Adaptation Strategies at Catchment Scale under Uncertainty, Non-Stationarity and Disaster Risks

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1 CEPAL, Santiago, Chile, June 4 th, 2015 Adaptation Strategies at Catchment Scale under Uncertainty, Non-Stationarity and Disaster Risks E M Mendiondo [1],[2], J A Marengo [1], D. A. Guzmán-Arias [2], D. Taffarello [2], G. L. Laurentis [2] [1] CEMADEN, MCTI, Center for Monitoring & Early Warning of Disasters, Min. Sci., Tech., & Innov., Brazil [2] EESC, USP, Sao Carlos School of Engineering, Univ. Sao Paulo, Brazil

2 TWO CONTRIBUTIONS FROM CEMADEN, BRAZIL TO MAPA-CEPAL, Santiago de Chile, May 2015 Elements of Comprehensive Governance and Policy for Disaster Risk Reduction based on Hyogo s and Sendai s Framework for Action st contribution 2 nd contribution

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4 Esquema de trabajo Implementación de la adaptación: Cuáles son las opciones y responsables, como las financiamos? Factores de Incertidumbre (I) Estrategias de Gestión/ Medidas de adaptación (A) Modelos/ Herramientas (M) Indicadores de Desempeño (D)

5 Purpose Adaptation strategies at the scale of river basin: - a menu on risk assessment, risk reduction and risk transfer - ecosystem-based adaptation (EbA) - risk transfer mechanism (insurance) - continuous training and education towards resilience - monitoring, modeling early warnings for disasters - optimizing decision making under uncertainty - and more

6 Examples of Uncertainty, non-stationarity and disaster risks Uncertainty Disaster Risk Non-stationarity Guzmán-Arias, D. A. (2015) Avaliação das incertezas na implementação de modelos de seguros e índices de secas sobre hipóteses de não estacionariedade, como estratégias para a gestão dos impactos das mudanças climáticas, PPG-SHS/EESC-USP, Sao Carlos-SP, Brasil

7 Examples of uncertainty of measured flows at challenging biomes, scales and land-use conditions Amazonas River at Obidos Station Catchment scale a km 2 Turcato Catchment at downstream section of urban-to-rural land-use Catchment scale a km m3/s m3/s Source: Mendiondo (2001) Source: Clarke, Mendiondo & Brusa (2000)

8 Adaptation strategies under uncertainty, non-stationarity and risk disasters Approach Step I: Ecosystem Assessment Step II: Ecosystem Sustainability Step III: Ecosystem Resilience Risk-based Analysis (adapted from IPCC, 2014) Vulnerability, Hazards and Exposure (2), (3), (4) Impacts from drivers of change (1), (2), (3), (4) Adaptation, Mitigation and Risk- Transfer Options (2), (3), (4) Water Footprint (adapted from Hoekstra et al, 2011) Scope and Accounting (3), (4) Sustainability Assessment and hotspots (3), (4) Response and Decision Making Scenarios (2), (3), (4) Ecosystem-based Adaptation (EbA; from CBD, 2010) Ecosystem Services Assessment (2), (3), (4) Valuing ecosystem services (2), (3), (4) Water-PES Projects River Restoration (2), (3), (4) Source: Taffarello et al (2015-b) Payment of Ecosystem Services at Brazilian Atlantic Forest: Adaptation to Climate Change (under review) Legends: (1) Drivers of change: consumption habits, population, land-use and land-use change (LULC), climate change, institutional governance evolution, (2): Ecohydrological monitoring, (3) Field investigation and data mining, (4) modeling. All approaches and steps have continuous feedbacks and loops across spatiotemporal scales and among stakeholders

