本研究旨在利用多目標遺傳演算法對分布型水文-土壤-植被模式進行參數最佳化優選，並建立不同的土地利用情境，探討土地利用變遷對逕流量與其他水循環分量之影響。
本研究之研究區域為石門水庫子集水區，研究區域之地文資料，包括高程、土壤及土地利用資料，將其整理為分布型水文-土壤-植被模式所需之網格型輸入資料，包括高程資料、研究區邊界資料、流動方向資料、土壤類型資料、土壤深度資料、植被類型資料、河道資料，並結合多目標遺傳演算法同時將水文模式對兩衝突目標函數進行水文模式最佳參數組合之探討，並確立一符合研究區特性之最佳參數組合。
同時蒐集研究區不同之土地利用驅動因子，利用二元邏輯式回歸確立各土地利用與驅動因子之關係，搭配土地利用變遷模式對未來各土地利用面積之控制假設，來達到未來土地利用情境的建置。本研究對未來土地利用之情境設置為假設各土地利用面積以歷史線性成長外差獲得，另一較劣情境為假設都市面積成增長率相較歷史成長80個百分點。
在探討土地利用之變化造成水文量與其他水循環分量之衝擊時，常使用固定水文事件進行模擬，模擬時間以分別為2014~2015年間9月至5月發生嚴重乾旱之時段，與2015年蘇迪勒、杜鵑颱風過境所造成之暴雨事件，探討不同情境下之水文量之變化。在乾旱時段中最劣土地利用情境相較觀測之土地利用流量減少10%；在蘇迪勒、杜鵑颱風侵台時段中最劣土地利用情境相較觀測之土地利用流量增加5%；
可見都市化面積之上升在乾旱期間對逕流量減量之趨勢，而在暴雨期間對逕流量增加之趨勢，其中增加幅度與建地面積呈正相關。

Due to the influence of multiplicity of factors including spatial variations in precipitations, the composition of soil layers and human activities, the complexity of parameter optimization for the physics-based distributed hydrology-soil-vegetation model (DHSVM) goes far beyond the capability of the traditional optimization method. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is a new scheme to find the solution to this problem. To reduce the uncertainty parameter in the DHSVM, the optimization only focuses on streamflow sensitive parameters, such as porosity, lateral saturated hydraulic conductivity, maximum infiltration, field capacity, and the exponential decrease rate of lateral saturated hydraulic conductivity with soil depth. The design of the optimization is illustrated in this thesis by defining the encoding method and devising the conflict fitness value function. The optimization method is implemented on the Shihmen Reservoir Catchment in northwestern Taiwan, and it shows that the satisfactory result of parameter estimation is achieved. The genetic algorithm is feasible in optimizing parameters of DHSVM model.
After establishing the optimal parameters, we assess the impacts of various scenarios of land-use change on hydrological cycles. In this study, CLUE-s model is additionally used to predict the possible changes in land use in Shihmen sub-watershed in 2020.
Finally, a case study of the various land-use scenarios, such as Linear trend of land use demand with restriction areas, Linear trend of land use demand without restriction areas, and Higher rate of land transformation. Peak flow rates under Higher rate of land transformation and Linear trend of land use demand without restriction areas are 5.2% and 3.4%, which are respectively greater than the rate under the no land-use change scenario. Furthermore, land use change leads to dramatically changes in the hydrological processes, especially for distributions of ground water and soil moisture in the downstream watershed.

