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【Objective】 The increased demand for water due to economic development coupled with dwindling water supply is the double whammy facing most provinces in north China. Given agriculture is the biggest water use sector, understanding the change in agricultural water demand is critical to improving water resources management. The purpose of this paper is to present a new method to estimate agricultural water use changes at provincial scale in the north of China. 【Method】 The proposed method was based on discrete wavelet transform (DWT), fractional-order grey model (FGM(1,1)), weighted Markov Chain (WMC), and autoregressive moving average (ARMA) model. The time series of agricultural water demand was firstly decomposed into approximate series and detailed series, respectively, using DWT. FGM(1,1) was then used to describe the approximate series, with the errors corrected by WMC. The Fisher optimal segmentation method was used to divide the state intervals of the predicted errors, and the state intervals were predicted using a probability transfer matrix. The predicted intervals and values of the approximate series were obtained from the boundary values and median of the predicted error state intervals. In comparison, the detailed series were predicted using the ARMA model based on the Akaike Information Criteria. These were used to predict the agricultural water demand and its intervals. We applied the models to agricultural water demand in Shaanxi and Inner Mongolia provinces, with data measured from 2002 to 2015 used to train the model and those measured from 2016 to 2019 to validate the model. We compared the results calculated from the proposed model with those estimated from the traditional GM (1,1), DWT- GM(1,1)-ARMA, and DWT-FGM(1,1)-ARMA models. 【Result】 The average absolute error of the proposed model for the two provinces was 1.25% and 1.01%, respectively, much less than those given rise to by other models. The predicted agricultural water demand intervals showed that after correction by WMC, the model provided reliable short-term fluctuation intervals in agricultural water demand in both provinces. 【Conclusion】 The proposed model for predicting agricultural water demand at provincial scale was accurate and robust. It can also predict the intervals which describe the short-term fluctuation in agricultural water demand. The model has an implication in helping improve water management and developing sustainable agriculture. |
