| Summary: | 博士 === 國立臺灣大學 === 森林環境暨資源學研究所 === 98 === Watershed hydrology, especially stream flow, is expected to be highly sensitive to the influences of global climate change. Traditional studies have integrated the General Circulation Models (GCMs) with the Generalized Watershed Loading Function (GWLF) model to estimate stream flow rates. However, using these models in the context of a transitioning climate and on a large spatial is problematic, particularly for the estimation of two important parameters, evapotranspiration (ET) and cover coefficient (CV).
This study focuses on an integrated analysis of the hydrological cycle using remote sensing techniques to estimate the ET and the CV. Furthermore, we improved on older studies by integrating the Surface Energy Balance Algorithm for Land (SEBAL) model, the First Version of the Canadian Global Coupled Model (CGCM1), and the Markov model which allows us to predict land-use and ET change. The results were applied to assess the future impacts of global warming on hydrological cycles of northern Taiwan. Our methods include applying hybrid image classification to generate the land-use maps of the northern Taiwan using Landsat-5 images; using digital terrain model (DTM) and the SEBAL model to calculate 16 environmental parameters relevant to ET. We then compared the differences among different land-use types; (1) investigating the effects of two ecosystem classification systems (i.e., watershed division method and geographic climate method) at various spatial scales on environmental parameters using stepwise discriminant analysis; (2) comparing stream flow simulations according to the GWLF model with two CV values derived from remote sensing and traditional methods; (3) integrating the Markov model and the CGCM1 model to predict future land-use and CV parameters for evaluating the effect of land-use change and ET change; and (4) finally, assessing the future impacts on hydrological cycle of the northern Taiwan.
The results indicated that the study area was classified into seven land types (i.e., forest, building, water, farmland, fallow farmland, cloud-covered, and shadow-covered) with 89.09% classification accuracy. These last two land types could not be analyzed further. A comparison of daily ET values among different land-use types revealed differences. In this study, forest ET is the largest (January: 0.723cm; November: 0.395cm) while building is the smallest (January: 0.220cm; November: 0.088cm). These differences contrive to exist for ecosystem classification systems at various scales, but depend on the selected environmental parameters and the number of parameters included in the model. Two parameters, a normalized difference vegetation index and an emissivity are important factors for discriminating land types. On the aspect of land-use and ET effects on hydrological simulations, the stream flows simulated by two estimated CVs were different. The stream flow simulation using the remote sensing approach (wet season: 1.245; dry season: 0.851) presented more accurate hydrological characteristics than the traditional approach (wet season: 0.842; dry season: 0.717). Meanwhile, according to the result of regression analysis, the flow simulation using RSCV (remote sensing based CV; regression coefficient = 0.877) would represent truer flow characteristics than the use of REFCV (reference CV; regression coefficient = 0.853). In the prediction of future land-use and ET, due to the increase of building area from 13.36% in 1995 and 14.05% in 2002 to 38.91% in 2030, 52.13% in 2052, and 62.36% in 2086, the predicated CV values for next three periods display a decreasing trend no matter under which climatic change storyline. In addition, land-use and ET change indeed affect the predicted stream flows. The predicted flows with consideration of these two factors were lower than those without consideration. Finally, the impact assessment on the hydrology of the northern Taiwan indicated that the flow volumes increase due to urban expansion, ET decline, and climate change, and it will lead to the increase of stream flow.
From above results, obviously the integration of remote sensing, the SEBAL model, the CGCM1 model, and the Markov model is a feasible scheme to predict future land-use, ET change, and stream flows. Therefore, it can be extended to the further studies in water resource management and global environmental change.
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