This study focused on an integrated analysis of the hydrological cycle in north Taiwan using remote sensing. Three kinds of models such as the SEBAL model, the GWLF model, and the CGCM1 model were integrated with the purposes of estimating the daily ET among various land-use types; the effects of ecosystems classifications and spatial scales on environmental parameters; the potential effects of land-use change and ET change on future stream flow simulations; and the assessment of future impacts on the hydrology of north Taiwan. The main conclusions of the study are as follows:
1. Using the hybrid classification method and Landsat-5 images, the generated land-use maps of the study area were classified into seven categories including forest, buildings, farmland, fallow farmland, water, cloud, and shadow. The accuracy of land-use classification evaluated by test areas was 89.09%. This implies that hybrid classification is a useful approach to generate a land-use map. In turn, the generated land-use maps are suitable for estimating the CV and actual ET.
2. The values of energy balance parameters among various land-use types were different. Forestland had higher values with NDVI and actual ET, and had lower values of surface temperature, soil heat flux and sensible heat flux. The calculated parameters conformed to our expectations that surface temperature, NDVI and
actual ET had higher values in July, and sensible heat flux and soil heat flux had higher values in November. Similarly, the mean daily ET in July (0.531 cm/day) was higher than in November (0.233 cm/day). Meanwhile, the ET values derived from the SEBAL model were different among different land-use types. For example, in this study, forest has larger ET (January: 0.723cm; November: 0.395cm) than building (January: 0.220cm; November: 0.088cm).
3. Environmental parameters among various land-use types were different among five land-use types, and the required parameters and the numbers of parameters for discriminating five land-use types were also different under different ecosystem classification systems at various scales. However, NDVI and emissivity seem to be the most significant parameters no matter what the kind of spatial scales and ecosystem classification systems.
4. The calculations of CV using two approaches have large differences in different seasons. For example, the calculated CV for the wet season (RSCV: 1.245; REFCV:
0.842) was higher than the value of the dry season (RSCV: 0.851; REFCV: 0.717).
Moreover, the overall trend of CV values derived from remote sensing techniques (July 20: 1.245; November 25: 0.851) were larger than the CV of the traditional approach (July 20: 0.842; November 25: 0.717). This result affected the stream flow simulations.
5. The assessment of CV effects on stream flow simulation indicated that, the flows obtained from RSCV were more accurate than that from REFCV, regardless of the monthly stream flow, the mean value of monthly stream flow, and the annual total stream flow from 1995 to 2002. The regression analysis also pointed out that the flow simulation using RSCV (regression coefficient = 0.877) would represent truer flow characteristics than the use of REFCV (regression coefficient = 0.853).
6. Goodman’s Chi-squared statistic indicates that the procedures of land-use change during 1995 to 2002 were not random. The prediction of overall land-use status from 1995 to 2002, 2030, 2058, and 2086, respectively showed that the building area increased rapidly 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 revealed a decreasing trend no matter which climatic change storyline was chosen.
7. Flow simulations were affected by the predicted future land-use change and ET change. Regardless of the monthly stream flow, mean value of monthly stream flow, and annual total stream, the predicted flows considering land-use change and ET change were lower than those calculated without considering the two effects.
8. Impact assessment of north Taiwan hydrology indicated that, even though some values were reduced, the overall results predicted a raising tendency for future flow
change. That is, That is, the impacts of urban expansion, ET decline, and climate change will increase the flow volumes.
The conclusions summarized from this study indicate that land-use type and spatial scales affected the estimation of ET, and their effects can be investigated by integrating remote sensing techniques, SEBAL model and multivariate statistical analysis. Stream flow simulation using remote sensing-based CV could present truer hydrological characteristics than the traditional approach. The integration of the SEBAL model, the CGCM1 model, and the Markov model is also a feasible scheme to estimate the future land-use status and CV values for stream flow. The consideration of land-use change and ET change indeed affects the predicted flows. 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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