Abstract
The Digital Earth (DE) metaphor is very useful for both end users and for hydrological modellers (i.e., the coders). However, in literature it can promote the erroneous view of models as a commodity, that is a basic good used in commerce that is interchangeable with other goods of the same type, without any warning about the fact that some models work better than others, some models just work while others can be simply wrong. These distinctions are at the core of doing good science. This opinion contribution, on the one hand, tries to accept the challenge of adopting models as commodities but, on the other, it wants to show that this acceptance comes with some consequences as to how models must be structured. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate infrastructure that provides some basic services for connection to input data and allows for a modelling-by-components strategy, which, we argue, is the right one for accomplishing the requirements of the DE. The urgency for DARTHs to be Open Source and well written pieces of codes is discussed here in light of the Open Science movement and its ideas. The need to tie predictions to an estimated confidence interval is also supported. Finally, it is argued that DARTHs must promote a new participatory way of doing hydrological science, where researchers can contribute cooperatively to characterize and control model outcomes in various territories. Furthermore, this has consequences for the engineering of the systems.
Highlights
The Digital Earth (DE) concept was first developed by the US Vice President Al Gore in a speech for the opening of the California Science Center in 1998
In literature it can promote the erroneous view of models as a commodity, that is a basic good used in commerce that is interchangeable with other goods of the same type, without any warning about the fact that some models work better than others, some models just work while others can be wrong
Digital eARTh Hydrological (DARTH) should be equipped with model operator components that are able to use states, fluxes and ancillary information derived from Earth Observation (EO) data to directly ingest brightness temperature and backscatter observations (De Lannoy and Reichle, 2016; Lievens et al, 2017; 535 Modanesi et al, 2021)
Summary
The Digital Earth (DE) concept was first developed by the US Vice President Al Gore in a speech for the opening of the California Science Center in 1998. The convergent forces of the Spatial Agencies and the products of their satellites have given substance to the pos sibility of really getting hyper-resolution models of Earth (Wood et al, 2011) These efforts have triggered the interest of the computer science community, with its high-performance, distributed computing infrastructures and all the related technologies. 50 This paper looks at this goal from the perspective of those who deploy ”community” hydrological models, meaning models developed by a community of researchers that freely gathers and discusses ideas about hydrological and Earth system science, produces model parts and commits them to common, decentralized repositories Their contribution usually encompasses theoretical achievements, implementation design, science verification from data, and deployment for applications, which are seen as the natural outcome and source of the most fundamental research of hydrologic processes. How is science certified? What role can remote sensing play? Is Machine Learning the solution?
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