A Time-Based Framework for Evaluating Hydrologic Routing Methodologies Using Wavelet Transform

Abstract

In this study we explore a method which provides an insight into the effectiveness of various hydrologic models' routing components based on their ability to accurately represent flood peak times and shapes. The method is based on using Cross-Wavelet Transforms to estimate the phase (time) difference between the time series of the observed and the simulated discharges. In this article we evaluate two routing components, the Routing Application for Parallel Computation of Discharge (RAPID), which is based on the simplified Muskingum routing method, and the routing component of the nonlinear Hillslope-Link hydrologic Model (HLM) produced in the Iowa Flood Center (IFC). Both routing components are driven by the same source of runoff and used the same channel network to ensure that the discrepancies between the simulated stream discharges are due to channel routing alone. We also explore the suitability of different wavelet shapes for our application, and how the difference in wavelet shape can affect our evaluation results. Unlike the conventional statistical skill scores used to evaluate model performance (e.g. Root Mean Squared Error, correlation coefficient, and Nash Sutcliff efficiency index), which give an estimate of the overall hydrograph performance, our method conveniently provides time-localized information with higher resolution at peak location. We perform our evaluation at multiple stream gauge locations, covering a wide range of scales (700 to 16,862 km 2 ), located in the eastern part of the state of Iowa. Our results show that the proposed wavelet method is effective in evaluating the performance of the routing components in simulating peak times across spatial scales. Generally, the non-linear routing method employed in the HLM outperformed the Muskingum based method employed in RAPID. In addition, our results suggest that the Paul wavelet is more effective in detecting and separating individual peaks than the Morlet wavelet, which in turn leads to a more accurate evaluation of the routing components.

An Investigation of Errors in Distributed Models' Stream Discharge Prediction Due to Channel Routing

“…Since both routing components receive the same runoff estimates and the runoff is eventually routed through the channels; the LSM VIC seems to be a major contributor to this underestimation in the overall water volume. This is supported by the results presented in ElSaadani and Krajewski (), where the Noah‐MP runoff estimates were used as input for the HLM routing component using the same parameter configuration. The results show improved peak magnitude estimates when Noah‐MP runoff is used.…”

“…Second, evaluating the HLM routing component with runoff estimates from a variety of LSMs. Finally, a more comprehensive technique of estimating the peak time difference based on continuous wavelet transform, which also accounts for peak magnitude and range, is described in ElSaadani and Krajewski (). We hope that our study will be followed by many similar investigations by others in different parts of the country and collectively will contribute to improve national flood estimation and forecasting platforms.…”

Assessment of Changes in Flood Frequency Due to the Effects of Climate Change: Implications for Engineering Design

“…More details about the HLM equations, configuration, and numerical solver are provided in [33] and [32]. Examples of HLM applications are provided in [32,33,[36][37][38][39][40][41][42][43].…”

A spatio-temporal dynamical evaluation of satellite rainfall products in hydrologic applications

“…We chose the climatology-based ET due to its simplicity. Although this approach successfully represents the seasonal variability of ET, it does not capture the daily fluctuations nor the inter-annual variability of ET.Researchers at the IFC are currently working on producing a more sound ET estimation methodology for the IFC model; this will be the subject of future studies.A major component in our second hydrologic model used in this study(ElSaadani and Krajewski, 2017) is the Noah-MP LSM. Noah-MP requires atmospheric forcing data that can be obtained either from the Weather Research and Forecasting (WRF) model, or retrospective atmospheric forcings such as those provided by the NASA Phase 2 North…”