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The purpose of this work is to evaluate the applicability of five drought indices (Surface Water Supply Index—SWSI, Reclamation Drought Index—RDI, Streamflow Drought Index—SDI, Standardized Precipitation Index—SPI, and Evaporative Demand Drought Index—EDDI) as tools for monitoring, in identifying the duration, intensity, and frequency of hydrological droughts in the basins of the Banabuiú, Castanhão, and Orós reservoirs, located in the state of Ceará, Brazil. The analysis focused on determining the performance of the indexes for capturing the droughts characteristics from 2002 to 2020. Thus, the comparison of the values of the indexes with the Target Levels of the reservoir’s operation were used, as well as the analysis of six decision criteria: robustness, tractability, transparency, sophistication, extensibility, and dimensionality, to compare the behavior of drought indices. The results of the evaluation criteria showed that the SPI was superior to the other indices, being able to significantly represent the drought episodes, capturing a greater number of events. Thus, SPI received the highest total weighted score (118), followed by SWSI (97), EDDI (95), SDI (95), and RDI (88). In this context, it was found that the SPI and SWSI are the most suitable indices to monitor drought in the region and that the use of longer time scales can be recommended to manage hydrological droughts, in order to improve planning and management of water resources.

International audience; In 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.

The joint WWRP–WCRP Subseasonal to Seasonal Prediction Project (e.g., Robertson et al. 2014) created a global repository of experimental or operational near-real-time S2S forecasts and reforecasts (hindcasts) from 11 international meteorological institutions, cohosted by ECMWF and CMA (Vitart et al. 2017). These data are publicly accessible by researchers and users (https://apps.ecmwf.int/datasets/data/s2s and http://s2s.cma.cn/index). With the exception of the fourth case study, which uses GloSea5 forecasts (MacLachlan et al. 2015), all case studies use selected S2S forecasts and reforecasts that are available from this repository, providing a consistent basis for S2S forecast skill assessment and evaluation of their utility. The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale. DD gratefully acknowledges support from the Swiss National Science Foundation through project PP00P2_170523. For case study 1, ACP and WTKH were funded by the U.K. Climate Resilience Programme, supported by the UKRI Strategic Priorities Fund. RWL was funded by NERC Grant NE/P00678/1 and by the BER DOE Office of Science Federal Award DE-SC0020324. TS was funded by NERC Independent Research Fellowship (NE/P018637/1). CMG and DB were funded by the Helmholtz Young Investigator Group “SPREADOUT” Grant VH-NG-1243. Case study 2 was supported by the U.K. Global Challenges Research Fund NE/P021077/1 (GCRF African SWIFT) and the Tertiary Education Trust Fund (TETFUND) of Nigeria TETFund/DR&D/CE/NRF/STI/73/VOL.1. EO thanks Adrian Tomkins of ICTP, Italy, for his contribution. Case study 3 was undertaken as part of the Columbia World Project, ACToday, Columbia University (https://iri.columbia.edu/actoday/). Case study 4 was supported by the ForPAc (Towards Forecast-based Preparedness Action) project within the NERC/FCDO SHEAR Programme NE/P000428/1, NE/P000673/1, and NE/P000568/1. Case study 5 was undertaken as part of the International Research Applications Project, funded by the U.S. National Oceanic and Atmospheric Administration. EO thanks IRAP project colleagues at The University of Arizona, Indian Meteorological Department, Regional Integrated Multi-Hazard Early Warning System for Africa and Asia, and two of Bihar’s State Agricultural Universities for their contributions. For case study 6, CASC thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico Process 305206/2019-2 and Fundação de Amparo à Pesquisa do Estado de São Paulo Process 2015/50687-8 (CLIMAX Project) for their support. For case study 7, DW’s contributions were carried out under contract with the National Aeronautics and Space Administration. Case study 8 was funded by the EU Horizon 2020 Research and Innovation Programme Grant 7767874 (S2S4E). We also acknowledge the Subseasonal-to-Seasonal Project’s Real-Time Pilot Initiative for providing access to real-time forecasts. For case study 9, TIC-LCPE Hydro-04 was funded by the University of Strathclyde’s Low Carbon Power and Energy program. JB was supported by EPSRC Innovation Fellowship EP/R023484/1. We thank Andrew Low and Richard Hearnden from SSE Renewables for their input. Case study 10 was supported by the Earth Systems and Climate Change Hub under the Australian Government’s National Environmental Science Program, and the Decadal Climate Forecasting Project (CSIRO). Case study 11 was funded by the Technologies for Sustainable Built Environments Centre, Reading University, in conjunction with the EPSRC Grant EP/G037787/1 and BT PLC. Case study 12 was funded through the framework service contract for operating the EFAS Computational Center Contract 198702 and the Copernicus Fire Danger Computations Contract 389730 295 in support of the Copernicus Emergency Management Service and Early Warning Systems between the Joint Research Centre and ECMWF. Peer Reviewed "Article signat per 60 autors/es: Christopher J. White, Daniela I. V. Domeisen, Nachiketa Acharya, Elijah A. Adefisan, Michael L. Anderson, Stella Aura, Ahmed A. Balogun, Douglas Bertram, Sonia Bluhm, David J. Brayshaw, Jethro Browell, Dominik Büeler, Andrew Charlton-Perez, Xandre Chourio, Isadora Christel, Caio A. S. Coelho, Michael J. DeFlorio, Luca Delle Monache, Francesca Di Giuseppe, Ana María García-Solórzano, Peter B. Gibson, Lisa Goddard, Carmen González Romero, Richard J. Graham, Robert M. Graham, Christian M. Grams, Alan Halford, W. T. Katty Huang, Kjeld Jensen, Mary Kilavi, Kamoru A. Lawal, Robert W. Lee, David MacLeod, Andrea Manrique-Suñén, Eduardo S. P. R. Martins, Carolyn J. Maxwell, William J. Merryfield, Ángel G. Muñoz, Eniola Olaniyan, George Otieno, John A. Oyedepo, Lluís Palma, Ilias G. Pechlivanidis, Diego Pons, F. Martin Ralph, Dirceu S. Reis Jr., Tomas A. Remenyi, James S. Risbey, Donald J. C. Robertson, Andrew W. Robertson, Stefan Smith, Albert Soret, Ting Sun, Martin C. Todd, Carly R. Tozer, Francisco C. Vasconcelos Jr., Ilaria Vigo, Duane E. Waliser, Fredrik Wetterhall, and Robert G. Wilson"

