Abstract
Climate-change influences rainfall-runoff process by changing weather input and basin's characteristics. This study uses remote sensing based vegetation index (NDVI) to evaluate the change of vegetation under 1xCO₂ and 2xCO₂ conditions. Some studies shown that vegetation belt shifts up with temperature on a rate of +180m/1°C. NDVI distribution curve simulates this shift of vegetation, and then calculates new evapotranspiration parameter of a rainfall-runoff model. CCCM, a general circulation model of NCAR, provides monthly precipitation and temperature on 2xCO₂ conditions, and daily data were generated with a first-order Markov chain model. Four basins located on difference water resource regions are processed to calculate flow duration curves (FDC). FDC result indicates that four basins have higher flood flow. Northern and eastern regions have smaller flow rate during drought period but the other two regions have larger values. Mean flow rate (Q₅₀) on southern region in 2xCO₂ is five times then that in 1xCO₂ condition.

Introduction

Basin-scale Modeling
Many satellite remote sensing programs have been launched for studying atmospheric vapor and their land surface relationship. General procedure is developing a series of algorithms that integrate data from remote sensing for simulating Water-Energy-Carbon, Carbon and Biogeochemistry. On the other hand, General Circulation Model (GCM) provides global scale solution of precipitation and temperature while carbo-dioxide concentration rises. Many GCMs have concluded that more precipitation falling in high latitude area and causing more runoff. In low latitude area, runoff is decreased due to less rainfall and higher temperature. Simultaneously increasing precipitation and temperature makes more flood and drought problems. However, water resource engineers do not satisfy the answer because the broad scale is non-suitable for operation purpose. What the engineers needed is a “basin-scale” result. This study present a short cut that develops a basin-scale model simulating runoff under 2xCO₂ condition by combining GCM output and remote sensing data.

Study method
Study procedure is arranged as Fig.1:

• Developing a hydrologic model that has hydrologic meaning parameters.
• Estimating the relationship between parameter and physiographic factor, remote sensing helps to represent the vegetation condition of watershed and to bridge parameter and climate change.
• Generating daily rainfall and temperature data from GCM’s monthly output.
• Estimating new vegetation pattern from GCM output on 2xCO₂ condition, and new model parameter from vegetation-parameter relationship.
• Combining the results of step 3 and step 4 to simulate runoff and follow up assessment.

Fig 1. Study Flow Chart
Hydrologic Model

Model Structure
We develop a simplified SWM (Lau, 1996) which has three storage layers simulating fast response (Fig.2), slow response, and ground water of a watershed. Model detail has described in original paper and briefly express as following:


Fig 2.Structure of Hydrologic Model
Fast Response Zone The zone simulates basin’s top depress and top soil layer. While precipitation (P) fall, water stay in the zone that is called Fast Response Zone Storage (FRS). Flow can be represented as:
FRS(t)=P(t)+FRS(t-1)-F(t-1)-SR(t-1)-FET(t-1)………………(1)
In eq. 1, t stands for time period. Surface runoff, SR, is calculated by Manning’s equation controlled by a roughness and slope parameter GEO. FET is assumed a free evaporation that estimated by smaller value between potential evapotranspiration (PET) and FRS. PET can be calculated with Blaney Criddle (1958) method by given average temperature.

Slow Response Zone
Slow response zone emulates lower soil zone. Moisture in the zone is called Slow Response Zone Storage (SRS) that can be formulated as:
SRS(t)= F(t)+SRS(t-1) -QI(t-1)-DP(t-1)-SET(t-1)………………(2)
F is decided by infiltration coefficient A, SRC and water content SRS. QI is calculated by interflow recession constant,KI. SET is determined by a vegetation related coefficient CET and water content SRS. DP is just decided by the ration of water content and zone’s capacity (SRC)s

Ground Water Zone
Ground Water zone Storage (GWS) receives deep percolation (DP) and outlet ground water (QG):
GWS(t)= DP(t)+GWS(t-1) -QG(t-1)……………………………(3)
Estimation of ground water is from recession constant (KG):
QG(t)=(1-KG) GWS(t)…………………………………………(4)
Outlet discharge from basin is the total of SR, QI and QG. There are total five parameters (Table 1) needed to calibrate by fitting estimated and historical discharge, and two recession constants are estimated by recession section of hydrographies. To study correlation between physiographic factor and parameters, remotely sensed data transfer two indexes: vegetation and soil index that makes it possible to bridge parameter and physiographic factor.