Bayesian uncertainty assessment of a semi-distributed integrated catchment model of phosphorus transport

Abstract

Process-based models of nutrient transport are often used as tools for management of eutrophic waters, as decision makers need to judge the potential effects of alternative remediation measures, under current conditions and with future land use and climate change. All modelling exercises entail uncertainty arising from various sources, such as the input data, selection of parameter values and the choice of model itself. Here we perform Bayesian uncertainty assessment of an integrated catchment model of phosphorus (INCA-P). We use an auto-calibration procedure and an algorithm for including parametric uncertainty to simulate phosphorus transport in a Norwegian lowland river basin. Two future scenarios were defined to exemplify the importance of parametric uncertainty in generating predictions. While a worst case scenario yielded a robust prediction of increased loading of phosphorus, a best case scenario only gave rise to a reduction in load with probability 0.78, highlighting the importance of taking parametric uncertainty into account in process-based catchment scale modelling of possible remediation scenarios. Estimates of uncertainty can be included in information provided to decision makers, thus making a stronger scientific basis for sound decisions to manage water resources.