Francisco de Assis de Souza Filho

Improved water allocation utilizing probabilistic climate forecasts: short-term water contracts in a risk management framework.

Received 4 February 2009; revised 29 July 2009; accepted 6 August 2009; published 11 November 2009. [1] Probabilistic, seasonal to interannual streamflow forecasts are becoming increasingly available as the ability to model climate teleconnections is improving. However, water managers and practitioners have been slow to adopt such products, citing concerns with forecast skill. Essentially, a management risk is perceived in ‘‘gambling’’ with operations using a probabilistic forecast, while a system failure upon following existing operating policies is ‘‘protected’’ by the official rules or guidebook. In the presence of a prescribed system of prior allocation of releases under different storage or water availability conditions, the manager has little incentive to change. Innovation in allocation and operation is hence key to improved risk management using such forecasts. A participatory water allocation process that can effectively use probabilistic forecasts as part of an adaptive management strategy is introduced here. Users can express their demand for water through statements that cover the quantity needed at a particular reliability, the temporal distribution of the ‘‘allocation,’’ the associated willingness to pay, and compensation in the event of contract nonperformance. The water manager then assesses feasible allocations using the probabilistic forecast that try to meet these criteria across all users. An iterative process between users and water manager could be used to formalize a set of short-term contracts that represent the resulting prioritized water allocation strategy over the operating period for which the forecast was issued. These contracts can be used to allocate water each year/season beyond long-term contracts that may have precedence. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser optimization model that can support such an allocation process is presented. The application of this conceptual model is explored using data for the Jaguaribe Metropolitan Hydro System in Ceara, Brazil. The performance relative to the current allocation process is assessed in the context of whether such a model could support the proposed short-term contract based participatory process. A synthetic forecasting example is also used to explore the relative roles of forecast skill and reservoir storage in this framework.

Climate risk management for water in semi–arid regions

BackgroundNew 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.MethodsThis 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.ResultsIn 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.ConclusionsThis 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.

Avaliação de desempenho dos modelos do CMIP5 quanto à representação dos padrões de variação da precipitação no século XX sobre a região Nordeste do Brasil, Amazônia e bacia do Prata e análise das projeções para o cenário RCP8.5

The global models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) are evaluated over Northeast Brazil (NEB), the Amazon region and La Plata Basin regarding the representation of precipitation for the period 1901 to 1999. Furthermore, the projections of precipitation for scenario RCP8.5 for the XXI century are analyzed. The evaluation is performed using data from the Climatic Research Unit (CRU) and 20th Century Reanalysis V2 reanalysis of the National Oceanic and Atmospheric Administration (NOAA). The models are classified using indices that indicate how patterns of seasonal, interannual and decennial variation are represented. The evaluation identified CANESM as the best models for NEB, whereas for La Plata Basin, the French model CNRM_CM5_r1i1p1 runs were superior. For the Amazon region, the model GISS Model-E2-R_r1i1p1 exhibited the best performance. Over NEB, most models indicate largest changes during the pre-season, but differ on the sign of the anomaly. In the Amazon region the models suggest greater chances of rainfall reduction up to 20.5%, 33.6 and 39.5% for the periods 2010-2039, 2040-2069 and 2070-2099, respectively. Over the La Plata region, the, ensemble projects few changes for the 2010 to 2039 period .

Verificação das previsões de tempo para precipitação usando ensemble regional para o estado do Ceará em 2009

Este artigo apresenta um estudo de verificacao das previsoes de chuva de um sistema de previsao do tempo por ensemble regional. O conjunto e composto por seis membros, dos quais quatro utilizam o modelo RAMS 6.0 e dois usam o WRF 3.1, inicializados com dados dos modelos globais do CPTEC ou GFS e diferentes parametrizacoes de conveccao. A verificacao foca nas previsoes de chuva de 24, 48 e 72 horas e nos limiares de precipitacao de 1mm, 5mm e 10mm sobre o Estado do Ceara. Os membros do ensemble apresentaram resultados superiores a persistencia em todo o dominio avaliado. O modelo RAMS apresenta maior indice de acerto, principalmente no litoral norte do Estado, porem um maior falso alarme em comparacao com o modelo WRF. O sistema de previsao de chuva diminui sua qualidade com o aumento dos horizontes e a intensidade da chuva que se quer prever.

Forecasting the impacts of climate variability: lessons from the rainfed corn market in Ceará, Brazil

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.

