Significant uncertainty in global scale hydrological modeling from precipitation data errors

JOURNAL OF HYDROLOGY

卷:529

Article

Weiland, Frederiek C. Sperna^1,2  Vrugt, Jasper A.^3,4,5  van Beek, Rens (L. ) P. H.²  Weerts, Albrecht H.^1,6  Bierkens, Marc F. P.^2,7 

[1] Deltares, NL-2600 MH Delft, Netherlands

[2] Univ Utrecht, Dept Phys Geog, NL-3508 TC Utrecht, Netherlands

[3] Univ Calif Irvine, Dept Civil & Environm Engn, Irvine, CA 92697 USA

[4] Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA 92697 USA

[5] Univ Amsterdam, Inst Biodivers & Ecosyst Dynam, Amsterdam, Netherlands

[6] Univ Wageningen & Res Ctr, Environm Sci, NL-7600 AA Wageningen, Netherlands

[7] Deltares, NL-3508 AL Utrecht, Netherlands

关键词: Global hydrological modeling;    Forcing uncertainty;    Model calibration;    Parameter uncertainty;   

DOI  :  10.1016/j.jhydrol.2015.08.061

来源: Elsevier

【 摘 要 】

In the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on large-scale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5 degrees and smaller. (C) 2015 Elsevier B.V. All rights reserved.

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