Attribution of long-term changes in peak river flows in Great Britain

We investigate the evidence for changes in the magnitude of peak river flows in Great Britain. We focus on a set of 117 near-natural "benchmark" catchments to detect trends not driven by land use and other human impacts, and aim to attribute trends in peak river flows to some climate indices such as the North Atlantic Oscillation (NAO) and the East Atlantic (EA) index. We propose modelling all stations together in a Bayesian multilevel framework to be better able to detect any signal that is present in the data by pooling information across several stations. This approach leads to the detection of a clear countrywide time trend. Additionally, in a univariate approach, both the EA and NAO indices appear to have a considerable association with peak river flows. When a multivariate approach is taken to unmask the collinearity between climate indices and time, the association between NAO and peak flows disappears, while the association with EA remains clear. This demonstrates the usefulness of a multivariate and multilevel approach when it comes to accurately attributing trends in peak river flows

Attribution of large-scale drivers of peak river flows in Ireland

Several large flooding events in recent years have led to increased concerns that climate change may be affecting the risk of flooding. At-site tests assessing whether change can be detected in observed data are not very powerful and cannot fully differentiate between possible confounders. It is also difficult to detect fully climate-driven trends, and separate these from other anthropogenic impacts such as urbanisation. We propose a change in focus from detection only towards both detecting and attributing trends in peak river flows to large-scale climate drivers such as the North Atlantic Oscillation index. We focus on a set of near-natural “benchmark” catchments in Ireland in order to detect those non-human driven trends. In order to enhance our ability to detect a signal, we model all stations together in a Bayesian framework which is implemented through Stan

Can GPS and GRACE data be used to separate past and present-day surface loading in a data-driven approach?

Glacial isostatic adjustment (GIA) and the hydrological cycle are both associated with mass changes and vertical land motion (VLM), which are observed by GRACE and GPS, respectively. Hydrology-related VLM results from the instantaneous response of the elastic solid Earth to surface loading by freshwater, whereas GIA-related VLM reveals the long-term response of the viscoelastic Earth mantle to past ice loading history. Thus, observations of mass changes and VLM are interrelated, making GIA and hydrology difficult to quantify and study independently. In this work, we investigate the feasibility of separating these processes based on GRACE and GPS observations, in a fully data-driven and physically consistent approach. We take advantage of the differences in the spatio-temporal characteristics of the GIA and hydrology fields to estimate the respective contributions of each component using a Bayesian hierarchical modelling framework. A closed-loop synthetic test confirms that our method successfully solves this source separation problem. However, there are significant challenges when applying the same approach with actual observations and the answer to the main question of this study is more nuanced. In particular, in regions where GPS station coverage is sparse, the lack of informative data becomes a limiting factor

The scope of the Kalman filter for spatio-temporal applications in environmental science

The Kalman filter is a workhorse of dynamical modeling. But there are challenges when using the Kalman filter in environmental science: the complexity of environmental processes, the complicated and irregular nature of many environmental datasets, and the scale of environmental datasets, which may comprise many thousands of observations per time-step. We show how these challenges can be met within the Kalman filter, identifying some situations which are relatively easy to handle, such as datasets which are high-resolution in time, and some which are hard, like areal observations on small contiguous polygons. Overall, we conclude that many applications in environmental science are within the scope of the Kalman filter, or its generalizations

Attribution of large-scale drivers of peak river flows in Ireland

Several large flooding events in recent years have led to increased concerns that climate change may be affecting the risk of flooding. At-site tests assessing whether change can be detected in observed data are not very powerful and cannot fully differentiate between possible confounders. It is also difficult to detect fully climate-driven trends, and separate these from other anthropogenic impacts such as urbanisation. We propose a change in focus from detection only towards both detecting and attributing trends in peak river flows to large-scale climate drivers such as the North Atlantic Oscillation index. We focus on a set of near-natural “benchmark” catchments in Ireland in order to detect those non-human driven trends. In order to enhance our ability to detect a signal, we model all stations together in a Bayesian framework which is implemented through Stan

An integrated, data-driven approach for estimating global glacial isostatic adjustment, VLM, land ice, hydrology and ocean mass trends

Poster at ICCC Workshop "Geodesy for Climate Research“, online, 29-31 March 202