Weighted risk assessment of critical source areas for soil phosphorus losses through surface runoff mechanisms
Weighted risk assessment of critical source areas for soil phosphorus losses through surface runoff mechanisms
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DOI：
10.1016/j.catena.2023.107027
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发表时间：
2023-05
期刊：
影响因子：
6.2
通讯作者：
E. Hayes;S. Higgins;J. Geris;Gillian Nicholl;D. Mullan
中科院分区：
文献类型：
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作者：
E. Hayes;S. Higgins;J. Geris;Gillian Nicholl;D. Mullan
在集约化畜牧区，土壤中的养分通常高于农艺最佳值，这会增加养分流失的风险，并导致水体生态状况不良。土壤养分含量存在较大的田间变异性，由于土壤采样制度不理想，磷（P）热点风险很少量化。本研究的目的是解决这个问题，开发和评估一个改进的分类，通过加权风险评估模型，结合网格化土壤采样数据与模拟在现场地表径流途径的P转移风险在子字段规模。在北方爱尔兰使用两种不同的采样技术，在6个领域规模的网站内的土壤磷的变异性进行了量化;传统的散装现场土壤采样（即散装分析的W模式采样）和网格采样（在35米的分辨率）旁边的插值。结果表明，传统的混合采样方法不能很好地反映土壤磷素含量的空间变异性。这可能会导致表面沃茨的化学和生态状况差，经常低估土壤养分含量，并未能确定潜在的土壤磷流失的贡献源。相比之下，较高强度的网格化采样和插值揭示了广泛的土壤磷含量的田间空间变异性，有利于识别磷损失的贡献来源，水质差，并有助于表征营养物质流失到水道的风险。水文模拟在外地径流途径表明几个磷源可能有助于径流为基础的磷流失。我们的加权风险评估模型是成功的，在确定磷热点和转移潜力的水道，说明了一个类似的方法可以应用于世界上任何地方，过量的磷造成水质问题。模型验证使用即时水质采样数据，这表明，较高的风险加权模型的结果与较差的水质条件。这一方法可以成为一个有用的管理工具，帮助各国实现其国家水质目标。
In intensive livestock areas, soils commonly contain elevated nutrients above the agronomic optimum which increases the risk of nutrient losses and contributing to poor ecological status waterbodies. Large within-field variability in soil nutrient content exists, and at-risk phosphorus (P) hotspots are rarely quantified due to sub-optimal soil sampling regimes. This study aims to address this issue by developing and evaluating an improved classification of P transfer risk at a sub-field scale through a weighted risk assessment model that combines gridded soil sampling data with modelled in-field surface runoff pathways. Within-field soil P variability was quantified at six field-scale sites in Northern Ireland using two different sampling techniques; traditional bulked field soil sampling (i.e. bulk analysis of W pattern sampling) and gridded sampling (at 35 m resolution) alongside interpolation. Results show that traditional bulked sampling failed to account for the sub-field scale spatial variability in soil P content. This may contribute to the poor chemical and ecological status of surface waters by frequently under-predicting soil nutrient content, and failing to identify potential contributing sources of soil P losses. In contrast, higher intensity gridded sampling and interpolation revealed wide in-field spatial variability in soil P content, facilitating the identification of contributing sources of P losses to poor water quality and aiding in the characterisation of risk for nutrient losses to waterways. Hydrological modelling of in-field runoff pathways indicated several P sources potentially contributing to runoff-based P losses. Our weighted risk assessment model was successful in identifying P hotspots and transfer potential to water courses, illustrating that a similar approach could be applied anywhere in the world where excess P poses a problem for water quality. Model validation took place using instream water quality sampling data, which showed that higher risk weighting model results correlated to poorer water quality conditions. This methodology could be a useful management tool to help countries meet their national water quality targets.