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A DECISION SUPPORT SYSTEM FOR DROUGHT FORECASTING AND RESERVOIRSMANAGEMENT IN NORTHEAST-BRAZIL

Marcos Airton de Sousa Freitas

Water Resources, Environmental Eng. andApplied Computation Research Group

University of Fortaleza - UNIFOR, Brazil.Av. Washington Soares, 1321 CEP 60811-341

e-mail: freitas@feq.unifor.br

ABSTRACT

Different methods and models have been developed, applied and incorporated in a Decision SupportSystem towards the regional analysis of the droughts in Northeast-Brazil. Methods for droughtforecasting were statistical and neuro-fuzzy systems analysis of the patterns of atmospheric andoceanic conditions of the tropical Atlantic and Pacific oceans. For the drought management byreservoirs, stochastic simulation models were evaluated by statistical parameters, required reservoircapacity and drought characteristics.

RESUMO

Para a análise regional integrada do fenômeno das secas no Nordeste do Brasil foram desenvolvidos eaplicados diversos métodos e modelos, que foram incorporados a um Sistema de Suporte à Decisão.Para a previsão de secas foram empregados modelos estatísticos e sistemas neuro-fuzzy a partir dospadrões das condições atmosféricas e oceânicas no Atlântico e Pacífico tropicais. Já na gestão dosrecursos hídricos durante períodos de secas foram usados modelos estocásticos para a simulação daoperação de reservatórios, levando-se em consideração a reprodução dos parâmetros estatísticos nasséries geradas, a capacidade normalizada requerida pelo reservatório e os parâmetros característicosdos períodos secos.

1. INTRODUCTION

The droughts of Northeast-Brazil have recurrently led to starvation, mass exodus and socialconflicts, and their eventual prediction remains a central concern (MAGALHÃES, 1993). Thevulnerability of agricultural production due to water deficit and the development of large-scale multi-purpose water supply systems implies that drought analysis is required at a regional scale.

A comprehensive approach for studying regional drought problems includes (ROSSI et al.,1992): a) identification of meteorological causes and drought forecast; b) evaluation of hydrologicaldrought characteristics; c) analysis of economic, environmental and social effects of drought, and d)definition of appropriate measures for controlling drought effects.

For the regional analysis of the droughts of Northeast-Brazil different methods and modelshave been developed, applied and incorporated in a Decision Support System. A Decision SupportSystem (DSS) is an advisory system for management, usually computed-based, that utilizes databases,models, and communication/dialog system to provide decision markers with management information(GRIGG, 1995). In this Decision Support System emphasis is given on drought forecasting anddrought management (FREITAS, 1997).

2. DROUGHT FORECASTING

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2.1 Statistical Analysis

The identification of the meteorological causes and the development of a method to predict thedroughts of Northeast-Brazil have been done by analysis of the global circulation systems, especiallythe ENSO (El Niño-Southern Oscillation), and the patterns of atmospheric and oceanic conditions ofthe tropical Atlantic. Correlation between the precipitation in Northeast-Brazil and global circulationindices like sea surface temperature (SST) anomaly patterns in the Pacific and Atlantic, wind stressand air pressure gradient in the Pacific (Darwin-Tahiti) allows an estimation of future precipitation.

In this study different data sources have been used: for SST of the Pacific the indices ofWRIGHT (1989), for SST and wind stress of the Atlantic the data of PICAUT et al. (1985), SERVAINet al. (1987) and SERVAIN & LUKAS (1990). The objective of the statistical analysis was to obtainon the basis of 30 precipitation stations located in the Federal State of Ceará the severity ofdependency between the incidence of the El Niño and the incidence of droughts in Ceará (FREITAS,1997).

2.2 Neuro-Fuzzy-Systems

Recently, significant progress in the fields of pattern recognition and system theory have beenmade by artificial neural network modelling (KOSKO, 1992). Neural networks have a flexiblemathematical structure, and are capable of identifying nonlinear relationships and of describingcomplex processes. The combination of neural network and fuzzy sets allows the use of additionalinformation, which is actually not well defined (FREITAS & BILLIB, 1997).

In this study different approaches for the drought prediction of Northeast-Brazil have beentested: modelling the time series of precipitation by the use of neural network for reference stations,and neuro-fuzzy systems analysis for the pattern recognition of the SST data of both Pacific and thetropical Atlantic. For the 30 precipitation stations of Ceará a regional rainfall departure indexaccording to LAMB et al. (1986) has been calculated for the rainy season and correlated with the SSTof the Atlantic. Only the SST data of the region where the correlation is higher than 0.3 have been usedas input to the neuro-fuzzy model.

