Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth-Wallace?

摘要

Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman-Monteith (P-M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO-56. In this paper the Shuttleworth-Wallace (S-W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere-Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA-AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S-W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data-poor or ungauged large basins. A comparison is made between the FAO-56 method and the S-W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10-day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S-W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non-linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S-W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions.