming hiatus. Science, 348(6242), 1469-1472. doi: 10.1126/science.aaa5632
62. Katz, R., Parlange, M., & Naveau, P. (2002). Statistics of extremes in hydrology. Advances In Water Resources, 25(8-12), 1287-1304. doi: 10.1016/s0309-1708(02)00056-8
63. Kay, S., & Marple, S. (1981). Spectrum analysis—A modern perspective. Proceedings Of The IEEE, 69(11), 1380-1419. doi: 10.1109/proc.1981.12184
64. Kim, D., Kim, M., Cha, J., & Kim, S. (2008). Numerical investigation on thermal–hydraulic performance of new printed circuit heat exchanger model. Nuclear Engineering And Design, 238(12), 3269-3276. doi: 10.1016/j.nucengdes.2008.08.002
65. Kim, N., Chung, I., & Won, Y. (2004a). The Development of Fully Coupled SWAT-MODFLOW Model (I) Model Development. Journal Of Korea Water Resources Association, 37(6), 499-507. doi: 10.3741/jkwra.2004.37.6.499
66. Kim, N., Chung, I., & Won, Y. 2004(b). The Development of Fully Coupled SWAT-MODFLOW Model (II) Evaluation of Model. Journal Of Korea Water Resources Association, 37(6), 509-515. doi: 10.3741/jkwra.2004.37.6.509
67. Kirchner, J., Feng, X., & Neal, C. (2000). Fractal stream chemistry and its implications for contaminant transport in catchments. Nature, 403(6769), 524-527. doi: 10.1038/35000537
68. Knisel, W. G. (1980). CREAMS: a field scale model for Chemicals, Runoff, and Erosion from Agricultural Management Systems [USA]. United States. Dept. of Agriculture. Conservation research report (USA)
69. Krause, P., Boyle, D. P., & Bäse, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in geosciences, 5, 89-97
70. Lee, K. S., & Chung, E. S. (2007). Hydrological effects of climate change, groundwater withdrawal, and land use in a small Korean watershed. Hydrological Processes: An International Journal, 21(22), 3046-3056
71. Legates, D. R., & McCabe, G. J. (1999). Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water resources research, 35(1), 233-241
72. Leonard, R. A., Knisel, W. G., & Still, D. A. (1987). GLEAMS: Groundwater loading effects of agricultural management systems. Transactions of the ASAE, 30(5), 1403-1418
73. Liang, W., Bai, D., Wang, F., Fu, B., Yan, J., Wang, S., Yang, Y., Long, D., & Feng, M. (2015). Quantifying the impacts of climate change and ecological restoration on streamflow changes based on a Budyko hydrological model in China's Loess Plateau. Water Resources Research, 51(8), 6500-6519. doi: 10.1002/2014wr016589
74. Liu, R., Liu, S., Shiu, C., Li, J., & Zhang, Y. (2016). Trends of regional precipitation and their control mechanisms during 1979–2013. Advances In Atmospheric Sciences, 33(2), 164-174. doi: 10.1007/s00376-015-5117-4
75. Liu, Y., Li, S., Chen, F., Yang, S., & Chen, X. (2010). Soil water dynamics and water use efficiency in spring maize (Zea mays L.) fields subjected to different water management practices on the Loess Plateau, China. Agricultural Water Management, 97(5), 769-775
76. Malamud, B. D., & Turcotte, D. L. (1999). Self-affine time series: measures of weak and strong persistence. Journal of statistical planning and inference, 80(1-2), 173-196
77. McDonald, M. G., & Harbaugh, A. W. (1988). A modular three-dimensional finite-difference ground-water flow model (Vol. 6, p. A1). Reston, VA: US Geological Survey
78. McWhorter, D. B., & Sunada, D. K. (1977). Ground-water hydrology and hydraulics. Water Resources Publication
79. Mikhil, U. & Sarda, V. K. (2016). STREAMBED HYDRAULIC CONDUCTIVITY-A STATE OF ART. IJMTER International Journal Of Modern Trends in Engineering and Research, 3(6)
80. Montanari, A., Young, G., Savenije, H. H. G., Hughes, D., Wagener, T., Ren, L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S. J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., & Belyaev, V. (2013). Panta Rhei—Everything Flows: Change in hydrology and society—The IAHS Scientific Decade 2013– 2022. Hydrological Sciences Journal, 58 (6), 1256–1275
81. Monteith, J. (1965). Light Distribution and Photosynthesis in Field Crops. Annals Of Botany, 29(1), 17-37. doi: 10.1093/oxfordjournals.aob.a083934
82. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900
83. Muleta, M. K., & Nicklow, J. W. (2005). Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. Journal of hydrology, 306(1-4), 127-145
84. Narsimlu, B., Gosain, A. K., Chahar, B. R., Singh, S. K., & Srivastava, P. K. (2015). SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting. Environmental Processes, 2(1), 79-95
85. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of hydrology, 10(3), 282-290
86. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute
87. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., & Williams, J. R. (2005a). Soil and Water Assessment Tool Input/Output File Documentation Version 2005, Grassland. Soil and water research laboratory Angriculture research services & Black land research Center Texas Agricultual Experiment station
88. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., & Williams, J. R. (2005b). Soil and water assessment tool input/output file documentation, version 2005. US Department of Agriculture-Agricultural Research Service, Grassland Soil and Water Research Laboratory, Temple, TX
89. Nicholls, R. J., & Cazenave, A. (2010). Sea-level rise and its impact on coastal zones. Science, 328(5985), 1517-1520
90. Parajuli, P. B., Nelson, N. O., Frees, L. D., & Mankin, K. R. (2009). Comparison of AnnAGNPS and SWAT model simulation results in USDA‐CEAP agricultural watersheds in south‐central Kansas. Hydrological Processes: An International Journal, 23(5), 748-763
91. Park, S., & Bailey, R. (2019). Retrieved from https://swat.tamu.edu/media/115048/swat-modflow-tutorial.pdf
92. Park, S., Nielsen, A., Bailey, R. T., Trolle, D., & Bieger, K. (2018). A QGIS-based graphical user interface for application and evaluation of SWAT-MODFLOW models. Environmental modelling & software, 111, 493-497
93. Press, W. H. (1978). Flicker noises in astronomy and elsewhere. Comments on Astrophysics, 7, 103-119
94. Priestley, C., & Taylor, R. (1972). On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Monthly Weather Review, 100(2), 81-92. doi: 10.1175/1520-0493(1972)100<0081:otaosh>2.3.co;2
95. Rahman, K., Maringanti, C., Beniston, M., Widmer, F., Abbaspour, K., & Lehmann, A. (2013). Streamflow Modeling in a Highly Managed Mountainous Glacier Watershed Using SWAT: The Upper Rhone River Watershed Case in Switzerland. Water Resources Management, 27(2), 323-339. doi: 10.1007/s11269-012-0188-9
96. Refsgaard, J. C. (1997). Parameterisation, calibration and validation of distributed hydrological models. Journal of hydrology, 198(1-4), 69-97
97. Reichert, J., Backes, A. R., Schubert, P., & Wilke, T. (2017). The power of 3D fractal dimensions for comparative shape and structural complexity analyses of irregularly shaped organisms. Methods in Ecology and Evolution, 8(12), 1650-1658
98. Relano, A., Gómez, J. M. G., Molina, R. A., Retamosa, J., & Faleiro, E. (2002). Quantum chaos and 1/f noise. Physical review letters, 89(24), 244102
99. Richardson, C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation. Water resources research, 17(1), 182-190
100. Santhi, C., Arnold, J., Williams, J., Dugas, W., Srinivasan, R., & Hauck, L. (2001). VALIDATION OF THE SWAT MODEL ON A LARGE RWER BASIN WITH POINT AND NONPOINT SOURCES. Journal Of The American Water Resources Association, 37(5), 1169-1188. doi: 10.1111/j.1752-1688.2001.tb03630.x
101. Schubert, S. (1994). A weather generator based on the European ‘Grosswetterlagen’. Climate Research, 4, 191-202
102. Selker, J. S., & Haith, D. A. (1990). Development and Testing of Single‐Parameter Precipitation Distributions. Water resources research, 26(11), 2733-2740
103. Sexton, A. M., A. M. Sadeghi, X. Zhang, R. Srinivasan, & A. Shirmohammadi. (2010). Using NEXRAD and Rain Gauge Precipitation Data for Hydrologic Calibration of SWAT in a Northeastern Watershed. Transactions Of The ASABE, 53(5), 1501-1510. doi: 10.13031/2013.34900
