Slide show

GWSP, Bonn, Dec 2010
Hydrologic Risk Transfer Models as Adapting Strategies for Changing Scenarios of Water Vulnerability:
Challenges and Opportunities
by
Eduardo Mario Mendiondo
Sao Carlos School of Engineering
University of Sao Paulo
Brazil
“Strategies for sustainable development depict a learning transition from uncertainty analysis to uncertainty management, encouraging new rules, attitudes and scenarios capable of being profited in water (risk) problems” (Mendiondo & Valdes, 2002).
Giovanni Benzoni
(1809-1873)
“Il Pompeiani”
Contents
•
•
•
•
Hydrologic Risk Transfer Models ‐ HRTMs
Working hypotheses and approaches
Methods and applications
Opportunities for GWSP
Adapting to “hydrorisks”
Marengo (2010)
Adapting measures to hydrologic risks are particularly relevant at watersheds threatened by climate extremes, especially
those rivers being highly impacted by progressive transformation, in North America and South America (Mendiondo &
Valdes, 2002) with ungauged or poor gauged basins’ responses (Sivapalan et al, 2003).
First, a Water Footprint Generator module (WFG) is mathematically oriented into
a new multiscale water balance equation with traditional components of
precipitation, evapotranspiration and runoff, as well as with water supply, sewer
production and virtual water fluxes, including garbage production.
Brazilian Federal Law of Urban Waters
#11.445
P
ETR
Water
Footprint
Generator
Water
Supply
Virtual water of
goods and services
Virtual Water of
Garbage & Residues
Domestic/sector’s
sewage
Q (urban
drainage)
WORKING HYPOTHESES ‐ Conversely, if it is generalized into a distributed
approach, modeled pixels are envisaged as water footprint generators of local
ecohydrology with side attributes of self‐resilience, ‐continuity, ‐diversity, ‐
dynamics and self‐vulnerability (Mendiondo, 2008).
...with further refinements to Global Water System Projects. What new limitations/scaling thresholds to medium and large river basins?*
waterfootprint
64
4744
8
⎞
⎛ }*
⎟
⎜
P + Supply + VWI
Dilution
Q
Sewage
Garbage
[1] = {
+
,
,
{ ∑
1
2
3
⎟
⎜
green
green
blue
blue
14444⎝442444444
3⎠
grey
}
[2] = {
ETR + Q + Sewage + Garbage
1444
424444
3
green
*
grey
grey
* Mendiondo et al (2010)
The objective is twofold: firstly, to link Global
Circulation Model outputs to regional HRTMs at
representative watersheds; secondly, to explore
several how combinations of hypothesis testing on
climate change and land-use scenarios should
elicit water insurance models in order to propose
new indexes of water vulnerability and resilience,
adaptation thresholds of more vulnerable society’s
sectors and mitigation policies of the whole
economical system.
HRTMs and scenarios of change
Second, the Risk‐Transfer Balance module (RTB) performs long‐term scenario
runs through intensively‐generated optimal efficiency predictions of features
of recovering insurance tradeoffs. RTB boundary conditions runs range from
climate variability conditions to land‐use change with water use intensity.
Sensitivity of insurance fund under prospective scenarios and
under different nominal areal-specific premiums
Flood insurance fund [ $.km-2 ]
= 835 US$/Km2
= 458 US$/Km2
= 352 US$/Km2
80000
70000
60000
50000
40000
30000
20000
10000
0
0
10
20
Time [ year ] 30
40
50
Novel HRTMs are derived on long-term scenarios and modeling runs of possible climate and land use
changes (Mendiondo et al, 2005). Other works with HRTMs relate to resilience and demonstrate how
scientific methods can be merged into feasible strategies of risk transfer at the watershed scale with
vulnerable sectors and stakeholders (Mendiondo, 2010).
HRTMs are scenario‐based models performing a system analysis linked to
regular hydrologic models and oriented to reduce vulnerability through
balance equations, i.e. insurance funds related to hazards` payments and
premiums
efficient
scenarios
nonefficient
scenarios
HRTMs
Averaged premium’s revenues and scenario efficiencies related
to progressive premium coverage from 30 stochastic runs
throughout 50-year period, and for maximum annual floods.
Efficiency depicts the relative
number of favourable longterm scenarios at which
insurance fund is selfsustainable. 'Efficiency' is as
an alternative indicator to
express how cope with flood
vulnerability at catchment
scale
'Revenue of premium'
outlines, in percent, the
difference between initial
(non-optimized) and final
(optimum) premium. This is
an alternative index to
emulate the resilient
capacity of insurance to risk
transference device.
To address resilience and vulnerability indexes, the insurance model emulate
stochastically-based scenarios based upon ‘fund tank’ solvency as well as
time-averaged efficiency throughout long-term scenarios of land-use. Then
climate change and land-use scenarios could be tested as end-points or mixed
situations which generate a wide range of possibilities
HRTMs used for water extremes and insurance options
across governance policy scenarios under climate change**
The x-axis is the magnitude of the disturbance of ecosystem services, measured by the number of people affected.
