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Hydrological Extremes and Human societies

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This is the talk given by Giuliano di Baldassarre at the Summer School on Hydrological Modeling kept in Cagliari this here. The topic is very up-to-date and important. He presented an analysis of a few case studies and suggested some literature.

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Hydrological Extremes and Human societies

  1. 1. Giuliano Di Baldassarre Uppsala University, Uppsala, Sweden Centre for Natural Disaster Science, Uppsala, Sweden UNESCO-IHE Institute for Water Education, Delft, Netherlands Hydrological Extremes and Human Societies Cagliari University, July 2017 Visiting Professor Programme
  2. 2. WE (HUMANS) ARE UNFAIR.
  3. 3. History
  4. 4. History • Early 1960s, Italy • Construction of the Vajont Dam (280m)
  5. 5. Vajont dam disaster • 9 October 1963 at 22:39 • Landslide into this “brand new” hydroelectric reservoir • Giant wave
  6. 6. Dam Flood wave (source: Alberto Viglione, 2012)
  7. 7. Vajont dam disaster • 9 October 1963 at 22:39 • Giant wave raised by a landslide into this “brand new” hydroelectric reservoir • The wave affected five towns, killing 1918 people Longarone (BEFORE 9 October 1963) Longarone (AFTER 9 October 1963)
  8. 8. An alternative story
  9. 9. • Late 1950s, Italy • Roberto Camorani, Minister of Public Works An alternative story
  10. 10. • Following the advices of some concerned geologists, Camorani did NOT authorize the Vajont dam construction • The Vajont dam disaster did NOT happen An alternative story Longarone (BEFORE 9 October 1963) Longarone (AFTER 9 October 1963)
  11. 11. An alternative story • Would the strictness of Roberto Camorani be appreciated? • Would he be rewarded for avoiding the Vajont disaster? • Would History actually remember him? *DISCLAIMER: Roberto Camorani is a fictious name. The picture of this presentation is of Friedrich August von Hayek, economist and philosopher (Nobel Price, 1974)
  12. 12. “everybody knows that you need more prevention than treatment, but few reward acts of prevention” N.N. Taleb (2007)
  13. 13. This module: Mutual shaping of hydrology and society
  14. 14. Panta Rhei: Everything flows IAHS scientific decade (2013-2022) Over 400 water scientists This module: Mutual shaping of hydrology and society
  15. 15. • Introduction o Human influence and response to hydrological extremes (drought and floods) • Empirical and theoretical research o Human-flood interactions o Human-drought interactions o Droughts and floods in the Anthropocene • Case studies o Vajont, Bangladesh and Rome This module
  16. 16. Introduction Human influence and response
  17. 17. Today: over 100 million people affected per year, more than 25,000 fatalities and annual economic damages above 15 billion US dollars (UN-ISDR) Near future: fatalities and economic losses are expected to increase Risk management: understand past changes and project future trajectories to reduce negative impacts, while maintaining ecological benefits Hydrological extremes: droughts and floods
  18. 18. Humans alter frequency, magnitude and distribution of hydrological extremes • Deliberately (water management): dams and reservoirs, levees, etc. • Not deliberately (land use): urbanization, deforestation, etc. • Numerous hydrological studies Human influence
  19. 19. (Kareiva et al., Science, 2007; Savenjie et al., Hydrology and Earth System Sciences, 2014) • Most river basins are rapidly changing • Human activities alter the hydrological regime Human influence
  20. 20. Increasing degree of regulation Dams and reservoirs
  21. 21. (Liu et al., Water, 2014) Urbanization and floods
  22. 22. Human response Hydrological extremes (in turn) trigger demographic and institutional change • Individuals, communities, institutions • Informal (spontaneous processes) or formal (disaster risk reduction) • Numerous socio-economic studies
  23. 23. Human response Los Angeles: Drought and water demand (Garcia et al., HESS, 2016) 1976–1977 1987–1992 (longer drought, persistent impact)
  24. 24. Society shapes hydrological extremes, while (at the same time) hydrological extremes shape society Mutual shaping (Di Baldassarre et al., Earth System Dynaics, 2017) Climate influences outside the system Human influences outside the system Hydrological extremes (frequency, magnitude, spatial distribution) Society (demography, institution, governance) Impacts and perceptions Policies and measures River basins, floodplains or cities as human-water systems
