請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70929
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 余化龍 | |
dc.contributor.author | Chia-Ying Lee | en |
dc.contributor.author | 李佳穎 | zh_TW |
dc.date.accessioned | 2021-06-17T04:44:21Z | - |
dc.date.available | 2023-08-06 | |
dc.date.copyright | 2018-08-06 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-03 | |
dc.identifier.citation | Abbaspour, K. C., Johnson, C., & Van Genuchten, M. T. (2004). Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal, 3(4), 1340-1352.
Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., . . . Srinivasan, R. (2007). Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333(2-4), 413-430. Amorocho, J., & Espildora, B. (1973). Entropy in the assessment of uncertainty in hydrologic systems and models. Water Resources Research, 9(6), 1511-1522. Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., . . . Van Liew, M. W. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508. Bogaert, P., & Fasbender, D. (2008). Nonlinear spatial prediction with non-Gaussian data: a maximum entropy viewpoint. In geoENV VI–Geostatistics for Environmental Applications (pp. 445-455): Springer. Borah, D. K., & Bera, M. (2004). Watershed-scale hydrologic and nonpoint-source pollution models: Review of applications. Transactions of the ASAE, 47(3), 789. Boyd, S., & Vandenberghe, L. (2004). Convex optimization: Cambridge university press. Cao, S., & Knight, D. W. (1997). Entropy-based design approach of threshold alluvial channels. Journal of Hydraulic Research, 35(4), 505-524. Chapman, T. G. (1986). Entropy as a measure of hydrologic data uncertainty and model performance. Journal of Hydrology, 85(1-2), 111-126. Claps, P., Fiorentino, M., & Oliveto, G. (1996). Informational entropy of fractal river networks. Journal of Hydrology, 187(1-2), 145-156. Dalezios, N. R., & Tyraskis, P. A. (1989). Maximum entropy spectra for regional precipitation analysis and forecasting. Journal of Hydrology, 109(1-2), 25-42. Deng, Z.-Q., & Singh, V. P. (1999). Mechanism and conditions for change in channel pattern. Journal of Hydraulic Research, 37(4), 465-478. Dingman, S. L., & Dingman, S. L. (1994). Physical hydrology (Vol. 575): Prentice Hall Upper Saddle River, NJ. Fiorentino, M., Claps, P., & Singh, V. P. (1993). An entropy‐based morphological analysis of river basin networks. Water Resources Research, 29(4), 1215-1224. Green, W. H., & Ampt, G. (1911). Studies on Soil Phyics. The Journal of Agricultural Science, 4(1), 1-24. Hargreaves, G. H. (1975). Moisture availability and crop production. Transactions of the ASAE, 18(5), 980-0984. Hargreaves, G. H., & Samani, Z. A. (1982). Estimating potential evapotranspiration. Journal of the Irrigation and Drainage Division, 108(3), 225-230. Hargreaves, G. H., & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied engineering in agriculture, 1(2), 96-99. Hargreaves, G. L., Hargreaves, G. H., & Riley, J. P. (1985). Agricultural benefits for Senegal River basin. Journal of irrigation and Drainage Engineering, 111(2), 113-124. Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical review, 106(4), 620. Jensen, M., Burman, R., & Allen, R. (1990). Evapotranspiration and irrigation water requirements. 