Sampling Design of Hydraulic and Quality Model Calibration Based on a Global Sensitivity Analysis Method

Moghaddam, Vahid Abbasi; Tabesh, Massoud

doi:10.1061/(asce)wr.1943-5452.0001390

Cited by 3 publications

(1 citation statement)

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“…As it is infeasible, if not impossible, to fully calibrate continental or global scale models, a common approach is to perform bias correction. There is extensive literature on hydrologic model bias correction methods [18][19][20][21][22]. These bias correction approaches can be generally classified as preprocessors or postprocessors, which adjust model inputs or outputs, respectively [23].…”

Section: Introductionmentioning

confidence: 99%

SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models

et al. 2022

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Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, and less extensive local calibration and validation. Thorough calibration and validation are difficult because the quantity of observations needed for such scales do not exist or is inaccessible to modelers. We present the Stream Analysis for Bias Estimation and Reduction (SABER) method for bias correction targeting large models. SABER is intended for model consumers to apply to a subset of a larger domain at gauged and ungauged locations and address issues with data size and availability. SABER extends frequency-matching postprocessing techniques using flow duration curves (FDC) at gauged subbasins to be applied at ungauged subbasins using clustering and spatial analysis. SABER uses a “scalar” FDC (SFDC), a ratio of simulated to observed FDC, to characterize biases spatially, temporally, and for varying exceedance probabilities to make corrections at ungauged subbasins. Biased flows at ungauged locations are corrected with the scalar values from the SFDC. Corrected flows are refined to fit a Gumbel Type 1 distribution. We present the theory, procedure, and validation study in Colombia. SABER reduces biases and improves composite metrics, including Nash Sutcliffe and Kling Gupta Efficiency. Recommendations for future work and a discussion of limitations are provided.

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