Among several learning methods the best known is the so-called Standard_Backpropagation(ZELL, 1996). A modified version of these method with a learning rate �, a 'momentum' term and 'flatspot elimination' has been tested. The algorithms QUICKPROP and BPTT (BackPropagation ThroughTime) have also been applied. All data have been normalized (interval 0.0 - 0.9) and the logisticfunction has been used as activation function.

For some rainfall stations neural networks were applied in order to predict the monthly rainfallbased on the SST-patterns of the Pacific Ocean. Figure 1 and 2 show the result of this analysis to theIpaguassu station using the algorithms QUICKPROP e BPTT, respectively. Four-layer networks havebeen used. The years 1911-40 have been used for training and the years 1941-88 for verification. Theapplication to all 30 stations gives a regional distribution of rainfall prediction classified by wet,normal and dry. As a consequence of these results, an early warning could be given to farmers 6months before rainy season starts.

3. RESERVOIRS MANAGEMENT

3.1 General aspects

The controlling of drought effects can be done in Northeast-Brazil by appropriate managementof the reservoirs. For the evaluation of the risk by droughts stochastic simulation models can beapplied. Due to the climatic and geological conditions of the region most rivers are intermittent, sospecial simulation models are necessary for the generation of the runoff.

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For the management of the water resources in semi-arid regions it is very important to analyzethe impacts of extreme droughts. This can be done by generating long time series and Monte-Carloanalysis. In this study several stochastic models are selected, applied and evaluated by comparing thestatistical parameters, potential reservoir capacity, and drought characteristics like duration, severity,and magnitude.

Figure 1: Normalized Monthly Rainfall at Station Ipaguassu Using the QUICKPROP Algorithm(without adaptive process)

Figure 2: Normalized Monthly Rainfall at Station Ipaguassu Using the BPTT Algorithm (withoutadaptive process)

3.2 Evaluation of stochastic models

In a first step the following monthly models has been evaluated by statistical parameters(FREITAS, 1995):

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- PAR-Model (Thomas/Fiering) with modification by CLARKE(1973)- PAR-Model (Thomas/Fiering) with transformation by MATALAS(1967)- Two-tier model (PAR(1)/AR(1) with log-gamma distribution)- Two-tier model (PAR(1)/AR(1) with log-normal distribution)- Two-tier model (PAR(1)/GAR(1) by FERNANDEZ & SALAS; 1990)- Fragment method for AR(1) with log-gamma distribution- Fragment method for AR(1) with log-normal distribution- Fragment method for GAR(1)- Disaggregation model/AR(1) by VALENCIA & SCHAAKE(1973)

The models have been applied to four catchments of different area by generation of 100 seriesof 50 years. In the second step two models, both with one year time step, the AR(1)- and the GAR(1)-model, have been evaluated by use of the Sequent-Peak-Algorithm (LOUCKS et al., 1981) for thecalculation of the necessary reservoir capacity. For both models 1,000 fifty-year synthetic streamflowsequences have been adjusted to the Weibull probability distribution. One important finding is thatalmost no streamflow sequences have been generated containing more extreme drought periods thanthe historical sequence.

As the stochastic models should be used for drought simulation, they were studied in the thirdstep through the analysis of the statistical parameters of the drought characteristics (duration,cumulative deficit and intensity). The GAR(1)-Model showed better results than the AR(1)-Model, butneither was sufficient for the reproduction of drought characteristics.

3.3 Alternating Renewal Reward Model (ARR-Model)

To get a better simulation of drought characteristics, the Alternating Renewal Reward Model(KENDALL & DRACUP, 1992) was chosen: A basic assumption of this model is that each droughtevent is part of an independent population, but it depends on the duration. For the simulation of the wetand dry periods the geometric distribution and for the cumulative deficit the gamma distribution havebeen used.

Due to the short sample two procedures have been applied for the identification of theprobability distribution of the duration and the cumulative deficit: decimation and standardization. Theapplication of the Alternating Renewal Reward Model coupled with the Fragment Method showed thebest results, compared with the historical sequence by the Index Sequential Method.

