Spatiotemporal characteristics of extreme droughts and their association with sea surface temperature over the Cauvery River basin, India

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.

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

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2020-4-99-111 ◽

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

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

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2021-4-99-111 ◽

2021 ◽

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

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Long-Lead Seasonal Prediction of Streamflow over the Upper Colorado River Basin: The Role of the Pacific Sea Surface Temperature and Beyond

Journal of Climate ◽

10.1175/jcli-d-20-0824.1 ◽

2021 ◽

pp. 1-47

Author(s):

Siyu Zhao ◽

Rong Fu ◽

Yizhou Zhuang ◽

Gaoyun Wang

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Sea Surface Temperature ◽

River Basin ◽

Colorado River ◽

Sea Surface ◽

Colorado River Basin ◽

Lead Times ◽

Upper Colorado River Basin ◽

The Pacific

AbstractWe have developed two statistical models for extended seasonal predictions of the Upper Colorado River Basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST Predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than four months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ~0.50). Since these land-surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ~0.50 using PSPs alone for lead times from six to nine months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ~0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time, and can provide useful climate information for water supply planning and decisions.

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