Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, N-0349 Oslo, Norway.
Environ Sci Process Impacts. 2014 Jul;16(7):1578-87. doi: 10.1039/c3em00619k.
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.
基于过程的养分输运模型通常被用作富营养化水体管理的工具，因为决策者需要根据当前条件以及未来土地利用和气候变化，判断替代修复措施的潜在影响。所有的建模工作都存在不确定性，这些不确定性来自于多个方面，如输入数据、参数值的选择以及模型本身的选择。在这里，我们对磷的综合集水区模型（INCA-P）进行了贝叶斯不确定性评估。我们使用自动校准程序和包含参数不确定性的算法来模拟挪威低地河流流域的磷输运。定义了两个未来情景来举例说明参数不确定性在生成预测中的重要性。虽然最坏情况下的情景产生了增加磷负荷的稳健预测，但最佳情况下的情景只有在概率为 0.78 时才会导致负荷减少，这突出了在基于过程的集水区尺度模拟可能的修复情景中考虑参数不确定性的重要性。不确定性的估计可以包含在提供给决策者的信息中，从而为管理水资源做出明智决策提供更强有力的科学依据。
