[1] Groundwater interacts with soil moisture through the exchanges of water between the unsaturated soil and its underlying aquifer under gravity and capillary forces. Despite its importance, groundwater is not explicitly represented in climate models. This paper developed a simple groundwater model (SIMGM) by representing recharge and discharge processes of the water storage in an unconfined aquifer, which is added as a single integration element below the soil of a land surface model. We evaluated the model against the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage change (DS) data. The modeled total water storage (including unsaturated soil water and groundwater) change agrees fairly well with GRACE estimates. The anomaly of the modeled groundwater storage explains most of the GRACE DS anomaly in most river basins where the water storage is not affected by snow water or frozen soil. For this reason, the anomaly of the modeled water table depth agrees well with that converted from the GRACE DS in most of the river basins. We also investigated the impacts of groundwater dynamics on soil moisture and evapotranspiration through the comparison of SIMGM to an additional model run using gravitational free drainage (FD) as the model's lower boundary condition. SIMGM produced much wetter soil profiles globally and up to 16% more annual evapotranspiration than FD, most obviously in arid-to-wet transition regions.

[1] This paper develops a simple TOPMODEL-based runoff parameterization (hereinafter SIMTOP) for use in global climate models (GCMs) that improves the runoff production and the partitioning of runoff between surface and subsurface components. SIMTOP simplifies the TOPMODEL runoff formulations in two ways: (1) SIMTOP represents the discrete distribution of the topographic index as an exponential function, not as a three-parameter gamma distribution; this change improves the parameterization of the fractional saturated area, especially in mountainous regions. (2) SIMTOP treats subsurface runoff as a product of an exponential function of the water table depth and a single coefficient, not as a product of several parameters that depend on topography and soil properties; this change facilitates applying TOPMODEL-based runoff schemes on global scale. SIMTOP is incorporated into the National Center for Atmospheric Research (NCAR) Community Land Model version 2.0 (CLM 2.0). SIMTOP is validated at a watershed scale using data from the Sleepers River watershed in Vermont, USA. It is also validated on a global scale using the monthly runoff data from the University of New Hampshire Global Runoff Data Center (UNH-GRDC). SIMTOP performs favorably when compared to the baseline runoff formulation used in CLM2.0. Realistic simulations can be obtained using two distinct saturated hydraulic conductivity (K sat ) profiles. These profiles include (1) exponential decay of K sat with depth (as is typically done in TOPMODEL-based runoff schemes) and (2) the definition of K sat using the soil texture profile data (as is typically done in climate models) and the concordant reduction of the gravitational drainage from the bottom of the soil column.

[1] We use sensitivity analysis to identify the parameters that are most responsible for controlling land surface model (LSM) simulations and to understand complex parameter interactions in three versions of the Noah LSM: the standard version (STD), a version enhanced with a simple groundwater module (GW), and version augmented by a dynamic phenology module (DV). We use warm season, high-frequency, near-surface states and turbulent fluxes collected over nine sites in the U.S. Southern Great Plains. We quantify changes in the pattern of sensitive parameters, the amount and nature of the interaction between parameters, and the covariance structure of the distribution of behavioral parameter sets. Using Sobol 0 's total and first-order sensitivity indexes, we show that few parameters directly control the variance of the model response. Significant parameter interaction occurs. Optimal parameter values differ between models, and the relationships between parameters also change. GW decreases unwarranted parameter interaction and appears to improve model realism, especially at wetter study sites. DV increases parameter interaction and decreases identifiability, implying it is overparameterized and/or underconstrained. At a wet site, GW has two functional modes: one that mimics STD and a second in which GW improves model function by decoupling direct evaporation and base flow. Unsupervised classification of the posterior distributions of behavioral parameter sets cannot group similar sites based solely on soil or vegetation type, helping to explain why transferability between sites and models is not straightforward. Our results suggest that the a priori assignment of parameters should also consider the climatic conditions of a study location.

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