The NCEP Regional Spectral Model (RSM), with horizontal resolution of 60 km, was used to downscale the ECHAM4.5 AGCM (T42) simulations forced with observed SSTs over northeast Brazil. An ensemble of 10 runs for the period January-June 1971-2000 was used in this study. The RSM can resolve the spatial patterns of observed seasonal precipitation and capture the interannual variability of observed seasonal precipitation as well. The AGCM bias in displacement of the Atlantic ITCZ is partially corrected in the RSM. The RSM probability distribution function of seasonal precipitation anomalies is in better agreement with observations than that of the driving AGCM. Good potential prediction skills are demonstrated by the RSM in predicting the interannual variability of regional seasonal precipitation. The RSM can also capture the interannual variability of observed precipitation at intraseasonal time scales, such as precipitation intensity distribution and dry spells. A drought index and a flooding index were adopted to indicate the severity of drought and flooding conditions, and their interannual variability was reproduced by the RSM. The overall RSM performance in the downscaled climate of the ECHAM4.5 AGCM is satisfactory over Nordeste. The primary deficiency is a systematic dry bias for precipitation simulation.

Os modelos globais do Coupled Model Intercomparison Project Phase 5 (CMIP5) são avaliados para a região Nordeste do Brasil (NEB), região Amazônica e bacia do Prata quanto à representação da precipitação para o período de 1901 a 1999. Além disso, são analisadas as projeções de precipitação para o cenário RCP8.5 para o século XXI. A avaliação é realizada utilizando-se os dados do Climatic Research Unit (CRU) e a reanálise 20th Century Reanalysis V2 do National Oceanic and Atmospheric Administration (NOAA). Os modelos são classificados através de índices que indicam como os padrões de variação sazonal, interanual e decenal são representados. A avaliação apontou como melhores modelos para o NEB as rodadas do modelo CANESM, enquanto para a bacia do Prata, a rodada do modelo francês CNRM_CM5_r1i1p1. Já para a Amazônia, destacam-se as rodadas do modelo GISS-E2-R. Na região NEB, a maioria dos modelos mostra maiores impactos na pré-estação, porém divergem quanto ao sinal da anomalia. Na região Amazônica, os modelos sugerem maiores possibilidades de redução na precipitação, em até 20,5%,33,6 e 39,5% para os períodos de 2010 a 2039, 2040 a 2069 e 2070 a 2099, respectivamente. Na região do Prata, o conjunto dos modelos projeta poucas alterações no período de 2010 a 2039.

No abstract

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.

Abstract:Seasonal streamflow forecasts based on climate information can guide water managers toward superior reservoir operations, leading to improved water resources management efficiency. Uncertainty, however, is always present in seasonal streamflow forecasts, affecting the forecast value. Thus, a forecast should not be considered complete without a description of its uncertainty, which is critical for climate risk and water resources management. This study investigates the uncertainties of a seasonal streamflow forecast system for Northeastern Brazil based on climate precipitation forecasts and hydrologic modeling. These two sources of uncertainty are treated independently and then compared in order to guide future investments in the forecast system. Sea surface temperature is considered to be the primary source of uncertainty for the seasonal precipitation forecasts, based upon a 10-member climate model ensemble. Parameter uncertainty is considered to be the only source of uncertainty for the hydrologic model. Estimation of parameter uncertainty is estimated by the Shuffled Complex Evolution Metropolis algorithm, which employs a Markov Chain Monte Carlo scheme to provide the posterior distribution of the parameters and form uncertainty bounds on streamflow forecasts. Results indicate that uncertainties associated with the climate forecast are much larger than those from parameter estimation in the hydrologic model. Although model structure has not been considered in the evaluation of hydrologic uncertainties, this study indicates that future efforts to address the predominant source of uncertainty should focus on the climate prediction models.

