Centro de Investigação e Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, Ap 1013, 5001-801 Vila Real, Portugal.
Centro de Investigação e Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, Ap 1013, 5001-801 Vila Real, Portugal; Centro de Química de Vila Real, Universidade de Trás-os-Montes e Alto Douro, Ap 1013, 5001-801 Vila Real, Portugal.
Sci Total Environ. 2021 Dec 1;798:149322. doi: 10.1016/j.scitotenv.2021.149322. Epub 2021 Jul 27.
Water management decisions are complex ever since they are dependent on adopted politics, social objectives, environmental impacts, and economic determinants. To adequately address hydric resources issues, it is crucial to rely on scientific data and models guiding decision-makers. The present study brings a new methodology, consisting of a combined GIS-MCDA, to prioritize catchments that require environmental interventions to improve surface water quality. A Portuguese catchment, Ave River Basin, was selected to test this methodology due to the low water quality. First, it was calculated the contamination risk of each catchment, based on a GIS-MCDA using point source pressures, landscape metrics, and diffuse emissions as criteria. This analysis was compared to local data of ecological and chemical status through ANOVA and the Tukey test. The results showed the efficiency of the method since the contamination risk was lower for catchments under a good status and higher in catchments with a lower classification. In a second task, it was calculated the intervention complexity using a different GIS-MCDA. For this approach, it was chosen five criteria that condition environmental interventions, population density, slope, percentage of burned areas, Strahler order, and the number of effluent discharge sites. Both multicriteria methods were combined in a graphical analysis to rank the catchments intervention priority, subdividing the prioritization into four categories from 1st to 4th giving a higher preference for catchments with high contamination risk and low intervention complexity. As a result, catchments with a good status were dominantly placed under low intervention priority, and catchments with a lower ecological status were classified as a high priority, 1st and 2nd. In total, 248 catchments were spatially ranked, which is an essential finding for decision-makers, that are willing to safeguard the catchment water quality.
水资源管理决策非常复杂,因为它们取决于所采用的政治、社会目标、环境影响和经济决定因素。为了充分解决水资源问题,必须依靠指导决策者的科学数据和模型。本研究提出了一种新的方法,即结合 GIS-MCDA,对需要进行环境干预以改善地表水水质的集水区进行优先排序。由于水质较低,选择了葡萄牙的阿韦河流域作为测试该方法的流域。首先,基于使用点源压力、景观指标和漫射排放作为标准的 GIS-MCDA,计算了每个集水区的污染风险。通过方差分析和 Tukey 检验,将该分析与生态和化学状况的本地数据进行了比较。结果表明该方法的效率,因为污染风险较低的集水区状况良好,而分类较低的集水区风险较高。在第二项任务中,使用不同的 GIS-MCDA 计算了干预的复杂性。对于这种方法,选择了五个条件环境干预的标准,即人口密度、坡度、燃烧面积百分比、Strahler 等级和污水排放点数量。将这两种多标准方法结合在图形分析中,根据污染风险和干预复杂性对集水区的干预优先级进行排序,将优先级划分为四个等级,从第 1 级到第 4 级,对污染风险高且干预复杂性低的集水区给予更高的优先级。结果,状况良好的集水区主要被归类为低干预优先级,而生态状况较低的集水区则被归类为高优先级,第 1 级和第 2 级。总共有 248 个集水区进行了空间排序,这对于决策者来说是一个重要的发现,他们愿意保护集水区的水质。