An appraisal of the performance of data-infilling methods for application to daily mean river flow records in the UK

Abstract

River flow records are fundamental for the sustainable management of water resources and even very short gaps can severely compromise their utility. Suitably-flagged flow estimates, derived via judicious infilling, are potentially highly beneficial to data users. The UK National River Flow Archive provides stewardship of, and access to, UK river flow records. While many datasets held on the archive are complete, gaps remain across a wide range of flow records. A comprehensive assessment of existing techniques for infilling these gaps is currently lacking. This paper therefore assesses 15 simple infilling techniques (including regression, scaling and equipercentile approaches), each relying upon data transfer from hydrologically-similar donor stations, to generate estimates of flow at 26 representative gauging stations. Results reveal the overall superiority of equipercentile and multiple regression techniques compared to the poorer capability of catchment area scaling. Donor station choice has a strong influence on technique performance. Modifying datasets to improve homogeneity, by seasonally grouping flows or excluding certain periods, offers improved performance. These findings provide a foundation upon which guidance on infilling river flow records can be based in future, allowing hydrometric practitioners and data end-users alike to adopt a consistent and auditable approach towards infilling.