Meta-water-modelling (Meta-WaM): A new framework for increasing applicability of digital water modelling

Abstract

[Eng] Water Modelling (WaM) faces numerous challenges to increase its social usefulness. To boost its applicability, a broader and more robust methodological framework fis needed to face the most important WaM challenges. This research aims to provide a broad and stochastic framework, called Meta-Water-Modelling (Meta-WaM), through a surrogate model approach based on the main WaM challenges. Conceptually, this is performed through a compartmental modelling type. The Meta-WaM potential is highlighted through a detailed development of the challenge modelling “Uncertainty through a Predictive development from the Stochastic Hydrology, UPSH”; one of the seven challenges that Meta-WaM addresses. An exhaustive analytical framework on the key factors of Uncertainty, Variability and Randomness was developed. Regarding numerical results, on one hand, when a model data contains maximum uncertainty, it is recommended its analysis with 100 % chance through a causal approach (Causality). This provides an average degree of uncertainty incorporation (quality) of 36.88 %. Data set with high variability should be appropriately modelled through Multivariate approach (100 % chance), with a quality of 36.15 %. In the case of samples for modelling with high randomness, the results are not as definitive. Here the highest percentage of recommendation is in favour of the non-parametric approaches (57.14 %), with a quality of 42.03 %, in line with the data characteristics. UPSH module has been able to highlight that the randomness parameter is a crucial issue to improve hydrological behaviour. Ultimately, Meta-WaM aims to become a comprehensive stochastic decision support system to improve the applicability of water modelling developments.