9 Strategy 1: Decision Making under Uncertainty of Regional Impacts from Global Changes WAVES: Water use modeling and scenario development (01LK9705) The specific issue of the sensitivity of computed irrigation water use on the applied climate data and on climate change is discussed in section 6.2. Left: number of municipalities under water droughts in Northeast Brazil. Right: scenarios of change of water withdrawal (1996/98 to 2025) ll figures are related to regional planning scenarios (RS-A, globalization and cash-crops, RS-B, descentralization through agropoles) and climate change models (ECHAM4 and HadCM2). Sources: Araujo ET AL (2004) and WAVES/BMBF Report (2001) Fig. 3. Withdrawal water use in the 332 municipalities of Piauí and Ceará, in mm/yr: a) total withdrawals 1996/98 b) change of total withdrawals until 2025, RS A, c) change of total withdrawals until 2025, RS B, d) withdrawals for irrigation 1996/98, e) change of total

10 Strategy 1: Decision Making under Uncertainty of Regional Impacts from Global Changes Left: Changes of water supply costs as compared to changes in water use between 1996/1998 period and year 2025, in the four scenario regions of water scarcity region, Ceara State, Brazil. Source: Döll et al (2001) Upper right: Operation and Maintenance Costs (period ) in Jaguaribe River Basin under scenarios of climate change and drought management. Source: Mendiondo & Valdés (2002). Lower Right: Regionalization of specific, spatial-based O&M and Investment costs of water demand for two reference scenarios under drought management until year 2025, depicting results for Jaguaribe sub-basins. Source: adapted from Mendiondo & Valdés (2002).

11 Erro relativo (%) Áreas irrigadas (ha) Strategy : Decision Making under Uncertainty of Regional Impacts from Global Changes ? Ano WAVES_Max WAVES_Min PISF_Max PISF_Min NLTA_Max NLTA_Min $ ? Perímetro de irrigação (ha) Banabuiú Exemplo de incertezas (coeficiente de variação, em %) das demandas de irrigação para o diagnóstico (ano 2010) em função do tamanho do perímetro de irrigação. Fonte: Bacia do Jaguaribe, CNARH e COGERH (com. Pessoal) & NLTA/World Bank (2013) 20$ 10$ Piranhas!Açu$River$Basin$ (Year:2010)$ 0$!10$ 0$ 0.2$ 0.4$ 0.6$ 0.8$ 1$?!!20$!30$ Uncertainties of water withdrawal at river basin of Piranhas-Acu, Brazil. Source: NLTA/World Bank (2013)

12 Strategy 2: Ecosystem-based Adaptation (EbA) through Payment for Ecosystem Services & Ecohydrological Restoration

13 Strategy 2: Ecosystem-based Adaptation (EbA) through Payment for Ecosystem Services (PES) & Ecohydrological Restoration Taffarello et al (2015-c; under rev.)

14 Piracicaba River Basin (MG/SP, Brazil)

15 Strategy 3: Demand Management using a Composite Private-Public Partnership through Adaptation on Consumption, Technology and Ecosystem Conservation (with monitoring) Demand Management Plan until year Total Saving Water until year 2050 = 17.6 m3/s. Water Withdrawal from Cantareira System in April 2015 = 15.6 m3/s 2 0 Reducing Mean Consumption per Capita ("from Consumers") Reducing Water Leaks ("from Water Supply Company") Ecosystem-Based Adaptation with PES ("Government Policy Part") 0.70 U$ 1.2 billion Demand Management Plan until year 2050: compared to São Paulo Metropolitan Region GDP(%) U$ 0.6 billion U$ 0.14 billion 0.00 Reducing Mean Consumption per Capita ("from Consumers") Reducing Water Leaks ("from Water Supply Company") Ecosystem-Based Adaptation with PES ("Government Policy Part")

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17 Natural uncertainty from monitoring and data Source: Taffarello et al (2015, under review)

18 Strategy 4: insurance and disaster risk-transfer (floods) Financial return Economic & sustainable solvency Mendiondo, E M (2010) Reducing vulnerability to water-related disasters in urban areas of humid tropics, In: J. Parkinson et al, Integrated Urban Water Management: Humid Tropics, CRC Press UNESCO Urban Water Series, Vol 6, ISSN , Chapter 6: Willingness to pay for Ecosystem Services