Background: New sources of hydroclimate information based on forecast models and observational data have the potential to greatly improve the management of water resources in semi-arid regions prone to drought. Better management of climate-related risks and opportunities requires both new methods to develop forecasts of drought indicators and river flow, as well as better strategies to incorporate these forecasts into drought, river or reservoir management systems. In each case the existing institutional and policy context is key, making a collaborative approach involving stakeholders essential. Methods: This paper describes work done at the IRI over the past decade to develop statistical hydrologic forecast and water allocation models for the semi arid regions of NE Brazil (the "Nordeste") and central northern Chile based on seasonal climate forecasts. Results: In both locations, downscaled precipitation forecasts based on lagged SST predictors or GCM precipitation forecasts exhibit quite high skill. Spring-summer melt flow in Chile is shown to be highly predictable based on estimates of previous winter precipitation, and moderately predictable up to 6 months in advance using climate forecasts. Retrospective streamflow forecasts here are quite effective in predicting reductions in water rights during dry years. For the multi-use Oros reservoir in NE Brazil, streamflow forecasts have the most potential to optimize water allocations during multi-year low-flow periods, while the potential is higher for smaller reservoirs, relative to demand. Conclusions: This work demonstrates the potential value of seasonal climate forecasting as an integral part of drought early warning and for water allocation decision support systems in semi-arid regions. As human demands for water rise over time this potential is certain to rise in the future.

This study aims to find a seasonal streamflow forecast model simultaneous to all stations of SIN using periodic autoregressive models with exogenous variables (PARX) using climate indexes. Comparing the results from PAR and PARX Models, this research analyzes the impact on forecasts by using climate information. The proposed models for streamflow forecast has been carried out using natural streamflow data from Operador Nacional do Sistema (ONS) and statistical techniques (such as multiple linear regression and stepwise method to choose explanatory variables). On 27 climate indexes utilized, 4 of them are suggested in this work. The performance analysis methodology is based on the ELECTRE method further the NASH coefficient, the mean absolute percentage error, the multi-criteria distance and correlation. Forecasts with one month lead, the PAR models present better results for most stations of SIN within seasons DJF, MAM, and JJA, while for SON season there is greater efficiency from PARX model. This kind of model shows better performance during dry season in the basins at Northern Brazil -Amazonas and Araguaia-Tocantins; Central-Eastern Brazil -Eastern Atlantic and the most rivers located in the Paraná basin.Keywords: Monthly streamflow forecast; Climate information; National Interconnected System. RESUMOEste estudo propõe um modelo de previsão simultânea de vazões sazonais para todos os locais SIN através de modelos periódicos autorregressivos simples (PAR) e com variáveis exógenas (PARX) utilizando índices climáticos. Os modelos propostos de previsão de afluência utilizam os dados de vazões naturais gerados pelo Operador Nacional do Sistema (ONS) e técnicas estatísticas como as de regressão linear múltipla e o método stepwise para escolha de variáveis explanatórias. São utilizados 27 índices climáticos, dos quais 4 foram sugeridos neste trabalho. A análise de desempenho das metodologias é baseada no método ELECTRE com o uso do coeficiente de NASH, do erro médio percentual absoluto, da distância multicritério e da correlação. Para previsões com um mês de antecedência, os modelos do tipo PAR apresentam melhores desempenhos na maioria dos postos do SIN nos trimestres DJF, MAM e JJA, enquanto para o período SON a uma maior eficiência do modelo PARX. O PARX apresenta melhor desempenho no período seco das bacias do norte do Brasil -Amazonas e Araguaia-Tocantins; centro-leste brasileiro -Atlântico Leste e na maioria dos rios que formam a Bacia do Paraná.Palavras-chave: Previsão sazonal; Índices climáticos; Setor elétrico.

A number of studies show that climatic shocks have significant economic impacts in several regions of the world, especially in, but not limited to, developing economies. In this paper we focus on a drought-related indicator of well-being and emergency spending in the Brazilian semi-arid zone -rainfed corn market -and estimate aggregate behavioral and forecast models for this market conditional on local climate determinants. We find encouraging evidence that our approach can help policy makers buy time to help them prepare for drought mitigating actions. The analysis is applicable to economies elsewhere in the world and climatic impacts other than those caused by droughts.