Improving Hillslope Link Model Performance from Non-Linear Representation of Natural and Artificially Drained Subsurface Flows

Nicolás Velásquez,Felipe Quintero,Witold Krajewski,Morgan Fonley,Ricardo Mantilla

doi:10.3390/hydrology8040187

Nicolás Velásquez, Felipe Quintero + Show 3 more

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https://doi.org/10.3390/hydrology8040187

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Journal: Hydrology	Publication Date: Dec 20, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: University of Iowa, Alma College, University of Manitoba

Abstract

This study 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 provides 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 the overall improvement of the model. 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 found 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.

Highlights

Flood forecasts that are calculated using regional distributed hydrological models are becoming more common and relevant because they provide information about internal watershed processes in large domains, along with predicted hydrographs for all streams in the river network
The diagnostic case has a better match to the Iowa River at Tama (Figure 5b), while the prognostic setup exhibits a better match to the White Breast Creek (Figure 5a) and at the Cedar River (Figure 5c)
It shows that Equation (8) can improve the streamflow representation, given the correct set of parameters that are obtained

Summary

Introduction

Flood forecasts that are calculated using regional distributed hydrological models are becoming more common and relevant because they provide information about internal watershed processes in large domains, along with predicted hydrographs for all streams in the river network. These forecasts are expected to be accurate at the region’s ungauged watersheds [1] as a consequence of appropriate spatial representation of processes and parameters in the model. Recession becomes more challenging because its non-linearity increases with the spatial scale [3,4,5] Landscape properties such as topography, soil, and the stream network seem to be involved in the recession variability [6,7,8].

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