The y-axis is the likelihood of an extreme ecosystem event of a given magnitude.
The total area under each curve is the same (for each scenario the probabilities of all event magnitudes must sum to one 1).
Order from Strength - OS
Local outbreaks of diseases and pests (especially in poorer and heavily
populated countries) that can spread globally despite border controls, famine,
crop collapse, landslides, storm damage and fisheries collapse under CLIMATE
CHANGE.
,
VERY LOCAL SECURITY MARKETS, HIGH-REACTIVE INSURANCE
Probability of Event
Global Orchestration - GO
Adapting Mosaic - AM
Local outbreaks of diseases and pests that can have global impacts (eg;
SARS and H1N1) until controls are put in place by global cooperation.
Breakdown in local and regional hydrological cycles, accumulation of
nutrients, and pollutants of local but not global significance, global ecological
problems (including CLIMATE CHANGE and decline of international fisheries)
that have major impacts before being recognized and mitigated by global
cooperation .REACTIVE INSURANCE, GLOBAL SECURITY MARKETS
Results of failed experiments at local scales (e.g. failed rehabilitation of
wetlands, inland or costland fisheries, flood mitigation), Small scale
technological failures under CLIMATE CHANGE.
AM1: (on)LEARNING INSURANCE? , TRANSITION SECURITY MARKET
Technogarden - TG
1,000,000
Event size
(number of people)
Broader scale events that become problems due to lack of
communication/cooperation between local and/or regional communities (e.g.
outbreaks of pests like hydrological problems that span multiple watersheds,
responses to CLIMATE CHANGE, failure to develop regional-scale strategies
for biodiversity protection or regulation of river flows and water withdrawals).
Adaptation and learning under transition.
AM2: INSURANCE UNDER ADAPTATION?, LOCAL SECURITY MARKET.
Breakdown in ecosystem processes at local scale because aspects of
ecosystem function under CLIMATE CHANGE have been overlooked in the
application of environmental technologies. The problems are of low
likelihood but if they occur, they have locally major impacts.
TG: “GREEN” INSURANCE, GLOBAL SECURITY MARKET
**Mendiondo (2009)
adapted from Millennium Ecosystem Assessment (2005)
Emerging South American flood insurance market (ca. US$ 80 billion/year) from scenarios during period 2000‐2100 * * “FPC”: ‘Flood Poverty Cycles’
To further mitigate
preventable water
disasters, mitigate
related
catastrophes and
transfer those
growing water
risks, new
insurance models,
herewith known as
Hydrological Risk
Transfer Models
(HRTM), are
proposed coupling
hydrologic models
with economic
layouts regarding a
scenario approach.
* * Mendiondo (2005).
Some applications of HRTMs
Marengo (2009)
Demonstrative pilot projects (1)
Marengo
(2009)
Source:
Marengo (2010)
The purpose is preliminary to approach case studies at representative biomes of
Subtropical and Semi-Arid areas, thereby underpinning yardsticks of how to replicate
intensive HRTM runs. Future scenarios from GCMs, with uncertainty intervals between
period of years 2000-2050 and 2050-2100, will be the inputs to HRTMs, acting as a
dynamic ‘fund tank’ of the insurance device.
Special cases of HRTM uses
Flash floods
River Floods
Droughts
RATIONALE ‐ When derived impacts across several spatiotemporal scales are
converted into disaster likelihoods, the inherent resilience of composite
biogeochemical processes is enhanced through risk‐transfer mechanisms.
Under a lumped single scheme, this working approach, on the one hand,
envisages risk assessment (hazard, vulnerability and exposure) of hydro‐
climatic regimes modified at watershed scales; on the other hand, it
acknowledges steps of manageable actions under uncertainty assessments.
.
RISK ASSESSMENT:
from Crichton, 1999.
risk − assessment ( components )[ IMPACT ,VULNERABILITY ]
[ ADAPTATION ; risk − management ( steps )]
⎧6
44444474444448 644
4444
474444444
8⎫
vu
erability
ln
after ( reconstruction )" ⎪
"before"
⎪6
exp osure
"during " 6"4
hazard
4 48
4 6
4
47444
8
4
74
8 6
64
4
744
8 647
4
74
8
7
8
⎪
N− j ⎪
⎛
⎞
⎛
⎞⎪
⎞
⎛
Tr
A
n
⎛
⎛
⎞
⎞
⎛
⎞
t
t
t
1
Δ
⎪ f
⎛
⎞
f ,Tr
f ,Tr
conc ⎟
rescue
S
v ⎟ ⎜
⎜
⎜
⎟⎬
⎟⎟ ,
⎟⎟, 1 − ⎜1 −
⎟⎟ , ⎜⎜
⋅
Risk ⎨⎜⎜
,
, ⎜⎜
⋅
⎟
nb ⎠ j ⎜⎝ S past t f ⎟⎠ ⎜⎝ tearly − w ⎟⎠ ⎝ tconc ⎠ ⎜⎝ ⎝ T * ⎠ ⎟⎠⎪
⎪⎝ T * ⎠ j ⎝ Ab
j
j
⎪
⎪
⎪
⎪
Risk Transfer Models under Long‐Term Changes depending upon policy scenarios for sustainable development:
“HydroResilientAdaptometers”
Assessed‐and‐Managed‐Risk Indicators step (AMRI)
gathers composite indicators of assessing and managing
risks in order to perform scenario runs under policy
options (Mendiondo, 2010).