19 Vazão específica (m 3.s -1.km -2 ) Os erros de volume ( V ) e da Q 90 ( Q 90) tam bém foram considerados Strategy 4: insurance and disaster risk-transfer (droughts) aceitáveis, sendo mais vantajosos se próximos de zero. Aceitou-se uma variação em Figura 18. Unidades de Resposta Hidrológica para o ano de 1985 (Fonte: Elaborado pelo autor). até 15 pontos percentuais, para mais ou para menos em relação aos dados observados, conforme sugerido por Moriasi et al. (2007). As incertezas inerentes aos dados de entrada do modelo e à operação do Sistema Cantareira na bacia contribuem para uma piora nos valores destas FO. Na Figura 30 são plotados os valores específicos (l.s -1.km -2 ) das vazões médias e da Q90 (observadas e simuladas), com relação à área de drenagem dos postos. Figura 19. Unidades de Resposta Hidrológica para o ano de 2010 (Fonte: Elaborado pelo autor) Vazão específica x Área Área (km 2 ) Media_obs esp Media_sim esp Q90_obs esp Q90_sim esp Source: Laurentis, G. (2012) Modelos de transferência de riscos como estratégia de adaptação às mudanças globais segundo cenários de vulnerabilidade de recursos hídricos, M Sc. Diss., PPG-SHS/EESC/USP, Sao Carlos ( Figura 30. Comparação dos valores de vazões específicas diárias médias (observadas e

20 Prejuízo (R$/km 2 ) Unidades monetárias (R$) Strategy 4: insurance and risk-transfer (droughts) Tempo (anos) #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14 #15 #16 #17 #18 #19 #20 #21 #22 #23 #24 #25 #26 #27 #28 #29 #30 Figura 83. Simulação do MTRH através da otimização das séries equiprováveis. Atores de mudança Cenários Clima Uso da terra Disp. hídrica Sem mudança Cli ClUso ClDis ClUsDi SMud Fonte: Elaborado pelo autor. Curvas de dano representativas dos cenários 8000 para a sub-bacia do rio Jaguari 7000 Os cenários propostos que consideraram modelagem hidrológica foram Cli, Resultados por sub-bacia Resultado da simulação do MTRH para as 30 séries equiprováveis na sub-bacia do rio Atibaia no cenário III 3,500,000 ClUso, 6000 ClDis e ClUsDi. O cenário SMud, considerado sem mudanças, contemplou Figura a 84. Exemplo de resultado da otimização das séries e do prêmio ótimo médio do cenário. TR cobertura Prejuízo médio (R$/km2) Prêmio ótimo médio (R$/km2) repetição 5000estatística dos fenômenos e processos atuais na bacia. Nas linhas a seguir (anos) I II III IV V I II III IV V 4000 são descritos os atores de mudança considerados, e as premissas relacionadas a cada 10 0,0 0,0 0,0 0,0 9,7 0,0 0,0 0,0 0,0 8, um ,1 0,1 0,0 0,0 289,0 0,1 0,1 0,0 0,0 276,9 3,000,000 Camanducaia 2,500,000 TR cobertura Prejuízo médio (R$/km2) Prêmio ótimo médio (R$/km2) 2,000,000 (anos) I II III IV V I II III IV V 1,500,000 1,000, ,5 9,5 9,5 9,5 15,2 9,9 9,9 9,9 9,9 15, ,3 14,3 14,3 14,3 131,9 15,4 15,4 15,4 15,4 144,7 500, ,2 66,2 66,2 66,2 137,8 75,3 75,3 75,3 75,3 151, ,2 66,2 66,2 66,2 137,8 75,3 75,3 75,3 75,3 151, Média 39,1 39,0 39,1 39,0 105,7 44,0 44,0 44,0 44,0 115,8 Prêmio ótimo do cenário Prêmio ótimo médio dos cenários Séries equiprováveis Prejuízo médio anual do cenário Prejuízo médio anual dos cenários ,3 75,7 28,5 12,6 330,6 90,4 92,5 37,0 15,7 322, Mudanças 0 climáticas ,8 96,5 28,5 27,7 330,8 117,7 121,3 37,0 36,2 322, Tempo de retorno (anos) A mudança nas condições climáticas foi considerada através das projeções do Média 42,8 43,1 14,3 10,1 240,0 52,0 53,5 18,5 13,0 232,6 Cli ClUso ClDis ClUsDi SMud modelo climático Eta-CPTEC, para o período , de acordo com o item Atibaia deste Figura capítulo. 77. Source: Curvas Cenários Laurentis, de que dano consideraram para G. (2012) os cenários esta Modelos variação propostos, de utilizaram transferência em variáveis função de riscos climáticas do período como estratégia de de adaptação às mudanças globais segundo retorno, para cenários a sub-bacia de vulnerabilidade do rio Jaguari (Fonte: de recursos Elaborado hídricos, pelo PPG-SHS/EESC/USP, autor). TR Sao cobertura Carlos ( Prejuízo médio (R$/km 2 ) Prêmio ótimo médio (R$/km 2 ) geradas pelo modelo climático, assim como a precipitação corrigida, para a Jaguari