  25. 25. Open questions How do human-water interactions shape wealth and recovery trajectories? Wealth Time Bouncing back disaster Wealth Time Bouncing forward disaster (Di Baldassarre et al., in preparation) Wealth Time Collapsing disaster
  26. 26. Human-flood interactions
  27. 27. Levee effect Levee building (less frequent floods) • Unintended consequences: Risk can increase after raising protection levels! • Levee paradox, already described by G. White in 1940s • Self-reinforcing feedback (tends to lock-in) RIVER FLOODPLAIN rare-but-catastrophic disasters
  28. 28. Levee/forgetting effect Rare events associated to increasing vulnerability Example: Rome, Italy (Di Baldassarre et al., Advances in Geoscience, 2016) 0 200,000 400,000 600,000 1860 1890 1920 1950 1980 2010 Floodplainpopulation Levees 1870Flooding
  29. 29. (Di Baldassarre et al., Earth System Dynamics, 2017) Adaptation/learning effect Frequent events associated to decreasing vulnerability Example: Bangladesh 1 10 100 1000 1970 1980 1990 2000 2010 Fatalitiesbyfloodedarea
  30. 30. Dynamics around the world (Examples from Kates et al., PNAS, 2006; Wind et al., WRR, 1999; Bohensky et al., 2014; Penning-Rowsell, GR, 1996) Levee effect Rare events associated with increasing vulnerability Adaptation effect Frequent events associated with decreasing vulnerability
  31. 31. • Traditional methods cannot capture these dynamics • Unrealistic interpretation of past changes and future projections o Less frequent events don’t necessarily lower risk, e.g. levee effect o Unintended consequences, e.g. protection paradox and lock-in New methods accounting for the mutual shaping of hydrology and society based on interdisciplinary frameworks (e.g. social-ecological systems, ecological economics, environmental history and socio-hydrology) Flood risk assessment time Extreme events Societies risk Currentapproach …scenarios CLIMATE DEVELOPMENT Extreme events Societies …dynamics Novelapproach feedback CLIMATE DEVELOPMENT time (Di Baldassarre et al., Water Resources Research, 2015)
  32. 32. After flooding events, societies build “flood memory” and respond via: (a) Non-structural measures (e.g. resettlement) (b) Structural measures (e.g. levees) Structural measures (in turn) change the frequency and magnitude of flooding Green system Technical system Human-flood interactions: Hypotheses
  33. 33. Key concept: Flood memory • Built after flood events, proportional to losses • Memory decays over time (Anastasio et al., 2014; Hanak et al., 2011) 20 40 60 80 100 0 10 20 30 40 50 Percentageretained(%) Retention interval (years) Human forgetting data 0,50 0,75 1,00 1,25 1,50 1996 1998 2000 2002 2004 2006 2008 Policiespercapita(%) Calendar year California's flood insurance coverage 1997 Flood
  34. 34. Conceptualizing human-flood interactions Human and flood systems are interlinked and gradually co-evolve while being abruptly altered by the occurrence of flood events • Focus on interactions and feedbacks between floods and societies
  35. 35. F = flood losses W = high water level H = levee height 1. Flooding • Protection measures change flood levels, and avoid smaller events • Higher water levels lead to higher flood losses Empirical studies Our model Actual water level Po River (Jongman et al., 2012; Di Baldassarre et al., 2009; Heine & Pinter, 2012) Flood depth Relativelosses(0-1)
  36. 36. 2. Demography • Floodplain population tends to increase over time • It decreases after events, but growth resumes as memory decays Empirical studies Our model F = flood losses D = population density M = social memory (Di Baldassarre et al., 2013; Collenteur et al., 2015) 4000 5000 6000 7000 8000 9000 10000 11000 1870 1910 1950 1990 Floodplainpopulation Calendar year Occhiobello, Italy 1951 Flood
  37. 37. 3. Memory • Memory is built after events, proportional to flood losses • Memory decays over time Empirical studies Our model F = flood losses D = population density M = social memory (Hanak, 2011; Anastasio et al., 2014) 0,50 0,75 1,00 1,25 1,50 1996 1998 2000 2002 2004 2006 2008 Policiespercapita(%) Calendar year California's flood insurance coverage1997 Flood
  38. 38. 4. Technology • Flood protection level is updated after major events • Protection measures decay over time Empirical studies Our model ( ) 0= -+= -- R HHWR HT xe Actual water level Adige River (Da Deppo & Datei, 1997; Di Baldassarre et al., 2013) R = levee heightening H = levee height
  39. 39. Flood system Human system F = flood losses W = high water level H = levee height D = population density R = levee heightening M = flood memory Modeling human-flood interactions (Di Baldassarre et al., Water Resources Research, 2015)
