The Impact Of Climate Change On The São Francisco River’s Hydroelectric Production

Pieter de Jong

Doutorado em Engenharia Industrial (PEI - UFBA );

Mestrado em Engenharia Industrial (PEI - UFBA );

Bacharel em Engenharia Elétrica (Universidade Monash).

Universidade Federal da Bahia, Salvador-BA, Brazil

2017

Background

•The NE region´s hydroelectric potential is saturated.

•The Northeast (NE) is driest part of Brazil and suffers from droughts.

•The drought in the NE from 2012-2017 is the worst in recorded history.

•2014-2017 the São Francisco streamflow has been the lowest on record.

•Climate change will negatively impact rainfall in the NE and hydroelectric availability.

São Francisco basin (SF) & the Northeast region of Brazil

SF

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

Northeast region

Schematic of hydroelectric plants along the São Francisco River

Northeast region

Xingó

Sobradinho PauloAlfonso

Luiz Gonzaga/ Itaparica

HydroelectricDam

Três Marias

Apolônio Sales

Source: ONS

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

APOLÔNIO SALES /

Itaparica

Apolônio Sales

Xingó

Paulo Alfonso

Sobradinho

Hydroelectric plants

Nov 2015 & 2017 the NE’s hydroelectric availability dropped to only 5% of its total storage capacity.

NE Reservoir Volume Levels / Stored Energy 2005-2017 (percentage of the total capacity) Disponibilidade hidroeléctrica mensal

Source: ONS - Operador Nacional do Sistema Elétrico, 2017

Sources of the Northeast’s ElectricityFontes de energia elétrica para o Nordeste

• “Thermal” electricity generation in the NE is from fossil fuels & biomass. “Imported” consists mostly of hydro from other Brazilian regions.

• From 2005-2007 hydro was responsible for more than 87% of the NE’s electricity supply, however, by 2016 this figure dropped to 25%.

• Source: ONS (2016).

30%

25%

25%

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

• IPCCC model rainfall projections are compared to the lineartrend-line of historical average rainfall extrapolated until 2100.

• Various studies that predict the percentage of average rainfallreduction by 2100, considering different IPCCC models, areexamined.

• Historical data from 1961-2017 for the average monthly rainfallwithin the São Francisco basin area was provided by CPTEC/INPE(Centro de Previsão de Tempo e Estudos Climáticos / InstitutoNacional de Pesquisas Espaciais).

• Historical monthly streamflow data for the São Francisco River issourced from the ONS (Operador Nacional do Sistema Elétrico).

• The elasticity factor, which is the reduction in streamflowrelative to the decline in rainfall during periods of low rainfall, isestimated from 2013-2016.

Objective, method and materials

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

Projected changes in precipitation until 2071-2100

Northeast region

Source: Marengo, 2008 and Marengo & Ambrizzi et al, 2007.

• Climate change will cause reduced rainfall (up 60% reductions in the semi-arid NE).

Projected temperature increases until 2071-2100

Source: Marengo, 2008 and Marengo & Ambrizzi et al, 2007.

• Climate change will cause higher temperatures and wind speeds.

Northeast region

Source: Marengo et al (2012) considering the IPCC B2 emission scenario.

Projected climate change for Brazilian river basins

• According to Tanajura et al (2010) rainfall could decline 25–50%in the in semi-arid areas of Bahia and up to 80% in coastal areasby 2100.

• According to Marengo et al (2012) annual rainfall in the SãoFrancisco basin will decrease by 35% by 2100 relative to the1961–1990 baseline considering the B2 (≈RCP6.0) reducedemissions scenario.

• Considering the A2 (RCP8.5) high emission scenario the predictedrainfall reduction could be 40-60% (Marengo, 2008).

• However, the linear trend-lines of historical rainfall andstreamflow (see next slide) show that they have already declinedby 25% and 33%, respectively, relative to the 1961–1990baseline.

• This show that the IPCC models have large uncertainties andcould have underestimated the decline in rainfall in the region.