1990. New York, NY, USA American Society of Civil Enginners. Google Scholar. Kannan, N., Jeong, J., & Srinivasan, R. (2010). Hydrologic modeling of a canal-irrigated agricultural watershed with irrigation best management practices: Case study. Journal of Hydrologic Engineering, 16(9), 746-757. Kassam, A., & Smith, M. (2001). FAO methodologies on crop water use and crop water productivity. Paper presented at the Expert meeting on crop water productivity, Rome. Li, M., Guo, P., & Singh, V. P. (2016). An efficient irrigation water allocation model under uncertainty. Agricultural Systems, 144, 46-57. Liu, B., Chen, X., Lian, Y., & Wu, L. (2013). Entropy-based assessment and zoning of rainfall distribution. Journal of Hydrology, 490, 32-40. Liu, J., Liu, T., Bao, A., De Maeyer, P., Feng, X., Miller, S. N., & Chen, X. (2016). Assessment of different modelling studies on the spatial hydrological processes in an arid alpine catchment. Water resources management, 30(5), 1757-1770. Martino, G. D., Fontana, N., Marini, G., & Singh, V. P. (2012). Variability and trend in seasonal precipitation in the continental United States. Journal of Hydrologic Engineering, 18(6), 630-640. Mishra, A. K., Özger, M., & Singh, V. P. (2009). An entropy-based investigation into the variability of precipitation. Journal of Hydrology, 370(1-4), 139-154. Mishra, A. K., Özger, M., & Singh, V. P. (2010). Association between uncertainties in meteorological variables and water-resources planning for the state of Texas. Journal of Hydrologic Engineering, 16(12), 984-999. 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, 23(5), 748-763. 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. Qi, Z., Kang, G., Chu, C., Qiu, Y., Xu, Z., & Wang, Y. (2017). Comparison of SWAT and GWLF Model Simulation Performance in Humid South and Semi-Arid North of China. Water, 9(8), 567. Rajsekhar, D., Mishra, A. K., & Singh, V. P. (2012). Regionalization of drought characteristics using an entropy approach. Journal of Hydrologic Engineering, 18(7), 870-887. Regulwar, D. G., & Gurav, J. B. (2011). Irrigation planning under uncertainty—a multi objective fuzzy linear programming approach. Water resources management, 25(5), 1387-1416. Ritchie, J. T. (1972). Model for predicting evaporation from a row crop with incomplete cover. Water Resources Research, 8(5), 1204-1213. Sakaguchi, A., Eguchi, S., & Kasuya, M. (2014). Examination of the water balance of irrigated paddy fields in SWAT 2009 using the curve number procedure and the pothole module. Soil science and plant nutrition, 60(4), 551-564. Sakaguchi, A., Eguchi, S., Kato, T., Kasuya, M., Ono, K., Miyata, A., & Tase, N. (2014). Development and evaluation of a paddy module for improving hydrological simulation in SWAT. Agricultural water management, 137, 116-122. Santhi, C., Arnold, J. G., Williams, J. R., Dugas, W. A., Srinivasan, R., & Hauck, L. M. (2001). validation of the swat model on a large RWER basin with point and nonpoint sources 1. JAWRA Journal of the American Water Resources Association, 37(5), 1169-1188. SCS. (1972). Hydrology In National Engineering Handbook. Shannon, C., & Weaver, W. (1949). The mathematical theory of communication university of illinois press urbana google scholar. Singh, N., & Singh, K. (2016). SIMULATION MODELLING OF CROP WATER DEMAND USING SWAT MODEL: A CASE STUDY OF BUTANA DISTRIBUTARY, HARYANA, INDIA. J. Indian Water Resour. Soc, 36(4). Singh, V. (1997). The use of entropy in hydrology and water resources. Hydrological Processes, 11(6), 587-626. Singh, V. (2010). Derivation of rating curves using entropy theory. Transactions of the ASABE, 53(6), 1811-1821. Singh, V. (2012). Derivation of furrow geometry using entropy theory. Transactions of the ASABE, 55(3), 987-993. Sonuga, J. (1972). Principle of maximum entropy in hydrologic frequency analysis. Journal of Hydrology, 17(3), 