As a result of climate change, sea level is changing all over the world at unprecedented rates. Sea-level change can have significant impacts on coastal communities, infrastructure and global economy, as most of the major cities are located near to or at the coast. Rising sea levels can lead to, for instance, more severe and more frequent flooding, increasing coastal erosion and salt water intrusion. In addition, sea-level change can also influence coastal ecosystems, by altering the habitats of many plant and animals species. Therefore, it is crucial that we understand what is causing sea-level change and at what rate sea levels are changing.

Global mean sea level has been rising at a rate of about 3.4 millimetres per year over the last 30 years. Regionally, however, sea level can be changing at a much higher or lower rate. That is because local processes, such as ocean dynamics and gravitational effects associated with continental ice mass changes, cause regional deviations from the global average. But what is causing sea level to change at a specific location? Is sea level changing because the oceans are warming, and thus expanding? Or because the ice from glaciers and ice sheets are melting? The attribution of sea-level change to these and other drivers can be done using a sea-level budget approach. Sea-level budget studies can be used to constrain missing or poorly known contributions and to validate climate models. While the global mean sea-level budget is considered closed within uncertainties, closing the budget on a regional to local scale is still challenging.

In this thesis, I focused on the question: Can we close the regional sea-level budget in the satellite altimetry era on a sub-basin scale consistently for the entire world? For this, we need not only high quality observations of sea-level change and each component, but also of the uncertainties within each process. Therefore, in Chapter 2 and 3, I explored the main drivers of regional sea-level change, focusing on the uncertainty characterization of each component. I then looked at which spatial scale is optimal for analysing the regional sea-level budget, and compared the sum of the drivers with the total observed change in these regions in Chapter 4.