NPG Nonlinear Processes in Geophysics NPG Nonlin. Processes Geophys. 1607-7946 Copernicus Publications Göttingen, Germany 10.5194/npg-20-683-2013 A top-down model to generate ensembles of runoff from a large number of hillslopes Furey P. R. 1 Gupta V. K. 2 Troutman B. M. 3 NorthWest Research Associates, Boulder, CO, USA Dept. of Civil, Environmental and Architectural Engineering, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA Lakewood, CO, USA 25 09 2013 20 5 683 704 Copyright: © 2013 P. R. Furey et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://npg.copernicus.org/articles/20/683/2013/npg-20-683-2013.html The full text article is available as a PDF file from https://npg.copernicus.org/articles/20/683/2013/npg-20-683-2013.pdf

We hypothesize that total hillslope water loss for a rainfall–runoff event is inversely related to a function of a lognormal random variable, based on basin- and point-scale observations taken from the 21 km<sup>2</sup> Goodwin Creek Experimental Watershed (GCEW) in Mississippi, USA. A top-down approach is used to develop a new runoff generation model both to test our physical-statistical hypothesis and to provide a method of generating ensembles of runoff from a large number of hillslopes in a basin. The model is based on the assumption that the probability distributions of a runoff/loss ratio have a space–time rescaling property. We test this assumption using streamflow and rainfall data from GCEW. For over 100 rainfall–runoff events, we find that the spatial probability distributions of a runoff/loss ratio can be rescaled to a new distribution that is common to all events. We interpret random within-event differences in runoff/loss ratios in the model to arise from soil moisture spatial variability. Observations of water loss during events in GCEW support this interpretation. Our model preserves water balance in a mean statistical sense and supports our hypothesis. As an example, we use the model to generate ensembles of runoff at a large number of hillslopes for a rainfall–runoff event in GCEW.

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