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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許少瑜 | zh_TW |
| dc.contributor.advisor | Shao-Yiu Hsu | en |
| dc.contributor.author | 劉哲佑 | zh_TW |
| dc.contributor.author | Che-You Liu | en |
| dc.date.accessioned | 2023-08-30T16:04:55Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-30 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-14 | - |
| dc.identifier.citation | Akaike, H. 1971. Autoregressive model fitting for control. Annals of the Institute of Statistical Mathematics 23(1), 163-180.
Andradóttir, S. (2006) Simulation, pp. 617-631. Asghari, K., Masdari, M., Gharehchopogh, F.S. and Saneifard, R. 2021. Multi‐swarm and chaotic whale‐particle swarm optimization algorithm with a selection method based on roulette wheel. Expert Systems 38(8). Bearden, W.O., Sharma, S. and Teel, J.E. 1982. Sample size effects on chi square and other statistics used in evaluating causal models. Journal of marketing research 19(4), 425-430. Berger, M., Eisner, S., van der Ent, R., Florke, M., Link, A., Poligkeit, J., Bach, V. and Finkbeiner, M. 2018a. Enhancing the Water Accounting and Vulnerability Evaluation Model: WAVE. Environ Sci Technol 52(18), 10757-10766. Berger, M., van der Ent, R., Eisner, S., Bach, V. and Finkbeiner, M. 2014. Water accounting and vulnerability evaluation (WAVE): considering atmospheric evaporation recycling and the risk of freshwater depletion in water footprinting. Environ Sci Technol 48(8), 4521-4528. Bergstra, J. and Bengio, Y. 2012. Random Search for Hyper-Parameter Optimization. Journal of Machine Learning Research 13, 281-305. Borah, D.K. and Ashraf, M.S. 1990. Modeling Storm Runoff and Sediment with Seasonal Variation. American Society of Agricultural Engineer. Bosznay, M. 1989. Generalization of SCS Curve Number Method. Journal of Irrigation and Drainage Engineering 115(1), 139-144. Castro, L.M., Gironás, J. and Fernández, B. 2014. Spatial estimation of daily precipitation in regions with complex relief and scarce data using terrain orientation. Journal of Hydrology 517, 481-492. Cavanaugh, N.R., Gershunov, A., Panorska, A.K. and Kozubowski, T.J. 2015. The probability distribution of intense daily precipitation. Geophysical Research Letters 42(5), 1560-1567. Chang, C.L. and Liao, C.S. 2012. Assessing the risk posed by high-turbidity water to water supplies. Environ Monit Assess 184(5), 3127-3132. Charles Batchelor, J.H., Jean-Marc Faurès and Livia Peiser (2017) Water_accounting_sourcebook, FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS. Chen, C.-W., Tung, Y.-S., Liou, J.-J., Li, H.-C., Cheng, C.-T., Chen, Y.-M. and Oguchi, T. 2019. Assessing landslide characteristics in a changing climate in northern Taiwan. Catena 175, 263-277. Chen, J. and Brissette, F.P. 2014. Stochastic generation of daily precipitation amounts: review and evaluation of different models. Climate Research 59(3), 189-206. Cheng, K.-S., Chen, C.-T., Hsu, H.-H., Yeh, K.-C. and Lin, L.-Y. 2016. Preface to the Special Issue on “Climate Changes and Their Impacts in Taiwan”. Terrestrial, Atmospheric and Oceanic Sciences 27(5), I-II. D’Asaro, F., Grillone, G. and Hawkins, R.H. 2018. Curve Numbers Seasonal Variation in Mid-Mediterranean Area. Journal of Irrigation and Drainage Engineering 144(9). Deng, X., Liu, Q., Deng, Y. and Mahadevan, S. 2016. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences 340-341, 250-261. DiCiccio, T.J. and Efron, B. 1996. Bootstrap confidence intervals. Statistical Science 11(3). Efron, B. 1987. Better Bootstrap Confidence Intervals. Journal of the American Statistical Association 82(397), 171-185. Escriva-Bou, A., McCann, H., Hanak, E., Lund, J., Gray, B., Blanco, E., Jezdimirovic, J., Magnuson-Skeels, B. and Tweet, A. 2020. Water Accounting in Western US, Australia, and Spain: Comparative Analysis. Journal of Water Resources Planning and Management 146(3). Eshel, G. 2003. The yule walker equations for the AR coefficients. Internet resource 2, 68-73. Gajbhiye, S., Mishra, S.K. and Pandey, A. 2013. Effects of Seasonal/Monthly Variation on Runoff Curve Number for Selected Wtersheds of Narmada Basin. International Journal of Environmental Sciences. Garen, D.C. 1993. Revised Surface‐Water Supply Index for Western United States. Journal of Water Resources Planning and Management 119(4), 437-454. Gudmundsson, L., Bremnes, J., Haugen, J. and Skaugen, T.E. 2012. Downscaling RCM precipitation to the station scale using quantile mapping–a comparison of methods. Hydrology & Earth System Sciences Discussions 9(5), 6185-6201. Guo, Y., Huang, S., Huang, Q., Wang, H., Fang, W., Yang, Y. and Wang, L. 2019. Assessing socioeconomic drought based on an improved Multivariate Standardized Reliability and Resilience Index. Journal of Hydrology 568, 904-918. Haith, D.A. and Shoemaker, L.L. 1987. Generalized Watershed Loading Functions for Stream Flow Nutrients1. JAWRA Journal of the American Water Resources Association 23(3), 471-478. Hamon, W.R. 1963. Estimating Potential Evapotranspiration. Transactions of the American Society of Civil Engineers 128(1), 324-338. Haro-Monteagudo, D., Palazón, L. and Beguería, S. 2020. Long-term sustainability of large water resource systems under climate change: A cascade modeling approach. Journal of Hydrology 582. He, C.-Y., Tung, C.-P. and Lin, Y.-J. 2021. Applying the DRCA Risk Template on the Flood-Prone Disaster Prevention Community Due to Climate Change. Sustainability 13(2). Heim, R.R. 2002. A Review of Twentieth-Century Drought Indices Used in the United States. Bulletin of the American Meteorological Society 83(8), 1149-1166. Howe, C.W., Schurmeier, D.R. and Shaw, W.D. 1986. Innovative Approaches to Water Allocation: The Potential for Water Markets. Water Resources Research 22(4), 439-445. Huang, S., Huang, Q., Leng, G. and Liu, S. 2016. A nonparametric multivariate standardized drought index for characterizing socioeconomic drought: A case study in the Heihe River Basin. Journal of Hydrology 542, 875-883. Hughes, D.A. and Mallory, S.J.L. 2008. Including environmental flow requirements as part of real‐time water resource management. River Research and Applications 24(6), 852-861. Hunink, J., Simons, G., Suárez-Almiñana, S., Solera, A., Andreu, J., Giuliani, M., Zamberletti, P., Grillakis, M., Koutroulis, A., Tsanis, I., Schasfoort, F., Contreras, S., Ercin, E. and Bastiaanssen, W. 2019. A Simplified Water Accounting Procedure to Assess Climate Change Impact on Water Resources for Agriculture across Different European River Basins. Water 11(10). Ilyas, A., Manzoor, T. and Muhammad, A. 2021. A Dynamic Socio‐Hydrological Model of the Irrigation Efficiency Paradox. Water Resources Research 57(12). Jacobi, J., Perrone, D., Duncan, L.L. and Hornberger, G. 2013. A tool for calculating the Palmer drought indices. Water Resources Research 49(9), 6086-6089. John A. Dracup, K.S.L., and Edwin G. Paulson, JR. 1980. On the definition of droughts. Water Resources Research VOL. 16. Karimi, P., Bastiaanssen, W.G.M. and Molden, D. 2013. Water Accounting Plus (WA+) – a water accounting procedure for complex river basins based on satellite measurements. Hydrology and Earth System Sciences 17(7), 2459-2472. Keys, P.W., Porkka, M., Wang-Erlandsson, L., Fetzer, I., Gleeson, T. and Gordon, L.J. 2019. Invisible water security: Moisture recycling and water resilience. Water Secur 8, 100046. Krzysztofowicz, R. 1983. Why should a forecaster and a decision maker use Bayes Theorem. Water Resources Research 19(2), 327-336. Lee, J.