Assessment of rainfall‐runoff models based upon wavelet analysis

By Stuart N. Lane. 2007

65 citations recorded by the source corpus.

A basic hypothesis is proposed: given that wavelet‐based analysis has been used to interpret runoff time‐series, it may be extended to evaluation of rainfall‐runoff model results. Conventional objective functions make certain assumptions about the data series to which they are applied (e.g. uncorrelated error, homoscedasticity). The difficulty that objective functions have in distinguishing between different realizations of the same model, or different models of the same system, is that they may have contributed in part to the occurrence of model equifinality. Of particular concern is the fact that the error present in a rainfall‐runoff model may be time dependent, requiring some form of time localization in both identification of error and derivation of global objective functions. We explore the use of a complex Gaussian (order 2) wavelet to describe: (1) a measured hydrograph; (2) the same hydrograph with different simulated errors introduced; and (3) model predictions of the same hydrograph based upon a modified form of TOPMODEL. The analysis of results was based upon: (a) differences in wavelet power (the wavelet power error) between the measured hydrograph and both the simulated error and modelled hydrographs; and (b) the wavelet phase. Power difference and wavelet phase were used to develop two objective functions, RMSE(power) and RMS(phase), which were shown to distinguish between simulated errors and model predictions with similar values of the commonly adopted Nash‐Sutcliffe efficiency index. These objective functions suffer because they do not retain time, frequency or time‐frequency localization. Consideration of wavelet power spectra and time‐ and frequency‐integrated power spectra shows that the impacts of different types of simulated error can be seen through retention of some localization, especially in relation to when and the scale over which error was manifest. Theoretical objections to the use of wavelet analysis for this type of application are noted, especially in relation to the dependence of findings upon the wavelet chosen. However, it is argued that the benefits of localization and the qualitatively low sensitivity of wavelet power and phase to wavelet choice are sufficient to warrant further exploration of wavelet‐based approaches to rainfall‐runoff model evaluation. Copyright © 2006 John Wiley & Sons, Ltd.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/a8af1a1a81e0320eab7478676846ea13722bb7c3

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Citations in the corpus

Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, methodology): “…analysis has been applied to examine rainfall-runoff relationships in Karstic watersheds (Labat et al., 1999), to evaluate rainfall-runoff models (Lane, 2007), to forecast river flow (Adamowski, 2008; Adamowski and Sun, 2010), to forecast groundwater levels (Adamowski and Chan, 2011), to…”
Multi-models for SPI drought forecasting in the north of Haihe River Basin, China (2017, methodology): “…2012), to forecast future precipitation (Partal and KisI 2007), and for drought forecasting (Kim and Valdes 2003), to assess rainfall-runoff models(Lane 2007), to check up the rainfall-runoff relationship in a Karstic watershed (Labat et al. 1999), to characterize daily streamflow (Saco and Kumar…”
Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. (2010, background): “…1999), to characterize daily streamflow (Smith et al., 1998; Saco and Kumar, 2000) and monthly reservoir inflow (Coulibaly et al., 2000), to evaluate rainfall–runoff models (Lane, 2007), to analyze streamflow trends (Adamowski et al., 2009), and to forecast river flow (Adamowski, 2007, 2008a,b).”
Characterising performance of environmental models (2013, methodology): “In the first, Lane (2007) uses a discrete approximation of the Continuous Wavelet Transform (CWT) to analyse performance of rainfallerunoff model results.”