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 non-linear 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 km2), 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.
Highlights
Distributed hydrologic models usually consist of two major components that together produce stream discharge estimates
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)
We start our evaluation of RAPID and the HLM routing by visually comparing their Continuous Wavelet Transform (CWT) against the CWT we obtained from the observed flow
Summary
Distributed hydrologic models usually consist of two major components that together produce stream discharge estimates. The first component is a Land Surface Model (LSM) that decomposes the terrain into a regular (e.g. rectangular or triangular) or irregular (e.g. terrain-fit polygons outlining hillslopes) grid where the energy and mass exchange between the land and the atmosphere are modeled This process produces estimates of the potential excess surface and subsurface runoff depths that will enter the stream channels. The second routing component we evaluate in this study is the one implemented in the Hillslope-Link hydrologic Model (HLM) developed and used by the Iowa Flood Center (IFC) This routing component is nonlinear and accounts for the momentum equation in a simplified form ([6] [7] and [8]), and the stream velocity is determined based on the nonlinear relationship between the discharge and the served area ([9] [10]). This means we assume the runoff depth immediately enters the streams in their corresponding upstream junctions before the channel routing takes place
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