Dotto C B S, Deletic A, Fletcher T D
Department of Civil Engineering and eWater CRC, Institute for Sustainable Water Resources, Monash University, Victoria 3800, Australia.
Water Sci Technol. 2009;60(3):717-25. doi: 10.2166/wst.2009.434.
Uncertainty is intrinsic to all monitoring programs and all models. It cannot realistically be eliminated, but it is necessary to understand the sources of uncertainty, and their consequences on models and decisions. The aim of this paper is to evaluate uncertainty in a flow and water quality stormwater model, due to the model parameters and the availability of data for calibration and validation of the flow model. The MUSIC model, widely used in Australian stormwater practice, has been investigated. Frequentist and Bayesian methods were used for calibration and sensitivity analysis, respectively. It was found that out of 13 calibration parameters of the rainfall/runoff model, only two matter (the model results were not sensitive to the other 11). This suggests that the model can be simplified without losing its accuracy. The evaluation of the water quality models proved to be much more difficult. For the specific catchment and model tested, we argue that for rainfall/runoff, 6 months of data for calibration and 6 months of data for validation are required to produce reliable predictions. Further work is needed to make similar recommendations for modelling water quality.
不确定性是所有监测项目和所有模型所固有的。它实际上无法消除，但有必要了解不确定性的来源及其对模型和决策的影响。本文的目的是评估径流和水质雨水模型中的不确定性，这些不确定性源于模型参数以及用于流量模型校准和验证的数据可用性。对澳大利亚雨水实践中广泛使用的MUSIC模型进行了研究。分别采用频率论方法和贝叶斯方法进行校准和敏感性分析。研究发现，在降雨/径流模型的13个校准参数中，只有两个参数起作用（模型结果对其他11个参数不敏感）。这表明该模型可以简化而不损失其准确性。水质模型的评估要困难得多。对于所测试的特定集水区和模型，我们认为对于降雨/径流，需要6个月的数据进行校准和6个月的数据进行验证，才能得出可靠的预测结果。需要进一步开展工作，以便为水质建模提出类似的建议。
