TY - JOUR

T1 - UPH Problem 20 – reducing uncertainty in model prediction: a model invalidation approach based on a Turing-like test

AU - Beven, Keith

AU - Page, Trevor

AU - Smith, Paul

AU - Kretzschmar, Ann

AU - Hankin, Barry

AU - Chappell, Nick

PY - 2024/4/18

Y1 - 2024/4/18

N2 - This study proposes using a Turing-like test for model evaluations and invalidations based on evidence of epistemic uncertainties in event runoff coefficients. Applying the consequent “limits of acceptability” results in all the 100 000 model parameter sets being rejected. However, applying the limits, together with an allowance for timing errors, to time steps ranked by discharge, results in an ensemble of 2064 models that can be retained for predicting discharge peaks. These do not include any of the models with the highest (> 0.9) efficiencies. The analysis raises questions about the impact of epistemic errors on model simulations, and the need for both better observed data and better models.

AB - This study proposes using a Turing-like test for model evaluations and invalidations based on evidence of epistemic uncertainties in event runoff coefficients. Applying the consequent “limits of acceptability” results in all the 100 000 model parameter sets being rejected. However, applying the limits, together with an allowance for timing errors, to time steps ranked by discharge, results in an ensemble of 2064 models that can be retained for predicting discharge peaks. These do not include any of the models with the highest (> 0.9) efficiencies. The analysis raises questions about the impact of epistemic errors on model simulations, and the need for both better observed data and better models.

U2 - 10.5194/piahs-385-129-2024

DO - 10.5194/piahs-385-129-2024

M3 - Journal article

VL - 385

SP - 129

EP - 134

JO - Proceedings of the International Association of Hydrological Sciences

JF - Proceedings of the International Association of Hydrological Sciences

SN - 2199-899X

ER -