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Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting.

Abstract. Understanding the propagation of prolonged meteorological drought helps solve the problem of intensified water scarcity around the world. Most of the existing literature studied the propagation of drought from one type to another (e.g., from meteorological to hydrological drought) with statistical approaches; there remains difficulty in revealing the causality between meteorological drought and potential changes in the catchment water storage capacity (CWSC). This study aims to identify the response of the CWSC to the meteorological drought by examining the changes of hydrological-model parameters after drought events. Firstly, the temporal variation of a model parameter that denotes that the CWSC is estimated to reflect the potential changes in the real CWSC. Next, the change points of the CWSC parameter were determined based on the Bayesian change point analysis. Finally, the possible association and linkage between the shift in the CWSC and the time lag of the catchment (i.e., time lag between the onset of the drought and the change point) with multiple catchment properties and climate characteristics were identified. A total of 83 catchments from southeastern Australia were selected as the study areas. Results indicated that (1) significant shifts in the CWSC can be observed in 62.7 % of the catchments, which can be divided into two subgroups with the opposite response, i.e., 48.2 % of catchments had lower runoff generation rates, while 14.5 % of catchments had higher runoff generation rate; (2) the increase in the CWSC during a chronic drought can be observed in smaller catchments with lower elevation, slope and forest coverage of evergreen broadleaf forest, while the decrease in the CWSC can be observed in larger catchments with higher elevation and larger coverage of evergreen broadleaf forest; (3) catchments with a lower proportion of evergreen broadleaf forest usually have a longer time lag and are more resilient. This study improves our understanding of possible changes in the CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system under climate change.

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