Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Abstract

Land surface models (LSMs) and hydrologic models are parameterized models. The number of involved parameters is often large. Sensitivity analysis (SA) is a key step to understand the complex relationships between parameters and between state variables and parameters. SA is also critical to understand system dynamics and to examine the parameter identifiability. In this paper, parameter SA for a fully coupled, physically based, distributed land surface hydrologic model, namely, the Flux-Penn State Integrated Hydrologic Model (Flux-PIHM), is performed. Multiparameter and single-parameter tests are performed to examine the three dimensions of identifiability: distinguishability, observability, and simplicity. Results show that Flux-PIHM model predictions of discharge, water table depth, soil moisture, land surface temperature, and surface heat fluxes are very sensitive to the selection of parameter values. Parameter uncertainties produce large uncertainties in hydrologic and land surface variable predictions. The van Genuchten parameters a and b and the Zilitinkevich parameter C zil are the most identifiable among the 20 tested parameters. Results indicate that the land surface and the subsurface are closely coupled. Hydrologic parameters have significant influence on land surface simulations. At the same time, land surface parameters have considerable impacts on hydrologic simulations; the evapotranspiration prediction prior to a strong precipitation event is critical for initializing accurate prediction of discharge peaks. Results also show that parameter identifiability depends on seasons and canopy wetness. Parameter identifiability at high and low flow conditions can be extremely different. Complex system dynamics have been revealed during the SA.

Improving the representation of hydrologic processes in Earth System Models

“…Interestingly, with the notable exception of the Catchment model , none of the TOPMO-DEL implementations represent the impact of subgrid variability in water table depth on soil moisture dynamics. Some more recent model implementations do explicitly represent the impact of spatial variability in water table depth, either through calculating the lateral flux from grid-to-grid [Fan and Miguez-Macho, 2011;Miguez-Macho and Fan, 2012a,b;Shi et al, 2014;Maxwell et al, 2015] or by calculating the lateral flux among a sequence of tiles in a subgrid hillslope [Subin et al, 2014].…”

Simulating high‐resolution soil moisture patterns in the Shale Hills watershed using a land surface hydrologic model

“…The land surface scheme integrates the Penman potential evaporation scheme of Mahrt and Ek (), the multiple‐layer soil model of Mahrt and Pan (), the canopy model of Pan and Mahrt (), and the canopy resistance approach of Noilhan and Planton () and Jacquemin and Noilhan (). Detailed descriptions and formulations of PIHM and Flux‐PIHM are provided by Qu (), Qu and Duffy (), and Shi et al (; ).…”

“…The other parameters are kept the same among all simulations. For details about those parameters, please see Shi et al (; ).…”

Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment

“…The parameters to be estimated in this study and their a priori values are presented in Table : the effective porosity , the van Genuchten [] soil parameter α, the van Genuchten soil parameter β, the Zilitinkevich [] parameter C zil , the minimum stomatal resistance R c min , and the reference canopy water capacity S . These six parameters show high distinguishability, observability, and simplicity [ Zupanski and Zupanski , ; Nielsen‐Gammon et al ., ] in the parameter sensitivity analysis [ Shi et al ., ]. High distinguishability, observability, and simplicity have been proven critical for EnKF parameter estimation [ Nielsen‐Gammon et al ., ; Hu et al ., ; Aksoy et al ., ].…”