21 Strategy 5: PWRI(IVA) Participative Water Resilience Index through Vulnerability, Impact and Adaptation Factors PWRI IVA : risk assessment * risk management. PWRI IVA acknowledges: multisource variables for several types of hazards flexible layouts: each group can redefine their own PWRI IVA at participatory/comparable platforms Non-dimensional weights factoring hazard, exposure, vulnerability, and management steps before, during and after the occurrence of hazards Mendiondo, E.M., J A Marengo, W Leyh, J Rotava, J Porto, J Ueyama, V Caramori Souza (2013) Towards Participatory-based Water Resilience Index for Coupling Vulnerability, Impacts and Adaptation Strategies at Areas Under Land Use Change, In: Global Water System Project Conference Water in the Anthropocene, Bonn, Germany, May, 2013, Session: Working with uncertainties: Models & Data I, GWSP Press/BMBF/DFG

22 Strategy 5: PWRI(IVA) Participative Water Resilience Index PWRI IVA PWRI is a composite index expressing the lack of resilience related to water extremes at an area under land-use change æ ç ç risk assessment (impact scores) ç ç hazard,vulnerability,exposure,"before","during", "after" ç ç ç è climate change variables land use maps socio-economic surveys risk management through "participative", "collaborative", "voluntary" (adaptation strategies of disaster management) long-term early -warning ability emergency &rescue capacity non -structural measures mitigation & reconstruction system structural measures Mendiondo, E.M., J A Marengo, W Leyh, J Rotava, J Porto, J Ueyama, V Caramori Souza (2013) Towards Participatory-based Water Resilience Index for Coupling Vulnerability, Impacts and Adaptation Strategies at Areas Under Land Use Change, In: Global Water System Project Conference Water in the Anthropocene, Bonn, Germany, May, 2013, Session: Working with uncertainties: Models & Data I, GWSP Press/BMBF/DFG ö ø

23 Strategy 5: PWRI(IVA) Participative Water Resilience Index Water Resilience Opportunity-WRO Water Resilience Opportunity: adaptation capacity of comparing PWRI IVA values over time between reactive and proactive scenarios. WRO lets monetary values be included, demonstrative pilot experiments like signboards or web-mapping collaborative scores from PWRI IVA. Water Resilience Opportunity

