Model Calibration
Subjected by officer of Water Resource Bureau, four basins located on different regions, namely Fusan (northern region), Chichawan (middle), Sandiman (southern), and Lisan (eastern), are selected due to their scarcely human-activity. Raingages inside watershed were counted for calculating average daily rainfall. Corresponding temperature and runoff data were put together to calibrate parameters with constrain Rosenbork algorithm. Model parameters are shown on table 1.
Table 1. List of Calibrated Parameters

Parameter	Fusan	Chichiawan	Sandiman	Lisan
Recession constant of interflow, KI	0.827	0.888	0.812	0.828
Groundwater recession constant, KG	0.953	0.971	0.916	0.967
Fast Response Zone Capacity, FRC	263	176	221	163
Slow Response Zone Capacity, SRC	1231	784	756	853
Surface Runoff Coefficient, GEO	4.75	2.6	1.54	1.67
Infiltration coefficient, A	1009	1206	1345	1297
Evaportranspiration coefficient, CET	91	83	76	112

Climate-Change Scenario

Output of General Circulation Model
Usually, assessment of climate-change impact uses scenario on double CO₂ (2xCO₂) concentration condition. There are three ways to composite scenario:

• Building forecasting model by analyzing historical data;
• Implementing sensitive analysis by changing statistics of historical data;
• disaggregating GCM’s monthly output to daily rainfall and temperature data.

Intergovernment Pannel on Climate Change (IPCC) does no suggest the first two approaches because history cannot forecast future in non-linearly complex system. This study adopts output of GCM to estimate 2xCO₂ scenario. Tung (1996) uses output of four major GCMs (CCCM, GFDL, GISS, and UK89) from NCAR to estimate rainfall ratio and temperature difference of Taiwan’s major meteorologic stations. Data were classified into 4 regions, only UK89 show different results in northern and southern regions. GISS show low values in southern stations. Tung suggests that taking output from different grid causes inconformity. For this study, only CCCM provides output at grid 121.9°E 26.3°N (Table 2) and thereby is selected for generating daily data.
Table 2. Weather change ratio from output of CCCM

Month	P2xCO2 / P1xCO2	T2xCO2 - T1xCO2	Month	P2xCO2 / P1xCO2	T2xCO2 - T1xCO2
January	0.81	2.71	July	1.00	2.09
February	1.05	3.66	August	1.19	1.75
March	0.67	4.73	September	1.40	2.62
April	1.11	4.16	October	1.01	2.45
May	1.13	4.21	November	0.86	2.39
June	1.39	2.52	December	0.66	3.42
			Average		3.05

Synthetic daily precipitation and temperature
Output of GCM provides monthly average data. A Markov chain process is applied to disaggregate them to daily data. The chance of rainfall existence is decided by preceding day’s rainfall and the probability of monthly rainfall (Richardson, 1981). Once rainfall chance is decided, amount of rainfall can be described by one-parameter Weibull distribution (Tung, 1995)
F_xm(x)=1-Exp[-(1.191X/L_m)^0.75 ] ……………………………………(5)
Here L_m is the average rainfall of month-m. Computation starts from generating random number of [0,1] uniform distribution and then calculating F_xm, and rainfall X.

Because only daily average temperature is needed, a first-order Markov-chain model is developed to calculate daily temperature using standard deviation and auto-correlation of monthly temperature:
T_ij=Tavg_i+ACF_i[ T_i,j-1-Tavg_i] +TSD_i+NORM_x(1-ACF_i²)^0.5 …………(6)
Here T_ij is temperature of month I, day J. Tavg_I is average temperature of mouth I, ACF_I is auto-correlation of mouth-i. TSD_I is the standard deviation of temperature of mouth-i. NORMx is normal distribution random number.

Spatial distribution of Vegetation Index

literatures review
Normalized different vegetation index (NDVI) derived by satellite remotely sensed data contributes spatial distribution of biomass. Frieal etl. (1995) believes that NDVI is a major tool to utilize remote sensing in hydrology for deriving model parameter. Ozenda and Borel (1990) found that 1°C rise of temperature causes 180 meters raising of vegetation belt from data of the mountains of Alps. Houerou (1992) found similar trend from Mediterranean data that vegetation zone lifts up 100 meter with temperature rising 0.55°C. He also concluded Mediterranean plant may lifting up 545 meters due to 3°C rising of temperature . Because of lack of similar study in Taiwan, the trend of 180 meter/1°C is adopted in this study to quantify temperature-induced vegetation change.

Relationship between vegetation and hydrologic-model parameters
Landsat 5/TM images acquiesced on January 9, 1995 at 9:33 morning were used. Solar zenith angle of the scene was 58.36 and azimuth was 140.36. Original 30-meter-resolution data are rectified and re-sampled into 25-meter resolution, and were registered to Transverse Mercator (WGS84) projection. Bi-direction reflectance distribution function is used to adjust topographic effect.

Five model-parameters relate to surface roughness, soil infiltration, plant evapotranspiration were counted for their relationship with remote sensing index. After different tries to soil brightness and vegetation indexes, results show that evapotranspiration coefficient (CET) appears better correlation with NDVI (Table.3 and Fig.3).
Table 3. NDVI and CET values of 1xCO₂ and 2xCO₂ conditions

Basin	1 x CO2		2 x CO2
	N D V I	CET	N D V I’	CET’
Fusan	0 .4 0 0 2	91	0 .3436	91
Chichiawan	0.3108	83	0.3701	95
Sandiman	0.2215	76	0.3034	85
Lisan	0.4212	112	0.4591	108

Fig.3 Regression plot of NDVI via CET
NDVI values in Table 3 are average of all pixels inside basins. When running 2xCO₂ scenario, a rising annual temperature (3°C) makes a right shift of NDVI-elevation curve 540 meters (3°C x 180 meter / 1°C) (Fig.4). A new NDVI average is estimated and new CET can be calculated from regression equation (CET = 147.9NDVI+40.44).

Fig.4 Elevation-NDVI curves of Fusan