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.

The purpose of this work is to evaluate the applicability of five drought indices (Surface Water Supply Index—SWSI, Reclamation Drought Index—RDI, Streamflow Drought Index—SDI, Standardized Precipitation Index—SPI, and Evaporative Demand Drought Index—EDDI) as tools for monitoring, in identifying the duration, intensity, and frequency of hydrological droughts in the basins of the Banabuiú, Castanhão, and Orós reservoirs, located in the state of Ceará, Brazil. The analysis focused on determining the performance of the indexes for capturing the droughts characteristics from 2002 to 2020. Thus, the comparison of the values of the indexes with the Target Levels of the reservoir’s operation were used, as well as the analysis of six decision criteria: robustness, tractability, transparency, sophistication, extensibility, and dimensionality, to compare the behavior of drought indices. The results of the evaluation criteria showed that the SPI was superior to the other indices, being able to significantly represent the drought episodes, capturing a greater number of events. Thus, SPI received the highest total weighted score (118), followed by SWSI (97), EDDI (95), SDI (95), and RDI (88). In this context, it was found that the SPI and SWSI are the most suitable indices to monitor drought in the region and that the use of longer time scales can be recommended to manage hydrological droughts, in order to improve planning and management of water resources.

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.

Resumo O presente artigo faz uma revisão bibliográfica e aborda os principais índices utilizados no monitoramento e análise de secas. Além disso, o texto ainda discorre sobre as metodologias empregadas mundialmente no monitoramento e previsão desses eventos. As secas ocorrem quando existe um déficit de precipitação comparado à média histórica da região. Elas podem ser diagnosticadas por meio de índices que identificam os períodos de escassez ou de disponibilidade hídrica através de equações. Dessa forma, é possível determinar a intensidade, frequência e duração dos eventos de seca. Nesse âmbito, alguns países implementaram seus próprios sistemas de monitoramento e alerta precoce das secas como o Monitor de Secas dos Estados Unidos, o Monitor de Secas da Europa e o Monitor de Secas do Brasil. Onde estes, utilizam de forma conjunta ou isolada as informações provenientes dos índices, a fim de identificar e caracterizar os eventos de seca. Desse modo, pode haver um melhor planejamento das instituições responsáveis, adoção de planos emergenciais e mitigação de impactos. Abstract The present work is a bibliographic review and addresses the main indexes used in the monitoring and analysis of droughts. In addition, the text also discusses the methodologies utilized worldwide in monitoring and forecasting these events. Droughts occur when there is a rainfall deficit compared to the region's historical average. They can be diagnosed by means of indices that identify periods of scarcity or availability hydric through equations. In this way, it is possible to determine the intensity, frequency, and duration of drought events. In this context, some countries have implemented their own drought monitoring and early warning systems such as the United States Drought Monitor, Europe's Drought Monitor and Brazil's Drought Monitor. Where they use information from indexes together or in isolation, in order to identify and characterize drought events. Thus, there can be better planning by the responsible institutions, adoption of emergency plans and mitigation of impacts.

Drought is widely known as a complex natural hazard, not just by its climatological features but also by human experiences and socio-economical impacts. Drought preparedness is the only way a society can mitigate effects and better cope with droughts. Here we present a methodological approach to guide the implementation of proactive drought plans, specially designed for hydrossystems and cities scales. We highlight strategies to engage local stakeholders in constructing such plans and build a participatory methodology. The preparedness drought plan methodology was developed and applied to two hydrosystems and two cities located in the Piranhas-Açu river basin, a drought-prone area of Brazilian Semi-arid. Our findings suggest that participatory socio-technical methodologies, built only from the system operators' tacit knowledge, can achieve good results when data and resources are limited. Still, results can be enhanced by hydrologic and hydraulic modeling to assess vulnerability, scenarios and strategies. We illustrate and analyze the process by storytelling to develop a meaningful and convincing narrative that speaks to theory and practice, and we provide recommendations to facilitate this approach.

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 S&atilde, o 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 &minus, 30% to &minus, 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.

Water crises have risen over the past decade. In the Brazilian context, extreme events have been intensified over the country undermining water systems in regions that already face water shortages. Although existing studies focused on individual regions of Brazil, an overview of the entire country will contribute to developing a national action plan for mitigating water inequalities. Therefore, we reviewed and analyzed water security in Brazil, presenting a diagnosis focused on the peculiarities of each geographic region and hydrographic area. One of the biggest threats is the slow-paced steps towards a robust policy for strengthening national water security. As shown here, water security depends on several variables such as availability, quality, and external factors such as climate forcing and anthropogenic pressure. Therefore, water governance needs to integrate human needs with the ecosystem functioning considering climate uncertainties to move towards better water resources planning.

International audience; The semi-arid Northeast Brazil (NEB) is just recovering from a very severe water crisis induced by a multiyear drought. With this crisis, the question of water resources management has entered the national political agenda, creating an opportunity to better prepare the country to deal with future droughts. In order to improve climate predictions, and thus preparedness in NEB, a circulation pattern (CP) classification algorithm offers various options. Therefore, the main objective of this study was to develop a computer aided CP classification based on the Simulated ANnealing and Diversified RAndomization clustering (SANDRA) algorithm. First, suitable predictor variables and cluster domain setting are evaluated using ERA-Interim reanalyses. It is found that near surface variables such as geopotential at 1,000 hPa (GP(1,000)) or mean sea level pressure (MSLP) should be combined with horizontal wind speed at the upper 700 hPa level (UWND700). A 11-cluster solution is favoured due to the trade-offs between interpretability of the cluster centroids and the explained variances of the predictors. Second, occurrence and transition probabilities of this 11-cluster solution of GP(1,000) and UWND700 are analysed, and typical CPs, which are linked to dry and wet conditions in the region are identified. The suitability of the new classification to be potentially applied for statistical downscaling or CP-conditional bias correction approach is analysed. The CP-conditional cumulative density functions (CDFs) exhibit discriminative power to separate between wet and dry conditions, indicating a good performance of the CP approach.

