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Meteorological forcing variables

Land surface models should be driven with reliable meteorological forcing variables such as temperature, precipitation, wind, vapor pressure, and downward longwave and shortwave radiation. The temperature, precipitation, wind speed are measured routinely at many meteorological stations. Unfortunately, downward longwave and shortwave radiation are usually unavailable for large river basins. A possible radiation data source is the output from atmospheric GCMs. But the biases in GCMs are generally still too large for hydrological models (Boyle, 1998; Gadgil and Sajani, 1998; Lau et al., 1996). One approach to obtaining radiation data is to calculate the missing data as a function of avaiblable observations.

In this study, the routine meteorological data from 120 meteorological stations inside and closed to the study basin were obtained from the China Meteorological Administration (CMA). The data set is available from 1982 to 2000 with the daily precipitation, mean temperature, maximum and minimum temperatures, surface relative humidity and sunshine duration. The hourly meteorological data offers the greater accuracy for estimating energy flux in land surface model. Several techniques are available for approximating the diurnal temperature curve through the use of daily maximum and minimum temperatures. From the simplest to the most complex, these are averaging, single triangulation, double triangulation, single sine, double sine, and mixed curve (Zalom et al., 1983; Baskerville and Emin, 1969; Allen, 1976). These curves are often used by ecologists and biologists. Cesaraccio et al. (2001) gave a mixed sine and square-root curve (TM model) and compared it with published models. The TM model was used in this study to estimate the hourly mean temperture. The TM model output hourly temperature data was shifted to fit the observed daily mean temperature. The vapor pressure was then estimated from observed relative humidity and temperature (Allen et al., 1998). The downward shortwave radiation data was reconstructed from sunshine duration. Angstrom formula gave an empirical linear relationship between downward shortwave radiation and the mean daily sunshine fraction (Angstrom, 1924). Revfeim (1981) improved the Angstrom formula with a physically meaningful form using hourly sunshine fractions. A method was then proposed with a plausible houly pattern of sunshine for the daily fraction (Yang et al., 2001; Revfeim, 1997) and was used in this study. The downward longwave radiation at the Earth's surface was calculated using the Stefan-Boltzmann relationship. There are many parameterization schemes to estimate the atmospheric emissivity with the effect of clouds e.g. Brunt (1932); Brutsaert (1975); Jiménez et al. (1987). Niemelä et al. (2001) presented a comparison of several downward longwave radiation flux parameterizations with hourly averaged pointwise surface-radiation observations in Finland. They found all the longwave radiation schemes usually underestimated the downward clear-sky flux. Although the method has shortcomings, it allows the development of forcing datasets for historic periods. The downward longwave radiation flux parameterization was used in this study following Jiménez et al. (1987).

The grided precipiation and temperature data is produced from station observations. Several methods were investigated to interpolate the daily station observations. These included surface-fitting procedure thin-plate splines (Hutchinson, 1995), Thiessen polygon area averaging (Thiessen, 1911), and angular distance weighted (ADW) averaging (New et al., 2000). The thin-plate splines interpolation was found to be unsuitable because there were considerable undershoot and overshoot in the edge of the study area. Thiessen polygon interpolation employ a limited number of data points in the estimation of grid point values. The ADW method was selected for this study. In estimating each grid point using ADW method, eight nearest stations regardless direction and distance are used to contribute to grid point estimation and form the distance weighting function (Piper and Stewart, 1996). Weights for the eight stations were determined in a two-stage process following New et al. (2000). All stations were first weighted by distance from the gird point. The second component of the distance weight was determined by the directional (angular) isolation of each the eight selected stations (New et al., 2000; Jones et al., 1997). Figure 2 displays the patterns of the variables in the forcing dataset (precipitation, mean, minimum and maximum temperature, relative humidity, vapor pressure, wind speed, sunshine duration, downward shortwave radiation, and downward longwave radiation). Figure 3 shows the time series of monthly averaged forcing variables from 1982 to 2000.

Figure 2: Forcing variables in the study area (1982-2000)
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Figure 3: Time series of monthly forcing variables (1982-2000)
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next up previous
Next: Vegetation and soil properties Up: Study area and model Previous: Study area and hydrological
TANG 2006-02-16