Trajectories of Adaptation: A Retrospectus for Future Dynamics

Sustainable adaptation to climate change needs to be assessed beyond the present time and location to include the way that current forms of adaptation might influence future response options. An analysis of past dynamics of adaptation, what we call “trajectories,” might hold the key to understanding how the adaptive outcomes of past responses to climate stress constrain or open avenues to future adaptation. Adaptation research often focuses on particular actions, technologies, or institutions which may positively influence these relationships in order to build resilience and reduce vulnerability. However, relationships are complex and often behave in unexpected ways. There is no simple cause and effect, but rather actions are modified and transmitted through a web of linkages and feedbacks that are both physical and social. This complexity challenges our ability to predict the outcome of particular actions and there remain gaps in the understanding of system interactions that would permit a more accurate assessment of future development trajectories. The work presented here is an analysis of change in the climate vulnerability of dryland farmers in Northeast Brazil over four decades. The analytical framework, which links biophysical characteristics with a socio-economic context and indicators, permits an analysis that captures the dynamic relationship of adaptive capacities and consequent changes in vulnerability. The analysis of trajectories provides a foundation for future assumptions about human behavior and the relationship with the environment.

Monthly streamflow forecast for National Interconnected System (NIS) using Periodic Auto-regressive Endogenous Models (PAR) and Exogenous (PARX) with climate information

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.

Reservoir yield intercomparison of large dams in Jaguaribe Basin-CE in climate change scenarios

Climate changes can have different impacts on water resources. Strategies to adapt to climate changes depend on impact studies. In this context, this study aimed to estimate the impact that changes in precipitation, projected by Global Circulation Models (GCMs) in the fifth report by the Intergovernmental Panel on Climate Change (IPCC-AR5) may cause on reservoir yield (Q90) of large reservoirs (Castanhão and Banabuiú), located in the Jaguaribe River Basin, Ceará. The rainfall data are from 20 GCMs using two greenhouse gas scenarios (RCP4.5 and RCP8.5). The precipitation projections were used as input data for the rainfall-runoff model (SMAP) and, after the reservoirs’ inflow generation, the reservoir yields were simulated in the AcquaNet model, for the time periods of 2040-2069 and 2070-2099. The results were analyzed and presented a great divergence, in sign (increase or decrease) and in the magnitude of change of Q90. However, most Q90 projections indicated reduction in both reservoirs, for the two periods, especially at the end of the 21th century.

STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS

The modeling of seasonal and interannual streamflow forecasting at northeastern Brazil represents a great relevance problem to the use and management of water resources; which demands greater prediction ability models. This is still a difficult task to solve due to the seasonal and interannual climate variability at the semi-arid region. This work presents the artificial neural networks (ANN) as an alternative for modeling the seasonal to interannual climate prediction,. For the development of this task the hydropraphic Oros weir Basin was chosen due to its importance as water resources in the State of Ceara. According to recent studies, the temperatures of the North Atlantic, South Atlantic and equatorial Pacific can be satisfactorily as predictors for the Northeast climate. The proposed model predicts, in July, the next rainy season (January to June) river flow regime. This time frame is of great relevance for the allocation of water resources. Among the studied models, those using the average temperature anomalies of April, May and June preceding the predicted year as input data showed the highest Nash-Suttcliffe efficiency (0.80).

From Drought to Water Security: Brazilian Experiences and Challenges

Water security is a relevant concept for public well-being and sustainable development. The word ‘security’ often refers to the idea of predictability, control, and assurance. These are relevant concepts in our changing world. Change involves social and natural processes on a planetary scale that shape and transform local realities. In this framework, the concept of water security must be understood as dialectically related to the concept of risk. In the past few years, the concept of water security has been increasingly disseminated in Brazil, owing to severe droughts that have struck several of the country’s regions. Between 2013 and 2015, South-East Brazil experienced the worst drought ever recorded there. In North-East Brazil, a similar episode began in 2011 and still persists in 2017. The impacts of these events, which are associated with climate risk and societal adaptation, have placed the issue of water security on the Brazilian political agenda, but decision makers’ conceptual approach is still fragile. This chapter describes the Brazilian experience with water security that emerged from the water crises in two large metropolitan regions: Sao Paulo, in Sao Paulo State, the economic power of the wet South-East Region; and Fortaleza, in Ceara State, a semi-arid part of the North-East Region that has dealt with the impacts of drought throughout its history. The respective droughts are described, as well as the water security strategies that were adopted during each of those crises, the lessons learned, and challenges for the future.