Crockford L, O'Riordain S, Taylor D, Melland A R, Shortle G, Jordan P
The Agricultural Catchments Programme, Teagasc, Johnstown Castle, Wexford, Ireland.
Geography, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland.
Environ Monit Assess. 2017 Aug 21;189(9):461. doi: 10.1007/s10661-017-6174-1.
Modelling changes in river water quality, and by extension developing river management strategies, has historically been reliant on empirical data collected at relatively low temporal resolutions. With access to data collected at higher temporal resolutions, this study investigated how these new dataset types could be employed to assess the precision and accuracy of two phosphorus (P) load apportionment models (LAMs) developed on lower resolution empirical data. Predictions were made of point and diffuse sources of P across ten different sampling scenarios. Sampling resolution ranged from hourly to monthly through the use of 2000 newly created datasets from high frequency P and discharge data collected from a eutrophic river draining a 9.48 km catchment. Outputs from the two LAMs were found to differ significantly in the P load apportionment (51.4% versus 4.6% from point sources) with reducing precision and increasing bias as sampling frequency decreased. Residual analysis identified a large deviation from observed data at high flows. This deviation affected the apportionment of P from diffuse sources in particular. The study demonstrated the potential problems in developing empirical models such as LAMs based on temporally relatively poorly-resolved data (the level of resolution that is available for the majority of catchments). When these models are applied ad hoc and outside an expert modelling framework using extant datasets of lower resolution, interpretations of their outputs could potentially reduce the effectiveness of management decisions aimed at improving water quality.
对河流水质变化进行建模，并进而制定河流管理策略，历来依赖于以相对较低时间分辨率收集的经验数据。由于能够获取以更高时间分辨率收集的数据，本研究调查了如何利用这些新的数据集类型来评估基于较低分辨率经验数据开发的两种磷（P）负荷分配模型（LAMs）的精度和准确性。针对十种不同的采样场景对磷的点源和非点源进行了预测。通过使用从一条排水面积为9.48平方公里的富营养化河流收集的高频磷和流量数据创建的2000个新数据集，采样分辨率从每小时到每月不等。研究发现，随着采样频率降低，两种LAMs在磷负荷分配方面存在显著差异（点源分别占51.4%和4.6%），精度降低且偏差增大。残差分析表明，在高流量情况下与观测数据存在较大偏差。这种偏差尤其影响了非点源磷的分配。该研究表明，基于时间分辨率相对较低的数据（大多数集水区可获得的分辨率水平）开发像LAMs这样的经验模型存在潜在问题。当在使用现有较低分辨率数据集的情况下，在专家建模框架之外临时应用这些模型时，对其输出结果的解释可能会降低旨在改善水质的管理决策的有效性。