Figure 3. Historic and synthetic statistical parameters (mean) of the station Fazenda Cajazeiras (RioAcaraú)

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4. CONCLUSIONS

The droughts of Northeast-Brazil can be partially predicted by conditional probabilities of therainfall related to the SST-index of the Pacific ocean. The likelihood of below-, near-, and abovenormal rainfall for each station can be calculated. The use of the Atlantic data improved the rainfallprediction by the application of the neuro-fuzzy systems analysis.

Based on the drought prediction, the actual reservoir management is developed by stochasticsimulation. By the comparison of the performance of the stochastic models the Alternating RenewalReward Model describes best the drought characteristics. The results of the whole droughtmanagement approach show that the developed methods give satisfactory predictions of droughts andappropriate tools for the reservoir management in Northeast-Brazil.

5. REFERENCES

CLARKE, R.T.: Mathematical Models in Hydrology, FAO Irrigation and Drainage Paper, N° 19,1973.FERNANDEZ, B. ; SALAS, J.D.: Gamma-Autoregressive Models for Stream-flow Simulation,Journal of Hydr. Eng., 116(11), 1403-1414, 1990.FREITAS, M.A.S.: Stochastische Abflussgenerierung in intermittierenden semiariden Gebieten(Nordost-Brasilien), Abschlussarbeit, Weiterbildendes Studium Bauingenieurwesen - Wasser undUmwelt, Universität Hannover, Deutschland, 1995.FREITAS, M.A.S.: Regional Drought Analysis by Statistic Methods und Neuro-Fuzzy-Systemswith Application to Northeast-Brazil (in German), PhD Thesis, University Hannover,Germany,1997.FREITAS, M.A.S.; BILLIB, M.H.A.: Drought Prediction and Characteristic Analysis in Semi-Arid Ceará / Northeast Brazil, in: Proceedings of Rabat Symposium “Sustainability of WaterResources Under Increasing Uncertainty”, IAHS Publ. N° 240, 105-112, Rabat, 1997.GRIGG, N.S.: Water Resources Management, Mc Graw-Hill, 1995.KENDALL, D.R.; DRACUP, J.A.: On the Generation of Drought Events Using an AlternatingRenewal-Reward Model. Stochastic Hydrol. Hydraul. 6, 55-68, 1992.KOSKO, B.: Neural Networks and Fuzzy Systems, A Dynamical Systems Approach to MachineIntelligence, Prentice-Hall, Inc.,Englewood Cliffs, N.J., USA, 1992.LAMB, P.J.; PEPPLER, R.A.; HASTENRATH, S.: Interannual Variability in the Atlantic, Nature ,322, 238-240, 1986.LOUCKS, D.P.; STEDINGER, J.R.; HAITH, D.A.: Water Resource Systems Planning andAnalysis, Prentice-Hall, Inc., Englewood Cliffs, N. J, 1981.MATALAS, N.C.: Mathematical Assessment of Synthetic Hydrology, Water Resources Research,3(4), 937-945, 1967.MAGALHÃES, A.R.: Drought and Policy Responses in the Brazilian Northeast. in: D.A. Wilhite(ed.), 1993: Drought Assessment, Management, and Planning: Theory and Case Studies. KluwerAcad. Publ., Dordrecht, 1993.PICAUT, J.; SERVAIN, J.; LECOMTE, P.; LUKAS M.; ROUGIER G: Climatic Atlas of theTropical Atlantic Wind Stress and Sea Surface Temperature 1964-1979, Université de BretagneOccidentale - University of Hawaii, 467p., 1985.ROSSI, G.; BENEDINI M.; TSAKIRIS G.; GIAKOUMAKIS S.: On Regional Drought Estimationand Analysis. Water Resources Management 6, 249-277, 1992.SERVAIN, J., SEVA M., LUKAS S. ; ROUGIER G.: Climatic Atlas of the Tropical Wind Stressand Sea Surface Temperature: 1980-1984, Ocean-Air Interactions, 1, 109-182, 1987.SERVAIN, J. ; LUKAS S.: Climatic Atlas of the Tropical Atlantic Wind Stress and Sea SurfaceTemperature 1985-1989: Oceans Tropicaux Atmosphere Globale, ORSTOM, 1990.VALENCIA, D. ; SCHAAKE, J.C.: Disaggregation Processes in Stochastic Hydrology, WaterResources Research, 9(3), 580-585, 1973.WRIGHT, P.B.: Homogeneized Long-Period Southern Oscillations Indices, Int. Journal ofClimatology, 9, 33-54, 1989.ZELL, A : Simulation Neuronaler Nezte, Addison-Wesley, 1994.