On the contribution of statistical bias correction to the uncertainty in the projected hydrological cycle

• Published in: Geophysical research letters Vol. 38; no. 20
• Main Authors: Chen, Cui, Haerter, Jan O., Hagemann, Stefan, Piani, Claudio
• Format: Journal Article
• Language: English
• Published: Washington, DC Blackwell Publishing Ltd 01.10.2011; American Geophysical Union; John Wiley & Sons, Inc
• Subjects: Climate change; climate projection; comparative analysis; Earth; Earth sciences; Earth, ocean, space; Exact sciences and technology; hydrological cycle; Hydrology; Scientific apparatus & instruments; statistical bias correction; Uncertainty; projects; General circulation models; atmospheric precipitation; concentration; Climate models; global; digital simulation; Modeling; greenhouse gas; Interannual variation; drainage basins; hydrologic cycle; extreme value; Runoff; discharge; Error correction; Forcing; uncertainties; Comparative study
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2011GL049318

Global hydrological modeling is affected by three sources of uncertainty: (i) the choice of the global climate model (GCM) used to provide meteorological forcing data; (ii) the choice of future greenhouse gas concentration scenario; and (iii) the choice of the decade used to derive the bias correction parameters. We present a comparative analysis of these uncertainties and compare them to the inter‐annual variability. The analysis focuses on discharge, integrated runoff and total precipitation over ten large catchments, representative of different climatic areas of the globe. Results are similar for all catchments, all hydrological variables and throughout the year with few exceptions. We find that the choice of different decadal periods over which to derive the bias correction parameters is a source of comparatively minor uncertainty, while other sources play larger and similarly significant roles. This is true for both the means and the extremes of the studied hydrological variables. Key Points Uncertainty study in the projected hydrological cycle Comparative analysis of uncertainties Improve climate model data for impact study in hydrology