Resumo O Nordeste do Brasil apresenta acentuada variabilidade interanual da precipitação com alguns anos secos e outros chuvosos. Nessa região, ocorrem os veranicos, que são dias consecutivos com ausência de precipitação durante o período da estação chuvosa, que ocasionam perdas nas safras agrícolas. O presente artigo tem como objetivo utilizar um indicador de veranicos já estabelecido por outros autores e identificar sua relação com o índice de Distribuição de Chuva e regionalizar de acordo com as macrorregiões do estado, bem como, compreender as possíveis consequências, de forma qualitativa e quantitativa, sobre as perdas de safra no Ceará. A área de estudo compreendeu o estado do Ceará e as análises foram realizadas para o período de 1980 a 2018.A partir dos dados de rendimento do IBGE, foram calculadas as perdas das culturas do milho e feijão, e através dos resultados pode-se inferir que: as correlações realizadas com os dados de perdas de safra, veranicos e o IDC evidenciaram que para a maior parte do estado existe correlação direta, ou seja, quanto maior a frequência de eventos de veranicos maior a perda de safra. Além disso, verificou-se que, quando há uma melhor distribuição da precipitação ao longo das regiões do estado ocorre menor perda de safra. Abstract Northeastern Brazil has marked interannual rainfall variability with some dry and other rainy years. In this region, there are dry spells, which are consecutive days with no rainfall during the rainy season, causing losses in agricultural crops. This article aims to use an indicator of Indian summers already established by other authors 2020 and identify its relationship with the Rain Distribution Index and regionalize according to the state's macro-regions, as well as understand the possible consequences, in a qualitative and quantitative way. on crop losses in Ceará. The study area comprised the state of Ceará and the analyzes were carried out for the period from 1980 to 2018. From the IBGE yield data, the losses of corn and beans crops were calculated, and through the results it can be inferred that: the correlations made with data on crop losses, summer and the IDC showed that for the largest part of the state there is a direct correlation, that is, the higher the frequency of summer events, the greater the crop loss. In addition, it was found that when there is a better distribution of precipitation over the regions of the state, less crop loss occurs.

This paper develops the quasi analytic Bayesian analysis of the generalized least squares (GLS)(BGLS) model introduced by Reis et al. (2005, https://doi.org/10.1029/2004WR003445) into an operationaland statistically comprehensive GLS regional hydrologic regression methodology to estimate oodquantiles, regional shape parameters, low ows, and other statistics with spatially correlated ow records.New GLS regression diagnostic statistics include a Bayesian plausibility value, pseudo adjusted R2,pseudo analysis of variance table, and two diagnostic error variance ratios. Traditional leverage andinuence are extended to identify rogue observations, address lack of t, and support gauge network designand regionofinuence regression. Formulas are derived for the Bayesian computation of estimators,standard errors, and diagnostic statistics. The use of BGLS and the new diagnostic statistics are illustratedwith a regional logspace skew regression analysis for the Piedmont region in the Southeastern U.S. Acomparison of ordinary, weighted, and GLS analyses documents the advantages of the Bayesian estimatorover the method ofmoment estimator of model error variance introduced by Stedinger and Tasker (1985,https://doi.org/10.1029/WR021i009p01421). Of the three types of analyses, only GLS considers thecovariance among concurrent ows. The example demonstrates that GLS regional skewness models can behighly accurate when correctly analyzed: The BGLS average variance of prediction is 0.090 for SouthCarolina (92 stations), whereas a traditional ordinary least squares analysis published by the U.S.Geological Survey yielded 0.193 (Feaster & Tasker, 2002, https://doi.org/10.3133/wri024140). BGLSprovides a statistical valid framework for the rigorous analysis of spatially correlated hydrologic data,allowing for the estimation of parameters and their actual precision and computation of several diagnosticstatistics, as well as correctly attributing variability to the three key sources: time sampling error, modelerror, and signal

International audience; The northeastern region of Brazil is the most densily populated and biodiverse semi-arid regions of the planet. Effects of the natural climate variability and colonization on the landscape have been described since the beginning of the 16 th century but little is known about their effects on natural resources. Climate projections predict temperatures above 40 °C and an increase in the number and duration of droughts at the end of the 21 st century with strong societal impacts. Here, we analyze the influence of public policies, human activities and natural climate variability on the environment over the last 60 years. Our study is based on sedimentological and environmental reconstructions from two sediment cores collected in two dam lakes on the river Acaraú in the State of Ceará. Multiproxy analyses of both cores (inorganic geochemistry, pollen, charcoal, remote sensing) at an annual resolution showed that 1) at interannual scale composition and distribution of the dry forest (known as Caatinga) were not affected by the alternance of drought and high moisture episodes; 2) at decadal scale human activities such as agriculture were reflected by changes in vegetation cover and fishery by progressive changes in lake trophic status; 3) public policies were able to promote changes in the landscape e.g., land colonization with the regression of the dry forest and irrigation plan able to amplify the deforestation and change the floristic composition. Thanks to paleoscience approach, our environmental diagnosis should help future decision-making and provide guidelines for preservation of resources and wellbeing of the inhabitants.

