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RESUMO O Enquadramento dos Corpos Hídricos em Classes de Usos Preponderantes, de acordo com a Resolução Conama nº 357/2005, possibilita o estabelecimento de metas a serem alcançadas, ou mantidas, em um segmento de corpo d'água de acordo com seus usos preponderantes. Sua proposição é responsabilidade dos Comitês de Bacia Hidrográfica, na Região Serrana do estado do Rio de Janeiro; o Comitê Piabanha definiu como prioridade o Enquadramento do Rio Piabanha. Nesse sentido, o objetivo deste artigo foi comparar em que medida o Enquadramento comporta-se como um processo de reabilitação da saúde dos rios. Busca-se construir um referencial teórico e definir diretrizes metodológicas para projetos de enquadramento de recursos hídricos. Nas conclusões, são destacadas cinco recomendações consideradas chave para o processo de enquadramento. ABSTRACT The Framing of Water Bodies in Preponderant Uses Classes, according to Conama Resolution nº 357/2005, enables the establishment of goals to be achieved, or maintained, in a body of water according to its predominant uses. Its proposal is the responsibility of the River Basin Committees, in the Mountain Region of the state of Rio de Janeiro; The Piabanha Committee has set the Framing of the Piabanha River as a priority. In this sense, the objective of this article was to compare the extent to which the Framing behaves as a process of rehabilitation of river health. It seeks to build a theoretical approach and define methodological guidelines for water resources framing projects. In the conclusions, five recommendations that are considered key to the framing process are emphasized.

Water quality has been a global concern, as evidenced by UN Sustainable Development Goals. The current paper has focused on the Piabanha River rehabilitation as a case study which can be generalized to other similar watersheds. A monitoring program during a hydrological year was carried out, and different databases were used to calibrate and validate the QUAL-UFMG water quality model. Sanitation is the major problem in the watershed, notably in its headwater catchments, which concentrate the most urbanized regions where water quality is worse in the dry season due to low river flows. Thus, simulations of the river water quality have been performed through computational modeling suggesting organic load reductions in some sub-basins. In conclusion, some strategies to improve water quality have been discussed: (i) The water quality rehabilitation must consider progressive goals of pollution reduction starting with an initial implementation in a reduced area. The monitoring should be based on a few parameters relevant and simple to monitor. (ii) Pollution reduction ought to be carried out strategically with deadlines and intermediate goals that must be agreed upon between the stakeholders in the watershed. (iii) Watershed committees should supervise projects to improve water quality in partnership with the State Prosecutor&rsquo, s Office.

Fifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQINSF) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

The provision of raw water to urban supply systems is a global endeavor, and a great challenge to water resource managers of large metropolitan regions. A sustainable supply requires the integrated management of water quantity and quality, as well as the system’s adaption to land use and climate changes (e.g., deforestation, droughts). The available water security models inform about the current security status of a system based on the assessment of quantity, quality and adaption indicators. But they barely include risk variables in the analysis, which could help to improve the security assessment considering the historical records of indicators or their future projections. In the present study, a new method is developed that couples a security assessment based on the rating of pressure indicators acting on a watershed, such as water demand, ordinary and accidental contaminants, droughts, and environmental settings (e.g., share of forest cover), with a risk assessment based on pressure properties such as severity, occurrence and detectability. The method is framed in the “Failure Mode and Effects Analysis”, and was applied to the Rio das Velhas system that supplies a portion of Belo Horizonte metropolitan region (Minas Gerais, Brazil) with >4 million inhabitants. The results exposed unacceptable risks for water demand, ordinary contaminants and droughts, because of their high severity and frequency. The water demand risks were explained by data on current (2019) water scarcity and population growth projections (until 2036) that are likely to raise water consumption. The ordinary contaminant risks were deduced from information on current deviations of water quality parameters (e.g., dissolved oxygen, turbidity) from legal thresholds, and records (since 2007) of stream water contamination by untreated domestic effluents. Finally, the drought risks were explained by information on the persistence of stream flows below a legal reference flow, coupled with the identification of many (23) dry years in the available stream flow record (48 years). Overall, the proposed model was efficient in the evaluation of water security risk in the basin. The results obtained for the study area help to act on the required mitigation through prioritization of risk attenuation. Given the simplicity and modest data requirements, the method is helpful to decision makers and suited for replication in other similar regions around the globe.

Water quality monitoring is a complex issue that requires support tools in order to provide information for water resource management. Budget constraints as well as an inadequate water quality network design call for the development of evaluation tools to provide efficient water quality monitoring. For this purpose, a nonlinear principal component analysis (NLPCA) based on an autoassociative neural network was performed to assess the redundancy of the parameters and monitoring locations of the water quality network in the Piabanha River watershed. Oftentimes, a small number of variables contain the most relevant information, while the others add little or no interpretation to the variability of water quality. Principal component analysis (PCA) is widely used for this purpose. However, conventional PCA is not able to capture the nonlinearities of water quality data, while neural networks can represent those nonlinear relationships. The results presented in this work demonstrate that NLPCA performs better than PCA in the reconstruction of the water quality data of Piabanha watershed, explaining most of data variance. From the results of NLPCA, the most relevant water quality parameter is fecal coliforms (FCs) and the least relevant is chemical oxygen demand (COD). Regarding the monitoring locations, the most relevant is Poco Tarzan (PT) and the least is Parque Petropolis (PP).

Guanabara Bay, a natural tropical embayment in Southeast Brazil adjacent to Rio de Janeiro, is important to the Brazilian economy as it provides areas for shipping, industry, recreation, and tourism. But commercial and residential urban development in the watershed results in water quality degradation. In Guanabara Bay, strong water quality gradients as a function of inputs and tidal flushing were evident. In the watershed, the urban and industrial areas degraded stream water quality, and the best water quality was in the upstream, rural areas. Steps have been initiated to collect or contain litter (ecoboats and ecobarriers) and reduce sewage discharge (sewage treatment plants). In spite of this, urban development continues to degrade Guanabara Bay ecosystem health. Large-scale international events hosted by Rio de Janeiro have brought widespread attention to Guanabara Bay pollution issues. Management actions have been identified to improve Guanabara Bay ecosystem health, including establishing a strong governance structure, restoring water quality in the rivers, bay and beaches, restoring habitats in Guanabara Bay and its watershed, and mitigating for climate change impacts. An ecosystem health report card was generated using monitoring data with stakeholder engagement for both Guanabara Bay and its watershed. The scientifically rigorous and transparent report card process can be used to track Guanabara Bay restoration progress.