Bridging the Gap Between Software and Hydraulic Engineering: The Development of a Climatic Open-Access Tool

Currently, academics use research software as part of their everyday work, and this trend should increase in the future. In many cases, this scientific software is developed by the researchers themselves who make them available to the rest of the scientific community. Despite the fact that the relationship between science and software is unquestionably useful, it is not always carried out in a successful manner. Some of the key challenges faced by scientists are the lack of training in software development, the shortage of time and resources or the difficulty in effective cooperation with other colleagues. This could lead to make scientific software slow, non-reusable, invisible, and in the worst case, faulty and unreliable.

Conversely, scientific software, developed primarily by software engineers, often lacks the physical interpretation or the correct validation of results. The primary reason of this issue is related to the lack of expert knowledge in the scientific discipline of the software. The availability of massive datasets and the application of cutting-edge technologies, such as data mining or deep learning, does not in itself mean that reliable scientific software is built.

A multidisciplinary framework that covers both scientific and software engineer demands is needed to tackle this situation successfully. Nevertheless, a multidisciplinary team is not necessarily always enough to solve this problem. It is necessary to have some kind of link between these two groups: scientists with deep development training or software engineers with solid scientific background.

This multidisciplinary approach is being used in the framework of the PROTOCOL project, and more particularly in the development of its applied software tools. In this paper, a tool for the characterization of the climatic agents is presented. The main guidelines in the development process comprise, among others, modularity, distributed control version, unit testing, profiling, inline documentation and the use of best practices and tools.

This methodology has enabled us to have a single source code to generate multiple products addressing different users. Moreover, a new addition to the source code is immediately available to all the products, and thus to every user. Deliverables currently available are tutorials in Jupyter notebooks, friendly automated reports or relational databases. Some of the potential users include public and private managers, specialized technicians, engineering students, stakeholders or other scientists.