scholarly journals Possible Linkages of Hydrological Variables to Ocean–Atmosphere Signals and Sunspot Activity in the Upstream Yangtze River Basin

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1361
Author(s):  
Ruting Yang ◽  
Bing Xing
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Spatiotemporal Variability ◽  
Sunspot Activity ◽  
Monthly Precipitation ◽  
Time Frequency ◽  
Hydrological Variables

Profiling the hydrological response of watershed precipitation and streamflow to large-scale circulation patterns and astronomical factors provides novel information into the scientific management and prediction of regional water resources. Possible contacts of El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), sunspot activity to precipitation and streamflow in the upper Yangtze River basin (UYRB) were investigated in this work. Monthly precipitation and streamflow were utilized as well as contemporaneous same-scale teleconnections time series spanning a total of 70 years from 1951 to 2020 in precipitation and 121 years from 1900 to 2020 in streamflow. The principal component analysis (PCA) method was applied so as to characterize the dominant variability patterns over UYRB precipitation time series, with the temporal variability of first two modes explaining more than 80% of total variance. Long-term evolutionary pattern and periodic variation characteristics of precipitation and streamflow are explored by applying continuous wavelet transform (CWT), cross-wavelet transform (XWT) and wavelet coherence (WTC), analyzing multi-scale correlation between hydrological variables and teleconnections in the time-frequency domain. The results manifest that ENSO exhibits multiple interannual period resonance with precipitation and streamflow, while correlations are unstable in time and phase. PDO and sunspot effects on precipitation and streamflow at interannual scales vary with time-frequency domains, yet significant differences are exhibited in their effects at interdecadal scales. PDO exhibits a steady negative correlation with streamflow on interdecadal scales of approximately 10 years, while the effect of sunspot on streamflow exhibits extremely steady positive correlation on longer interdecadal scales of approximately 36 years. Analysis reveals that both PDO and sunspot have significantly stronger effects on streamflow variability than precipitation, which might be associated with the high spatiotemporal variability of precipitation.

scholarly journals Hybrid method for assessing the multi-scale periodic characteristics of the precipitation–runoff relationship: a case study in the Weihe River basin, China

2016 ◽  
Vol 8 (1) ◽  
pp. 62-77 ◽  
Author(s):  
Aijun Guo ◽  
Jianxia Chang ◽  
Qiang Huang ◽  
Yimin Wang ◽  
Dengfeng Liu ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
River Basin ◽  
Hybrid Method ◽  
Hydrological Cycle ◽  
Monthly Precipitation ◽  
Time Frequency ◽  
Multi Scale ◽  
Frequency Domains ◽  
Weihe River ◽  
Weihe River Basin

Fully elucidating the precipitation–runoff relationship (PRR) is of great significance for better water resources planning and management and understanding hydrological cycle processes. For investigating the multi-scale PRR variability in the Weihe River basin in 1960–2010, a new hybrid method is proposed in which ensemble empirical mode decomposition (EEMD) and cross wavelet transform and wavelet transform coherence are used in combination. With the application of mutual information entropy, monthly precipitation and runoff are decomposed into two parts: high- (HFC) and low-frequency components (LFC). The results show that HFCs are characterized by inter- and intra-annual variations in precipitation and runoff, whereas LFCs display approximately two-year periodicity and contain abundant abnormal information of the raw data. Therefore, the PRR between HFCs exhibited significant correlations at the 95% confidence level over the whole time period. However, the correlations of the PRR between LFCs are not significant for many of the time-frequency domains. Additionally, the phase relations are disordered in these time-frequency domains, and no certain trend in phase angle variations can be identified. Through comparative analysis of the anthropogenic activities and climatic events with PRR variations, it can be concluded that the hybrid method can efficiently capture the PRR in various time-frequency domains.

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

Climate ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 53
Author(s):  
Heng Qian ◽  
Shi-Bin Xu
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Future Climate Change ◽  
Climate Indices ◽  
The Yangtze River ◽  
Second Mode ◽  
The Yangtze River Basin

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.

Future Climates from Bias-Bootstrapped Weather Analogs: An Application to the Yangtze River Basin

2010 ◽  
Vol 23 (13) ◽  
pp. 3509-3524 ◽  
Author(s):  
Boris Orlowsky ◽  
Oliver Bothe ◽  
Klaus Fraedrich ◽  
Friedrich-Wilhelm Gerstengarbe ◽  
Xiuhua Zhu
Keyword(s):  
Time Series ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Central China ◽  
The Yangtze River ◽  
Linear Temperature ◽  
Validation Experiment ◽  
Single Station ◽  
The Yangtze River Basin

Abstract The authors describe a statistical analog resampling scheme, similar to the “intentionally biased bootstrap,” for future climate projections whose only constraint is a prescribed linear temperature trend. It provides a large ensemble of day-to-day time series of single-station weather variables and other climatological observations at low computational cost. Time series are generated by mapping time sequences from the observed past into the future. The Yangtze River basin, comprising all climatological subregions of central China, is used as a test bed. Based on daily station data (1961–2000), the bootstrap scheme is assessed in a cross-validation experiment that confirms its applicability. Results obtained for the projected future climates (2001–40) include climatological profiles along the Yangtze, annual cycles, and other weather-related phenomena (e.g., floods, droughts, monsoons, typhoons): (i) the annual mean temperature and, associated with that, precipitation increase; (ii) the annual cycle shows an extension of the Asian summer monsoon season with increasing rainfall, linked to a small summer temperature reduction in the Yangtze lower reaches; (iii) coupling between monsoon circulation and monsoon rainfall strengthens; (iv) while drought occurrence is reduced, Yangtze floods do not change considerably; and (v) the number of typhoon days in the East China Sea shows a reduction of about 25%; the proportion of intense typhoons with landfall increases. GCM scenario simulations produce similar results.

Using multi-temporal analysis to classify monthly precipitation based on maximal overlap discrete wavelet transform

2019 ◽  
Vol 21 (4) ◽  
pp. 541-557 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Farhad Alizadeh
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Spatial Clustering ◽  
Superior Performance ◽  
Discrete Wavelet ◽  
Monthly Precipitation ◽  
Time Frequency ◽  
Clustering Model ◽  
Multi Temporal

AbstractIn the present study, a hybrid methodology was proposed in which temporal pre-processing and spatial classification approaches were used in a way to take advantage of multiscale properties of precipitation series. Monthly precipitation data (1960–2010) for 31 rain gauges were used in the proposed classification approaches. Maximal overlap discrete wavelet transform (MODWT) was used to capture the time–frequency attributes of the time series and multiscale regionalization was performed by using self-organizing maps (SOM) clustering model. Daubechies 2 function was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the wavelet (W) and scaling (V) coefficients were used to determine the input dataset as a basis of spatial clustering. Four input combinations were determined as single-cycle and the remaining four combinations were determined with multi-temporal dataset. These combinations were determined in a way to cover all possible scales captured from MODWT. The proposed model's efficiency in spatial clustering stage was verified using Silhouette Coefficient index. Results demonstrated superior performance of MODWT-SOM in comparison to historical-based SOM approach. It was observed that the clusters captured by MODWT-SOM approach determined homogenous precipitation areas very well (based on physical analysis).

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