Abstract
Abstract. Physical processes, including anthropogenic feedbacks, sculpt planetary surfaces (e.g., Earth's). A fundamental tenet of Geomorphology is that the shapes created, when combined with other measurements, can be used to understand those processes. Artificial or synthetic Digital Elevation Models (DEMs) might be vital in progressing further with this endeavour. Morphological data, including metrics and mapping (manual and automated) are a key resource, but at present their quality is typically weakly constrained (e.g., by mapper inter-comparison). In addition to examining inaccuracies caused by noise, relatively rare examples illustrate how synthetic DEMs containing a priori known, idealised morphologies can be used perform "synthetic tests" to make strong "absolute" statements about landform detection and quantification; e.g., 84 % of valley heads in the real landscape are identified correctly. From our perspective, it is vital to verify such statistics as ultimately they link physics-driven models of processes to morphological observations, allowing quantitative hypotheses to be formulated and tested. Synthetic DEMs built by directly using governing equations that encapsulate processes are another key part of forming this link. Thus, this note introduces synthetic tests and DEMs, then it outlines a typology of synthetic DEMs along with their benefits, challenges and future potential to provide constraints and insights. The aim is to discuss how we best proceed with uncertainty-aware landscape analysis to examine physical processes.