177-191. Tsuchiya, R., Kato, T., & Jeong, J. (2016). Development of SWAT-PADDY for Simulating Lowland Paddy Fields. Van Liew, M., Arnold, J., & Garbrecht, J. (2003). Hydrologic simulation on agricultural watersheds: Choosing between two models. Transactions of the ASAE, 46(6), 1539. Waldrip, S., Niven, R., Abel, M., & Schlegel, M. (2016). Maximum entropy analysis of hydraulic pipe flow networks. Journal of Hydraulic Engineering, 142(9), 04016028. Woznicki, S. A., Nejadhashemi, A. P., & Parsinejad, M. (2015). Climate change and irrigation demand: Uncertainty and adaptation. Journal of Hydrology: Regional Studies, 3, 247-264. Wu, Y., & Chen, J. (2013). Estimating irrigation water demand using an improved method and optimizing reservoir operation for water supply and hydropower generation: a case study of the Xinfengjiang reservoir in southern China. Agricultural water management, 116, 110-121. Yesuf, H. M., Melesse, A. M., Zeleke, G., & Alamirew, T. (2016). Streamflow prediction uncertainty analysis and verification of SWAT model in a tropical watershed. Environmental Earth Sciences, 75(9), 806. Zhang, Q., Maeda, S., & Kawachi, T. (2007). Stochastic multiobjective optimization model for allocating irrigation water to paddy fields. Paddy and Water Environment, 5(2), 93-99. Zheng, J., Li, G.-y., Han, Z.-z., & Meng, G.-x. (2010). Hydrological cycle simulation of an irrigation district based on a SWAT model. Mathematical and Computer Modelling, 51(11-12), 1312-1318. 水利署. (1998). 台灣地區水資源開發綱領計畫. 朱榮彬. (1988). 農田高度利用與有效排水系統設置之研究. 台灣水利, 36(1), 23-48. 行政院農業委員會. (2015). 農業灌溉白皮書. 吳孟庭. (2015). 應用貝氏最大熵法於臺北盆地水文地質推估. 臺灣大學生物環境系統工程學研究所學位論文, 1-79. 胡江堂. 最大熵模型讀書筆記: 北京大學軟件與微電子學院. 陳述, 姚銘輝, & 陳守泓. (2008). 利用潛熱通量資料驗證水稻田蒸發散模式. 作物, 環境與生物資訊, 5 (1):, 29-39. 陳豐文, & 劉振宇. (2013). 水收支平衡應用於水田灌溉用水消耗特性之評估. 農業工程學報, 59(1), 77-98. 楊純明. (2009). 農作物生長的量測與追蹤. 技術服務, 20(4), 33-36. 楊偉甫. (2010). 台灣地區水資源利用現況與未來發展問題. 台灣水環境再生協會, 用水合理化與新生水水源開發論壇. 經濟部水利署. (2002). 農業節餘水量有效運用策略研析. 經濟部水利署水利規劃試驗所. (2005). 桃園地區農地耕作調整促進水資源利用研究. 經濟部水利署北區水資源局. (2007). 多元化水資源開發桃園及石門新竹地區農業迴歸水調查與可行性評估. 經濟部水利署北區水資源局. (2008). 石門水庫供水區域各標的用水中長期規劃暨區域產業發展探討及推動之研究. 經濟部水利署北區水資源局. (2014). 石門水庫供水區水資源活化計畫. 萬洪濤, 萬慶, & 周成虎. (2012). 流域水文模型研究的進展. 地球信息科學學報, 2(4), 46-50. 鍾閔光, & 林俐玲. (2015). 土壤水文評估模式之介紹. 龐靖鵬, 徐宗學, & 劉昌明. (2007). SWAT 模型研究應用進展. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70929 | - |
dc.description.abstract | 台灣由於其氣候條件,造成整體可利用水資源相當不足,根據2016年水利署統計資料,灌溉用水佔了65.23%的總用水比例,因此如何適當地進行灌溉配水對水資源的調度有很大的幫助,由於灌溉配水過程充滿不確定性,需要考量推估過程中的所有不確定性,並以序率(stochastic)的架構去探討灌溉配水量。
本研究以SWAT模式(Soil and Water Assessment Tool)進行石門灌區水文過程的模擬,並可推估出灌區的需水量。配水端則採用最大熵法(maximum entropy method),使用最大熵法分析灌溉配水過程的優勢在於能在有限的資訊下做推估,且可以考慮現有操作下之不確定性,其所採取的原則就是要盡可能保留推估過程的所有不確定性,使推估結果盡可能保持客觀,其限制式以區間表示不確定性,並嘗試放鬆限制式的不確定性範圍後進行模式收斂,達到隨需而供的灌溉配水架構。 在此不確定性的配水架構下,灌溉系統中的影響參數皆可表示為機率密度函數(probability density function),例如:支渠配水量、地面水取水量、田間灌溉用水量和灌溉系統中之流量,若有更多可靠的資料,滿足更多的限制式,可以使配水量不確定性的範圍縮小,使推估的結果更精確。此序率的配水架構並可結合風險分析領域,提供決策者農業水資源調度的參考,期待本研究對台灣的灌溉配水方式有所幫助。 | zh_TW |
dc.description.abstract | Due to the climatic conditions in Taiwan, the overall available water resources are quite inadequate. According to the statistics of the Taiwan’s Water Resources Agency in 2016, irrigation water accounts for 65.23% of the total water consumption. Therefore, how to properly implement irrigation water allocation will greatly help the water resources management. Because the process of irrigation water allocation is full of uncertainties, it is necessary to incorporate all the uncertainties into the estimation process, and to analyse the amount of irrigation water in a stochastic framework.
