Abstract
Abstract. Many factors influence irrigation water requirement on the basin scale, which make it difficult to obtain comprehensive data. Despite the advantage of less needing historical data, the prediction precision of traditional trend prediction methods is hard to guarantee. For water scarce basins, the artificial influence on irrigation requirement should be thought of as important impact factors. In this paper, the PCA (principal component analysis) method is used to identify the main influencing factors, such as precipitation, irrigation area, water saving technology and so on. Based on that, an irrigation water demand prediction model considering multiple factors is developed for water shortage regions. The method is applied in the Haihe River basin as an example. The study results show that the irrigation water demand forecasting method considering multiple factors in this paper can achieve higher modelling accuracy, compared with the traditional trend prediction method and the method that does not consider the human influence. In view of the small average relative error, 1.32%, it has good values for application.
Highlights
Irrigation water is the main component of off-stream water uses
Farmland irrigation water is influenced by climate and human factors, five factors including rainfall, irrigation area, food production, planting structure and agricultural water saving level were selected as the impact factors to be analysed
Where WWis the forecasted irrigation water demand, ff(PP, FF, ... ) is a linear regression equation based on the principal component, P is the predicted rainfall, FF is the predicted irrigation area, α is the annual average coefficient of water saving progress, tt is the forecasting year, and tt0 is the start year of the data series
Summary
Irrigation water is the main component of off-stream water uses. It is important to reasonably estimate irrigation water demand. The Simulation model prediction method, which considers many factors, but is limited in data scarce regions. Leenhardt (2004) set up a simulation platform called ADEAUMIS, which includes a bio-decision model and a specialized input database necessary to run it. It considered factors such as irrigation area, climate and irrigation techniques. In water-short regions, which have a large intensity of water-saving irrigation, only considering climate factors is obviously inadequate. This has barely been considered in the above studies. The method in this paper is based on principal component analysis (PCA) and regression analysis methods, and considers the influence of water saving
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