Sustaining community-managed rural water supply systems in severe water-scarce areas in Brazil and Tunisia

International audienceIn many countries, the challenge of sustaining rural water supplies is entrusted to community organizations, which have difficulties in performing durably the operation, maintenance and cost recovery of rural water supply systems. This paper analyzes how rural communities struggle to ensure a sustainable access to water, while seeking close interaction with outside actors such as the State, NGOs, and politicians. The analysis is based on field observations, interviews and participatory workshops in four community-managed water supply systems in Brazil and Tunisia. To sustain the access to water, communities limit their dependance on community-managed water supply systems and diversify water sources for different uses; they adapt the technical and organizational dimensions of water supply systems through bricolage ; and use political leverage to obtain financial and technical support. Understanding how communities adapt the infrastructure and the organization of rural water supply, in close interaction with external actors, may inspire water providers in designing more resilient water systems.Dans de nombreux pays, l’approvisionnement en eau en milieu rural est confié aux organisations communautaires, qui éprouvent des difficultés à en assurer durablement l’exploitation, la maintenance et le recouvrement des coûts. Cet article analyse comment les communautés rurales luttent pour assurer un accès durable à l’eau, en sollicitant l’État, des ONG et des élus. L’analyse est basée sur des observations de terrain, des entretiens et des ateliers participatifs dans quatre communautés dans un contexte d’extrême rareté de l’eau au Brésil et en Tunisie. Pour maintenir un accès durable à l’eau, les communautés limitent leur dépendance à l’égard des systèmes collectifs et diversifient les sources d’eau pour différents usages ; elles adaptent l’infrastructure et l’organisation des systèmes collectifs par le bricolage ; et elles utilisent l’influence politique pour obtenir des soutiens des acteurs externes. Comprendre comment les communautés adaptent l’infrastructure et l’organisation de l’accès à l’eau, en étroite interaction avec les acteurs externes, peuvent inspirer les fournisseurs d’eau dans la conception de systèmes d’approvisionnement en eau plus résilients

Relação Entre Duração dos Eventos de El Niño com as Condições do Atlântico Tropical e a Precipitação no Ceará

Resumo O artigo investiga como eventos de El Niño de diferentes durações afetam a circulação atmosférica no Atlântico Tropical Norte (ATN), quais consequências dessas alterações sobre a variabilidade interanual da temperatura da superfície do mar (TSM) na região e qual a resposta, em termos de anomalias de precipitação, sobre o Ceará. A análise foi feita através da composição de variáveis oceanográficas e atmosféricas para 18 eventos de El Niño, divididos em eventos de curta e longa duração. Os resultados mostram que os eventos de maior duração resultaram em anomalias positivas de TSM sobre a região do ATN, favorecendo, assim, ao desenvolvimento do gradiente inter-hemisférico de anomalias de TSM positivo e um regime de precipitação abaixo da média no Ceará. Por outro lado, as alterações na circulação atmosférica sobre o ATN não se mostraram tão intensas nos anos em que o El Niño apresentou menor duração, resultando em anomalias de TSM no ATN próximas a zero e consequentemente não foi observado um gradiente de anomalias de TSM no Atlântico Tropical. A composição das anomalias de precipitação sobre o Ceará próximas de zero nesses anos condiz com essa não formação de um gradiente de anomalias de TSM

Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil

The 2012–2018 drought was such an extreme event in the drought-prone area of Northeast Brazil that it triggered a discussion about proactive drought management. This paper aims at understanding the causes and consequences of this event and analyzes its frequency. A consecutive sequence of sea surface temperature anomalies in the Pacific and Atlantic Oceans, at both the decadal and interannual scales, led to this severe and persistent drought. Drought duration and severity were analyzed using run theory at the hydrographic region scale as decision-makers understand impact analysis better at this scale. Copula functions were used to properly model drought joint characteristics as they presented different marginal distributions and an asymmetric behavior. The 2012–2018 drought in Ceará State had the highest mean bivariate return period ever recorded, estimated at 240 years. Considering drought duration and severity simultaneously at the level of the hydrographic regions improves risk assessment. This result advances our understanding of exceptional events. In this sense, the present work proposes the use of this analysis as a tool for proactive drought planning

Long-Term Series of Chlorophyll-a Concentration in Brazilian Semiarid Lakes from Modis Imagery

