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Improvements in Performance of the Hillslope Link Model in Iowa using a Non-linear Representation of Natural and Artificially Drained Subsurface Flows
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  • Nicolas Velasquez,
  • Ricardo Mantilla,
  • Witold F Krajewski,
  • Morgan Fonley
Nicolas Velasquez
The university of iowa

Corresponding Author:[email protected]

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Ricardo Mantilla
IIHR - Hydroscience & Engineering
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Witold F Krajewski
IIHR-Hydroscience & Engineering
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Morgan Fonley
Alma College
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This evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation allows a better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify overall model improvement. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors find discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations.