Abstract
This paper developed a multi-space prediction model for seasonal precipitation using a high-resolution grid dataset (0.5° × 0.5°) together with climate indices. The model is based on principal component analyses (PCA) and artificial neural networks (ANN). Trend analyses show that mean annual and seasonal precipitation in the area is increasing depending on spatial location. For this reason, a multi-space model is especially suited for prediction purposes. The PCA-ANN model was examined using a 64-grid mesh over the source region of the Yangtze River (SRYR) and was compared to a traditional multiple regression model with a three-fold cross-validation method. Seasonal precipitation anomalies (1961–2015) were converted using PCA into principal components. Hierarchical lag relationships between principal components and each potential predictor were identified by Spearman rank correlation analyses. The performance was compared to observed precipitation and evaluated using mean absolute error, root mean squared error, and correlation coefficient. The proposed PCA-ANN model provides accurate seasonal precipitation prediction that is better than traditional regression techniques. The prediction results displayed good agreement with observations for all seasons with correlation coefficients in excess of 0.6 for all spatial locations.
Highlights
Precipitation is an important resource that affects ecological health, agricultural yield, and economic growth
The North Atlantic Oscillation (NAO), POL, Southern Oscillation Index (SOI), and Scandinavia Pattern (SCA) events have an influence on precipitation in the source region of the Yangtze River (SRYR) during the cold season, while NAO, Pacific Decadal Oscillation (PDO), and SOI are more important on precipitation in the SRYR during the cold season, while NAO, PDO, and SOI are more important for the warm season
This study aimed to develop a multi-space model for seasonal precipitation prediction applied to the SRYR with a minimum loss of information
Summary
Precipitation is an important resource that affects ecological health, agricultural yield, and economic growth. The ability to predict future precipitation anomalies has received considerable research attention [1,2,3,4,5]. Predictive models can help decisionmakers to better manage finite future water resources. The source region of the Yangtze River (SRYR), the origin of the longest river in. China, is located in the central Tibet Plateau. With occurring wetland degradation and biodiversity reduction, the variability of precipitation has a significant influence on the sustainability of the basin itself and downstream areas [6,7]. The ability to predict future precipitation in this area is of great importance
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