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Testing the realism of model structures to identify karst system processes using water quality and quantity signatures
Author(s) -
Hartmann A.,
Wagener T.,
Rimmer A.,
Lange J.,
Brielmann H.,
Weiler M.
Publication year - 2013
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/wrcr.20229
Subject(s) - karst , identifiability , identification (biology) , system identification , computer science , conceptual model , representation (politics) , data mining , process (computing) , sensitivity (control systems) , hydrology (agriculture) , environmental science , machine learning , geology , geotechnical engineering , engineering , ecology , paleontology , biology , database , electronic engineering , politics , political science , law , measure (data warehouse) , operating system
Many hydrological systems exhibit complex subsurface flow and storage behavior. Runoff observations often only provide insufficient information for unique process identification. Quantitative modeling of water and solute fluxes presents a potentially more powerful avenue to explore whether hypotheses about system functioning can be rejected or conditionally accepted. In this study we developed and tested four hydrological model structures, based on different hypotheses about subsurface flow and storage behavior, to identify the functioning of a large Mediterranean karst system. Using eight different system signatures, i.e., indicators of particular hydrodynamic and hydrochemical characteristics of the karst system, we applied a novel model evaluation strategy to identify the best conceptual model representation of the karst system within our set of possible system representations. Our approach to test model realism consists of three stages: (1) evaluation of model performance with respect to system signatures using automatic calibration, (2) evaluation of parameter identifiability using Sobol's sensitivity analysis, and (3) evaluation of model plausibility by combining the results of stages (1) and (2). These evaluation stages eliminated three out of four model structures and lead to a unique hypothesis about the functioning of the studied karst system. We used the estimated parameter values to further quantify subsurface processes. The chosen model is able to simultaneously provide high performances for eight system signatures with realistic parameter values. Our approach demonstrates the benefits of interpreting different tracers in a hydrologically meaningful way during model evaluation and identification.