  40. 40. Numerical experiment to explore plausible trajectories in case of increasing flood levels e.g. climate change or sea level rise Comparison between: • Green system • Technical system Increasing flood levels Green system Technical system
  41. 41. Results Capture emerging patterns • Memory as a primary mechanism • Suggest data needs • Make tests and re-iterate (Di Baldassarre et al., WRR, 2015) Green system Technical system
  42. 42. Results 2/2 Diagrams show outcomes with decreasing memory decay rate Keeping memory high is crucial, especially in technical systems
  43. 43. Model evaluation (Ciullo et al.,Hydrological Sciences Journal, 2017)
  44. 44. e.g. uncertainty due to differences in sequence of floods only 0 1000 2000 yrs Log(wealth) Probability Initial condition Bimodal distribution (Viglione et al., Journal of Hydrology, 2014) Models as hypotheses Not predictions!
  45. 45. Case studies
  46. 46. The River Tiber and the Foundation of Rome About 2,700 years ago, the King of Alba Longa, Amalius, abandoned to die the newborn twins Romulus and Remus in the Tiber river Luckily, flooding occurred at the same time and Amalius did not manage to abandon them in the main river channel Instead, he had to abandon them in the calmer waters of the floodplain A she-wolf (“lupa”) rescued (and breastfed) the twins Some years after, Romulus and Remo founded the city of Rome Romulus was the first King of Rome This is a myth, but it shows the long “love-hate relationship of Rome and the Tiber” (Aldrete, 2007)
  47. 47. Rome and the Tiber: over centuries • The ancient Rome mostly developed on (seven) hills • Tiber’s floodplain was mainly exploited for agricultural purposes • Small communities settled in the riparian areas of the Tiber, but they had a peaceful relationships with the frequent occurrence of flooding events • Over centuries, flood events have been part of the history of Rome and its relationship with the Tiber river • Inundation risk influenced the city's landscape development Number of flooding events in Rome (over 25 centuries!) (Aldrete, 2007)
  48. 48. Rome and the Tiber: today • Nowadays, more than 600,000 people live in the Tiber’s floodplain, often unaware of their exposure to potentially catastrophic flooding (Academy Award’s winning movie “The Great Beauty”, Sorrentino, 2013)
  49. 49. Tiber’s floodplain as fully coupled human-water system (McDonald, 1997; Di Baldassarre, HESS, 2015) Socio-hydrological dynamics in Rome hydrological processes (flood changes) human interventions (policies, structures) human experience (memory, learning) socio-economic processes (population changes)
  50. 50. Observations hydrological processes (flood changes) human interventions (policies, structures) human experience (memory, learning) socio-economic processes (population changes) Social information • People’s “relationship” with the river and flooding • Historical studies Demographic data • Urbanization and land-use • Official census by districts Hydrological data • Number of flooding events • High water marks • Maximum water levels Policy information • Engineering works • Building policies
  51. 51. Turning or tipping point? 1870: Rome experiences a large flooding event 1871: Rome becomes Italy’s Capital
  52. 52. Flood defence: the walls (”muraglioni”) • Discussion on possible options to mitigate flooding in Rome • Garibaldi was for a flood-relief channel • Following examples of other European capitals, such as London and Paris, embankments/walls were designed and built • Walls’ level at 18,45 m a.s.l. (1870’s maximum flood level was 17,22 m a.s.l.) • The walls were built at the end of the century • Rome and its relationship with the Tiber river were significantly transformed
  53. 53. Flood defence: the walls (”muraglioni”) (Raccolta Roma Sparita; Sacca et al., 2015)
  54. 54. Floodplain development Many modern districts were created in the Tiber’s floodplain: e.g. Prati in 1887 and 1934
  55. 55. Flood levels Level of flood protection Floodplain population Shift from frequent flooding (3-6 inundation events per century) to rare (1-in-200 years?), but potentially catastrophic events (“levee effect”) Data analysis
  56. 56. • Is Rome safe from flooding now? (as, for instance, Wikipedia suggests!) • Last (big) flooding was in 1870, Rome is mainly perceived as “flood-proof” Historical analysis helps raise risk awareness e.g. levels of flood protection is 18.45m, which is above 1870 flood levels (17.22m), but below the maximum historical level of 1598 (19.56m)! Map of flood extent in 1870 (green) and 1598 (blue) Is Rome safe from flooding?