1.李光敦. (2012)。水文學。五南圖書出版股份有限公司。
2.王守荣，黄荣辉，丁一汇(2002)。分布式水文-土壤-植被模式的改进及气候水文 Off—line 模拟试验。气象学报，60(3)，290-300。
3.刘姝(2010)。分布式水文仿真系统 DHSVM 的 Java 实现。电子科技大学。
4.吳振發(2011)。臺灣鄉村景觀變遷模擬之 CLUE-s 模式最佳參數試驗。地理學報(62)，103-125。
5.林子平(2015)。土地與氣候變遷情境對流量之影響-以大屯溪流域為例。臺灣大學生物環境系統工程學研究所學位論文，1-102。
6.林焜詳(2016)。支撐向量機與隨機森林應用於颱風時雨量預報之比較。成功大學水利及海洋工程學系學位論文，1-105。
7.姚长青，杨志峰，赵彦伟(2006)。分布式水文-土壤-植被模型与 GIS 集成研究。水土保持学报，20(1)，168-171。
8.郝振纯，梁之豪，梁丽乔，郭晓玉，鞠琴(2012)。DHSVM 模型在宝库河流域的径流模拟适用性分析。水电能源科学，30(11)，9-12。
9.康丽莉，王守荣，顾骏强(2008)。分布式水文模型=DHSVM对兰江流域径流变化的模拟试验。热带气象学报。
10.張曜麟。(2005)。都市土地使用變遷之研究。成功大學都市計劃學系學位論文，1-120。
11.黃寶萱(2017)。應用分布水文-土壤-植被模式探討氣候變遷對水文量之影響。成功大學水利及海洋工程學系學位論文，1-68。
12.雷諮曼(2017)。應用 Dyna-CLUE 模式預測石門子集水區土地利用改變。成功大學自然災害減災及管理國際碩士學位學程學位論文，1-127。
13.熊勤学(2015)。基于土壤植被水文模型的县域夏收作物渍害风险评估。农业工程学报，31(21)，177-183。
14.蕭政宗，黃景裕(2010)。以遺傳演算法推導考量二衝突缺水指標之南化水庫多標的最佳限水策略。農業工程學報，56(4)，27-41。
15.戴巧雯(2014)。多目標遺傳演算法應用於滯洪池最佳化優選。成功大學水利及海洋工程學系學位論文，1-75。
16.謝宜桀(2015)。應用分布型水文-土壤-植被模式探討土地利用變遷對水文量之影響。成功大學水利及海洋工程學系學位論文，1-123。
17.Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and Rasmussen, J. (1986). An introduction to the European Hydrological System—Systeme Hydrologique Europeen,“SHE”, 1: History and philosophy of a physically-based, distributed modelling system. Journal of hydrology, 87(1-2), 45-59.
18.Bian, H., Lü, H., Sadeghi, A. M., Zhu, Y., Yu, Z., Ouyang, F., ... and Chen, R. (2017). Assessment on the Effect of Climate Change on Streamflow in the Source Region of the Yangtze River, China. Water, 9(1), 70.
19.Bowling, L. C., and Lettenmaier, D. P. (2001). The effects of forest roads and harvest on catchment hydrology in a mountainous maritime environment. Land use and watersheds: human influence on hydrology and geomorphology in urban and forest areas, 145-164.
20.Chu, H. J., Lin, Y. P., Huang, C. W., Hsu, C. Y., and Chen, H. Y. (2010). Modelling the hydrologic effects of dynamic land‐use change using a distributed hydrologic model and a spatial land‐use allocation model. Hydrological Processes, 24(18), 2538-2554.
21.Confesor, R. B., and Whittaker, G. W. (2007). Automatic calibration of hydrologic models with multi‐objective evolutionary algorithm and Pareto optimization. JAWRA Journal of the American Water Resources Association, 43(4), 981-989.
22.Cuo, L., Giambelluca, T. W., and Ziegler, A. D. (2011). Lumped parameter sensitivity analysis of a distributed hydrological model within tropical and temperate catchments. Hydrological Processes, 25(15), 2405-2421.
23.Cuo, L., Giambelluca, T. W., Ziegler, A. D., and Nullet, M. A. (2006). Use of the distributed hydrology soil vegetation model to study road effects on hydrological processes in Pang Khum Experimental Watershed, northern Thailand. Forest Ecology and Management, 224(1-2), 81-94.
24.Cuo, L., Lettenmaier, D. P., Mattheussen, B. V., Storck, P., and Wiley, M. (2008). Hydrologic prediction for urban watersheds with the Distributed Hydrology–Soil–Vegetation Model. Hydrological Processes, 22(21), 4205-4213.
25.Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
26.DeJong, K. A. (1975). Analysis of the Behavior of a Class of Genetic Adaptive Systems. Dept. Computer and Communication Sciences, University of Michigan, Ann Arbor.
27.Dickerson‐Lange, S. E., and Mitchell, R. (2014). Modeling the effects of climate change projections on streamflow in the Nooksack River basin, Northwest Washington. Hydrological Processes, 28(20), 5236-5250.
28.Fonseca, C. M., and Fleming, P. J. (1993, June). Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization. In Icga (Vol. 93, No. July, pp. 416-423).
29.Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on systems, man, and cybernetics, 16(1), 122-128.
30.Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence.
31.Kelleher, K. D. (2006). Streamflow calibration of two sub-basins in the Lake Whatcom watershed, Washington using a distributed hydrology model.
32.Knowles, J., & Corne, D. (1999). The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on (Vol. 1, pp. 98-105). IEEE.
33.Leung, L. R., Wigmosta, M. S., Ghan, S. J., Epstein, D. J., and Vail, L. W. (1996). Application of a subgrid orographic precipitation/surface hydrology scheme to a mountain watershed. Journal of Geophysical Research: Atmospheres, 101(D8), 12803-12817.
34.Li, F.-F., and Qiu, J. (2015). Multi-objective reservoir optimization balancing energy generation and firm power. Energies, 8(7), 6962-6976.
