The impact of standard and hard‐coded parameters on the hydrologic fluxes in the Noah‐MP land surface model

Abstract

Land surface models incorporate a large number of process descriptions, containing a multitude of parameters. These parameters are typically read from tabulated input files. Some of these parameters might be fixed numbers in the computer code though, which hinder model agility during calibration. Here we identified 139 hard‐coded parameters in the model code of the Noah land surface model with multiple process options (Noah‐MP). We performed a Sobol' global sensitivity analysis of Noah‐MP for a specific set of process options, which includes 42 out of the 71 standard parameters and 75 out of the 139 hard‐coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated at 12 catchments within the United States with very different hydrometeorological regimes. Noah‐MP's hydrologic output fluxes are sensitive to two thirds of its applicable standard parameters (i.e., Sobol' indexes above 1%). The most sensitive parameter is, however, a hard‐coded value in the formulation of soil surface resistance for direct evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard‐coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities because of their tight coupling via the water balance. A calibration of Noah‐MP against either of these fluxes should therefore give comparable results. Moreover, these fluxes are sensitive to both plant and soil parameters. Calibrating, for example, only soil parameters hence limit the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard‐coded model parameters that were exposed in this study when calibrating Noah‐MP.

Toward seamless hydrologic predictions across spatial scales

“…We selected soil porosity as an example to visualize existing shortcomings because it is one of the most common parameters in many LSMs/HMs. This parameter controls the dynamic of several state variables and fluxes such as soil moisture, latent heat, and soil temperature, and its sensitivity has been demonstrated in various studies (Goehler et al, 2013;Cuntz et al, 2015;Mendoza et al, 2015;Cuntz et al, 2016). A representation of the porosity of the top 2 m soil column in these models over the Pan-European domain (Pan-EU) is shown in Fig.…”

“…Unfortunately, in this case, the parameters (or coefficients) of regularization functions were not subject to parameter estimation or to the verification of their ability to predict fluxes and states across various scales. The use of empirical point-scale-based relationships to link geophysical characteristics with LSM/HM parameters and the assumption that their coefficients are universally applicable with certainty (e.g., the coefficients in the Clapp and Hornberger (1978) pedotransfer functions) are the major reasons for the proliferation of hidden parameters in LSM/HM code (Mendoza et al, 2015;Cuntz et al, 2016). It is of pivotal importance to understand that these point-scale relationships should not be applied beyond the scale at which they were derived.…”

“…Since every LSM/HM (e.g., those mentioned in Sect. 2) contains "effective" model parameters, depending on heuristic relationships (that are hidden in the source code in many cases; Mendoza et al, 2015;Cuntz et al, 2016), it is logical that existing LSMs/HMs are subject to parameter uncertainty. These models can be treated as stochastic models, even though their governing equations are deterministic in nature and based on physical principles such as the conservation of mass and energy .…”

“…-lack of significant progress on the applicability of seminal upscaling theories (Miller and Miller, 1956;Dagan, 1989;Gelhar, 1993;Neuman, 2010;Kitanidis and Vomvoris, 2010) developed for subsurface hydrologic problems into LSMs/HMs, and -lack of transparency in most of the existing LSM/HM source codes with respect to the meaning, origin, and uncertainty associated with the hard-coded numerical values (i.e., parameters) either in the code or in the lookup tables (Mendoza et al, 2015;Cuntz et al, 2016). …”

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

“…Examples of missing parameters include those that define the temporal decay of snow albedo and the recession characteristics of shallow aquifers. In such situations process-based hydrologic and land models often treat these uncertain parameters as physical constants, adopting hard-coded parameters that are selected based on orderof-magnitude considerations or on limited experimental data (Mendoza et al, 2015;Cuntz et al, 2016). For other parameters the available spatial information is limited to broad landscape characteristics, e.g., the parameters controlling carbon assimilation and stomatal conductance are typically tied to vegetation type (Bonan et al, 2011;Niu et al, 2011), or the available soil maps impose the same hydraulic properties over large areas (Miller and White, 1998).…”