A key step in model development is selection of an appropriate representational system, including both the representation of what is observed (the data), and the formal mathematical structure used to construct the input-state-output mapping. These choices are critical, because they completely determine the questions we can ask, the nature of the analyses and inferences we can perform, and the answers that we can obtain. Accordingly, a representation that is suitable for one kind of investigation might be limited in its ability to support some other kind.

Arguably, how different representational approaches affect what we can learn from data is poorly understood. This paper explores three complementary representational strategies as vehicles for understanding how catchment-scale hydrological processes vary across hydro-geo-climatologically diverse Chile. Specifically, we test a lumped water-balance model (GR4J), a data-based dynamical systems model (LSTM), and a data-based regression-tree model (Random Forest). Insights were obtained regarding system memory encoded in data, spatial transferability by use of surrogate attributes, and informational deficiencies of the dataset that limit our ability to learn an adequate input-output relationship. As expected, each approach exhibits specific strengths, with LSTM providing the best characterization of dynamics, GR4J being the most robust under informationally deficient conditions, and RF being most supportive of interpretation.

Overall, the complementary nature of the three approaches suggests the value of adopting a multi-representational framework in order to more fully extract information from the data. Our results show that a multi-representational approach better supports the goals of prediction, understanding, and scientific discovery in Hydrology.