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. Abstract This paper investigates how El Niño events of different durations affect the atmospheric circulation over the Tropical North Atlantic (TNA) region, what are the consequences of these alterations in the sea surface temperature (SST) interannual variability and what are the responses, in terms of precipitation anomalies, over Ceará. Composite analyses of oceanographic and atmospheric variables were made for 18 El Niño years, divided between events of short and long duration. Events that lasted longer resulted in positive SST anomalies in the TNA region, leading to an interhemispheric gradient of SST anomalies in the Tropical Atlantic and below normal precipitation anomalies over Ceará. On the other hand, the alterations in the atmospheric circulation on TNA, during years of short duration El Niño, have not shown to be as intense as those in years of long duration El Niño. This resulted in SST anomalies on TNA close to zero and consequently, the interhemispheric gradient of SST anomalies was not observed. The near normal precipitation anomalies composites are consistent with the no formation of a SST anomalies gradient.

The teleconnection between the Atlantic Niño and the Pacific El Niño Southern Oscillation (ENSO) is revisited using obser-vational and reanalysis data for the 1905-2014 period. Two types of Atlantic Niño are significantly negatively correlated with ENSO, with Atlantic leading ENSO by 6-month to 1-year. The first one is the already well-known connection between the boreal summer Atlantic Niño (ATL3: 3° N-3° S, 20° W-0°) and the subsequent winter ENSO (Niño3: 5° N-5° S, 150° W-90° W). This relationship is strong in the first and last decades of the study period. It is shown that a second Atlantic Niño in boreal fall/early winter (October-December, here in after called winter Atlantic Niño) is also significantly correlated with the following year ENSO. This winter Atlantic Niño leads to an early development of ENSO from boreal summer onwards, with a marked multidecadal modulation of the lead time. A nearly 1-year leading connection between winter Atlantic Niño and the following ENSO is generally observed in the mid-twentieth century, mostly when the summer Atlantic Niño teleconnection with the subsequent winter ENSO is weak. The same mechanism of the Atlantic-Pacific Niño connection, which involves the Walker circulation, operates for the two types of Atlantic Niño. Our analysis supports the leading influence of the summer and winter Atlantic equatorial modes on climate variability in South America. These results suggest the relevance of different types of Atlantic Niño for the 6-month to 1-year predictability of ENSO and its climatic impacts.

Precipitation and temperature projections from global models, made available through the Coupled Model Intercomparison Project Phase 5 (CMIP5), and actually used in the AR5 by Intergovernmental Panel on Climate Change - IPCC-AR5), are assessed here for Sao Francisco River Basin. RCP 4.5 and RCP 8.5 are considered througout the period 2011 to 2100. Incidentally, global models are evaluated concerning their representativity related to 1961-2000 climatology from Brazilian National Meteorological Institute (INMET) data. Two indexes were evaluated: correlation and square mean error. The analisys of projections was performed through the assessment of yearly average values of 30 years period (2011-2040, 2041-2070 e 2071-2100). Additionally to evaluate trends and variability it was considered 10 year moving averages, linear regression and Mann-Kendall-Sen method. Approximately 28% analyzed models do not capture the seasonal precipitation reliably. All models present positive trend for temperature, and despite of divergence on precipitation, the models projected anomalies between -20 and 20 for each time-slice for this variable.

The state of Para&iacute, ba is part of the semi-arid region of Brazil, where severe droughts have occurred in recent years, resulting in significant socio-economic losses associated with climate variability. Thus, understanding to what extent precipitation can be influenced by sea surface temperature (SST) patterns in the tropical region can help, along with a monitoring system, to set up an early warning system, the first pillar in drought management. In this study, Generalized Additive Models for Location, Scale and Shape (GAMLSS) were used to filter climatic indices with higher predictive efficiency and, as a result, to perform rainfall predictions. The results show the persistent influence of tropical SST patterns in Para&iacute, ba rainfall, the tropical Atlantic Ocean impacting the rainfall distribution more effectively than the tropical Pacific Ocean. The GAMLSS model showed predictive capability during summer and southern autumn in Para&iacute, ba, highlighting the JFM (January, February and March), FMA (February, March and April), MAM (March, April and May), and AMJ (April, May and June) trimesters as those with the highest predictive potential. The methodology demonstrates the ability to be integrated with regional forecasting models (ensemble). Such information has the potential to inform decisions in multiple sectors, such as agriculture and water resources, aiming at the sustainable management of water resources and resilience to climate risk.

The 2012&ndash, 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&ndash, 2018 drought in Cear&aacute, 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.

