An overview of current applications, challenges, and future trends in distributed process-based models in hydrology

Abstract

46Process-based hydrological models have a long history dating back to the 1960s. 47Criticized by some as over-parameterized, overly complex, and difficult to use, a more 48 nuanced view is that these tools are necessary in many situations and, in a certain class of 49 problems, they are the most appropriate type of hydrological model. This is especially the 50 case in situations where knowledge of flow paths or distributed state variables and/or 51 preservation of physical constraints is important. Examples of this include: spatiotemporal 52 variability of soil moisture, groundwater flow and runoff generation, sediment and 53 contaminant transport, or when feedbacks among various Earth's system processes or 54 understanding the impacts of climate non-stationarity are of primary concern. These are 55 situations where process-based models excel and other models are unverifiable. This article 56 presents this pragmatic view in the context of existing literature to justify the approach where 57 applicable and necessary. We review how improvements in data availability, computational 58 resources and algorithms have made detailed hydrological simulations a reality. Avenues for 59 the future of process-based hydrological models are presented suggesting their use as virtual 60 laboratories, for design purposes, and with a powerful treatment of uncertainty. 61

Mapping dominant runoff processes: an evaluation of different approaches using similarity measures and synthetic runoff simulations

“…This approach was inspired by Weiler and McDonnell (2004), who suggested using numerical experiments to isolate hypotheses and investigate their influence on the model output. In a recent review paper, Fatichi et al (2016) acknowledge these studies to be different from the ones aiming at comparing performances of different models or validating model results. The word "synthetic" implies therefore that the focus is exclusively on how the different DRP maps influence the simulated runoff, and not on how well the model reproduces a measured discharge.…”

HESS Opinions: The complementary merits of competing modelling philosophies in hydrology

“…The above points do however not contest the immense value of physically based models as recently discussed in detail by Fatichi et al (2016). Rather, detailed implementations of these models, in spite of the associated limitations, have in the past been shown to be powerful tools to reproduce and understand spatially heterogeneous system-internal flux and state dynamics as well as patterns that emerge from the interaction of small-scale processes (e.g.…”

“…. Fatichi et al (2016) argue that this suggests that uncertainties in observed system input and output data and the resulting biased parameters in calibrated models (e.g. Renard et al, 2010) outweigh uncertainties introduced by insufficient heterogeneity and/or an unsuitable scale.…”

“…Models at the lowresolution and low-complexity end of the continuum are criticized for lacking a robust physical or theoretical basis and for their inability to meaningfully represent spatial patterns (e.g. Paniconi and Putti, 2015;Fatichi et al, 2016), whereas models at the high-resolution and high-complexity end are often viewed as having inferior representations of sub-grid variability (e.g. Beven and Cloke, 2012) and as being not sufficiently agile to represent the dominant processes in different environments (e.g.…”

How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

“…Top-down models and parameterisations may be too simplistic and, therefore, require calibration (e.g. Fatichi et al, 2016), whereas physically-based models may be too data demanding and not 20 flexible enough to cope with emergent patterns at large scales (Beven, 2000).…”