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Improving land‐surface model hydrology: Is an explicit aquifer model better than a deeper soil profile?
Author(s) -
Gulden Lindsey E.,
Rosero Enrique,
Yang ZongLiang,
Rodell Matthew,
Jackson Charles S.,
Niu GuoYue,
Yeh Pat J.F.,
Famiglietti James
Publication year - 2007
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2007gl029804
Subject(s) - aquifer , environmental science , hydrology (agriculture) , forcing (mathematics) , parameter space , monte carlo method , data assimilation , soil science , groundwater , soil horizon , geology , soil water , meteorology , mathematics , climatology , statistics , geotechnical engineering , physics
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.