Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models

Xueli Huo,Qingyun Duan,Wei Gong,Hoshin Gupta,Guo‐Yue Niu

doi:10.1029/2018ms001573

Abstract

AbstractDynamical 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.

Highlights

To properly represent the intricate feedback mechanisms between components of a complex system, dynamical environmental systems models (DESMs) are highly parameterized with empirical equations, resulting in many uncertain parameters
It is useful to be able to perform parameter screening, via sensitivity analysis, to identify the most important parameters. 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
We explore a novel approach to the problem of global sensitivity analysis (GSA) for spatially distributed applications of DESMs

Summary

Introduction

To properly represent the intricate feedback mechanisms between components of a complex system, dynamical environmental systems models (DESMs) are highly parameterized with empirical equations, resulting in many uncertain parameters. Herman et al (2013) applied the Hydrology Laboratory Research Distributed Hydrologic Model to only 78 grids covering the 1,248‐km Blue River basin in southern Oklahoma, USA, and reported that use of the Sobol' GSA method to obtain reliable estimates of parameter importance would require over six million model runs. This implies that proper application of GSA over even larger areas (e.g., continental scale) would be effectively impossible.

Methods

Results

Conclusion