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.
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.
Summary 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 visco-elastic 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.
<p><span xml:lang="EN-GB" data-contrast="auto"><span>It is important to close the</span><span>&#160;sea-level budget</span><span>&#160;(SLB)</span><span>, to validate&#160;</span><span>the independent observation systems of its components and to understand changes in time in those component contributions.&#160;</span><span>Recent studies have</span><span>,</span><span>&#160;</span><span>in turn</span><span>,</span><span>&#160;</span><span>clos</span><span>ed</span><span>&#160;the global-mean sea-level budget for the `golden era&#8217; of&#160;</span><span>highest</span><span>&#160;</span><span>spatio</span><span>-temporal coverage</span><span>&#160;from observations</span><span>&#160;(from 2005&#8212;2016)</span><span>,&#160;</span><span>closed&#160;</span><span>regional&#160;</span><span>sea-level&#160;</span><span>budgets,&#160;</span><span>and&#160;</span><span>extended the budget to present.</span><span>&#160;In the&#160;</span><span>latter</span><span>,&#160;</span><span>careful consideration is needed on&#160;</span><span>the</span><span>&#160;treatment of the</span><span>&#160;observation gap between GRACE and GRACE-FO</span><span>,</span><span>&#160;</span><span>and&#160;</span><span>apparent</span><span>&#160;</span><span>recent&#160;</span><span>drifts in&#160;</span><span>other&#160;</span><span>observation systems</span><span>.&#160;</span><span>Here, we</span><span>&#160;present&#160;</span><span>a statistical</span><span>-</span><span>model-</span><span>based&#160;</span><span>SLB&#160;</span><span>from observations</span><span>&#160;for the extended period 2003&#8212;2020</span><span>&#160;in a simultaneous inversion</span><span>.&#160;</span><span>We use</span><span>&#160;Argo and in-situ steric sea level change, GRACE&#160;</span><span>mass</span><span>&#160;change</span><span>&#160;as equivalent water height</span><span>, satellite altimetry-derived sea surface height change, and a compilation of altimetry and DEM-differencing ice mass change data products</span><span>. The Bayesian hierarchical model (BHM)&#160;</span><span>solve</span><span>s</span><span>&#160;for</span><span>&#160;the&#160;</span><span>spatial and temporal evolution of the&#160;</span><span>underlying components of the&#160;</span><span>SLB</span><span>: land ice mass, land hydrology, ocean manometric sea level and steric sea level.</span><span>&#160;</span><span>The solution&#160;</span><span>provides probability distributions for the time series of</span><span>&#160;each component&#8217;s contribution to the sea-level budget at&#160;</span><span>each mesh node, integrating to&#160;</span><span>catchment and sub-ocean basin scale</span><span>&#160;and</span><span>&#160;the</span><span>&#160;global-mean.&#160;</span><span>Thus</span><span>&#160;the probabilistic solution allows</span><span>&#160;interrogation of where the largest uncertainties remain.</span><span>&#160;</span><span>We will discuss the component contributions&#160;</span><span>from this solution,&#160;</span><span>their uncertainties</span><span>, and where and why these differ from other SLB approaches</span><span>.</span><span>&#160;</span><span>In addition, the BHM can be run both backward (hindcast) and forward (forecast) in time to provide calibrated projections for each process and the integral both globally and regionally.</span></span><span>&#160;</span></p>
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