Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.936 IF 4.936
  • IF 5-year value: 5.615 IF 5-year
    5.615
  • CiteScore value: 4.94 CiteScore
    4.94
  • SNIP value: 1.612 SNIP 1.612
  • IPP value: 4.70 IPP 4.70
  • SJR value: 2.134 SJR 2.134
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 107 Scimago H
    index 107
  • h5-index value: 63 h5-index 63
Volume 23, issue 2
Hydrol. Earth Syst. Sci., 23, 741–762, 2019
https://doi.org/10.5194/hess-23-741-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 741–762, 2019
https://doi.org/10.5194/hess-23-741-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 08 Feb 2019

Research article | 08 Feb 2019

Parameter-state ensemble thinning for short-term hydrological prediction

Bruce Davison1, Vincent Fortin2, Alain Pietroniro1, Man K. Yau3, and Robert Leconte4 Bruce Davison et al.
  • 1Environment and Climate Change Canada, Saskatoon, Saskatchewan, Canada
  • 2Environment and Climate Change Canada, Montreal, Quebec, Canada
  • 3McGill University, Montreal, Quebec, Canada
  • 4Université de Sherbrooke, Sherbrooke, Quebec, Canada

Abstract. The main sources of uncertainty in hydrological modelling can be summarized as structural errors, parameter errors, and data errors. Operational modellers are generally more concerned with predictive ability than model errors, and this paper presents a new, simple method to improve predictive ability. The method is called parameter-state ensemble thinning (P-SET). P-SET takes a large ensemble of continuous model runs and applies screening criteria to reduce the size of the ensemble. The goal is to find the most promising parameter-state combinations for analysis during the prediction period. Each prediction period begins with the same large ensemble, but the screening criteria are free to select a different sub-set of simulations for each separate prediction period. The case study is from June to October 2014 for a small (1324 km2) watershed just north of Lake Superior in Ontario, Canada, using a Canadian semi-distributed hydrologic land-surface scheme. The study examines how well the approach works given various levels of certainty in the data, beginning with certainty in the streamflow and precipitation, followed by uncertainty in the streamflow and certainty in the precipitation, and finally uncertainty in both the streamflow and precipitation. The approach is found to work in this case when streamflow and precipitation are fairly certain, while being more challenging to implement in a forecasting scenario where future streamflow and precipitation are much less certain. The main challenge is determined to be related to parametric uncertainty and ideas for overcoming this challenge are discussed. The approach also highlights model structural errors, which are also discussed.

Publications Copernicus
Download
Short summary
This paper explores a new method of predicting streamflow using a complex model. It makes use of streamflow observations to reduce an existing ensemble of model runs for predictive purposes. The study illustrated that the method could work given the proper constraints, which were only possible if there was enough knowledge about how the river responded to precipitation in the previous months. Ideas were discussed to allow the method to be used in a way to predict future streamflow.
This paper explores a new method of predicting streamflow using a complex model. It makes use of...
Citation