24 Strategy 5: PWRI(IVA) Participative Water Resilience Index Ensembles: Eta_HadCM3 (A1B;40kmx6h) Runs: Low-, midi-, high-, ctrl Time periods : ; ; ; Application variable [unit.] Definition prcv [kg/m2/day] convective precipitation agpl [kg/m2/day] instantaneous precipitation water prge [kg/m2] large scale precipitation rnof [kg/m2/s] runoff rnsg [kg/m2/s] acm bsfl-gdwr runoff Sites: São Carlos: S, W Rio Claro: S, W Campinas: S, W Sao Paulo: S, W Santos: S, W Taubaté: S, W Mendiondo, E.M., J A Marengo, W Leyh, J Rotava, J Porto, J Ueyama, V Caramori Souza (2013) Towards Participatory-based Water Resilience Index for Coupling Vulnerability, Impacts and Adaptation Strategies at Areas Under Land Use Change, In: Global Water System Project Conference Water in the Anthropocene, Bonn, Germany, May, 2013, Session: Working with uncertainties: Models & Data I, GWSP Press/BMBF/DFG

25 Strategy 5: PWRI(IVA) Participative Water Resilience Index PWRI-F : preliminary results of spatial variability transect of water hazard across areas under change, according to Q1% Q5% Brazilian Megacity Brazilian Urban Area Mendiondo, E.M., J A Marengo, W Leyh, J Rotava, J Porto, J Ueyama, V Caramori Souza (2013) Towards Participatory-based Water Resilience Index for Coupling Vulnerability, Impacts and Adaptation Strategies at Areas Under Land Use Change, In: Global Water System Project Conference Water in the Anthropocene, Bonn, Germany, May, 2013, Session: Working with uncertainties: Models & Data I, GWSP Press/BMBF/DFG

26 Strategy 5: PWRI(IVA) Participative Water Resilience Index PWRI-F: uncertainties from GCM s hazard factors. Change rate between & periods fraction of Q1% / Q5% (permanency curves) Courtesy work: Gustavo Romero (USP) Maria Clara Fava (USP) Mendiondo, E.M., J A Marengo, W Leyh, J Rotava, J Porto, J Ueyama, V Caramori Souza (2013) Towards Participatory-based Water Resilience Index for Coupling Vulnerability, Impacts and Adaptation Strategies at Areas Under Land Use Change, In: Global Water System Project Conference Water in the Anthropocene, Bonn, Germany, May, 2013, Session: Working with uncertainties: Models & Data I, GWSP Press/BMBF/DFG

27 Water Resilience Opportunity Strategy 5: PWRI(IVA) Participative Water Resilience Index PWRI-F : ; reactive & proactive scenarios PWRI/run#1 Mendiondo, E.M., J A Marengo, W Leyh, J Rotava, J Porto, J Ueyama, V Caramori Souza (2013) Towards Participatory-based Water Resilience Index for Coupling Vulnerability, Impacts and Adaptation Strategies at Areas Under Land Use Change, In: Global Water System Project Conference Water in the Anthropocene, Bonn, Germany, May, 2013, Session: Working with uncertainties: Models & Data I, GWSP Press/BMBF/DFG

28 Strategy 6: Human-Scale Adaptation using Disaster Risk Monitoring Prevenção e Monitoramento do Risco de Inundações em Bacias Urbanas Adaptado para escoamento em canais Sinalizacao nos locais de monitoramento experimental Adaptado para escoamento em sarjetas

29 Strategy 6: Human-Scale Adaptation using Disaster Risk Monitoring Sensor de pressao Rede WSN (online) USP-1, Sao Carlos Régua para observador voluntário (VGI) Sinalizador para Alerta e Preven;cao (Defesa Civil e Bombeiros) FINEP/MAPLU: Parceria UFAL-EESC/USP Executado: 95%

30 Strategy 7: Local Adaptations and Traditional Knowledge to Cope with Uncertainty, Non-Stationarity and Disaster Risks Source: Millennium Ecosystem Assessment: Vilcanota Sub-global Assessment Further reading: Apgar, J.M., Argumedo, A. & Allen, W. (2009) Building Transdisciplinarity for Managing Complexity: Lessons from Indigenous Practice. International Journal of Interdisciplinary Social Sciences. Volume 4, Number 5, pp

31 Gracias! Eduardo Mario Mendiondo