Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins

Abstract

The Community Land Model (CLM) represents physical, chemical, and biological processes of the terrestrial ecosystems that interact with climate across a range of spatial and temporal scales. As CLM includes numerous sub-models and associated parameters, the high-dimensional parameter space presents a formidable challenge for quantifying uncertainty and improving Earth system predictions needed to assess environmental changes and risks. This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States. The sensitivity of CLM-simulated water and energy fluxes to hydrological parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach. Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening. The basins are then classified according to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using Principal component analysis (PCA) and expectation-maximization (EM)based clustering approach. Similarities and differences among the parameter sensitivity-based classification system (S-Class), the hydrologic indices-based classification (H-Class), and the Koppen climate classification systems (K-Class) are discussed. Within each parameter sensitivity-based classification system (S-Class) with similar parameter sensitivity characteristics, similar inversion modeling setups can be used for parameter calibration, and the parameters and their contribution or significance to water and energy cycling may also be more transferrable. This classification study provides guidance on identifiable parameters, and on parameterization and inverse model design for CLM but the methodology is applicable to other models. A set of 3 experiments of model calibration were conducted to evaluate the transferability of model calibration strategies and parameter values within and between the classes. It was demonstrated that inverting parameters at representative sites belonging to the same class can significantly reduce parameter calibration efforts.

The Possibility of Applying the EM-PCA Procedure to Lake Water

“…Other similar satisfactory optimization results were determined [54] by his research on hydrological parameter classifi cation. [54] has modeled in his studies on study [55][56][57][58], showing that the values of parameters on PCA diagrams can be presented as tables correlating between investigated parameters. At the same time, in order to identify the missing data and their uncertainties in complex computational models, a reliable approach to analysis is needed.…”

On the applicability of surrogate‐based Markov chain Monte Carlo‐Bayesian inversion to the Community Land Model: Case studies at flux tower sites

“…Such a parameter-sensitivity-based classification system helps reduce the complex climate/hydrologic system into subsets of more homogeneous and smaller systems and provides necessary information to set up the parameter estimation or model calibration problem. Interested readers are referred to Ren et al [2016] for details.…”

“…[] and Ren et al . [] extended the sensitivity analysis framework in Hou et al . [] to 431 relatively pristine watersheds over the contiguous United States within minimal human perturbations.…”

“…The results also show that only a selected set of parameters, and not all hydrological parameters, could be correctly estimated from the fluxes. More recently, Huang et al [2013] and Ren et al [2016] extended the sensitivity analysis framework in Hou et al [2012] to 431 relatively pristine watersheds over the contiguous United States within minimal human perturbations. They confirmed the findings in Hou et al [2012] that surface energy fluxes (i.e., latent and sensible heat) and surface and subsurface runoff in CLM4 are highly sensitive to subsets of hydrological parameters depending on their hydrologic attributes; therefore, the parameter values and inversion procedure are potentially transferrable among watersheds in similar hydrologic regimes.…”

Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale

“…In the latter, we probabilistically solve monthly rainfall-runoff modelling problems for 270 catchments in the United States (US). Large-sample hydrological studies are increasingly carried out in the literature (see e.g., Bock et al 2018;Bourgin et al 2015;Coxon et al 2015;Farmer and Vogel 2016;Langousis et al 2016;Mouelhi et al 2006a,b;Papacharalampous et al 2018aPapacharalampous et al ,b, 2019aPapalexiou and Koutsoyiannis 2013;Perrin et al 2001;Ren et al 2016;Sawicz et al 2011;Tyralis and Koutsoyiannis 2017;Papacharalampous 2017, 2018;Tyralis et al 2017bTyralis et al , 2019aWeijs et al 2013;Xu et al 2018Xu et al , 2019, while this is the first study performing a largescale assessment of the MK blueprint methodology.…”