A study of non-linearity in rainfall-runoff response using 120 UK catchments

Mathias, S.A.; McIntyre, N.; Oughton, R.H.

Abstract

This study presents a catchment characteristic sensitivity analysis concerning the non-linearity of rainfall-runoff response in 120 UK catchments. Two approaches were adopted. The first approach involved, for each catchment, regression of a power-law to flow rate gradient data for recession events only. This approach was referred to as the recession analysis (RA). The second approach involved calibrating a rainfall-runoff model to the full data set (both recession and non-recession events). The rainfall-runoff model was developed by combining a power-law streamflow routing function with a one parameter probability distributed model (PDM) for soil moisture accounting. This approach was referred to as the rainfall-runoff model (RM). Step-wise linear regression was used to derive regionalization equations for the three parameters. An advantage of the RM approach is that it utilizes much more of the observed data. Results from the RM approach suggest that catchments with high base-flow and low annual precipitation tend to exhibit greater non-linearity in rainfall-runoff response. In contrast, the results from the RA approach suggest that non-linearity is linked to low evaporative demand. The difference in results is attributed to the aggregation of storm-flow and base-flow into a single system giving rise to a seemingly more non-linear response when applying the RM approach to catchments that exhibit a strongly dual storm-flow base-flow response. The study also highlights the value and limitations in a regionlization context of aggregating storm-flow and base-flow pathways into a single non-linear routing function.

Citation

Mathias, S., McIntyre, N., & Oughton, R. (2016). A study of non-linearity in rainfall-runoff response using 120 UK catchments. Journal of Hydrology, 540, 423-436. https://doi.org/10.1016/j.jhydrol.2016.06.039

• Journal Article Type: Article
• Acceptance Date: Jun 18, 2016
• Online Publication Date: Jun 20, 2016
• Publication Date: Sep 1, 2016
• Deposit Date: Jun 30, 2016
• Publicly Available Date: Jun 20, 2017
• Journal: Journal of Hydrology
• Print ISSN: 0022-1694
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 540
• Pages: 423-436
• DOI: https://doi.org/10.1016/j.jhydrol.2016.06.039
• Public URL: https://durham-repository.worktribe.com/output/1380145