Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge, and surface water body parameterization

Abstract

In global hydrological models, groundwater storages and flows are generally simulated by linear reservoir models. Recently, the first global gradient-based groundwater models were developed in order to improve the representation of groundwater-surface-water interactions, capillary rise, lateral flows, and human water use impacts. However, the reliability of model outputs is limited by a lack of data and by uncertain model assumptions that are necessary due to the coarse spatial resolution. The impact of data quality is presented in this study by showing the sensitivity of a groundwater model to changes in the only available global hydraulic conductivity dataset. To better understand the sensitivity of model output to uncertain spatially distributed parameters, we present the first application of a global sensitivity method for a global-scale groundwater model using nearly 2000 steady-state model runs of the global gradientbased groundwater model G 3 M. By applying the Morris method in a novel domain decomposition approach that identifies global hydrological response units, spatially distributed parameter sensitivities are determined for a computationally expensive model. Results indicate that globally simulated hydraulic heads are equally sensitive to hydraulic conductivity, groundwater recharge, and surface water body elevation, though parameter sensitivities vary regionally. For large areas of the globe, rivers are simulated to be either losing or gaining, depending on the parameter combination, indicat-ing a high uncertainty in simulating the direction of flow between the two compartments. Mountainous and dry regions show a high variance in simulated head due to numerical instabilities of the model, limiting the reliability of computed sensitivities in these regions. This is likely caused by the uncertainty in surface water body elevation. We conclude that maps of spatially distributed sensitivities can help to understand the complex behavior of models that incorporate data with varying spatial uncertainties. The findings support the selection of possible calibration parameters and help to anticipate challenges for a transient coupling of the model.

Towards the representation of groundwater in the Joint UK Land Environment Simulator

“…Recently, improvements have been made towards to coupling groundwater models below LSMs and global hydrological models (e.g., de Graaf et al, 2017; de Graaf, Sutanudjaja, Van Beek, & Bierkens, 2015; Fan, Li, & Miguez‐Macho, 2013; Fan & Miguez‐Macho, 2011; Ganji & Sushama, 2018; Gedney & Cox, 2003; Gulden et al, 2007; Gutowski Jr et al, 2002; Guzman et al, 2015; Koirala, Kim, Hirabayashi, Kanae, & Oki, 2019; Maxwell et al, 2011; Maxwell & Miller, 2005; Niu, Yang, Dickinson, Gulden, & Su, 2007; Reinecke et al, 2019; Tian et al, 2020; Tian, Li, Wang, & Hu, 2012; Vergnes & Decharme, 2012; Yeh & Eltahir, 2005a, 2005b; York, Person, Gutowski, & Winter, 2002; Zeng et al, 2018). These studies generally showed that adding the groundwater component led to a more realistic partitioning of water components at the land surface.…”

Hyper‐Resolution Continental‐Scale 3‐D Aquifer Parameterization for Groundwater Modeling

“…The GLHYMPS2.0 provides permeabilities that are higher than the original values but still show the same inconsistencies as it uses the same surface lithology map (GLiM Hartmann & Moosdorf, ). These inconsistencies have a major impact on model results of water table depths, water table fluctuations, and head declines when storage is changed (de Graaf et al, ; Maxwell et al, ; Reinecke et al, ).…”

GMD Perspective: the quest to improve the evaluation of groundwater representation in continental to global scale models

“…We further note that large-scale models have only been assessed to a very limited degree with respect to understanding, quantifying, and attributing relevant uncertainties. Expanding computing power, developing computationally frugal methods for sensitivity and uncertainty analysis, and potentially employing surrogate models can enable more robust sensitivity and uncertainty analysis such as used in regional-scale models (Habets et al, 2013;Hill, 2006;Hill & Tiedeman, 2007;Reinecke et al, 2019b). For now, we suggest applying computationally frugal methods such as the elementary effect test or local sensitivity analysis (Hill, 2006;Morris, 1991;Saltelli et al, 2000).…”

“…In mountain regions, large-scale models tend to underestimate steady-state hydraulic head, possibly due to over-estimated hydraulic conductivity in these regions, which highlights that model performance varies in different hydrologic landscapes. (de Graaf et al, 2015;Reinecke et al 2019b). Additionally, there are significant regional differences in performance with low flows for a number of large-scale models (Zaherpour et al 2018) likely because of diverse implementations of groundwater and baseflow schemes.…”