Potential improvements to statistical downscaling of general circulation model outputs to catchment streamflows with downscaled precipitation and evaporation

An existing streamflow downscaling model (SDM (original) ), was modified with the outputs of a precipitation downscaling model (PDM) and an evaporation downscaling model (EDM) as additional inputs, for improving streamflow projections. For this purpose, lag 0, lag 1 and lag 2 outputs of PDM were ind...

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Bibliographic Details
Published in:Theoretical and applied climatology 2015-10, Vol.122 (1-2), p.159-179
Main Authors: Sachindra, D. A., Huang, F., Barton, A., Perera, B. J. C.
Format: Article
Language:English
Subjects:
Analysis
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Calibration
Climatology
Earth and Environmental Science
Earth Sciences
Evaporation
Mathematical models
Original Paper
Precipitation
Precipitation (Meteorology)
Stream discharge
Stream flow
Streamflow
Waste Water Technology
Water Management
Water Pollution Control
Watersheds
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Summary:An existing streamflow downscaling model (SDM (original) ), was modified with the outputs of a precipitation downscaling model (PDM) and an evaporation downscaling model (EDM) as additional inputs, for improving streamflow projections. For this purpose, lag 0, lag 1 and lag 2 outputs of PDM were individually introduced to SDM (original) as additional inputs, and then it was calibrated and validated. Performances of the resulting modified models were assessed using Nash-Sutcliffe efficiency (NSE) during calibration and validation. It was found that the use of lag 0 precipitation as an additional input to SDM (original) improves NSE in calibration and validation. This modified streamflow downscaling model is called SDM (lag0_preci) . Then lag 0, lag 1 and lag 2 evaporation of EDM were individually introduced to SDM (lag0_preci) as additional inputs and it was calibrated and validated. The resulting models showed signs of over-fitting in calibration and under-fitting in validation. Hence, SDM (lag0_preci) was selected as the best model. When SDM (lag0_preci) was run with observed lag 0 precipitation, a large improvement in NSE was seen. This proved that if precipitation produced by the PDM can accurately reproduce the observations, improved precipitation predictions will produce better streamflow predictions.
ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-014-1288-7