On the Suitability of GCM Runoff Fields for River Discharge Modeling: A Case Study Using Model Output from HadGEM2 and ECHAM5

F. C. Sperna Weiland, L. P. H. van Beek, J. C. J. Kwadijk, M. F. P. Bierkens
2012 Journal of Hydrometeorology  
The representation of hydrological processes in land surface schemes (LSSs) has recently been improved. In this study, the usability of GCM runoff for river discharge modeling is evaluated by validating the mean, timing, and amplitude of the modeled annual discharge cycles against observations. River discharge was calculated for six large rivers using runoff, precipitation, and actual evaporation from the GCMs ECHAM5 and Hadley Centre Global Environmental Model version 2 (HadGEM2). Four methods
more » ... were applied: 1) accumulation of GCM runoff, 2) routing of GCM runoff, 3) routing of GCM runoff combined with temporal storage of subsurface runoff, and 4) offline hydrological modeling with the global distributed hydrological model PCRaster Global Water Balance (PCR-GLOBWB) using meteorological data from the GCMs as forcing. The quality of discharge generated by all four methods is highly influenced by the quality of the GCM data. In small catchments, the methods that include runoff routing perform equally well, although offline modeling with PRC-GLOBWB outperforms the other methods for ECHAM5 data. For larger catchments, routing introduces realistic travel times, decreased day-to-day variability, and it reduces extremes. Complexity of the LSS of both GCMs is comparable to the complexity of the hydrological model. However, in HadGEM2 the absence of subgrid variability for saturated hydraulic conductivity results in a large subsurface runoff flux and a low seasonal variability in the annual discharge cycle. The analysis of these two GCMs shows that when LSSs are tuned to reproduce realistic water partitioning at the grid scale and a routing scheme is also included, discharge variability and change derived from GCM runoff could be as useful as changes derived from runoff obtained from offline simulations using large-scale hydrological models.
doi:10.1175/jhm-d-10-05011.1 fatcat:sykbvxnizred7g64fkfxbsicse