Multiscale teleconnection analysis of monthly total and extreme precipitations in the Yangtze River Basin using ensemble empirical mode decomposition

Tao, Lizhi; He, Xinguang; Qin, Jianxin

doi:10.1002/joc.6624

Cited by 11 publications

(10 citation statements)

References 59 publications

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“…(2006), which indicates that summer precipitation in this specific basin during 1960–2002 showed stationarity with an insignificant upward trend, and Tao et al . (2020), which supports that SST over the East China Sea, South China Sea, Kuroshio and Bay of Bengal can provide predictive information for monthly precipitation in the basin. In light of these, the basin can be served as the case region to demonstrate the proposed precipitation forecasting approach.…”

Section: Applicationsupporting

confidence: 63%

“…Tao et al . (2020) reports a close correlation between monthly precipitation in the upper Yangtze River basin and SSTA over East China Sea, South China Sea, Kuroshio (22°–36°N, 122°–150°E), as well as Bay of Bengal. In our study, the SSTA poles distributions to some extent in accord with the report.…”

Section: Resultsmentioning

confidence: 98%

“…Since the new approach proposed in this study uses SST as the predictor, here the MLR, RF, and SVM models also use SST‐related indices as predictors, including the Niño3.4 index, North Atlantic Triple index (NAT), Western Pacific Warm Pool Strength index (WPWPS), and Indian Ocean Basin Mode (IOBM). These indices are regarded as typical SST indices that reveal SST conditions across three major oceans in the world, that is, Pacific Ocean, Atlantic Ocean, and Indian Ocean, and therefore are widely used in many literatures (Kayano et al ., 2016; Si and Ding, 2016; Wei, 2017; Tao et al ., 2020). Time series of these indices are from National Climate Center of China (http://cmdp.ncc-cma.net/), covering the period 1961–2020.…”

Section: Resultsmentioning

confidence: 99%

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Long‐range precipitation forecast based on multipole and preceding fluctuations of sea surface temperature

Guo

Qian

et al. 2022

Intl Journal of Climatology

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Long-range precipitation forecasting is crucial for flooding control and water resources management. However, making precise forecasting is rather difficult due to the complex climatic factors and large uncertainties arising from long lead times. Sea surface temperature anomaly (SSTA) is one of the strongest signals that influence regional precipitation, often used for the development of precipitation forecasts. Traditional models using SSTA for precipitation forecasting usually screen SSTA over fixed oceanic zones and neglect its preceding temporal fluctuation information. In this study, we introduce a multipole SSTA index and the preceding fluctuation modes of SSTA to develop a monthly precipitation forecasting model, which is applied to the upper Yangtze River basin in China where monthly precipitation during May-October for the period of 1961-2020 are forecasted. Results show that more significant SSTA poles correlated with precipitation are found for September than for the other months. The new approach is able to forecast monthly precipitation for May-October in the basin, particularly for September. It outperforms traditional statistical and dynamical models and has much more skill in forecasting precipitation for June-September when heavy precipitation is more likely to occur than for May or October. Our approach enriches the knowledge of the relationship between precipitation and SSTA, which is conducive to the improvement of long-range precipitation forecasting.

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Section: Applicationsupporting

confidence: 63%

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Long‐range precipitation forecast based on multipole and preceding fluctuations of sea surface temperature

Guo

Qian

et al. 2022

Intl Journal of Climatology

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“…Since the long-term rainfall series data in the Taihu basin for access are limited, three stations (Nanjing, Nantong and Ningguo) within about 30 km from the Taihu basin were used in this study. The monthly rainfall at these adjacent stations are also subject to the similar climatic condition [37,38]. The monthly rainfall datasets for the period 1961-2019 were obtained from the China Meteorological Data Service Centre, China Meteorological Administration (CMA) (http://data.cma.cn/data/ cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html (accessed on 27 February 2021)).…”

Section: Study Area and Datamentioning

confidence: 99%

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

Zhou

Chalov

et al. 2022

Water

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The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R2, RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions.

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“…The Yangtze River Basin (YRB), the largest river basin in China and the 12th largest river basin in the world, plays a crucial role in regional water cycle, economic and social development (Tao et al 2021). The middle and lower reaches of the YRB (MLYRB) have the most abundant water source in China, with the highest nationwide density and number of lakes (Li et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Historical and future runoff changes in the Yangtze River Basin from CMIP6 models constrained by a weighting strategy

Zhao

Wang

2022

Environ. Res. Lett.

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Based on the ERA5-Land datasets from 1981-2020, a decadal oscillation has been found in the variation of summer runoff in the middle and lower reaches of the Yangtze River Basin (MLYRB). The oscillation suggests that the MLYRB will experience increased runoff in the next few decades after 2020 which saw a record high runoff in the MLYRB. The decadal changes in summer runoff over the MLYRB under various climate change scenarios are then analyzed with direct runoff outputs from 28 general circulation models (GCMs) participating in the sixth phase of Coupled Model Intercomparison Project (CMIP6). Given that the equal-weighted multi-model ensemble mean could not well represent the historical runoff changes in MLYRB, in this paper we introduced a model weighting scheme that considers both the model skill and independence. It turns out that this scheme has well constrain the models to represent the observed decadal changes of summer runoff. The weighted mean projections suggest that the summer runoff in the MLYRB during 2015-2100 under all warming scenarios will be higher than the present-day; and 2021-2040 is likely to be a period with significantly increased summer runoff. Results of the present study are of great implication for flood control and effective water resources management over the MLYRB in the future, and the weighting approach used in this paper could be applied to a wide range of projections at both regional and global scales.

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Multiscale teleconnection analysis of monthly total and extreme precipitations in the Yangtze River Basin using ensemble empirical mode decomposition

Cited by 11 publications

References 59 publications

Long‐range precipitation forecast based on multipole and preceding fluctuations of sea surface temperature

Long‐range precipitation forecast based on multipole and preceding fluctuations of sea surface temperature

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

Historical and future runoff changes in the Yangtze River Basin from CMIP6 models constrained by a weighting strategy

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