Representing Intercell Lateral Groundwater Flow and Aquifer Pumping in the Community Land Model

Abstract

Groundwater supplies ∼40% of irrigation and household and ∼30% of industrial water use globally (Döll et al., 2012). The generally high-quality water, long residence time, and strong resilience to climate variability (Cuthbert et al., 2019) make groundwater a dependable freshwater resource in many (semi)arid areas, which has led to a rapid increase in groundwater use, especially to sustain rising water demands for increased food production (Pokhrel et al., 2015; Wada et al., 2014). Such groundwater overexploitation has caused an alarming rate of aquifer storage depletion worldwide (Gleeson et al., 2012; Pokhrel et al., 2015; Wada et al., 2010), making groundwater use fundamentally unsustainable that has not only impacted water supplies, but also gravely altered natural hydrologic regimes and deteriorated ecosystem health (Gleeson et al., 2012; Siebert et al., 2010). Hydrologically, groundwater acts as a buffer that directly modulates soil moisture and is coupled to surface water through a relatively slow, two-way water exchange; groundwater converges to streams and lakes as baseflow and recharges naturally by water percolating down from the surface (de

On the Precipitation‐Induced Uncertainties in Process‐Based Hydrological Modeling in the Mekong River Basin

“…The increased downstream accuracy is, however, contrary to some previous findings (e.g., Wang et al, 2016) on higher accuracies in the upstream. In our study, no adjustment is done to the subsurface runoff parameterizations (i.e., CLM5 employs a linear representation of the subsurface runoff which is a function of saturated soil thickness as explained in Section 2.2) that could technically be calibrated for improved performance (Bisht et al, 2018;Felfelani et al, 2021) especially at the upstream location of the MRB where the topography is relatively complex. The long-term average seasonal cycle of the simulated streamflow also compares well with observations both in terms of magnitude and timing (Figure 2 right panels).…”

Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication

“…In global water modelling, there are some more methodologies that can be tested to evaluate multi-model structures and model equations, which are also considered hypotheses on runoff generation, for example, the Rainfall-Runoff Modelling Toolbox (Wagener et al, 2001), the rejectionist framework (Vaché and McDonnell, 2006), the Framework for Understanding Structural Errors (FUSE, Clark et al, 2008), SU-PERFLEX (Fenicia et al, 2011), the Catchment Modelling Framework (CMF, Kraft et al, 2011), and the Structure for Unifying Multiple Modelling Alternatives (SUMMA, Clark et al, 2015b and c). Other methodologies can be used to evaluate parameter values, such as the Model Parameter Estimation Experiment (MOPEX: Duan et al, 2006), the multipletry DREAM(ZS) algorithm (Laloy and Vrugt, 2012), the Generalized Likelihood Uncertainty Estimation methodology (GLUE: Beven and Binley, 2014), perturbed parameter ensembles (Gosling, 2013), the Uncertainty Quantification Python Laboratory platform (UQ-PyL: Wang et al, 2016), and Multiscale Parameter Regionalization (MPR, Samaniego et al, 2010 and2017).…”

Introduction to Special Section: The Quest for Sustainability of Heavily Stressed Aquifers at Regional to Global Scales

“…Continental-to global-scale models are undergoing continual development (Bierkens et al, 2015;Döll et al, 2016). Felfelani et al (2020) describe the results of incorporating pumping, conjunctive-use, and lateral-flow capabilities into a widely used land surface model. Hartick et al (2021) present a terrestrial modeling approach that incorporates atmospheric, land surface, and subsurface flow models to assess near-term changes in subsurface storage using a probabilistic methodology similar to that for weather forecasting.…”