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A dual-pass error-correction technique for forecasting streamflow
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DOI：10.1016/j.jhydrol.2011.05.036.png)
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摘要
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This study reports the development and testing of a dual-pass method for correcting slowly varying errors in simulations of streamflow. Error correction is a form of data assimilation used to improve streamflow forecasts. The dual-pass method is ideally suited for catchments with long-lasting shifts in runoff efficiency that the hydrologic model poorly simulates. In a first pass, the simulation is rescaled (i.e. multiplicative correction is applied), based on the cumulative error over the prior 365 days. In a second pass, a correction is added to the adjusted series, based on the error from the most recent timestep. When tested on 330 Australian and 183 United States catchments, the dual-pass approach improved the median four-measure validation skill score from 0.83 to 0.89 for the GR4J model and from 0.56 to 0.79 for a naive coefficient model. The majority of improvement comes from the second pass (the short-memory component). The magnitudes of correction at each pass are controlled by two tuneable parameters: a global sensitivity analysis determined that satisfactory performance could be had without tuning of the long-memory (first pass) error-correction parameter. For most catchments, the use of the long-memory error-correction neither degrades nor significantly improves performance. International hydrological modeling datasets have relatively fewer catchments with slowly varying errors than might be encountered in operational forecasting environments, therefore, there is a need for better identification and study of such problem catchments. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
Keyword:
Forecast updating
Autoregressive model
Data assimilation
Non-stationarity
Time-series model
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期刊
IF:
6.3
论文数:
2.4W
被引数:
9.8W
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