Challenges in modeling detailed and complex environmental data sets: a case study modeling the excess partial pressure of fluvial CO2

Abstract

Advances in sensor technology enable environmental monitoring programmes to record and store measurements at a high temporal resolution, enhancing the capacity to detect and understand short duration changes that would not have been apparent in the past with monthly, fortnightly or even daily sampling. However, there are various challenges in terms of the processing and analysis of these environmental high-frequency data due to their complex behavior over the different timescales and the strong correlation structure that persists over a large number of lags. Here, we explore the complexities of modeling high-frequency data which arise from environmental applications. With increasing understanding of the importance of surface waters as a source of atmospheric(Formula presented.) we consider a high-resolution sensor-generated time series of the over-saturation of (Formula presented.), in a small order river system. We will present advanced statistical approaches to analyze and model the data, which include visualization tools for exploratory analysis, wavelets and additive models. These methods reveal the complex dynamics of (Formula presented.) over different timescales, and the multivariate relationships of (Formula presented.) with hydrology and temporal autocorrelation structures, which are time and scale dependent.