Abstract
The distribution of the daily average air temperature with high spatial resolution is vital for hydro-ecological applications. The air temperature usually recorded at fixed-point stations provides little distribution information and easily suffers from the scarce amount and uneven distribution of the stations in the data sparse regions. In this study, a method based on multisource spatial data was developed to estimate the spatial distribution of daily average temperature, especially for data sparse regions. In this method, the instantaneous temperature was retrieved first using the moderate resolution imaging spectroradiometer data, which was then transformed to a daily value using transformation equations. Second, the global land data assimilation system air temperature data were spatially downscaled and used to improve the data accuracy from step 1 at low temperatures. This method was applied in the Ili River basin in Central Asia, and the results were evaluated against data from two stations’ observations and in situ data from a field test site. The results showed the correlation coefficient varies from 0.90 to 0.94 and the root mean square deviation is ∼3°C , indicating the generated temperature matched the observations well. This suggests the method is an alternative for data sparse regions.
Highlights
Air temperature is one of the most important parameters for a wide range of applications in ecology and hydrology
−3.72ð1 − ALÞðcos i∕ cos z þ ðπ − sÞ∕πÞRs ↓ −3.41Δh; where T2m is the instant temperature at 2 m height and the unit is K; Land surface temperature (LST) is the instantaneous daytime land surface temperature from moderate resolution imaging spectroradiometer (MODIS) MOD11A1 in K; z is the solar zenith angle in rad; α is the solar azimuth start from the south in rad; AL is the surface albedo; i is the solar incident angle in rad; s is the slope in rad; Rs ↓ is the down-welling surface short-wave radiation flux in w∕m2; and Δh is the difference between the pixel elevation and the mean elevation within the vicinity of 20 km in km
This study was a part of the hydrological modeling in ungauged regions and was designed to provide daily average surface air temperature estimation for distributed hydrological models, especially in data sparse regions, and no further atmospheric correction was applied to the MODIS LST data
Summary
Air temperature is one of the most important parameters for a wide range of applications in ecology and hydrology. Near surface air temperature with varying spatial and temporal resolution is often required in many hydro-ecological modeling techniques. Spatial distribution of air temperature is vital for the regional hydro-ecological modeling. Researchers have explored various methods to generate spatial air temperature. Some researchers like Peterson et al.,[1] Anderson,[2] and Florio et al.[3] used spatial interpolation to estimate spatial distribution of air temperatures. The reliability of results from interpolations greatly depends on the density stations and the numbers of observations; these methods are limited for application over large area, especially in data sparse regions
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