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- 2019
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Cao, Qing, et al.
(författare)
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On the predictability of daily rainfall during rainy season over the Huaihe River Basin
- 2019
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Ingår i: Water. - : MDPI AG. - 2073-4441. ; 11:5
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Tidskriftsartikel (refereegranskat)abstract
- In terms of climate change and precipitation, there is large interest in how large-scale climatic features affect regional rainfall amount and rainfall occurrence. Large-scale climate elements need to be downscaled to the regional level for hydrologic applications. Here, a new Nonhomogeneous Hidden Markov Model (NHMM) called the Bayesian-NHMM is presented for downscaling and predicting of multisite daily rainfall during rainy season over the Huaihe River Basin (HRB). The Bayesian-NHMM provides a Bayesian method for parameters estimation. The model avoids the risk to have no solutions for parameter estimation, which often occurs in the traditional NHMM that uses point estimates of parameters. The Bayesian-NHMM accurately captures seasonality and interannual variability of rainfall amount and wet days during the rainy season. The model establishes a link between large-scale meteorological characteristics and local precipitation patterns. It also provides a more stable and efficient method to estimate parameters in the model. These results suggest that prediction of daily precipitation could be improved by the suggested new Bayesian-NHMM method, which can be helpful for water resources management and research on climate change.
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| 3. |
- Chen, Sichun, et al.
(författare)
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Spatiotemporal Changes in Precipitation and Temperature in the Huaibei Plain and the Relation between Local Precipitation and Global Teleconnection Patterns
- 2019
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Ingår i: Journal of Hydrologic Engineering. - 1084-0699. ; 24:8
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Tidskriftsartikel (refereegranskat)abstract
- The Huaibei Plain is one of the most severe water scarcity areas in China. Understanding of hydroclimatic variation in this area at different timescales and its relationship with global teleconnection patterns are important for assessment of water resources utilization. In this study, spatiotemporal changes of seasonal and annual precipitation and temperature, including trend, abrupt change, variability, and periodicity were examined to recognize the potential remarkable changes during the last 41 years. The relationship between precipitation in the Huaibei Plain and teleconnection patterns using climate indexes was revealed by applying singular value decomposition. Results showed a nonsignificant annual precipitation increase about 2.4 mm/year. The annual average temperature increased about 1.2°C during 1970-2010. The abrupt change of annual precipitation mainly occurred during the 1970s and 1980s, while the primary mutation points for temperature were detected in 1990s, especially in 1997. The mean areal precipitation is characterized by a statistically significant 2- to 4-year periodicity at different phases, and the 2- to 5-year band is the major cycle for annual average temperature in this region. A statistically strong 5- to 8-year periodicity for precipitation could be detected from the middle of the 1980s to the end of the 1990s. Precipitation has positive correlation with the West Pacific Pattern and El Nino Southern Oscillation. The investigated results might have considerable implications for managing water resources in the Huaibei Plain.
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| 4. |
- du, Yiheng, et al.
(författare)
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Multi-Space Seasonal Precipitation Prediction Model Applied to the Source Region of the Yangtze River, China
- 2019
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Ingår i: Water. - 2073-4441. ; 11:12
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Tidskriftsartikel (refereegranskat)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.
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