A soil moisture index as an auxiliary ANN input for stream flow forecasting

作者：Anctil F^*; Michel C; Perrin C; Andreassian V

来源：Journal of Hydrology, 2004, 286(1-4): 155-167.

DOI：10.1016/j.jhydrol.2003.09.006

• rainfall-runoff
• artificial neural networks
• soil moisture
• conceptual model
• antecedent precipitation index
• potential evapotranspiration

This study tests the short-term forecasting improvement afforded by the inclusion of low-frequency inputs to artificial neural network (ANN) rainfall-runoff models that are first optimized by using only fast response components, i.e. using stream flow and rainfall as inputs. Ten low-frequency ANN input candidates are considered: the potential evapotranspiration, the antecedent precipitation index (API(i), i = 7, 15, 30, 60, and 120 days) and a proposed soil moisture index time series (SMIA, for A = 100, 200, 400 and 800 mm). As the ANNs considered are for use in real-time lead-time-L forecasting, forecast performance is expressed in terms of the persistence index, rather than the conventional Nash-Sutcliffe index. The API(i) are the non-decayed moving average precipitation series, while the SMIA are calculated through the soil moisture accounting reservoir of the lumped conceptual rainfall-runoff model GR4J. Results, based on daily data of the Serein and Leaf rivers, reveal that only the SMIA time series are useful for one-day-ahead stream flow forecasting, with both the potential evapotranspiration and the API(i)time series failing to improve the ANN performance.

• 出版日期2004-1-30