Spatial scaling up and spatiotemporal variation of evapotranspiration based on Kc and NDVI
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Abstract:
In the catchment scale, evapotranspiration refers to the sum of evaporation from water surface and soil, and transpiration of vegetation in a specific region. Evapotranspiration can reflect heat exchange and water exchange between land and atmosphere, and is an important element in hydrological cycle. Therefore, the study of evapotranspiration variation is of great significance to investigate the law of water cycle, the planning and design of hydraulic engineering, and the efficient utilization of water resources. Due to the short time series of evapotranspiration observation, the small number of observation stations, and the heterogeneity of underlying surface at catchment scale, the spatial-temporal distribution of evapotranspiration can not be obtained using the measured data. Scaling up the observed short-term evapotranspiration data at a small scale is an efficient way to get long-term time series at regional scale. To this end, Liulin watershed was selected as the study area in which a large lysimeter was installed in 2021. Penman-Monteith model is a method for calculating reference crop evapotranspiration (ET0) determined by FAO based on the energy balance and water-air diffusion theory, and was employed in the Liulin watershed. Combined with the measured evapotranspiration (ETc) by the lysimeter from June 2020 to May 2021, the crop coefficient (Kc) was calculated by the equation ETc= Kc ×ET0. Studies have shown that crop coefficient (Kc) has a good linear correlation with normalized vegetation index (NDVI), and this relationship has also been used to study evapotranspiration in ecosystems with different crop types and uneven underlying surface. The linear relationship between Kc obtained by the lysimeter and NDVI was established based on the data from June 2020 to May 2021. Then the Kc with the grid size of 250 m was scaled up to Liulin watershed scale from 2000 to 2021 according to the spatial NDVI distribution from 2000 to 2021. Finally, the evapotranspiration with the same spatial and temporal resolution as NDVI in Liulin watershed was obtained. The annual evapotranspiration was also calculated by water balance equation based on long-term observed precipitation and runoff data, in order to verify the feasibility of the proposed evapotranspiration scaling up method. The results showed that: (1) The average annual potential evapotranspiration of Liulin watershed from 2000 to 2021 was 1135.6 mm with a decreasing trend. The average annual actual evapotranspiration was 591.4 mm with an increasing trend. Both the monthly mean potential evapotranspiration and actual evapotranspiration were unimodal, and the peaks occurred in June and July, respectively. (2) Spatially, the annual average evapotranspiration was high in the northwest and low in the southeast, and the spatial distribution characteristics of evapotranspiration in the four seasons were similar with the annual average. The distribution of evapotranspiration was extremely uneven in the four seasons. In summer, evapotranspiration reached 285.9 m, accounting for 48.3% of the whole year, while in winter, evapotranspiration was only 24.2 mm, accounting for 4.1% of the whole year. (3) The annual average crop coefficient of the basin was 0.52, and the spatial variation ranged from 0.34 to 0.73. The crop coefficient in summer was the largest, reaching 0.70, and that in winter was the smallest (0.20). The actual evapotranspiration calculated by the relation between Kc and NDVI was 43.5 mm higher than that calculated by the water balance method, and the relative error from 2000 to 2020 was 7.9% which was acceptable. This error might come from the errors in the fitting relation of crop coefficient under different meteorological conditions. With the increase of actual monitoring data, the relationship between Kc and NDVI can be refined according to different meteorological conditions. The accuracy of the method to calculate the actual evapotranspiration can be further improved. In general, the scaling up method was feasible, and can be applied to other similar regions, and can be tested in different climate zones.
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