Demonstrating the Value of Fine-resolution Optical Data for Minimising Aliasing Impacts on Biogeochemical Models of Surface Waters

Abstract:

There is increasing awareness that under-sampling may have resulted in the omission of important physicochemical information present in water quality signatures of surface waters - thereby affecting interpretation of biogeochemical processes. For dissolved organic carbon (DOC) and nitrogen this under-sampling can now be avoided using UV-visible spectroscopy measured in-situ and continuously at a fine-resolution e.g. 15 minutes (“real time”). Few methods are available to extract biogeochemical process information directly from such high-frequency data. Jones, Chappell & Tych (2014 Environ Sci Technol: 13289-97) developed one such method using optically-derived DOC data based upon a sophisticated time-series modelling tool. Within this presentation we extend the methodology to quantify the minimum sampling interval required to avoid distortion of model structures and parameters that describe fundamental biogeochemical processes. This shifting of parameters which results from under-sampling is called “aliasing”. We demonstrate that storm dynamics at a variety of sites dominate over diurnal and seasonal changes and that these must be characterised by sampling that may be sub-hourly to avoid aliasing. This is considerably shorter than that used by other water quality studies examining aliasing (e.g. Kirchner 2005 Phys Rev: 069902). The modelling approach presented is being developed into a generic tool to calculate the minimum sampling for water quality monitoring in systems driven primarily by hydrology. This is illustrated with fine-resolution, optical data from watersheds in temperate Europe through to the humid tropics.