  57. 57. Bangladesh: flows of water and people • Bangladeshi cities are rapidly growing, economies expanding • People continuously move to cope with hydrological changes, such as salt water intrusion, river erosion and flooding events • Videos… Giuliano Di Baldassarre, Kun Yan, Luigia Brandimarte and Md Ruknul Ferdous. IAHS Bologna 2014
  58. 58. Bangladesh: Salt water intrusion • Gridded Population of the World (1990-2000, 2000-2010) • Population change (%) • Migration from Southwest region –why?
  59. 59. Southwest region
  60. 60. Current narratives Flooding Cyclones Saline intrusion Migration Conflicts Climate change Sea level rise (IPCC, 2007; Reuveny, Political Geography, 2007; World Bank and UN reports) 0 3 6 9 12 15 18 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 SalinityHW(ppt) Yearly maximum salinity at Khulna 3,0% 3,5% 4,0% 4,5% 5,0% 5,5% 6,0% 1881 1901 1921 1941 1961 1981 2001 RatioofPopulatiom Years Ratio of Population (Study area vs Bangladesh)
  61. 61. Human activities, upstream • Farakka Barrage at Ganges River in India, since 1974 0 500 1 000 1 500 2 000 2 500 3 000 1930 1950 1970 1990 2010 Discharge(m3/s) Years Minimum Discharge at Hardinge Bridge 0 3 6 9 12 15 18 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 SalinityHW(ppt) Yearly maximum salinity at Khulna
  62. 62. Human activities, SW region • Polders (1960-1970) India -0,5 0,5 1,5 2,5 3,5 4,5 1940 1950 1960 1970 1980 1990 2000 2010 2020 WaterLevel(mPWD) Water Level of Rupsa-Pussur at Khulna Polder crest level -1,5 -0,5 0,5 1,5 2,5 3,5 4,5 1960 1970 1980 1990 2000 2010 WaterLevel(mPWD) Water Level of Rupsa-Pussur at Mongla High water level Low water level Polder crest level High water level Low water level Increased 3 mm/yr
  63. 63. Human experience and migration • Census data (Ruknul Ferdous, 2014) -0,4 -0,2 0,0 0,2 0,4 0,6 0,8 1,0 Migratedpopulation Millions Parmanent net migration from the Khulna Division Data missing
  64. 64. Survey, interviews 2% 22% 33% 43% 0% 10% 20% 30% 40% 50% 18-30 31-45 46-60 > 60 Numberofhouseholds Age Groups 9 4 2 1 16 6 3 9 25 21 13 9 7 75 0 10 20 30 40 50 60 70 80 Businessman Service holder Rural Doctor Labor Others Fisherman (Rivers) Fisherman (Rivers and sea) Fisherman Medium Farmer (land 2.5 -7.49 acres) Small Farmer (land 0.5 -2.49 acres) Landless Farmer Marginal Farmer (land 0.05 -0.49 acres) Large Farmer (land > 7.5 acres) Farmer House hold numbers
  65. 65. Results • Do people perceive hydrological changes? How? • Do people move? Where do they move? And why? – Migration is not a more response, but a way to cope with changes – Most time is about temporary, seasonal, or short-term movement – Permanent migration is rare (see also Penning-Rowsell et al., ESP, 2012) 2 22 41 65 3 5 10 17 35 0 20 40 60 80 Due to salinity Lost everything in cyclones Lost everything in floods Bio-physical and hydrological After 1971 war Political reasons Looking for better opportunities Hindu-Muslim conflict Social, political and environmental % of people migated Reasons for the migration 86,2% 94,2% 97,9% 13,8% 5,8% 2,1% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Khulna Bagerhat Satkhira Migration from the Districts Living within the district Parmanent migrants Permanent Permanent Permanent
  66. 66. Discussion Need to go beyond current narratives • Human activities matter • Societal dynamics, not a mere response • Socio-hydrology – Societies shape physical processes (human activities) – Physical processes shape societies (human experience) Flooding Cyclones Saline intrusion Migration Conflicts Climate change Sea level rise
  67. 67. Conclusions and perspectives • Need to understand the interplay between changes in hydrology and society to explain the emerging dynamics Flooding Cyclones Saline intrusion human activities (policies, structures) human experience (damage, memory) Wealth Migration Conflicts Climate change Sea level rise Other drivers of societal changes
