A Multi-Algorithm Approach to Suspended Sediment Modeling in the Colorado Front Range

Climatic and land cover changes present important uncertainties into the rates of streamflow and soil erosion in mountainous watersheds. Soil erosion adds constituents to streams, altering water chemistry and streambed morphology, which can impact drinking water treatment and water resources infrastructure. We applied five erosion and suspended sediment load algorithms within a common hydrologic framework to quantify uncertainty and evaluate predictability in two steep, forested catchments (\u3e 1,000 km2). The algorithms were chosen from among widely used sediment models, including empirical models: monovariate rating curve (MRC), and the Modified Universal Soil Loss Equation (MUSLE), a stochastic model: the Load Estimator (LOADEST), a conceptual model: the Hydrological Simulation Program—Fortran (HSPF), and a physically based model: the Distributed Hydrology Soil Vegetation Model (DHSVM). We coupled the algorithms with the Variable Infiltration Capacity Model (VIC), using hydrologic and meteorological inputs and fluxes generated from VIC. A multi-objective calibration was applied to the algorithms. Performance of optimized parameter sets from the calibration were validated over an ancillary period, as well as in an inter-basin transfer to a separate catchment to explore parameter robustness. This work highlights the tradeoffs in sediment prediction across a range of algorithm structures and catchments. Model performance showed consistent decreases when parameter sets were applied to time periods with greatly differing SSL magnitudes than the calibration period. Solutions from a joint algorithm calibration favored simulated streamflow partitioning into runoff and baseflow that optimized SSL timing, impacting the flexibility and robustness of the streamflow to adapt to different time periods. Transferability performance was highest in algorithms with lower dependence on streamflow performance, the HSPF and the DHSVM. We expect that these more flexible and robust algorithms would likely fair better in predicting future climate scenarios due to their inclusion of physical conditions, precipitation rates and vegetation coverage, rather than solely relying on streamflow as in the case of the MRC. Future work will include applying this multi-algorithm routine to the Western United States, covering a greater number of catchments across varying climate, topography and land use regimes