Bibliographic record
Abstract
[[abstract]]環境變遷與集水區水文循環之間的關係已成為環境規劃的重要課題。國內外雖有許多專家學者結合大氣環流模式(General Circulation Models, GCMs)與GWLF 河川流量模式(Generalized Watershed Loading Functions, GWLF)，探討氣候變遷對集水區水資源的衝擊效應，但前人研究在蒸發散覆蓋係數(Evapotranspiration Cover Coefficient, CV)之設定方面，大多根據國外文獻之參考值進行設定此外，土地使用型態和蒸發散量的逐年變化，亦會影響集水區之未來水文狀態，而傳統的流量模擬研究甚少針對此兩項因子之影響效應加以探討。故本研究以台灣北部地區為試區，旨在利用遙測技術推估真實地表的蒸發散量與CV值，以提昇流量模擬之正確性，進而結合SEBAL模式(Surface Energy Balance Algorithm for Land, SEBAL)、CGCM1 大氣環流模式 (The First Version of the Canadian Global Coupled Model, CGCM1)與Markov模式，模擬未來土地使用型及蒸發散量的變化，並分析其對未來流量模擬之影響，最後再綜合氣候、土地使用及蒸發散量等環境變遷因子，進一步評估台灣北部地區未來水文循環可能遭受之衝擊效應。研究結果指出，與傳統查表值相比，應用遙測推估之CV值更能反應真實之河川水文特性，此外，考慮未來土地使用變遷與蒸發散量變化兩項因子所進行模擬的流量，均較未考慮的流量值低；最後，未來集水區水資源衝擊評估結果指出，由於都市擴張、蒸發散量減少及氣候變遷等因子之綜合作用，將造成台灣北部地區河川流量之上升。綜合上述結果可知，整合SEBAL、CGCM1與Markov模式模擬土地使用變遷及蒸發散量變化，進而推估未來集水區之河川流量及評估區域水文循環之衝擊效應，確實為一有效、可行的方法。由於台灣北部地區為經濟商業之核心區域，無論在工商業發展及水資源供給方面均占有相當重要的地位，因此相關單位應重視此課題，並及早研擬因應策略。 This study aims to improve on stream flow simulation using the Generalized Watershed Loading Functions (GWLF) model by including remote sensing techniques to estimate the cover coefficient (CV), as well as integrating the SEBAL model, the CGCM1 model, and the Markov model to predict land-use and ET changes. Moreover, the results were adopted to assess the effect of environmental changes on future hydrology or the north of Taiwan. The processes include land-use classification using hybrid approach and Landsat-5 TM images, a comparison of stream flow simulations using the GWLF model with two CV values derived from remote sensing and traditional methods, and finally the prediction of future land-use and CV parameters for assessing the effect of land-use change and ET change. The results indicated that the study area was classified into seven land-use types with 89.09% classification accuracy. The stream flows simulated by two estimated CVs were different, and the simulated stream flows using the remote sensing approach presented more accurate hydrological characteristics than a traditional approach. In addition, the consideration of land-use change and ET change indeed affected the predicted stream flows under climate change conditions. The results of the hydrology analysis based on the SRES scenarios of CGCM1 model predicted that the river flows of north Taiwan will become greater due to the effects of climate change, land-use change and ET change. Therefore, the results obtained from this study can be extrapolated to the future studies of global environmental change and water resource management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".