Long-term streamflow forecast is of great significance for water resource application and management. However, accurate monthly streamflow forecasting is challenging due to its non-stationarity and uncertainty. Time series analysis methods have been proved to perform well in stationary time series forecasting, which can be derived from decomposition of the non-stationary sequence. As common decomposition methods in time domain, Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) are selected to decompose the components with different time-scale characteristics in the original hydrological time series in this study. The derived components are proved to be stationary by the stationarity test. Thus, Autoregressive Integrated Moving Average (ARIMA) model, a simple and effective time series analysis method, is used to forecast the components. A hybrid EMD/EEMD-ARIMA model is proposed in this study for long-term streamflow forecasting, which is applied to the upper stream of the Yellow River. The original daily streamflow time series of six years at the Tangnaihai station are firstly decomposed by EMD/EEMD into several stationary or simple non-stationary sub-series to explore detailed data information with different time scales. ARIMA models with appropriate parameters are then established for each subsequence to forecast the stream flow of the next year. Predicted ten-day and monthly stream flow is finally obtained combing the predictions of all the components. The EMD-ARIMA hybrid model performs best in forecasting high and moderate value of streamflow and fits best with the observation compared with EEMD-ARIMA and ARIMA models. The results not only verify the effectiveness of the proposed hybrid EMD/EEMD-ARIMA model in exploiting comprehensive information to improve the prediction but also indicate that the EMD-ARIMA model with end points disposal performs the best and can be used for long-term hydrological forecasting.

Global warming has changed both the amount of global precipitation and the atmospheric capacity to retain water. In this paper, a novel definition of the long‐term Capturability of Atmospheric Water (CAW) based on horizontal atmospheric water transport is proposed, describing the ability of a certain area to intercept and convert the atmospheric water transported by horizontal moisture flux into local precipitation. The significant decrease of the CAW in Amazon and Congo rainforests and Inside Greenland indicates that these areas were having less precipitation with the same water vapor in the past 42 years, while in Asia (especially China), CAW is showing a large‐scale increasing trend, verifying the regional humidifying. Considering the change of both the CAW and the background atmospheric water simultaneously, their mismatch degree is also investigated. The positive mismatch in Qinghai Tibet Plateau, Greenland, and the Andes, suggests higher susceptibility to climate change, and in the areas of negative mismatch (Amazon, Maritime Continent, southeastern China, the Eastern United States, India, and Japan), a more stable precipitation response to climate change is expected. The proposed concept of CAW provides a novel perspective to analyze the precipitation response to climate change on a global scale.

Long-term prediction of solar radiation intensity plays an important role in the planning and design of photovoltaic power stations. Unlike previous research on solar radiation prediction requiring various meteorological and topographic data, this study proposed a rolling prediction model combining Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN) techniques with the need for historical solar radiation data only. To overcome the inconsistency problem of the number of intrinsic mode functions derived from the EMD, they are classified into high-frequency term, low-frequency term, and trend item, which are taken as the input parameters of the ANN model. With the historical data one-year after as the output parameter, the ANN model implies the complex, non-linear relationship between the adjacent periods, and it can be used to predict long-term solar radiation. The proposed methodology is applied to Gonghe county in the Qinghai province of China, where a large-scale photovoltaic power plant is under planning. The results indicate that the correlation coefficients between the daily and monthly predicted value and the historical data are 0.698 and 0.930, respectively, which are comparable to previous studies with a greater data requirement and a simpler model.