104. Sharma, A., Wasko, C., & Lettenmaier, D. (2018). If Precipitation Extremes Are Increasing, Why Aren't Floods?. Water Resources Research, 54(11), 8545-8551. doi: 10.1029/2018wr023749
105. Singh, J., Knapp, H. V., & Demissie, M. (2004). Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. ISWS CR 2004-08. Champaign, Ill.: Illinois State Water Survey
106. Singh, S., Srivastava, P., Gupta, M., Thakur, J., & Mukherjee, S. (2014). Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine. Environmental Earth Sciences, 71(5), 2245-2255. doi: 10.1007/s12665-013-2628-0
107. Sloan, P., & Moore, I. (1983). Modeling subsurface stormflow on steeply sloping forested watersheds. Water Resources Research, 20(12), 1815-1822. doi: 10.1029/wr020i012p01815
108. Soil Survey and Remediation Lab | Nation Taiwan University, Department of Agriculttural Chemistry. (2019). Retrieved from http://lab.ac.ntu.edu.tw/soilsc/en/index_en.html
109. Solomon, S. (2007b). Climate change 2007. Cambridge: Cambridge University Press
110. Solomon, S., Qin, D., Manning, M., Averyt, K., & Marquis, M. (Eds.). (2007). Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC (Vol. 4). Cambridge university press
111. Sophocleous, M. (2005). Groundwater recharge and sustainability in the High Plains aquifer in Kansas, USA. Hydrogeology Journal, 13(2), 351-365. doi: 10.1007/s10040-004-0385-6
112. Srivastava, P., Han, D., Rico-Ramirez, M., & Islam, T. (2014). Sensitivity and uncertainty analysis of mesoscale model downscaled hydro-meteorological variables for discharge prediction. Hydrological Processes, 28(15), 4419-4432. doi: 10.1002/hyp.9946
113. Steffen, W., Richardson, K., Rockstrom, J., Cornell, S., Fetzer, I., Bennett, E., Biggs, R., Carpenter, S., Vries, W., de Wit, C. Folke, C., Heinke, J., Mace, G., Persson, L., Ramanathan, V., Reyers, B., & Sörlin, S. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347(6223), 1259855-1259855. doi: 10.1126/science.1259855
114. Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., & Midgley, P. M. (2013). Climate change 2013: The physical science basis
115. Tickell, C. (1993). The Human Species: A Suicidal Success?. The Geographical Journal, 159(2), 219. doi: 10.2307/3451413
116. Tseng, H., Gan, T., & Yu, P. (2015). Composite Drought Indices of Monotonic Behaviour for Assessing Potential Impact of Climate Change to a Water Resources System. Water Resources Management, 29(7), 2341-2359. doi: 10.1007/s11269-015-0945-7
117. Tsuang, B. J., Wu, M. C., Liu, C. C., & Chen, H. H. (1998). Climatic change and prediction in Taiwan. Journal of Nature, 58, 106-112
118. Tung, C. P., & Haith, D. A. (1995). Global-warming effects on New York streamflows. Journal of Water Resources Planning and Management, 121(2), 216-225
119. Uniyal, B., Jha, M., & Verma, A. (2015). Assessing Climate Change Impact on Water Balance Components of a River Basin Using SWAT Model. Water Resources Management, 29(13), 4767-4785. doi: 10.1007/s11269-015-1089-5
120. USDA, S. (1972). National engineering handbook, section 4: Hydrology. Washington, DC
121. Van Griensven, A., & Bauwens, W. (2003). Multiobjective autocalibration for semidistributed water quality models. Water Resources Research, 39(12)
122. Van Liew, M. W., Arnold, J. G., & Garbrecht, J. D. (2003). Hydrologic simulation on agricultural watersheds: Choosing between two models. Transactions of the ASAE, 46(6), 1539
123. Vanrolleghem, P., Insel, G., Petersen, B., Sin, G., De Pauw, D., & Nopens, I. et al. (2003). A comprehensive model calibration procedure for activated sludge models. Proceedings Of The Water Environment Federation, 210-237. doi: 10.2175/193864703784639615
124. Vaze, J., Jordan, P., Beecham, R., Frost, A., & Summerell, G. (2011). Guidelines for rainfall-runoff modelling: towards best practice model application. Bruce, Australia
125. Vazquez-Amábile, G. G., & Engel, B. A. (2005). Use of SWAT to compute groundwater table depth and streamflow in the Muscatatuck River watershed. Transactions of the ASAE, 48(3), 991-1003
