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Hydrological Extremes and Human societies

The document discusses the complex relationship between hydrology and human societies, highlighting historical disasters like the Vajont dam and ongoing global challenges such as droughts and floods impacting millions annually. It presents case studies from Italy and Bangladesh, outlining how human actions have influenced hydrological extremes while also calling for better risk management strategies. Additionally, it emphasizes the need for interdisciplinary approaches to understand and mitigate these risks in the Anthropocene era.

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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
WE (HUMANS) ARE UNFAIR.
History
History • Early 1960s, Italy • Construction of the Vajont Dam (280m)
Vajont dam disaster • 9 October 1963 at 22:39 • Landslide into this “brand new” hydroelectric reservoir • Giant wave
Dam Flood wave (source: Alberto Viglione, 2012)
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)
An alternative story
• Late 1950s, Italy • Roberto Camorani, Minister of Public Works An alternative story
• 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)
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)
“everybody knows that you need more prevention than treatment, but few reward acts of prevention” N.N. Taleb (2007)
This module: Mutual shaping of hydrology and society
Panta Rhei: Everything flows IAHS scientific decade (2013-2022) Over 400 water scientists This module: Mutual shaping of hydrology and society
• 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
Introduction Human influence and response
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
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
(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
Increasing degree of regulation Dams and reservoirs
(Liu et al., Water, 2014) Urbanization and floods
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
Human response Los Angeles: Drought and water demand (Garcia et al., HESS, 2016) 1976–1977 1987–1992 (longer drought, persistent impact)
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
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
Human-flood interactions
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
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
(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
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
• 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)
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
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
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
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)
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
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
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
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)
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
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
Results 2/2 Diagrams show outcomes with decreasing memory decay rate Keeping memory high is crucial, especially in technical systems
Model evaluation (Ciullo et al.,Hydrological Sciences Journal, 2017)
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!
Case studies
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)
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)
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)
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)
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
Turning or tipping point? 1870: Rome experiences a large flooding event 1871: Rome becomes Italy’s Capital
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
Flood defence: the walls (”muraglioni”) (Raccolta Roma Sparita; Sacca et al., 2015)
Floodplain development Many modern districts were created in the Tiber’s floodplain: e.g. Prati in 1887 and 1934
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
• 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?
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
Bangladesh: Salt water intrusion • Gridded Population of the World (1990-2000, 2000-2010) • Population change (%) • Migration from Southwest region –why?
Southwest region
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)
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
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
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
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
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
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
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
Human-drought interactions
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)
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?
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)
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
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)
“Prediction is very difficult, especially about the future!” Niels Bohr (1885-1962)
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
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)
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
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)
Dams and reservoirs • Unintended consequences • Supply-demand cycle • Reservoir effect
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
(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
Global analysis Reservoir capacity vs. water demand (worldwide) • GRanD database • World Bank (GRanD database; Lehner et al. 2012)
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
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
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)
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?
Drought and Floods in the Anthropocene
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)
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
(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
(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
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
• 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
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)
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
Summary and perspectives
• 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
• 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
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
Debate
What are these? What do they have in common?!
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)
Black Swans (observer)
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!
We can’t predict everything, but we can still reduce losses!
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)
Top-down and bottom-up approaches (source: Bloeschl et al., 2013)(Bloeschl et al., Climate Vulnerability, 2013)

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Hydrological Extremes and Human societies

