The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments. Issue 4 (3rd April 2015)

• Record Type: Journal Article
• Authors: Finger, David; Vis, Marc; Huss, Matthias; Seibert, Jan
• Is Part Of: Water resources research. Volume 51:Issue 4(2015:Apr.)
• Page Start: 1939
• Page End: 1958
• Publication Date: 2015-04-03
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2014WR015712
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>The assessment of snow, glacier, and rainfall runoff contribution to discharge in mountain streams is of major importance for an adequate water resource management. Such contributions can be estimated via hydrological models, provided that the modeling adequately accounts for snow and glacier melt, as well as rainfall runoff. We present a multiple data set calibration approach to estimate runoff composition using hydrological models with three levels of complexity. For this purpose, the code of the conceptual runoff model HBV‐light was enhanced to allow calibration and validation of simulations against glacier mass balances, satellite‐derived snow cover area and measured discharge. Three levels of complexity of the model were applied to glacierized catchments in Switzerland, ranging from 39 to 103 km<sup>2</sup>. The results indicate that all three observational data sets are reproduced adequately by the model, allowing an accurate estimation of the runoff composition in the three mountain streams. However, calibration against only runoff leads to unrealistic snow and glacier melt rates. Based on these results, we recommend using all three observational data sets in order to constrain model parameters and compute snow, glacier, and rain contributions. Finally, based on the comparison of model performance of different complexities, we postulate that the availability and use of different data sets to calibrate hydrological models might be more important than model complexity to achieve realistic estimations of runoff composition.</p> </abstract>