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. Streamflow timing errors (in the units of time) are rarely explicitlyevaluated but are useful for model evaluation and development. Wavelet-based approaches have been shown to reliably quantify timing errorsin streamflow simulations but have not been applied in a systematic waythat is suitable for model evaluation. This paper provides a step-by-stepmethodology that objectively identifies events, and then estimates timingerrors for those events, in a way that can be applied to large-sample,high-resolution predictions. Step 1 applies the wavelet transform to theobservations and uses statistical significance to identify observed events. Step 2 utilizes the cross-wavelet transform to calculate the timing errors for the events identified in step 1; this includes the diagnostic of model event hits, and timing errors are only assessed for hits. Themethodology is illustrated using real and simulated stream discharge datafrom several locations to highlight key method features. The method groupsevent timing errors by dominant timescales, which can be used to identifythe potential processes contributing to the timing errors and the associated model development needs. For instance, timing errors that are associated with the diurnal melt cycle are identified. The method is also useful for documenting and evaluating model performance in terms of defined standards. This is illustrated by showing the version-over-version performance of the National Water Model (NWM) in terms of timing errors.

A wavelet-based approach to streamflow event identification and modeled timing error evaluation