On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes

Abstract

Part 1 of this study discussed the concept of using a form of Turing‐like test for model evaluation, together with eight principles for implementing such an approach. In this part, the framing of fitness‐for‐purpose as a Turing‐like test is discussed, together with an example application of trying to assess whether a rainfall‐runoff model might be an adequate representation of the discharge response in a catchment for predicting future natural flood management scenarios. It is shown that the variation between event runoff coefficients in the record can be used to create some limits of acceptability that implicitly take some account of the epistemic uncertainties arising from lack of knowledge about errors in rainfall and discharge observations. In the case study it is demonstrated that the model used cannot be validated in this way across all the range of observed discharges, but that behavioural models can be found for the peak flows that are the subject of interest in the application. Thinking in terms of the Turing‐like test focusses attention on the critical observations needed to test whether streamflow is being produced in the right way so that a model is considered as fit‐for‐purpose in predicting the impacts of future change scenarios. As is the case for uncertainty estimation in general, it is argued that the assumptions made in setting behavioural limits of acceptability should be stated explicitly to leave an audit trail in any application that can be reviewed by users of the model outputs.

Interpolation of rainfall observations during extreme rainfall events in complex mountainous terrain

“…The estimation of RCs for the Kent catchment was made using the method of Beven (2019) for calculating ‘event discharge’ which is most applicable to fast‐responding catchments. This is the case because, in fast‐responding catchments it is possible estimate ‘event discharge’ by using recession curve extrapolation using a master recession curve and hence calculate an event RC (Beven, 2019; Beven et al, 2022). Beven et al (2022) showed that the RCs for the Kent catchment (where rainfall was initially interpolated using the Thiessen Polygon method ) may have RCs greater than 1 (i.e., more event discharge than apparent event rainfall).…”

“…This is the case because, in fast‐responding catchments it is possible estimate ‘event discharge’ by using recession curve extrapolation using a master recession curve and hence calculate an event RC (Beven, 2019; Beven et al, 2022). Beven et al (2022) showed that the RCs for the Kent catchment (where rainfall was initially interpolated using the Thiessen Polygon method ) may have RCs greater than 1 (i.e., more event discharge than apparent event rainfall). This was also the case for some of the largest events on record where the estimates of discharge might also depend on epistemic uncertainties in the rating curve extrapolation.…”

Deciding on fitness‐for‐purpose‐of models and of natural flood management

“…An application of this evaluation strategy to the River Kent catchment in Cumbria (UK) was presented in Beven et al (2022). The topic of that paper was the prediction of major flood peaks.…”

“…These runoff coefficients then reflect the potential epistemic errors in input and output volumes contained in the observations. A distribution of runoff coefficients for ‘similar’ events (estimated in Beven et al, 2022, on the basis of the nearest neighbours defined by event rainfall volume, antecedent flow and peak flow) then allows limits of acceptability to be defined for any particular event used to evaluate the model outputs.…”

“…The evaluation of model simulations for the Kent catchment defined by the location of Sedgwick river gauging station has been discussed at length in Beven et al (2022). That study made use of limits of acceptability based on an analysis of runoff coefficients from past events in the historical record.…”