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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #326399
Title: Evaluating hydrological model performance using information theory-based metrics

Author
item Pachepsky, Yakov
item MARTINEZ, GONZALO - Universidad De Cordoba
item PAN, FEBG - University Of Utah
item WAGENER, THORSTEN - University Of Bristol
item NICHOLSON, THOMAS - Us Nuclear Regulatory Commission

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/26/2016
Publication Date: 5/26/2016
Citation: Pachepsky, Y.A., Martinez, G., Pan, F., Wagener, T., Nicholson, T. 2016. Evaluating hydrological model performance using information theory-based metrics. Hydrology and Earth System Sciences. doi:0.5194/hess-2016-46.

Interpretive Summary: Hydrologic model performance or accuracy is commonly evaluated by comparing differences between simulated vs. measured values at various observation locations and times. It is often observed that different models may have very similar accuracy metrics and yet be based on completely different conceptualizations of processes and their interactions. Metrics are needed that can discriminate between models based on the qualitative correspondence between measured and simulated values. We evaluated a new metric that compares the shape of flow rate dependences on time. This new metric appears to be very efficient in discriminating between models with similar accuracy. The results of this work will be useful for researchers and practitioners-hydrologists, and hydrological model users in that it offers a new efficient tool for choosing between models and justifying the choice.

Technical Abstract: The accuracy-based model performance metrics not necessarily reflect the qualitative correspondence between simulated and measured streamflow time series. The objective of this work was to use the information theory-based metrics to see whether they can be used as complementary tool for hydrologic model evaluation and selection. We simulated 10-year streamflow time series in five watersheds located in Texas, North Carolina, Mississippi, and West Virginia. Eight model of different complexity were applied. The information theory based metrics were obtained after representing the time series as strings of symbols where different symbols corresponded to different quantiles of the probability distribution of streamflow. The symbol alphabet was used. Three metrics were computed for those strings – mean information gain that measures the randomness of the signal, effective measure complexity that characterizes predictability and fluctuation complexity that characterizes the presence of a pattern in the signal. The observed streamflow time series has smaller information content and larger complexity metrics than the precipitation time series. Watersheds served as information filters and streamflow time series were less random and more complex than the ones of precipitation. This is reflected by the fact that the watershed acts as the information filter in the hydrologic conversion process from precipitation to streamflow. The Nash Sutcliffe efficiency metric increased as the complexity of models increased, but in many cases several model had this efficiency values not statistically significant from each other. In such cases, ranking models by the closeness of the information theory based parameters in simulated and measured streamflow time series can provide an additional criterion for the evaluation of hydrologic model performance.