Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models. (10th September 2019)

• Record Type: Journal Article
• Authors: Huo, Xueli; Gupta, Hoshin; Niu, Guo‐Yue; Gong, Wei; Duan, Qingyun
• Is Part Of: Journal of advances in modeling earth systems. Volume 11:Number 9(2019)
• Page Start: 2896
• Page End: 2909
• Publication Date: 2019-09-10
• Subjects: parameter sensitivity analysis -- progressive Latin hypercube sampling -- grouping‐based ranking -- sample design -- robustness to sampling variability; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2018MS001573
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Dynamical environmental systems models are highly parameterized, having large numbers of parameters whose values are uncertain. For spatially distributed continental‐scale applications, such models must be run for very large numbers of grid locations. To calibrate such models, it is useful to be able to perform parameter screening, via sensitivity analysis, to identify the most important parameters. However, since this typically requires the models to be run for a large number of sampled parameter combinations, the computational burden can be huge. To make such an investigation computationally feasible, we propose a novel approach to combining spatial sampling with parameter sampling and test it for the Noah‐MP land surface model applied across the continental United States, focusing on gross primary production and flux of latent heat simulations for two vegetation types. Our approach uses (a) progressive Latin hypercube sampling to sample at four grid levels and four parameter levels, (b) a recently developed grouping‐based sensitivity analysis approach that ranks parameters by importance group rather than individually, and (c) a measure of robustness to grid and parameter sampling variability. The results show that a relatively small grid sample size (i.e., 5% of the total grids) and small parameter sample size (i.e., 5 times the number of parameters) are sufficient to identify the most important parameters, with very high robustness to grid sampling variability and a medium level of robustness to parameter sampling variability. The results ensure a dramatic reduction in computational costs for such studies. Key Points: Perform sensitivity analysis of spatially distributed model without significant information loss by running model at subset of grids Rank parameters by importance group identified via a grouping‐based sensitivity analysis approach High importance parameters can be identified by running model on 5% of total grids for 5 times the number of parameters