Recommended Citation
Postprint version. Published in Journal of Hydrology, Volume 464-465, September 25, 2012, pages 233-248.
NOTE: At the time of publication, the author Christopher G. Surfleet was not yet affiliated with Cal Poly.
The definitive version is available at https://doi.org/10.1016/j.jhydrol.2012.07.012.
Abstract
A wide variety of approaches to hydrologic (rainfall–runoff) modeling of river basins confounds our ability to select, develop, and interpret models, particularly in the evaluation of prediction uncertainty associated with climate change assessment. To inform the model selection process, we characterized and compared three structurally-distinct approaches and spatial scales of parameterization to modeling catchment hydrology: a large-scale approach (using the VIC model; 671,000 km2 area), a basin-scale approach (using the PRMS model; 29,700 km2 area), and a site-specific approach (the GSFLOW model; 4700 km2 area) forced by the same future climate estimates. For each approach, we present measures of fit to historic observations and predictions of future response, as well as estimates of model parameter uncertainty, when available. While the site-specific approach generally had the best fit to historic measurements, the performance of the model approaches varied. The site-specific approach generated the best fit at unregulated sites, the large scale approach performed best just downstream of flood control projects, and model performance varied at the farthest downstream sites where streamflow regulation is mitigated to some extent by unregulated tributaries and water diversions. These results illustrate how selection of a modeling approach and interpretation of climate change projections require (a) appropriate parameterization of the models for climate and hydrologic processes governing runoff generation in the area under study, (b) understanding and justifying the assumptions and limitations of the model, and (c) estimates of uncertainty associated with the modeling approach.
Disciplines
Environmental Sciences
Copyright
2012 Elsevier.
URL: https://digitalcommons.calpoly.edu/nrm_fac/61