@misc{ReusserBlumeSchaeflietal.2009,
  author    = {Reusser, Dominik and Blume, Theresa and Schaefli, Bettina and Zehe, Erwin},
  title     = {Analysing the temporal dynamics of model performance for hydrological models},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-45114},
  year      = {2009},
  abstract  = {The temporal dynamics of hydrological model performance gives insights into errors that cannot be obtained from global performance measures assigning a single number to the fit of a simulated time series to an observed reference series. These errors can include errors in data, model parameters, or model structure. Dealing with a set of performance measures evaluated at a high temporal resolution implies analyzing and interpreting a high dimensional data set. This paper presents a method for such a hydrological model performance assessment with a high temporal resolution and illustrates its application for two very different rainfall-runoff modeling case studies. The first is the Wilde Weisseritz case study, a headwater catchment in the eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The second is the Malalcahuello case study, a headwater catchment in the Chilean Andes, simulated with the physicsbased model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of classically used performance measures for a moving window. The key of the developed approach is a data-reduction method based on self-organizing maps (SOMs) and cluster analysis to classify the high-dimensional performance matrix. Synthetic peak errors are used to interpret the resulting error classes. The final outcome of the proposed method is a time series of the occurrence of dominant error types. For the two case studies analyzed here, 6 such error types have been identified. They show clear temporal patterns, which can lead to the identification of model structural errors.},
  language  = {en}
}
@article{SchaefliHuss2010,
  author    = {Schaefli, Bettina and Huss, Matthias},
  title     = {Simulation of high mountainous discharge : how much information do we need?},
  issn      = {1812-2108},
  doi       = {10.5194/hessd-7-8661-2010},
  year      = {2010},
  abstract  = {The hydrologic cycle of high mountainous catchments is frequently simulated with simple precipitation-discharge models representing the snow accumulation and ablation behavior of a very complex environment with a set of lumped equations accounting for altitudinal temperature and precipitation gradients. In this study, we present a methodology to include sparse snow depths measurements into the calibration process. Based on this methodology, we assess for a case study, the Rhonegletscher catchment (Switzerland), how much observed information we need to reliably calibrate the model, such that it reproduces the dominant system dynamics, discharge, as well as glacier mass balance. Here, we focus on the question whether observed discharge is sufficient as a calibration variable or whether we need annual or even seasonal glacier mass balance data. Introducing seasonally variable accumulation and ablation parameters is sufficient to enable the simple model to reproduce observed seasonal mass balances for the Rhonegletscher. Furthermore, our results suggest that calibrating the hydrological model exclusively on discharge can lead to wrong representations of the intra- annual accumulation and ablation processes and to a strong bias in long term glacier mass balance simulations. Adding only a few annual mass balance observations considerably reduces this bias. Calibrating exclusively on annual balance data can, in turn, lead to wrong seasonal mass balance simulations. Even if these results are case study specific, our conclusions provide valuable new insights into the benefit of different types of observations for calibrating hydrological models in glacier catchments. The presented multi-signal calibration framework and the simple method to calibrate a semi-lumped model on point observations has potential for application in other modeling contexts.},
  language  = {en}
}