126. Viviroli, D., Dürr, H., Messerli, B., Meybeck, M., & Weingartner, R. (2007). Mountains of the world, water towers for humanity: Typology, mapping, and global significance. Water Resources Research, 43(7). doi: 10.1029/2006wr005653
127. Voss, R. F. (1985). Random fractal forgeries Fundamental Algorithms for Computer Graphics ed RA Earnshaw
128. Wagener, T., & Wheater, H. (2006). Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty. Journal Of Hydrology, 320(1-2), 132-154. doi: 10.1016/j.jhydrol.2005.07.015
129. Wang, J., Sun, Y., Lu, M., Wang, J., & Yan, X. (2019). Study on the Thermal-hydraulic Performance of Sinusoidal Channeled Printed Circuit Heat Exchanger. Energy Procedia, 158, 5679-5684. doi: 10.1016/j.egypro.2019.01.568
130. Williams, J. R. (1990). The erosion-productivity impact calculator (EPIC) model: a case history. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 329(1255), 421-428
131. Williams, J. R., & Berndt, H. D. (1977). Sediment yield prediction based on watershed hydrology. Transactions of the ASAE, 20(6), 1100-1104
132. Woolhiser, D. A., & Pegram, G. G. S. (1979). Maximum likelihood estimation of Fourier coefficients to describe seasonal variations of parameters in stochastic daily precipitation models. Journal of Applied Meteorology, 18(1), 34-42
133. Woolhiser, D. A., & Roldan, J. (1982). Stochastic daily precipitation models: 2. A comparison of distributions of amounts. Water resources research, 18(5), 1461-1468
134. Woolhiser, D. A., & Roldán, J. (1986). Seasonal and regional variability of parameters for stochastic daily precipitation models: South Dakota, USA. Water resources research, 22(6), 965-978
135. Yatheendradas, S., Wagener, T., Gupta, H., Unkrich, C., Goodrich, D., Schaffner, M., & Stewart, A. (2008). Understanding uncertainty in distributed flash flood forecasting for semiarid regions. Water Resources Research, 44(5). doi: 10.1029/2007wr005940
136. Ye, W., Bates, B., Viney, N., Sivapalan, M., & Jakeman, A. (1997). Performance of conceptual rainfall-runoff models in low-yielding ephemeral catchments. Water Resources Research, 33(1), 153-166. doi: 10.1029/96wr02840
137. Yeh, C. F., Wang, J., Yeh, H. F., & Lee, C. H. (2015). Spatial and temporal streamflow trends in northern Taiwan. Water, 7(2), 634-651
138. Yen, H., Ahmadi, M., White, M., Wang, X., & Arnold, J. (2014a). C-SWAT: The Soil and Water Assessment Tool with consolidated input files in alleviating computational burden of recursive simulations. Computers & Geosciences, 72, 221-232. doi: 10.1016/j.cageo.2014.07.017
139. Yen, H., Jeong, J., & Smith, D. R. (2016). Evaluation of dynamically dimensioned search algorithm for optimizing SWAT by altering sampling distributions and searching range. JAWRA Journal of the American Water Resources Association, 52(2), 443-455
140. Yu, P. S., Yang, T. C., & Wu, C. K. (2002). Impact of climate change on water resources in southern Taiwan. Journal of Hydrology, 260(1-4), 161-175
141. Zanwar, D. R., Deshpande, V. S., Modak, J. P., Gupta, M. M., & Agrawal, K. N. (2015). Determination of mass, damping coefficient, and stiffness of production system using convolution integral. International Journal of Production Research, 53(14), 4351-4362
142. Zhang, J., Gao, G., Fu, B., & Zhang, L. (2018). Explanation of climate and human impacts on sediment discharge change in Darwinian hydrology: Derivation of a differential equation. Journal of hydrology, 559, 827-834
143. Zheng, Y., & Keller, A. A. (2007). Uncertainty assessment in watershed‐scale water quality modeling and management: 1. Framework and application of generalized likelihood uncertainty estimation (GLUE) approach. Water resources research, 43(8)
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2022-07-01起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2022-07-01起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw