daily mean discharge Articles
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Regional climate model and model output statistics method uncertainties and the effect of temperature and precipitation on future river discharges in Scandinavia
We analyze the importance of regional climate models (GCM-RCMs) and model output statistics (MOS) methods as uncertainty sources for future changes of various hydrological variables in Scandinavia. The Hydrological Predictions for the Environment (HYPE) model, driven with daily mean temperature and precipitation, is used to simulate changes in river discharges and other hydrological ...
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Evaluation of the impacts of future hydrological changes on the sustainable water resources management of the Richmond River catchment
The conceptual rainfall–runoff (HBV model) is applied to evaluate impacts of future climate changes on the hydrological system of the Richmond River catchment, Australia. Daily observed rainfall, temperature and discharge and long-term monthly mean potential evapotranspiration from the hydro-meteorological stations within the catchment over the period 1972–2014 were used to run, calibrate and ...
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Integration of remote sensing evapotranspiration (ET) model and hydrologic model for mapping daily ET time series at river basin scale
Hydrological models and remote sensing evapotranspiration (ET) models usually are used to estimate regional ET. This study aims to integrate the advantages of both the models to simulate the daily ET processes. A compromise between these two methodologies is represented by improving the optimization of the hydrological model on the basis of a new probability optimal ET series, which is ...
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Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China
Modeling of hydrological time series is essential for sustainable development and management of lake water resources. This study aims to develop an efficient model for forecasting lake water level variations, exemplified by the Poyang Lake (China) case study. A random forests (RF) model was first applied and compared with artificial neural networks, support vector regression, and a linear ...
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