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Evaluation of diffuse pollution model applications in EUROHARP catchments with limited data

机译:评估有限数据在EUROHARP流域中的扩散污染模型应用

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The application of diffuse pollution models included in EUROHARP encompassed varying levels of parameterisation and approaches to the preparation of input data depending on the model and modelling team involved. Modellers consistently faced important decisions in relation to data interpretation, especially in those catchments with unfamiliar physical or climatic characteristics, where catchment conditions were beyond the range for which a particular model was originally developed, or where only limited input data were available. In addition to a broad discussion of data issues, this paper compares the performance of the four sub-annual output models tested in EUROHARP (EveNFlow, NL-CAT, SWAT and TRK) in three test catchments without the modelling teams having sight of measured flow and nitrate concentration data. Model performance in this "blind test" indicate that the range of predictions generated by any individual models pre and post calibration exceed the differences between the estimates yielded by all four models. Comparison of Analysis of Variance (ANOVA) statistics for simulated and observed flow, concentration and loads underscores the benefits of calibration for these intermediate and complex model formulations. Interpretation of input data (e.g. rainfall interpolation method and pedotransfer functions selected) appeared equally (or more) important than process representation. In the absence of calibration data, modeller unfamiliarity with a particular catchment and its environmental processes sometimes resulted in questionable assumptions and input errors which highlight the problems facing modellers charged with implementing policies under the Water Framework Directive (2000/60/EC) in poorly monitored catchments. Catchment data owners and modellers must therefore work more closely given that the output from diffuse pollution models is clearly modeller-limited as well as model-limited.
机译:EUROHARP中包含的弥散污染模型的应用包括不同级别的参数化和根据所涉及的模型和建模团队准备输入数据的方法。建模人员始终面临与数据解释有关的重要决策,尤其是在那些物理或气候特征不熟悉的流域,流域条件超出了最初开发特定模型的范围,或者只有有限的输入数据可用的流域。除了对数据问题进行广泛讨论外,本文还比较了在三个测试流域中在EUROHARP中测试的四个次年度输出模型(EveNFlow,NL-CAT,SWAT和TRK)的性能,而建模团队看不到测量流量和硝酸盐浓度数据。此“盲测”中的模型性能表明,在校准前后,任何单个模型生成的预测范围都超过了所有四个模型得出的估计之间的差异。模拟和观察到的流量,浓度和载荷的方差分析(ANOVA)统计数据的比较强调了对这些中间模型和复杂模型公式进行校准的好处。输入数据的解释(例如选择的降雨插值方法和pedotransfer函数)显得与过程表示同等(或更重要)。在缺乏校准数据的情况下,建模者对特定流域及其环境过程的不了解有时会导致可疑的假设和输入错误,这突显了建模者在监督不力的情况下负责实施《水框架指令》(2000/60 / EC)政策的问题流域。因此,鉴于扩散污染模型的输出显然受到建模者的限制以及模型的限制,流域数据的所有者和建模者必须更加紧密地合作。

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