Transcript AGWA: Risk Management Framework for Water Resources Climate

AGWA: Risk Management Framework for
Water Resources Climate Adaptation
Rolf Olsen,1 PhD
Eugene Stakhiv,1,2 PhD
1Institute
for Water Resources
U.S. Army Corps of Engineers
Alexandria, Virginia, USA
2Johns
Hopkins University
Baltimore , Maryland, USA
Outline
• Background on Alliance for Global Water Adaptation
(AGWA)
• Risk Management Framework
– Breakout sessions
– Next steps – stress tests
• Examples
– Flood risk
– Reservoir regulation
• Application of framework to United States-Canada
Great Lakes Study
– Example: ecosystems
AGWA: A Brief Overview
• The Alliance for Global Water Adaptation is a
group of regional and global development banks,
aid agencies and governments, a diverse set of
non-governmental organizations (NGOs), and the
private sector focused on how to manage water
resources in way that is sustainable even as
climate change alters the global hydrological cycle.
• Focused on how to help practitioners, investors,
and water planners and managers make
systematic, consistent, and resilient decisions
AGWA network • alliance4water.org
Development banks and capacity-building groups.
The World Bank, the Asian Development Bank, European Investment Bank, KfW,
the Inter-American Development Bank, GiZ, the Cooperative Programme on Water
and Climate.
Non-governmental Organizations
Conservation International, the Delta Alliance, International Water Association, the
Swedish Environmental Institute (IVL), the Global Water Partnership, Deltares,
Environmental Law Institute (ELI), Stockholm Environmental Institute (SEI),
Organization for European Cooperation and Development (OECD), Stockholm
International Water Institute, Wetlands International, IUCN, The Nature
Conservancy, ICIMOD, WWF.
Governmental
US Army Corps of Engineers, US State Department, NOAA, UN Water, UN Habitat,
UNECE, Water Utilities Climate Alliance, WMO, CONAGUA, Seattle Public Utilities,
The Private Sector
Ceres, UNEP FI, World Business Council for Sustainable Development
Key partners
Water & Climate Coalition, the Adaptation Partnership, the Global Environment
Facility, Nairobi Work Programme
Uncertainty
Source: Wilby & Dessai, 2010, Weather
Traditional approaches amplify
or hide uncertainty
• Models not developed for
adaptation purposes but for
testing hypotheses about
greenhouse gas mitigation.
• Low confidence, especially
for quantitative purposes
• Little agreement across
models, scenarios
• Often result in a series of
“no regret” options
• Stakeholders often feel
disempowered by process,
which is often experienced
as deterministic
Source: AGWA, “Caveat Adaptor,” 2013
UZH R. watershed [Dneister R. Basin]
(Zhelezhniak, et al. 2013)
Return Period, yrs
100
TOPKAPI (1961-1990)
DHSVM (1961-1990)
TOPKAPI (2011-2050)
DHSVM (2011-2050)
observed
80
60
40
20
0
0
200
400
600
Discharge, cms
800
Top-down vs. bottom-up approaches
top-down approaches
to risk assessment
1. Downscale
climate model
projections
decision-scaling risk
assessment
3. Assess plausibility and
test vulnerability
2. Estimate shifts
in water supply
3. Determine
system responses
to changes in these
variables
2. Assemble multiple
climate data sources and
link to breaking points
1. Define your system’s
breaking points
Weaver et al., 2012, WIREs Climate Change
Purpose
• Purpose of these sessions: Develop a risk
assessment of the performance of water
resources management under the threat of
future climate changes and variability using a
‘bottom-up’ approach.
• A bottom-up approach is a stakeholder driven
process to assess vulnerability rather than a
reliance on predictive models of the future.
Risk-Informed Decision Making
Analyze Risks
Evaluate Risks
Monitor, Evaluate, Modify
Identify Risks
Risk Assessment
Consult, Communicate and
Collaborate
Establish Decision Context
Risk Mitigation
Adapted from ISO 31000- Risk Management—Principles and Guidelines
Background
• Risk management has two basic parts: assessing risks
and developing solutions.
• Risks can be assessed either qualitatively or
quantitatively
• Vulnerabilities such as flood inundation and flood
damages can be quantified.
• Other vulnerabilities (ecosystems) can be categorized
qualitatively by stakeholders in terms of ‘coping
zones’ and relative degrees of ‘risk tolerance.’
• Risk management options (solutions) need to take
into account both types of information.
Defining System Objectives
• For each sector (flood risk, ecosystems, and
agriculture), what specific objectives are you trying
to achieve?
– Flood risk examples: reduce long-term flood damages;
reduce vulnerability of infrastructure to disruption; reduce
human fatalities from flooding
– Ecosystem examples: improve biodiversity; preserve
wetlands; increase fish stocks
– Agriculture examples: increase agricultural production;
increase farm income
Measuring System Performance
• What metrics would you use to define success or
failure?
– Flood risk examples: reduction in flood damages
– Ecosystem examples: biodiversity indicators; fish
biodiversity and catch amounts; health of indicator species
– Agriculture examples: area of land irrigated; crop yields
• A metric is a measurable quantity that can be used to
measure the performance of a system.
Identify Problems
• Identify climate concerns, hazards and thresholds.
What river flow and climate conditions are
associated with these hazards?
– Flood risk: What are the current flooding problems in the
Dniester river basin? At what flood water levels and flood flow
values are populations affected? At what flood water levels and
flood flow values does major infrastructure become unusable?
– Ecosystems: What are the current major ecosystem problems?
What is the major source/cause of ecosystem disruption
(infrastructure, floods, droughts, or pollution)? What river flow
values support these ecosystems? How has drought affected
ecosystems? Have changes in flow patterns caused by reservoir
regulation altered ecosystems?