International audience; A relationship between oceanic conditions in the northwestern equatorial Atlantic (NWEA) and the seasonal rainfall over the northern part of Brazilian Northeast (NNEB) allows large climate events to be forecasted with a delay of a few months. Observed sea surface variables (sea surface temperature, wind stress and latent heat flux) and reanalyzed temperature and salinity profiles at depths of 0 - 150 m are used during 1974-2008. Perturbations in the Wind-Evaporation-SST mechanism over the NWEA during the last months of the year and the first months of the following year are of primary importance in evaluating the risk that strong climate events will affect the subsequent seasonal rainfall (in March-April) over the NNEB. Especially interesting are the Barrier Layer Thickness (BLT) and Ocean Heat Content (OHC) in the NWEA region from August-September through the subsequent months, during which a slow and steady evolution is apparent, with the highest signal occurring in October-November. Through their relationship with the local surface dynamic conditions, such BLT and OHC perturbations during the last months of the year can be used as a valuable indicator for forecasting wet or dry events over the NNEB during the subsequent rainfall season. A proposal is discussed to deploy additional temperature/conducti- vity sensors down to a depth of 140 m at three PIRATA moorings located in the NWEA region. That will be necessary if the BLT and other parameters of energy exchange between the ocean and atmosphere are to be estimated in real time and with a sufficiently high vertical resolution.

This sixth edition of explaining extreme events of the previous year (2016) from a climate perspective is the first of these reports to find that some extreme events were not possible in a preindustrial climate. The events were the 2016 record global heat, the heat across Asia, as well as a marine heat wave off the coast of Alaska. While these results are novel, they were not unexpected. Climate attribution scientists have been predicting that eventually the influence of human-caused climate change would become sufficiently strong as to push events beyond the bounds of natural variability alone. It was also predicted that we would first observe this phenomenon for heat events where the climate change influence is most pronounced. Additional retrospective analysis will reveal if, in fact, these are the first events of their kind or were simply some of the first to be discovered. Last year, the editors emphasized the need for additional papers in the area of “impacts attribution” that investigate whether climate change’s influence on the extreme event can subsequently be directly tied to a change in risk of the socio-economic or environmental impacts. Several papers in this year’s report address this challenge, including Great Barrier Reef bleaching, living marine resources in the Pacific, and ecosystem productivity on the Iberian Peninsula. This is an increase over the number of impact attribution papers than in the past, and are hopefully a sign that research in this area will continue to expand in the future. Other extreme weather event types in this year’s edition include ocean heat waves, forest fires, snow storms, and frost, as well as heavy precipitation, drought, and extreme heat and cold events over land. There were a number of marine heat waves examined in this year’s report, and all but one found a role for climate change in increasing the severity of the events. While humancaused climate change caused China’s cold winter to be less likely, it did not influence U.S. storm Jonas which hit the mid-Atlantic in winter 2016. As in past years, the papers submitted to this report are selected prior to knowing the final results of whether human-caused climate change influenced the event. The editors have and will continue to support the publication of papers that find no role for human-caused climate change because of their scientific value in both assessing attribution methodologies and in enhancing our understanding of how climate change is, and is not, impacting extremes. In this report, twenty-one of the twenty-seven papers in this edition identified climate change as a significant driver of an event, while six did not. Of the 131 papers now examined in this report over the last six years, approximately 65% have identified a role for climate change, while about 35% have not found an appreciable effect. Looking ahead, we hope to continue to see improvements in how we assess the influence of human-induced climate change on extremes and the continued inclusion of stakeholder needs to inform the growth of the field and how the results can be applied in decision making. While it represents a considerable challenge to provide robust results that are clearly communicated for stakeholders to use as part of their decision-making processes, these annual reports are increasingly showing their potential to help meet such growing needs.

Aim The primary objectives of this study were (1) to assess, in the light of palaeoecological reconstruction, the climate stability hypothesis used by evolutionary biologists to explain high diversity in historically stable areas, and (2) to identify the response mechanisms of a tropical rain forest microrefugium to climatic variability. Location North-eastern Brazil, Serra de Maranguape. Methods Vegetation and climatic changes were reconstructed using a pollen record in a sediment core from a forest hollow, and the chronology was based on accelerator mass spectrometry radiocarbon analyses. Results Past vegetation dynamics consisted of three main forest types, shown by major compositional changes in rain forest assemblages between 5000 and 1000 cal. yr bp. Dense ombrophilous forest was abruptly replaced by heliophilous early successional tree taxa at 4275 cal. yr bp. These early successional tree taxa were established over a period of c. 100 years, and their dominance lasted for c. 750 years and was associated with dry conditions until 3525 cal. yr bp. Subsequently, the expansion of secondary successional tree taxa over a period of c. 550 years enabled the recovery of ombrophilous forest. Main conclusions The vegetation changes in the Serra de Maranguape provide evidence for the high sensitivity of this rain forest microrefugium to climatic variability on a multidecadal to millennial time-scale during the mid- to late Holocene. Despite the substantial compositional and climatic changes, this microrefugium apparently was continuously forested and responded to climatic instability by recruiting key species to its highly diverse stock. This evidence helps to address the joint concerns of evolutionary biologists and palaeoecologists regarding how forests can persist during periods of climatic variability by showing that some tropical regions can remain continuously forested despite reorganization during abrupt and short-term climatic changes. (Resume d'auteur)

