Journal cover Journal topic
Advances in Geosciences An open-access journal for refereed proceedings and special publications
Journal topic

Journal metrics

Journal metrics

  • CiteScore value: 1.02 CiteScore
    1.02
  • SNIP value: 0.614 SNIP 0.614
  • SJR value: 0.435 SJR 0.435
  • IPP value: 0.97 IPP 0.97
  • h5-index value: 11 h5-index 11
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 32 Scimago H
    index 32
Volume 29
Adv. Geosci., 29, 109-118, 2011
https://doi.org/10.5194/adgeo-29-109-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Geosci., 29, 109-118, 2011
https://doi.org/10.5194/adgeo-29-109-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  27 Apr 2011

27 Apr 2011

Reliability of autoregressive error models as post-processors for probabilistic streamflow forecasts

M. Morawietz, C.-Y. Xu, and L. Gottschalk M. Morawietz et al.
  • Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway

Abstract. In this study, the reliability of different versions of autoregressive error models as post-processors for probabilistic streamflow forecasts is evaluated. Rank histograms and reliability indices are used as performance measures. An algorithm for the construction of confidence intervals to indicate ranges of reliable forecasts within the rank histograms is presented. To analyse differences in performance of the post-processors, scatter plots of the standardized residuals of the error models are generated to assess the homoscedacity of the residuals with respect to streamflow. A problem of distorted impressions may appear when such plots are generated with a regular x-scale. The problem is analysed with both synthetic and real data, and a rank scaled x-axis is proposed to remedy the problem. The results of the study reveal large differences in the reliability of the post-processors. Versions with empirical distribution functions are clearly superior to those with standard normal distribution, but for validations with independent data their rank histograms still lie outside of the confidence bands for reliable forecasts.

Download
Citation
Share