HRTMs used for participatory perception of hydrologic
risks under flash floods
f
lo o
days
Percepción del riesgo en la cuenca hidrográfica…
… y la “acción” casera para el riesgo, local!
Fuente: PPG-UFSCar
from Giuntoli & Mendiondo (2008)
HRTMs used for large non-stationary rivers, with more vulnerable settlements
(Corrientes Station, Parana River Basin, Area= 2 million Km2, Series: 1904-2000)
HRTMs used for environmental services from river drought regimes*
How do we recover resilient quantitative-qualitative aspects of river regimes under change?
A.N.A.(2002)
HRTMs for 2010-2050 period
UGRHI 13: Tietê-Jacaré
1200
Oferta = Q7,10
Oferta até Q95%
Uso consuntivo
Uso não-consuntivo
m3 / ano.habitante
1000
800
600
400
200
0
2010
2010
2025
2025
2050
2050
HRTMs for 2010-2050 period
Surface Water Balance Superficial (m3/s)*
Experimental Pilot Project with River Basin
Authority of Tiete-Jacare, Sao Paulo, Brazil
Source: Mendiondo
(2008) Braz. J.
Biol. vol.68 no.4 suppl
. Nov. 2008
15 m3/s
0 m3/s
“O”: Water availability
“D”: Water demand
2010
O
D
2025
O
D
2050
O
D
time
Example
Sensitivity analysis of regional impacts of water scarcity at 184 municipalities of Ceara State (Northeastern Brazil) from models ECHAM4 and HadCMx until year 2025 o ano 2025) and prospective adaptation measures towards hydrologic emergency (Araujo et al, 2004)
Original database: Araujo et al (2004)
0,2
Relative frequency
HRTMs used fro
storylines scenarios of
water footprint
0,1
Brazilian
Water
Footprint
0,0
0
500
1000
1500
2000
2500
3000
Individual water footprint (m3/cap.year)
Prospective storylines
(100% = 2009WF)
200
150
100
50
0
2010
individual water footprint*
2015
2020
2025
2030
2035
2040
Year
Mendiondo et al (2009*)
2045
2050
HRTMs linked to Virtual Water Fluxes at Demonstrative Pilot Project
Virtual Water Flux
(from):
Farm & Forest
Industry
Non-consuntive
** Mendiondo (2008)
Opportunities: HRTMs linked to regional modeling strategy
Source: Marengo
(2010)
New climate change scenarios runs through Brazilian CPTEC-ETA model, globally-constrained by either
HadCM3 or ECHAM4, from baseline scenarios A1B, A2 and B1 for the period 1960-2100, also include
indexes of extreme precipitation, temperature and water scarcity indexes here used.
Eduardo Mario Mendiondo
emm@sc.usp.br
mendiondo@uol.com.br
e.mario.mendiondo@gmail.com
Thank you !
Thank you!
emm@sc.usp.br
e.mario.mendiondo@gmail.com
mendiondo@uol.com.br
APPLICATION ‐ All modules, WFG, RTB and AMRI can be adaptively scalable
and linked under flexible layouts, either under matrix pixels or free‐irregular
topology objects capable of being modeled and controlled, when feasible, by
remote sensing, web‐GIS and wireless sensor web technology
(HUMAN‐BASED SCALE)
.
Examples
3
1
2
4
Table 1‐ Averaged green water footprint of a A4‐paper according to Brazilian forest stands at demonstrative pilot projects (Source: E M Mendiondo, 2010, WWF/ACE Internal Report)
Brazilian demonstrative forest
forest
wood
yield
green water
footprint of a A4paper [liter]
neutral water footprint
management practices
moderate
10
high
2 Amazon-savannah transition forest
rapid
35
moderate
3 Amazon rainforest
rapid
42
moderate
4 Subtropical ancient forest
low
83
very low
1 Caatinga, water scarcity forest
Hazard prediction risks from Flashfloods also need of calibration,
validation and identifiability
approaches
Non-stationary relationship between radar-based
(dBZ) Vs ground precipitation during a flash-flood at
Gregorio creek, São Carlos, Brazil. Traced lines and
numbers depict a 30-min discretized time sequence
(Gonçalves & Mendiondo, 2008)