Articles | Volume 20, issue 2
Hydrol. Earth Syst. Sci., 20, 669–683, 2016
https://doi.org/10.5194/hess-20-669-2016
Hydrol. Earth Syst. Sci., 20, 669–683, 2016
https://doi.org/10.5194/hess-20-669-2016

Research article 11 Feb 2016

Research article | 11 Feb 2016

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

M. F. Müller and S. E. Thompson M. F. Müller and S. E. Thompson
  • Department of Civil and Environmental Engineering, Davis Hall, University of California, Berkeley CA, USA

Abstract. The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs.

This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash–Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

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Short summary
We compare a stochastic (process-based) and statistical (data-based) method to predict flow duration curves in ungauged basins, under stationary and non-stationary conditions, and using Nepal as a case study. Both methods worked well in stationary conditions, with performances driven by the main source of runoff heterogeneity (climate vs. recession). The stochastic model worked better under change, and the performance of the statistical model was determined by the resilience of the flow regime.