  68. 68. Human-drought interactions
  69. 69. Nile River Example: the Nile River Basin • World’s longest river (6670 km) • Basin covers about 10% of Africa • 11 African countries • River flow from Ethiopia (Blue Nile) and Lake Victoria (White Nile)
  70. 70. Hydrology of the Nile Ancient Egyptian civilization River flows vital to Egyptian agriculture (nutrients, fertility) The hydrological behavior of the Nile river led to one of the first scientific questions Thales of Miletus (624-546 BC) tried to understand the “hydrological paradox” of the Nile Why does flooding occur in summer when it does not rain in Egypt?
  71. 71. Hydrological data Nilometers: River gauge stations along the Nile river in Egypt measuring river flows (i.e. water depth) Nile flows to determine the levels of tax to be paid 10 12 14 16 18 20 Abundance Security Happiness Suffering Hunger Disaster NILOMETERREADINGINELLS 1ELL=1.1m (floods)(droughts) (Eagleson et al., 1991)
  72. 72. Future water availability: part of the mystique of the Ancient Egyptian priesthood Predictions based on observations (no runoff models at that time!) Example: Roda Nilometer (near Cairo) Annual minimum flows from 700 to 800 AD Linear regression (red line) > Negative trend 800 1000 1200 1400 1600 700 710 720 730 740 750 760 770 780 790 800 Year Minimumflow(m 3 s -1 ) Drought predictions
  73. 73. Drought predictions 800 1000 1200 1400 1600 700 710 720 730 740 750 760 770 780 790 800 Year Minimumflow(m 3 s -1 ) ? Let’s assume we are in the 800AD. What would you predict? A) Minimum flows will stabilize at the level of 800AD B) Minimum flows will further drop (even more droughts) C) Minimum flows will rise (less droughts)
  74. 74. “Prediction is very difficult, especially about the future!” Niels Bohr (1885-1962)
  75. 75. Roda Nilometer One of the longest time series of hydrological data Annual minimum flows from 622 AD to 1284 AD 25-year moving average (red line) Climate Variability 800 1000 1200 1400 1600 622 722 822 922 1022 1122 1222 Year Minimumflow(m 3 s -1 ) 700-800 (Di Baldassarre et al., Hydrological Sciences Journal, 2011) Roda Nilometer, full series
  76. 76. meteorological drought soil moisture drought hydrological drought climate variabilitydrivers consequences ecological impacts socio-economic impacts Human-drought interactions (Van Loon et al., Nature Geoscience, 2016)
  77. 77. meteorological drought soil moisture drought hydrological drought climate variability human activitiesdrivers consequences land use irrigation dam building water abstraction ecological impacts socio-economic impacts responses anthropogenic climate change (Van Loon et al., Nature Geoscience, 2016) Human-drought interactions
  78. 78. Dams and reservoirs • Water shortages: Supply-below-demand events • Reservoirs’ intended benefits: Secure water supply • More than 50% in GRaND database (Source: Jim Wilson/The New York Times, Hoover Dam in Colorado)
  79. 79. Dams and reservoirs • Unintended consequences • Supply-demand cycle • Reservoir effect
  80. 80. River basins as human-water systems • Intended benefits (short term) • Unintended consequences (medium-long term) Our hypothesis Water Shortage Economic Losses Public Pressure Reservoir Storage Water Supply Water Demand Agricultural, industrial or urban expansion VulnerabilityDependency + + + + + + + _ + + + Intended Benefits Supply-Demand Cycle Reservoir effect
