Farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall

Abstract

The invention discloses a farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall. The method comprises the steps of obtaining meteorologicaldata of a farmland crop growth environment; calculating a crop coefficient of the prediction reference date according to the reference crop evapotranspiration of the prediction reference date and theactually measured evapotranspiration of the farmland; respectively constructing a training set and a test set, and carrying out preprocessing; establishing a feedforward neural network model considering crop coefficient dynamic change and rainfall influence, and performing training optimization; and utilizing the optimized feedforward neural network model to predict the evapotranspiration of the farmland crops in a short term according to the test set data. The influence of crop coefficient change and rainfall on the farmland crop evapotranspiration is considered, the nonlinear relation between the farmland reference crop evapotranspiration and driving factors of the farmland reference crop evapotranspiration is effectively constructed, accordingly, the crop evapotranspiration better meeting the actual growth condition of crops can be obtained, and a scientific basis is provided for future moisture management of the farmland underlying surface.

Description

technical field [0001] The invention belongs to the technical field of crop transpiration prediction, and in particular relates to a short-term prediction method for farmland evapotranspiration considering the dynamic change of crop coefficient and rainfall. Background technique [0002] The evapotranspiration process is an important part of the terrestrial hydrological cycle, and the evapotranspiration (Evapotransspiration, ET c ) has important guiding significance for irrigation planning and regional water resource allocation. Accurate prediction of evapotranspiration can save irrigation water to a certain extent. Therefore, in order to better manage crop irrigation water and improve crop water use efficiency, accurate prediction of crop evapotranspiration is urgently needed. [0003] At present, the prediction methods of evapotranspiration are mainly divided into four categories: time series method, gray model method, empirical formula method and neural network model met...

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Problems solved by technology

[0005] To sum up, many studies on evapotranspiration prediction have been carried out, but there are two main problems at present: (1) The fixed or simple difference crop coefficient recommended by FAO-56 is suitable for the prediction of evapotranspiration in a long period, while The applicability of short-term evapotranspiration prediction is low, and the basic crop coefficient curve in the crop coefficient method is only linearly differenced by three determined nodes, which simplifies the processing of the crop growth process, which will cause large deviations; ( 2) Due to the uncertainty of rainfall, there are few studies on evapotranspiration prediction models considering rainfall. Only considering deterministic meteorological factors will inevitably cause deviations in evapotranspiration prediction under typical weather, and the applicability is weak

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