In this study, the SWAT model (Soil and Water Assessment Tool) was used to simulate the hydrological process in the Shimen irrigation area so that the irrigation water demand was estimated. The approach adopted to allocate irrigation water is maximum entropy method. The most important advantage of using the maximum entropy method to analyze the irrigatioin water allocation is that the random variables can be estimated under limited information, and the uncertainty under existing irrigation systens can be considered. The principle of the maximum entropy method is to keep all the uncertainties of the estimation process in order to keep the estimation results as objective as possible. The constraints of estimation process were expressed as intervals to indicate uncertainties. After attempting to relax the limited range of uncertainty, the model converges and the uncertainty-based framework of irrigation water allocation is available on water demand. Under this uncertainty-based framework, the influence parameters in the irrigation system can be expressed as probability density functions, such as irrigation water allocation in branch canals, surface water withdrawals, irrigation water demand and flow rates in the irrigation system. Also, If there are more reliable data and more constraints are satisfied, the range of uncertainty in the irrigation water allocation can be reduced so that the estimated results would be more accurate. Furthermore, the framework can be further combined with risk analysis to provide some useful information for the decision-makers to manage the agricultural water resources. It is expected that this study would be beneficial to the allocation methods of irrigation water in Taiwan. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T04:44:21Z (GMT). No. of bitstreams: 1 ntu-107-R05622029-1.pdf: 8899017 bytes, checksum: 4bc7d47255f952b43b32321fb4152b88 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract iv 目錄 v 圖目錄 vii 表目錄 ix 第一章 前言 1 第二章 文獻回顧 5 第三章 研究方法 9 3.1 SWAT模式 9 3.1.1 地表逕流 11 3.1.2 蒸發散量 13 3.1.3 土壤水 17 3.1.4 地下水 19 3.2 SWAT-CUP 20 3.3 最大熵法 Maximum Entropy 21 3.3.1 熵 Entropy 21 3.3.2 最大熵原理 Principle of Maximum Entropy 22 3.4 凸優化 Convex Optimization 23 第四章 研究區域介紹 25 4.1 地理位置 25 4.2 氣象因子 26 4.3 灌溉系統 27 4.4 水系 29 4.5 農事耕作制度 30 4.6 灌溉方法 31 4.7 計畫配水量 31 第五章 灌溉配水模式建置 32 5.1 推估灌區需水量 32 5.2 SWAT模式檢定 36 5.3 最佳化灌溉配水過程 44 第六章 結果與討論 53 6.1 田間需水量趨勢變化 53 6.2 支渠配水量趨勢變化 55 6.3 地面水取水量趨勢變化 57 6.4 水庫和地面水供水趨勢變化 59 6.5 石門水庫供水打折情境模擬 61 6.6 灌溉系統影響參數之機率分布 63 6.7 推估中小給水路輸水損失率 65 第七章 結論與建議 66 7.1 結論 66 7.2 建議 67 第八章 參考文獻 69 | |
dc.language.iso | zh-TW | |
dc.title | 應用SWAT模式結合最大熵法模擬灌溉配水過程–以石門灌區為例 | zh_TW |
dc.title | Application of SWAT Model Coupled with Maximum Entropy Method to Simulate Irrigation Water Allocation - A Case Study of Shimen Irrigation Area | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳主惠,江莉琦,張煜權,陳豐文 | |
dc.subject.keyword | 灌溉配水,不確定性,SWAT,最大熵法, | zh_TW |
dc.subject.keyword | irrigation water allocation,uncertainty,SWAT,maximum entropy method, | en |
dc.relation.page | 72 | |
dc.identifier.doi | 10.6342/NTU201802443 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-03 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
顯示於系所單位: | 生物環境系統工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-107-1.pdf 目前未授權公開取用 | 8.69 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。