35.Meyer, P. D., Rockhold, M. L., Gee, G. W. (1997). “Uncertainty analyses of infiltration and subsurface flow and transport for SDMP sites”, Nuclear Regulatory Commission, Washington, DC (United States). Div. of Regulatory Applications; Pacific Northwest National Lab., Richland, WA (United States), No. NUREG/CR-6565; PNNL-11705.
36.Park, C. H., Joo, J. G., and Kim, J. H. (2012). Integrated washland optimization model for flood mitigation using multi-objective genetic algorithm. Journal of Hydro-environment Research, 6(2), 119-126.
37.Rawls, W. J., Brakensiek, D. L., and Miller, N. (1983). Green-Ampt infiltration parameters from soils data. Journal of hydraulic engineering, 109(1), 62-70.
38.Reddy, M. J., and Kumar, D. N. (2006). Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resources Management, 20(6), 861-878.
39.Reddy, M. J., and Kumar, D. N. (2007). Multiobjective differential evolution with application to reservoir system optimization. Journal of Computing in Civil Engineering, 21(2), 136-146.
40.Rogger, M., Agnoletti, M., Alaoui, A., Bathurst, J. C., Bodner, G., Borga, M., Bloschl, G. (2017). Land use change impacts on floods at the catchment scale: Challenges and opportunities for future research. Water Resources Research, 53(7), 5209-5219. doi:10.1002/2017wr020723
41.Sadeghi, J., and Niaki, S. T. A. (2015). Two parameter tuned multi-objective evolutionary algorithms for a bi-objective vendor managed inventory model with trapezoidal fuzzy demand. Applied Soft Computing, 30, 567-576.
42.Storck, P., Bowling, L., Wetherbee, P., and Lettenmaier, D. (1998). Application of a GIS‐based distributed hydrology model for prediction of forest harvest effects on peak stream flow in the Pacific Northwest. Hydrological Processes, 12(6), 889-904.
43.Thyer, M., Beckers, J., Spittlehouse, D., Alila, Y., and Winkler, R. (2004). Diagnosing a distributed hydrologic model for two high‐elevation forested catchments based on detailed stand‐and basin‐scale data. Water Resources Research, 40(1).
44.Veldkamp, A., and Fresco, L. O. (1996). CLUE: a conceptual model to study the conversion of land use and its effects. Ecological modelling, 85(2-3), 253-270.
45.Veldkamp, A., and Verburg, P. H. (2004). Modelling land use change and environmental impact.
46.Verburg, P. H., Overmars, K. P., and Witte, N. (2004). Accessibility and land‐use patterns at the forest fringe in the northeastern part of the Philippines. The Geographical Journal, 170(3), 238-255.
47.Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., and Mastura, S. S. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30(3), 391-405.
48.Westrick, K. J., Storck, P., and Mass, C. F. (2002). Description and evaluation of a hydrometeorological forecast system for mountainous watersheds. Weather and Forecasting, 17(2), 250-262.
49.Whitaker, A., Alila, Y., Beckers, J., and Toews, D. (2003). Application of the distributed hydrology soil vegetation model to Redfish Creek, British Columbia: model evaluation using internal catchment data. Hydrological Processes, 17(2), 199-224.
50.Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P. (1994). A distributed hydrology‐vegetation model for complex terrain. Water resources research, 30(6), 1665-1679.
51.Wigmosta, M. S., Nijssen, B., Storck, P., and Lettenmaier, D. (2002). The distributed hydrology soil vegetation model. Mathematical models of small watershed hydrology and applications, 7-42.
52.Xiong, Q. (2015). Risk evaluation of sub-surface waterlogging of summer crops based on DHSVM model on county scale. Transactions of the Chinese Society of Agricultural Engineering, 31(21), 177-183.
53.Yandamuri, S. R., Srinivasan, K., and Murty Bhallamudi, S. (2006). Multiobjective optimal waste load allocation models for rivers using nondominated sorting genetic algorithm-II. Journal of water resources planning and management, 132(3), 133-143.
54.Yao, C., and Yang, Z. (2009). Parameters optimization on DHSVM model based on a genetic algorithm. Frontiers of Earth Science in China, 3(3), 374-380.
55.Yeh, C. H., and Labadie, J. W. (1997). Multiobjective watershed-level planning of storm water detention systems. Journal of Water Resources Planning and Management, 123(6), 336-343.
56.Zhao, G., Gao, H., and Cuo, L. (2016). Effects of urbanization and climate change on peak flows over the San Antonio River Basin, Texas. Journal of Hydrometeorology, 17(9), 2371-2389.
57.Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on Evolutionary Computation, 3(4), 257-271.