Comparison of IHM and MIKE SHE Model Performance for Modeling Hydrologic Dynamics in Shallow Water Table Settings. Issue 7 (17th July 2015) - Zhang, Jing; Ross, Mark
Abstract : Modeling hydrologic dynamics behavior in shallow water table settings provides unique challenges for integrated hydrologic models. The Integrated Hydrologic Model (IHM), a comprehensive distributed‐parameter model, was developed based on deterministic and physically based soil and vegetative properties. In the paper, the IHM was compared with the popular MIKE SHE model in the study of a shallow water table site in west‐central Florida. The theoretical basis and integration pathways of IHM are discussed. The models were compared in terms of their performance in predicting streamflow and water table depth. Performance was evaluated using the coefficient of determination ( R 2 ) and the Nash–Sutcliffe efficiency (Ens). The P factor and R factor were used as uncertainty statistics, comparing observed data with the 95% prediction band. Both models performed reasonably well and reliably in predicting monthly water table depth and streamflow, with acceptable R 2 values (0.49–0.86) and Ens values (0.41–0.78) over a 3‐yr period. Overall, MIKE SHE was the more robust model, producing slightly better streamflow predictions than IHM. However, there was no major difference in the ability of the models to predict depth of the water table, using existing parameter sets. The generalized likelihood uncertainty estimation (GLUE) was used to quantify parameter sensitivity and the uncertainty of predictions made by the IHM and MIKE SHE models. A sensitivity analysis (SA) was conducted in the form of Sobol's method with first‐ and second‐order sensitivity indices, based on the Ens values for the two models. Pair‐wise correlations between parameters as well as uncertainties associated with equifinality in model parameter estimation were also explored. It is concluded that both models performed adequately after calibration, and parameter identification in both was subject to considerable uncertainties.