  81. 81. (Kallis, Ecological Economics, 2007) Supply-demand cycle • Increasing water supply generates (per se) increasing water demand • In the medium-long term this can offset the initial benefits of reservoirs Example: Athens, Greece • Spiral of increasing supply and demand (co-evolution) time 1940 Population 1.1 million 1961 Population 1.8 million 1981 Population 3 million 1971 Population 2.5 million 1931 Completion of Marathon dam 1941 Water shortage 1951 Repeated Water shortages 1958 Completion of Iliki aqueduct 1944 Proposal for Lake Iliki transfer 1954 Decision for Lake Iliki transfer 1968 Decision for Mornos project 1974 Water system bought back by State 1980 Completion of Mornos dam 1990-1992 Repeated Water shortages 1941 German occupation 1949 End of Civil War 1967 Military dictatorship 1974 Democracy 1989-1991 Repetitive elections
  82. 82. Global analysis Reservoir capacity vs. water demand (worldwide) • GRanD database • World Bank (GRanD database; Lehner et al. 2012)
  83. 83. Global analysis Reservoir capacity vs. water demand (worldwide) • 1960s and 70s: Faster growth in reservoir capacity • From 1980s: Faster growth in water demand (likely more shortages) 0,5 1,0 1,5 2,0 2,5 1960 1970 1980 1990 2000 2010 NormalizedValues Reservoir Capacity Water Demand
  84. 84. River basins as human-water systems • Intended benefits (short term) • Unintended consequences (medium-long term) Our hypothesis Water Shortage Economic Losses Public Pressure Reservoir Storage Water Supply Water Demand Agricultural, industrial or urban expansion VulnerabilityDependency + + + + + + + _ + + + Intended Benefits Supply-Demand Cycle Reservoir effect
  85. 85. Reservoir effect From frequent events to rare-but-catastrophic disasters Example: Maja collapse • Additional storage of water brought benefits and allowed agricultural growth, but increased dependence on water making people more vulnerable • Prolonged drought conditions as a plausible hypothesis for collapse (Aimers & Hodell, Nature, 2011; Lucero, Am Anthropol, 2002; Kuil et al., WRR, 2016)
  86. 86. Counterargument? Frequent shortages might erode local resilience • Systems under frequent stress might get closer and closer to a tipping point, and potentially catastrophic shifts (Rockström, 2003; Proença and Fernández-Manjarrés, 2015) What are the circumstances in which these two opposite dynamics emerge?
  87. 87. Drought and Floods in the Anthropocene
  88. 88. Drought and floods in the Anthropocene Hydrological change triggered by social change, and vice versa • Empirical and theoretical research • Social, engineering and natural sciences • Both flood and drought events (why both?) Climate influences outside the system Human influences outside the system Hydrological extremes (frequency, magnitude, spatial distribution) Society (demography, institution, governance) Impacts and perceptions Policies and measures River basins, floodplains or cities as human-water systems (Di Baldassarre et al., Earth System Dynamics, 2017)
  89. 89. Flood trends (Di Baldassarre et al., Geophysical Research Letters, 2010) 0 3000 6000 9000 12000 15000 1950-1969 1970-1989 1990-2009 Floodfatalities Population growth as main driver of increasing flood losses and fatalities in Africa, while climate change has so far played a smaller role
  90. 90. (Winsemius et al., Nature Climate Change, 2016; Di Baldassarre et al., Earth System Dynamics, 2017) Longer or more severe drought conditions, might have triggered the tendency to increase river proximity and therefore made more people exposed to flooding (hypothesis, still to be tested) What about drought? 0 3000 6000 9000 12000 15000 1950-1969 1970-1989 1990-2009 Floodfatalities
  91. 91. (Di Baldassarre et al., Earth System Dynamics, 2017) Sequence effect Response to drought exacerbates the impact of floods (and vice versa) Example: Brisbane, Australia • Flood retention reservoir built upstream Brisbane in the 1970s • Prolonged multi-year drought period, Millennium Drought (2001-2010) • Reservoir operation rules changed to mitigate drought conditions • Not “optimal" to mitigate the 2011 flood, which was devastating