A agricultura é um dos setores que mais se ressentem da variabilidade e irregularidade das chuvas, típicas do clima semiárido que predomina no Estado do Ceará. Embora seja menos dependente do que a agricultura de sequeiro, culturas irrigadas também sofrem os impac­tos, principalmente, em períodos prolongados de seca quando os reservatórios não recebem aporte de água significativo. Neste artigo são apresentados os principais Sistemas de Infor­mação Agrometeorológica desenvolvidos pela FUNCEME para subsidiar a tomada de decisão para uma gestão sustentável no setor agrícola. São soluções tecnológicas disponibilizadas pela internet e que permitem identificar áreas com potencial de plantio em função da quali­dade da estação chuvosa, estimar o rendimento das culturas de forma antecipada, decidir o momento de plantar e realizar o manejo da irrigação com base nas condições atmosféricas.

Surface soil samples were collected in three mountainous massifs in north-eastern Brazil to characterize the different vegetation types according to their respective pollen assemblages. Complementary approach between pollen and vegetation data shows that the pollen rain accurately reflects the following three main forest types: i) a dense ombrophilous forest (or tropical moist broadleaf forest) characterized by Myrtaceae associated with high percentages of Miconia, Guapira, Ilex, Moraceae-Urticaceae undif. or Byrsonima, ii) a seasonal semi-deciduous montane forest characterized by an increase of Arecaceae associated with Fabaceae-Mimosideae, Myrtaceae, Piper, Cecropia, Urera and Mitracarpus, and iii) a seasonal deciduous forest dominated by Fabaceae-Mimosideae and Arecaceae tree taxa associated with Alternanthera, Cyperaceae and Mitracarpus. Using of botanical data from several plots of ombrophilous forest, in which several surface soil samples have been collected, allows to roughly estimate the over- and underrepresentation of pollen taxa relative to their floristic abundance. Furthermore, distribution of surface soil samples at different altitude and mountain sides also allows to characterize vegetation variation according to several environmental parameters. The precipitation increase with altitude is confirmed as the main environmental factor controlling vegetation distribution. However, the forests located close to the crest with a proportion increase of pollen taxa characteristic of heliophilous and pioneer trees (Alchornea, Miconia, Clusia), are also influenced by changes of edaphic conditions. In addition to provide useful information in under standing of fossil pollen records, this approach improves our understanding of the ecosystem functioning in mountainous massifs in north-eastern Brazil. A useful knowledge for conservation or restoration purposes.

RESUMO As projeções de precipitação e temperatura dos modelos globais do Coupled Model Intercomparison Project Phase 5 (CMIP5, utilizados no quinto relatório do Intergovernmental Panel on Climate Change - IPCC-AR5) são analisadas para a bacia do rio São Francisco para o período de 2011 a 2100 para os cenários RCP 4.5 e RCP 8.5. Além disso, os modelos são avaliados quanto à representação da climatologia da precipitação no período de 1961 a 2000. Para a avaliação, os dados das estações do Instituto Nacional de Meteorologia (INMET) são utilizados. A avaliação baseia-se em dois índices de desempenho: a correlação e o erro quadrático médio. Para a análise das projeções, são calculadas as anomalias das médias anuais divididas em períodos de 30 anos (2011 a 2040, 2041 a 2070 e 2071 a 2100). Para avaliação de tendências e variabilidade são usados: a média móvel de dez anos, regressão linear e o método de Mann-Kendall-Sen. Aproximadamente 28% dos modelos analisados não representam adequadamente a sazonalidade da precipitação. Todos os modelos apresentaram tendência positiva para a temperatura, e apesar da divergência na precipitação, os mesmos projetam anomalias entre -20% e 20% em cada período de 30 anos para essa variável. ABSTRACT Precipitation and temperature projections from global models, made available through the Coupled Model Intercomparison Project Phase 5 (CMIP5), and actually used in the AR5 by Intergovernmental Panel on Climate Change - IPCC-AR5), are assessed here for São Francisco River Basin. RCP 4.5 and RCP 8.5 are considered througout the period 2011 to 2100. Incidentally, global models are evaluated concerning their representativity related to 1961-2000 climatology from Brazilian National Meteorological Institute (INMET) data. Two indexes were evaluated: correlation and square mean error. The analisys of projections was performed through the assessment of yearly average values of 30 years period (2011-2040, 2041-2070 e 2071-2100). Additionally to evaluate trends and variability it was considered 10 year moving averages, linear regression and Mann-Kendall-Sen method. Approximately 28% analyzed models do not capture the seasonal precipitation reliably. All models present positive trend for temperature, and despite of divergence on precipitation, the models projected anomalies between -20 and 20 for each time-slice for this variable.

