隨著氣候變遷和不透水表面的蔓延，都市地區越發面臨著徑流增加和洪水氾濫的嚴峻挑戰。這項研究旨在探討綠色基盤（Green Infrastructure, GI）對於淹水減緩的貢獻。文獻指出，規劃者在以綠色基盤進行淹水改善策略擬定時，應考量綠色基盤的地景特徵。因此，本研究以景觀指數（Landscape Metrics, LMs）進行台中市地景特徵之定量計算，配合地文性淹水模擬軟體（Physiographic Drainage-Inundation Model, PDIM）評估在不同綠色基盤大小、形狀和位置之改善策略下，不同地景特徵對淹水減緩效果之影響。
本研究中的研究方法是模擬方法（Simulation Approach）。淹水模型是由國立成功大學的蔡長泰教授根據Cunge（1975）（Shiau，2012）的淹水理論進行編程而開發。本研究的實證案例是基於綠色基盤在不同的地理分佈下探討綠色基盤景觀特徵與淹水的關係，比較依據為台中市目前景觀指數與綠色基盤分布下的初始模擬結果。新的模擬結果將通過局部空間自相關（Local Spatial Autocorrelation）（包括單變量局部空間自相關和雙變量局部空間自相關）和敘述統計做進一步分析。
這項研究將綠色基盤配置於現況地理分布下局部空間自相關結果為H_L（洪水高度高，但綠色基盤景觀指數低）之網格，這確實在宏觀模擬中實現了大幅度的洪水減少，且綠色基盤策略在SA情境（洪水高度高但綠色基盤的Area 景觀指數低）中實現了最高的減緩效果和聚類現象。最後，這項研究從SA情境採樣了8個網格，以觀察這些細胞周圍的微觀水文。流向和水文圖表明，策略實施對每個目標單元及其周圍單元的影響程度和位置各不相同，這表明了洪水的動態特徵，並為規劃者提供了更全面的環境綜合參考。
本研究的結果並非規畫人員針對所有都市綠色基盤規劃時的原則，但該過程適用於各個城市。因此，本研究更像是對綠色基盤定位規劃框架的測試，同時考慮綠色基盤的景觀特徵，探索綠色基盤策略的影響。

Amid climate change and sprawling of impervious cement surface, urban area encounters severe challenges from increasing run-off and frequency of floods around the
world. This research interests in the contribution of Green Infrastructure (GI) to flood reduction. Literature pointed out that taking advantage of GI to increase vegetation and to reduce flood, planners should consider the landscape characteristic of GI. Therefore, this
research assesses the effectiveness to mitigate flood with different landscape characteristics of urban GI, such as parks, schools, squares, in terms of the size, shape, and location of GI, which are quantitatively calculated with Landscape Metrics (LMs) in this research.
The research approach in this study is Simulation Approach. The flood is simulated by Physiographic Drainage-Inundation Model (PDIM), which is developed based on the inundation theory presented by Cunge (1975) (Shiau, 2012) and programmed by Professor
Chang-Tai Tsai in National Cheng Kung University, and the flood simulation is run under different GI distribution. The relationship between GI distribution and flood is examined in an empirical case study based on the initial result of the simulation and the LMs of GI. New simulation results in the empirical study are further analyzed with Local Spatial Autocorrelation (including Univariate Local Spatial Autocorrelation and Bivariate Local Spatial Autocorrelation) and Narrative statistics, which are the research methods in this
research.
This research designates GI at H_L (flood height high but GI LMs low) cell, which does engender generous flood reduction in macro simulation, and the GI strategy achieves the highest effect and clustering phenomenon in SA (flood height high but GI Area LMs low). Finally, this research samples 8 cells from SA to observe micro hydrology around these cells. Flow direction and hydrology graph show that the influence of strategy implementation
to every target cell and its surrounding cells differs in extent and location, which signifies
the dynamic feature of flood and provides planners a more concrete reference in the respective of comprehensive environment characteristics.
The result of this research is not a discipline for planners to plan GI for all urban area, but the process is applicable in various urban areas. Therefore, this research is more like a test of a framework for GI locating planning with the exploring of influence from GI strategy with the consideration of landscape characteristic of GI.

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