Where are the limits of model predictive capabilities?. Issue 26 (24th October 2016)

• Record Type: Journal Article
• Authors: Refsgaard, J.C.; Højberg, A.L.; He, X.; Hansen, A.L.; Rasmussen, S.H.; Stisen, S.
• Is Part Of: Hydrological processes. Volume 30:Issue 26(2016)
• Page Start: 4956
• Page End: 4965
• Publication Date: 2016-10-24
• Subjects: distributed hydrological model -- minimum scale of predictive capability -- Representative Elementary Scale (RES); Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.11029
• Languages: English
• ISSNs: 0885-6087

Abstract

Abstract: Distributed hydrological models can make predictions with much finer spatial resolution than the supporting field data. They will, however, usually not have a predictive capability at model grid scale due to limitations of data availability and uncertainty of model conceptualizations. In previous publications, we have introduced the Representative Elementary Scale (RES) concept as the theoretically minimum scale at which a model with a given conceptualization has a potential for obtaining a predictive accuracy corresponding to a given acceptable accuracy. The new RES concept has similarities to the 25‐year‐old Representative Elementary Area concept, but it differs in the sense that while Representative Elementary Area addresses similarity between subcatchments by sampling within the catchment, RES focuses on effects of data or conceptualization uncertainty by Monte Carlo simulations followed by a scale analysis. In the present paper, we extend and generalize the RES concept to a framework for assessing the minimum scale of potential predictability of a distributed model applicable also for analyses of different model structures and data availabilities. We present three examples with RES analyses and discuss our findings in relation to Beven's alternative blueprint and environmental modeling philosophy from 2002. While Beven here addresses model structural and parameter uncertainties, he does not provide a thorough methodology for assessing to which extent model predictions for variables that are not measured possess opportunities to have meaningful predictive accuracies, or whether this is impossible due to limitations in data and models. This shortcoming is addressed by the RES framework through its analysis of the relationship between aggregation scale of model results and prediction uncertainties and for considering how alternative model structures and alternative data availability affects the results. We suggest that RES analysis should be applied in all modeling studies that aim to use simulation results at spatial scales smaller than the support scale of the calibration data.