-W., Hong, E.-M., Kim, J.-U., Jang, W.-J., Jung, C.-G. and Kim, S.-J. 2022. Evaluation of agricultural drought in South Korea using socio-economic drought information. International Journal of Disaster Risk Reduction 74. Li, C., Singh, V.P. and Mishra, A.K. 2012. Simulation of the entire range of daily precipitation using a hybrid probability distribution. Water Resources Research 48(3). Lin, C.Y., Chua, Y.J., Sheng, Y.F., Hsu, H.H., Cheng, C.T. and Lin, Y.Y. 2014. Altitudinal and latitudinal dependence of future warming in Taiwan simulated by WRF nested with ECHAM5/MPIOM. International Journal of Climatology 35(8), 1800-1809. Lipowski, A. and Lipowska, D. 2012. Roulette-wheel selection via stochastic acceptance. Physica A: Statistical Mechanics and its Applications 391(6), 2193-2196. Liu, S., Shi, H. and Sivakumar, B. 2020. Socioeconomic Drought Under Growing Population and Changing Climate: A New Index Considering the Resilience of a Regional Water Resources System. Journal of Geophysical Research: Atmospheres 125(15). Liu, X., Zhu, X., Pan, Y., Li, S., Liu, Y. and Ma, Y. 2016. Agricultural drought monitoring: Progress, challenges, and prospects. Journal of Geographical Sciences 26(6), 750-767. Liu, Y., Gupta, H., Springer, E. and Wagener, T. 2008. Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management. Environmental Modelling & Software 23(7), 846-858. Maliva, R. and Missimer, T. 2012. Aridity and Drought. Arid Lands Water Evaluation and Management, 21-39. Marengo, J.A. and Bernasconi, M. 2014. Regional differences in aridity/drought conditions over Northeast Brazil: present state and future projections. Climatic Change 129(1-2), 103-115. Massey, F.J. 1951. The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association 46(253), 68-78. McLennan, M., Group, S. and Group, Z.I. 2022 The Global Risks Report 2022, World Economic Forum, World Economic Forum. Mehran, A., Mazdiyasni, O. and AghaKouchak, A. 2015. A hybrid framework for assessing socioeconomic drought: Linking climate variability, local resilience, and demand. Journal of Geophysical Research: Atmospheres 120(15), 7520-7533. Mishra, A.K. and Singh, V.P. 2010. A review of drought concepts. Journal of Hydrology 391(1-2), 202-216. Molden, D. 2019. Scarcity of water or scarcity of management? International Journal of Water Resources Development 36(2-3), 258-268. Mooney, C.Z. (1997) Monte carlo simulation, Sage. Naresh Kumar, M., Murthy, C.S., Sesha Sai, M.V.R. and Roy, P.S. 2009. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Meteorological Applications 16(3), 381-389. Obasi, G.O.P. 1994. WMO's Role in the International Decade for Natural Disaster Reduction. American Meteorological Society. Pathak, A.A., Channaveerappa and Dodamani, B.M. 2016. Comparison of two hydrological drought indices. Perspectives in Science 8, 626-628. Pedro-Monzonís, M., Solera, A., Ferrer, J., Estrela, T. and Paredes-Arquiola, J. 2015. A review of water scarcity and drought indexes in water resources planning and management. Journal of Hydrology 527, 482-493. Peng, T.-R., Liu, K.-K., Wang, C.-H. and Chuang, K.-H. 2011. A water isotope approach to assessing moisture recycling in the island-based precipitation of Taiwan: A case study in the western Pacific. Water Resources Research 47(8). Perret, S.R., Babel, M.S. and Mullick, M.R.A. 2013. Marginal benefit based optimal water allocation: case of Teesta River, Bangladesh. Water Policy 15(S1), 126-146. Rajsekhar, D., Singh, V.P. and Mishra, A.K. 2015. Multivariate drought index: An information theory based approach for integrated drought assessment. Journal of Hydrology 526, 164-182. Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N. and Rafaj, P. 2011. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change 109(1-2), 33-57. Shi, H., Chen, J., Wang, K. and Niu, J. 2018. A new method and a new index for identifying socioeconomic drought events under climate change: A case study of the East River basin in China. Sci Total Environ 616-617, 363-375. Shi, W. and Wang, N. 2020. An Improved SCS-CN Method Incorporating Slope, Soil Moisture, and Storm Duration Factors for Runoff Prediction. Water 12(5). SHIAU, J.T. 2003. Water Release Policy Effects on the Shortage Characteristics for the Shihmen Reservoir System during Droughts. Shibata, R. 1976. Selection of the order of an autoregressive model by Akaike's information criterion. Biometrika 63(1), 117-126. Shiu, C.-J., Liu, S.C. and Chen, J.-P. 2009. Diurnally Asymmetric Trends of Temperature, Humidity, and Precipitation in Taiwan. Journal of Climate 22(21), 5635-5649. Sivapalan, M. and Blöschl, G. 1998. Transformation of point rainfall to areal rainfall: Intensity-duration-frequency curves. Journal of Hydrology 204(1-4), 150-167. Sohoulande Djebou, D.C. 2017. Bridging drought and climate aridity. Journal of Arid Environments 144, 170-180. Spinoni, J., Vogt, J., Naumann, G., Carrao, H. and Barbosa, P. 2015. Towards identifying areas at climatological risk of desertification using the Köppen-Geiger classification and FAO aridity index. International Journal of Climatology 35(9), 2210-2222. Stagge, J.H., Tallaksen, L.M., Gudmundsson, L., Van Loon, A.F. and Stahl, K. 2015. Candidate Distributions for Climatological Drought Indices (SPI and SPEI). International Journal of Climatology 35(13), 4027-4040. TaiCCAT_VA 2015 TaiCATS 臺灣氣候調適科技服務 TaiCCAT_VA, 國立台灣大學 生物環境系統工程學系 永續發展研究室. Townsend, J.T. 1971. Theoretical analysis of an alphabetic confusion matrix. Perception & Psychophysics 9(1), 40-50. Tsakiris, G., Loukas, A., Pangalou, D., Vangelis, H., Tigkas, D., Rossi, G. and Cancelliere, A. 2007. Drought characterization. Drought management guidelines technical annex 58, 85-102. Tu, X., Wu, H., Singh, V.P., Chen, X., Lin, K. and Xie, Y. 2018. Multivariate design of socioeconomic drought and impact of water reservoirs. Journal of Hydrology 566, 192-204. V. García, R.A.M., J. S. Sánchez (2009) Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions. van der Ent, R.J. 2014. A new view on the hydroloigical cycle over continents. van der Ent, R.J. and Savenije, H.H.G. 2010. Length and time scales of atmospheric moisture recycling. Atmospheric Chemistry and Physics. Veneziano, D. and Furcolo, P. 2002. Multifractality of rainfall and scaling of intensity-duration-frequency curves. Water Resources Research 38(12), 42-41-42-12. Verma, R.K., Verma, S., Mishra, S.K. and Pandey, A. 2021. SCS-CN-Based Improved Models for Direct Surface Runoff Estimation from Large Rainfall Events. Water Resources Management 35(7), 2149-2175. Vicente-Serrano, S.M., López-Moreno, J.I., Gimeno, L., Nieto, R., Morán-Tejeda, E., Lorenzo-Lacruz, J., Beguería, S. and Azorin-Molina, C. 2011. A multiscalar global evaluation of the impact of ENSO on droughts. Journal of Geophysical Research 116(D20). Vicente-Serrano, S.M., Miralles, D.G., Domínguez-Castro, F., Azorin-Molina, C., El Kenawy, A., McVicar, T.R., Tomás-Burguera, M., Beguería, S., Maneta, M. and Peña-Gallardo, M. 2018. Global Assessment of the Standardized Evapotranspiration Deficit Index (SEDI) for Drought Analysis and Monitoring. Journal of Climate 31(14), 5371-5393. Wambua, R.M. 2019. Hydrological Drought Forecasting Using Modified Surface Water