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

Autor: Ben Livneh, Joseph R. Kasprzyk, Balaji Rajagopalan, J. Stewart, William J. Raseman, J. T. Minear
Rok vydání: 2017
Předmět:
Zdroj: Journal of Advances in Modeling Earth Systems
ISSN: 1942-2466
DOI: 10.1002/2017ms001120
Popis: 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 km2). 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.
Key Points Multialgorithm sediment modeling is a novel way to incorporate uncertainty into land surface model estimates of sedimentThere are trade‐offs when finding an equilibrium between suspended sediment and the partitioning of streamflow into runoff and base flowTransferability of calibrated suspended sediment parameters is sensitive to the variability of the observed data
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje