A Multialgorithm Approach to Land Surface Modeling of Suspended Sediment in the Colorado Front Range

Abstract

A new paradigm of simulating suspended sediment load (SSL) with a Land Surface Model (LSM) is presented here. Five erosion and SSL algorithms were applied within a common LSM framework to quantify uncertainties and evaluate predictability in two steep, forested catchments (>1,000 km 2 ). The algorithms were chosen from among widely used sediment models, including empirically based: monovariate rating curve (MRC) and the Modified Universal Soil Loss Equation (MUSLE); stochastically based: the Load Estimator (LOADEST); conceptually based: the Hydrologic Simulation Program—Fortran (HSPF); and physically based: the Distributed Hydrology Soil Vegetation Model (DHSVM). The algorithms were driven by the hydrologic fluxes and meteorological inputs generated from the Variable Infiltration Capacity (VIC) LSM. A multiobjective calibration was applied to each algorithm and optimized parameter sets were validated over an excluded period, as well as in a transfer experiment to a nearby catchment to explore parameter robustness. Algorithm performance showed consistent decreases when parameter sets were applied to periods with greatly differing SSL variability relative to the calibration period. Of interest was a joint calibration of all sediment algorithm and streamflow parameters simultaneously, from which trade‐offs between streamflow performance and partitioning of runoff and base flow to optimize SSL timing were noted, decreasing the flexibility and robustness of the streamflow to adapt to different time periods. Parameter transferability to another catchment was most successful in more process‐oriented algorithms, the HSPF and the DHSVM. This first‐of‐its‐kind multialgorithm sediment scheme offers a unique capability to portray acute episodic loading while quantifying trade‐offs and uncertainties across a range of algorithm structures.

A model for continental-scale water erosion and sediment transport and its application to the Yellow River Basin

“…It has been shown that sediment transport equations developed from fluvial bed load equations perform poorly in representing overland flow processes (Ali et al, 2013;Hessel & Jetten, 2007). Attempts have also been made (Fagundes et al, 2021;Mao et al, 2010;Stewart et al, 2017;Tan et al, 2017) to test the validity of such models for region-scale simulations. However, some current regional sediment discharge simulations (Fagundes et al, 2021;Stewart et al, 2017;Tan et al, 2017) overlook the detachment-transport coupling approach, which is crucial to describe regionalscale processes adequately.…”

“…Attempts have also been made (Fagundes et al, 2021;Mao et al, 2010;Stewart et al, 2017;Tan et al, 2017) to test the validity of such models for region-scale simulations. However, some current regional sediment discharge simulations (Fagundes et al, 2021;Stewart et al, 2017;Tan et al, 2017) overlook the detachment-transport coupling approach, which is crucial to describe regionalscale processes adequately. The sediment yield models with and without the transport process are mixed (Fagundes et al, 2021;Stewart et al, 2017;Tan et al, 2017), which do not allow for a complete assessment of the existing sediment yield equations (Fagundes et al, 2021).…”

Estimating Reservoir Sedimentation Rates and Storage Capacity Losses Using High‐Resolution Sentinel‐2 Satellite and Water Level Data

“…However, they suffer from representing catchment systems homogeneously and estimating outputs based on a single event (Pechlivanidis et al, 2011). Recent advancements combine land surface models with these empirical models to model space-time variability of sedimentation (e.g., Stewart et al, 2017). Limited data, calibration, and parametrization inhibit them from widespread applications.…”