Supply Index (SWSI) and Streamflow Drought Index (SDI) in Conjunction with Artificial Neural Networks (ANNs). International Journal of Service Science, Management, Engineering, and Technology 10(4), 39-57. Wang, T., Tu, X., Singh, V.P., Chen, X., Lin, K., Lai, R. and Zhou, Z. 2022. Socioeconomic drought analysis by standardized water supply and demand index under changing environment. Journal of Cleaner Production 347. Wang, Y., Liu, S., Huang, S., Zhou, Z. and Shi, H. 2023. Bivariate assessment of socioeconomic drought events based on an improved socioeconomic drought index. Journal of Hydrology 623. Wilhite, D.A. and Glantz, M.H. 2009. Understanding: the Drought Phenomenon: The Role of Definitions. Water International 10(3), 111-120. Wittenberg, A.T. 2009. Are historical records sufficient to constrain ENSO simulations? Geophysical Research Letters 36(12). Wu, B., Ma, Z. and Yan, N. 2020. Agricultural drought mitigating indices derived from the changes in drought characteristics. Remote Sensing of Environment 244. Wu, H., Hayes, M.J., Wilhite, D.A. and Svoboda, M.D. 2005a. The effect of the length of record on the standardized precipitation index calculation. International Journal of Climatology 25(4), 505-520. Wu, J., Miao, C., Tang, X., Duan, Q. and He, X. 2018. A nonparametric standardized runoff index for characterizing hydrological drought on the Loess Plateau, China. Global and Planetary Change 161, 53-65. Wu, Q., Bell, D. and McGinnity, M. 2005b. Multiknowledge for decision making. Knowledge and Information Systems 7(2), 246-266. Yu, P.-S., Yang, T.-C. and Kuo, C.-C. 2006. Evaluating Long-Term Trends in Annual and Seasonal Precipitation in Taiwan. Water Resources Management 20(6), 1007-1023. Zhou, X., Zhang, Y., Sheng, Z., Manevski, K., Andersen, M.N., Han, S., Li, H. and Yang, Y. 2021. Did water-saving irrigation protect water resources over the past 40 years? A global analysis based on water accounting framework. Agricultural Water Management 249. 林皓華, 黃群展 and 許少瑜 2021. 應用水會計架構初探農業水資源狀態-以桃園地區為例. 中國水利工程學刊 第33卷第3期. 許泰文, 陳. 2009. 氣候變遷對乾旱期聚衝擊與調適研究. 行政院農業委員會(財團法人成大水利海洋研究發展文教基金會. 童慶斌, 劉子明, 林嘉佑, 曹榮軒 and 李明旭 2015. 氣候變遷水資源風險評估與調適決策之探討. 土木水利 42(4), 30-45. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89152 | - |
| dc.description.abstract | 通過考慮供水系統的資源稀缺抗性和季節性脆弱性閾值，本研究引入水會計 (Water accounting) 和 開發了 改良版水會計系統脆弱性評估 （modified WAVE+）模型。該模型基於水文-社會經濟活動互動及對偶雙系統的概念 (Hydrological-social interaction and dual system)，從供水系統的角度有效評估和量化水文乾旱的多重原因。本研究使用石門水庫供水系統的歷史數據驗證了modified WAVE+ 模型在乾旱檢測和診斷方面的性能。這種新方法能夠評估乾旱的嚴重程度、強度和持續時間，表現優於同時考慮了入流量、儲水量、消耗量的兩種乾旱指標：社會經濟乾旱指數 (SEDI) 和改進的多元標準化恢復力和可靠性指數 (IMSRRI) 等現有方法。本研究基於上述討論新開發的模塊化條件數據庫乾旱指數（MCDBDI）框架集成了水文隨機過程和modified WAVE+模型，可以計算乾旱發生的條件機率。條件概率可以支持用水量和水庫調度的實時政策調整。此外，本研究開發了一個對偶雙系統序列模擬過程來評估氣候變化和用水量變化情景下的乾旱事件特徵演變。結果表明，在預測的氣候變化情境（RCP 2.6 和 RCP 8.5）下，台灣北部乾旱事件的發生次數有明顯減少，而乾旱嚴重程度及乾旱持續時間在平均值與變異數相差不大的情況下極端嚴重以及極端長的持續時間有下降的趨勢。 | zh_TW |
| dc.description.abstract | By considering the water stress robustness and the seasonal vulnerability threshold of the water supply system, I developed a modified water accounting and vulnerability evaluation plus (WAVE+) model. Based on the dual system concept, the model effectively assesses and quantifies the multi-causalities of drought from the water supply system point of view. I validated the performance of the modified WAVE+ model in drought detection and diagnosis using the historical data from the Shihmen Reservoir water supply system. This new approach enables the evaluation of drought severity, intensity, and duration, outperforming existing methods such as the SocioEconomic Drought Index (SEDI) and Improved Multivariate Standardized Resilience and Reliability Index (IMSRRI). Additionally, the