IPCC Projections of rainfall in the São Francisco basin

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

São Francisco (SF) basin Monthly Rainfall and Streamflow 1961-2017(2 year rolling average)

Source: ONS (2017) and CPTEC / INPE (2017).

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

Extrapolating Rainfall and Streamflow relative to long term averagesTable 1 (based on linear trend-lines)

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

Rainfall Rainfall reduction relative to 1961-90 avg:

Year (mm) (mm) (%)

1961-90 90.2 Decrease per year 0.58 0.65%

1995 80.0 Decrease in 20 years 10.2 11.3%

2016 67.8 Decrease in 41 years 22.4 24.9%

2030 59.6 Decrease in 55 years 30.6 33.9%

2050 47.9 Decrease in 75 years 42.3 46.9%

2085 27.5 Decrease in 110 years 62.7 69.5%

2100 18.7 Decrease in 125 years 71.5 79.2%

Rainfall reduction relative to 1961-90 avg: Streamflow Streamflow reduction relative to 1931-90 avg:

Year (MWavg) (MWavg) (%)

1931-90 8500 Decrease per year 67 0.79%

1995 7144 Decrease in 20 years 1356 16.0%

2016 5735 Decrease in 41 years 2765 32.5%

2030 4796 Decrease in 55 years 3704 43.6%

2050 3454 Decrease in 75 years 5046 59.4%

2085 1106 Decrease in 110 years 7394 87.0%

2100 100 Decrease in 125 years 8401 98.8%

Annual rainfall (trend-lines) in the São Francisco basin 1961-2080

Source: CPTEC / INPE (2017) and and Marengo et al (2012).

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

Annual rainfall has been below its 1961-1990 long-term average every year since 1997.

• Linearly extrapolating rainfall reduction in the São Franciscobasin from 1961-1990 to 2071-2100 (110 years) (as per the tablein the previous slide) would see a reduction of approximately70%.

• Due to non-linear hydrologic processes, reductions in streamflowcan actually be amplified 2 to 3 times relative to reductions inrainfall (SAFT et al, 2015 and TIMBAL et al, 2015).

• This elasticity factor is due to a larger percentage of rainfall beinglost to infiltration, irrigation and reservoir evaporation.

• It was estimate that irrigation and reservoir evaporation equateto 7% and 11%, respectively, of the São Francisco’s long termaverage streamflow (1931-1991).

• However, as a result of the drought, irrigation and reservoirevaporation equated to 20% and 30%, respectively, of the SãoFrancisco’s streamflow in 2015.

Linear projections for the São Francisco basin

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

São Francisco monthly rainfall and streamflow 2005-2017 vslong term averages (streamflow 1931 to 1990 & rainfall 1961-1990)

• The slope of the respective trend-lines demonstrate that the drop in the São Francisco’s streamflow is more extreme when compared to the decline in rainfall. E.g. 2013-2016.

• From June to November streamflow in the São Francisco reservoirs is at its lowest level which are typically the months that wind energy generation is at its highest.

Source: ONS (2017) and CPTEC / INPE (2017).

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

São Francisco monthly rainfall, streamflow, hydro storage and hydro generation 1996-2017 (12 month rolling averages)

Streamflow in the São Francisco basin dropped below 3000MWavg and is continuing to drop.

Source: ONS (2017) and CPTEC / INPE (2017).

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

• During the last 4 years average rainfall dropped 34% to below60mm per month. As a result, since 2015 the averagestreamflow in the São Francisco dropped by 60% below itsbaseline and is continuing to drop. The average streamflow for2017 is predicted to be 75% below the baseline average.

• Therefore, an elasticity factor of 1.7-2 is assumed for the basin

Rainfall and streamflow under non-drought conditions

• Linearly extrapolating the long term average monthly rainfallwould see it drop to only 60mm by 2030, which is a reduction of34% relative to the 1961-1990 baseline (see table).

