Models of everywhere revisited: A technological perspective

Historical development of rainfall‐runoff modeling

“…There are many other issues relating to rainfall‐runoff modeling that could be discussed within the framework of a primer on RRM, but space precludes their inclusion. For example (a) calibration and evaluation (Bathurst, Ewen, Parkin, O'Connell, & Cooper, 2004; Duan et al, 2006; Ewen & Parkin, 1996; Fowler et al, 2018; Fowler, Peel, Western, Zhang, & Peterson, 2016; Gupta, Kling, Yilmaz, & Martinez, 2009; Klemeš, 1986a, 1986b; Parkin et al, 1996; Saft, Peel, Western, Perraud, & Zhang, 2016; Vaze et al, 2010), (b) equifinality (Beven, 2006; Beven & Freer, 2001; Khatami, Peel, Peterson, & Western, 2019; Savenije, 2001), (c) uncertainty (Beven, 2019a; Kavetski, Kuczera, & Franks, 2006a, 2006b; Nearing et al, 2016; Nearing & Gupta, 2015); (d) consistent modeling across multiple time steps (Ficchi, Perrin, & Andréassian, 2019); (e) modeling framework, methodology and philosophy (Clark et al, 2008, 2011, 2015; Crooks, Kay, Davies, & Bell, 2014; Fenicia et al, 2011; Hrachowitz & Clark, 2017); (f) plausibility and influence of internal fluxes (Ficchi et al, 2019; Guo, Westra, & Maier, 2017; Khatami et al, 2019); and (g) models of everywhere (Beven, 2007; Beven, 2019b; Blair et al, 2019; Wood et al, 2011). The reference list in this primer would be incomplete if reference was not made to “Rainfall‐Runoff Modelling The Primer” in which Beven (2012) deals with the evolution of rainfall‐runoff modeling including the above topics and more.…”

On doing large-scale hydrology with Lions: Realising the value of perceptual models and knowledge accumulation

“…Large scale hydrologic models depend on appropriate datasets to define their parameters (and potentially model structure), and workflows to integrate models and data have become increasingly sophisticated and efficient (Turuncoglu et al, 2013;Leonhard and Duffy, 2014;Leonhard et al, 2016;Blair et al, 2019). Leonhard and Duffy (2013) provide an example of such a workflow that combines web services and data-model workflows to integrate what they refer to as Essential Terrestrial Variables (ETV) into distributed watershed models.…”

Models in the Cloud: Exploring Next Generation Environmental Software Systems

“…There is a great desire to move the science on from this complexity, which we believe could be achieved through abstraction. Indeed, to foster greater understanding of uncertainty in models of environmental systems, models runs may need to be more efficient and run many more times in many more places with approaches such as models of everywhere [1]. Software engineering achieves the separation of concerns through abstraction using modularity, frameworks and defined interfaces; these practices are not often seen in day to day environmental modelling, possibly due to a lack of SE training, but mainly due to the absence of tools at a usable level of abstraction for this domain.…”

“…A vision where models form part of a service fabric of technologies that can be recombined and run an unlimited number of times to explore the specifics of place and rationalise about uncertainty. The work underpins Blair's ten challenges to environmental data science community [1] and leverages the elasticity and flexibility of cloud computing to provide on demand, scale-able computing.…”

“…Abstracting from underlying compute infrastructure and defining interfaces and datastores for environmental models will allow models to be connected and run more efficiently, and to allow scientists to concentrate on the science, resulting in a better understanding of phenomena and uncertainties which would hopefully be reflected in better policy informed by results. This work forms a core pillar of technology enabling the concept of 'models of everywhere' -a vision to have models of everywhere, models of everything and models at all times, being constantly re-evaluated against the most current evidence [1].…”