newly developed Modularized Conditional Data Base Drought Index (MCDBDI) framework, which integrates hydrologic stochastic processes and the modified WAVE+ model, enables the calculation of conditional probability for drought occurrences. The conditional probability could support real-time policy adjustments in water consumption and reservoir operations. Furthermore, I developed a sequential dual system simulation process to evaluate the drought characteristic evolution under climate change and water consumption change scenarios. The results indicate a reduction in occurrence numbers and the distribution tail shrinkage on event severity and duration in Northern Taiwan under the projected climate change scenarios (RCP 2.6 and RCP 8.5). | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-30T16:04:54Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-30T16:04:55Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
謝誌 i 中文摘要 ii ABSTRACT iii CONTENTS iv FIGURE LIST vi TABLE LIST ix Chapter 1 Introduction 1 Chapter 2 Background Information. 4 2.1 Drought review 4 2.2 Water Accounting and Vulnerability Evaluation Plus 7 2.3 Drought indicators considering demand and supply 9 Chapter 3 Methodology and Material 14 3.1 Dual system concept and DRCA Template 15 3.2 Local BIER estimation 19 3.3 Modified WAVE+ model 21 3.4 Hydrological drought event detection and diagnosis 24 3.5 Weather Generator 29 3.6 The hydrological deterministic model: Pseudo GWLF model 37 3.7 Water consumption scenario construction 41 3.8 MCDBDI framework with Monte-Carlo simulation. 42 3.9 The process of the Dual System sequential simulation. 44 3.10 The Intensity-Duration-Frequency curve analysis of the hydrological drought 46 3.11 Study case: Northern Taiwan, Shihmen reservoir water supply system 48 3.12 Taiwan climate change scenario and Data 52 Chapter 4 Result and Discussion 55 4.1 Dual system and modified WAVE+ model application 55 4.2 Stochastic and Deterministic model performance evaluations 77 4.3 MCDBDI performance and implementation 92 4.4 Hydrological drought characteristics change, under different climate change and different water consumption scenarios 104 Chapter 5 Conclusion 115 Chapter 6 Suggestions and Future works 117 REFERENCE 120 Appendix 126 | - |
| dc.language.iso | en | - |
| dc.subject | 隨機過程 | zh_TW |
| dc.subject | 水文模擬 | zh_TW |
| dc.subject | 異常事件偵測及診斷 | zh_TW |
| dc.subject | 氣候變遷 | zh_TW |
| dc.subject | 水資源管理 | zh_TW |
| dc.subject | 時間序列分析 | zh_TW |
| dc.subject | Time series analysis | en |
| dc.subject | Climate change | en |
| dc.subject | Water resource management | en |
| dc.subject | Abnormal event detection and diagnosis | en |
| dc.subject | Stochastic process | en |
| dc.subject | Hydrological simulation | en |
| dc.title | 氣候變遷下水文乾旱的偵測、診斷與預測—水會計觀點 | zh_TW |
| dc.title | Hydrological Dought Detection, Diagnosis, and Forecasting, under Climate Change from the Water Accounting Point of View | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 鄭克聲;廖國偉;陳憲宗 | zh_TW |
| dc.contributor.oralexamcommittee | Ke-Sheng Cheng;Kuo-Wei Liao;Shien-Tsung Chen | en |
| dc.subject.keyword | 異常事件偵測及診斷,氣候變遷,水資源管理,時間序列分析,水文模擬,隨機過程, | zh_TW |
| dc.subject.keyword | Abnormal event detection and diagnosis,Climate change,Water resource management,Time series analysis,Hydrological simulation,Stochastic process, | en |
| dc.relation.page | 139 | - |
| dc.identifier.doi | 10.6342/NTU202301576 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-07-17 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物環境系統工程學系 | - |
| dc.date.embargo-lift | 2028-07-13 | - |
| 顯示於系所單位： | 生物環境系統工程學系 | |
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