Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices

Abstract

Autumn precipitation (AP) has important impacts on agricultural production, water conservation, and water transportation in the middle and lower reaches of the Yangtze River Basin (MLYRB; 25°–35° N and 105°–122° E). We obtain the main empirical orthogonal function (EOF) modes of the interannual variation in AP based on daily precipitation data from 97 stations throughout the MLYRB during 1980–2015. The results show that the first leading EOF mode accounts for 30.83% of the total variation. The spatial pattern shows uniform change over the whole region. The variance contribution of the second mode is 16.13%, and its spatial distribution function shows a north-south phase inversion. Based on previous research and the physical considerations discussed herein, we include 13 climate indices to reveal the major predictors. To obtain an acceptable prediction performance, we comprehensively rank the climate indices, which are sorted according to the values of the new standardized algorithm of information flow (NIF, a causality-based approach) and correlation coefficient (a traditional climate diagnostic tool). Finally, Tropical Indian Ocean Dipole (TIOD), Arctic Oscillation (AO), and other four indicators are chosen as the final predictors affecting the first mode of AP over the MLYRB; NINO3.4 SSTA (NINO3.4), Atlantic-European Circulation E Pattern (AECE), and other four indicators are the major predictors for the second mode. In the final prediction experiment, considering the time series prediction of principal components (PCs) to be a small-sample problem, the Bayesian linear regression (BLR) model is used for the prediction. The experimental results reveal that the BLR model can effectively capture the time series trends of the first two modes (the correlation coefficients are greater than 0.5), and the overall performance is significantly better than that of the multiple linear regression (MLR) model. The prediction factors and precipitation prediction results identified in this study can be referenced to rapidly obtain climatological information for AP over the MLYRB and improve the regional prediction of AP elsewhere, which will also help policymakers prepare appropriate adaptation and mitigation measures for future climate change.

The Relationship between River Flow Regimes and Climate Indices of ENSO and IOD on Code River, Southern Indonesia

“…In contrast, the other ENSO indices were mostly based on the sea surface temperature anomalies in the tropical Pacific Ocean, such as NINO 3.4. The NINO 3.4 was identified as the most representative index for ENSO since the publication in 1997 [28] and was frequently used in recent studies (e.g., [19,29,30]). SOI and NINO 3.4 were well-correlated and not statistically different in most of the seasons [28].…”

Spatiotemporal Analysis on the Teleconnection of ENSO and IOD to the Stream Flow Regimes in Java, Indonesia

“…It has been generally used in early [15] and recent [16] research. The Niño 3.4 is a sea surface temperature (SST) index, located in 5 • N-5 • S and 170 • W-120 • W, that has been acknowledged as the most representative index for ENSO [17], and was frequently used in many recent studies [18][19][20]. The SOI, Niño 3.4, and DMI data sets were obtained from the USA National Oceanic and Atmospheric Administration (NOAA).…”

The relative importance of antecedent soil moisture and precipitation in flood generation in the middle and lower Yangtze River basin

“…Most of the precipitation comes between June and September, the premise of persistent heavy rain in the Yangtze River basin is the frequent activity of weak cold air in the north (Tao et al, 1980) and the intersection of mid-latitude air mass and monsoon air mass (Kato et al, 1985). Studies have found that both annual precipitation and the frequency of extreme precipitation events have increased in the middle and lower reaches of the Yangtze River (Qian et al, 2020;Fu et al, 2013). As a result, floods have occurred frequently in the middle and lower reaches of the Yangtze River, where most of the population in the YZRB live .…”