Abstract
This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA) models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI). About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR) data were selected to develop the ARIMA models from the erecting stage to the maturity stage of winter wheat (early March to late May in each year at a ten-day interval) of the years from 2000 to 2009. We take the study area overlying on the administration map around the study area, and divide the study area into 17 parts where at least one weather station is located in each part. The pixels where the 17 weather stations are located are firstly chosen and studied for their fitting models, and then the best models for all pixels of the whole area are determined. According to the procedures for the models’ development, the selected best models for the 17 pixels are identified and the forecast is done with three steps. The forecasting results of the ARIMA models were compared with the monitoring ones. The results show that with reference to the categorized VTCI drought monitoring results, the categorized forecasting results of the ARIMA models are in good agreement with the monitoring ones. The categorized drought forecasting results of the ARIMA models are more severity in the northeast of the Plain in April 2009, which are in good agreements with the monitoring ones. The absolute errors of the AR(1) models are lower than the SARIMA models, both in the frequency distributions and in the statistic results. However, the ability of SARIMA models to detect the changes of the drought situation is better than the AR(1) models. These results indicate that the ARIMA models can better forecast the category and extent of droughts and can be applied to forecast droughts in the Plain.
Highlights
Earth experiences numerous extreme weather-induced disasters: droughts, floods, hurricanes, heat waves, bushfires, insect infestations and many others
The main objective of the present study is to model time series of Vegetation Temperature Condition Index (VTCI) for drought forecasting by applying Seasonal AutoRegressive Integrated Moving Average (SARIMA) models in the Guanzhong Plain of China
Having tentatively decided d and D, we further identify the orders of the SARIMA process and the principal tools of autocorrelation function (ACF) and partial autocorrelation function (PACF) as well
Summary
Earth experiences numerous extreme weather-induced disasters: droughts, floods, hurricanes, heat waves, bushfires, insect infestations and many others. Drought is one of the most damaging environmental phenomena [1]. Information about the onset of droughts, their extent, intensity, duration and impact is useful for alleviating the losses of life, human suffering and decreasing damages to economy and environment. Droughts have been occurring frequently, and their impacts are being aggravated by the rise in water demand and the variability in hydro-meteorological variables due to climate change [2]. The causes for the occurrence of droughts are complex, because they depend
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