O monitoramento da agua e realizado em amostragens temporais e espaciais tanto no que se refere a aspectos quantitativos como qualitativos. Para ser eficiente, a amostragem deve considerar processos fisicos, quimicos e biologicos que afetam os parâmetros de interesse. O monitoramento da qualidade de agua para reservatorios do Ceara, localizados no semiarido do Brasil, e atualmente realizado mensalmente com base em medidas de perfis verticais para alguns parâmetros fisicos e quimicos, e em um ponto amostral (proximo a superficie d’agua) para todos os outros parâmetros. O objetivo deste artigo e descrever o processo de mistura em lagos em baixas latitudes e estabelecer criterio para o monitoramento de qualidade de aguas nestas regioes. E demonstrado que a estratificacao e a mistura ocorrem em um ciclo diurno governado por variaveis climaticas, tais como radiacao solar e vento. Assim, a variabilidade mensal nao e somente devido a variabilidade sazonal, mas tambem a diferencas no horario da medida realizada. Como um resultado, a estrategia de monitoramento deve levar em consideracao a variabilidade diurna de tais parâmetros e dos processos de mistura e estratificacao, definindo, por exemplo, uma hora padrao para a medida, o que pode ser muito caro e operacionalmente impossivel.

Apresenta-se um problema de otimizacao da remediacao de aguas subterrâneas por meio da tecnica bombear-e-tratar. Dois objetivos sao perseguidos: 1) minimizar a pluma contaminante; e 2) minimizar do custo total da remediacao. A resolucao desse problema e feita aplicando-se dois algoritmos evolucionarios (AE) multipopulacao juntamente com a tecnologia de redes neurais artificiais (RNA). As vazoes dos pocos sao colhidas para a RNA, que calcula a massa restante de contaminante no sitio. As respostas dadas pela rede sao avaliadas pelos algoritmos evolucionarios a fim de efetuar a otimizacao.

This paper considers use of the generalized Pareto ∥GP) distribution with a Poisson model for arrivals to describe peaks over a threshold. This yields a three-parameter generalized extreme value (GEV) distribution for the annual maximum series. Maximum likelihood estimates of the GP shape parameter к can result in absurd estimates in small samples. These problems are resolved by addition of a prior distribution on к yielding a generalized maximum likelihood estimator. Results show that a three- parameter partial duration series (PDS) analysis yields quantile estimators with the same precision as an annual maximum series (AMS) analysis when the generalized maximum likelihood (GML) GP and GEV estimators are adopted. For к ≤ 0 the GML quantile estimators with both PDS and AMS have the best performance among the quantile estimators examined (moments, L moments, and GML). The precision of flood quantiles derived from a PDS analysis is insensitive to the arrival rate λ, so that a year of PDS data is generally worth about as much as a year of AMS data when estimating the 100-year flood.

This paper considers use of historical information with partial duration series (PDS) and annual maximum series (AMS) flood risk models. A generalized Pareto distribution for exceedances over a threshold combined with the Poisson arrival model yields a three-parameter generalized extreme value (GEV) distribution for the AMS. When fitting three-parameter GEV models using generalized maximum likelihood estimators, the average gains from use of historical information are about the same with both AMS and PDS frameworks, though the exact values depend upon the shape parameter к. The effect of the arrival rate λ is modest. In general, average gains are higher when к = 0.0 as opposed to when −0.3 ≤ к ≤ −0.1. When fitting two-parameter models (exponential—Poisson and Gumbel), the average gains are less than those observed with the corresponding three-parameter models with к = 0. Fitting a two-parameter AMS lognormal distribution to lognormal data yielded higher average gains with use of historical information than were obtained with the two-parameter AMS/Gumbel distribution.