  92. 92. Daniel Kahneman (Nobel Prize, 2002) o Humans are NOT rational  o Prospect theory o Cognitive biases and heuristics • Confirmation bias • Anchoring effect • Availability heuristic Cognitive biases and heuristics
  93. 93. • Theoretical background o Decision makers estimate probabilities not only on robust evidence, but also “by the ease with which relevant instances come to mind” o Availability heuristic (Tversky and Kahneman, 1973) o Humans are not “rational” • Fundamental hypothesis o Memories built after events, and then decays • Modelling Example o Feedback mechanisms in reservoir operation Modeling example
  94. 94. Modeling feedback mechanisms in reservoir operation Human-modified outflow (Q) derived from the “natural” inflow (QN) using a linear reservoir approximation with a variable storage coefficient (k) Variables Units Description Mf [.] flood memory Md [.] drought memory Q [L3/T] human-modified outflow Parameters Units Description kf [T] coefficient to cope with flood kd [T] coefficient to cope with drought μ [1/T] memory decay rate a [T] overflow coefficient b [.] bias parameter (Di Baldassarre et al., Earth System Dynamics, 2017)
  95. 95. Example and results • Brisbane streamflow data as “natural” inflows into the reservoir • Diagram shows the resulting outflows (Di Baldassarre et al., Earth System Dynamics, 2017) 0 10 20 30 40 50 60 1973 1983 1993 2003 2013 MeanAnnualFlow(m3s-1) Coping with Flood Coping with Drought Human-modified Outflow Millennium drought Modeling feedback mechanisms in reservoir operation
  96. 96. Summary and perspectives
  97. 97. • Empirical and theoretical work • Dynamics emerging from the mutual shaping of hydrology and society • Levee and adaptation effects, supply-demand cycle and sequence effect • Understanding past changes and projecting future trajectories to support the making of strategies for sustainable water management, disaster risk reduction, and climate change adaptation Summary
  98. 98. • Open questions: o Site-specific dynamics or generic patterns? o What can(not) be generalized? o What are the social and hydrological conditions in which they emerge? Why? o How do they change across scales? • Empirical and theoretical studies, as well as global comparative analyses • Unprecedented opportunity: “flood” of global data and archives o Human influence (e.g. dams and reservoirs, irrigation, protection standards) o Human response (e.g. proxies of economic activity, population density) Perspectives Nightlights Reservoirs and Dams
  99. 99. Giuliano Di Baldassarre Uppsala University, Uppsala, Sweden Centre for Natural Disaster Science, Uppsala, Sweden UNESCO-IHE Institute for Water Education, Delft, Netherlands Hydrological Extremes and Human Societies Cagliari University, July 2017 Visiting Professor Programme
  100. 100. Debate
  101. 101. What are these? What do they have in common?!
  102. 102. Black Swans • Black Swan event is a surprise (to the observer) • Black Swan event has a major impact • Black Swan event appears as if it could have been expected (retrospective predictability)
  103. 103. Black Swans (observer)
  104. 104. Debate • Small group discussion (1 hour) – Was the Vajont dam disaster a black swan event? – If so, for whom? And, why? – Could it be prevented? How? – “Local knowledge” versus “experts” • Group “leader” present – 2/3 slides (5/10 minutes) • Debate -be open!
  105. 105. We can’t predict everything, but we can still reduce losses!
  106. 106. Unrepeatable chain of events and cascade of contingencies 0 2 4 6 8 10 12 1920 1930 1940 1950 1960 FloodLevels(mabovedatum) Human-water system SURPRISE! Example: Piave River at Ponte delle Alpi (Italy) (Di Baldassarre et al., Hydrlogical Sciences, Journal, 2016)
  107. 107. Top-down and bottom-up approaches (source: Bloeschl et al., 2013)(Bloeschl et al., Climate Vulnerability, 2013)

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