  • 1.
    Giuliano Di Baldassarre UppsalaUniversity, 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.
  • 3.
  • 4.
    History • Early 1960s,Italy • Construction of the Vajont Dam (280m)
  • 5.
    Vajont dam disaster •9 October 1963 at 22:39 • Landslide into this “brand new” hydroelectric reservoir • Giant wave
  • 6.
  • 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.
  • 9.
    • Late 1950s,Italy • Roberto Camorani, Minister of Public Works An alternative story
  • 10.
    • Following theadvices 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.
    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.
    “everybody knows thatyou need more prevention than treatment, but few reward acts of prevention” N.N. Taleb (2007)
  • 13.
    This module: Mutual shapingof hydrology and society
  • 14.
    Panta Rhei: Everythingflows IAHS scientific decade (2013-2022) Over 400 water scientists This module: Mutual shaping of hydrology and society
  • 15.
    • Introduction o Humaninfluence 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.
  • 17.
    Today: over 100million 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.
    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.
    (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.
    Increasing degree ofregulation Dams and reservoirs
  • 21.
    (Liu et al.,Water, 2014) Urbanization and floods
  • 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.
    Human response Los Angeles:Drought and water demand (Garcia et al., HESS, 2016) 1976–1977 1987–1992 (longer drought, persistent impact)
  • 24.
    Society shapes hydrologicalextremes, 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.
    Open questions How dohuman-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.
  • 27.
    Levee effect Levee building (lessfrequent 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.
    Levee/forgetting effect Rare eventsassociated 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.
    (Di Baldassarre etal., 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.
    Dynamics around theworld (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.
    • Traditional methodscannot 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.
    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.
    Key concept: Floodmemory • 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.
    Conceptualizing human-flood interactions Human andflood 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.
    F = floodlosses 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.
    2. Demography • Floodplainpopulation 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.
    3. Memory • Memoryis 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.
    4. Technology • Floodprotection 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.
    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.
    Numerical experiment to exploreplausible 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.
    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.
    Results 2/2 Diagrams showoutcomes with decreasing memory decay rate Keeping memory high is crucial, especially in technical systems
  • 43.
    Model evaluation (Ciullo etal.,Hydrological Sciences Journal, 2017)
  • 44.
    e.g. uncertainty dueto 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.
  • 47.
    The River Tiber andthe 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)
  • 48.
    Rome and theTiber: 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)
  • 49.
    Rome and theTiber: 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)
  • 50.
    Tiber’s floodplain asfully 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)
  • 51.
    Observations hydrological processes (flood changes) humaninterventions (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
  • 52.
    Turning or tippingpoint? 1870: Rome experiences a large flooding event 1871: Rome becomes Italy’s Capital
  • 53.
    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
  • 54.
    Flood defence: the walls(”muraglioni”) (Raccolta Roma Sparita; Sacca et al., 2015)
  • 55.
    Floodplain development Many moderndistricts were created in the Tiber’s floodplain: e.g. Prati in 1887 and 1934
  • 56.
    Flood levels Level offlood 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
  • 57.
    • Is Romesafe 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?
  • 59.
    Bangladesh: flows ofwater 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
  • 60.
    Bangladesh: Salt waterintrusion • Gridded Population of the World (1990-2000, 2000-2010) • Population change (%) • Migration from Southwest region –why?
  • 61.
  • 62.
    Current narratives Flooding Cyclones Saline intrusion Migration Conflicts Climatechange 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)
  • 63.
    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
  • 64.
    Human activities, SWregion • 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
  • 65.
    Human experience andmigration • 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
  • 66.
    Survey, interviews 2% 22% 33% 43% 0% 10% 20% 30% 40% 50% 18-30 31-4546-60 > 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
  • 67.
    Results • Do peopleperceive 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
  • 68.
    Discussion Need to gobeyond 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
  • 69.
    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
  • 70.
  • 71.
    Nile River Example: theNile 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)
  • 72.
    Hydrology of theNile 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?
  • 73.
    Hydrological data Nilometers: River gaugestations 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)
  • 74.
    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
  • 75.
    Drought predictions 800 1000 1200 1400 1600 700 710720 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)
  • 76.
    “Prediction is verydifficult, especially about the future!” Niels Bohr (1885-1962)
  • 77.
    Roda Nilometer One ofthe 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
  • 78.
    meteorological drought soil moisture drought hydrological drought climate variabilitydrivers consequences ecologicalimpacts socio-economic impacts Human-drought interactions (Van Loon et al., Nature Geoscience, 2016)
  • 79.
    meteorological drought soil moisture drought hydrological drought climate variability human activitiesdrivers consequences land use irrigation dambuilding water abstraction ecological impacts socio-economic impacts responses anthropogenic climate change (Van Loon et al., Nature Geoscience, 2016) Human-drought interactions
  • 80.
    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)
  • 81.
    Dams and reservoirs •Unintended consequences • Supply-demand cycle • Reservoir effect
  • 82.
    River basins ashuman-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
  • 83.
    (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
  • 84.
    Global analysis Reservoir capacityvs. water demand (worldwide) • GRanD database • World Bank (GRanD database; Lehner et al. 2012)
  • 85.
    Global analysis Reservoir capacityvs. 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
  • 86.
    River basins ashuman-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
  • 87.
    Reservoir effect From frequentevents 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)
  • 88.
    Counterargument? Frequent shortages mighterode 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?
  • 89.
    Drought and Floodsin the Anthropocene
  • 90.
    Drought and floods inthe 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)
  • 91.
    Flood trends (Di Baldassarreet 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
  • 92.
    (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
  • 93.
    (Di Baldassarre etal., 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
  • 94.
    Daniel Kahneman (NobelPrize, 2002) o Humans are NOT rational  o Prospect theory o Cognitive biases and heuristics • Confirmation bias • Anchoring effect • Availability heuristic Cognitive biases and heuristics
  • 95.
    • Theoretical background oDecision 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
  • 96.
    Modeling feedback mechanisms inreservoir 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)
  • 97.
    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
  • 98.
  • 99.
    • Empirical andtheoretical 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
  • 100.
    • Open questions: oSite-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
  • 101.
    Giuliano Di Baldassarre UppsalaUniversity, 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
  • 102.
  • 103.
    What are these? Whatdo they have in common?!
  • 104.
    Black Swans • BlackSwan 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)
  • 105.
  • 106.
    Debate • Small groupdiscussion (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!
  • 107.
    We can’t predicteverything, but we can still reduce losses!
  • 108.
    Unrepeatable chain ofevents 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)
  • 109.
    Top-down and bottom-upapproaches (source: Bloeschl et al., 2013)(Bloeschl et al., Climate Vulnerability, 2013)