Abstract
We use Monte Carlo analysis to show that explicit representation of an aquifer within a land‐surface model (LSM) decreases the dependence of model performance on accurate selection of subsurface hydrologic parameters. Within the National Center for Atmospheric Research Community Land Model (CLM) we evaluate three parameterizations of vertical water flow: (1) a shallow soil profile that is characteristic of standard LSMs; (2) an extended soil profile that allows for greater variation in terrestrial water storage; and (3) a lumped, unconfined aquifer model coupled to the shallow soil profile. North American Land Data Assimilation System meteorological forcing data (1997–2005) drive the models as a single column representing Illinois, USA. The three versions of CLM are each run 22,500 times using a random sample of the parameter space for soil texture and key hydrologic parameters. Other parameters remain constant. Observation‐based monthly changes in state‐averaged terrestrial water storage (dTWS) are used to evaluate the model simulations. After single‐criteria parameter exploration, the schemes are equivalently adept at simulating dTWS. However, explicit representation of groundwater considerably decreases the sensitivity of modeled dTWS to errant parameter choices. We show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a congruent set.
Highlights
[3] Which of these methods best represents subsurface hydrology at a monthly time scale? We address this question for three different levels of parameter uncertainty: (1) when an optimal set of subsurface hydrologic parameters can be inferred from observations; (2) when no information about effective parameters can be obtained; and (3) when only ranges for parameter values are known
[5] Three questions frame our analysis: (1) When given a surrogate optimal parameter set, which of the ways to represent subsurface hydrology results in the most realistic simulation of monthly change in terrestrial water storage? (2) When no reliable information regarding effective subsurface hydrologic parameters exists, which process representation most consistently gives the best performance? (3) Does knowledge of approximate values for hydrologic parameters guarantee reasonably accurate simulation of monthly change in terrestrial water storage? Our results will inform land-surface model (LSM) model development; more important, they characterize the level of confidence that can be placed in LSM-generated hydrologic predictions, especially when observations are scarce
0.01 – 0.25 uniform semi-uniform uniform uniform log uniform uniform aCLM calculates hydraulic conductivity and matric potential as a function of percent sand and percent clay according to the methods of Clapp and Hornberger [1978] and Cosby et al [1984]
Summary
[2] With the growing recognition of groundwater – atmosphere interaction as a potentially significant influence on spatial and temporal climate variability, researchers in the field of terrestrial hydrometeorology have focused increasing attention on improving the process representations of subsurface hydrology within land-surface models (LSMs). We address this question for three different levels of parameter uncertainty: (1) when an optimal set of subsurface hydrologic parameters (e.g., percent sand, porosity, and specific yield) can be inferred from observations (the ‘‘ideal’’ case); (2) when no information about effective parameters can be obtained (the ‘‘worst’’ case); and (3) when only ranges for parameter values are known (the ‘‘real life’’ case). [5] Three questions frame our analysis: (1) When given a surrogate optimal parameter set, which of the ways to represent subsurface hydrology results in the most realistic simulation of monthly change in terrestrial water storage? (2) When no reliable information regarding effective subsurface hydrologic parameters exists, which process representation most consistently gives the best performance? (3) Does knowledge of approximate values for hydrologic parameters guarantee reasonably accurate simulation of monthly change in terrestrial water storage? [5] Three questions frame our analysis: (1) When given a surrogate optimal parameter set, which of the ways to represent subsurface hydrology results in the most realistic simulation of monthly change in terrestrial water storage? (2) When no reliable information regarding effective subsurface hydrologic parameters exists, which process representation most consistently gives the best performance? (3) Does knowledge of approximate values for hydrologic parameters guarantee reasonably accurate simulation of monthly change in terrestrial water storage? Our results will inform LSM model development; more important, they characterize the level of confidence that can be placed in LSM-generated hydrologic predictions, especially when observations are scarce
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