By monitoring the chlorophyll a concentration (chla), it is possible to keep track of the eutrophication status of a lake and to describe the temporal dynamics of the phytoplankton biomass. Such monitoring must be both extensive and intensive to account for the short- and long-term biomass variations. This may be achieved by the remote estimation of chla through an orbital sensor with high temporal resolution. In this study, we used MODIS imagery to produce 21-year time series of chla for three strategic lakes of the Brazilian semi-arid region: Eng. Armando Ribeiro Gonçalves, Castanhão, and Orós. We used data collected in 13 lakes of the region to test new and published regression models for chla estimation. The selected model was validated and applied to daily MODIS images for the three largest lakes. The resulting chla time series revealed that the temporal dynamics of the phytoplankton biomass is associated with the hydraulic regime of the lakes, with chla plummeting upon intense water renewal and keeping high during persistent dry periods. The intense rainy season of 2004 reduced the phytoplankton biomass and its effects even extended to the subsequent years. Our results encourage the exploration of the MODIS archived imagery in limnological studies

Long-Term Series of Chlorophyll-<i>a</i> Concentration in Brazilian Semiarid Lakes from Modis Imagery

By monitoring the chlorophyll a concentration (chla), it is possible to keep track of the eutrophication status of a lake and to describe the temporal dynamics of the phytoplankton biomass. Such monitoring must be both extensive and intensive to account for the short- and long-term biomass variations. This may be achieved by the remote estimation of chla through an orbital sensor with high temporal resolution. In this study, we used MODIS imagery to produce 21-year time series of chla for three strategic lakes of the Brazilian semi-arid region: Eng. Armando Ribeiro Gonçalves, Castanhão, and Orós. We used data collected in 13 lakes of the region to test new and published regression models for chla estimation. The selected model was validated and applied to daily MODIS images for the three largest lakes. The resulting chla time series revealed that the temporal dynamics of the phytoplankton biomass is associated with the hydraulic regime of the lakes, with chla plummeting upon intense water renewal and keeping high during persistent dry periods. The intense rainy season of 2004 reduced the phytoplankton biomass and its effects even extended to the subsequent years. Our results encourage the exploration of the MODIS archived imagery in limnological studies

Projection of Climate Change and Consumptive Demands Projections Impacts on Hydropower Generation in the São Francisco River Basin, Brazil

Climate change impacts may influence hydropower generation, especially with the intensification of extreme events and growing demand. In this study, we analyzed future hydroelectric generation using a set of scenarios considering both climate change and consumptive demands in the São Francisco River Basin. This project will increase consumptive demands for the coming decades. Five models from the recently released Coupled Model Intercomparison Project Phase 6 and two scenarios, SSP2-4.5 and SSP5-8.5, were considered to estimate climate change projections. The affluent natural flows, regulated flows, and the hydroelectric energy generated were estimated for four multi-purpose reservoirs considering all existing and new demands. The conjunction of scenarios indicated a possible significant reduction in water availability, increased consumptive demands, especially for irrigation, and reduced power generation. Only at the Sobradinho hydroelectric plant, the decrease ranged from −30% to −50% for the period 2021 to 2050 compared to the historical period (1901 to 2000). The results can provide insights into future energy generation and water resources management in the basin

Deep Learning for Streamflow Regionalization for Ungauged Basins: Application of Long-Short-Term-Memory Cells in Semiarid Regions

Rainfall-runoff modeling in ungauged basins continues to be a great hydrological research challenge. A novel approach is the Long-Short-Term-Memory neural network (LSTM) from the Deep Learning toolbox, which few works have addressed its use for rainfall-runoff regionalization. This work aims to discuss the application of LSTM as a regional method against traditional neural network (FFNN) and conceptual models in a practical framework with adverse conditions: reduced data availability, shallow soil catchments with semiarid climate, and monthly time step. For this, the watersheds chosen were located on State of Ceará, Northeast Brazil. For streamflow regionalization, both LSTM and FFNN were better than the hydrological model used as benchmark, however, the FFNN were quite superior. The neural network methods also showed the ability to aggregate process understanding from different watersheds as the performance of the neural networks trained with the regionalization data were better with the neural networks trained for single catchments

Deep Learning for Streamflow Regionalization for Ungauged Basins: Application of Long-Short-Term-Memory Cells in Semiarid Regions

Rainfall-runoff modeling in ungauged basins continues to be a great hydrological research challenge. A novel approach is the Long-Short-Term-Memory neural network (LSTM) from the Deep Learning toolbox, which few works have addressed its use for rainfall-runoff regionalization. This work aims to discuss the application of LSTM as a regional method against traditional neural network (FFNN) and conceptual models in a practical framework with adverse conditions: reduced data availability, shallow soil catchments with semiarid climate, and monthly time step. For this, the watersheds chosen were located on State of Cear&aacute;, Northeast Brazil. For streamflow regionalization, both LSTM and FFNN were better than the hydrological model used as benchmark, however, the FFNN were quite superior. The neural network methods also showed the ability to aggregate process understanding from different watersheds as the performance of the neural networks trained with the regionalization data were better with the neural networks trained for single catchments