– Agriculture: What are the current problems for irrigated
agriculture? Discuss problems during past droughts.
Non-climate Causes of System Stress
• Identify key drivers and stressors.
– Drivers are forces that can have major influences on the
system of interest. Potential drivers could be of physical,
biological or economic origin (i.e., climate, invasive
species, population growth, etc.).
– Stressors are changes that occur that are brought about by
the drivers.
– Examples:
• Flood: population living in floodplain; important infrastructure in
flood plain
• Ecosystems: water quality (toxic chemicals, dissolved oxygen,
water temperature); overfishing; invasive species
• Agriculture: irrigation infrastructure not performing as designed;
soil fertility
Risk Tolerance
• Risk tolerance is the willingness to bear a known risk
based on its severity and likelihood
• What range of conditions would have unfavorable
though not irreversible agricultural impacts? How often
can you tolerate such conditions?
– Flood examples: disruption of transportation; damaged homes;
reduced economic output
– Ecosystem examples: loss of wetlands; diminished fish stocks
– Agricultural examples: reduced farm income; lower agricultural
productivity
• What range of conditions would have severe, long-lasting
or permanent adverse impacts?
– Flood example: population does not return and rebuild after a
flood
– Ecosystem example: extinct species
– Agricultural examples: farmland is abandoned
Coping Range: Coping range
represents the magnitude or rate of
disturbance various systems like
communities, enterprises, or
ecosystems can tolerate without
significant adverse impacts or the
crossing of critical thresholds.
Resilience Range: Resilience
range is the magnitude of damage a
system can tolerate, and still
autonomously return to its original
state.
Failure Range: Failure range
starts from the point where
magnitude of damage is such that a
system can no longer tolerate it
without significant adverse
impacts.
Risk Matrix
Likelihood
• Likelihood is the chance of something happening,
whether defined, measured or determined
objectively or subjectively, qualitatively or
quantitatively.
• Statistical models are generally based on assumption
of stationarity, that is the past is representative of
future.
• Estimating probabilities for Global Climate Models is
problematic; uncertainty is not quantifiable.
Confidence
• Confidence is the validity of a finding based on the
type, amount, quality, and consistency of evidence
(e.g., mechanistic understanding, theory, data,
models, expert judgment) and on the degree of
agreement (Intergovernmental Panel on climate
Change Fifth Assessment Report, 2013).
• Our confidence in the likelihood and potential
consequences will influence our decision.
Robustness Tests
• How well does the system remain functioning under
a range of circumstances?
• System is tested with an array of approaches
–
–
–
–
Observed hydrology
Stochastic hydrologic sequences
Global climate model projections
‘Weather generator’ if available
• Test risk management solutions across a range of
possible conditions.
Stress test: drought characteristics
Duration, Severity and Intensity
Severity = Volume/Duration
Stress test: climate events
4000
640
Historical
Climate 1
Historical - lake level
Climate 1 - lake level
3500
635
630
2500
2000
625
1500
620
1000
615
500
0
610
1
13
25
37
Month #
49
Lake level (ft.)
Monthly average inflow (cfs)
3000
Dniester Basin Flooding
LEB - Lower
Error Bound
Q
S
Frequency
Flood Discharge (Q)
P
Q
Flood Stage (S)
UEB - Upper Error Bound
Frequency
Flood Stage (S)
Uncertainty and Flood Damage Calculation
UEB
LEB
S
D
Flood Damage (D)
P
D
Flood Discharge (Q)
Flood Damage (D)
U.S. Army Corps of Engineers Procedures - HEC-FDA;1992
Quantifying Frequency
Description
Flood Zone
Coping
Range
Remote
Mid-point
estimated
frequency
Approximate Rank
numerical
value
events/
year
1 in 500 yr
0.002
1
1 in 100 yr
0.01
2
1 in 20 yr
0.05
3
Occasional
Flood Zone 1 <1 : 200 years
1:50 yrs - 1: 200
Flood Zone 2
years
1:10 years to 1: 50
Flood Zone 3
years
Flood Zone 4 1:5 yrs to 1:10 yrs
1 in 7 yr
0.15
4
Frequent
Flood Zone 5 1:2 yrs to 1:5 yrs
1 in 4 yr
0.25
5
Regular
Flood Zone 6 1:1 yr to 1:2 yrs
1 in 1.5 yr
0.50
6
Common
Flood Zone 7 0.2 yr to 1:1 yr
1 in 0.3 yr
0.70
7
Rare
Infrequent
Frequency Range
Quantifying Severity/Consequences
Economic/Safety/Health
Approximate
Numerical value
Description
Ranking
Equivalent fatalities
per event
Minor Damages
< $ 103/ 0.005
More serious damages e.g.
multiple minor injuries
Major injuries/property
damage
2 (significant)
3 (moderate)
Multiple Major / single fatality
4 (Major)
Multiple fatalities (1-10)
Severe economic damages
Multiple fatalities (10 to 100)
Catastrophic damages
Multiple fatalities (>100)
1 (minor)
5
6 (Severe)
> $109/1000 fatalities
7 (catastrophic)
Choosing Management Alternatives
Dniester Dams and Reservoirs
Reservoir Regulation
• Potential adaptation measures
– Provide more naturalized flow patterns for ecosystems
while maintaining economic benefits
– Change allocation of storage space to different uses
– “Dynamic rule curves”: Shift reservoir storage allocation
based on current hydrological conditions in basin.
– More use of forecasts in reservoir operations
Reservoir Rule Curves and Storage Allocation
Allocation of Reservoir Storage Space
New Bullards Bar Reservoir, California, USA
Less
Precipitation
Shasta Reservoir, California, USA
More
Precipitation