Lack of observed data for model calibration hinders the application of hydrological models in many poorly-gauged catchments, particularly in the humid tropical region. Despite much less attention given, it is vital to assess transferability of calibrated parameters in order to apply hydrological models in such catchments to assist their water resources planning and management activities. Thus, this study investigated temporal transferability of a lumped conceptual hydrological model’s (MIKE 11 NAM) calibrated model parameters for rainfall-runoff simulations in two different time periods beyond its calibration period. Study area was selected as Gin catchment located in the humid tropical region. MIKE 11 NAM model was calibrated for the period 1995-1998 [Nash Sutcliffe coefficient (NSE) = 0.73, percent bias (PBIAS) = 3.9%, ratio of the root mean square error to the standard deviation of measured data (RSR) = 0.52] and validated for the period 1999-2002 (NSE=0.66, PBIAS = 8.7%, RSR = 0.59) for the Gin catchment. The temporal transferability of the calibrated model parameters was tested using two scenarios which formulated based on the temporal lag between the calibration period and the transfer period: scenario A having a 4-year time lag and scenario B having a 8-year time lag. Scenario A which evaluated the model performance using 2003-2006 streamflow data indicated only a marginal loss in the model performance in comparison to the calibration. It showed an overall ‘good’ performance (NSE=0.64, PBIAS = 8.6%, RSR = 0.59) including promising capability to reproduce the peak flows (<10th percentile) with Pearson’s correlation coefficient of 0.6. However, scenario B which evaluated the model performance using 2007-2010 streamflow data indicated ‘unsatisfactory’ model performance (NSE=0.42, PBIAS = 13.6%, RSR = 0.76). Therefore, this study suggests that the calibrated parameters of MIKE 11 NAM model can be temporally transferred within a catchment with a 4-year time lag from the calibration period implying the applicability of this modelling framework for rainfall-runoff simulations especially in the catchments where streamflow data is sparse.
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