• Applying the elasticity factor means that by 2030, the long termaverage streamflow could actually drop approximately 60%compared to the baseline average (1931-1990).

Elasticity factor for the São Francisco basin

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

Summary of results

• Linearly extrapolating rainfall data to 2050 sees a 47% decline inaverage rainfall and an 80% reduction in average streamflow.

• This means the rainfall estimates for 2100 based on IPCC B2 andA2 projections could actually eventuate as early as 2030 and2050, respectively.

• In short, the deficits in rainfall and streamflow experiencedduring the last 4 years could become the norm by 2030.

• Moreover, by 2030 the reduced streamflow in the São Franciscowill only allow hydroelectricity to generate approximately 10-15% of the NE’s electricity demand.

• Therefore, in the coming decades the São Francisco river will bemore important for urban water supplies, farming and irrigation.

de Jong Pieter, PEI, Politécnica, UFBA, Salvador – 2017.

Jucazinho Reservoir full – Pernambuco

Jucazinho Reservoir – Pernambuco 2017

PRINCIPAL REFERENCES

• ANEEL – Agência Nacional da Energia Elétrica, BIG – Banco de Informações de Geração; 2014. Available at: http://www.aneel.gov.br/aplicacoes/capacidadebrasil/capacidadebrasil.asp accessed on 14/03/2017.

• CPTEC / INPE - Centro de Previsão de Tempo e Estudos Climáticos / Instituto Nacional de Pesquisas Espaciais; 2017.

• de Jong P, Kiperstok A, Torres E A. Economic and environmental analysis of electricity generation technologies in Brazil. Renewable and Sustainable Energy Reviews 2015; Accepted for publication.

• de Jong P, Sánchez A S, Esquerre K, Kalid R A, Torres E A. Solar and wind energy production in relation to the electricity load curve and hydroelectricity in the Northeast region of Brazil. Renewable and Sustainable Energy Reviews 2013; 23: 526–535.

• Lucena A F P, Szklo A S, Schaeffer R, Dutra R M. The vulnerability of wind power to climate change in Brazil. Renewable Energy2010; 35: 904-12.

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• Marengo J A. Regional Climate Change Scenarios for South America-The CREAS project. Conference on Climate Change and Official Statistics, Oslo Norway, 2008.

• Marengo J A, Chou S C, Kay G, Alves L M, Pesquero J F, Soares W R, et al. Development of regional future climate change scenarios in South America using the Eta CPTEC/HadCM3 climate change projections: climatology and regional analyses for the Amazon, SãoFrancisco and the Paraná River basins. Climate Dynamics 2012; 38: 1829–1848.

• Marengo J A, Jones R, Alvesa L M, Valverde M C. Future change of temperature and precipitation extremes in South America as derived from the PRECIS regional climate modeling system. International Journal Of Climatology 2009; 29: 2241–2255.

• ONS – Operador Nacional do Sistema Elétrico. RE-3-128-2008 PEL 2009-2010; 2008.

• ONS – Operador Nacional do Sistema Elétrico. 2ª Revisão Quadrimestral das Projeções da demanda de energia elétrica do Sistema Interligado Nacional 2014-2018. EPE - Empresa Pesquisa Energética/ONS; 2014.

• ONS – Operador Nacional do Sistema Elétrico. Históricas da Operação - Geração de Energia; 2017. Available at: http://www.ons.org.br/historico/geracao_energia.aspx Accessed on 14/04/2017.

• Tanajura C A S, Genz F, Araujo H. Mudanças climáticas e recursos hídricos na Bahia: validação da simulação do clima presente do HadRM3P e comparação com os cenários A2 e B2 para 2070-2100. Revista Brasileira de Meteorologia ed. online; 2010. vol. 3. p. 345-58.

THANK YOU

Pieter de Jong.

Contact email: pieterj@ufba.br

Universidade Federal da Bahia, Salvador-BA, Brazil.

Escola Politécnica, Rua Aristides Novis, n 2, 6 Andar - Federaçáo, CEP 40.210-630.