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To test this hypothesis, a multiple linear regression model for monthly streamflow reconstruction is presented that expands the set of predictors to include annual streamflow reconstructions, reconstructions of global circulation, and potential differences among regional tree-ring chronologies related to tree species and geographic location.
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Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow.
Regionalization using mean annual and mean monthly streamflow values was used to delineate hydrologic regimes with distinct temporal flow variations.
BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread.
The NSCE value for monthly streamflow over a period of 22 years was 0.79.
Monthly streamflow forecasts with long lead time are being sought by water managers in Australia.
Finally, cloud forest conversion to pasture does not promote significant monthly streamflow change.
This study develops configurational entropy theory (CET) for monthly streamflow forecasting.
Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology.
The proposed methodology was demonstrated using monthly streamflow data from the Shavir Creek in Iran.
Then this approach is tested on a typical empirical hydrological model for monthly streamflow forecasting.
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