scholarly journals Linkage between global sea surface temperature and hydroclimatology of a major river basin of India before and after 1980

2017 ◽  
Vol 12 (12) ◽  
pp. 124002 ◽  
Author(s):  
Sonali Pattanayak ◽  
Ravi S Nanjundiah ◽  
D Nagesh Kumar
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Basin ◽  
Sea Surface ◽  
Before And After ◽  
Major River ◽  
Global Sea Surface Temperature

scholarly journals Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, Its Teleconnection to Global Sea Surface Temperature, and Its Forecasting by an Artificial Neural Network

2014 ◽  
Vol 142 (5) ◽  
pp. 1771-1791 ◽  
Author(s):  
Mohamed Helmy Elsanabary ◽  
Thian Yew Gan
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Basin ◽  
Principal Component ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Blue Nile ◽  
Artificial Neural ◽  
Mean Square Errors ◽  
Global Sea Surface Temperature

Abstract Rainfall is the primary driver of basin hydrologic processes. This article examines a recently developed rainfall predictive tool that combines wavelet principal component analysis (WPCA), an artificial neural networks-genetic algorithm (ANN-GA), and statistical disaggregation into an integrated framework useful for the management of water resources around the upper Blue Nile River basin (UBNB) in Ethiopia. From the correlation field between scale-average wavelet powers (SAWPs) of the February–May (FMAM) global sea surface temperature (SST) and the first wavelet principal component (WPC1) of June–September (JJAS) seasonal rainfall over the UBNB, sectors of the Indian, Atlantic, and Pacific Oceans where SSTs show a strong teleconnection with JJAS rainfall in the UBNB (r ≥ 0.4) were identified. An ANN-GA model was developed to forecast the UBNB seasonal rainfall using the selected SST sectors. Results show that ANN-GA forecasted seasonal rainfall amounts that agree well with the observed data for the UBNB [root-mean-square errors (RMSEs) between 0.72 and 0.82, correlation between 0.68 and 0.77, and Hanssen–Kuipers (HK) scores between 0.5 and 0.77], but the results in the foothills region of the Great Rift Valley (GRV) were poor, which is expected since the variability of WPC1 mainly comes from the highlands of Ethiopia. The Valencia and Schaake model was used to disaggregate the forecasted seasonal rainfall to weekly rainfall, which was found to reasonably capture the characteristics of the observed weekly rainfall over the UBNB. The ability to forecast the UBNB rainfall at a season-long lead time will be useful for an optimal allocation of water usage among various competing users in the river basin.

Experience in developing a method for long-term forecasting of the ice breakup time for the Vyatka River basin

2021 ◽  
Vol 4 ◽  
pp. 99-111
Author(s):  
Y.A Pavroz . ◽  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Basin ◽  
Geopotential Height ◽  
Sea Surface ◽  
River Ice ◽  
Northwest Pacific ◽  
Ice Breakup ◽  
The Impact ◽  
Breakup Time

An attempt is made to develop a method for long-term forecasting of the ice breakup time for the Vyatka River basin, to identify the impact of the distribution of sea surface temperature and geopotential height in the informative regions at the levels H100 and H500 over the Northern Hemisphere on the river ice breakup. The location and boundaries of the informative regions in the fields of H100 and H500 were revealed by the discriminant analysis, the EOF expansion coefficients of the fields of anomalies of monthly mean values of H100 and H500 for January and February and the anomalies of monthly mean sea surface temperature in the North Atlantic and Northwest Pacific were used as potential predictors. The stepwise regression analysis allowed deriving good and satisfactory (S/σ = 0.45–0.73) complex prognostic equations for forecasting the ice breakup time for the Vyatka River basin. The essential influence of H100 and H500 geopotential height fields and the spatial distribution of sea surface temperature anomalies in the North Atlantic and Northwest Pacific in January and February on the river ice breakup time is revealed. It is proposed to improve the method by considering the impact of air temperature, maximum ice thickness per winter, and other indirect characteristics on the processes of river ice breakup in the Vyatka River basin. Keywords: ice regime, long-range forecast, river ice breakup, expansion coefficients, geopotential height fields, spring ice phenomena, energy-active zones of the oceans, complex prognostic equation

scholarly journals Seasonal Covariability of Dryness or Wetness in China and Global Sea Surface Temperature

2020 ◽  
Vol 33 (2) ◽  
pp. 727-747
Author(s):  
Chunxiang Li ◽  
Chunzai Wang ◽  
Tianbao Zhao
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Sea Surface ◽  
Interdecadal Variability ◽  
Coupled Modes ◽  
Indian Ocean Basin Mode ◽  
Global Sea Surface Temperature

AbstractSeasonal covariability of the dryness/wetness in China and global sea surface temperature (SST) is investigated by using the monthly self-calibrated Palmer drought severity index (PDSI) data and other data from 1950 to 2014. The singular value decomposition (SVD) analysis shows two recurring PDSI–SST coupled modes. The first SVD mode of PDSI is associated with the warm phases of the eastern Pacific–type El Niño–Southern Oscillation (ENSO), the interdecadal Pacific oscillation (IPO) or Pacific decadal oscillation (PDO), the Indian Ocean basin mode (IOBM) in the autumn and winter, and the cold phase of the IOBM in the spring. Meanwhile, the Atlantic multidecadal oscillation (AMO) pattern appears in every season except the autumn. The second SVD mode of PDSI is accompanied by a central Pacific–type El Niño developing from the winter to autumn over the tropical Pacific and a positive phase of IPO or PDO from the winter to summer. Moreover, an AMO pattern is observed in all seasons except the summer, whereas the SST over the tropical Indian Ocean shows negligible variations. The further analyses suggest that AMO remote forcing may be a primary factor influencing interdecadal variability of PDSI in China, and interannual to interdecadal variability of PDSI seems to be closely associated with the ENSO-related events. Meanwhile, the IOBM may be a crucial factor in interannual variability of PDSI during its mature phase in the spring. In general, the SST-related dryness/wetness anomalies can be explained by the associated atmospheric circulation changes.

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